Relation between higher education and Governance

Dissertation-MueidAlRaee Dissertation-MueidAlRaee_1

The Nexus between governance and human Capital Details research PROPOSAL investigate the relation between spending on higher education and governance effectiveness MUST FOLLOW the ideas of a SPECIFIC BOOK MUST HAVE an ECONOMETRIC MODEL Nothing fancy, just good lit review and a decent linear regression proposal

Innovation and Diversification Policies
for Natural Resource Rich Countries

Mueid Al Raee

UNU MERIT, UM MGSoG

Supervisors
Professor Jo Ritzen
Dr. Denis de Crombrugghe

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Contents
1. Introduction …………………………………………………………………………………………………….. 7
Appendix 1-A ……………………………………………………………………………………………………. 16
2. Productivity and Innovation Policy …………………………………………………………………… 19
2.1. Introduction …………………………………………………………………………………………….. 20
2.2. Innovation policies and the path towards successful innovation ………………………. 24
2.3. Identification Strategy ………………………………………………………………………………. 29
2.4. Data ………………………………………………………………………………………………………. 32
2.5. Results ……………………………………………………………………………………………………. 36
2.5.1. Global ………………………………………………………………………………………………. 36
2.5.2. Arabian Gulf countries – A special case? ……………………………………………….. 42
2.6. Conclusions and Discussion ……………………………………………………………………….. 45
Appendix 2-A ……………………………………………………………………………………………………. 49
Appendix 2-B ……………………………………………………………………………………………………. 50
3. Policy and Economy in the GCC ………………………………………………………………………. 53
3.1. Introduction …………………………………………………………………………………………….. 55
3.2. Perspectives on innovation ………………………………………………………………………… 59
3.2.1. General …………………………………………………………………………………………….. 59
3.2.2. The literature on GCC countries ………………………………………………………….. 61
3.3. The Case of GCC – Policies and Enablers …………………………………………………… 68
3.3.1. Section Summary ……………………………………………………………………………….. 68
3.3.2. Development of education systems ……………………………………………………….. 71
3.3.3. Literacy, primary education, secondary education, reforms and performance 72
3.3.4. Tertiary education and vocational education …………………………………………. 77
3.3.5. R&D ………………………………………………………………………………………………… 80

3.3.6. Business and Entrepreneurship…………………………………………………………….. 85
3.3.7. Governance and Infrastructure …………………………………………………………….. 87
3.4. The Outputs of GCC – Indicators of Innovation and Diversification ……………….. 89
3.4.1. Section Summary ……………………………………………………………………………….. 89
3.4.2. Patents, Trademarks and Industrial designs ………………………………………….. 91
3.4.3. Non-traditional sector – share in the economy and labour productivity ……… 94
3.5. Connecting Policies, Enablers and Outcomes ……………………………………………….. 96
3.6. Summary, Discussion and Conclusion ………………………………………………………….. 98
Appendix 3-A ………………………………………………………………………………………………….. 103
4. Natural Resource Abundance: No Evidence of an Oil Curse ……………………………….. 107
4.1. Introduction …………………………………………………………………………………………… 109
4.2. Literature Review …………………………………………………………………………………… 110
4.3. Modelling the natural resource extraction and capital investment relationship … 113
4.4. Empirical Model …………………………………………………………………………………….. 113
4.5. Data …………………………………………………………………………………………………….. 121
4.6. Data Reliability ……………………………………………………………………………………… 124
4.7. Results ………………………………………………………………………………………………….. 127
4.8. Postestimation tests and robustness ………………………………………………………….. 133
4.9. Discussion and Conclusion ……………………………………………………………………….. 134
5. “Stars in their Eyes?” …………………………………………………………………………………….. 137
5.1. Introduction …………………………………………………………………………………………… 138
5.2. Background and Literature ………………………………………………………………………. 140
5.2.1. Diversification …………………………………………………………………………………. 140
5.2.2. Evaluation of Diversification Strategies ……………………………………………….. 140
5.2.3. Methodologies for Evaluation …………………………………………………………….. 143

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5.2.4. Oman and Saudi Arabia Evaluations ………………………………………………….. 144
5.3. The Predictive Model ……………………………………………………………………………… 146
5.4. Review of the Economic Plans of Oman and Saudi Arabia …………………………… 147
5.4.1. Oman …………………………………………………………………………………………….. 150
5.4.2. Saudi Arabia …………………………………………………………………………………… 151
5.4.3. Reference Condition and Scenarios ……………………………………………………… 152
5.5. Results and Discussion ……………………………………………………………………………. 154
5.6. Summary and Conclusion ………………………………………………………………………… 162
6. Conclusion …………………………………………………………………………………………………… 165
6.1. Background of the dissertation …………………………………………………………………. 165
6.2. Summary ………………………………………………………………………………………………. 166
6.3. Limitations and Suggestions for Future Research ………………………………………… 171
6.4. Integrated insights from the dissertation and policy implications …………………… 174
6.4.1. Institutional Effectiveness, Productive Efficiency, Human Capital, Education
and R&D …………………………………………………………………………………………………….. 174
6.4.2. Natural Resources, Oil, Productivity and Investment ……………………………. 176
6.4.3. Regional Infrastructure, International Trade and Peace ………………………… 178
7. References ……………………………………………………………………………………………………. 181

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1. Introduction

A challenge that many countries face today is the sustenance of their economic growth in the
face of increasing reliance on natural resources. The number of natural resource-driven
economies has increased from 58 in 1995 to 82 in 2017. 1 Among these countries, 57% were
low- and lower-middle-income countries, while only 13% were high-income countries. The
natural resource-driven countries were home to more than two-thirds of all people living in
extreme poverty. If low-income natural resource-driven economies engaged in sound policy
for effective and efficient utilisation of natural resources, aimed at broad economic
development, it is expected that almost half of the world’s poor could be lifted out of poverty
by 2030. 2 This number is more than the number of poor people lifted out of poverty due to
China’s rapid economic development from 1996 to 2015. In the face of the fast-evolving global
demand for natural resources, diversification of the economy offers a path for economic
development in low- and middle-income natural resource-driven economies and for sustaining
economic growth in the high-income ones.
The central aim of this dissertation is to examine the challenge faced by natural resource-
driven countries, in particular, the countries in the Arabian Peninsula, to diversify their
economies. We investigate what policies can help stimulate innovation and diversification in
natural resource-driven economies to ensure sustained development. The research carried out
in this dissertation draws upon the evidence of the policies for development in the global
context. This part of the dissertation is complemented by research on the state of

1 Natural resource-driven economies are defined as those that qualify under at least one of the criteria:
1. Natural resource rents are higher than 10% of the economic output of the country, and/or,
2. Natural resource rents amount to more than 20% of the fiscal revenue of the country, and/or,
3. Natural resource rents represent more than 20% of the total exports of the country.
The natural resource-driven classification is used by various sources (IMF, 2012; Dobbs, et al., 2013; Addison &
Roe, 2018). The 2017 classification of natural resource-driven countries is based on the author’s calculations.
More details are presented in Appendix 1-A, Table 1.1.

2 See Dobbs, et al. (2013, pp. 5, 135-136) for the methodology used in determining the estimated reduction of
poverty due to the effective and efficient utilisation of natural resource rents. The poverty line of 1.90 USD 2011
PPP a day is used to determine the number of people living under extreme poverty.

diversification and its relation to the policy measures undertaken in the Gulf Cooperation
Council (GCC) 3 and its member countries.
The complexity of the problem at hand can be understood by bringing together multiple
strands of academic literature. Some of the distinct yet interconnected areas that this
dissertation relies upon include economics and policy of innovation, growth economics, studies
of natural resource-driven economies and economic diversification.
A review of the innovation policy literature at the outset of the research for this dissertation
identified a need for more empirical and theoretical research in the area. 4 Addressing this
need is expected to push the field further in its current transition from pre-paradigmatic phase
towards a defined set of theories of innovation policy. It was observed that research from the
broad innovation policy perspective was limited, and more attention was needed for the
interaction of policy instruments with systemic conditions and institutional settings.5 Lastly
and most importantly, discussions of suitable methodologies were necessary to advance the
field and facilitate research that can close the gaps in the innovation policy literature
mentioned earlier. One of the propositions has been to understand the policy realm as having
two parallel spaces. The first space covers macro-level, systemic and institutional enablers
and determinants of innovation, such as governance, education, research and development
investment, business environment, fiscal policies, and infrastructure. The second space
encompasses the dynamics of the innovation process itself such as knowledge and skills
required, the creation of products and services, intellectual property protection, incentives for
innovation, production factors, value chains and feedback. The enablers and determinants in

3 The Gulf Cooperation Council (GCC) is the colloquial term used to refer to the Cooperation Council of the
Arab States of the Gulf (GCC). We use the abbreviation GCC to refer to the member countries as of 2017 –
Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates. These are synonymously referred
to as the countries of the Arabian Gulf, countries of the Arabian Peninsula, countries of the Persian Gulf and
Arab countries of the Gulf. Other countries on the Arabian/Persian Gulf such as Iran and Iraq, and countries
in accession talks such as Yemen, Jordan and Morocco are not included.

4 For more information on the evolution of the innovation policy literature landscape refer to Radosevic (2012),
Fagerberg (2016) and Borras and Edquist (2019).

5 According to Lundvall (2007) systems of innovation in a narrow sense “leave significant elements of innovation-
based economic performance unexplained”. In the “broad” sense the core knowledge-producing and disseminating
institutions (such as educational institutes and research units) are embedded in a wider socio-economic system
and the relative success of innovation policies is a function of influences and linkages beyond these core
institutions (Freeman, 2002).

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both these spaces form the focus of the innovation policy studies. Therefore, it is frequently
recommended to carry out more research that considers the collective effect of these
determinants of innovation (Freeman, 2002; Lundvall, 2007; Fagerberg, 2016).
The literature of systems of innovation from a growth theory perspective has been criticised
for having a narrow conceptual scope. 6 Additionally, the policy relevance of the results in this
line of research varies depending on the measures of growth and innovation used.7 The
research for innovation policy through the growth theory lens has been carried out using
various productivity measures, most prominently Multi-Factor Productivity (MFP).
However, its use is challenged because of the imperfect imputation for developing countries
and a lack of direct policy implications that can be derived from research using MFP. Other
measures also face challenges and their use is context and target-driven. Given this, the use
of labour productivity-based measures (such as labour productivity in the modern sector) 8
generally increases the policy relevance of the results and widens the geographical and
conceptual scope of the innovation policy studies. In addition to this, innovation studies have
also been criticized as being too centred on rich countries. This critique is made plain in the
question, “whether innovation systems and policies are only for the rich” (Perez, 2013, p. 90) 9.
The substantial focus of innovation studies on innovation in the developed or rich countries
has partially contributed to the lack of understanding about the determinants of innovation
in a broader country income level context.

6 For more details on the literature of systems of innovation from growth theory perspective see Fagerberg, et
al. (2013) and (Soete, et al., 2010).

7 The relationship between productivity and innovation, and a detailed discussion on the pros and cons of the
alternative measures of innovation are presented in Mohnen and Hall (2013).

8 In this dissertation the terms non-traditional sector and modern sector exclude the natural resource and
agricultural sectors, unless otherwise mentioned. The sectors where the productive efficiency is a function of the
knowledge and skills of the labour force is often called the productive sector or modern sector. In the empirical
literature, the modern sector has varyingly been defined to exclude either the natural resource sector, or the
agricultural sector, or both sectors. This usage is derived from the assumption that the natural resource and
agricultural sectors are predominantly non-productive sectors.

9 The question is posed by the editors of “Innovation Studies: Evolution and Future Challenges” Jan Fagerberg,
Ben R. Martin and Esben Sloth Andersen to Carlota Perez as a discussion point for her contribution in the book
(Fagerberg, et al., 2013).

One of the main limitations of the growth literature, in general, is to ignore the importance
of natural resources in the economy (Sachs & Warner, 1995; Auty, 2007). The same limitation
is also observed in the literature on innovation policy from the growth perspective.
Additionally, the innovation policy mix for countries that are close to the technological
frontier and for those that are far behind in the catching-up process is likely to be different
(Szirmai, et al., 2011; Aghion, et al., 2014). Countries with natural resources form a unique
subset of world economies. Among other reasons, the uniqueness originates in the use of
natural resource revenues as a facilitator of broader economic growth. Thus, investigations
that focus on the policy mix that has been used and can be used in these countries for catch-
up, diversification and stimulation of innovation are warranted. The natural resource
economics and policy literature for a long time has been focussed on the so-called natural
resource curse and the search for its determinants. However, some argue that the outcome of
the mismanagement of national revenues for a natural resource-driven economy does not differ
from the consequence of the mismanagement of revenues in countries that are not dependent
on natural resource revenues (Maloney, 2002). Even without the precise assignment of blame
for the economic woes of the countries rich in natural resources, the literature appears to lead
towards a probable solution. 10 The general inference is that the diversification of natural
resource-driven economies is key for ensuring their long-term economic development. This
concept is not dissimilar from the more generalised perspective that countries that provide
more diverse products and services are more likely to have higher output growth stability
(Krishnaa & Levchenko, 2013; Content & Frenken, 2016).
Given the reservations of the neo-classical literature in considering the role of natural
resources as a contributor to economic growth, the theoretical contributions in this area have
been limited. Also, the determinants of economic growth in developed countries have
frequently been used to try to explain the same in low income economies without
contextualising the research. Diversification policy has long been a part of the national
discourse of natural resource-driven economies. However, policy instruments in some countries

10 For more information on the natural resource economics literature and the natural resource curse debate see
Badeeb, et al. (2017) and Papyrakis (2017).

11
have been limited to monetary and fiscal policy instruments, and ineptly chosen targets for
economic policy programs make it hard to evaluate the attainment of a positive net stimulus
or the lack thereof (World Bank Institue, 2010). Such complications in policy design,
evaluation and implementation are symptomatic of the absence of research-based innovation
and diversification policymaking. There is a lack of comprehensive theoretical background
that adequately applies to a broad set of economies including natural resource-driven and
developing economies. This limitation indicates that the complacency of the academics and
economic policy consultants is as likely to be blamed as that of the national governments and
policymakers for the failure of many countries to have an effective innovation policy. Among
the natural resource-driven economies, the GCC countries have attracted the general curiosity
of economists. However, the scientific publications aimed at trying to understand the
economic structure, systems of innovation and the state of diversification in the GCC
countries have been limited. The most frequently given reason is the lack of data. In view of
the academic context described in the preceding paragraphs, we have an opportunity to look
for the key where it is dark 11.
This dissertation presents a conceptual framework that can be used to analyse the interplay
of the determinants of innovation and diversification. As a first step, this is used in an
empirical exercise to discover the effective relationship between selected determinants and
labour productivity growth in the modern sector. A discrepancy among the regions is observed
and explored further by mapping the enablers and inputs of innovation and diversification
for three GCC countries. The relationship of these determinants is discussed in light of the
innovation and labour productivity outputs. These undertakings support theorising in the
area of innovation policy studies and ensure that such theorising covers the economic reality
and systemic conditions of all countries regardless of their income levels.

11. The streetlight effect, or the drunkard’s search principle, is a type of observational bias that occurs when
people only search for something where it is easiest to look. A parable featuring the Seljuk Sufi mystic Nasrudin
Hodja is considered as the earliest form of the story (Shah, 1964, p. 70). The parable relates him as looking for
his key outside his house because there was more light outside, while he knew that the key was lost inside the
house. It was popularised in social sciences by Abraham Kaplan in his book “The Conduct of Inquiry:
Methodology for Behavioural Science” (Kaplan, 1964).

The introduction of the natural resource sector in this dissertation as an important
contributor to economic growth as well as to the development of the modern sector follows
the same logic as mentioned above. The aim is to ensure that the research contribution has
broad academic and policy applicability in terms of economic structure and country income
classification. The research places a special emphasis on the GCC countries to illustrate the
thesis that important economic questions can be addressed even in a state of data scarcity.
Economists should never be wary of research on a seldom studied region. Rather, the opposite
should be the norm, and, purposefully and promisingly, it has often been so for the last ten
years. The frameworks, tools and models developed in this dissertation can guide policymakers
to not only fix realistic targets for economic inputs and outputs but also embed an ex ante
and ex post evaluation of the achievements into their programs. Accordingly, the use of an
empirical model is presented to highlight the importance of research-based policymaking.
The findings in the various chapters of this dissertation illustrate that all the determinants
of the innovation system need to be working well in order to stimulate innovation and
productivity growth in the modern sector. The highlights, along with the structure of the
dissertation, are summarised in the following.
Chapter 2, titled “Productivity and Innovation Policy” presents a conceptual framework of
innovation policy and its empirical application in developed and developing countries. The
condition of the GCC region in terms of labour productivity growth in the modern sector is
explored within the context of this model. The results of Chapter 2 underscore the importance
of investment into enablers of innovation such as tertiary education, and research and
development expenditures. It is observed that not only the level of investment matters but
also the effectiveness of the system in which the policy is executed. This observation highlights
the importance of improving governance and investing in the development of institutions.
Chapter 2 also reveals that the modern sector in the GCC region is performing relatively
poorly compared to other regions in terms of labour productivity growth in the modern sector.
This is followed by a comprehensive review and comparative analysis of the broad innovation
system in three of the six GCC countries in Chapter 3, titled “Policy and Economy in the

13
GCC” in the form of an eclectic comparative case-study. We present a snapshot of the
development of enablers and policies for diversification and innovation in Oman, Saudi Arabia
and the United Arab Emirates and highlight the limitations of their systems. The low
innovation and diversification output in these countries is a consequence of the observed
limitations. This observation leads to the notion that the lowest-performing policy area limits
the system performance and that it is essential to ensure the robust performance of all the
determinants of the system.
Chapter 4 of the dissertation, “Natural Resource Abundance: No Evidence of an Oil Curse”,
outlines a theoretical model in order to examine the possibility of using natural resource
revenues to fund fixed capital investments and develop the modern sector. It also examines
empirically, to what extent oil wealth has been used for diversification by the six GCC
countries. Chapter 4 shows that the lower performance in the GCC region in terms of labour
productivity growth in the modern sector is not due to natural resource rents. The GCC
countries have been rather successful in investing their natural resource revenues into fixed
capital. The chapter highlights that natural resource rents can be used for the development
of the modern sector.
This is followed by an analysis of the stated economic diversification policies of Oman and
Saudi Arabia contrasted against the predicted outcomes in Chapter 5, titled “Stars in their
Eyes?”. It is an ex ante evaluation of national policy programmes based on the empirical
model developed in Chapter 2. The challenges and prospects for meeting the desired and
declared diversification targets are also discussed. The results of Chapter 5 project that Oman
and Saudi Arabia are not likely to meet their 2030 targets for diversification. A research-
backed discussion of the policy limitations and possible pathways to meet future targets is
thus presented. It is observed that the several enablers that may be improved through
investments must all be understood in the national and regional context in order to achieve
successful diversification and improvements in the innovation system of the country. In the
final Chapter 6, a short review of the background and results of the dissertation are presented.
It summarises the chapters of the dissertation, formulates academic research

recommendations, acknowledges some of the limitations of this dissertation, proposes an
integrated view of the dissertation and outlines the policy implications of the research carried
out. The synopsis of this dissertation is that there is no natural resource curse and that there
is no one policy or area of investment that drives growth and productivity in the modern
sector. A broad and holistic approach to academic enquiry in innovation policy and
productivity growth in the modern sector is justified and recommended. The policy relevance
of this dissertation, already discussed in the subsection “Integrated insights from the
dissertation and policy implications,” is exposed a second time in the “Valorisation
Addendum”.
The successes and limitations in the policy actions of the GCC countries brought to light in
this dissertation support a broad approach to policymaking for diversification. The GCC
countries started using their oil revenues for economic and social development while in a
position of relative poverty in comparison to the rest of the world (Khalaf & Hammoud, 1987;
Pamuk, 2006). 12 They invested in human capital, fixed capital and the improvement of the
standards of living of their population. As a result, the GCC countries successfully eradicated
extreme poverty in their countries, and by 2017 all the six GCC countries were included in
the list of “very high human development countries” based on their Human Development
Index (HDI) (UNDP, 2018; GCC-STAT, 2019). Along with using their natural resources for
human development, the GCC countries have attempted to stimulate diversification of their
economies and ensure sustained economic development. They have been successful to varying
degrees and are undertaking policy actions to deepen diversification aimed at securing and
increasing the prosperity gains of the last fifty years. The policy lessons from the GCC
countries are critical for other natural resource-driven economies. The empirical evidence from
this dissertation, on innovation policy, diversification and natural resource-based
development, helps illustrate the importance of innovation and diversification policy research.
The results demonstrate that low- and low-middle-income natural resource-driven countries

12 By 1970s almost all GCC countries had control over a substantial portion of the oil revenue generated from
oil extraction in their countries.

15
can utilise their natural resources effectively and efficiently and aim their policies at diversified
production and broad economic development. Such policymaking is expected to support
improvements in the state of their human development as it has been accomplished by the
GCC countries, and also help eradicate poverty in all forms.

Appendix 1-A
Table 1.1 – Classification of countries as natural resource-driven economies
Natural resource-driven economies are defined as those that qualify under at least one of the criteria:
Export: Natural resource rents represent more than 20% of the total exports of the country.
Revenue: Natural resource rents amount to more than 20% of the fiscal revenue of the country.
Output: Natural resource rents are higher than 10% of the economic output of the country.
Based on Addison and Roe’s (2018) definition in the book “Extractive Industries” which is adapted from
McKinsey & Company’s report “Reversing the curse” (Dobbs, et al., 2013). The resource dependence
classification, country income levels and poverty dynamics were inspired by the mentioned references and are
based on the author’s calculations using the World Bank (2019) data published under Creative Commons
Attribution 4.0 International License (CC-BY 4.0).
Country Name Exports Revenue Output World Bank Income Level
Algeria ● ● ● Upper Middle Income
Angola ● ● ● Upper Middle Income
Armenia ● Upper Middle Income
Australia ● ● High Income
Azerbaijan ● ● ● Upper Middle Income
Bahrain ● High Income
Benin ● ● Low Income
Bhutan ● Upper Middle Income
Bolivia ● Upper Middle Income
Brazil ● Upper Middle Income
Brunei Darussalam ● ● ● High Income
Burkina Faso ● ● ● Low Income
Burundi ● Low Income
Cameroon ● ● Lower Middle Income
Central African Republic ● ● ● Low Income
Chad ● ● ● Low Income
Chile ● ● ● High Income
Colombia ● ● Upper Middle Income
Comoros ● Lower Middle Income
Congo, Dem. Rep. ● ● ● Low Income
Congo, Rep. ● ● ● Lower Middle Income
Cote d’Ivoire ● Lower Middle Income
Ecuador ● ● Upper Middle Income
Egypt, Arab Rep. ● ● Lower Middle Income
Equatorial Guinea ● ● ● Upper Middle Income

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Country Name Exports Revenue Output World Bank Income Level
Eritrea ● Low Income
Ethiopia ● ● ● Low Income
Gabon ● ● ● Upper Middle Income
Gambia, The ● Low Income
Ghana ● ● ● Lower Middle Income
Guinea ● ● ● Low Income
Guinea-Bissau ● ● ● Low Income
Guyana ● ● ● Upper Middle Income
Indonesia ● ● Upper Middle Income
Iran, Islamic Rep. ● ● ● Upper Middle Income
Iraq ● ● ● Upper Middle Income
Kazakhstan ● ● ● Upper Middle Income
Korea, Dem. People’s Rep. ● Low Income
Kuwait ● ● ● High Income
Kyrgyz Republic ● ● Lower Middle Income
Lao PDR ● ● Lower Middle Income
Liberia ● ● Low Income
Libya ● ● ● Upper Middle Income
Madagascar ● ● ● Low Income
Malawi ● ● Low Income
Malaysia ● Upper Middle Income
Mali ● ● ● Low Income
Mauritania ● ● ● Lower Middle Income
Mongolia ● ● ● Upper Middle Income
Mozambique ● ● ● Low Income
Myanmar ● ● Lower Middle Income
New Caledonia ● High Income
Niger ● ● Low Income
Nigeria ● ● Lower Middle Income
Oman ● ● ● High Income
Papua New Guinea ● ● ● Lower Middle Income
Peru ● ● Upper Middle Income
Qatar ● ● ● High Income

Country Name Exports Revenue Output World Bank Income Level
Russian Federation ● ● ● Upper Middle Income
Rwanda ● ● Low Income
Saudi Arabia ● ● ● High Income
Senegal ● ● Lower Middle Income
Sierra Leone ● ● Low Income
Solomon Islands ● ● ● Lower Middle Income
Somalia ● ● ● Low Income
South Africa ● ● Upper Middle Income
Sudan ● ● Lower Middle Income
Suriname ● ● ● Upper Middle Income
Syrian Arab Republic ● ● Low Income
Tajikistan ● Low Income
Tanzania ● ● Low Income
Timor-Leste ● ● ● Lower Middle Income
Togo ● ● ● Low Income
Trinidad and Tobago ● ● ● High Income
Turkmenistan ● ● ● Upper Middle Income
Uganda ● ● ● Low Income
United Arab Emirates ● ● ● High Income
Uzbekistan ● ● ● Lower Middle Income
Venezuela, RB ● ● ● Low Income
Yemen, Rep. ● ● Low Income
Zambia ● ● ● Lower Middle Income
Zimbabwe ● ● Lower Middle Income

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2. Productivity and Innovation Policy
Education, Research & Development, Governance, Business, and Productivity
Abstract
In this chapter, we examine the relationship between “innovation policy” and labour
productivity growth in non-traditional sectors, for a cross-section of more than 95 developed
and developing countries. We consider that labour productivity growth in non-traditional
sectors is in part explained by innovation and catch-up. In developing countries, catch-up is
a substantial contributor to productivity increases in addition to new-to-the-world
innovations. The ability to catch-up is considered to be dependent on the absorptive capacities
of the countries. We term the policies that contribute to improvements in the absorptive
capacity as innovation policies. In this chapter, we include investments in tertiary education
as a percentage of gross domestic product (GDP), investments in research and development
(R&D) as a percentage of GDP, the freedom in the business environment, as well as overall
government effectiveness. Our results confirm the convergence of non-traditional sector labour
productivity amongst the countries. We could show a significant positive effect of the
interaction between, government effectiveness, and, the government expenditures in tertiary
education as a percentage of GDP, on labour productivity growth in non-traditional sectors.
Also, for developing countries, a positive and significant relationship between the growth
variable and effective R&D expenditures was observed. We could not uncover a relationship
between other policies considered in this chapter and labour productivity growth in non-
traditional sectors. Non-traditional sector labour productivity growth in the oil-rich Arabian
Gulf countries was observed to be consistently slower than western countries. We propose
that there is the likelihood of higher oil prices crowding-out innovation in oil-rich countries
while stimulating innovation in oil-importing countries.
Keywords: Innovation policy, labour productivity, catch-up, structural change, government
effectiveness, developing countries, Arabian Gulf countries.
JEL Classification: O2, O3, O38, O43, O47

2.1. Introduction
In this chapter, we analyse how individual innovation policies and their interactions influence
labour productivity in non-traditional sectors and innovation globally (both in developed as
well as in developing countries). We consider the different strategies that may be required to
innovate under various conditions of development. We also explore the relationship between
labour productivity growth and innovation policies in Arabian Gulf countries, that are
characterised by a high share of natural resource rents in the economy.
Knowledge, technological change, and innovation have been introduced as drivers of growth
in the growth economics literature as it has moved beyond only considering capital and labour
as sources of growth 13. It discusses healthy institutions as necessary for technological change
and points towards innovation policy to nurture the institutions that promote knowledge
production and technological progress. The lumping together of the factors that contribute
to human capital, physical capital, and institutional capabilities has been considered in the
1960s and 1970s as a common deficit in the literature. The need for a more in-depth enquiry
of complementarities in policies that affect economic activities, capabilities and institutional
arrangements has been emphasized by Easterly & Levine (2001), Freeman (2002), Aghion et
al. (2009) and notably the winner of the 2018 Nobel Memorial Prize in Economics, Paul
Romer (1994). As such, “innovation policy” including, education policy, R&D policy, business
policy, and governance is considered in this chapter.
The role of education and R&D policy for innovation and modern sector labour productivity
growth has been explored in the context of developed and developing countries. This has
promoted the need for enquiries on whether higher education and R&D expenditures have
dissimilar returns for developed and developing countries (Krueger & Lindahl, 2001; Keller,
2006; Aghion & Durlauf, 2009). An important insight is that countries could be more or less

13 Solow’s works, and studies by Denison, showed that something other than labour and capital was responsible
for increasing growth rates in the US (Solow, 1957; Denison, 1963). Romer (1986) incorporated technology as
an endogenous factor in constructing a model of increasing returns of technology and knowledge for long-run
growth.

21
efficient in the translation of innovation policies to productivity growth and innovation
(Griffith, et al., 2004; Ritzen & Soete, 2011; Loukil, 2014). Therefore, we consider the
effectiveness of the government in analysing the relationship between labour productivity
growth in non-traditional sectors and innovation policy. Considering that innovation-based
growth is less likely to affect traditional industries (Becheikh, et al., 2006), we consider the
response of innovation policies, for both developed and developing countries, to exhibit in the
non-traditional sector labour productivity growth. That is labour productivity growth
exclusive of natural resource rents and agricultural value added.
Productivity can increase in three ways: through economies of scale, through the utilisation
of unused capacity and by innovation. Innovation encompasses creating new products and
services, adopting technology that is not available anywhere else, adapting technology that
has been available elsewhere and as such moving towards the best practice, improving
management techniques, and marketing novelty. Sectors where capital investment is more
likely to increase economies of scale in the short-run include traditional natural resource
extraction and agricultural sectors (Behrens, et al., 2007; Kislev & Peterson, 1996). As such,
labour productivity excluding these traditional sectors is expected to be more closely
associated with innovation. We term this part of the economy as the modern sector
(interchangeably, non-traditional sector). In the modern sector increasing output by utilising
the same input generally requires improving management techniques (for enhancing
production), better supply chain (for delivering increased inputs and production outputs),
and marketing innovation (for ensuring that production is consumed in the market).
Productivity is defined as the index of output over the index of input. Labour productivity is
one of the ways to measure productivity. It is the total output over the total amount of labour
employed. As such, it does not account for inputs of capital in the denominator. Another
approximate measure is the Total Factor Productivity also termed as Multi-Factor
Productivity (MFP). It is measured as the Solow residual or using the output of the economy
divided by weighted inputs of capital investment and labour. The standard weighting of 0.7
for labour and 0.3 for capital is often employed. A concern with the output measure used for

the MFP is that quality improvement is not accounted for within this indicator as price
changes do not necessarily reflect the changes in quality. Using output and input deflator is
also inaccurate as we may be undervaluing output deflators or overvaluing input deflators
and by doing this, we might be reflecting price effects and not necessarily innovation effects.
In their work “Innovation and Productivity: An Update”, Mohnen and Hall (2013) provide a
brief review of the empirical literature and conclude that innovation leads to better
productivity performance. They note that innovation affects productivity increases through
both real output and the price. Innovators take advantage of the two effects in order to
increase their profitability. Firstly, they are able to sell their products or services at above
competitive prices, as in monopolistic competition at least for some time. Secondly, the
product or service itself is higher quality and delivers higher value to the consumer – this is
called the real innovation output. In terms of the limitation of the productivity measure,
Mohnen and Hall find that the innovation price effect and the innovation product effect (real
output) are hard to disentangle in the absence of good individual price measures. It is
important to note that in this chapter, we are interested in capturing catch-up effects as well
as new-to-the-world innovation effects. The innovation price effect and innovation product
effect are both important in this context and the disentanglement is not necessitated. Product,
process, organisational and marketing innovations all improve productivity through either or
both of these effects, in other words, innovation leads to better output per employee
performance.
Innovation counts are widely used to represent innovation, specifically to account for the
innovation product effect. However, innovation counts are not without limitations. In fact,
the reason that researchers are unable to find a stronger association between productivity and
innovation may be related to the use of imperfect measures to represent innovation (Mohnen
& Hall, 2013). It is thus suggested that recording innovation counts in a continuous measure
including quality of innovation instead of a binary “yes or no” format would lead to more
meaningful and robust estimates on the relationship between productivity and innovation.
Patents while promising are controversial as they represent only a small part of innovation.

23
Some patents may never be converted to finished and marketed products or services. At the
same time, some innovation may never be patented. Also, new-to-the-country and new-to-
the-region innovations may not be reflected in patent data. This type of innovation is vital
in developing countries and accounts for the majority of increases in labour productivity
through innovation.
In this chapter, we are interested in increases in labour productivity associated with
innovation policy. As discussed above, we ensure that labour productivity is closely related
to innovation by using labour productivity in the modern sector. The issue of selecting the
most appropriate measure is however complicated further when considering the availability
of data for a broad set of countries and, in particular, for the Arab countries of the Gulf that
are the central focus of this dissertation. One commonly quoted disadvantage of productivity
data is that of varying recording standards among countries. We find that labour productivity
data is most widely available across countries and time. In order to account for the probable
variation in recording standards, we use standardised data from a reliable source.
Additionally, it is also important for us to use a measure that has direct policy implications.
In the construction of MFP, which is derived as the residual of the Cobb-Douglas equation,
the unit of MFP does not have a simple economic interpretation and appears to be a modelling
artefact. The use of labour productivity in the modern sector ensures the ability to derive
simple economic interpretation of the results that can inform policymakers on innovation as
well as diversification-concerns.
The catch-up process in developing countries is dependent on absorptive capacity. The
countries as such can induce catch-up and increases in labour productivity by improving
education, R&D, business environment and governance. Innovation policy is a broad term
and successful innovation is related to many systemic processes working in sync. As such, we
acknowledge that defining the ideal combination of institutions and policies that are
important for innovation is a complex task. We consider innovation policies as those directed
at improving the innovative and absorptive capacity of the countries. In this chapter
innovation policy is characterised by the interaction of expenditures on education with the

effectiveness of government, the interaction of expenditures on R&D with the effectiveness of
government and business policy. Our identification strategy comprises five-year labour
productivity growth rates regressed as a function of initial labour productivity, relevant lagged
innovation policy variables, control variables and dummy variables.
The literature on innovation, growth and productivity, innovation policies, education, R&D,
business, governance and the associated relationships is discussed in Section 2.2. Our
identification strategy is drawn out in Section 2.3. The data are presented in Section 2.4. The
results related to the effect of innovation policies in the global context, as well as for developed
and developing countries are presented in Section 2.5. Finally, we discuss the results, their
policy implications, and present our concluding remarks in Section 2.6.
2.2. Innovation policies and the path towards successful innovation
The innovation literature is distributed between “narrow” and “broad” focus on innovation
policies. In the “narrow” sense, only formal R&D systems and organizations systematically
active in knowledge generation and diffusion are the focus. An example of the application of
the systems of innovation framework in the former sense is the World Bank Knowledge
Assessment Methodology (Chen & Dahlman, 2005). However, systems of innovation in a
narrow sense “leave significant elements of innovation-based economic performance
unexplained” (Lundvall, 2007). In the “broad” sense the core knowledge-producing and
disseminating institutions are embedded in a wider socio-economic system, and the relative
success of innovation policies is a function of influences and linkages beyond these core
institutions (Freeman, 2002; Soete, et al., 2010). Among the works that discuss new-to-the-
world innovation in the latter sense, Furman, Porter and Stern (2002) integrate ideas-driven
growth theory, microeconomics-based models of national competitiveness and industrial
clusters theory. They consider R&D manpower, knowledge and technology base as important
sources of innovation. Archibugi & Coco (2004) define innovation system through patents,
publications, ICT, electricity consumption, and education. To formulate a comprehensive
narrative, we draw from literature sources focused on individual policy variables, governance,

25
policy interaction, growth, labour productivity, and innovation. This conceptualisation is
carried out with the frame of reference that growth is primarily driven by technological change
and innovation. The three main factors of growth are generally considered to include MFP,
labour input, physical capital and human capital. Technology growth and efficiency are
considered as the sub-sections of MFP. Efficiency improvement, in the very least, need
management and process innovation. Together we term these components as innovation. This
is the origin of the consideration that innovation and technological change is the primary
contributor to economic growth (Solow, 1957; Denison, 1963; Romer, 1986). At the same
time, Nelson and Winter’s (1982) work on the evolutionary approach views the free-market
economic structure as continuously evolving with emphasis on the influence of institutions
and government policies on economic activity.
Education is one of the policy variables that affect innovation. Aghion et. al (2009) set out
to estimate causality of the effect of education on growth, using actual measures of investment
in education. They question the adequacy of using lagged spending – in their previous work
(Vandenbussche, et al., 2004) – as an instrument to overcomes biases caused by omitted
variables such as institutions, especially in the case of small low variation data. They show
that there are positive effects of exogenous increases in education expenditure related to four
years’ tertiary education in the states of the United States of America (US) that are close to
the world frontier (Aghion, et al., 2009). Krueger and Lindahl (2001) find societal returns to
schooling in terms of increased growth in cross-country analysis; the relationship is
statistically significant and positively associated with subsequent growth for countries with
the lowest level of initial education.
Faster growing countries in Asia have had higher expenditures on primary education. Keller
(2006) does not obtain positive significant results consistently for the effect of such
investments on growth. However, it is suggested in the paper that inefficiencies in resource
allocation of secondary and tertiary education expenditures may be the reason behind. In
other studies, government expenditures on education relate positively to growth in developing
countries (Bose, et al., 2007). Also, the inclusion of other policy variables in the studies, such

as openness, public spending, and health variables results in a lower estimated impact of
education on growth (Benos & Zotou, 2014). From these studies, we conclude that education
should be considered as a part of “broad” policies for innovation-based growth.
Loayza et al. (2005) find that regulatory burden reduces growth. However, a higher quality
of institutional framework leads to the negative effects of excessive regulation on growth to
be lessened. In a simple model Djankov et al. (2006) observe the effect of business regulations
as represented by the doing business indicators while considering the effect of initial level of
growth, and control variables and other determinants of growth that include corruption, law
and order, the political system, primary and secondary school enrolment, and civil conflict.
They find that going from the worst to the best quartile of business regulation shows a 2.3%
increase in annual growth rate. They also observe that the effects of improvement in primary
and secondary education from worse to better quartiles of policy or output are significantly
lower than the effects of business regulation on growth rate. Hanusch (2012) suggests that
regulations related to credit, contract enforcement, costs, time, starting a business, registering
property, and protection of investors within the realm of business policies are statistically
significantly related to economic growth.
Griffith and team (2004) find that R&D, as represented by BERD, is statistically and
economically important in the catch-up process as well as for stimulating innovation directly
and suggest that the social rate of return of R&D has been underestimated in the literature
as many studies only focus on the US. A look into cross-country labour productivity
differences due to investment in R&D reveals that R&D investment has a significant positive
impact on productivity (Lichtenberg, 1993). Nadiri and Kim (1996) find rates of returns of
domestic R&D expenditures to be in the range of 14 to 16% and adding the effect of spill-
overs of international R&D spending for six (6) advanced economies showed the returns to
be 23 to 26% varying amongst the countries. Hall, Mairesse, and Mohnen (2010) in their
review of the econometric literature measuring the private and social returns to R&D find
that the literature identifies private returns to R&D as strongly positive, social returns to be
greater than private returns and public-funded R&D to be less productive in terms of private

27
returns. In many research avenues, the incentives to invest in R&D is determined on the basis
of private returns and not social returns. It is also observed that despite having higher social
returns to R&D investments developing countries are not able to achieve the maximum
potential in R&D. This may be due to inappropriate or inadequate social policies (Griffith,
et al., 2004).
Jalilian, Kirkpatrick and Parker (2007) find that there is a strong causal link between
government regulation, regulatory quality indices, and economic performance. Other cross-
sectional studies also report causal effects of governance on long-run income per capita, using
instrumental variables (Kaufmann & Kraay, 2002). Also, the mechanism behind this causal
link has been examined and it has been pointed out that one path through which government
effectiveness improves economic performance is by creating a better investment environment
(Kirkpatrick, et al., 2006). It is likely that government effectiveness translates into high
economic growth not only through the path of providing a good investment environment but
also by creating a good environment for innovation policies to be effective.
The common theme that emerges from the literature is that policies work in coherence with
each other and have a combined and complementary effect on growth and productivity. The
translation of policy to increased labour productivity growth must go through the
governments’ ability to effectively convert inputs of policy into innovative products and
services, as well as innovative management, production, and marketing practices. In this
respect, non-traditional sector labour productivity is closely associated with innovation, across
developed as well as developing countries. Most of the literature referenced here is aimed at
determining the relationship between various policies and growth measures such as GDP,
Income per Capita and Labour Productivity. In this chapter, we try to study the relationship
between selected policies and innovation proxied by labour productivity growth in the modern
sector. As such we are delving deeper into the relationship between one of the proximate
causes of growth – technological change or innovation and some of the fundamental causes of
growth – the policies and institutional settings that drive growth. In this sense in the literature
we have discussed is relevant in two ways – one, in pointing out the relationship of

fundamental causes of growth to growth itself; and the second, by pointing out that there is
a gap in trying to understand the link between the fundamental and proximate causes of
growth.

Figure 2.1 – Innovation Policy Framework Conditions
Figure 2.1 above represents our interpretation of how the flows of knowledge enable an
increase in innovation. The innovation eco-system is thus arranged into conditions, linkages,
the firms and the market itself. The increase in innovation is linked to increasing labour
productivity through innovative management, design, production, and marketing techniques.
The change in the state of these conditions is determined through natural transformation and

29
policy. The education condition is affected by government policies, such as government
financing of the tertiary education system, policies determining graduate ratios in science and
technology fields, alignment to labour demand from the market, university autonomy, and
others. Similarly, research and development conditions are impacted by the government
expenditure on research and development, type of research grants, targeted scientific field
grants, the competitiveness of grants, intellectual property regime, and private-sector research
funding, and so forth. Business conditions are related to industrial policies, competition policy,
entrepreneurship policy, taxation policy, financial policy, the health of the financial sector,
availability of finance, and market access for firms that create new products or services.
Infrastructure conditions include the availability of ICTs, Transport, Energy, Standard-
Setting, Metrology, Security, among others. Finally, it is considered that without efficient
and effective linkages the production of knowledge, as well as transfer of knowledge for the
creation of new products and services, would be hampered. For innovation to thrive in the
production space it is important that the innovation environment conditions are healthy,
governed by sound policy, with effective linkages across various conditions, the production
space and the market for consumption of the innovations.
2.3. Identification Strategy
We use the framework in Figure 2.1 above to understand policy factors that promote
innovation. Consequently, we explore how individual innovation policies and their interactions
influence innovation globally, in developed, and developing countries. As such, labour
productivity growth in the non-traditional sector is modelled as a function of innovation
policy and the effectiveness of innovation policy.
We assume the drivers of innovation to be: the initial level of labour productivity, the
interaction of government effectiveness with educational expenditures, the interaction of
government effectiveness with research and development and a facilitative business
environment. We estimate the relationship with an Ordinary Least Squares regression with
exogenous variation in explanatory variables of policy.

The response variable is defined as the natural log of the ratio of final to initial labour
productivity in non-traditional sectors, where final labour productivity is taken to be five
years after the initial measure. We use four years averages starting from the initial year to
smoothen out one-off effects for the countries. The explanatory variables are lagged one year
and include the natural logarithm of initial labour productivity, interaction of government
effectiveness and government expenditures in tertiary education as a percentage of GDP
(alternatively called effective tertiary education expenditures in this chapter), interaction of
government effectiveness and gross expenditures on R&D as a percentage of GDP
(alternatively called effective R&D expenditures in this chapter) and index of economic
freedom. Innovation is a medium to long term phenomenon, and innovation policies typically
take a long time to bear fruit. Using lagged average variables accommodates for long term
nature of innovation and also provides a way to exclude reverse causality.
The literature provides evidence that initial level of labour productivity is a determinant of
labour productivity growth. As such, we account for the initial level of labour productivity in
the estimation equation (Barro, 1991). Also, the initial level of education has an impact on
how innovation policies influence the role of tertiary education expenditures on innovation
itself (Keller, 2006). Natural resources and agricultural endowments also influence the growth
path of a country (Lederman & Maloney, 2007). Finally, a country’s regional situation
influences its growth trajectory as well (Moreno & Trehan, 1997). We introduce regional
dummies, educational attainment in terms of years of education from primary to tertiary
level, natural resource rents as a percent of GDP, and agricultural value added as a percent
of GDP as additional control variables. Total natural resource rents as a percent of GDP is
defined as the sum of oil rents, natural gas rents, coal rents, mineral rents, and forest rents
(World Bank, 2014). Agriculture in Agricultural value added corresponds to International
Standard Industrial Classification (ISIC) divisions 1-5 and includes forestry, hunting, and
fishing, as well as cultivation of crops and livestock production.
The estimation equation thus takes the form;

31
Equation 2.1: Δ tN-t1ln(labprod) = α0 + βo • labprod t1 + β1 • goveff t1 • edu t1 + β2 • goveff t1 • r&d t1 + β3 •
econfreedom t1 + natresrents t1 + agrirents t1 + eduattain t1 + regional dummies +
Table 2.1 – Variable Definitions
Variable Definition
ΔtN-t1 ln(labprod)
Productivity Growth
Natural log of the ratio of final to initial labour productivity in non-
traditional sectors (alternatively including traditional; natural resource and
agriculture sectors – see discussion in the main text)
labprod t1
Initial Productivity
Natural log of initial labour productivity
goveff t1 • edu t1
Effective Tertiary Education
Interaction of government effectiveness and government expenditures on
tertiary education as a percent of GDP – initial
goveff t1 • r&d t1
Effective R&D
Interaction of government effectiveness and gross expenditures on research
and development as a percent of GDP – initial
econfreedom t1
Economic Freedom
Index of economic freedom – initial
natresrents t1
Natural Resource Rents
Natural resource rents as a percentage of GDP – initial
agrirents t1
Agricultural Value Added
Agricultural value added as a percentage of GDP – initial
eduattain t1
Educational Attainment
Number of years of schooling from primary to tertiary level – initial
Note: The subscript “t1” in Equation 2.1 and the reference “initial” in Table 2.1 specifies the magnitude of the
variable during the initial year(s) considered. The subscript “tN” in Equation 2.1 and the reference “final” in
Table 2.1 specifies the final year. As such the growth is considered between t1 and tN. In this chapter, this
period of growth is 5 years and the policy variables are the average of four years, lagged by one year from the
final year for which a five-year growth rate is considered.

In addition, we evaluate the same equation for labour productivity growth including natural
resource rents and agricultural value added. This helps us identify differences in the influence
of innovation policies on innovation-based growth versus mixed innovation and traditional
sector growth and confirm the robustness of our results. We also estimate the model for
developed and developing country groups separately to understand the differences in the
influence of innovation policies and analyse the need for varying policies for both the groups.
The 15-year data from 1998 to 2013 is regressed in three groups of five years. The results for

each period are observed in order to understand period-specific differences. These period-
specific differences are controlled-for through a period dummy in the pooled dataset regression
that is aimed at generating a larger data set leading to significant and robust coefficients.
2.4. Data
Labour productivity is calculated in terms of real GDP per labour force. The labour
productivity indicator is constructed by using the GDP from World Bank Development
Indicators (World Bank, 2014) and the number of employees’ data from Penn Worlds Table
Version 8.1 (Feenstra, et al., 2015). The use of Purchasing Power Parity (PPP) Constant
2010 USD GDP ensures that the data is comparable across time and countries in level and
growth rate. Literature suggests that innovation-based growth is less likely to reflect in
traditional industries such as those in natural resource and agricultural sectors (Becheikh, et
al., 2006). As such the labour productivity growth measure excludes natural resource rents
and agricultural value added.
Data for government expenditure on tertiary education as a percentage of GDP is acquired
from the subset of the UNESCO Institute of Statistics education dataset that is related to
financial resources (UIS.STAT, 2016). UIS.STAT receives data on education expenditure from
country governments responding to UIS’s annual survey on formal education. Tertiary
education is considered as one of the most important contributors to innovation. When
interpreting this indicator, however, we should keep in mind that in some countries, the
private sector and/or households may fund a higher proportion of total funding for education,
thus making government expenditure appear lower than in other countries. Educational
attainment is based on years of school life expectancy primary to tertiary.
The gross domestic expenditure on research and development (GERD) as a percentage of
GDP is the total intramural expenditure on R&D performed in a country or region during a
given year, expressed as a percentage of GDP of the country or region (UIS.STAT, 2016).
The data is used as an indication of research and development policy. The ideal case would
be to use GovERD, that is government expenditure on research and development as a

33
percentage of GDP. We use the GERD measure because it captures wider geographical space
and time, and is a good representative of what similar higher expenditures can achieve.
We use the Index of Economic Freedom as an indicator of government policy towards
business. The Index of Economic Freedom is an annual index and ranking created by The
Heritage Foundation and The Wall Street Journal in 1995 to measure the degree of economic
freedom in the world’s nations. The creators of the index took an approach similar to Adam
Smith’s in The Wealth of Nations, that “basic institutions that protect the liberty of
individuals to pursue their own economic interests result in greater prosperity for the larger
society” (Heritage Foundation & Wall Street Journal, 2016). The index of economic freedom
is based on ten quantitative and qualitative factors. These ten factors are property rights,
freedom from corruption, fiscal freedom, government spending, business freedom, labour
freedom, monetary freedom, trade freedom, investment freedom, and financial freedom. Each
factor is graded on a scale of 0 to 100. A country’s overall score is derived by averaging these
ten economic freedoms, with equal weight being given to each.
It would have been ideal to use Ease of Doing Business data from the World Bank Doing
Business Indicators. The ten constitutive measures used in the composite ease of doing
business indicator are, starting a business, dealing with construction permits, getting
electricity, registering property, getting credit, protecting minority investors, paying taxes,
trading across borders, enforcing contracts and resolving insolvency (World Bank, 2015). As
such ease of doing business accounts for objective as well as subjective measures that are
directly related to business policy in the country. However, due to limited time-period
availability, we resort to using the Index of Economic Freedom that relates to the business
environment in a relative bird-eye manner.
Government effectiveness captures, “perceptions of the quality of public services, the quality
of the civil service and the degree of its independence from political pressures, the quality of
policy formulation and implementation, and the credibility of the government’s commitment
to such policies” (World Bank, 2015). Notably, the indicator is a mix of quality and perception
of infrastructure, bureaucratic, state, and policy stability. As such, it is used as a measure of

expected effectiveness of innovation policies as related to the enabling conditions that affect
linkages amongst various policy conditions and knowledge flow necessary for innovation (See
Figure 2.1 – Innovation Policy Framework Conditions).
Governance is difficult to account for using any kind of measure. We find it important to
touch upon the topic of the selection of Government Effectiveness as an interaction term for
the policy measures of expenditures in tertiary education and research and development in
more detail. The representative sources for constructing this indicator include quality of
bureaucracy, institutional effectiveness, excessive bureaucracy or red tape, infrastructure,
quality of primary education, satisfaction with public transportation system, satisfaction with
roads and highways, satisfaction with education system, basic health services, drinking water
and sanitation, electricity grid, transport infrastructure, maintenance and waste disposal,
infrastructure disruption, state failure, and policy instability. The composite is constructed
from a weighted average of the individual indicators obtain through an Unobserved
Components Model (UCM). The UCM assigns greater weight to data sources that tend to be
more strongly correlated with each other. This weighting improves the statistical precision
of the aggregate indicators and typically does not affect the ranking of countries much on the
aggregate indicators. There are two rationales for using Government Effectiveness. First, it is
indicative of the governments’ ability to implement their policies and as such the interactive
term represents the efficiency of each dollar spent. Second, the interaction of Government
Effectiveness with the expenditures can be looked at with much simpler view that is of
representing the policies as related to the governance environment. Both explanations relate
well to the definition of Government Effectiveness indicator and its use in the context of this
paper and the framework represented graphically in Figure 2.1.
The variable, effective government expenditures on tertiary education, is constructed by
interacting the index of government effectiveness with the government expenditures on
tertiary education as a percent of GDP. The same approach is taken to construct the variable
effective GERD as a percent of GDP. The prefix “effective” signifies an interaction with the
measure of government effectiveness. Effective expenditure is obtained by the interaction of

35
government effectiveness that runs from 0 to 1 by actual percent expenditures per GDP in
the relevant policy areas. As such government effectiveness is translated as the percentage of
effectiveness of each dollar spent or simply the interaction of the governance environment
with the policy measures.
Table 2.2 – Summary Statistics
Variable Countries Years Mean Std Dev Min Max
Δ yN-y1 ln(labprod)
Productivity Growth
150 1998-2013 0.559 0.364 -0.452 1.540
labprod y1
Initial Productivity
157 1998-2013 9.614 1.183 6.884 12.084
goveff.edu
Effective Tertiary Education
164 1998-2013 0.483 0.427 0.024 2.198
goveff.r&d
Effective R&D
129 1998-2013 0.485 0.716 0.003 3.427
econfreedom
Economic Freedom
177 1998-2013 57.740 12.190 8.900 89.060
natresrents
Natural Resource Rents
187 1998-2013 6.290 10.560 0 86.170
agrirents
Agricultural Value Added
164 1998-2013 16.830 14.510 0 61.800
eduattain
Educational Attainment
182 1998-2013 11.790 3.210 3.100 20.230
Note: Description of abbreviations is provided in Table 2.1
The indicator used to represent innovation-based growth is the natural log of the ratio of final
to initial labour productivity excluding natural resource rents and agricultural value added.
It is noteworthy that the number of countries for which these data points are available varies
from 129 for effective GERD as a percent of GDP to 177 for the index of economic freedom
for the year between 1998 to 2013. However, in our regression between 95 to 106 countries
are represented depending on the time period and extent of the data available. The correlation
coefficient of effective tertiary education expenditures and effective research and development
expenditures is 0.57. The same for Economic Freedom with effective tertiary education

expenditures is 0.47 and with the effective research and development expenditures is 0.51.
The pairwise correlation between our explanatory variables of concern is considered moderate
and is not expected to have an effect on the coefficients of the estimation. In order to make
sure that this is the case, we also regress excluding two of the explanatory variables and
compare the results with the original estimation.
2.5. Results
2.5.1. Global
Here we present the observed influences of the explanatory variables of concern, on the
response variable i.e. labour productivity growth excluding natural resource and agricultural
rents. Second, we present the results separately for developed and developing countries.
Finally, we glance at how labour productivity growth in the Arabian Gulf countries compares
with western countries (See footnote 18 associated with Appendix 2-B).
The first result that is observed and presented in Table 2.3 below is that of “beta-convergence”.
This term implies that the partial correlation between growth in income or productivity over
time, and its initial level is negative. It refers to a process in which poorer regions grow faster
than richer ones and therefore catch-up on them. We observe that the initial labour
productivity is negatively and statistically significantly correlated to labour productivity
growth for pooled data for three periods. An increase of 1% in the country’s initial labour
productivity results in the ratio of final to initial labour productivity to be lower by 0.045%.
Countries with relatively lower labour productivity are able to grow faster and hence converge
to the frontier. In this chapter, we make the observation for innovation represented by labour
productivity excluding natural resource rents and agricultural value added. In this context,
the results confirm the convergence of labour productivity between countries on the
innovative frontier and those away from it.

37
Table 2.3 – Labour Productivity Growth and Policy Variables
Response Variable Productivity Growth
(Net of natural resource rents and agricultural value added)

Period 1 Period 2 Period 3 Pooled Pooled Pooled

1998-2003 2003-2008 2008-2013 Period
1 & 2
Period
2 & 3
Period
1, 2 & 3

Initial Productivity -0.111*** -0.051 0.039 -0.075*** -0.022 -0.045**

(0.038) (0.037) (0.028) (0.025) (0.027) (0.021)
Effective Tertiary Education 0.024 0.035 0.041* 0.029 0.048* 0.041*

(0.040) (0.041) (0.023) (0.028) (0.025) (0.021)
Effective R&D -0.005 -0.037 -0.01 -0.023 -0.022 -0.018

(0.029) (0.030) (0.019) (0.020) (0.020) (0.016)
Economic Freedom 0.003 0 -0.003 0.002 -0.002 -0.001

(0.002) (0.002) (0.002) (0.001) (0.001) (0.001)
Arabian Gulf Dummy -0.118 -0.268** -0.148 -0.215** -0.278*** -0.269***

(0.142) (0.125) (0.114) (0.087) (0.097) (0.078)
Period 1

-0.068*** 0.103***

(0.017) (0.019)
Period 2

0.149*** 0.147***

(0.018) (0.017)
Root Mean Squared Error 0.107 0.126 0.090 0.113 0.126 0.120
Adj. R-squared 0.404 0.446 0.138 0.463 0.342 0.383
N 91 96 100 187 196 287
* p<0.10, ** p<0.05, *** p<0.01 Note: Regional dummies, educational attainment, natural resource rents, and agricultural value added included as control variables We observe that effective expenditures on tertiary education as a percent of GDP have a positive relationship in all periods with the explanatory variables. The pooled data for the three periods shows that there is a statistically significant positive relationship between effective tertiary education spending as a percent of GDP of the country and labour productivity growth. This is statistically significant at the 10% level for two sets of pooled data and for the third period. In this case, the magnitude of the increase is considerable i.e. 38 an increase of 1% in average effective tertiary education expenditure as a percentage of GDP would result in an increase of 4.2% in labour productivity growth. To simplify, a country effectively investing 1% of their GDP in tertiary education will improve their growth rate by 4.2% if they invest an equivalent of 2% of their GDP in tertiary education. Since this variable is represented by an interaction of government effectiveness and tertiary education expenditure as a percent of GDP it is useful to break down this result. A hypothetical country with 1% effective expenditure on tertiary education as a percent of GDP driven by 0.45 government effectiveness, investing 2.2% of GDP as tertiary education expenditure and having an annual labour productivity growth rate of 3% can improve its productivity growth rate to 3.13% (that is an increase by 4.2%) by increasing its effective expenditure to 2% that could be accomplished either by improving government effectiveness to 0.9 or tertiary education expenditure as a percent of GDP to 4.4%. Note that non-traditional sector labour productivity is being discussed here, as it is a measure representing innovation that covers developing countries as well as developed countries. We do not observe positive results for effective R&D expenditure percent of GDP for the complete set of countries. We observe no statistically significant results for the Index of Economic freedom. The magnitudes are small and the pooled data for the three periods show inconsistent correlation for the business policy and labour productivity growth. The estimation is robust to the inclusion of interaction variable’s constituents – government effectiveness, tertiary education expenditure as a percent of GDP, and GERD as a percent of GDP – in the estimation in addition to the interaction variables named effective tertiary education and effective R&D. Also, the signs of the coefficients for effective tertiary education, effective R&D and Economic Freedom do not vary and the magnitudes do not vary by a considerable extent when included individually in the estimation, that is, when the remaining two explanatory policy variables are excluded. This confirms that the moderate pairwise correlation for our explanatory variables discussed in Section 4 has no influence on the results. Table 2.4 shows the results for developed and developing countries. It provides a perspective into differences in the relation of innovation policy to labour productivity between developing 39 countries and developed countries. We observe in Table 2.4 a negative relationship between effective research and development expenditures and labour productivity in the modern sector for developed countries. The results may seem precarious at first. However, the difficulty in finding a relationship between productivity and innovation in the developed countries is well known and termed as the “productivity puzzle” and “Solow paradox”. We can observe for developing countries in Table 2.4 that the effective R&D expenditures variable has a positive effect on labour productivity growth for developing countries14 and the relationship is statistically significant at 10% level for the pooled data for three periods. An increase of 1% in the effective R&D expenditures as a percent of GDP in developing countries would result in the labour productivity growth rate to increase by 27.5% for pooled data of periods 1, 2 and 3. However, we do not find the same for developed economies. The differences between developed countries and developing countries are indicative of different stages of development. The developed countries may be more prominently engaged in new-to-the-world type innovation. At the same time, the developing countries are benefiting from catch-up type innovation and associated R&D. The productivity output of such research is considered to be higher and have lower lag. The lag is higher in the case of new-to-the-world innovation as was observed in the case of the acceleration in productivity growth that started in the technology sector and spread to the overall economy only many years later leading to the rapid productivity growth period of 1995 to 2004. A recession followed this period, as such overall productivity growth was decreasing in the developed countries in our sample set. We discuss these result along with the “productivity puzzle” further in Section 2.6. 14 Note that when resource and agricultural dependency dummies are used instead of actual resource rents and agricultural value added the pooled data for three periods shows significant results at 10% for both effective tertiary education and R&D expenditures. 40 Table 2.4 – Labour Productivity Growth and Policy Variables – High Income OECD and Developing Countries Separately Response Variable Productivity Growth Developed Countries Productivity Growth Developing Countries Pooled Pooled Pooled Pooled Pooled Pooled Period 1 & 2 Period 2 & 3 Period 1, 2 & 3 Period 1 & 2 Period 2 & 3 Period 1, 2 & 3 Initial Productivity -0.001 0.057 0.032 -0.076** -0.024 -0.043* (0.088) (0.083) (0.066) (0.030) (0.033) (0.026) Effective Tertiary Education 0.022 0.023 0.020 0.024 0.038 0.036 (0.038) (0.035) (0.028) (0.041) (0.033) (0.028) Effective R&D -0.006 -0.009 -0.009 0.164 0.165* 0.166* (0.020) (0.019) (0.015) (0.103) (0.097) (0.079) Economic Freedom -0.002 0.002 0.000 0.002 -0.004 -0.002 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Educational Attainment 0.001 0.001 -0.002 0 0.004 0.001 (0.006) (0.007) (0.006) (0.004) (0.007) (0.005) Root Mean Squared Error 0.077 0.069 0.071 0.129 0.143 0.136 Adj. R-squared 0.172 0.632 0.491 0.349 0.326 0.321 N 44 45 67 127 135 196 * p<0.10, ** p<0.05, *** p<0.01, Note: Regional dummies, time dummies, educational attainment, natural resource rents, and agricultural value added included as control variables This result is consistent with Nadiri and Kim (1996) who find the rate of return for domestic R&D spending to be between 23% and 26% varying amongst different countries. The breakdown of government effectiveness and R&D expenditures can be explained in similar terms as effective education expenditures as a percent of GDP. A hypothetical country with 1% effective expenditure on research and development as a percent of GDP driven by 0.45 government effectiveness, investing 2.2% of GDP as research and development expenditure and having an annual growth rate of 3 percent can improve its growth rate to 3.825% (that is an increase by 27.5%) by increasing its effective expenditure to 2%. This increase in effective 41 expenditure could be accomplished either through improving government effectiveness to 0.9 or research and development expenditure as a percent of GDP to 4.4%. As in the case of the regression where all countries are included, we find that the signs of the coefficients for effective tertiary education, effective R&D and Economic Freedom do not vary and the magnitudes do not vary by considerable extent when included individually in the estimation. We find a positive effect of effective tertiary education on labour productivity growth for both developed and developing countries. However, the coefficients are not significant as it was observed in the case of the pooled data for all countries. We observe that effective tertiary education expenditures are important for labour productivity growth in the non-traditional sector in addition to initial educational attainment represented by average years of schooling. Also, the exclusion of the educational attainment represented by average years of schooling does not affect the results. Robustness tests show that the inclusion of natural resource rents and agricultural value added in the regression equation (instead of the resource dependency dummy) does not change the results. We have excluded the possibility of reverse causality. In this chapter, we have accounted for the initial economic state of the country, the initial level of educational attainment in the country region-specific differences, and time-specific differences. Also, the labour productivity growth variable is lagged by a period of five years in order to exclude the possibility of reverse causality. In terms of omitted variable bias, we acknowledge that not accounting for capital investment in the estimation equation may lead to a bias in the estimation. The important question is whether we can assume plausible causality in the case where we observe statistically significant relationships in our empirical outcome or not. We account for lagged labour productivity. The lagged capital investment itself is expected to be associated with lagged labour productivity, which in turn is expected to be associated with labour productivity growth. In this situation, if we are interested in determining the exact magnitude of the effect of lagged labour productivity on labour productivity growth in the modern sector then the omitted variable bias is of serious concern. However, we are trying to determine whether policies such as tertiary education expenditures, research and development 42 expenditures and business environment affect labour productivity in the modern sector. Considering lagged labour productivity in the estimation model we are able to account for much of the omitted variable bias concerning policies as the missing factors would be associated with the lagged labour productivity. As such, it is plausible that the interaction of higher government effectiveness and higher investment in tertiary education as a percent of GDP leads to higher labour productivity growth excluding natural resource and agricultural rents. We also consider that tertiary education expenditures, research and development expenditures and government effectiveness may be associated with other omitted variables. For example, educational attainment can be correlated to tertiary education expenditures and research and development expenditures. Considering that governments with higher educational attainment may be able to invest more in both education and R&D simply because of the availability of educated populace. This leads to a simultaneity problem. Investing in tertiary education and R&D would most probably not be the first choice of the government of a country that has overall lower educational attainment. In this sense, we note that our estimation suffers for the omitted variable bias related to simultaneity concerns. This might be leading to an over-estimation or under-estimation of the relationship between the explanatory variables and our variable of interest labour productivity in the non-traditional sectors. However, we have a choice to make in terms of selecting our variables of interest and are restricted by the coverage of data that we intend to keep geographically wide. 2.5.2. Arabian Gulf countries - A special case? We also present results for Arabian Gulf country dummies in contrast to the reference region (includes North America, Western Europe and Nordic countries - See Appendix 2-B for more details) in Table 2.5 below and compare them to those already seen in Table 2.3 above. We observe that the growth in labour productivity in the non-traditional sector in the Arabain Gulf region is much lower in comparison to the reference group. With rising oil prices from 2003 onwards most of the growth in Arabian Gulf economies appears to have been mostly based on resource rents (Ftiti, et al., 2016). We observe in Table 2.5 below, that, the same regression without excluding natural resource rents and agricultural value added, results in 43 diminished statistical significance for the Arabian Gulf countries’ dummy variable for the pooled sets. This result indicates that the growth in the non-natural resource sector has been slower in comparison with the reference group. It is noteworthy that the coefficient of the Arabian Gulf Dummy is significant for Period 2 in both cases where labour productivity growth excludes and includes natural resources rents. Periods 1 and 3 also corresponds with low oil prices. Table 2.5 – Total Labour Productivity Growth and Policy Variables Response Variable Productivity Growth (Inclusive of natural resource rents and agricultural value added) Period 1 Period 2 Period 3 Pooled Pooled Pooled 1998-2003 2003-2008 2008-2013 Period 1 & 2 Period 2 & 3 Period 1, 2 & 3 Initial Productivity -0.084*** -0.053** 0.009 -0.074*** -0.041 -0.065*** (0.026) (0.026) (0.022) (0.018) (0.027) (0.020) Effective Tertiary Education 0.013 0.036 0.011 0.022 0.013 0.007 (0.028) (0.029) (0.018) (0.020) (0.025) (0.020) Effective R&D -0.014 -0.050** -0.006 -0.030** -0.027 -0.019 (0.020) (0.021) (0.015) (0.015) (0.020) (0.016) Economic Freedom 0.001 -0.002 -0.001 -0.001 -0.003 -0.002* (0.001) (0.002) (0.001) (0.001) (0.002) (0.001) Arabian Gulf Dummy -0.098 -0.164* -0.08 -0.112* -0.081 -0.065 (0.098) (0.088) (0.092) (0.064) (0.097) (0.074) Root Mean Squared Error 0.073 0.089 0.072 0.083 0.127 0.114 Adjusted R-squared 0.617 0.381 0.049 0.521 0.112 0.228 N 91 96 100 187 196 287 * p<0.10, ** p<0.05, *** p<0.01 Note: Regional dummies, time dummies, educational attainment, natural resource rents, and agricultural value added included as control variables As such in the following, we attempt to substantiate the effect of oil price on non-traditional sector labour productivity growth. In Figure 2.2 the predicted labour productivity growth excluding natural resource rents and agricultural value added for two Arabian Gulf countries 44 (Oman and Saudi Arabia) and two reference group countries (Netherlands and Norway) is plotted against the annual growth rate of crude oil price. The predicted labour productivity growth function is computed for each country by using their respective data points and estimation results of pooled data for periods 1, 2 and 3 as shown in Table 2.3. In Figure 2.2 it is observed that lower non-traditional sector labour productivity growth in the Arabian Gulf countries Oman and Saudi Arabia is associated with higher oil prices and vice versa ,, 15 but not for the two countries from the reference group Norway and Netherlands. This provides confirmation that oil prices partly drive the non-traditional sector labour productivity growth and innovative development in the Arabian Gulf countries. Figure 2.2 – Predicted labour productivity growth as a function of the annual growth rate of crude oil prices 15 The years 1999 and 2000 witnessed strong oil price recovery after the oil price crash related to Asian Financial Crises. Excluding 1999 and 2000 would results in an even stronger correlation of oil price growth with labour productivity growth. 45 2.6. Conclusions and Discussion This chapter presents the analyses of the relationship between innovation policy and productivity growth related to innovation and catch-up. It establishes the correlation and plausible causality between innovation policies and labour productivity growth in non- traditional sectors in a cross-sectional evaluation among countries. A selection of innovation policies was chosen based on the literature review and the state-of-the-art “broad” innovation policy approach. Innovation policy in this chapter is represented by indicators of education, research and development, and business. The policy implementation capability and potential of the governments are also analysed. In our results, we observe the convergence between countries with lower labour productivity and those at the innovative frontier. This result is in line with earlier findings of convergence in labour productivity between richer and poorer countries – beta-convergence (Barro, 1991; Barro, 2012). Also, a study by Verspagen (1991) confirms the catching-up of relatively backward countries through technological spill-overs. Further, we observe that there is a significant and positive relationship between the interaction of government effectiveness and government expenditures in tertiary education, and labour productivity in the modern sector. This observation answers one of the questions raised in Keller (2006), where the returns to tertiary education are not found to be consistently positive. Keller (2006) hypothesizes that tertiary education expenditures might be inefficiently allocated. We consider the multiplicative term of government efficiency and tertiary education investment while including tertiary education investment. We found that the interaction of government efficiency and tertiary education expenditures as a percent of GDP were positively and significantly related to labour productivity growth in non-traditional sectors. We could also challenge the notion that primary and secondary investment has priority over tertiary education investment, on the basis of economic returns, by including the initial educational attainment in the form of years of schooling in the explanatory variables. The initial level of educational attainment in the country turns out to be not significantly correlated with labour 46 productivity growth in the modern sector, while tertiary education is. This is important for policymakers as it demonstrates substantial societal returns to tertiary education. When separating developing countries and developed countries, we do not observe a significant effect of effective tertiary education. At the same time, for developing countries, the coefficients of the effective R&D expenditures show a consistently positive and statistically significant effect on labour productivity growth. This relationship contrasts with findings elsewhere, which often highlight the importance of research and development expenditures for developed countries, speculating the opposite for developing countries. For example, Griffith et al. (2004) point out that developing countries are not able to achieve the maximum potential in R&D. They see this as a consequence of inappropriate social policies. Our results indeed highlight that the influence of the interaction between the government effectiveness and R&D expenditures is positive. Through these results, the importance of looking at innovation policies as a complete set within an innovation eco-system rather than only looking at them individually is highlighted further. These results are unique and to the best of our knowledge first of their kind in confirming the interaction of sound governance and innovation policy measures such as expenditures in tertiary education and R&D. In line with the academic literature, we find that near-term lagged effective investments in R&D do not result in increased productivity in developed countries. Following the moderate growth of the 1980s, the developed countries witnessed high productivity growth in the years from 1995 to 2004. This productivity growth episode was associated with the maturity of the technological revolution. The rapid growth in the application of technological advances in productivity-enhancing innovations, and semi-conductor and computer manufacturing lead to rapid labour productivity increases from the mid-1990s (Manyika, et al., 2001). This period was followed by a recession in 2008. There are thus two main reasons we do not find positive returns of effective R&D expenditures for developed countries. Firstly, as observed during the technological revolution, the effects of R&D investment and new-to-the-world innovation take more time to yield productivity increases than we have considered in this chapter. The 47 productivity increases in the first two periods were associated with R&D expenditures that were mainly carried out in the last 10 to 20 years and not during the preceding five years. The findings suggest evidence on the “Solow paradox” or the “productivity paradox” that is found in the manufacturing outside technology-producing sectors (Acemoglu, et al., 2014). There are three other possible explanations of the “Solow Paradox”. One argument is that the current innovations are not as impactful as those of the first and second industrial revolutions, such as the steam engine, electricity, piped water and sanitation, and antimicrobial drugs (Gordon, 2012). The second argument is of secular stagnation, that is, the decline of growth due to the ageing population and lower investments in capital, despite the productivity- inducing innovations (Eggertsson, et al., 2016). Finally, the third argument is related to the mismeasurement of productivity, such as the difficulties in measuring the output of cheaper software and accounting for the benefits of internet-based services (Mokyr, 2013). We do not find any relationship between labour productivity growth and the index of economic freedom. In other words, we cannot demonstrate that a good business environment as defined by the index of economic freedom is conducive to the transformation of knowledge and research into marketed goods and services. The results may be a consequence of the type of indicator we have selected to represent the quality of the business environment. The variable used presents only a bird-eye view of the business environment. Also, our specification fails to catch a potentially shorter response time to business policies. It would be ideal in future research to work with different time lags for business conditions and to work with indicators that objectively represent the business policy and environment in the countries. Overall, Arabian Gulf countries experience lower labour productivity growth in the non- traditional sector as the oil prices increase. For these countries, a crucial policy implication is to devote resources towards tertiary education and R&D, while improving government effectiveness, if they want to grow independent of oil and gas resources. Concerning the limitations, it is important to note that this chapter is not intended to be a part of the extensive literature that tries to explain factors of growth. We acknowledge that 48 a vast amount of literature on the explanations of the productivity growth of countries exist. In this literature, many different and competitive explanatory variables are inserted in the regression for productivity growth. This includes but is not limited to the investment rate, the growth rate of employment and population. Durlauf et al. (2008), for example, find evidence of macroeconomic policy effects and a role for unexplained regional heterogeneity in explaining aggregate growth. The importance of this chapter lies in trying to determine the effect of the interaction of effectiveness measures with policy measures – in this case, tertiary education and R&D expenditures. We were able to expand the geographical coverage of the study and point out the differences that exist between developed and developing countries. This chapter forms an important basis for future research exploring the relationship of innovation policy with innovation, catch-up, diversification and labour productivity growth. It is expected that the increased availability of data for a wider set of countries will provide more insights into the topic. 49 Appendix 2-A Table 2.6 – Data Sources Variable Source GDP PPP World Bank – World Development Indicators (World Bank, 2014) Number of employees Penn Worlds Tables – Version 8.1 (Feenstra, et al., 2015) Natural resource rents World Bank – World Development Indicators (World Bank, 2014) Agricultural value added World Bank – World Development Indicators (World Bank, 2014) Government effectiveness World Bank – World Governance Indicators (World Bank, 2015) Government expenditures on tertiary education as a percent of GDP UNESCO Institute for Statistics (UIS.STAT, 2016) Gross expenditures on research and development as a percent of GDP UNESCO Institute for Statistics (UIS.STAT, 2016) Index of economic freedom Heritage Foundation and Wall Street Journal (Heritage Foundation & Wall Street Journal, 2016) Years of school life expectancy primary to tertiary UNESCO Institute for Statistics (UIS.STAT, 2016) 50 Appendix 2-B Table 2.7 – Regional groups and countries Region Countries East Asia & Pacific Papua New Guinea, Australia, Japan, Korea - Republic, New Zealand, China, Hong Kong, Korea - Democratic People's Republic, Mongolia, Taiwan Eastern Bloc 16 Belarus, Albania, Armenia, Bulgaria, Czech Republic, Estonia, Georgia, Hungary, Kyrgyzstan, Latvia, Lithuania, Moldova, Poland, Romania, Russian Federation, Slovakia, Tajikistan, Ukraine, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, Serbia, Slovenia Gulf Cooperation Council (GCC) Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates Latin America and the Caribbean Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), Suriname, Turks and Caicos Islands, Uruguay, Venezuela, Virgin Islands U.S. Middle East 17 Iran, Iraq, Jordan, Lebanon, Palestine, Syrian Arab Republic, Yemen North Africa Algeria, Djibouti, Egypt, Libya, Morocco, Tunisia North America & Europe 18 (Reference Group) Austria, Belgium, France, Germany, Greece, Ireland, Israel, Italy, Luxembourg, Netherlands, Portugal, Spain, United 16 The name applied to the former communist states of eastern Europe, including Yugoslavia and Albania, as well as the countries of the Warsaw Pact (Hirsch, et al., 2002). 17 Middle Eastern countries on the Asian continent, excluding countries of the Gulf Cooperation Council (GCC), Turkey and Israel 18 Countries of Western Europe, Northern Europe excluding Eastern Bloc, Mediterranean excluding Arab and African countries, North America excluding Latin American countries 51 Kingdom, Cyprus, Switzerland, Turkey, Greenland, Denmark, Finland, Iceland, Norway, Sweden, Canada, United States Small Nations and Islands Bermuda, Åland Islands, American Samoa, Andorra, Anguilla, Antarctica, Bonaire, Sint Eustatius and Saba, Bouvet Island, British Indian Ocean Territory, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Falkland Islands (Malvinas), Fiji, French Guiana, French Polynesia, French Southern Territories, Gibraltar, Guadeloupe, Guam, Guernsey, Heard Island and McDonald Islands, Holy See (Vatican City State), Jersey, Kiribati, Malta, Marshall Islands, Martinique, Mayotte, Micronesia, Montserrat, Nauru, New Caledonia, Niue, Norfolk Island, Northern Mariana Islands, Palau, Pitcairn, Réunion, Saint Barthélemy, Saint Helena, Saint Pierre and Miquelon, Samoa, Solomon Islands, South Georgia and the South Sandwich Islands, Svalbard and Jan Mayen, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Virgin Islands, British, Wallis and Futuna, Western Sahara, Channel Islands, Faroe Islands, Isle of Man, Liechtenstein, Monaco, San Marino South Asia Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka South-East Asia Cambodia, Indonesia, Lao, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, Vietnam Sub-Saharan Africa Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Comoros, Congo - Democratic Republic, Congo - Republic, Côte d'Ivoire, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea- Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, Tanzania, Uganda, Zambia, 52 53 3. Policy and Economy in the GCC Policies, Innovation and Transition in Oman, Saudi Arabia and United Arab Emirates Abstract This paper attempts to assess how policy measures have affected innovation and sectoral labour productivity. We use an eclectic qualitative approach incorporating descriptive statistics from Oman, Saudi Arabia (KSA) and the United Arab Emirates (UAE) for the period 1990 to 2016. The policy measures and outputs are rated on a seven-point scale. The inputs covered include policies of primary, secondary, tertiary and vocational education, research and development (R&D), governance, and business enablers. We use the term ‘enablers’ because these policies are expected to contribute to an environment that boosts innovation and diversification. The innovation outputs and diversification outputs covered include patents, variation in the fields of patents, industrial designs, sectoral labour productivity and share of outputs by sectors. The scores are based on relative expenditures, quality and performance of the enablers and output measures. We find that the state of the ultimate outputs of innovation activity and diversification for the three countries are far from their full potential. We suggest that this is due to the moderate performance of the enablers and relevant policy areas. Oman has limited natural resources and has followed a sequential development of the components of its innovation system. For example, Oman has first focussed on basic education, then expanded expenditures on tertiary education and subsequently increased R&D investments. KSA with more ample oil reserves has approached education, R&D and industrialisation in parallel. It has also engaged in a pull strategy for the setup of R&D units from multinational organisations. The third country under review, the UAE has focused on creating a welcoming business environment. This includes establishing business and logistic free zones and information technology (IT), media, tertiary education and health industry clusters. With education as a weaker element of its innovation system, the UAE has relied on foreign labour. 54 We find that each country’s policy affected innovation and productivity growth as follows: - Oman has limitations in vocational education and R&D enablers. Difficulties in getting credit and resolving insolvency limit the entrepreneurial arena in Oman. Overall, Oman exhibits slow-paced innovation activity and slightly positive progress in the diversification arena. - KSA, despite troubles in the education and governance arena, is engaged in basic research with relatively high patent and design activity. Ease of trading across borders and enforcing contracts has a moderate score in KSA that is uniquely lower than other GCC countries. The labour productivity in certain sectors in KSA has improved while the share of those sectors is declining. - UAE has at least ‘slightly positive’ status on most enablers. However, it appears to be lagging in the development of R&D clusters and R&D funding. The strongest enabler is the development of business clusters. This strength, in addition to the use of the expatriate workforce, appears to reflect positively on the relative innovation output. However, explicit positive trends are not evident in terms of diversification. We observe that the ultimate outputs in GCC countries are constrained by the lowest- performing policy areas that may be termed as the “limiting enablers”. The policy implication of this work is that a piecemeal policy focus is not adequate as all enablers in the system have to function well in order to support innovation and diversification. It is important to note that the relationship between inputs and outputs where expressed is subjective to the rationale discussed in the chapter. Keywords: Innovation policy, innovation, technological change, structural change, government effectiveness, developing countries, Arabian Gulf countries. JEL Classification: O2, O3, O38, O57 3.1. Introduction The countries of the Gulf Cooperation Council (GCC) 19 have long been considered as mainly oil and gas-based economies. Currently, the countries face the challenge of planning for a post-oil and gas economy while facing high population growth. It is expected that the population of the GCC will increase to 66.5 million by 2030 in comparison to 56.5 million at the end of 2018 (World Bank Group, 2019). Meanwhile, the GCC countries have been dependent on foreign labour, and 48% of the current population of the GCC is composed of the expatriate workforce (Abyad, 2018; GCC-Stat, 2019). Among the GCC countries, UAE and Qatar have one of the highest reliance on foreign labour in the world with 94% and 95% of their workforce being composed of expatriates, respectively (GCC-Stat, 2019; Arab News, 2019). This high reliance on the foreign workforce is symptomatic of a lack of human capital development. The GCC countries have enacted varying policies for innovation and diversification. Their endeavours are in the context of population growth, an anticipation of a post-oil and gas economy and the associated need for employment creation. The ultimate aim is the sustenance of a dignified standard of living for their citizens. This aspiration is expected to be accomplished by job creation through economic growth associated with innovation and diversification. These ambitions are also enshrined in the Articles 17, 23, 24, 29 and 30 of the GCC Human Right Declaration (GCC Secretariat-General, 2014). The GCC countries’ governments have invested in the development of indigenous human capital. Also, industrialisation and diversification efforts have led to the emergence of new industries and a revival of old ones. Bahrain, Oman, Qatar and UAE have increased focus on tourism. Trade and logistics sectors have seen development in all the GCC countries. Oman and UAE that have a rich history of shipping and trading, have both invested in the shipping 19 Also referred to as the countries of the Arabian Gulf, countries of the Arabian Peninsula and countries of Persian Gulf and Arab countries of the Gulf. Our preferred terminology in this work is the countries of the Gulf Cooperation Council (GCC) which is colloquial term used to refer to the Cooperation Council of the Arab States of the Gulf (GCC). Geographically, Iraq is also an Arab country on the Arabian/Persian Gulf, Iran is also a country on the Arabian/Persian Gulf. Jordan and Yemen are countries on the Arabian Peninsula that have both been in talks to join the GCC. Finally, Morocco despite being geographically distant from the peninsula and the Gulf has also been in accession talks since 2011. In order to avoid any confusion, we use the term GCC to refer to the GCC members as of 2017 – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. 56 industry (OBG, 2015; Baxter, 2018). Additionally, the GCC countries have set their eyes on moving from a monopolistic and rent-seeking private sector to generating a more dynamic, innovative and productive economy (Hvidt, 2013). KSA has seen growth in its petrochemical conversion industry, and the UAE has invested in setting up various clusters (Seznec, 2011). Despite the GCC having interesting development dynamics, not much academic work has been carried out to analyse the changing economic structures of the GCC countries. The knowledge related to policies for innovation and diversification in the GCC has remained in a black-box, mostly inaccessible or untapped. This inaccessibility may be due to previous limitations in data availability or the wide-spread misunderstanding that there is nothing more to the GCC economies than oil and gas. However, it is evident from the preceding discussion, that the policies undertaken by the GCC countries in order to diversify and develop their innovative capacity are an important subject matter for academic research. A country’s innovative capacity is based not only on its resources and other endowments that offer a natural comparative advantage. The concept of innovation is studied as effective competitive performance in a dynamic context in “National innovation systems: a comparative analysis" (Nelson, 1993). There, it was observed that countries decide on policies, that create a comparative advantage in selected areas (Nelson, 1993). Two features – education and the macroeconomic climate, both subjects of government policy – were identified by Nelson (1993) to affect the ability and incentives of firms to innovate. This was derived from 15 case studies on innovation. In the preceding chapter, we discussed the effect of higher education and R&D policy on innovation in interaction with the quality of governance. In that chapter, innovation is studied as labour productivity in non-traditional sectors, excluding the natural resource and agricultural sectors. It was also observed that the GCC countries are not at par with other countries when considering the features of innovation policy and diversification. Through this approach in the previous chapter we observed that the labour productivity growth in the non-natural resource sector in the GCC group of countries for the years from 1998 to 2013 has been much slower than the group of countries 57 in Western Europe, Northern Europe, European countries of the Mediterranean, USA, and Canada together 20. The observation of much weaker growth in labour productivity in the non-traditional sector for GCC country group in comparison with the reference group becomes important when seen in the light of the other observations as well. With rising oil prices from 2003 onwards, a majority of the growth in Arabian Gulf economies was based on natural resource rents and the development of other sectors was found to be correlated with oil prices. In continuation of this we explored the relationship further and found the oil price and sectoral productivity correlation to be dissimilar for each country – with Oman’s transportation and communication sectors performing better in terms of labour productivity as oil prices increased, UAE’s Manufacturing sector performing better, and no clear relationship for KSA’s sectoral labour productivity performance with oil prices (See Chapter 3 - Appendix 3-A). With these variations in view, we explore the role of policies, enablers and capabilities in driving the non- oil sector and attempt to identify the country-specific conditions that may explain these variations. We compare three GCC countries – Oman, KSA and UAE, both in terms of enablers and innovation.21 The governments in the GCC have a broad sphere of influence in economic structure and workings of their countries. The exports of petroleum remain key to all the three countries Oman, KSA and UAE. Threats such as oil price volatility, depletion of oil resources, climate change, the emerging international consensus to move away from fossil fuels, and a youth bulge in their respective populations, has made it important for these fossil fuel-dependent GCC countries to drive the diversification process and develop highly productive and innovative economies. 20This relationship is scrutinized on country level and the predicted labour productivity growth in non-traditional sectors in Oman and Saudi Arabia is found to be correlated with the annual growth rate of oil prices. In comparison no correlation of non-traditional sector labour productivity growth with oil prices in Netherlands and Norway – two countries from the reference group was found (See Chapter 2) 21 The conceptual scope of innovation in this work is beyond technological innovation and includes management, process, service, product and marketing innovation as well. 58 Oman has a reputation as a young oil-based economy and makes it an ideal case in trying to understand the types of paths countries in early stages of development and limited natural resources can take to develop a sustainable, diversified and innovation-based economy.22 KSA is an oil-rich country with a relatively conservative socio-political setup in comparison to its GCC neighbours. It is one of the twenty largest economies in the world and a member of the G20. KSA remained the largest producer and exporter of oil for a long period. However, from 2002 to 2018 its production numbers were in close proximity to Russia and were overtaken by the US in 2018. From 1987 onwards till now as of March 2019, KSA remains the largest exporter of oil in the world. KSA provides a unique setup to study the invigoration of innovation and diversification through government policies in resource-abundant scenarios. The UAE also has vast oil and natural gas reserves. Its most populous city Dubai occupies the position of an important global city and international trade, shipping, and aviation hub. The UAE's economy is considered to be the most diversified among the six countries of the GCC. This is attributed to the efforts of the federal government and the constituent emirates. A very low indigenous population and high reliance on foreign labour also make it an interesting case for studying innovation and diversification policies. This chapter offers insights from individual countries and is a first of its kind cross-country comparative narrative for the GCC. In the following section, 3.2, we present a review of the literature of innovation studies. In section 3.3, the state of policies and innovation in the GCC countries is explored. Sub-section 3.3.2 addresses the development of the education systems, 3.3.3 focuses on literacy, primary education, secondary education, education reforms and performance, 3.3.4 on tertiary education and vocational training. Sections 3.3.5, 3.3.6 and 3.3.7 cover R&D policy, business and entrepreneurship, and governance and infrastructure, respectively. Section 3.4 covers the outputs of the GCC economies, with sub-sections 3.4.2 focussed on the innovation outputs, and 3.4.3 focussed on the share of individual sectors in the economy and sectoral labour productivity in the GCC countries. The policy inputs and 22 Oman secured control over a substantial portion of the oil revenue generated from oil extraction in the country by early 1970s. 59 associated outcomes are discussed in combination in section 3.5. Finally, section 3.6 contains the summary, closing discussion and conclusion of this chapter. 3.2. Perspectives on innovation 3.2.1. General The innovation literature is distributed between “narrow” and “broad” focus on innovation policies. In the “narrow” sense only formal R&D systems and organisations systematically active in knowledge generation and diffusion are the focus. An example of the application of the systems of innovation framework in the former sense is the World Bank Knowledge Assessment Methodology (Chen & Dahlman, 2005). However, systems of innovation in a narrow sense “leave significant elements of innovation-based economic performance unexplained” (Lundvall, 2007). In the “broad” sense the core knowledge-producing and disseminating institutions are embedded in a wider socio-economic system and the relative success of innovation policies is a function of influences and linkages beyond these core institutions (Freeman, 2002). Among the works that discuss new-to-the-world innovation in the latter sense Furman, Porter and Stern (2002) integrate ideas-driven growth theory, microeconomics-based models of national competitiveness and industrial clusters theory and considers R&D manpower, knowledge and technology base as important sources of innovation, and Archibugi & Coco (2004) define innovation system through patents, publications, ICT, electricity consumption, and education. The common theme that emerges from the literature is that innovation policies work in coherence with each other and have a combined and complementary effect on growth. The translation of policy to growth must go through the governments’ ability to effectively turn inputs of policy into growth. 23 Figure 3.1 below represents our interpretation of how the flows of knowledge enable growth in innovation through an increase in productivity in an economy. The innovation eco-system is thus arranged into conditions, linkages, the firms and the market 23 See Chapter 2 for more details. 60 itself. The change in the state of these conditions is determined through natural transformation and policy. The education condition is affected by government policies as government financing of the tertiary education system, policies determining graduate ratios in science and technology fields, alignment to labour demand from the market, university autonomy, and others. Similarly, research and development conditions are impacted by government expenditure on research and development, type of research grants, targeted scientific field grants, the competitiveness of grants, intellectual property regime, and private- sector research funding, and so forth. Business conditions are related to industrial policies, competition policy, entrepreneurship policy, taxation policy, financial policy, the health of the financial sector, availability of finance, and market access for firms that create new products or services. Infrastructure conditions include the availability of Information and Communication Technologies (ICTs), Transport, Energy, Standard-Setting, Metrology, Security, and so on. Figure 3.1 – Innovation Policy Framework Conditions 61 Finally, it is considered that without efficient and effective linkages, the production of knowledge, as well as transfer of knowledge for the creation of new products and services, would be hampered. For innovation to thrive in the production space it is important that the innovation environment conditions are healthy, governed by sound policy, with effective linkages across various conditions as well as the production space and consequently the market that would consume the innovations and drive the productivity growth. Fagerberg and Srholec (2008) show that “Innovation System” and “Governance” – as understood in the context of the factor analysis carried out in their work – are important for economic development. In this chapter, we bring governance, business policy, education, R&D and infrastructure within the policy sphere of innovation systems. The concept of capabilities or enablers is discussed in conjunction with structural change policies in Rodrik’s (2013b) work where slow growth is explained as an outcome of a high level of capabilities and fundamentals, accompanied with slow structural change. One of the most influential studies for understanding the characteristics of innovation systems looks at innovation systems in the context of technological innovation through fifteen country case-studies (Nelson, 1993). The work illustrates that across the countries there was a variation amongst the most important feature of the country’s innovation system and that is associated to the policies and decision that the country implements to create a comparative advantage (Nelson, 1993). 3.2.2. The literature on GCC countries The GCC countries offer a setting where the government influence spans across more spheres than the countries that have been discussed above in the innovation policy literature. The government holdings in oil and gas production provide GCC countries financial resources to invest in developing human capital, improving R&D enablers, governance and business environments, and to create business and innovation clusters. The GCC countries being relatively young oil-based economies, offer ideal grounds for comparing the pathways that countries with natural resource availability in early stages of development may take to develop 62 a sustainable, diversified and innovative economy. The literature offers very little in the way of understanding the variation in policies between the GCC countries. To the best of our understanding, none of the works discusses the effect of policy inputs on innovation through intermediate outputs and the governments’ ability to implement the policies. A number of works discuss selected specific policy measures in individual GCC countries and a smaller subset offers a comparative narrative. Education policies are perhaps one of the most discussed in the context of GCC countries. The literature discussing primary education policies in the GCC countries, points out that Oman offers a story of success in terms of expanding primary education opportunities and rapid rise in literacy levels, KSA appears to have a problem of low enrolment in primary education, whereas UAE’s primary education system has been plagued with inefficiencies and faces challenges on the supply of teaching staff (Watkins, 2000; Gonzalez, et al., 2008; McMinn, et al., 2015). The secondary education system in GCC is plagued with the so-called middle-child syndrome 24 with secondary school sector being much smaller than the primary school sector, and the majority of secondary school graduates from Oman, KSA and UAE facing a skills and knowledge set mismatch for entering and adjusting to the standards of the job market and tertiary education system (Abouchakra, et al., 2008; Issan & Gomaa, 2010; McGlennon, 2015). The secondary education in GCC also lacks an early transfer of practical skills and knowledge useful for a smooth transition into vocational education programs. Moreover, there is a shortage of adequate vocational and technical training beyond the secondary level (Gonzalez, et al., 2008). Tertiary Education in the GCC countries under discussion has a gap in inputs including the quality of high school graduates and funding (Alyahmadi, 2006; McGlennon, 2015). The quality of both primary and secondary education is not on par with international standards (Gonzalez, et al., 2008). While tertiary education standards in both Oman and KSA are assured by internal as well as external accreditation, UAE has inadequate higher education management systems and inefficient governance that 24 To be neglected because of being in the middle of two others. In this case we mean that primary and tertiary education get more policy attention while policies to strengthen secondary education are weak. However, it may also be that the education expenditure statistics are not accurate in allotting expenditures between primary and secondary education at K-12 public schools. 63 have led to a quality deficit in higher education outputs in UAE (Darandari & Cardew, 2013; McGlennon, 2015). McGlennon (2006) points out that UAE has an open-door policy for universities, leading to the universities offering programs that require minimum investment on their part. The programs have demand mostly from foreign inhabitants in the country. Thus, the majority of tertiary education programs in the UAE are business and IT-related and the actual needs of UAE nationals, local job market alignment and development targets are ignored. The GCC countries’ governments acknowledge the challenges facing the education system and have initiated reforms at various levels. The reforms include; a move from teacher-centred to student-oriented education in the primary and secondary education sector, efforts to match resources to desired outputs, enhancing skills and knowledge at secondary, post-secondary and tertiary level so as to diminish job market mismatch, enhancing linkages, and targeting education design geared towards developing a globalized knowledge economy (The Ministry fo Education, Saudi Arabia, et al., 2004; Alyahmadi, 2006; Abouchakra, et al., 2008; Gonzalez, et al., 2008). Oman, KSA and UAE have faced varied challenges in their pursuit of setting up a world- class R&D system. Lack of coordination within the country and lack of collaboration with foreign researchers is an issue of concern for the Omani R&D system (Thomson Reuters, 2011; UNCTAD, 2014). The literature points towards a lack of a central science and research institute for coordination of research policy up until recently for both Oman and UAE (McGlennon, 2006; Al-Balushi, 2016). Insufficient investment in R&D and the youth of R&D systems are often discussed in the context of GCC countries. 25 UAE has a greater problem of limited availability of tertiary education graduates and their quality. Meanwhile, the limited number of researchers is the main problem in Oman (Al-Balushi, 2016). In contrast to Oman and UAE, the academic R&D research shows a relatively positive picture in KSA. The Saudi government began investing in knowledge basis from the early 2000s with strong government support and alignment of government policy. Shin, et al. (2012) report that research 25 The first formal university in the GCC is the King Saud University, established in 1957 by KSA. In 1966, Kuwait established their first university, followed by Bahrain two years later in 1968, Qatar in 1973, and the UAE in 1976. The last GCC country to open up its first formal university is Oman, where the Sultan Qaboos University was opened in 1986. 64 collaboration in KSA was stable while scientific productivity was increasing dramatically since the late 2000s. They also find that technological development in KSA relies on prior technology i.e. the majority of patents in KSA refer to previously existing foreign patents. The investments into developing a knowledge base in KSA are paying off as they are attracting graduates from around the world and KSA has been able to establish PhD education with the number of PhDs doubling from 2006 to 2010 (Shin, et al., 2012, p. 323). The GCC countries are overall aggressively pushing for stronger R&D systems for transition to a knowledge and innovation-based economy (Al-Hammadi, et al., 2010). Relating foreign direct investment (FDI) and various factors affecting FDI in the GCC countries, Mina (2007) finds that higher trade openness, infrastructure development, and institutional quality have a positive influence on FDI. The study uses trade inflows and outflows to represent trade openness, penetration of telephone and mobile services for representing infrastructure, and rule of law as a proxy for institutional quality. As such it is not clear if the results would hold if alternative definitions for the explanatory variables are used. The GCC countries offer economic stability, political stability, low taxes, legal stability and protection for foreign investors, intellectual property rights, and robust physical infrastructure. Boparikar (2015) argues that the Oman government has pushed entrepreneurship in its development agenda. Shachmurov (2009) also points out that Oman’s economy is one of the most open in the region. In addition to that Al-Ghassani (2010) finds that the education system in Oman has made significant strides to include entrepreneurship education at all levels. While UAE has a strong focus on Free Trade Zones that contribute 80% of the UAE non-oil exports. The free trade zones have been identified in studies as one of the strongest pillars of the country’s diversification strategy and put UAE on the world map as the third most important re-export centre in the world (Shayah & Qifeng, 2015). In “Mapping Entrepreneurship ecosystems of Saudi Arabia” (2010) Khan finds that the business environment in KSA is in early stages of development and does not provide a complete range of support services, financing instruments, institutions and policy actions that have been discussed in the literature to have a positive influence pertaining to big and small businesses, 65 start-ups, and entrepreneurs. The governments of KSA and other GCC countries have taken many steps to develop the SME regime, and the institutionalisation of the policy sphere is expected to deliver positive results in the future (Khan, 2010). The critical question in the context of policy implementation is not only related to the policy itself but also the effectiveness and capacity of the government to implement the policies and providing the essential infrastructure to do so. The works done to study the effectiveness of governance in GCC countries are limited. The economic development in Oman has led to the vast development of infrastructure in Oman. The spending has been focused to establish and improve institutional as well as physical infrastructures, transportation, competitively driven dynamic telecommunication service industry, and power and water sectors (Abdelal, et al., 2008; Rajasekar & Al Raee, 2012; Oxford Business Group, 2014; BMI, 2017). Abdelal et al. (2008) consider that Oman, owing to its geographical, political and economic position in GCC is pulling closer to the international financial system. The move to increase international linkages is accomplished through the development of free-trade zones for manufacturing and services, and the setting up of recreational facilities that attract businesses, highly-skilled labour, knowledge workers and tourists. Oman also has a public-private partnership framework that is considered as one of the more robust ones in the GCC (BMI, 2017). This strength enables Oman to use governance systems to attract private investment (BMI, 2017). Oman is considered to have paid particular attention to governance and regulatory quality in its development path and as such earned the benefits of the development that comes along improved institutional quality (Looney, 2013). The presence of good physical and communication infrastructure in KSA complements the effectiveness of governance and the ability of the Saudi government to implement the policy design (Al Shehry, et al., 2006). However, Al Shehry, et al. (2006) also find that there is a need for enhancing efficiency in terms of cost reduction, fairness, streamlining procedures and reducing fraud and over- crowdedness in KSA’s public services. The participants in the study clearly outline the presence of a gap between planning and implementation. The work also points at “distance” challenges in KSA. First, being the personal distance referring to the limitations in 66 communication between men and women at workplace, public and private sector organisations. The second relates to the physical size of KSA. This natural and social distance among the participant of the KSA’s economy creates inefficiencies in the policy realm (Al Shehry, et al., 2006). Al-Yahya (2010) finds that governance in Oman is constrained not by lack of skills but underutilization of skills, as such effective utilization of talent is an important source of improvement in the quality of governance. The situation in UAE, however, is different with UAE and its constituent Emirates not having a clear government vision, limited technical and entrepreneurial talent, restricted R&D budgets, weak regulatory systems, and inefficiencies in the labour market among others (Schiliro, 2013; Byat & Sultan, 2014; Haouas & Heshmati, 2014). The policies that affect innovation, labour productivity in non-traditional sectors, or sectoral diversity are discussed together in a very limited set of literature. Within this set of literature, it is observed that the level of readiness of GCC countries with respect to key knowledge economy pillars is low. Oman lies amongst the 10th percentile of countries for innovation pillar, KSA lags behind in human resources development and the weakest knowledge economy pillar for UAE is the education pillar (Al-Rahbi, 2008; Ahmed & Alfaki, 2010; Bashehab & Buddhapriya, 2013). In a dedicated chapter on KSA in the Global Innovation Index (GII) Report for 2012, the authors point out at the lack of indigenous engineers and scientists in KSA (Sultan & Zaharnah, 2012). This is in contrast to its GCC neighbour Oman that boasts the highest ratio of science and engineering graduates per capita in the world (Cornell University, INSEAD, and WIPO, 2016). The strengths of the system in UAE according to the literature include; targeted R&D initiatives, sovereign wealth funds, the trust in UAE’s Financial sector despite high exposure and the recent efforts to provide a clear strategy for creating an innovation-based economy. KSA with 40 years of diversification strategy under its belt has succeeded in establishing industrial competitiveness in petrochemicals and downstream products of oil (Seznec, 2011). However, KSA suffers from a lack of entrepreneurial activity and the primary source of transition and innovation in KSA is dependent on large corporations that dominate the industrial landscape in KSA. The 67 discussion on innovation in KSA cannot be complete without discussing the science and technology clusters. Dahran technovalley, KAUST Research Park and Innovation Cluster, and PetroRabigh offer examples of KSA’s efforts to boost R&D and innovation through creating clusters and pulling international organisations to set up their middle east research units in KSA but the success of these units is yet to be observed (Sultan & Zaharnah, 2012). An important observation therein is that the Gulf States have had economic diversification on their agenda for the last half a century (Hvidt, 2013). The work by Hvidt (2013) focuses on the history of diversification and current plans in the countries of the Gulf Cooperation Council. They find that the plans of the GCC countries are targeting a shift towards a production-oriented model where the participants of the economy are encouraged to produce actual goods and services. In another work a comparison of cumulative data for GCC countries KSA, Kuwait and UAE and BRICS region is carried out and the findings point that the development of an effective innovation-based economy is not necessarily associated with expenditures on R&D but rather on efficient allocation of resources and rigorous implementation of a strong innovation policy (Gackstatter, et al., 2014). The above literature provides a good background for our work. There are, however, several points missing. Firstly, there is no explicit empirical evidence about the impact of the composition of education, R&D, business, and governance systems on intermediate and final outputs in the GCC countries in the literature. Secondly, the impact of the combination of the policies that essentially drive the various components of a comprehensive innovation system in GCC is not discussed. These limitations in the literature are not without reason as until recently, data from the countries of the GCC has been sparsely available. In this chapter, inputs, intermediate outputs and the components of the system of innovation in the selected GCC countries are discussed along with the possible impact of the state of these enablers on diversification and innovation in individual countries. An attempt is made to understand the causes of improvements or the absence of innovation and diversification in these countries. However, the eclectic qualitative nature of this work means causation is not established. 68 In the last four decades, the GCC countries have been able to provide a high standard of living for their citizens through economic development based on the exploitation of oil and gas resources. However, as the previous chapter shows, this has not been accompanied by diversification and innovation as was hoped. This fact is alarming in the face of the threats facing the region, including but not limited to, oil price volatility, depletion of oil resources, climate change and the emerging international consensus against the use of fossil fuels. In light of a failure of the policies to deliver sectoral diversification, development of highly productive and innovative sectors; the GCC countries will not be able to maintain economic stability and the high standard of living for their citizens. The high rate of population growth in the region – Oman 2%, KSA 1.5% and UAE 2.5% per year during the period 2016 - make the challenge of sustaining per capita incomes even greater. This work provides a critical comparative study for Oman, KSA and UAE on the policies, enablers, and the outcomes of these on the share of value added of individual sectors in the economy, sectoral labour productivity, and innovation indicators. In the following, we study the innovation policies and their influence on innovation, labour productivity and diversification in the three GCC countries Oman, KSA and UAE. 3.3. The Case of GCC – Policies and Enablers 3.3.1. Section Summary Table 3.1 below highlights the overall strength of policy measures. In terms of education, Oman and the UAE perform equally well in the primary and secondary education policy and outcomes. However, overall government spending in primary education is comparatively lower in Oman and the UAE than it is in KSA. KSA with relatively higher spending in primary education has not been able to achieve positive outcomes in primary education students’ Math and Science results as well as in primary to secondary transition and secondary education students’ results. Oman faces shortcoming in vocational and technical education policy and outcomes, as well as R&D spending, number of scientific researchers and number of research staff. KSA and UAE perform relatively well in vocational and technical education, and R&D 69 spending and R&D staff, while facing challenges in terms of the total number of scientists active in research. The development of R&D clusters and enactment of R&D reforms and coordinated policy appear to be a positive development in Oman’s R&D policy component and relatively stronger in KSA. UAE lags in both of these elements in terms of their R&D policy. Oman appears to be the only one out of the three countries providing targeted research funding programmes through a central R&D administration. In terms of the business environment, all three countries perform low on the ease of resolving insolvency and ease of getting credit indicators. KSA has been slower in the Free Zone implementation policy with the announced free-zones in KSA only reaching operational maturity from 2015 onwards. UAE has used the Free Zone model as a way to boost individual sectors through clustering. Meanwhile, Oman has three active and mature free trade zones focussed on industry, manufacturing and logistics, with the first one established in 2002. While there is a scope for improvement in governance in all three countries, the UAE scores relatively higher in government effectiveness and control of corruption. Oman has all governance indicators positive, with voice and accountability at the mean value. KSA performs positively only on the rule of law component of governance. 70 Table 3.1 – Comparing Innovation Policies and Enablers Country  Innovation Enablers  Oman KSA UAE Primary and Secondary Education Primary Education Spending – – + – – Primary Education Enrolment and Literacy +++ +++ +++ Primary Education Results (TIMMS) + – + Secondary Education Enrolment (Primary to Secondary Transition) ++ – – + Secondary Education Results (TIMMS) + – + Teaching Quality +++ + +++ Primary & Secondary Education Reforms ++ + ++ Summary ++ – + Vocation, Technical and Tertiary Education Vocational and Technical Education – – – + – Tertiary Education Enrolment (Upper Secondary to Tertiary Transition) + ++ + The ratio of Science, Engineering and Technology Graduates +++ + o Summary o + o Research and Development R&D Spending – – + + Number of Researchers – – – Total R&D Staff – ++ + Development of R&D Clusters + ++ – R&D Reforms & Coordinated Policy + ++ o Research Funding Programmes + – – Summary – + – Business Environment Starting a business +++ ++ +++ Getting credit – o – Paying Taxes +++ +++ +++ Trading across borders ++ o ++ Enforcing contracts + o + Resolving insolvency – – – – – Free Trade Zones + o +++ Summary + – + Governance and Infrastructure Government Effectiveness + o ++ Voice and Accountability o – – – Political Stability – – + Regulatory Quality + o + Rule of Law + + + Control of Corruption + o ++ Summary + – ++ Note: Description of the quality of or level of success in policy, enables or outcomes – – – Extremely Negative – – Negative – Slightly Negative o Moderate +++ Highly Positive ++ Positive + Slightly Positive 71 3.3.2. Development of education systems Oman: The first university in Oman was established in 1985. By the early 1990s, the baby- boom population following early development in Oman’s economy was reaching post- secondary age. In 1995 providing education and job opportunities to the young became an important part of the government's agenda. Oman developed its Vision 2020 that focussed on the development of a diversified economy. However, there was no evidence of an increased focus on tertiary education immediately after 1995. The changes in spending trends in tertiary education appear much later. Since 2001 Oman witnessed steady drops in primary education spending as a percent of GDP, that went down from their peak of 2.0% in 2001 to 1.4% in 2011. Similarly, secondary education expenditures as a percent of GDP reduced from 2.1% in 1998 to 1.6% in 2013. We observe that a major portion of this spending was moved towards tertiary education expenditures. This change was especially visible as the expenditure as a percent of GDP on tertiary education was on a growth trajectory increasing from a dip of 0.25% in 2005 to 1.2% in 2009. There was an apparent move from investing in primary and secondary education towards the development of higher education capacity. As basic education infrastructure reached stability and primary education needs of the population were being met, the government had a policy shift to spend more towards higher education. This shift was also in line with a stated effort to strengthen knowledge capabilities for a diversified and innovative economy. KSA has one of the most wide-ranging education systems in GCC countries, mainly due to its early focus on vocational education in addition to tertiary education. It offers universal primary education, secondary education with tracking that includes, science, social and vocational education, and tertiary education. The social track is synonymous to the Islamic education track and occupies a similar position in the Saudi education system as the Secondary Modern Schools occupied in the Tripartite Education System in the United Kingdom from 1945 to 1970. UAE: Data from UAE reveals that the expenditure on education as a percent of GDP in 1998 was 1.2% and was distributed between pre-primary, primary, lower-secondary and upper- 72 secondary education in ratios of 6.6%, 47%, 24% and 21.6% respectively. Expenditure on tertiary education was not included in the overall expenditure. Primary and secondary education, both received close to 50% of the overall education spending. As such, it appears that the government has had the objective to ensure the development of both primary and secondary education. The tertiary education system in UAE, however, lacks a coordinated policy. 3.3.3. Literacy, primary education, secondary education, reforms and performance Literacy and primary education: The three countries of the GCC under discussion – Oman, KSA and United Arab Emirates (UAE) – have performed exceptionally well in terms of youth literacy despite not having compulsory basic education. Starting with modern education in 1970s, Oman had achieved above 97% literacy by early 1990s. As such Oman was the earliest amongst the three, while KSA and UAE were still below 95% in terms of literacy till the early 2000s. By 2015 the youth literacy rates for Oman, KSA and UAE were 99.1%, 99.5% and 99.3% respectively, the gross primary school enrolment was above 100% for the three countries and the primary school enrolment gender parity index was 1.035, 0.995, and 1.03 respectively. The net enrolment rates were 94.5%, 97.6% and 93.4% respectively. The adult literacy at the same time was 93%, 94% and 93% respectively. In Oman, from 1997 to 2001 the education expenditures per primary student have stayed between USD 4000 and USD 5000 per student, thereon we see a marked increase from 2002 onwards, and the expenditures range between USD 6000 and USD 7000 per student. 73 Figure 3.2 – Net enrolment rate, by education level, both sexes (%) – Primary and secondary education (Selected years for which data for all three countries is available) Secondary education: In Oman, the secondary education expenditures have varied overall without any pattern that may be helpful in extracting any links to the quality of education as well as economic development. The one thing that appears from the fluctuating finance at the secondary education level (between USD 5,000 to 10,000 per student on average) is that Oman lacks a clear coordinated secondary education strategy. From 2010 to 2014 we observe a drop in enrolment at the secondary school level that follows a related drop in primary school enrolment, and by 2015 Oman achieved 95% gross (85% net) enrolment in secondary education. For UAE, whereas primary education survival rates are as high as 100%, the completion situation in secondary education is of concern. Firstly, between 2008 and 2012 between 77% to 80% lower secondary school-age children were enrolled for lower secondary school education. This ratio has improved to 88% and 92% in the years 2013 and 2014. Secondly, the gross intake ratio to the last grade of lower secondary school between 2008 and 2014 has been as low as 66% to 71% for both sexes. For KSA, we notice that the enrolment in secondary education (2015) is roughly 20% lower than that of the enrolment in primary school six (6) years earlier (2009). This drop out rate represents a daunting figure with no clear indication of how the children who drop out of education at the age of 12 contribute to their personal development, the society and the economy. Note that the same figure is 7% and 4% for Oman and UAE respectively (See Figure 3.2 above). 65 70 75 80 85 90 95 100 1999 2000 2009 2011 2014 2015N et e nr ol m en t ra te , b y ed uc at io n le ve l, bo th s ex es (% ) Primary Oman Secondary Oman Primary Saudi Arabia Secondary Saudi Arabia Primary United Arab Emirates Secondary United Arab Emirates 74 Figure 3.3 – Net intake ratio to grade 1 of education level, both sexes (%) – Primary and secondary education (Selected years for which data for all three countries is available) Teachers: In Oman, the pupil-teacher ratio at primary and secondary level was 20 and 17, respectively. The same in KSA was close to 10 for both primary and secondary school. While in UAE, the pupil-teacher ratio, at the primary and secondary levels of education, stands at 23 and 16 respectively in 2015. The job satisfaction of science and math teachers at primary education level was very high, with Oman ranked at number three (3) for grade four (4) and number eight (8) for grade eight (8) teachers. 73-74% of grade four (4) and 61-64% grade eight (8) students in Oman were being taught by teachers who were very satisfied with their working conditions. KSA performs a little lower in teachers’ job satisfaction with 55-61% of the students being taught by teachers who were very satisfied with their job. The job satisfaction of science and math teachers at primary education level in UAE is one of the top five (5) in the world with 64-70% students taught by teachers reporting that they were very satisfied and only between 3% to 5% (depending on grade level and subject) of students being taught by teachers who reported less than satisfied on job satisfaction in the year 2015. In Oman, for grade 4 between 58-64% of pupils are taught by math and science teachers who have majored in both the concerned subject and education. For the remaining students, around 33% are taught by teachers who have majored only in the subject concerned or only in education. For grade eight (8) between 36-40% of pupils are taught by math and science teachers who have majored in both the concerned subjects and education, and around 50-60% are taught by teachers who have majored only in the subject concerned or only in education. 80 85 90 95 100 105 110 115 2010 2011 2012 2013 2014 2015 N et in ta ke r at io to g ra de 1 o f ed uc at io n le ve l, bo th s ex es (% ) Primary Oman Secondary Oman Primary Saudi Arabia Secondary Saudi Arabia Primary United Arab Emirates Secondary United Arab Emirates 75 In KSA the percentage of students taught by teachers with both the concerned subjects (Math or Science) and Education qualification was lower and between 17-34%. In the UAE, between 25 to 26% of students are taught by math and science teachers who have majored in both the concerned subject and education. For the remaining students, around 50 to 60% are taught by teachers who have majored only in the subject concerned. Figure 3.4 – Job satisfaction of school teachers as reported in TIMMS 2015 Reforms: In Oman, an important reform – the Basic Education System – was introduced in 1997. This system was to replace all the General Education System gradually and the first schools started with the Basic Education System in 1998. The degree of prevalence of Basic Education System in Oman is not determined at the time of writing this paper. The system promises to be student-oriented rather than the previous teacher-oriented design. The Basic Education Reforms may be the second part of the story of the marked increase in results of Mathematics and Science by 2015. UAE has followed suit in both primary and secondary education. Whereas primary and secondary education reform have been recommended for 0 10 20 30 40 50 60 70 80 Grade 4 Science Grade 4 Math Grade 8 Science Grade 8 Math Jo b sa tis fa tio n of te ac he rs a s pe rc en ta ge o f s tu de nt s ta ug ht b y te ac he rs w ho r ep or t b ei ng " ve ry s at is fie d" (% ) Oman Saudi Arabia United Arab Emirates 76 KSA for some time by experts, any concrete action towards a comprehensive education reform has yet to be seen. Performance: The resource allocation changes and education reforms may be partially responsible for the improved TIMMS results of primary education and secondary education in 2015 in Oman. In terms of the translation of primary education spending to the quality of education, the scenario in Oman has been encouraging. The TIMMS scores for students of 4th Grade age were a low of 385 for Mathematics and 377 for Science in 2011. Similarly, the 8th Grade scores for Mathematics and science were 372 and 423 in 2007, respectively. The increase is visible from 2011 to 2015 where Oman made to the top of TIMMS greatest improvers list showing an increase of 4th Grade score from 385 to 425 for Mathematics and from 377 to 431 in Science. The 8th Grade scores in Mathematics increased from 366 to 403, and those in Science increased from 420 to 455 during the same period. Between 2011 and 2015 KSA has witnessed a fall in the Trends in International Mathematics and Science Scores (TIMMS) Math and Science scores for 4th and 8th Grade students. The scores for 4th Grade Math results went from 410 to 383 and the science result dropped from 429 to 390 respectively. Similarly, the results for 8th Grade Math and Science dropped from 394 to 368 and 436 to 396. It is hard to say what forces are behind this drop as the data related to total and per capita education expenditures for the years 2011 to 2015 was not available. However, the last data point available shows that 2.2% of GDP was spent on primary education. It is also clear from the TIMMS analysis that roughly 90% of that students were taught in a condition whereby they faced shortages in resources for Math and Science teaching for both 4th and 8th grade. The TIMMS scores in the United Arab Emirates showed an improvement from 434 to 452 and 456 to 465 for 4th Grade and 8th Grade Maths scores between 2011 to 2015. The science scores improved from 428 to 451 and 465 to 477 in the same period for grades 4th and 8th, respectively. To put things in perspective the students in the highest-scoring countries scored around 620, and in European countries and Anglo-Saxon countries, the students scored around 500 to 550. One reason for lower scores in the GCC countries may be that students were starting education in primary level at a later age than their peers in other countries 77 Table 3.2 – TIMMS Score for Grade 4 and 8 students in Science and Mathematics Score Country 2011 2015 Trend Grade 4 Science Oman 377 431 + Saudi Arabia 429 390 – United Arab Emirates 428 451 + Grade 4 Math Oman 385 425 + Saudi Arabia 410 383 – United Arab Emirates 434 483 + Grade 8 Science Oman 420 455 + Saudi Arabia 436 396 – United Arab Emirates 465 477 + Grade 8 Math Oman 366 403 + Saudi Arabia 394 368 – United Arab Emirates 456 465 + 3.3.4. Tertiary education and vocational education Tertiary education: In 1997, the total number of students enrolled in tertiary education in and outside Oman was hardly approaching 10,000. By 2005 the number of tertiary education students had increased to 48,500 with government spending a meagre 0.25% of the total GDP on tertiary education. This spending accounted for roughly 290 million USD constant 2011 PPP total expenditure on tertiary education or 6,000 USD constant 2011 PPP per student. It was a drop from the 1997 per student expenditure of roughly 20,000 USD constant 2011 PPP. However, by 2005 onwards the spending increased to 25,000 USD constant 2011 PPP per student, with 127,000 students enrolled in tertiary education leading to a total expenditure of roughly 3.2 billion USD constant 2011 PPP per student or 2.0% of total GDP. Tertiary education expenditures in Oman have been an average of USD 20,000 per from 1997 onwards. 78 Even though the expenditures per student have remained somewhat constant on average the overall expenditure has increased since 2005 from 0.25% of the GDP to nearly 2.0% of GDP by 2015. This increase is reflected in a larger number of Omanis graduating from tertiary education institutes in Oman and foreign countries. Also, there was a marked increase in natural science, mathematics, information technology, statistics and engineering graduates. The share of graduates in these fields of education increased from 27% of total graduates in 2007 to 45% in 2015. The number of higher education graduates in sciences has been acknowledged as the greatest strength of Oman’s young innovation system as Oman has the highest ratio of higher education science and engineering graduates when compared to any other country in the world. This success is rather recent, yet it is one that is bound to reflect positively in building innovation capacity in Oman. Figure 3.5 - Percentage of graduates from tertiary education graduating from various tertiary education (by programme type) In KSA, the transition from upper secondary to tertiary education has a ratio close to one (1). KSA has at least 35 universities and around 70 plus tertiary education institutes. 35% of 47.3% 68.4% 70.7% 42.5% 23.9% 21.8% 6.9% 5.7% 6.3% 3.0% 1.8% 1.0% 0.3% 0.2% 0.2% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2015 2015 2015 Agriculture, Forestry, Fisheries and Vetrinary Services Health & Welfare Natural Sciences, Mathematics & Statistics, Information & Communication Technologies, Engineering, Manufacturing & Construction Education, Arts & Humanities, Social Science Journalism & Law 79 the tertiary education graduates are from tertiary education pertain from engineering, manufacturing, construction, natural science, mathematics, statistics, information and communication technology, and health and welfare programs. Another 15% of graduates are from the education field. Whereas around 50% finish their tertiary education in social science, law, business, and service sector related programmes. In terms of tertiary education, we observe that UAE has two universities ranked at 51-60 in the QS University 50 under 50 ranking. Overall, for tertiary education, we also observe that business, administration, and law programmes form close to 50% of the total graduates in the UAE. This share may be related to the strong focus on trade in the UAE economy. The UAE is the largest host of international branch campuses in the world. It has done very well in increasing the intake of secondary school graduates into tertiary education. In 1993 only 25% of the students that were enrolled in the upper-secondary education two years earlier from were enrolled in tertiary education. This ratio has increased to roughly 80% by 2015. The attrition rate at every level of education is a cause of concern. The technical or vocational education in the UAE is highly focused on business and IT as such it is not clear how the country aims to include the national population in the process of setting up a diversified and innovative economy. Another cause of concern is the type of programmes offered by the UAE’s education system. The branch campuses have become a highlight of the UAE education policy. However, most of them offer the same easy to set up, low-cost, high return programs, such as business administration and information technology. The biggest of these private universities have lower than 15% enrolment of national students. Since there is a large foreigner population, there is currently no mechanism through which the objectives of these universities can be aligned with the labour force, social, and economic growth objectives of the United Arab Emirates. The open and free-market style education system results in branch campuses popping up since 2005 target profitability and foreign student demand rather than future labour market requirements. The result is that the UAE has less than 15% of tertiary education graduates completing natural science, statistics, mathematics, technology, engineering, or construction programmes. 80 Gonzalez et al. (2008) have pointed out for Oman, that secondary school graduates who do not make it to tertiary education or higher education are not ready for vocational and service jobs and there is not adequate post-secondary vocational, technical and service education available thereon. The data for KSA does not show the numbers related to vocational secondary education. The problem of getting the youth ready for the labour market in UAE is compounded by the fact that there is no coordinated policy for vocational education. Some vocational training institutes operate in the private and public sector, however, since UAE has a very high expatriate population, the education provided by the institutes is not always aimed at the requirements of UAE rather the monetary objectives that may be greatly driven by the educational demands of the foreigners living in the UAE. 3.3.5. R&D The condition of the R&D policy sphere is essentially different from the education sphere for GCC countries. Whereas Oman performs relatively well in education outcomes, KSA has a clear advantage on the R&D inputs side of the picture. Oman spends the least in proportion to its GDP on R&D followed by UAE with KSA having the highest spending on R&D as a percent of its GDP. The number of researchers in Oman is also lower than its GCC neighbours. KSA has more high-ranking universities and higher rating on research capabilities of these universities. UAE performs better than Oman, whereas Oman lags in the research arena. The development of R&D parks and sovereign funds focussed on R&D and Innovation has also entered the agendas of all three countries however these agendas will take time to show results. Expenditure and Inputs: The gross expenditure on research and development as a percent of GDP for Oman has hovered between 0.14% to 0.25% during the period 2011 to 2016. The headcount number of researchers per million population has dropped gradually from 450 to 129 during the same period. The research expenditure has shifted from higher education institutes to the government between 2011 and 2012. The total GERD has ranged between USD 150 million and 250 million per year (constant USD 2005 PPP). The contribution of business enterprises towards R&D has been in the range of 24 to 29% in the recent year. 81 KSA, in contrast, has been spending between 0.8 to 0.9% as a percent of its GDP on R&D. The UAE spent only 0.5% of its GDP on Research and Development (R&D) in 2011 with 0.2% spent by the public sector, another 0.14% by higher education institutes, and only 0.14% by business enterprises. The Gross Expenditure on Research and Development (GERD) in the UAE increased to 0.7% by 2014 with 0.5% carried out by the government and 0.2% by private enterprises. The GERD as a percent of GDP in the UAE was 0.96% in 2016. Figure 3.6 - GERD as a percentage of GDP - (Selected years for which data for all three countries is available) Whereas Oman lags in most inputs that contribute to research and development, it boasts the highest ratio of science and engineering graduates as a portion of its total graduates when compared to rest of the world, as discussed earlier. The headcount of researchers shows that the number of researchers active in research was 1446, 1301 and 1235 in 2011, 2012 and 2013 respectively. The total R&D personnel in the UAE numbered at 11400 in 2011 that is roughly 1300 per million inhabitants. The same data was not available for KSA however is expected to be higher than both Oman and UAE. 0 0.2 0.4 0.6 0.8 1 1.2 2009 2010 2011 2012 2013 2014 2015 2016 G ER D a s a pe rc en ta ge o f G D P (% ) Oman Saudi Arabia United Arab Emirates 82 Figure 3.7 - R&D personnel, headcount basis, per million inhabitants (in the year 2015, breakdown for UAE not available) Performance: The average QS Universities Ranking for top three universities in Oman has hovered around 9 (0 being the lowest and 100 being the highest). However, it must be noted that Oman with 3 million inhabitants has only one university ranked in QS top 700. It is noteworthy that this university is ranked 61 in universities under 50 years old. The top 10 universities in UAE showed a rating of medium to high in terms of research, with the highest rating possible being very high. Only two universities the United Arab Emirates University and Khalifa University performed at the level of high ranking in research according to QS University Ranking 2016. Oman scores a meagre 4.2 (0=weakest 100=strongest) on the R&D in the Innovation Input sub-index of the Global Innovation Index 2017 report. This performance followed a gradual drop from 9.0 in the previous 5 years. KSA performed very well in its R&D in the Innovation Input sub-index of the Global Innovation Index, having a score of 41.2 and ranked 22 in the world. UAE was not doing as well as it would appear and had a score of 18.2 on the R&D sub-index. Reforms, Coordinated Policy and Central Research Institution: To improve the situation of research and development and drive the research and innovation ecosystem in Oman towards a positive direction the Research Council (TRC) with it research and innovation section was set up in 2005. Since then, the research council has launched many initiatives. The UAE lacks 0 1000 2000 3000 4000 5000 6000 7000 8000 Oman Saudi Arabia United Arab Emirates R & D s ta ff , h ea d co u n t b a si s, p er m il li o n i n h a b it a n ts Total Researchers Technicians Other Supporting Staff 83 a central authority and a central policy for directing Research and Development in the country. The main weaknesses observed include a weak intellectual property regime, non- clarity of the government in terms in terms of the R&D priorities that the industry and academia is to follow, and absence of an overreaching regulatory body that can communicate the governments R&D priority areas with the ability to facilitate and/or enforce partnerships between national and international stakeholders. Whereas Oman is the first one to have a central research coordination institution, a research agenda has only been recently outlined. However, it seems that KSA does quite well without a central research institution. KSA does not have a coordinated research policy or a dedicated central research institution and in UAE research policy is handled by the Ministry of Higher Education and Research. In the same regard, the UAE has also tried to improve its R&D footprint by trying to establish a central policy and induce a coordinated R&D effort. Research Funding: Oman has the Open Research Grants Program that allocates funds to small and medium-sized research in areas highlighted as priorities in the National Research Strategy26. In 2011, the fifth cycle of the Open Research Grants Program set 13 million constant 2011 USD PPP for the funding of 20 Research Proposals that included funding towards 12 PhD and 20 Masters students associated with the projects. In 2010 the Industrial Innovation Centre (IIC) took place of the Industrial Innovation assistance program that existed before. Between 2009 and 2013 this program invested 7.8 million constant 2011 USD PPP for 31 projects of which 2.6 million USD were contributed by the IIC and the remaining from the Industrial Partners in the Projects. The TRC also had the Strategic Research Program since 2012, and the Faculty Mentored Undergraduate Research Award Program (FURAP) since 2013 that sponsored 249 projects by 2016. The annual spending of the FURAP program is roughly half a million USD. The TRC also established the publication & awareness department to create a learning culture using media. Their initiatives included TRC youth science and innovation show, print media coverage, 26 The areas of research highlighted as priorities in the National Research Strategy include; Culture & Basic Sciences, Energy & Industry, Environment & Biological Resources, Education & Human Resources, Health & Social Services, Information & Communication Technology 84 social media coverage, television coverage, and showcasing research activities in exhibitions. Since there are no central research institutes in KSA and UAE it is hard to estimate the research grant and funding of research project in these countries R&D and Innovation Clusters and Sovereign Funds: In 2016 The Research Council (TRC) in Oman launched Ibtikar, an innovation-investment company with a 360 million USD constant 2011 PPP equivalent initial capital to invest in start-ups and also to attract international companies that will utilise Oman's natural and human capital and benefit Oman directly with knowledge transfer and development. In 2016, the TRC finalised the development of its Innovation Park Muscat (IPM) with 560,000 square meters of area research, innovation, incubation integrated facilities available for research and development activities. The program has an estimated 8 to 10 years maturation period, and after that it is expected to generate jobs, provide direct revenue and establish a flow of companies to Oman while increasing foreign direct investment. KSA has established at least four (4) major research clusters that include the KAUST Research and Technology Park, Riyadh Valley Makkah Park, and Dahran TechnoValley (DTV). DTV is particularly interesting as it started with four (4) major international business and two (2) Saudi corporates setting up their research centres in 2011. By 2017 there are now 12 research centres belonging to major and international businesses and four (4) for Saudi firms in DTV. There are plans of a 2 trillion USD sovereign fund for R&D and innovation projects that will be accompanied by a national transformation plan. UAE does not have dedicated research and innovation cluster or sovereign fund for setting up technology, innovation, and research related institutes and organisation, however, three significant clusters – Dubai Science Park, Dubai Internet City and Dubai Silicon Oasis – are established for businesses with the expectation of R&D and innovation spill-overs. 85 3.3.6. Business and Entrepreneurship Index of Economic Freedom: Oman has maintained relatively stable scores on the overall Index of Economic Freedom. The overall score is composed of many different indices amongst these we have selected business freedom, trade, investment, and monetary indices as they directly relate with government policies to boost businesses (100 representing the frontier). We observe that Oman scores high on business freedom and trade conditions with the scores being in the range of 70 to 80 during most of the period from 1995 to 2015. During the same period, monetary and investment conditions have improved continuously with recent scores on the sub-indices being 60 and 65, respectively. The same is true for KSA with the scores on business freedom and trade freedom, improving regularly between 2005 and 2016 from 50 and 60 to 80 and 70, respectively. However, the scores on the index for business freedom are currently lower than those observed in 1995 where KSA scored 85 on the business freedom sub-index. Fiscal freedom has remained at 100 while investment freedom has been below 50 and hovered from 30 to 40 for most of the period 1995 to 2016. The UAE has a long tradition of business and entrepreneurship and in particular international trade. From 1996 to 2002 we observe that business freedom was at 85 out of 100 on the business freedom sub-index of the Index of Economic Freedom (IOEF). From 2002 to 2008, we observe a steady drop in the business freedom sub-index, and after that by 2016, it increases to 80. Trade freedom sub- index has maintained around 80 points from 1995 to 2016. Fiscal freedom has been steady at 100 or close to 100 on the IOEF index. However, investment freedom appears to be on the lower side and has been below 50 on the index and in most years closer to 30. Doing Business: The Doing Business indicators present a more detailed picture of the business policies and the business environment. Figure 3.8 below, shows distance from the frontier (DTF) for various business-related policies in Oman. The measure DTF is ordered from 0 being the furthest from the frontier and 100 being at the frontier. We notice that since 2008 Oman has made great strides in improving the Starting a Business measure, going from 49.22 in 2008 to 92.85 in 2017. This improvement has been achieved by reducing the number of days, procedures, and capital required to start a business. For example, the paid-in minimum 86 capital was reduced to 0% of the income per capita. It is apparent that policies to boost entrepreneurship have been put in place in Oman. Paying taxes and Trading across borders measures maintained close to the frontier in the last 13 years and were at 90.6 and 80.17 in 2017, respectively. Enforcing contracts measure has seen modest improvement in from 2014 to 2015 and was steady at 61.55 in 2017. The areas where Oman is far behind the frontier includes that of Resolving insolvency and Getting credit at 42.65 and 35 during 2017. Figure 3.8 – Distance to frontier (DTF) on Doing Business indicators – Oman, KSA and UAE (100 being the frontier) – 2004 to 1017 KSA has been close to the frontier with the value of Paying Taxes DTF close to 100 between 2013 to 2015 owing to low tax requirements and ease of tax payment systems, however, in the last two years 2016 and 2016 the Paying Taxes DTF has fallen to 80. The ease of starting a business DTF saw an improvement in 2008 and jumped from 30 to 70 and since then has slowly hovered close to a value of 80 still below the leader in ease of starting a business among the GCC countries that is Oman. The situation regarding ease of resolving insolvency is alarming as it appears that KSA is furthest from the frontier. The ease of resolving insolvency can be paramount in terms of attracting businesses and in this case could be a potential 0 10 20 30 40 50 60 70 80 90 100 Starting a Business - DTF Getting Credit - DTF Paying Taxes - DTF Trading across Borders - DTF Enforcing Contracts - DTF Resolving Insolvency - DTF  Oman  Saudi Arabia  United Arab Emirates 87 inhibitor of risk-taking behaviour among entrepreneurs leading to slow down of innovation efforts. United Arab Emirate has a low tax regime, and this appears in the Doing Business indicators with UAE close to the frontier in terms of Ease of Paying Taxes. Trading across borders had always been one of the strong points of the UAE economy however, during 2015 and 2016 the UAE has witnessed movements away from the frontier. The ease of starting a business that is critical for entrepreneurial activity has steadily improved going from 63 to 91 from 2004 to 2016. Finally, resolving insolvency, getting credit and enforcing contracts remain areas where the UAE needs further efforts and policy to get closer to the frontier. Trade Zones: There are at least 24 free trade zones in the UAE, with most of them concentrated in the Emirate of Dubai. The companies that set up in free trade zones in the UAE can benefit from 100% foreign ownership, full repatriation of capital and profits, no requirement for minimum capital investment, quick approval procedures for setup and no corporate or personal income taxes. However, a free trade-zone company in UAE cannot trade directly with the UAE or broader GCC market. Such trade can be made only through locally appointed distributors that need to have appropriate trade licenses and 51% ownership by UAE National. A customs duty of 5% is applicable when the free zone company sells its products or services in the local market. Through the establishment and effective operation of trade zones, UAE has been able to accomplish two direct goals. Firstly, the country profits from real estate development, and secondly, it can make products and services from all around the world to be available at its doorsteps. Whereas the target of diversification of the economy is clearly helped through free trade zone, it is not very clear how the policies related to activities in the free trade-zones impact the aim of inducing innovation in the UAE. 3.3.7. Governance and Infrastructure The worldwide governance indicators (WGI) from World Bank show that Oman has improved in the rule of law and control of corruption indices from 1995 up to the early 2000s and there 88 on seen a drop. Regulatory quality and government effectiveness data show a regular drop from 1995 to 2013. The situation is not alarming as Oman is above the threshold of 50 points for all governance indices indicating reasonable governance structure and confidence in the public authorities. However, it can be observed that there is a lot of room for improvements. The countries with the best quality and perceptions of governance exhibit scores of above 90 points in terms of government effectiveness, regulatory quality, rule of law and control of corruption. Figure 3.9 – World Governance Indicators – Oman, KSA, UAE – 1996-2015 The main improvements are apparent in the Regulator Quality in Oman that went up from 50.4 to 64.2 between the years 2000 and 2002. Other indicators including Government Effectiveness, Political Stability, Rule of Law, and Control of Corruption have stayed somewhat stable between 1996 and 2017. The indicator that seems to show the worst performance is that of Voice and Accountability. The was a steady drop from 39.76 to 27.61 points between 1996 and 2006 and 2007 onwards the indicator has been stable around 30 points. 0 10 20 30 40 50 60 70 80 90 100 Voice and Accountability Political Stability Government Effectiveness Regulatory Quality Rule of Law Control of Corruption  Oman  Saudi Arabia  United Arab Emirates 89 The WGI show that KSA has the lowest levels for all Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality and Control of Corruption. Political Stability dropped from 50 to 40, while control of corruption has improved from 40 to 50 from 2000 to 2015. Regulatory quality and rule of law have remained relatively steady at approximately 50 and 55, respectively. Government effectiveness has steadily improved from 45 to 55 from 1996 to 2015. Finally, voice and accountability measure is low as expected for GCC countries. The World Governance Indicator shows that the UAE has been steadily improving its governance. Government effectiveness has improved from 63.75 to 80.74 from in the last 20 years, from 1996 to 2015. Political Stability and Rule of Law appear to be in a stable and healthy position since at a level around 65 out 100 on the indices. Regulatory quality had seen a dip from 1996 to 2010 and thereafter improvements have been observed. The regulatory quality from 2010 to 2015 has improved from 57 points to 72.5 points on the index. As is often the case in the GCC region, Voice and Accountability is seen to be low and dropping from 40 to 30 points from 1996 to 2015. 3.4. The Outputs of GCC – Indicators of Innovation and Diversification 3.4.1. Section Summary Table 3.3 below shows that patent activity in Oman is lower than both KSA and UAE. Meanwhile, industrial design activity is relatively higher in Oman and UAE than KSA on a per capita basis. In terms of sectoral diversification, Oman and UAE have explicit positive trends in tourism and trade sectors respectively, and KSA has explicit positive trends in all sectors other than industry and manufacturing sector. An increase in the share of value added of industry and manufacturing sector in the economy of Oman was observed between 1990 and 2013. While the same was true for UAE, yet the overall increase was observed to go through cycles of fluctuation during the period. While the trade, financial and business service and public sector were shrinking in Oman the overall productivity in the sectors was increasing. The industry and manufacturing sector witnessed a decrease in labour 90 productivity. The UAE also witnessed a similar trend in industry and manufacturing, while the opposite was true in KSA where the overall share of manufacturing reduced as the labour productivity in these sectors grew. The overall trend shows that Oman improved in the tourism sector with the value added from the tourism sector as a percent of total GDP being stable with increasing labour productivity. KSA performing well in trade sector with both the share of value added of the trade sector and labour productivity of the trade sector improving. KSA’s share in value added for both tourism, and, finance and business-related service sector was stable with increasing labour productivity, while the public, education and health sectors increased in size with stable labour productivity. UAE has a clear positive trend in only trade sector where the share of value added increased with labour productivity being stable. Table 3.3 - Comparing Innovation and Diversification Outputs Country  Innovation and Diversification Outputs  Oman KSA UAE Innovation Patent Applications – – + o Variation in Patents by Fields of Technology – – o + Industrial Design ++ + ++ Summary - o + Share of Value Added (VA) & Labour Productivity (LP) by Sector Industry – Chemical & Metals (VA) ++ – + Industry – Machinery, Equipment and Other Manufacturing (VA) ++ – + Trade – Wholesale and Retail (VA) – ++ + Service – Tourism Related (VA) o o o Service – Finance and Business (VA) – o o Public Sector, Education and Health (VA) – + – Manufacturing (LP) – + – Trade (LP) ++ + o Service – Tourism Related (LP) ++ + o Service – Finance and Business (LP) ++ + – Public, Education, and Health (LP) ++ o + Summary ++ o - Note: Description of the quality of or level of success in policy, enablers or outcomes – – – Extremely Negative – – Negative – Slightly Negative o Moderate +++ Highly Positive ++ Positive + Slightly Positive 91 3.4.2. Patents, Trademarks and Industrial designs The patent, trademark and industrial design data show that Oman is slowly progressing in a positive direction. The number of patent applications abroad had risen to 24 by the year 2012 coming back to around 10 in the year 2015. Resident Patent application has also seen a positive trend. It is noteworthy that a major proportion of the patent applications is related to Civil Engineering. Non-Resident Industrial Design Registrations have tripled from 2007 to 2015. Oman’s innovation output is dwarfed in comparison to KSA that has developed its R&D and innovation systems for a longer period than Oman. Additionally, Oman’s pace in producing patent and industrial design applications and grants is much slower than the UAE which appears to be active in the arena since 2001 onwards (See Figure 3.10 and Figure 3.12). Figure 3.10 – Patent, Trademark & Industrial Design Data – Oman (WIPO Statistics Database) 92 KSA appears to have benefited from the conversion industry set up during the early phases of its development, and by 2015 it had 200 resident patent grants from those filed previously while filing 1000 resident patent applications during the year. The top five fields in which patents are filed in KSA are associated with chemical and material industry, and this association clarifies the significant role of the chemical process industry in development in KSA. Industrial design applications in both Oman and KSA seem to have picked up pace since 2009. KSA also shows a higher number of patent applications, perhaps, due to having set up a local patent office as well as having the GCC patent office located in its borders. Figure 3.11 – Patent, Trademark & Industrial Design Data – KSA (WIPO Statistics Database) The patent, trademark and industrial design data for UAE shoes that patents application in UAE from 2001 and 2015 are from in many different areas. Civil engineering patents are related to the real 93 estate development in UAE, whereas computer technology, engines and machine patent application, information technology and medical technology patent application are associated with the various free zones that have been instated to develop the economy and push local innovation. Figure 3.12 – Patent, Trademark & Industrial Design Data - UAE (WIPO Statistics Database) Overall trends exhibit that patenting, design and trademark registration activity when considered on a per capita basis is roughly twenty (20) times higher in KSA than Oman, and ten (10) times higher in UAE than Oman. Meanwhile, Oman and UAE have similar levels of activity in terms of industrial design applications and registrations on a per capita basis that is roughly ten (10) times higher than the activity in this area in KSA. 94 3.4.3. Non-traditional sector - share in the economy and labour productivity In terms of share of value added of sectors as a percent of total output in Oman, we observe that value added in the mining and quarrying sector increased from 7.0% to 12.0%. In addition to that, we observe increases in Chemical Manufacturing, Metal Manufacturing and Machinery and transport Manufacturing in the range of 1.0% to 2.5%. One exception to this apparent growth in the share of sectors in addition to the Mining and Quarrying sector is that of the Hotel and Restaurant sector within the Services broad category. Despite having a strong policy to promote the tourism sector in Oman, the sector has not grown in absolute terms. We observe in Figure 3.13 that the share of Hotels, Restaurants, and Transport services sector in the economy has remained virtually stagnant in the past few years. KSA, on the other hand, observed a drop in the share of value added as a percent of total output from the Mining and Quarrying sector along with Petroleum, Chemical and Non-Metallic manufacturing sectors, Metal products sector. Wholesale and Retail Trade is the leading sector in private industry that appears to be improving from 2002 onwards. UAE’s Mining and Quarrying Sector has also reduced in the share of value added from 2002 onward while Electrical and Machinery, Transport Equipment and Other Manufacturing have seen a rise in their share of value added as a percent of total output in the UAE economy. 0% 5% 10% 15% 20% 25% 30% S h a re o f V a lu e A d d ed in t o ta l O u tp u t (% )  Oman  Saudi Arabia  United Arab Emirates 95 Figure 3.13 – Share of value added by sector as a percent of total output for Oman, KSA and United Arab Emirates (1990 to 2013) Figure 3.14 – Labour Productivity by sector, KSA and United Arab Emirates (1990 to 2013) The labour productivity changes in the various sector provide additional information on sectoral diversification in the three countries. Figure 3.14 above shows that labour productivity in all sectors except Mining and Quarrying, Manufacturing, and Electricity, Gas & Water saw a steady increase between 1998 and 2008. Consequently, Manufacturing in Oman has dropped from being the most productive to the second most productive sectors in Oman. The productivity increase in the transport and communication sector may be related to two events. In the transport sector Oman Air a government-owned airline that was formed in 1993 became a part of IATA and expanded its operations in 1998. Also, in the transport sector, Oman Shipping Company, a government-owned enterprise was incorporated in 2003 and started operations. Oman also deregulated the telecommunications sector in 2004, privatised part of the government-owned telecommunication company, and opened the licensing market for an additional private player in the market. These three events coincide 100 200 300 400 500 600 700 800 900 1,000 L a b o u r P ro d u ct iv it y (U S D p er e m p lo y ee )  Oman  Saudi Arabia  United Arab Emirates 96 with the first and second increase in labour productivity in the transportation and communication sectors. KSA’s labour productivity in all sectors appears to be stagnant from 2007 onwards. This levelling followed the rise and fall of labour productivity in all sectors except Mining and Quarrying from 1994 to 2007 with the peak productivity observed in most sectors in the years 2001 and 2002. The mining sector has retained productivity levels around to USD 200,000 per employee with a slightly increasing trend. In the UAE, the mining and quarrying sector and manufacturing sector that altogether contribute less than 20% of the value-added in the economy have become the most productive sectors in the economy. It is interesting to note that the trade sector while being the largest employer in the economy is amongst the lowest productive sectors in the economy of the UAE. It contributes approximately 10% of value- added in the overall economy of the UAE. 3.5. Connecting Policies, Enablers and Outcomes The comparative analysis of the three countries discusses indicates that all three of them have had very little success in terms of innovative activity and diversification in the economy away from natural resources such as oil and gas. This result indicates that the level of success achieved by the countries in their intention to diversify is closely matched to the quality and level of policy inputs and condition of enablers in the country. The pattern indicates that it is often the lowest ranking policy measure or enabler that decides the highest possible level of success in innovation and diversification. Leading to the understanding that the condition of all inputs needs to be strong in order for innovation activity as well as diversification to prosper. Oman’s failure to have substantial progress in innovation activity and only achieving moderate results is coincident with moderate policy measures in the research and development arena. Similarly, KSA’s failure to have a strong industrial and sectoral development policy associated with an excellent business environment has led to moderate success in diversification. Not only that, but the relatively moderate conditions of the primary and 97 secondary education policy and quality expresses in vocational, tertiary graduates and overall labour quality. Even though primary and secondary education quality may be considered as indirectly related to innovation and diversification efforts of a country, yet there is a direct correlation observed between the highest quality of education in the country to the highest level of success achieved in the ultimate outputs of innovative activity and diversification. United Arab Emirates is another example of the same trend where vocational, technical and tertiary education and the condition of the R&D policy and enablers are leading to moderated levels of diversification despite the relatively stronger business environment and governance regime in comparison to the other two countries. It is important to note that the slightly positive output trends for Oman’s diversification arena and those for KSA and UAE in the area of innovation activity are just that – slightly positive. The innovation and diversification goals of these countries are far from their full potential and constrained by the lowest- performing policy areas. Table 3.4 – Summary of policy and enablers condition to innovation and diversification output in the three GCC countries Oman, KSA and United Arab Emirate Country Input Trend and Overall Rating Output Trend Oman Primary & secondary education ++ Innovation (Patenting activity and industrial design) – Vocation, technical & tertiary education o Research & development – Business environment and industrial/sectoral development policy + Diversification (sectoral value added and labour productivity) ++ Governance & infrastructure + KSA Primary & secondary education – Innovation (Patenting activity and industrial design) + Vocation, technical & tertiary education + Research & development + Business environment and industrial/sectoral development policy – Diversification (sectoral value added and labour productivity) o Governance & infrastructure – UAE Primary & secondary education + Innovation (Patenting activity and industrial design) + Vocation, technical & tertiary education o Research & development – Business environment and industrial/sectoral development policy + Diversification (sectoral value added and labour productivity) – Governance & infrastructure ++ Note: Description of the quality of or level of success in policy, enablers or outcomes – – – Extremely Negative – – Negative – Slightly Negative 98 o Moderate +++ Highly Positive ++ Positive + Slightly Positive The summary of these policy inputs and enablers has been discussed in Section 3.3 on page 68 and the detail of these have been presented in the sub-sections. Similarly, the summary and details of the innovation and diversification outputs have been discussed in Section 3.4 on page 89. 3.6. Summary, Discussion and Conclusion The overall conclusion for the three GCC countries discussed is that the innovation and diversification goals of these countries are constrained by the comparatively lowest-performing policy area and the lowest quality enablers. The governments of the GCC countries have focussed in varying degrees on the required policies for improving, education, R&D, governance and business environment. However, we find that as long as all policy areas and enabler are not improved adequately, the diversification and innovation output in these countries will remain far from their maximum potential. The cases of Oman, KSA and UAE show that no one policy alone leads to improvements in innovation and diversification outputs. Overall, the “limiting enabler” for both Oman and UAE are research and development policy. The implementation of the policy set in Oman and UAE generates differing result in the two countries. Oman is most constrained in terms of innovation output and UAE in terms of diversification output. The limiting enablers in the policy set of KSA are primary and secondary education policy, business environment, industrial and sectoral policy, and governance and infrastructure. KSA performs moderately on both innovation and diversification outputs. On the whole, the three countries are far from their full potential in term of the pace of diversification and innovative activity in the economy. The systems of education in the GCC countries developed distinctly. Oman delayed the development of a tertiary education system in subsequence to successes in primary and secondary education in order to provide an adequate base for the next level. In contrast, KSA developed its vocational and tertiary education in parallel to primary and secondary education systems. The UAE seems to have focused equally on the development of primary and 99 secondary education. However, the UAE lacks a specific policy in terms of tertiary and vocational education. The literacy and primary education performance has been excellent for Oman, KSA and UAE. Secondary education seems to lack a clear policy for both Oman and KSA. Oman has the highest enrolment rate in secondary education, whereas KSA and UAE do not perform very well in primary to secondary transfer. The job satisfaction of primary school teachers in Oman and UAE is among the best in the world while KSA lags slightly behind. In terms of qualifications of teachers, Oman does relatively better than KSA and UAE, and a higher proportion of students in Oman are taught by teachers who have both concerned subjects (Math or Science) qualification and education as their major. Oman has been the first in the region to phase-out primary education reforms moving towards a student-oriented approach. UAE has followed suit in both primary and secondary education. Whereas primary and secondary education reform have been recommended for KSA for some time by experts, any firm action towards comprehensive education reform has yet to be seen. The performance improvement in both Oman and UAE has been observed following reforms. The TIMMS scores for maths and science improved in Oman and UAE, while KSA witnessed a drop in TIMMS scores leading to concerns in terms of system performance. In Oman, it is also apparent that the government has put in efforts to increase enrolment in tertiary education. It is estimated that the government funds roughly half of the students in tertiary education. While not all secondary school graduates in Oman enter tertiary education yet the tertiary education science, technology and engineering graduates’ ratio to the total graduates is 45%, that is highest in the world. The transition ratio from upper secondary to tertiary in KSA is close to 1 and in UAE is as low as 0.25. The ratio of natural science, technology and engineering graduates to the total number of graduates in KSA is also high being close to 35%. In the UAE it is only 15%, and most of the tertiary education graduates pertain from the law, social science and business programs. All three countries appear to suffer from lack of technical training, vocational education programs and coordinated education policy 100 The condition of the R&D policy sphere is substantially different from the education sphere for the individual GCC countries. KSA has a clear advantage on the R&D inputs side of the picture. Oman spends the least in proportion to its GDP on R&D followed by UAE with KSA having the highest spending on R&D as a percent of its GDP. The number of researchers in Oman is also lower than its GCC neighbours. KSA has more high-ranking universities and higher rating on research capabilities of these universities. UAE performs better than Oman whereas Oman lags in the research arena. The Development of R&D parks and sovereign funds focussed on R&D and innovation has also entered the agendas of all three countries however these agendas will take time to show results. In understanding the business policies and environment through the lens of Index of Economic Freedom, we observe that Oman does the best among the three countries in terms of providing investment freedom with a score of 70 (out of 100). The scores for investments freedom in both KSA and UAE have remained below 50 (out of 100). The three countries perform similar to each other in terms of business freedom, trade freedom and fiscal freedom with scores close to 70, 80 and 100, respectively. When we look at the business environment through the lens of Doing Business indicators we find that UAE and Oman have the most attractive tax regimes, ease of starting a business and ease of trading across borders with KSA lagging relatively behind. In terms of getting credit and enforcing contracts, the three countries seem to perform equally moderate. KSA has no system installed to ensure smooth insolvency resolution while Oman and UAE perform moderately in this aspect. In terms of governance, Oman and UAE perform well with most scores being in the range of 50 to 70. Oman has seen improvements in regulatory quality and UAE in control of corruption from 2002 onwards. KSA lags with scores ranging from 40 to 55 in all aspect of governance. Voice and accountability is a critical area for attention in order to develop and improve the overall standard of governance in the GCC countries. Both KSA and UAE are performing better than Oman in terms of innovation output. KSA’s patent output seems to be concentrated on chemical and conversion industry. Whereas, UAE seems to have patent activity in a variety of areas. The development of R&D systems in 101 parallel with education systems, development of the conversion industry and pulling innovation and R&D centres of multinational firms seems to be paying off for KSA in terms of high patent and design activity. UAE has also benefited from its free zone and clusters model triggering innovative activity in the particular areas that have dedicated market clusters in the country. Despite having a stronger education base, Oman lacks the relevant market, business environment and industrial setup for the high paced and diversified innovative activity. These sets of policy instruments and conditions act as enablers for the market to innovate and the economy to diversify. The limitations in inputs, policy and enablers that are common for Oman, KSA, and UAE are vocational education, R&D systems and provision of financial instruments to boost entrepreneurship. The different limitations for the three countries in relation to each other are R&D for Oman, primary and secondary education for KSA, and tertiary education in science and engineering disciplines for UAE. In this chapter, we are able to isolate and discuss the performance of policy measures and enablers. We observe that there are limitations in the policy area. Similarly, we have also been able to identify the gaps and relative performance differences in innovation and diversification for the three countries. We suggest that these performance differences are correlated to limitations in the input policies enablers and intermediate outputs of the input policies. However, due to this chapter being a qualitative study, we are unable to confirm the causal inferences. It is recommended that a quantitative study is carried out to relate the lowest-performing policy areas to innovation and diversification output. The main challenge in such a study would be to normalise differing policy and enabler on a standard scale. The seven-point scale established in this work offers a starting point for such investigations. In conclusion, the innovation and diversification outputs of the GCC countries are much below the ‘highly positive’ level. It is expected that the most competitive countries and regions in the world are performing at or striving to perform at the highest level of output. In the context of a highly competitive global economy, the GCC countries are facing a systemic risk of falling behind rather than catching up. It is evident that some of the policy measures and 102 enablers in place are not at the highest performance levels. In comparison to each other UAE performs better in terms of innovation and Oman perform better in terms of diversification. Yet, these “better” performances are far from full potential. The fact that the countries are not investing in the full spectrum of policies is leading to the diversification efforts being hindered and innovation being slow-paced. 103 Appendix 3-A Sectoral Labour Productivity vs. Oil Price Y-Axis – Labour Productivity, Current USD per employee, X-Axis – Oil Price, USD Figure 3.15 - Labour Productivity by Sector vs. Oil Price – Oman y = -976.94x + 329351 0 100000 200000 300000 400000 500000 0 20 40 60 80 100 120 Mining and Quarrying y = 163.91x + 3395.7 0 5000 10000 15000 20000 25000 0 20 40 60 80 100 120 Agriculture y = 7268.8x + 88726 0 200000 400000 600000 800000 1000000 0 20 40 60 80 100 120 Transport and communications y = 214.55x - 260.79 0 5000 10000 15000 20000 25000 30000 0 20 40 60 80 100 120 Fishing y = 557.45x + 1126.5 0 10000 20000 30000 40000 50000 60000 70000 0 20 40 60 80 100 120 Education, Health and Other Services y = -2174x + 533028 0 100000 200000 300000 400000 500000 600000 700000 0 20 40 60 80 100 120 Manufacturing 104 Y-Axis – Labour Productivity, Current USD per employee, X-Axis – Oil Price, USD Figure 3.16 - Labour Productivity by Sector vs. Oil Price – KSA y = 63.093x + 176556 0 50000 100000 150000 200000 250000 0 20 40 60 80 100 120 Mining and Quarrying y = 68.642x + 7026.8 0 5000 10000 15000 20000 0 20 40 60 80 100 120 Agriculture y = 378.4x + 48618 0 50000 100000 150000 200000 250000 0 20 40 60 80 100 120 Transport and communications y = 61.761x + 9208.80 5000 10000 15000 20000 25000 0 20 40 60 80 100 120 Fishing y = 81.587x + 13821 0 5000 10000 15000 20000 25000 30000 35000 0 20 40 60 80 100 120 Education, Health and Other Services y = 1259.9x + 82058 0 200000 400000 600000 800000 1000000 0 20 40 60 80 100 120 Manufacturing 105 Y-Axis – Labour Productivity, Current USD per employee, X-Axis – Oil Price, USD Figure 3.17 - Labour Productivity by Sector vs. Oil Price – United Arab Emirates y = 2794.8x + 69310 0 50000 100000 150000 200000 250000 300000 350000 400000 0 20 40 60 80 100 120 Mining and Quarrying y = 291.14x + 14650 0 10000 20000 30000 40000 50000 0 20 40 60 80 100 120 Agriculture y = 85.586x + 110720 0 20000 40000 60000 80000 100000 120000 140000 160000 0 20 40 60 80 100 120 Transport and communications y = 501.54x + 3885.4 0 10000 20000 30000 40000 50000 60000 0 20 40 60 80 100 120 Fishing y = 128.89x + 114050 0 50000 100000 150000 200000 0 20 40 60 80 100 120 Education, Health and Other Services y = 5810.3x + 97959 0 100000 200000 300000 400000 500000 600000 700000 800000 0 20 40 60 80 100 120 Manufacturing 106 107 4. Natural Resource Abundance: No Evidence of an Oil Curse Abstract This chapter examines the relationship between labour productivity, capital formation, and natural resource extraction in countries with natural resource reserves. We develop a theoretical two-sector model for a closed economy, that maximises consumption over time, and examine how the control variables - natural resource extraction and the savings rate – determine fixed capital investment. We find that in a closed economy, the overall labour productivity is a positive function of capital investment per labour. That is in turn related to the externally given natural resource price, natural resource reserves and the resource extraction ratio. High natural resource prices and extraction rates provide opportunities to increase the overall investment in fixed capital and thus boost labour productivity. We empirically test this model for oil as a natural resource. The data covers 36 years from 1980 to 2015 and includes 149 countries. Eighty-five (85) of these countries possessed commercially recoverable oil reserves in at least a part of the period covered. We are able to exploit the panel and carry out the estimation using two-way fixed effects. We observe that oil price has an overall positive impact on labour productivity growth in the modern sector. The savings rate and schooling are positively correlated to labour productivity growth as well as fixed capital formation per capita. We find that the oil sector variables – oil reserves and oil extraction ratio – do not contribute to labour productivity growth directly, rather through increased capital formation per capita. An important finding is that higher oil prices lead to increased fixed capital formation per capita. We also observe, while accounting for time period fixed effects and country-level heterogeneities, that countries with larger oil extraction ratios have higher fixed capital formation per capita. We do not observe a statistically significant relationship between higher oil reserves ratio and fixed capital formation. However, we find that countries with larger oil reserves are found to have higher fixed capital formation during higher oil price periods. 108 Simply stating, higher oil revenues are related to greater investments into fixed capital. There are other mechanisms for higher fixed capital formation in oil extracting countries during high oil price periods that have not been considered here. One such mechanism may be that high oil prices increase the desirability of oil-rich countries as a destination for foreign direct investments (FDI). The determination of the mechanism for the impact of high oil prices on non-oil economies is beyond the scope of this chapter and thesis. However, for non-oil economies, a positive relationship may be driven by FDI from oil-rich countries, increased exports of capital goods and consumption goods (and services) to oil-rich countries, and/or innovation in non-oil economies to counter the effects of reduced marginal returns because of increased energy costs per unit produced. We find that the oil-rich countries of the Gulf Cooperation Council (GCC) are able to exploit high oil prices and invest into fixed capital owing to their relatively large oil reserves per capita. Qatar is the most successful among the GCC countries in diverting its natural resource wealth towards fixed capital. The results indicate that natural resources by themselves do not constitute a curse. Countries with large natural resource reserves per capita can use their natural resource outputs to increase overall fixed capital formation. Investment into fixed capital, in turn, leads to higher labour productivity growth. We find that increasing the efficiency of the production systems through improved schooling can also lead to higher fixed capital formation and labour productivity growth in the modern sector. Thus, smart management of natural resources can support the diversification of the economy. The so- called “natural resource curse” originates not in “simply having” natural resources. It is instead the mismanagement of natural resource rents that leads to lower productivity in the modern sector. There is no evidence that natural resource-rich countries are more or less prone to mismanage their rents. Keywords: structural change, natural resource curse, GCC, theoretical modelling, empirical application, capital formation JEL Classification: E21, E24, O13, O47, Q32 109 4.1. Introduction Countries rich in natural resources have been concerned about the extractive nature of their economies. The most important assets of these countries – natural resources – have not always been valued highly at world markets. After the 1970s and 1980s, the world witnessed periods of high natural resource prices, but the volatility has remained high. For example, for a hundred years before 1973, oil had stayed at around 20 USD per barrel in constant 2010 prices. It then went as high as 100 USD a barrel by 1980 and as low as 30 USD by 1989. By 1999 the oil prices had fallen to 15 USD per barrel. The 2000s saw oil prices as high as 130 USD and as low as 30 USD per barrel. The other natural resources followed similar patterns. The policymakers in the natural resource-rich countries have long desired to diversify and move away from their heavy dependence on natural resources. Diversification has been on their agenda for three to four decades. Despite this effort, many natural resource-rich countries have failed to diversify (van der Ploeg, 2011). In this chapter, we explore if this failure to diversify provides evidence of a natural resource curse. In recent years, the focus on the “natural resource curse” research has led to the consensus that the curse is highly context-specific. The curse is best described as a lowering of labour productivity with an increase in revenues from the extraction of natural resources. The literature emphasises the complexities and conditionalities of the curse. This has led to a notion that the curse is not given but is rather a result of the specific policies and the conditions of the ecosystem in which the institutions exist. The presence and intensity of the resource curse depend on the types of resources, socio-political institutions and the linkages with the rest of the economy (Papyrakis, 2017). In this chapter, we focus on the relation between natural resource extraction and productivity. Richard M. Auty (2007) argues that the models formulated in the mid-twentieth century, like the Cobb-Douglas and Harrod-Domar models, did not adequately account for the role of natural resources in the economy. We aim to estimate the dynamics of capital formation and the decision factors in oil extraction and investment. We start from the following premise: If 110 the use of non-renewable extractive natural resources, such as oil, gas, and minerals, does not lead to the accumulation of other forms of productive capital, but instead is used to support only consumption, there will be no income-generating assets to replace it when it is exhausted (Canuto & Cavallari, 2012). We develop a two-sector closed economy theoretical model with the natural resource sector and modern sector being the only two sectors in the economy. The decision variable (control) for the natural resource sector is limited to the natural resource extraction ratio. The second control variable is the savings ratio: the percentage savings from the income generated by the modern sector and the natural resource sector, leaving the rest for consumption. These savings are assumed to be invested into fixed capital. Next, we explore the empirical application of the theoretical model with oil as a natural resource, using data covering a period of 36 years and 149 countries. We impute the productivity and investment estimations using time period and country fixed effect. Accounting for the time period effects as well as individual country level heterogeneities enables us to comment on the causal mechanisms related to labour productivity growth in the modern sector and fixed capital formation. Also, we are able to make additional inferences about the productivity and fixed capital formation in the individual Gulf Cooperation Council (GCC) countries. The chapter is structured as follows. In section 4.2, we present a brief review of the relevant literature. In section 4.3, we develop the theoretical model. Then, we describe the empirical model, data, data reliability, results and postestimation tests in sections 4.4 to 4.8, respectively. Finally, we summarise our main findings, discuss the limitations of this chapter and propose an outlook into future research avenues in section 4.9. 4.2. Literature Review Academics studying the natural resource-based economies have argued varyingly whether these countries are cursed or blessed (van der Ploeg, 2011). The resource curse thesis “interprets a mineral boom as a net economic loss, where the present value of the positive effects of the boom is more than offset by the present value of the negative effects.” (Davis, 111 1995, p. 4). It is also argued that the wealth resulting from natural resources should lead to prosperity as rents from natural resources can lead to government investment in public goods, infrastructure, and other development project expenditures. Such investment would not have been possible if these resources would not have been available. The welfare improving nature of resource-based development is often recognized as a consequence of a new equilibrium with higher incomes and higher consumption. Albeit so, to a great extent the increase in consumption of goods and services is based upon increased imports. There is historical evidence that the development in several European countries and the United States of America (US) followed a similar natural resource-based trajectory (Lederman & Maloney, 2008). This is represented in the work of Alan Gelb and Associates as, “there is evidence that, at least in some cases, high-rent activities… have provided an important stimulus to growth” (Gelb & Associates, 1988, p. 33). The discussion in the literature shows that the short-term gains in welfare are often at the expense of long-term growth. Jeffery Sachs and Andrew Warner (1995) have presented a model for this discrepancy between long term and short term gains of natural resource richness based on the concept that manufacturing and non-resource tradable goods and services are better for growth due to their characteristic learning by doing effects and positive technological spillovers. However, Lederman and Maloney (2007) not only question the robustness of the resource curse finding on econometric grounds but also question the validity of the argument that the natural resource sector is inferior to manufacturing in its growth- enhancing characteristics. Many scholars have been critical of an idea of resource-based development, including Prebisch (1959), Singer (1950), Richard M. Auty (2001), and Jeffrey Sachs and Andrew Warner (2001). However, data from Maddison (1994) shows that from 1913 to 1950, resource-rich countries grew faster than the industrialized countries (Ferranti, et al., 2002). Also, Maloney et al. (2002) and Stijns (2005) find no negative association between resource abundance and growth. The argument on whether resources are an actual curse to nations’ development has come around to a point where it is considered that the curse essentially lies in the management or rather mismanagement of the resource revenues 112 (Amuzegar, 1982, pp. 817-821; Levy, 1978; Davis, 1995, pp. 1773-1776), and thus can be avoided by learning and sound policy implementation (Stijns, 2005). A review of the natural resource curse studies by Papyrakis (2017) concludes that the presence or absence of the resource curse is dependent on the type of resources, socio-political institutions and linkages with the rest of the economy. Good institutions that ensure property rights protection can discourage rent-seeking behaviour in mineral-rich contexts and hence prevent the resource curse phenomena and stimulate economic development (Boschini, et al., 2007; Sarmidi, et al., 2014). The central message is that good institutional setup, in the form of secure property rights, efficient bureaucracies and low levels of corruption, ensures better natural resource sector output management and can turn the curse into a blessing (Anshasy & Katsaiti, 2013; Mehlum, et al., 2006). The inability of the neoclassical growth models to account for the role that natural resources play in a country’s path towards economic growth and stability has been highlighted, among others, by Sachs & Warner (1995) and Auty (2007). In an attempt to introduce non-renewable resources in addition to labour and capital in the neo-classical model, Henry Thompson does not find evidence of natural resource rent contribution to maintaining a stable per capita income (Thompson, 2012). Even though the majority of the countries in the world rely on natural resources for meeting their economic objectives to a considerable extent, we also find that theoretical models accounting for the natural resource sector are rare. Keeping in mind this critical limitation in the literature, we have attempted to construct a theoretical model that accounts for natural resource output in the total economic output that is either consumed or saved for investment in fixed capital. In addition to that, we explore the empirical application of the theoretical model that we develop. In order to do this, we build upon the literature that focuses on the mechanisms and policies that create a robust economy through investments from the natural resource sector. In the following section, we present the model accommodating the decision factors in oil extraction and investment into fixed capital. 113 4.3. Modelling the natural resource extraction and capital investment relationship We consider a two-sector economy where the total output is the sum of the output of the natural resource sector and the non-natural resource sector. The output of the non-natural resource sector (Barro & Salai-i-Martin, 2004) is given by, 𝑄𝑄 = 𝐾𝐾 𝛼𝛼𝐴𝐴𝐴𝐴1−𝛼𝛼 4.1 Where Q is the output of the non-oil sector, K is the total capital in the non-oil sector, L is the labour force in the economy. Assuming constant growth of labour, �̇�𝐴 = 𝛽𝛽𝐴𝐴 4.2 A is the efficiency parameter and is often considered to be the absorptive capacity of the economy. It is considered exogenous in this equation and represents the efficiency with which the capital and labour are converted to output. The output elasticity of the capital is represented by α. It is the responsiveness of output to a change in the level of capital used in production, ceteris paribus. For example, if α = 0.75, a 1% increase in capital usage would approximately lead to a 0.75% increase in output. The output elasticity of labour is 1 - α. The growth rate of labour is β. Dividing Equation 4.1, 𝑄𝑄 = 𝐴𝐴𝐾𝐾𝛼𝛼𝐴𝐴1−𝛼𝛼, by L on both sides we get: 𝑄𝑄 𝐴𝐴 = 𝐴𝐴� 𝐾𝐾 𝐴𝐴 � 𝛼𝛼 4.3 Let 𝑞𝑞 be defined as total output per labour and 𝑘𝑘 be defined as total capital per labour in the economy, 𝑞𝑞 = 𝑄𝑄 𝐴𝐴 = 𝐴𝐴𝑘𝑘𝛼𝛼 4.4 Where, 𝑘𝑘 = 𝐾𝐾 𝐴𝐴 4.5 114 Let us consider the example of the oil sector as the only natural resource sector in the economy. R represents the producible oil reserves in constant price equivalent of barrels. In such a case, the change in the natural resource reserves is given by the total reserves extracted (Hoel, 2015). 𝑂𝑂 = −�̇�𝑅 4.6 Where O is the oil extraction expressed as the total number of barrels produced (evaluated in constant prices; note that in case of other natural resources, this is simply the total units of the natural resource produced, evaluated at constant prices). With an externally given price of oil Poil and the total producible reserves of oil in barrels Rbbl, the producible oil reserves in constant price equivalent of barrels are given as 𝑅𝑅 = 𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙. 𝑅𝑅𝑏𝑏𝑏𝑏𝑙𝑙. Also, the extracted reserves are a fraction of the total reserves. This fraction is given by ‘o’ and is called the natural resource extraction ratio 𝑂𝑂 = 𝑜𝑜𝑅𝑅 4.7 Dividing both sides with L we get, 𝑂𝑂 𝐴𝐴 = 𝑜𝑜𝑅𝑅 𝐴𝐴 4.8 Let qoil be defined as the total output of the oil sector per labour and r be defined as total producible oil reserves per labour in the economy, 𝑞𝑞𝑜𝑜𝑜𝑜𝑙𝑙 = 𝑂𝑂 𝐴𝐴 = 𝑜𝑜𝑅𝑅 𝐴𝐴 = 𝑜𝑜𝑜𝑜 4.9 Where, 𝑜𝑜 = 𝑅𝑅 𝐴𝐴 4.10 Taking the derivative with respect to time, we obtain the change in reserves per labour as follows: �̇�𝑜 = �̇�𝑅 𝐴𝐴 − 𝛽𝛽𝑜𝑜 = −(𝑜𝑜 + 𝛽𝛽)𝑜𝑜 4.11 115 Combining equation 4.4 and 4.9, the total output of the economy per labour is given by: 𝑦𝑦 = 𝑞𝑞 + 𝑞𝑞𝑜𝑜𝑜𝑜𝑙𝑙 = 𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑜𝑜 4.12 The change in the capital stock in the economy is driven by the depreciation of the capital stock and new investment. Assuming δ is the depreciation rate of capital and ‘I’ is the total new investment into the non-oil sector of the economy, �̇�𝐾 = 𝐼𝐼 − 𝛿𝛿𝐾𝐾 4.13 ‘I’ is a function of the total output and the savings rate for the economy, as given by, 𝑠𝑠 = 𝑆𝑆 𝑌𝑌� 4.14 where S is total saving and S=I 𝐼𝐼 = 𝑠𝑠𝑌𝑌 4.15 Dividing by L on both sides gives us, 𝑖𝑖 = 𝑠𝑠𝑦𝑦 4.16 Where, 𝑖𝑖 = 𝐼𝐼 𝐴𝐴 4.17 And, 𝑦𝑦 = 𝑌𝑌 𝐴𝐴 4.18 Given Equation 4.12 and Equation 4.16: 𝑖𝑖 = 𝑠𝑠(𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜) 4.19 With the total output of the economy given as Y = Q + O �̇�𝐾 = 𝑠𝑠𝐴𝐴𝑘𝑘 𝛼𝛼𝐴𝐴 − 𝑠𝑠𝑜𝑜𝑅𝑅 − 𝛿𝛿𝑘𝑘𝐴𝐴 4.20 Also, taking the derivative of 𝑘𝑘 = 𝐾𝐾 𝐿𝐿 with respect to time: 116 �̇�𝑘 = 𝑠𝑠𝐴𝐴𝑘𝑘 𝛼𝛼 − 𝑠𝑠𝑜𝑜𝑜𝑜 − (𝛿𝛿 + 𝛽𝛽)𝑘𝑘 4.21 Let consumption of the population be given by c, that is the total output of the economy excluding investment per labour, 𝑐𝑐 = 𝑦𝑦 − 𝑖𝑖 4.22 Putting y and i from Equations 4.12 and 4.19 in this equation we get: 𝑐𝑐 = 𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜 − 𝑠𝑠(𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑜𝑜) 4.23 𝑐𝑐 = (1 − 𝑠𝑠)(𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜) 4.24 Presenting Equation 4.11 and 4.21, �̇�𝑜 = −(𝑜𝑜 + 𝛽𝛽)𝑜𝑜 �̇�𝑘 = 𝑠𝑠𝐴𝐴𝑘𝑘 𝛼𝛼 − 𝑠𝑠𝑜𝑜𝑜𝑜 − (𝛿𝛿 + 𝛽𝛽)𝑘𝑘 The Hamiltonian function can be set up as follows to maximise consumption per capita accounting for per capita changes in capital and oil reserves (Barro & Salai-i-Martin, 2004): 𝐻𝐻 = 𝑐𝑐 + 𝜆𝜆1�̇�𝑘 + 𝜆𝜆2�̇�𝑜 4.25 𝐻𝐻 = (1 − 𝑠𝑠)(𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜) + 𝜆𝜆1[𝑠𝑠𝐴𝐴𝑘𝑘𝛼𝛼 − 𝑠𝑠𝑜𝑜𝑜𝑜 − 𝛿𝛿(𝑘𝑘 + 𝛽𝛽)𝑘𝑘] − 𝜆𝜆2(𝑜𝑜 + 𝛽𝛽)𝑜𝑜 4.26 Since the technological change is exogenous, oil price 𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙 is given externally, and total reserves in barrels per effective labour are given as 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙. Substituting for 𝑜𝑜 = 𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙. 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙, we have four equations by taking partial derivative of the Hamiltonian with respect to the control and state variables: 𝜕𝜕𝐻𝐻 𝜕𝜕𝑠𝑠 = −(𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙) + 𝜆𝜆1(𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙) 4.27 𝜕𝜕𝐻𝐻 𝜕𝜕𝑜𝑜 = (1 − 𝑠𝑠)𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 − 𝜆𝜆1𝑠𝑠𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 − 𝜆𝜆2𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 4.28 117 𝜕𝜕𝐻𝐻 𝜕𝜕𝑘𝑘 = (1 − 𝑠𝑠)𝐴𝐴𝐴𝐴𝑘𝑘𝛼𝛼−1 + 𝜆𝜆1(𝑠𝑠𝐴𝐴𝐴𝐴𝑘𝑘𝛼𝛼−1 − 𝛿𝛿 − 𝛽𝛽) 4.29 𝜕𝜕𝐻𝐻 𝜕𝜕𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 = (1 − 𝑠𝑠)𝑜𝑜𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙 + 𝜆𝜆1𝑠𝑠𝑜𝑜 − 𝜆𝜆2(𝑜𝑜 + 𝛽𝛽) 4.30 There is no analytical solution to this utility maximisation problem involving a fixed capital- driven sector and the extractive natural resource-based sector. Our purpose here is to understand the nature of the relationship between oil sector variables and productivity growth in the modern sector through savings invested in fixed capital. Computational solution for parameters can be carried out to explain the dynamics of the economy through sound reasoning. Given the time limitations, unfortunately, such an exercise remains out of scope for this dissertation. A useful exercise is to have separate controls for savings in the extractive sector and the fixed capital-driven sector where the oil sectors saving rate is connected to either the technological state of the economy or the level of fixed capital. This recommendation is based on two reasons. Firstly, the oil extraction decision is not a yes or no decision, but, it is related to the technological state of the economy. In reality, technological advances such as “Enhanced Oil Recovery” 27 can be used to increase the extraction ratios as well as the total extractable oil reserves. Secondly, investments in the modern sector are expected to be more productive in economies with higher absorptive capacity or technological state. Countries are more likely to invest more of their natural resource sector rents into the modern sector when they have higher absorptive capacity. Without such absorptive capacity their capital investments are likely to be inefficient. In equation 4.27, we observe that the change in consumption/savings per unit of output is a function of the productive efficiency of the economy (A), the fixed capital per labour (k), oil extraction ratio (o), the price of oil (Poil) and the oil reserves per labour (rbbl). In equation 4.29, we observe that the change in fixed capital per labour is a function of the productive efficiency of the economy (A), the fixed capital per labour (k) and the savings per unit of output (s). Equations 4.28 and 4.30 show that the oil reserves per capita (rbbl) and oil 27 Enhanced oil recovery (EOR), also called tertiary recovery, is the extraction of crude oil from an oil field that cannot be extracted without using EOR advancements. EOR can extract 30% to 60% or more of a reservoir's oil, compared to 20% to 40% using primary and secondary recovery. 118 extraction ratio (o) are endogenous and related to the savings rate per unit of output (s) and the exogenously given price of oil (Poil). The change in the stock of fixed capital per labour is as such a function of the initial fixed capital stock (ko), the efficiency of the economy (A), the extraction ratio of oil (o), the total oil reserves per labour (rbbl), the price of oil (Poil) and, the savings rate (s). The savings rate (s) determines the total contribution of the output of the economy towards the fixed capital formation. The savings per unit of output are determined after the consumption decision is made. The output of the economy used for consumption originates from both the modern sector as well as the natural resource sector. The functions for labour productivity growth in the modern sector and the fixed capital investment that we adapt in section 4.4 for the empirical application of our theoretical model are given as follows, ∆𝑞𝑞 𝑞𝑞𝑜𝑜 = 𝑓𝑓(𝐴𝐴, 𝑞𝑞𝑜𝑜, 𝑘𝑘𝑜𝑜, 𝑠𝑠, 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙, 𝑃𝑃, 𝑜𝑜) 4.31 ∆𝑘𝑘 = 𝑓𝑓�𝑘𝑘𝑜𝑜,, 𝐴𝐴, 𝑠𝑠, 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙, 𝑃𝑃, 𝑜𝑜� 4.32 4.4. Empirical Model We derive the functions of labour productivity growth in the modern sector and fixed capital formation. The first equation explains the one year growth rate of labour productivity in the modern sector as a function of initial labour productivity, initial stock of fixed capital in the economy, initial efficiency of the production system proxied by school life expectancy from primary to tertiary level, the savings rate lagged by one year, the oil price lagged by one year, the rate of extraction of the total oil reserves lagged by one year and the total oil reserves per capita as a ratio of the world oil reserves per capita lagged by one year. 119 Productivity Equation ∆𝑙𝑙𝑜𝑜𝑙𝑙(𝑙𝑙𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑝𝑝𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦 𝑖𝑖𝑖𝑖 𝑚𝑚𝑜𝑜𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖 𝑠𝑠𝑚𝑚𝑐𝑐𝑝𝑝𝑜𝑜𝑜𝑜)𝑡𝑡, 𝑡𝑡−1 = 𝐴𝐴𝑜𝑜 + 𝛽𝛽1 𝑙𝑙𝑜𝑜𝑙𝑙(𝑙𝑙𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑝𝑝𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦)𝑡𝑡−1 + 𝛽𝛽2 𝑙𝑙𝑜𝑜𝑙𝑙(𝑓𝑓𝑖𝑖𝑓𝑓𝑚𝑚𝑝𝑝 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑙𝑙 𝑠𝑠𝑝𝑝𝑜𝑜𝑐𝑐𝑘𝑘 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙)𝑡𝑡−1 + 𝛽𝛽3(𝑠𝑠𝑐𝑐ℎ𝑜𝑜𝑜𝑜𝑙𝑙𝑖𝑖𝑖𝑖𝑙𝑙)𝑡𝑡−1 + 𝛽𝛽4(𝑠𝑠𝑙𝑙𝑖𝑖𝑖𝑖𝑖𝑖𝑙𝑙𝑠𝑠 𝑜𝑜𝑙𝑙𝑝𝑝𝑚𝑚)𝑡𝑡−1 + 𝛽𝛽5 𝑙𝑙𝑜𝑜𝑙𝑙(𝑜𝑜𝑖𝑖𝑙𝑙 𝑝𝑝𝑜𝑜𝑖𝑖𝑐𝑐𝑚𝑚)𝑡𝑡−1 + 𝛽𝛽6 � 𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑜𝑜𝑖𝑖𝑙𝑙 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 𝑤𝑤𝑜𝑜𝑜𝑜𝑙𝑙𝑝𝑝 𝑙𝑙𝑖𝑖𝑚𝑚𝑜𝑜𝑙𝑙𝑙𝑙𝑚𝑚 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 � 𝑡𝑡−1 + 𝛽𝛽7(𝑜𝑜𝑖𝑖𝑙𝑙 𝑚𝑚𝑓𝑓𝑝𝑝𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝑜𝑜𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜)𝑡𝑡−1 + �𝛾𝛾𝑜𝑜(𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 149 𝑜𝑜=1 + �𝛾𝛾𝑗𝑗(𝑝𝑝𝑖𝑖𝑚𝑚𝑚𝑚 𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑜𝑜𝑝𝑝 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 8 𝑗𝑗=1 + 𝜖𝜖 4.33 The investment equation explains the change in gross fixed capital stock per capita (not including depreciation of existing assets) over one year by initial stock of fixed capital per capita, initial school life expectancy from primary to tertiary, the savings rate lagged by one year, one-year growth of GDP lagged by one year, oil price lagged by one year, the oil reserves per capita as a ratio of world oil reserves per capita lagged by one year and the oil extraction as a ratio of total reserves lagged by one year. 120 Investment Equation 𝑙𝑙𝑜𝑜𝑙𝑙(𝑙𝑙𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠 𝑓𝑓𝑖𝑖𝑓𝑓𝑚𝑚𝑝𝑝 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑙𝑙 𝑓𝑓𝑜𝑜𝑜𝑜𝑚𝑚𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙)𝑡𝑡 = 𝐴𝐴1 + 𝛾𝛾1 𝑙𝑙𝑜𝑜𝑙𝑙(𝑓𝑓𝑖𝑖𝑓𝑓𝑚𝑚𝑝𝑝 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑙𝑙 𝑠𝑠𝑝𝑝𝑜𝑜𝑐𝑐𝑘𝑘 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙)𝑡𝑡−1 + 𝛾𝛾2(𝑠𝑠𝑐𝑐ℎ𝑜𝑜𝑜𝑜𝑙𝑙𝑖𝑖𝑖𝑖𝑙𝑙)𝑡𝑡−1 + 𝛾𝛾3(𝑠𝑠𝑙𝑙𝑖𝑖𝑖𝑖𝑖𝑖𝑙𝑙𝑠𝑠 𝑜𝑜𝑙𝑙𝑝𝑝𝑚𝑚)𝑡𝑡−1 + 𝛾𝛾4(𝑙𝑙𝑝𝑝𝑝𝑝 𝑙𝑙𝑜𝑜𝑜𝑜𝑤𝑤𝑝𝑝ℎ)𝑡𝑡−1, 𝑡𝑡−2 + 𝛾𝛾5 𝑙𝑙𝑜𝑜𝑙𝑙(𝑜𝑜𝑖𝑖𝑙𝑙 𝑝𝑝𝑜𝑜𝑖𝑖𝑐𝑐𝑚𝑚)𝑡𝑡−1 + 𝛾𝛾6 � 𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑜𝑜𝑖𝑖𝑙𝑙 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 𝑤𝑤𝑜𝑜𝑜𝑜𝑙𝑙𝑝𝑝 𝑙𝑙𝑖𝑖𝑚𝑚𝑜𝑜𝑙𝑙𝑙𝑙𝑚𝑚 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 � 𝑡𝑡−1 + 𝛾𝛾7(𝑜𝑜𝑖𝑖𝑙𝑙 𝑚𝑚𝑓𝑓𝑝𝑝𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝑜𝑜𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜)𝑡𝑡−1 + �𝛾𝛾𝑜𝑜(𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 149 𝑜𝑜=1 + �𝛾𝛾𝑗𝑗(𝑝𝑝𝑖𝑖𝑚𝑚𝑚𝑚 𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑜𝑜𝑝𝑝 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 8 𝑗𝑗=1 + 𝜖𝜖 4.34 Where, t = 1982, 1983, 1984, … 2015, and, 𝛽𝛽 and 𝛾𝛾 are the coefficients of the explanatory variables in equations 4.33 and 4.34, respectively. In Table 4.1 we present the names and definitions of the variables used in the estimation models. We will refer to the variables by their short names describes in hereon. 121 Table 4.1 – Variable names and definitions Variable Definition Productivity Growth log of the ratio of final to initial labour productivity (one-year growth rate of labour productivity) Fixed Capital Formation log of gross fixed capital formation per labour in the economy (not including depreciation of existing stocks) Log of Initial Labour Productivity log of labour productivity lagged by one year Fixed Capital Stock log of fixed capital stock per capita lagged by one year Schooling school life expectancy primary to tertiary lagged by one year Lagged GDP Growth log of the ratio final GDP to initial GDP lagged by one year (one-year growth rate of GDP lagged by one year) Savings Rate savings rate lagged by one year Oil Price log of the price of oil lagged by one year Oil Reserves Ratio average of the ratio of country oil reserves per capita to the average world oil reserves per capita lagged by one year Oil Extraction Ratio oil extraction ratio lagged by one year 4.5. Data The data for labour productivity in the modern sector is calculated as real GDP in constant 2011 USD per number of employees excluding the share of natural resource and agricultural sector. The number of employees’ data is sourced from the Penn Worlds Table (PWT) Version 9.1 (Feenstra, et al., 2015). The share of natural resource rents and agricultural value added is sourced from World Bank Development Indicators dataset that is agglomerated from national accounts data (World Bank, 2019). Where, total natural resource rents as a 122 percentage of GDP the as the sum of; oil rents, natural gas rents, coal rents, mineral rents, and forest rents, and Agriculture in Agricultural value added corresponds to International Standard Industrial Classification (ISIC) divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production (World Bank, 2019). The real GDP in constant 2011 USD, the fixed capital stock data and the gross fixed capital formation data is sourced from “IMF Investment and Capital Stock Data, 2017” and is in constant 2011 USD (Gupta, et al., 2006; Kamps, 2004). According to the description of the IMF Investment and Capital Stock Data, “Information on public and private investment and GDP comes from three main sources: the OECD Analytical Database (August 2016 version) for OECD countries, and a combination of the National Accounts of the Penn World Tables and the IMF World Economic Outlook for non-OECD countries. Information on PPP investment comes from two main sources: The World Bank Private Participation in Infrastructure Database and European Investment Bank (EIB) data sourced from the European PPP Expertise Centre (EPEC) at the EIB. Information on country income groupings used in depreciation rates' assumptions is from the World Bank World Development Indicators.” The world bank definition of fixed capital includes “land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchase; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings” (World Bank, 2018). Moreover, it consists of resident producers’ investments, deducting disposals, in fixed assets during a given period. It also includes certain additions to the value of non-produced assets realised by producers or institutional units. Fixed assets are tangible or intangible assets produced as outputs from production processes that are used repeatedly, or continuously, for more than one year. PWT data on capital formation covers, “nine asset types: residential buildings, other structures, information technology, communication technology, other machinery, transport 123 equipment, software, other intellectual property products and cultivated assets (such as livestock for breeding and vineyards). These investment data are drawn from country National Accounts data, supplemented by estimates based on the total supply of investment goods (import plus production minus exports) and data on spending on information technology. Note that coverage is limited to assets currently covered in the System of National Accounts. This means that land and inventories are omitted, as well as other forms of intangible capital – such as from product design or organisation capital – and subsoil assets – such as oil or copper.” The Savings Rate is measured using the Final Consumption Expenditure as a percent of GDP that is sourced from the World Bank Development Indicators (World Bank, 2019). The Savings Rate is calculated as one (1) minus the Final Consumption Expenditure as a percent of GDP. The efficiency of the economy is proxied by the school life expectancy from primary to tertiary level (UNESCO, 2018). It is calculated as the sum of the age-specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high school life expectancy indicates greater probability for children to spend more years in education and higher overall retention within the education system. The oil extraction ratio is proxied by the total oil extraction by a country in a given year divided by total oil reserves. The data for oil extraction is based on total petroleum and other liquids production in barrels, and the data for oil reserves is based on proven crude oil reserves in barrels from Energy Information Administration (EIA, 2019). The data for total proven reserves of oil are taken to be those quantities that geological and engineering information indicates with reasonable certainty can be recovered in the future from known reservoirs under existing economic and geological conditions. Market-linked pricing is the main method for pricing crude oil in international trade. The current reference, or pricing markers, are Brent, West Texas Intermediate (WTI), and Dubai crudes. Note that the price variation of 124 Brent, WTI and Dubai crude resembles each other as such using any one of them for pricing serves the purpose well. However, in order to ensure representative world crude oil price in this chapter we have used the average price of oil indicated in World Bank Commodity Price Data. The world bank defines the price as the equally-weighted average spot price of Brent, Dubai and WTI crude oil (GEM Commodities, World Bank Group, 2019). In the case where we consider the time period and country fixed effects, the reference time period is period number eight (8) that runs from 2010 to 2015 and the reference country is the US. The time periods are based on the US business cycles reported by Nation Bureau of Economic Research (NBER). The economic “Peak” and “Trough” announcements provided by NBER since 1980 form the start and end year of each period (NBER, 2019). The only exception is the addition of period number five (5) where 1997 is considered as the Trough Year instead of 2001 March which was the first recession in the US following 1991 Peak according to NBER. The added period considers the Asian Financial Crises (1997), Russian Financial Crises (1998) and the oil price crash of 1999 and runs from 1997 to 2001. 4.6. Data Reliability The first concern that we address is the objection to the use of labour productivity in capital driven sectors defined to exclude the natural resource and agricultural sector. It is argued that modern agricultural technology, modern mineral recovery technology and enhanced oil recovery (EOR) are examples of change in productivity through fixed capital investments. In our theoretical background, such examples may be considered as part of the capital-driven sector. In terms of our estimation excluding the natural resource and agricultural output (as such the modern, fixed capital-driven part of this output) from the labour productivity is justified as the portion and usage of modern mineral recovery technology and EORs is much smaller in comparison to the usage of traditional recovery. The same is true for agricultural technology albeit the usage is higher. However, it is much more important for us to exclude the traditional productivity and productivity of the extractive sector in contrast to capturing 125 all capital-driven productivity. Owing to the data limitations, we find that our technique is the most suitable way to account for labour productivity in the modern sector. Another uncertainty that we face is that different countries and companies from which the oil data is agglomerated from have varying abilities to precisely estimate the recoverable oil reserves. We consider that our source EIA makes every effort to come up with a consistent series for reserves based on a common definition. However, the use of different methodologies is apparent in the data. An example in case is that of Bangladesh, Equatorial Guinea, Morocco, South Africa where the EIA data shows oil extraction exceeding oil reserves, or oil extraction for two subsequent years at a level very close to the total reserves. This by definition is impossible, as such the data was excluded from the set. The excluded data consists of data points for Bangladesh, Equatorial Guinea, Morocco and South Africa. We also observed that oil extracting countries that are facing war or sanctions tend to have very low oil extraction ratios. In addition to that, Canada shows very low oil extraction ratio because large quantities of difficult to extract and heavy tar sands distort the total recoverable oil reserves measure. Note that we also tested our models by excluding this set of countries and the results are discussed in the postestimation tests and reliability section. We also checked the pairwise correlation between our explanatory variables. The correlation is considered low and is not expected to have an effect on the coefficients of the estimation. Another important concern is that a small set of data points may affect the coefficients of the regression disproportionately. In this regard, we carry out an influence analysis for the regression with respect to the explanatory variable. We use dfbeta values that measure the difference between the regression coefficient calculated for all of the data and the regression coefficient calculated with the observation deleted, scaled by the standard error calculated with the observation deleted. This value can then be used to limit the variance in the estimations by using a rule of thumb. For this case, we tested for where n is the total number of observations. In the following, we term the data points with dfbeta higher than as outliers. We will go through the implications in our postestimation test and reliability section. The 126 data was also visually inspected in correlation matrix graphs and the data was confirmed to be free from outliers. The time range of the data runs from 1980 to 2015. As such the total number of years is 36. The minimum number of years for a country was four (4) years, and the maximum was 36 years. The explained variables for investment estimation include the growth of GDP over one year lagged by one year. As such, the first set of observations is from the year 1982. The average number of years per country is 23 years. The total number of countries in the data for both the productivity and investment estimations is 149. Out of these, 85 had an oil sector for at least part of the time period in consideration. We carry out ordinary least squares regression with estimates efficient for homoskedasticity and standard errors robust to heteroskedasticity and autocorrelation (HAC) using robust and Newey-West estimation with a lag of one (Newey & West, 1987). The summary statistics for the data used in productivity estimation and investment estimation are presented in Table 4.2. Table 4.2 – Summary statistics for productivity estimation Variable Countries Years Obs. Mean Std. Dev. Min Max Productivity Growth 149 1982-2015 3337 0.019 0.105 -1.415 1.475 Fixed Capital Formation 149 1982-2015 3337 8.026 1.505 2.915 11.352 Initial Labour Productivity 149 1982-2015 3337 9.536 1.397 4.713 12.017 Fixed Capital Stock 149 1982-2015 3337 10.471 1.378 5.212 12.960 Savings Rate 149 1982-2015 3337 0.193 0.156 -1.420 0.884 Schooling 149 1982-2015 3337 11.115 3.651 1.437 23.282 GDP Growth 149 1982-2015 3337 0.036 0.046 -0.670 0.296 Oil Price 149 1982-2015 3337 3.691 0.595 2.766 4.557 Oil Reserves Ratio 149 1982-2015 3337 0.847 4.391 0 60.027 Oil Extraction Ratio 149 1982-2015 3337 0.048 0.066 0 0.811 127 The mean and standard deviation of productivity growth in the modern sector over one year was 0.019 and 0.105, respectively. The fixed capital formation had a mean of 8.026 with a standard deviation of 1.505. The mean of log of initial labour productivity was observed to be 9.536 while the standard deviation was 1.397. The log of the initial stock of fixed capital had a mean of 10.471 with a standard deviation of 1.378. The mean of savings rate was 0.193 with a standard deviation of 0.156. The mean of school life expectancy from primary to tertiary (schooling) was 11.115, and the standard deviation was 3.651. The explanatory variables, lagged log of oil price, lagged oil reserves to world oil reserves ratio and lagged oil extraction ratio had means of 3.691, 0.847 and 0.048, respectively. Their respective standard deviations were observed to be 0.595, 4.391 and 0.066. 4.7. Results Here we present the observed influences of the explanatory variables of concern, on the explained variable that is labour productivity growth excluding natural resource and agricultural rents. Second, we present the results of the estimation for gross fixed capital formation explained by initial fixed capital stock, schooling, savings rate, GDP growth and oil sector variables including oil price, oil reserves and oil extraction ratio. See Equations 4.33 and 4.34, and Tables 4.1 and 4.2 for more details on the estimation and definitions of the variables. 128 Table 4.3 – Regression results – Productivity Equation Dependent Variable Productivity Growth (Net of natural resource rents and agricultural value added) A-1 A-2 A-3 A-4 A-5 A-6 A-7 A-7 Continued INT#Country Labour Productivity -0.029*** -0.028*** -0.028*** -0.177*** -0.177*** -0.177*** -0.202*** (0.010) (0.010) (0.010) (0.034) (0.034) (0.035) (0.036) Fixed Capital Stock 0.009 0.009 0.009 0.061** 0.062** 0.062** 0.070*** (0.007) (0.007) (0.007) (0.024) (0.024) (0.024) (0.023) Savings Rate 0.042** 0.041* 0.040* 0.036 0.025 0.028 0.037 (0.021) (0.021) (0.021) (0.038) (0.038) (0.040) (0.047) Schooling 0.006*** 0.006*** 0.005*** 0.011*** 0.009*** 0.009*** 0.011*** (0.001) (0.001) (0.001) (0.003) (0.003) (0.003) (0.003) Oil Price (OP) 0.004 0.023*** 0.011*** 0.023*** 0.023*** 0.018*** (0.004) (0.006) (0.004) (0.006) (0.006) (0.006) Oil Reserves Ratio (OR) 0.000 0.000 0.003 0.002 0.003 0.004 (0.001) (0.001) (0.003) (0.003) (0.004) (0.004) Oil Extraction Ratio (OE) 0.013 0.012 -0.001 -0.006 -0.007 0.035 (0.017) (0.017) (0.029) (0.029) (0.029) (0.036) OP#OR (INT) 0.000 0.005 (0.001) (0.020) Bahrain -0.040** -0.034* -0.035* -0.032 -0.013 (0.019) (0.019) (0.020) (0.031) (0.044) Kuwait -0.167 -0.134 -0.102 -0.456 -0.004 (0.149) (0.148) (0.159) (0.394) (0.020) Oman -0.023 -0.027 -0.026 -0.032 -0.005 (0.031) (0.031) (0.031) (0.108) (0.022) Qatar -0.002 0.006 0.014 0.181* -0.009 (0.053) (0.053) (0.056) (0.105) (0.020) KSA -0.090 -0.075 -0.065 -0.070 -0.006 (0.058) (0.057) (0.059) (0.138) (0.020) UAE -0.035 -0.024 -0.009 0.204 -0.008 (0.063) (0.063) (0.073) (0.212) (0.020) Time period fixed effects No No Yes No Yes Yes ... Yes Country Fixed Effect No No No Yes Yes Yes ... Yes Root Mean Square Error 0.104 0.104 0.103 0.099 0.099 0.099 ... 0.097 Adj.R-squared 0.024 0.023 0.029 0.114 0.120 0.119 ... 0.153 Countries 149 149 149 149 149 149 149 Years 23 23 23 23 23 23 23 Observations 3337 3337 3337 3337 3337 3337 ... 3337 * p<0.10, ** p<0.05, *** p<0.01 129 In Table 4.3: A-1 includes only the base explanatory variables, A-2 introduces oil price (OP), reserves ratio (OR) and extraction ratio (OE), A-3 adds only time period fixed effects and A- 4 only country fixed effects. A-5 includes both time and country fixed effects. A-6 introduces the interaction between oil price and oil reserves ratio (INT). Finally, A-7 adds the interaction of country dummies with INT. Note that we first discuss the results of productivity estimation model A-5 indicated in Table 4.3 based on equation 4.33. The first result that we observe in above Table 4.3 is that initial labour productivity is negatively correlated with growth in labour productivity in the modern sector. The coefficient is statistically significant, with a confidence level of 99%. The standard error is 0.034. A 1% higher initial labour productivity is observed to produce a 0.177 ± 0.034% lower labour productivity growth in the modern sector. The initial capital in the economy is statistically significantly correlated with the growth in labour productivity in the modern sector. Higher initial capital stocks lead to higher growth of labour productivity in the modern sector. The coefficient is statistically significant with a confidence level of 95%. A 1% increase in initial fixed capital stocks in the economy is observed to increase the labour productivity growth rate in the modern sector by 0.062 ± 0.024%. We observe that a year’s increase in schooling is expected to increase labour productivity growth in the modern sector by 0.009 ± 0.003% (99% confidence). We observe that the coefficient of the oil price variable is positive and statistically significant, with a confidence of 99%. An average oil price increase of 1% lead to a 0.023% higher modern sector labour productivity growth rate with a standard error of 0.006. The oil reserves ratio and the oil extraction ratio do not explain the change in labour productivity in the modern sector. The coefficient for all the GCC country dummies except that of Qatar is negative. Excluding Bahrain, the labour productivity growth in the modern sectors of the GCC countries is not statistically different from the reference country (US). Bahrain has the lowest growth in labour productivity in the modern sector among the GCC countries. The root mean squared error of the estimation is 0.099. The mean of the estimated labour productivity growth in the modern sector is 0.019% with 0.102 as the standard deviation of 130 the residuals. In comparison to this, the real mean of the explained variable is 0.019% with a standard deviation of 0.105. We observe in A-2 and A-3 that an increase in savings rate leads to higher labour productivity growth in the modern sector. In A-2 (without fixed effects) and A-3 (with time period fixed effects but without country fixed effects), a 1% change in savings rate is expected to yield 0.041 and 0.040 ± 0.021% increase in labour productivity growth in the modern sector, respectively. The relationship is statistically significant with confidence at a level of 90%. However, the relationship between the savings rate and labour productivity growth has lower confidence and a higher standard error when considering country fixed effects. Therefore, cross-country differences are important in explaining the effect of savings rate on labour productivity growth in the modern sector. In A-6 and A-7 we do not observe any statistically significant effect of the interaction (INT) of oil price with oil reserves ratio or the interaction of the country dummies with INT, on the labour productivity in the modern sector. However, in A-7, we observe that including the interaction of INT with country dummies makes the coefficient of the country dummy for Qatar larger and statistically significant with a confidence level of 90%. This observation is indicative that increased labour productivity in the modern sector in Qatar is not driven directly through returns from oil extraction. We will analyse this relationship further in the interpretation of the investment estimation in this section, and the conclusion and discussion section (Section 4.9). According to our theoretical model and the associated empirical interpretation, we expect that the effect of oil sector variables on labour productivity is driven through investments in fixed capital. In the following, the regression output of the empirical model explaining gross fixed capital formation through initial fixed capital stock, savings rate, GDP growth, schooling and oil sector variables is presented. 131 Table 4.4 – Regression results – Investment Equation Dependent Variable Fixed Capital Formation B-1 B-2 B-3 B-4 B-5 B-6 B-7 B-7 Continued INT#Country Fixed Capital Stock 0.902*** 0.915*** 0.923*** 0.734*** 0.731*** 0.749*** 0.739*** (0.011) (0.011) (0.011) (0.030) (0.029) (0.030) (0.037) Savings Rate 0.858*** 0.825*** 0.832*** 0.595*** 0.562*** 0.457*** 0.509** (0.075) (0.075) (0.074) (0.171) (0.168) (0.176) (0.213) Schooling 0.050*** 0.041*** 0.036*** 0.039*** 0.012** 0.013** 0.014** (0.003) (0.003) (0.004) (0.005) (0.006) (0.006) (0.006) GDP Growth 3.300*** 3.251*** 3.276*** 2.050*** 2.059*** 2.097*** 1.934*** (0.296) (0.294) (0.295) (0.192) (0.189) (0.186) (0.182) Oil Price (OP) 0.116*** 0.051* 0.138*** 0.084*** 0.077*** 0.063*** (0.012) (0.027) (0.012) (0.019) (0.019) (0.019) Oil Reserves Ratio (OR) 0.001 0.001 -0.001 -0.003 -0.026*** -0.023*** (0.001) (0.001) (0.006) (0.005) (0.008) (0.007) Oil Extraction Ratio (OE) 0.091 0.090 0.245* 0.256* 0.276** 0.453** (0.101) (0.101) (0.138) (0.133) (0.133) (0.200) OP#OR (INT) 0.011*** -0.597*** (0.003) (0.160) Bahrain -0.114 -0.124 -0.104 -0.585*** 0.864*** (0.081) (0.085) (0.085) (0.118) (0.211) Kuwait -0.011 0.043 -0.809** 0.732 0.597*** (0.279) (0.263) (0.326) (0.472) (0.160) Oman 0.108 0.010 -0.021 -0.667*** 0.641*** (0.068) (0.069) (0.067) (0.159) (0.161) Qatar 0.634*** 0.584*** 0.360*** 0.225 0.605*** (0.132) (0.128) (0.126) (0.195) (0.160) KSA -0.073 -0.050 -0.336** -1.233*** 0.617*** (0.128) (0.123) (0.133) (0.156) (0.160) UAE 0.273** 0.240* -0.184 -1.253*** 0.618*** (0.133) (0.127) (0.156) (0.315) (0.160) Time Period Effects No No Yes No Yes Yes ... Yes Country Fixed Effect No No No Yes Yes Yes ... Yes Root Mean Square Error 0.410 0.405 0.403 0.273 0.269 0.269 ... 0.253 Adj.R-squared 0.926 0.928 0.928 0.967 0.968 0.968 ... 0.972 Countries 149 149 149 149 149 149 149 Years 149 23 23 23 23 23 23 Observations 149 3337 3337 3337 3337 3337 ... 3337 * p<0.10, ** p<0.05, *** p<0.01 In Table 4.4: B-1 includes only the base explanatory variables, B-2 introduces oil price (OP), reserves ratio (OR) and extraction ratio (OE), B-3 adds only time period fixed effects and B- 4 only country fixed effects. B-5 includes both time period and country fixed effects. B-6 introduces the interaction between oil price and oil reserves ratio (INT). Finally, B-7 adds the interaction of country dummies with INT. Here we discuss the results of the investment estimation model B-5 based on Equation 4.34 as presented in Table 4.4. We observe that capital formation in the previous period is positively correlated with new capital formation. A 1% higher initial capital stock leads to 0.731% increase in fixed capital formation over a year. The standard error of the coefficient is 0.029, and the result is statistically significant, with a confidence of 99%. We also find that the savings rate, schooling and GDP growth are positively and statistically significantly related gross fixed capital formation. A 1% higher savings rate results in a 0.562% higher gross fixed capital formation. The coefficient is statistically significant at a confidence level of 99%, and the robust standard error is 0.168. A year’s increase in initial schooling is expected to increase the fixed capital formation in the final year by 0.012 ± 0.006%. The coefficient is statistically significant at the confidence level of 95%. We observe that Qatar is able to accumulate the highest fixed capital per capita in comparison to its neighbouring GCC countries. The coefficient of the country dummy for Qatar is positive and statistically significant, with a confidence level of 99%. The coefficient of the country dummy for UAE is also positive and statistically significant (confidence level 90%) showing higher fixed capital formation in comparison to the reference country (US). The fixed capital formation per capita in the remaining GCC countries is not statistically different from the reference country. Bahrain is the lowest-performing among the GCC countries in terms of the fixed capital formation in the country while Saudi Arabia is the second from the last. We observe that the coefficient of the oil price variable is positive and statistically significant, with a confidence level of 99%. An average oil price increase of 1% leads to a 0.084% higher capital formation. The standard error is 0.019. We observe that a higher oil extraction ratio 133 is expected to have a positive effect on fixed capital investment. The coefficient is observed to be 0.256 with a standard error of 0.133 and a confidence level of 90%. The root mean squared error of the estimation is 0.269. The mean of the estimated fixed capital formation is 8.039 with 0.387 as the standard deviation of the residuals. In comparison to this, the real mean of the explained variable is 8.026, with a standard deviation of 1.505. In order to delve further into the mechanism through which oil reserves and oil prices affect the capital formation, we test the interaction of oil price and oil reserves ratio (INT) in B-6. Also, we interact all country dummies with the interaction INT reported as INT#Country and discuss the results for the six GCC countries in B-7. In B-6 we observe that INT is positively correlated with the fixed capital formation with a coefficient of 0.011 and standard error of 0.003 (confidence level 99%). In B-7 we observe through the interaction of country dummies with INT that all GCC countries are able to exploit high oil prices for increased capital formation, owing to their large oil reserves ratio (See column for B-7 Continued INT#Country in Table 4.4). It is noteworthy that the coefficient of the country dummy of Qatar has a smaller magnitude in B-7 in comparison to B-6. We discussed a similar comparison for Qatar between A-7 and A-6 for the productivity estimation. There, we observed that the revenues generated from oil extraction in terms of labour productivity are not the direct drivers of Qatar’s modern sector labour productivity growth. In the investment estimation, it becomes clear that the revenues from oil extraction have a positive impact on the fixed capital formation in Qatar. This trend is most clearly observed from the coefficients for Qatar in both estimations, and the relationship is the same for all the GCC countries except Kuwait. All in all, the ability of all GCC countries to divert their natural resource profits to higher fixed capital formation is evident. 4.8. Postestimation tests and robustness In relation to the countries with very low oil extraction ratios (discussed in section presenting the Data Reliability), we repeated the estimation of both equation 4.33 and 4.34, excluding 134 the country group. The exclusion of countries with very low oil extraction ratios did not affect the outcome of the estimations with the coefficients and standard errors not changed to any considerable extent. The estimation results in Table 4.3 and 4.4 include these countries. The regressions result for equation 4.33 and 4.34 were also estimated excluding the outliers identified by calculating dfbetas. The standard error, significance and coefficients of the variables in the estimation model based on equations 4.33 and 4.34 were not meaningfully different when estimated excluding the outliers identified. The estimation results in Table 4.3 and 4.4 do not exclude the outliers identified by calculating the dfbetas. We find that the standard errors of the oil price variable are robust to changes in the selection of time dummy periods as well as when no time dummies are used. The oil extraction ratio standard errors are robust to selection of time dummy periods and also when no time dummies are used. Note that we have selected the time dummy periods to be based on the business cycles as reported by NBER with the addition of the Asian Financial Crises. However, we tested by using random six years periods and did not observe any substantial changes in the results of the estimation. The oil reserves per capita as a ratio of world oil reserves per capita coefficient and the oil extraction ratio are robust to these changes. 4.9. Discussion and Conclusion Our theoretical model predicts that the change in fixed capital formation per capita is a function of the initial stock of fixed capital, the savings rate, the productive efficiency of the economy, and output of the oil sector. The output of the oil sector is a function of the oil sector variables – oil price, oil reserves per capita, and the ratio of oil reserves extracted from the total oil reserves. The change in the productivity of the modern sector is a function of the initial labour productivity, initial fixed capital stock, productive efficiency of the economy and gross fixed capital formation. In our estimation, we test the theoretical model for 149 countries, including 85 countries with commercially extractable oil reserves and do no find any evidence of an “Oil Curse”. We find that higher oil prices affect labour productivity in the modern sector as well as fixed capital 135 formation. The oil reserves and the oil extraction ratio do not affect labour productivity growth directly. Also, we find that the oil reserves variable on its own does not have a statistically significant effect on fixed capital formation. However, countries with high oil reserves benefit from high oil prices in order to form new fixed capital. The oil extraction ratio has a positive effect on fixed capital formation. The mechanism for the increase of labour productivity in the modern sectors of countries with and without an active oil sector can be assumed to be different to a certain extent. The similarity between the two types of countries may be related to foreign direct investments (FDI). Countries without an oil sector may be benefiting from FDIs from the sovereign wealth funds of oil-rich nations. Firms in rich countries may be offsetting higher energy costs by moving production to lower-income countries for reducing overall costs of production, thus increasing productivity in the modern sectors and fixed capital investment there. Oil-rich countries may also become more attractive FDI destinations for international firms during high oil price periods because of liquidity and availability of finance. For countries without an active oil sector, the increase in labour productivity in the modern sector may also be driven by demand for capital and consumption goods (and services) from oil-rich countries that have surplus wealth available to consume as well as to invest during high oil price periods. Another possibility is that higher oil prices reduce the marginal returns for producing consumption goods due to high cost of energy per unit, this leads to innovation that is aimed at improving profitability, that in turn leads to higher productivity in the modern sector of non-oil-extracting countries. However, we expect that a mechanism whereby higher energy prices drive innovation would exhibit itself in the longer run, rather than one-year periods that we use in this chapter. It is recommended that further research is undertaken to further disentangle the mechanisms of the relationship between labour productivity growth in the modern sectors of countries with and without an oil sector. We observe that all the GCC Countries are able to generate higher amounts of fixed capital during high oil price periods owing to their large oil reserves per capita. We observe that Qatar has been able to generate higher fixed capital per capita in comparison to the reference 136 country (US), as well as in comparison to all other GCC countries. The UAE’s investments in fixed capital are higher in comparison to the reference country and other GCC countries, but lower than Qatar. We find that for Saudi Arabia and UAE, the formation of fixed capital is highly associated with their ability to benefit from high oil prices owing to their high oil reserves. A similar effect is observed for Oman and Bahrain, but the association is of a lesser magnitude than that of Saudi Arabia and UAE. Unlike the other GCC countries, Kuwait and Qatar appear to have a positive relationship of their individual country dummies with fixed capital formation even when the oil price, oil reserves and country dummies interaction is introduced. As such, it appears that oil income during high oil price periods is not the only source for fixed capital formation in these two countries. As far as the effect of the oil variables is concerned, it may well be that oil reserves offer countries a type of collateral or warranty inducing financial influx through financial intermediation or as mentioned earlier through FDIs. These can lead to an increase in fixed capital investments. However, future research may be carried out to focus on this aspect of the oil sector’s contribution to overall fixed capital formation. We observe that schooling has a positive effect on labour productivity growth in the modern sector as well as fixed capital formation. The relationship between labour productivity on the one hand and productive efficiency or education on the other is well known. We propose that the relationship between schooling and fixed capital formation is driven by the countries’ ability to invest in modern technology because of having a higher skill level of the population that is required for such investment. Simply stated, improving capital stocks without relevant education and skill is not expected to have analogous returns. As such countries only invest in fixed capital that is matching the productive efficiency level of the economy proxied here by schooling. Further research into the relationship between productive efficiency and/or schooling and fixed capital formation is recommended. All in all, we find that natural resources offer an important means for investing in the modern sector, diversify the economy and ensure economic sustainability. This is evident in the case of GCC countries investing in fixed capital using their oil wealth. 137 5. “Stars in their Eyes?” Evaluating the Development Visions of Oman and Saudi Arabia Abstract Oman and Saudi Arabia are two Gulf Cooperation Council (GCC) countries with ambitious development plans. In this chapter, we evaluate the quantitative targets set in these plans, using the model developed in Chapter 2. The model assumes relations between, on the one hand, modern sector labour productivity, and, on the other initial labour productivity, government effectiveness, tertiary education expenditure as a percent of GDP, research and development (R&D) expenditure as a percent of GDP, regional characteristics and time period status. The coefficients have been estimated using pooled OLS regression for 75 developing countries covering three consecutive 5 years periods from 1998 to 2013. We find that in the most probable scenario, by 2020, the modern sector labour productivity in both Oman and Saudi Arabia will be close to their respective targets. However, by 2030 both countries will fall substantially behind their targets. We consider two additional policy scenarios. In the first, Oman and Saudi Arabia do not improve tertiary education, R&D, governance and regional characteristics. In the second scenario, these are improved. The results show that a delay in improving these determinants will lead to a failure in achieving the 2030 targets. Swift policy action can help avoid this and may even facilitate the two countries to surpass their targets. Factors associated with regional characteristics are not disentangled in the model. However, we discuss the likely constraints that require policy action. These include the lack of direct cross-country transportation infrastructure, regional and international trade integration, peace, and security in the region. Finally, we suggest that upgrading human capital by employing knowledge workers from other countries while nurturing local expertise can fast-track development. Keywords: structural change, GCC (Gulf Cooperation Council), ex ante evaluation, complicated and complex policies, economic development JEL Classification: E21, E24, O13, O53 138 5.1. Introduction The oil glut of 1980s that culminated in the 1986 plunge (Koepp, 1986), the oil demand drop associated with the Asian financial crises of 1997-1999 (Manning, 1998), the oil price shock of 2014-2016 (Grigoli, et al., 2017), the promise of green technology affecting fossil fuel demand (Gerlagh, 2011), and the apparent limits to technically and commercially exploitable oil have all been strongly imprinted as an existential threat over the consciousness of the GCC 28 policymakers. The governments of the region have devised many strategic plans to encounter this urgent need to diversify away from fossil fuels as the main share in the output of their economies. Here we analyse the probability of success of the strategies and plans developed by Oman and Saudi Arabia for diversification away from oil rents as the main share in the total output of these economies. The government of Oman announced the Vision 2020 in 1995, which is culminating with the 9th five-year development plan (Oman Vision 2020) (Supreme Council for Planning, Oman, 2019). In addition to this, the National Program for Enhancing Economic Diversification of Oman (تنفیذ - Tanfeedh 29) is an action-oriented program derived from Oman’s five-year development plan (Supreme Council for Planning, Oman, 2017). This program is being followed by Oman Vision 2040 (Oman Vision 2040), the preliminary document for which has been published on 25th July 2019 (Oman Vision 2040, 2019). The latest plans and visions in Oman are complemented by The National Innovation Strategy (The Research Council, Oman, 2017). Similarly, the government of Saudi Arabia has presented its National Transformation Program (Saudi NTP) 30 highlighting its development policies up to 2020 and 28 The Gulf Cooperation Council (GCC) is the colloquial term used to refer to the Cooperation Council of the Arab States of the Gulf (GCC). We use the abbreviation GCC to refer to the member countries as of 2017 – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. These are synonymously referred to as the countries of the Arabian Gulf, countries of the Arabian Peninsula, countries of Persian Gulf and Arab countries of the Gulf. Countries on the Arabian/Persian Gulf such as Iran and Iraq, and countries in accession talks such as Yemen, Jordan and Morocco are not included. 29 Tanfeedh is the official short name of Oman’s National Plan for Enhancing Economic Diversification. It is an Arabic term that means achievement or accomplishment. 30 Saudi Arabia’s National Transformation Plan 2020 is presented in lieu of the 10th five-year plan 139 followed it with the ambitious Saudi Arabia Vision 2030 (Saudi Vision 2030) 31. This is complemented by the National Science Technology and Innovation Plan (معرفة – Ma’rifah 32) (Alsodais, 2013). To the best of our knowledge, there has not been any research into the success or failure of the plans, strategies and visions implemented, except for qualitative introspection and statistical reviews. We attempt to fill this gap. We first assess the quantifiable inputs of these strategies. They include primary inputs such as initial labour productivity, government effectiveness, education expenditure as a percent of Gross Domestic Product (GDP), research and development (R&D) expenditure as a percent of GDP, regional characteristics. These inputs are subsequently inserted in a quantitative model of economic diversification and innovation. Our last step is to compare the outcomes of this model in terms of labour productivity in the non-oil and gas-based sectors with the targets. We follow this with a discussion on the context, including, cross-country competitive pressures, regional cooperation, strategic development, trade and foreign direct investment. In Section 5.2 we present the relevant literature, the background for considering diversification for Oman and Saudi Arabia that is the reduction of oil dependency, as well as the model used for evaluating. Subsequently, in Section 5.3 we discuss the predictive model for assessing the performance of the modern sector. In Section 5.4 we take the first step to extract the inputs and the targets for Saudi Arabia and Oman from the available national strategy documents. In section 5.5 we present the output of the model and contrast it with the intended targets. In Section 5.5.1, we suggest options for realising the intended targets. Finally, we present the conclusion, limitations and recommendations on future research in Section 5.6. 31 The official website of Saudi Vision 2030 “https://vision2030.gov.sa” provides access to documents and information related to both the Saudi NTP and Saudi Vision 2030 (Saudi Vision 2030, 2016) 32 Ma’rifah is the official short name for Saudi Arabia’s National Science Technology and Innovation Plan. It is an Arabic word that means knowledge and awareness 140 5.2. Background and Literature 5.2.1. Diversification Our subject is the evaluation of diversification strategies of two countries: Oman and Saudi Arabia. Much of the recent literature has indeed focussed on diversification rather than specialisation as holding the key to economic stability (Rodrik, 2005; Hidalgo & Hausmann, 2009). In the past decades across the world, governments have set their eyes on industrialisation and innovation policy to facilitate diversification and innovation while limiting obstacles to the restructuring goals (Rodrik, 2005). Public policy addresses deficiencies in capabilities, behaviours, institutions and framework conditions that negatively affect the performance of the system (Arnold, 2004; Rodrik, 2005). This is in addition to or complementary with import substitution, planning, state ownership, economic liberalisation and the application of taxes and subsidies to correct market failures. The focus on diversification makes sense: countries that are more diversified in terms of capabilities, products, sectoral production and employment are more likely to have a higher income (Imbs & Wacziarg, 2003; Klinger & Lederman, 2004; Hidalgo & Hausmann, 2009). 5.2.2. Evaluation of Diversification Strategies In the innovation and diversification literature, we find many works that evaluate the success and failure of government programs. However, there is very little literature on ex post evaluation of government policy at the national level and none that evaluate national level policies ex ante by quantitative means using a model with interactions. Roessner (1989) analyses the evaluation strategies of four programs in the US. One of these was the state technical services (STS) program 1965 aimed at industrialisation, use of advanced technology, increasing employment and enhancing the competitive position of the US products. For this program, he finds that the public evaluation committee was not formulated and equipped to perform a systematic evaluation. At later stages, Arthur D. Little Inc. (ADL) was employed to evaluate STS (Roessner, 1989). With no mechanism set up to collect baseline and intermediate data, ADL collected the data and used “lower bound” methodology. The overall 141 lessons from STS and the other three programs studied by Roessner (1989) were that program evaluations should be designed early to help achieve program objective, program designs should not be rigid, and the credibility of evaluations should be maximised. In the new approaches of national industrial and innovation policy, a package of related measures is involved, with multiple instruments or multiple levels of governance. The OECD report on “Evaluation of industrial policy: Methodological issues and policy lessons” classifies this type of policy as “complicated” and in cases of high uncertainty “complicated/complex” (Warwick & Nolan, 2014). While such policies are proliferating 33, the evaluation of such policies through research is limited. Some evaluations that have been presented in the academic literature include those of South Africa, Namibia, Tanzania, Rwanda, Malaysia and Australia’s respective Visions (Qwabe, 2013; Marope, 2005; Mallya, 2000; Islam, 2010; Mason, et al., 2011). A qualitative assessment of South Africa’s Vision 2030 determines that the realisation of the targets is dependent on building the capability of the state (Qwabe, 2013). An extensive study into the adequacy of Namibia’s education system for meeting the goals of Namibia Vision 2030 finds that the education system has poor learning outcomes at all levels, inefficiencies, inequalities, lack of relevance to the economy, poor capacity for knowledge creation and innovation, and, inadequate and declining resources (Marope, 2005). As such the system cannot achieve the national development goals and the transformation as envisioned under Namibia’s Vision 2030. This is backed up by Amukugo et. al. (2010) who find that education enrolment in Namibia meets the targets. However, this is at the cost of quality. The understanding among the researchers is that without extensive investment to improve the conditions in schools and to provide teachers with appropriate training, the goals of Namibia’s Vision 2030 are at jeopardy (Marope, 2005; Amukugo, et al., 2010). A review of 33 To name a few in no particular order: Malaysia Vision 2020, Kenya Vison 2030, Oman Visions 2020 and 2040, Namibia Vision 2030, South Africa National Development Plan – Vision 2030, Australia Vision 2040, , Uganda Vision 2040, Qatar National Vision 2030, Tanzania Development Vision 2025, Rwanda Vision 2020, Bangladesh Vision 2021, Bahrain Vision 2030, UAE Vision 2021, Vision Kuwait 2035, Vision 2030 - Jamaica National Development Plan, Brunei Vision 2035, Cameroon Vision 2035 and so on are examples of such that can be classified as “complicated” policies and in many cases due to national, regional and international uncertainties labelled as “complicated/complex”. 142 Tanzania’s Development Vision 2025 finds that the goals are too many and over-ambitious. The goals do not relate to the capability of Tanzania’s government and economy in real terms, and as such, the goals are inappropriate for the selected time-frame (Mallya, 2000) An encouraging picture emerges from Rwanda in an article that presents a mid-term review of Rwanda’s Vision 2020. The country had up until 2010 met or exceeded targets on child mortality, school enrolment, government capacity and overall economic growth. However, this comes with a caveat – the benefits of the growth are concentrated at the top segment of the society, the policies are not farmer focussed, small scale landowner farmers have had difficulties in enduring as the income gap is becoming larger (Ansoms & Rostagno, 2012). A study evaluating success factors of Malaysia Vision 2020 applies an analytical hierarchy process and outlines nine main challenges that include creating a mature democratic government, pushing science and technology, and increasing economic prosperity, among other social and economic challenges (Islam, 2010). Another study finds that rather than catching-up Malaysia’s gap with other advanced knowledge economies is increasing in terms of most common knowledge economy indicators such as researchers per capita and investment into research and development (R&D) (Evers, 2003) An interesting policy programme is Australia Vision 2040 – A vision for Australia’s mineral future (Mason, et al., 2011). Its uniqueness is that it is developed for a natural resource-driven economy that is also a high-income OECD country. In addition to using mineral wealth for economic development, the Vision also focuses on the environment and society as such, creating targets using nested or embedded notion of sustainability. Some of the works delve into evaluating only one input, output, pillar or goal of such policies, such as Evers (2003) for Malaysia, Amukugo, et al. (2010) for Namibia and Qwabe (2013) for South Africa. Others try to present a broader picture trying to relate the results of multiple inputs individually to the desired goal such as Mallya (2000) for Tanzania, Islam (2010) for Malaysia and Ansoms & Rostagno (2012) for Rwanda. No work has – to the best of our knowledge – yet been able to deal with the “complicated/complex” nature of the policy sets, 143 as is our aim. Such an evaluation is critical in understanding the implications of the targets selected in these “complicated/complex” policies. In addition to that, it provides a clearer picture to the stakeholders for retrenching in order to achieve the intertwined objectives without losing the focus on the ultimate goal(s). 5.2.3. Methodologies for Evaluation Here we try to offer a literature review of the applicability of methodologies that have been attempted for evaluating “complicated/complex” policies. We also explore the best- recommended practice. In their paper O’Sullivan et. al. (2013) compare the “new” industrial policies of four industrialised countries United Kingdom, United States, Germany, and Japan. They present a matrix approach consisting of interlinking “intervention levels” and the “factor inputs” of the national manufacturing system and contrasting with the global manufacturing systems and market. They use it to analyse policy variations within and across the countries. The policy matrix approach is interesting to isolate policy goals. An important takeaway from their work is that the policies made in the context of national manufacturing systems involve complex networks integrated to varying degrees with the national innovation systems (O’Sullivan, et al., 2013). In Patton’s (2006) view developmental evaluations differ in design and learning goals from traditional evaluations. In terms of design, the evaluations are required to capture the dynamics of the system, the interdependencies and the emergent connections instead of linear cause and effect models. Meanwhile, the learning objectives are targeted at producing context-specific understanding that informs further policy development rather than to produce findings that are generalisable across space and time. In this context, Warwick & Nolan (2014) present a synopsis on White’s (2013) and Rogers’ (2008; 2011) works and their application in the arena of complex industrial and innovation policy. In scenarios where a package of measures is being evaluated in an uncertain and complex situation, counterfactuals may not be possible. The first approach in this regard is to construct a model for the policy or program, ensuring that all elements of the policy and the interrelationships are recognised and presented (Rogers, 2008). For complicated and complex polices with 144 multiple policy areas, inputs, determinants, and outcomes it is recommended to focus on the components to be evaluated quantitatively and exclude the areas that do no form the core of the policy evaluation. Thereon qualitative measures or discussions can be used to describe the interactions and systemic effects of the policy areas that have not been evaluated quantitatively with the policy outcome that is considered in the Quantitative evaluation (Rogers, 2008; 2011; Warwick & Nolan, 2014). This ensures that interactions within the broader program are accounted for even if the quantitative evaluation is limited to a small sub-set of the complicated or complex policy with many facets. 5.2.4. Oman and Saudi Arabia Evaluations Here we review the research that has already been conducted in terms of forecasting the economic situation in Oman and Saudi Arabia. Oman Al-Mawali et. al. (2016) carry out macro-economic modelling to estimate the dependence of the Omani economy on the oil sector revenues. They forecast that by 2020, the share of the oil sector in the total GDP of Oman will be 40.50%. They do not contrast this result against the targets of Oman’s 9th Five Year Plan 2015-2020 and Omani Vision 2020. It is important to mention that Vision 2020 that was presented in 1995 targeted the oil and gas sector share in GDP to be 9.0%. The Vision 2020 was however not rigid and the five (5) year plans introduced flexibility in the Vision’s goals. Oman’s 9th Five Year Plan 2015-2020 and Oman’s National Program for Enhancing Economic Diversification (Tanfeedh) put the same number at 32.4% by the end of the year 2020. It is apparent that the goals of Vision 2020 in their original shape are not expected to be met as the current share of the oil and gas sector in the total GDP of Oman is 43.0%. The referred study finds results that are pessimistic in comparison to Vision 2020 however not far from the target of Oman’s 9th Five Year Plan 2015-2020 and Tanfeedh. We find that the study is restricted in its forecasting ability as it takes only a linear approach to a complex problem. The variables in the model are limited to sectoral GDPs and ignore 145 the systemic nature of economic development as well as the “complicated/complex” nature of the development plans. This systemic nature requires us to include changes to the education, R&D, business, government and infrastructure arena among others. The complex nature of the policies requires us to discuss the alternative economic regimes under which the plans are expected to unfold so as to adequately account for the uncertainty. Saudi Arabia In an assessment of Saudi Vision 2030 by means of documental analysis Nurunnabi (2017) finds that the policy agenda in Saudi Arabia can only be effective if it includes further development of human capital, increased expenditure on R&D with a push for applied research, tackling unemployment and increasing the labour force participation of Saudis, particularly that of university-educated females. Albassam (2015) carries out a descriptive statistical review of the Saudi government’s five-year development plans from 1970 to 2015. The author finds that despite having diversification as the main focus of all the development plans, there has been no success in achieving economic diversification. The poor quality of institutions and the inability of the Saudi government to induce the private sector into actively contributing towards the diversification goal are pointed out as the main challenges to achieving it (Albassam, 2015). 146 5.3. The Predictive Model We use the output of the estimation model presented in Chapter 2 for predicting the state of the non-oil sector in Oman and Saudi Arabia The predictive model is given below: 𝐹𝐹𝑖𝑖𝑖𝑖𝑙𝑙𝑙𝑙 𝐴𝐴𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑃𝑃𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦 𝐺𝐺𝑜𝑜𝑜𝑜𝑤𝑤𝑝𝑝ℎ 𝑖𝑖𝑖𝑖 𝑚𝑚𝑜𝑜𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖 𝑆𝑆𝑚𝑚𝑐𝑐𝑝𝑝𝑜𝑜𝑜𝑜 = 𝐴𝐴 + 𝛽𝛽1 log(𝐼𝐼𝑖𝑖𝑖𝑖𝑝𝑝𝑖𝑖𝑙𝑙𝑙𝑙 𝐴𝐴𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑃𝑃𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦 𝑖𝑖𝑖𝑖 𝑝𝑝ℎ𝑚𝑚 𝑚𝑚𝑐𝑐𝑜𝑜𝑖𝑖𝑜𝑜𝑚𝑚𝑦𝑦) + 𝛽𝛽2(𝐼𝐼𝑖𝑖𝑖𝑖𝑝𝑝𝑖𝑖𝑙𝑙𝑙𝑙 𝐸𝐸𝑓𝑓𝑓𝑓𝑚𝑚𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑚𝑚 𝑇𝑇𝑚𝑚𝑜𝑜𝑝𝑝𝑖𝑖𝑙𝑙𝑜𝑜𝑦𝑦 𝐸𝐸𝑝𝑝𝑙𝑙𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝐸𝐸𝑓𝑓𝑝𝑝𝑚𝑚𝑖𝑖𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑜𝑜𝑚𝑚𝑠𝑠 𝑙𝑙𝑠𝑠 𝑙𝑙 𝑝𝑝𝑚𝑚𝑜𝑜𝑐𝑐𝑚𝑚𝑖𝑖𝑝𝑝 𝑜𝑜𝑓𝑓 𝐺𝐺𝐺𝐺𝑃𝑃) + 𝛽𝛽3(𝐼𝐼𝑖𝑖𝑖𝑖𝑝𝑝𝑖𝑖𝑙𝑙𝑙𝑙 𝐸𝐸𝑓𝑓𝑓𝑓𝑚𝑚𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑚𝑚 𝑅𝑅𝑚𝑚𝑠𝑠𝑚𝑚𝑙𝑙𝑜𝑜𝑐𝑐ℎ & 𝐺𝐺𝑚𝑚𝑖𝑖𝑚𝑚𝑙𝑙𝑜𝑜𝑝𝑝𝑚𝑚𝑚𝑚𝑖𝑖𝑝𝑝 𝐸𝐸𝑓𝑓𝑝𝑝𝑚𝑚𝑖𝑖𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑜𝑜𝑚𝑚𝑠𝑠 𝑙𝑙𝑠𝑠 𝑙𝑙 𝑝𝑝𝑚𝑚𝑜𝑜𝑐𝑐𝑚𝑚𝑖𝑖𝑝𝑝 𝑜𝑜𝑓𝑓 𝐺𝐺𝐺𝐺𝑃𝑃) + 𝛽𝛽4(𝑅𝑅𝑚𝑚𝑙𝑙𝑖𝑖𝑜𝑜𝑖𝑖𝑙𝑙𝑙𝑙 𝐶𝐶ℎ𝑙𝑙𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑠𝑠𝑝𝑝𝑖𝑖𝑐𝑐𝑠𝑠) + 𝛽𝛽5(𝐺𝐺𝑜𝑜𝑜𝑜𝑤𝑤𝑝𝑝ℎ 𝑃𝑃𝑚𝑚𝑜𝑜𝑖𝑖𝑜𝑜𝑝𝑝 𝐶𝐶ℎ𝑙𝑙𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑠𝑠𝑝𝑝𝑖𝑖𝑐𝑐𝑠𝑠) + 𝜖𝜖̂ The values of the respective coefficients are based on the developing countries model. The regional and growth period characteristic coefficients are based on the lowest and highest coefficients of the dummy variables in the estimated model for developing countries. The lowest coefficient for regional characteristics is based on the coefficient of the GCC region dummy, and the highest value of the coefficient are based on the South East Asia dummy coefficient. It reflects that the GCC region has had the worst regional characteristics that are conducive for labour productivity growth in the modern sector. At the same time, South East Asia perform best in terms of region-wide economic characteristics. We assume these to be reflective of regional transport and trade infrastructure connectivity, market access, regional peace and security. The lowest coefficient for growth period characteristics reflects a global economic downturn, corresponding to the time period dummy of the five-year period from 2008 to 2013 in the estimation. The highest coefficient reflects an economic upturn. This is based on the time period dummy of the five-year period from 2003 to 2008. The values are given in the following table. 147 Table 5.1 – Value of respective coefficients, their standard errors and root mean square error (RMSE) of the estimation Coefficient Standard Errors 𝜶𝜶 0.668 0.262 𝜷𝜷𝟏𝟏 -0.043 0.026 𝜷𝜷𝟐𝟐 0.036 0.028 𝜷𝜷𝟑𝟑 0.166 0.079 𝜷𝜷𝟒𝟒 Low -0.225 0.102 Medium 0 High +0.225 𝜷𝜷𝟓𝟓 Low -0.150 0.025 Medium 0 High +0.150 RMSE 0.136 Number of Observations: 196, Number of Countries: 75, Time: 1998-2013 (Three periods of five years), Adjusted R2 = 0.363, Method of Estimation: Pooled Ordinary Least Squares (OLS), Panel: Unbalanced. In the original model, we consider a time period of five years for the policies to reflect on labour productivity in the modern sector. The initial values of labour productivity, educational expenditures as a percent of GDP, R&D expenditures as a percent of GDP and government effectiveness are lagged by one year from the reference year as per the specification of the original empirical model. In the next section, we present an overview of the relevant economic plans of Oman and Saudi Arabia, isolate the inputs and targets of the Visions, and detail the reference condition based on the isolated inputs. 5.4. Review of the Economic Plans of Oman and Saudi Arabia We find that the targets of the Visions, Programs and Plans outlined in the respective documents for Oman and Saudi Arabia are often presented in a form that makes the accountability for the targets difficult or unfeasible34. An example of this in the case of Oman Vision 2020 is that the diversification target is stated in terms of the share of the non-oil sector in the economy. This target is dependent on the prevailing oil price in the year of 34 Note that an important exception to this is the preliminary release (Oman Vision 2040, 2019) of the Oman Vision 2040 where all targets are referenced with the source and both the baseline and targets for all indicators are defined explicitly. 148 evaluation. A low oil price would automatically make the relative share of the non-oil sector in the economy appear larger even though the actual size of the non-oil sector may remain unchanged. In the case of Saudi Arabia, the Saudi National Transformation Plan (Saudi NTP 2020) and Saudi Vision 2030 are both quiet about precise targets for the expansion of the non-oil sector. The original Saudi NTP 2020, in particular, shows the “Non-oil GDP as a percentage of GDP” target to be “under study”. The November 2018 version of Saudi NTP 2020 has no mention of a target for “Non-oil GDP as a percent of GDP”. The original document is mainly a combination of individual ministry targets with some effort dedicated to bringing together the targets at the national level. We find relevant 2020 and 2030 targets for our study through the 2020 targets of Vision 2030 whereby Saudi Arabia commits to be the regional leader in terms of having a diversified economy by 2020 and to be a global leader in terms of having a diversified economy by 2030. While Oman Vision 2020 and 9th Five Year Plan are mainly focused on outputs, the Saudi Vision 2030 and Saudi NTP 2020 are stronger in defining targets for some inputs that need to be met. We recommend that evaluation methods should be built-in, and both input and output targets should be explicit and balanced in the policy, vision and program designs. Such an approach would lead to addressing the national systemic limitation as well as ensuring accountability of the programs or policies. We include the strategies that complement the main policy or vision as a part of the same programme. As such when we mention the Economic Development Plan of Oman or Vision 2020, we mean to include Oman’s National Program for Enhancing Economic Diversification (Tanfeedh), the 9th Five Year Plan 2015-2020 in Oman and Oman Innovation Strategy, unless mentioned otherwise. Similarly, when we mention the Economic Development Plan of Saudi Arabia or Saudi Vision 2030, we mean to include the Saudi National Transformation Plan 2020, unless specified differently. In Figure 5.1 we present the conceptual scheme of the Oman Vision 2020 and Saudi Arabia Vision 2030. 149 In the following sub-sections, we discuss three (3) areas namely economic output, education and research & development, and, governance and business environment of the respective Visions. These are also highlighted in the conceptual scheme presented in Figure 5.1. Figure 5.1 – The conceptual scheme of the main elements of Oman Vision 2020 and Saudi Arabia Vision 2030 - highlighted to show the focus area of the ex ante quantitative evaluation carried out in this chapter. 35 35 Our construction of the policy model uses the visual format of Collaborative Institute for Research, Consulting and Learning Evaluation (CIRCLE, 2006) and the design is adapted from the recommendation of Rogers (2008) for the design of the logic model of complicated/complex policies. Education R&D Governance Economic Economic Output Overall Sectoral Sustainable Economic Development - Based on a mix of non-fossil fuel-based sources with considerable diversification away from oil & gas Varied and ubiquitous products Environmental and Social Sustainability Application of capacity to address challenges and seize opportunities FDI Misc. Identification of Capacity Development of Capacity Identification of Opportunity Development of Opportunity Greater Awareness and Participation by Stakeholders Innovation Capacity Capability & Capacity Other Opportunities Labour Participation & Employment High mobility and low inequality 150 5.4.1. Oman Target: Economic Output Oman has witnessed an average annual growth rate of GDP 4.5% from the commencement point of Oman’s Vision 2020 in 1990 till 2017. During the same period GDP per capita has dropped from 18,700 to 16,150 constant 2010 USD making the average annual GDP growth rate a negative of -0.8%. The average of the GDP per capita over the last 17 years was observed to be 18,000 USD. As such we can conclude that accounting for or excluding the economic and oil price cycles the GDP per capita in Oman has remained stagnant for the previous 17 years. The per capita economic growth target of Oman Vision 2020 was 3.8% and for the Oman 9th Five Year Development Plan was 2.8%. The total investment rate has grown from 10.3% in 2000 to 31.2% in 2015 against the target of 34% for 2020 under Oman Vision 2020. This is a remarkable achievement. The target for 2020 for the investment rate of the economy in Oman set in 2014 under the 9th Five Year Development Plan’s was 28%. However, this was already 32% in the year the target was set. The average annual growth rate of GDP in the non-oil sector has been 5.85% from 2000 to 2013. In order to meet the target of Oman Vision 2020, the labour productivity in the modern sector in Oman has to increase from 9,912 Constant 2010 USD to 13,250 Constant 2010 USD from 2015 to 2020. That represents an annual growth rate of 5.0%. Input: Education, Research & Development and Innovation Oman aims to be among the top 40 most innovative countries by 2020 and among the top 20 most innovative countries by 2040. This is in comparison to its position in 2016 of rank 73 on the GII. The current total education expenditure as a percent of GDP in 2017 was 6.7% and the current expenditure into tertiary education as a percent of GDP in Oman is at 1.9%. At the same time, Oman only invested 0.25% of its GDP into R&D. There are no explicit input targets for future education and R&D spending mentioned in any of the available relevant documents. 151 For the evaluation of the development policies we derive the inputs on education and R&D from the top 40 countries in the GII, so that the inputs devoted to education and research to reach the economic targets in 2020 are: 2.0% for tertiary education expenditure as a percent of GDP and 1.3% for R&D expenditures as a percent of GDP. Input: Governance and Business Environment Oman’s government effectiveness index was observed to be 0.3 on a scale of -2.5 to +2.5. The top 40 countries on the GII have an average of government effectiveness index of 1.35. Oman has a score of 76.3 out of 100 on the Index of Economic Freedom (IOEF) – Business Freedom subcategory. This is in comparison to the average of 78.3 for top 40 countries on the GII. We identify Oman’s target for government effectiveness and business environment to be scores of 1.35 and 78.3 for government effectiveness index and the business freedom sub-category of IOEF, respectively. 5.4.2. Saudi Arabia Target: Economic Output The Saudi Vision 2030 was announced in 2016. In the follow-up, the Kingdom of Saudi Arabia rebranded the 10th five-year plan to the Saudi National Transformation Plan (Saudi NTP 2020) and launched it in June of 2016 as an integral part of the Saudi Vision 2030. GDP per capita in Saudi Arabia in 2015 stood at 21,500 USD. The Saudi NTP 2020 does not set a target for the share of the non-oil sector. Saudi Vision 2030 sets the target for 2030 to be at the level of the 2016 regional benchmark for the share of non-oil GDP in the economy. This was 69%, while the share in Saudi Arabia was 58% in 2016. As the government of Saudi Arabia envisions no changes in the output of the oil sector, the entire growth of this share is expected to come from the non-oil sector. Input: Education, Research & Development and Innovation The government of Saudi Arabia had in 2016 the Gross Expenditure on R&D equivalent to 0.82% of the GDP. Total expenditure on tertiary education was around 1.6 % as a percent of 152 GDP. Unfortunately, Saudi NTP 2020, as well as the various programs under Saudi Vision 2030, do not mention expenditures the intended tertiary education and R&D expenditures 36. We use the average of the top 40 most innovative countries on the Global Innovation Index (GII) as the target for Saudi Arabia in 2020 and consider 2.0% as an input for the best policy scenarios. Input: Governance and Business Environment Saudi Arabia outlines a baseline of rank 26 and a target of 10 by 2030 for the Social Capital Index. However, there is no mention of the reference for the value of Social Capital. We were unable to find this value in World Economic Forum’s (WEF) Global Competitiveness Index (WEF, 2019) or in the Global Sustainable Competitiveness Index (SolAbility, 2019) or the Social Capital Pillar of Legatum Prosperity Index (Legatum, 2019). Saudi Arabia was indeed ranked as 26th in various years between 2013 to 2017 in relation to judicial independence, institutions, social institutions, firm ethics, flexibility, infrastructure quality or some other component of these indices. However, the reference is not explicit. One of the targets set by the Saudi Vision 2030 is that of reaching the rank of 10th country in the world on the Global Competitiveness Index. In addition to that, it highlights the aim for Saudi Arabia to go from the 80th rank on Government Effectiveness to the 20th rank by 2030. This would imply a target for government effectiveness index of 1.0 by 2020 and 1.8 by 2030. Both these targets appear difficult to achieve as it was only 0.25 in 2017. 5.4.3. Reference Condition and Scenarios Here we present the references initial condition for both the countries for most probable, inadequate policy measures and best practice policy action scenarios. The worst scenarios are based on maintaining the status quo on policy and input measures. The best practice scenarios are based on average economic growth and high improvements in regional characteristics for 36 Note that the Saudi Vision 2030 is an evolving plan. Some of the programs under the Saudi Vision 2030 like the Human Capital Development program was not published by the time this chapter was published (Saudi Vision 2030, 2019) 153 the GCC region. The governance, education and R&D expenditures for the best practice scenarios are based on the targets identified as best practice in the Visions of Oman and Saudi Arabia. We acknowledge that some of these choices are arbitrary. The purpose is to show the difference of outcome between what we have defined as inadequate, most probable and best practice policy. As such, we believe that our assignment of inputs for demonstrating the difference in output based on varying level and quality of policy action is justified. Table 5.2 – Reference Oman’s and Saudi Arabia’s respective Visions Oman 2020 Prediction Initial Reference Conditions (2014) Oman 2025 Prediction Initial Reference Conditions (2019) Oman 2030 Prediction Initial Reference Conditions (2024) Saudi 2020 Prediction Initial Reference Conditions (2014) Saudi 2025 Prediction Initial Reference Conditions (2019) Saudi 2030 Prediction Initial Reference Conditions (2024) G D P p er c a p it a C o n st a n t 2 0 1 0 U S D p e r C a p it a Most Probable 16,800 16,636 19,999 21,500 20,581 24,917 Inadequate Policy Not Applicable 17,080 16,687 Not Applicable 21,138 23,191 Best Practice Not Applicable 16,409 21,285 Not Applicable 20,298 16,604 L a b o u r P ro d u c ti v it y C o n st a n t 2 0 1 0 U S D p e r C a p it a Most Probable 9,912 12,044 15,799 12,147.50 15,168 19,684 Inadequate Policy Not Applicable 12,367 13,183 Not Applicable 15,579 17,092 Best Practice Not Applicable 11,880 16,185 Not Applicable 14,959 21,017 S h a re o f n o n -o il (m o d er n ) se ct o r a s a p er ce n t o f G D P Most Probable 59.0% 72.4% 79.0% 56.5% 73.7% 79.0% Inadequate Policy Not Applicable 72.4% 79.0% Not Applicable 73.7% 79.0% Best Practice Not Applicable 72.4% 79.0% Not Applicable 73.7% 79.0% E x p en d it u re o n R & D a s a p er ce n t o f G D P Most Probable 0.25% 1.30% 2.00% 0.82% 1.30% 2.00% Inadequate Policy Not Applicable 0.22% 0.22% Not Applicable 0.82% 0.82% Best Practice Not Applicable 1.3% 2.00% Not Applicable 2.00% 2.00% E x p en d it u r e o n te rt ia ry ed u ca ti o n a f Most Probable 1.90% 1.92% 2.00% 1.60% 1.60% 2.00% Inadequate Policy Not Applicable 1.92% 1.92% Not Applicable 1.60% 1.60% 154 Oman 2020 Prediction Initial Reference Conditions (2014) Oman 2025 Prediction Initial Reference Conditions (2019) Oman 2030 Prediction Initial Reference Conditions (2024) Saudi 2020 Prediction Initial Reference Conditions (2014) Saudi 2025 Prediction Initial Reference Conditions (2019) Saudi 2030 Prediction Initial Reference Conditions (2024) Best Practice Not Applicable 2.00% 2.00% Not Applicable 2.00% 2.00% G o v er n m en t E ff ec ti v en es s Most Probable 0.28 0.21 0.80 0.21 0.25 0.8 Inadequate Policy Not Applicable 0.00 0.00 Not Applicable 0.00 0.00 Best Practice Not Applicable 0.50 1.8 Not Applicable 1.8 1.8 R eg io n a l G ro w th C h a ra ct er is ti cs (C o ef fi ci en t) Most Probable Low (-0.225) Low (-0.225) Low (-0.225) Low (-0.225) Low (-0.225) Low (-0.225) Inadequate Policy Not Applicable Low (-0.225) Low (-0.225) Not Applicable Low (-0.225) Low (-0.225) Best Practice Not Applicable High (0.225) High (0.225) Not Applicable High (0.225) High (0.225) T im e P e ri o d C h a ra ct er is ti cs (C o ef fi ci en t) Most Probable Upturn (0.15) Upturn (0.15) Upturn (0.15) Upturn (0.15) Upturn (0.15) Upturn (0.15) Inadequate Policy Not Applicable Neutral (0.00) Neutral (0.00) Not Applicable Neutral (0.00) Neutral (0.00) Best Practice Not Applicable Neutral (0.00) Neutral (0.00) Not Applicable Neutral (0.00) Neutral (0.00) The population in Oman in 2020, 2025 and 2030 is projected to be 5.15, 5.57, 5.90 million people, and in Saudi Arabia it is projected to be 34.7, 37.3, 39.5 million people, respectively (UN DESA, 2019) 5.5. Results and Discussion The model of Table 5.1, together with the input data of Table 5.2 is used to compute the outcomes of the development strategies. These outcomes are compared with the 2020 and 2030 targets for Oman and Saudi Arabia. Confidence intervals are calculated using the root mean standard errors as these approximate the standard error of the predictions. 155 Table 5.3 – Most probable outcome of Oman and Saudi Arabia’s non-oil sector labour productivity Oman Saudi Arabia Target Actual Predicted Target Actual Predicted 2020 Labour Productivity in Modern Sector Constant 2010 USD 13,250 12,662* 12,547 Confidence Interval 10,952 to 14,375 18,595 15,836** 15,897 Confidence Interval 13,876 to 18,213 Annual Growth in Labour Productivity of Modern Sector (2015 to 2020) 5.0% 4.2% 4.0% Confidence Interval 1.7% to 6.4% 7.4% 4.5% 4.6% Confidence Interval 2.2% to 7.0% *Based on actual 2018 figure of 12,364 Constant 2010 USD and 1.2% annual growth expected as per April 2019 World Bank Economic Update for Oman. **Based on actual 2018 figure of 15,311 Constant 2010 USD and 1.7% annual growth expected as per April 2019 World Bank Economic Update for Saudi Arabia. In Table 5.3 we observe that Oman and Saudi Arabia are very likely to achieve their respective 2020 target for diversification. However, it must be noted that when compared to the original plans these targets were highly diluted in the last five-years plan. This is especially true for Oman where the Oman Vision 2020 documented the aim to increase the share of the non-oil sector in the economy to 91% of the total GDP by 2020. However, considering no changes in the size of the oil sector, the share of the non-natural resource sector in the economy is predicted to be between 65% and 75% (this range depends on the price of oil). The labour productivity in the modern sector target as per the original Oman Vision 2020 was 17,700 Constant 2010 USD. While Oman is successful in achieving the target of its 9th five-year development plan, it has failed in accomplishing Oman Vision 2020. The situation of Saudi Arabia is similar. The desire to completely diversify away from crude oil exports was implicit in Saudi policy for the last 20 years, however, Saudi Arabia at the time did not make it explicit through a formal long-term plan such as Oman Vision 2020 or its own Saudi Vision 2030. The first version of Saudi Arabia’s National Transformation Plan 2020 stated explicit targets, however, these were scrapped in the revised version that has officially replaced the original version. The only diversification target for 2030 that can be used to extrapolate intermediate 156 2025 targets is the global benchmark of 84% for non-oil sector contribution to GDP. One of the objectives of Saudi Arabia is to match global benchmarks by 2030. Another important target for Saudi Arabia in Saudi Vision 2030 is to be amongst the 15 largest economies in the world by 2030. We consider the target for Saudi Arabia to be equivalent to an estimated GDP of 1.7 trillion constant 2010 USD with a projected population of 39.5 million (UN DESA, 2019). With 83% of the output of the economy to be generated by the non-oil sector, this translates to an output per capita of 35,700 Constant 2010 USD for the non-oil sector. The preliminary version for Oman Vision 2040 released on 25th of July 2019 does not define the target for the output of the knowledge economy as a percent of total GDP for 2030 or 2040 and marks it as under development. For the purpose of comparison with the 2030 predicted outcome, we consider 35,700 constant 2010 USD as the labour productivity target for the non-oil sector for both Oman and Saudi Arabia for 2030. The following figure shows a comparison of the predicted productivity in the non-oil sector in Oman and Saudi Arabia with the intermediate targets and final target for 2030. Note that here we assume that by 2025 Oman and Saudi Arabia would have met their respective targets for the effectiveness of governance, investment in tertiary education and R&D. Here we present the comparison of the target for 2020, 2025 and 2030 with the most probable prediction. 157 Figure 5.2 – Predicted and Targeted Labour Productivity in the Modern Sector We also look into the best and worst policy scenarios in terms of tertiary education expenditures, research and development expenditures, government effectiveness, regional growth characteristic (regional connectedness, transport and trade infrastructure, regional peace and security and international trade, et cetera). 5000 10000 15000 20000 25000 30000 35000 40000 45000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Oman Target Most Probable Scenario 10000 15000 20000 25000 30000 35000 40000 45000 50000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Saudi Arabia Target Most Probable Scenario ⌶ ± 1 RMSE ⌶ ± 1 RMSE 158 Figure 5.3 – Labour productivity in the modern sector in varying policy conditions We observe that Oman and Saudi Arabia will achieve their respective 2020 targets. However, the differential between the target and the most probable scenario is going to increase with time. It appears that without deliberate policy action for improving the determinants of labour productivity in the modern sector the governments of Oman and Saudi Arabia will face stagnation of labour productivity growth in the modern sector. 5000 10000 15000 20000 25000 30000 35000 40000 45000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Oman Most Probable Scenario No Improvement Scenario Best Scenario ⌶ ± 1 RMSE 10000 15000 20000 25000 30000 35000 40000 45000 50000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Saudi Arabia Most Probable Scenario No Improvement Scenario Best Scenario ⌶ ± 1 RMSE 159 The predictions in Figures 5.2 and 5.3 have to be understood within the context of the model used, which supports plausible causal links between policies and productivity growth in the modern sector. While individual countries do not have any control over the global business cycle, they do, however, have some ability to affect their own regional growth characteristics. Efforts to increase regional integration and cohesion, regional peace and stability, enacting trade agreements, launching regional development programs, opening up trade routes that connect other important countries, and improving regional transportation networks are all examples of regional characteristics where individual countries can enact policies to improve their growth prospects. In the following, we present a discussion on what Oman and Saudi Arabia can do to reach their respective diversification and economic growth targets. Oman Currently, Oman spends 0.25% of its GDP on R&D and 1.90% of its GDP on tertiary education. The government effectiveness in Oman has fallen from its high point of 0.5 to the current level of 0.28. The apparent decline of the quality of governance in Oman is a cause of alarm. Looking at its level of R&D expenditures it is easy to recommend the government of Oman to stimulate an increase in the expenditure on R&D. However, it is most important to improve the quality of governance. Without a highly effective government, other policy measures are likely to be negatively affected by the inefficiencies in the system. Based on the trends of government effectiveness in Oman, a scenario where the quality of governance in Oman declines further is plausible. In such a scenario, the annual growth in the non-oil sector in Oman is estimated to be approximately 2.6%. This is not adequate to ensure the replacement of oil as the main driver of the economy by 2030. Oman is already taking measures to improve its connectivity to international trade routes. Oman’s new port in the open Arabian Sea (Indian Ocean) features prominently on the Chinese Belt and Road Initiative (BRI) and holds the promise to be one of the most important transport and trade hubs in the region. Oman has also held a neutral position in international strategic disputes and facilitated negotiations to dispel conflict in the region. Oman is engaged in expanding its 160 road and air networks and at the same time has commissioned the construction of a railway network through all major cities. It is planned to be eventually connected to the proposed GCC railways network. Oman’s contribution to improving regional characteristics through regional transportation integration, international trade links and maintaining peace and security is important to achieve the diversification goal. It is recommended that Oman should increase each of its R&D and tertiary education spending to 2.0% of GDP and improve its governance (for example, to the level of Denmark which had the 10th most effective governance system in the world in 2017). In the best case policy scenario, Oman could achieve labour productivity in the modern sector of 37,900 Constant 2010 USD by 2030. This is above the target of 35,700 Constant 2010 USD and much above 30,000 Constant 2010 USD that is more likely under the most probable scenario considered in this chapter. It must be noted that Oman’s military expenditure as a percent of GDP in 2016 was 15.3% (The International Institute for Strategic Studies (IISS), 2017). It is obvious that the suggested increases in spending are completely feasible in the case of Oman. Saudi Arabia What can Saudi Arabia do to ensure that 2020 targets are met? Government effectiveness and expenditure on R&D are also the areas where Saudi Arabia can make tremendous improvements. Regional growth trends will have a great influence on Saudi Arabia’s ability to achieve its targets. Saudi Arabia is the country that holds the key to improving many regional characteristics such as the perception of peace, trade links and regional cohesion. Two regional limitations impeding the economic development of the GCC are poor infrastructure and geopolitical tensions. Saudi Arabia can play a major role in overcoming these in favour of greater growth in the modern sector. The regional infrastructure can be improved by creating rail, air, road and maritime connections and improving trade networks with all middle eastern countries including Israel, Syria and Iran. Lasting peace could be achieved in the Arabian Peninsula by including Yemen, reintegrating and increasing the cohesion of the GCC, which includes ending the trade embargo of Qatar, and improving relationships with Iran. These measures are expected to improve the regional growth 161 characteristics that are key to the growth of Saudi Arabia’s modern sector itself. In their ambitions to diversify and improve economic and social stability the countries of the GCC can benefit from regional peace and stability, the Chabahar Port, the Belt and Road Initiative and the GCC wide rail link extending to the West. Saudi Arabia consistently ranks among the top military spenders in the past few years. By diverting resource towards governance, tertiary education, R&D, and improving regional characteristics, it should be feasible to achieve best practice policy scenario. This would result in labour productivity in the modern sector of Saudi Arabia equivalent to 47,800 Constant 2010 USD by 2030. In this scenario, Saudi Arabia could stand among the top thirty most productive economies in the world with its modern sector productivity at a level comparable to that of advanced western economies. Positive Regional Developments in GCC Duqm has a special place in the global and GCC trade. Unlike most of the ports in the region that are prone to access concerns, the Duqm port is in the open Arabian Sea and provides quick access to the Indian Ocean trade routes. The government of China through the Wan Fang Consortium has allocated 10.7 billion current 2017 USD to development of a transit- oriented industrial city named China-Oman Industrial Park in Duqm, Oman. China promises to complete 30% of the project within 5 years. The Chabahar Port in Iran is an Indian sponsored port on the Gulf of Oman that sisters with the Sohar Port of Oman. Meanwhile, Saudi Arabia is envisioning a road link between the Gwadar port in Pakistan and Oman and has agreed to a 10 billion grant investment in the China-Pakistan Economic Corridor (CPEC) that is a critical part of the Belt and Road Initiative. The GCC rail project appears to have been delayed, however, individual countries in the GCC are constructing their individual rail networks and Israel has shown interest to join the network. Oman and Saudi Arabia have the first land link in the form of a construction marvel road link through the world’s largest contiguous sand desert - the Empty Quarter. All signs indicate that the growth characteristics of GCC region for modern and non-oil and gas sector are going to move from “low growth characteristics” to “high growth characteristics” in the medium to long term. 162 5.6. Summary and Conclusion We evaluated ex ante the development strategies of Saudi Arabia and Oman, aimed at less oil dependency for the years 2020 through 2030. For that purpose, we used a predictive quantitative model assessing the targets under the respective policies, based on the econometric analysis carried out in Chapter 2. As a first step, we sought to extract inputs and quantifiable targets from the strategy documents of both countries. Wherever the inputs or targets by year were not explicitly mentioned, we extrapolated them from high-level targets (such as being top 40 in innovation by 2040 or top 10 in GDP by 2030). For Saudi Arabia, we had to deduce the target for the share of the non-oil sector in the economy, while for Oman, these targets are clearly stated for 2020. Since the share of the non-oil sector is dependent on the oil price, we have focused on the annual growth in labour productivity of the modern sector and the final level. In the most probable scenario, Oman and Saudi Arabia will both not be able to meet their targets for diversification away from an oil-based economy by 2030. One potential reason for this expected failure to meet the growth targets for the modern sector is related to not having the required systemic conditions, enablers and inputs. Another part of it comes from limitations in baseline analysis and simply having unrealistic targets. An example of this is the Oman Vision 2020 target for the non-oil and gas sector to contribute 91.0% of the total GDP by 2020. This is the “putting stars in their eyes” effect, as some of the targets presented were overly ambitious at best or ambiguous at worst. Poor policy action on the determinant discussed will mean that the countries will face stagnation in the growth of their respective modern sectors. In order to ensure that the long-term targets for their respective visions are met, it becomes imperative for the governments of Oman and Saudi Arabia to focus on all systemic conditions. Investment in education, R&D, improving government effectiveness, business environment and regional characteristics is crucial for ensuring successful diversification and sustainable economic development. 163 We observed in Chapter 2 that the regional growth characteristics in the GCC were worse than in other regions. In their ambitions to diversify and improve economic and social stability, the GCC and its countries can benefit from improving the regional growth characteristics in the coming 5 to 10 year. This can be achieved through a focus on regional peace and stability and investing in major regional infrastructure projects such as the Chabahar Port, connections to the Belt and Road Initiative and the GCC wide rail link extending to the West. The governments of Oman and Saudi Arabia will be able to maximise growth opportunities by opening the highly-qualified employment sector to the international job market in the short term, especially in the area of R&D and modern sector. At the same time plans should be drawn to settle Omani and Saudi citizens in highly-qualified employment position especially in the R&D arena as the modern sector expands in the long term. The governments of Oman and Saudi Arabia are interested in increasing the productivity of the country in general, and at the same time, they are strongly interested in employment creation. Therefore, expansion of the modern sector is the only key to both higher productivity as well as job creation. Note that higher productivity can also be achieved through increased efficiency. However, the population of Oman and Saudi Arabia is young, increasing and well educated. As such expansion and efficiency have to go hand in hand. Oman has the highest ratio of science and engineering graduates in the world and Saudi Arabia is also performing well in this measure. However, Oman and Saudi Arabia both perform very low in terms of R&D personnel per capita. While increasing investments in the R&D sector will help mitigate the current situation, the fastest and most plausible solution in order to fast track growth of the modern sector is to ease the job market participation of foreign R&D personnel in both Oman and Saudi Arabia. In the long run, it is expected that employment opportunities for science and engineering graduates in the expanding modern sector, entrepreneurship and R&D arena will be created through focussed programs and investment into the relevant areas. This research provides us with important avenues for future research, such as looking deeper into the regional characteristics that affect economic growth. Another area of research is to 164 study government policies for brain-import (hiring knowledge workers and highly skilled labour from other countries) versus local development of knowledge workers for creating a modern knowledge-based sector and ensure sustainable growth through diversification. We recommend that evaluation methods should be inbuilt, and both input and output targets should be explicit and balanced in the policy, vision and program designs. Such an approach would lead to addressing the national systemic limitation as well as ensuring accountability of the programs or policies. 165 6. Conclusion Background, Summary, Synthesis and Recommendations 6.1. Background of the dissertation Economic diversification and innovation are considered to positively influence sustained economic development (Fagerberg, 1994; Frenken, et al., 2007; Content & Frenken, 2016). Industrial specialisation and natural resource specialisation provide some growth benefits for countries (Krugman, et al., 2012). However, such strategies may make the economy susceptible to volatility and cyclical risks (Koren & Tenreyro, 2007). Economic diversification is expected to help counter the effects of downturns that affect particular industries or extractive sectors (Ramey & Ramey, 1995; Osakwe, 2007). Countries that offer a more diversified set of products and services are known to have higher levels of development in comparison to countries that do not (Krishnaa & Levchenko, 2013). Additionally, economies with higher skill levels, well-developed institutions and strong systems of innovation have been shown to be more robust in the face of shifting demand and evolving market structure (Chor, 2010). Effective utilisation of knowledge and streamlining of the adoption of transferrable skills are considered crucial for transition and diversification (Blanchard & Kremer, 1997). Strong institutions support this process and help guide the economy and its participants for transition (Costinot, 2009). The relevance of diversification, innovation, and investment in the modern sector 37 is amplified in scenarios where countries are highly dependent on natural resource extraction. Many countries with natural resource rents as the main share of their total output have to confront natural resource price volatility, risks associated with anticipated depletion of the natural resources, as well as associated strategic risks. It is not surprising that these countries have had diversification on their agendas for a considerable time. However, reports of outright success in creating a diversified economy are rare and those of failure are abundant. The literature highlights that institutional settings, education, research and development (R&D), governance, business environment, sound industrial and clusters policy, and increased capital investment into the modern sector are 37 non-natural resource and non-agricultural sectors 166 related to successful diversification and stimulation of innovation in the economy. The overarching gaps in the literature are highlighted in the Introduction to this dissertation and a detailed discussion on the relevant literature is presented in the literature review sections in Chapters 2 through 5. In the context outlined above, this dissertation aims to understand the causes of successful diversification and growth in labour productivity in the modern sector of an economy. The question at the heart of this dissertation is whether natural resources hamper the growth in the modern sector. Additionally, we explore the systematic strengths (or limitations) that lead to improved (or restricted) growth in the modern sector of a natural resource-rich economy. These enquiries are complemented by an effort to understand the state of “broad innovation policies” 38, the role of enablers39 and their relationship with diversification and innovation in the member states of the Gulf Cooperation Council (GCC) 40. Finally, the dissertation attempts to find out whether the GCC countries are on the path towards successful diversification. This is determined in terms of the predicted outcome of their diversification efforts contrasted against their stated targets. 6.2. Summary In this dissertation, several issues concerning policy for diversification, innovation and growth of labour productivity in the modern sector have been addressed. These are termed the enablers. They include government effectiveness, investment in education, R&D expenditures 38 According to Lundvall (2007) systems of innovation in a narrow sense “leave significant elements of innovation- based economic performance unexplained”. In the “broad” sense the core knowledge producing and disseminating institutions are embedded in a wider socio-economic system and the relative success of innovation policies is a function of influences and linkages beyond these core institutions (Freeman, 2002) 39 The ‘enablers’ include primary, secondary, tertiary and vocational education, research and development (R&D), quality and efficiency of governance, and business environment. The policies affecting these areas such as investment into education and R&D and regulation improving the governance and business environment are also covered under the term ‘enablers’ as they ultimately contribute to boosting innovation and diversification. It is synonymous to the term determinants. 40 The Gulf Cooperation Council (GCC) is the colloquial term used to refer to the Cooperation Council of the Arab States of the Gulf (GCC). We use the abbreviation GCC to refer to the member countries as of 2017 – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. These are synonymously referred to as the countries of the Arabian Gulf, countries of the Arabian Peninsula, countries of Persian Gulf and Arab countries of the Gulf. Countries on the Arabian/Persian Gulf such as Iran and Iraq, and countries in accession talks such as Yemen, Jordan and Morocco are not included. 167 and the state of the business environment. Furthermore, the growth of the modern sector is explored taking into account the role of fixed capital investments, the amount of commercially extractable oil reserves, the oil extraction ratio as a share of total oil reserves and the oil price. The main focus is on natural resource-based economies. The wider global perspective along with the policy consideration for non-high-income economies in contrast to high-income economies is also presented. The diversification and innovation policy in the case of GCC countries is explored in greater depth. Chapter 1 presents an introduction and summarises the rationale behind the research carried out for this dissertation. Chapter 2 explores whether “broad innovation policies” affect labour productivity in the modern sector. The question was addressed through an econometric model based on an innovation policy framework. The innovation policy framework is a conceptual framework that identifies factors influencing innovation in a market space. The policy focus in this chapter was limited to government effectiveness, expenditures on tertiary education, expenditures on R&D, and the condition of the business environment. Data from different sources were used to carry out pooled OLS regressions for a cross-section of 95 countries covering the time period of 1998 to 2013, and regional effects were considered. The policy measures were lagged by 5 years. The results indicate that government effectiveness played an important role in labour productivity growth in the modern sector. It was estimated that the interaction of government effectiveness and tertiary education expenditures has a statistically significant and positive association with labour productivity growth in the modern sector for the global data set. The positive association persisted for the set of non- OECD countries that includes non-high-income countries and GCC countries. Furthermore, for non-OECD countries, it was observed, that the effective R&D expenditure as a percent of GDP was important for labour productivity growth in the modern sector. We could not identify the effect of a good business environment (as defined by the index of economic freedom) on growth in labour productivity in the modern sector. The GCC region is lagging behind all other regions in terms of regional characteristics that positively affect labour productivity growth in the modern sector. In Chapter 2 we also explore the probability that oil prices affect the ability of the GCC countries to sustain the development of their modern 168 sector. The chapter highlights the importance of the interactions among various systemic conditions and enablers for stimulation of innovation and diversification. Overall, the results lead us to the exploration of the relationship of innovation and diversification output in the GCC countries with the systemic conditions and enablers as well as oil sector variables such as extraction ratio, reserves and oil prices. In Chapter 3, the analysis focussed on the performance of the GCC countries in terms of policies affecting diversification and innovation. The analysis was guided by an eclectic qualitative approach for the cases of Oman, Saudi Arabia and the United Arab Emirates. A qualitative discussion of the data, relating to several policy measures, enablers and outputs, was presented. The results revealed the state of each policy or enabler, and output on a seven- step scale ranging from “extremely negative” (---) to “extremely positive” (+++). The linkage between the state of the policies or enablers and the state of outputs was explored. Drawing upon this connection, the conclusions were presented. The discussed policies and enablers included seven (7) measures of primary and secondary education, four (4) measures of vocational, technical and tertiary education, six (6) measures of R&D, seven (7) measures of the business environment, and six (6) measures related to governance and infrastructure. In terms of outputs, three (3) innovation outputs and eleven (11) diversification outputs were discussed and graded on the scale. The assessment of the state of each policy, enabler and output by itself forms an important outcome of this chapter as it provides a snapshot of the innovation system in these countries. It was observed that Oman lags behind in innovation output (-) with relatively better performance on the diversification front (++). Saudi Arabia and the United Arab Emirates were performing slightly positively (+) on innovation outputs in relative terms. The diversification output for Saudi Arabia was rated as moderate (o) and that of the United Arab Emirates as slightly negative (-). Furthermore, the relationship between the inputs and outputs was discussed. The state of the outputs relates to a system of policies and enablers in the five overarching input areas where the three countries have different strengths and weaknesses. In order to improve the outputs, these countries have to strengthen all of the discussed policies and enablers. Additionally, there should be a special focus on the weakest areas. In the case of Oman, these are vocational education and R&D. 169 For Saudi Arabia, primary and secondary education is the first observed limitation within the scope of Chapter 3. The states of the business environment, governance and infrastructure represent additional weaknesses in the Saudi Arabian economic and innovation system. Finally, for the United Arab Emirates, the limiting enablers include vocational education, science and engineering education, and the lack of a coordinated R&D policy. Chapter 4 focuses on the oil industry variables – oil price, oil reserves, and oil extraction as a ratio of total oil reserves – in improving modern sector labour productivity and fixed capital formation. In order to explore this research question, we develop a theoretical model for a closed economy with a modern sector (in this case capital-driven) and a traditional sector (natural resource-based where the price of the natural resource is externally given). The theoretical model provides a background for the empirical model. The Hamiltonian maximisation for consumption indicates that investment in fixed capital is a function of the productive efficiency of the economy, the savings rate, the oil reserves, oil prices and oil extraction. Based on the interpretation of the theoretical model, given that the labour productivity growth in the modern sector is related to investment in fixed capital, natural resource extracting countries can utilise the rents from resource extraction to diversify effectively. The theoretical model was tested in an econometric specification with a panel of 149 countries for the period of 36 years from 1980 to 2015. The data set included 85 countries that had oil reserves in part of the time period considered. It was observed that countries with high levels of oil reserves per capita (as a ratio of world oil reserves per capita) benefited from high oil prices to generate higher fixed capital. In addition to this, the initial level of capital, the savings rate, schooling and GDP growth rate had positive relationships with fixed capital formation. The relationship of labour productivity growth in the modern sector with oil reserves and the oil extraction ratio was not found to be statistically significant. Higher oil prices were observed to have a positive relationship with labour productivity growth in the modern sector. The GCC countries demonstrated an ability to invest their oil wealth into fixed capital. The investment trend was positively related to a higher total value of the oil reserves for all the six GCC countries. That is, the interaction of oil reserves with the oil price 170 and country dummies for the individual GCC countries was positive and statistically significant. Qatar’s performance in terms of fixed capital investments was the best among the GCC countries, while Bahrain and Saudi Arabia were the worst performers among the GCC countries in terms of fixed capital formation. Chapter 4 shows that improving the productive efficiency of the country through better education not only has led to high labour productivity growth in the modern sector but also has improved the ability of the country to invest in fixed capital. All in all, according to our analysis, natural resources do not constitute a curse, rather, they offer countries the opportunities to grow their modern sector and diversify. In the final research chapter – Chapter 5, an evaluation of the economic diversification plans of Oman and Saudi Arabia was presented. The main question in this regard is whether the targets of these plans are realistic and achievable. The question was addressed through an ex ante evaluation of the policies of both countries using the predictive model constructed on the basis of the econometric analysis of Chapter 2. The results were computed for three states of global economic development and three states of regional characteristics. The most probable scenario was discussed in detail and it was observed that between 2015 and 2020 Oman and Saudi Arabia were predicted to achieve 4.2% and 4.5% annual growth in modern sector labour productivity. This is in comparison to the targeted 5.0% and 7.4% annual growth rates, respectively. It is estimated that in the most probable scenario the gaps between predicted and targeted labour productivity in the modern sectors are going to widen further by 2030. In addition to this, the worst-case scenario shows a likelihood of stagnation if special attention is not given to the state of enablers, and systemic conditions are not improved. However, the countries can improve governance, increase expenditure on education and research and development, and improve regional characteristics (such as cross-country transportation infrastructure, trade links and perception of peace and security, among others). This will result in Oman and Saudi Arabia meeting their desired diversification goals. Given the right policy response, it is not only possible to stimulate growth in the modern sector and diversify away from dependence on natural resources, but also to join the ranks of some of the most developed nations by 2030. 171 6.3. Limitations and Suggestions for Future Research The research carried out for this dissertation identifies and recommends pathways for successful diversification and increased innovation for natural resource-driven economies. At the same time, it also opens up many avenues for future research. In Chapter 2 the use of innovation policy framework conditions was limited to a few enablers and policies – namely government effectiveness, education policy, research and development policy and business environment. A limitation encountered there is a lack of appropriate indicators for proxying the state of the business environment. 41 The indicator used – Index of Economic Freedom – does not show a relationship with the modern sector labour productivity. The empirical model may be refined further, and a better indicator may be selected or constructed to identify the effect of the ease of doing business independent from other variables. An important element of Chapter 3 is the use of the seven-point scale for enablers and policies that affect innovation, diversification and economic growth in general. One of the limitations there is that the ranking on this scale is relative and the scores are based on the expertise and judgment of the authors. It is recommended to repeat the ranking of inputs and enablers on a similar scale for the same countries at different points of time, increase the number of expert reviewers for assigning scores/points on the scale and add more countries to the comparison. Additionally, the scale may be normalised and the relationship of the worst- performing input against an economic output may be explored quantitatively. This will provide a way to analyse the relationship of a “limiting policy” with innovation, economic growth and/or diversification. In the current discourse in economics, the discussion often leans more towards identifying what causes the output to be achieved rather than what limits it. In the system approach, all the enablers and inputs must be performing healthily, and the system’s level of performance may be considered as a function of the worst-performing input. Appropriate normalisation techniques need to be developed in order to ensure that the indicators of the various components of the innovation system are comparable. 41 The World Bank’s Doing Business indicators did not cover the full time period under observation. 172 The key contribution of Chapter 4 is the introduction of the natural resource sector as an important contributor in a two-sector economic development model. The observation that the natural resource rents can be successfully invested in fixed capital formation and contribute to the growth in the modern sector is not surprising and he relationship between fixed capital and economic output is generally accepted in the economic discourse. However, Chapter 4 also raises a queston about the efficiency of the new fixed capital in natural resource-rich economies, which is of great interest to the academic discourse. While our findings indicate that there is no “natural resource curse”, we must ask – Is the efficiency of the fixed capital investments in some countries lower than others? This question may be answered by disentangling the components of fixed capital investments. The role of the relatively shorter- term high return fixed assets (such as fixed asset software) confounds the analysis of economic growth based on fixed capital investments further and opens up new avenues for research in this direction. The positive effect of oil prices on the growth of labour productivity growth in the modern sector is an interesting result encountered in Chapter 4. Rising oil prices contribute to the acceleration of business investment and activity in the oil sector. This acceleration, in turn, is likely to positively affect the business activity and investment along the supply chain where the oil sector connects through to the modern sector. Thus, high oil prices may help in boosting the overall economic expansion. Countries that produce modern consumption goods, such as the US and some European countries, benefit during high oil price periods from increased exports to oil-exporting countries in the GCC and other regions. However, high oil prices also tend to limit the growth of exports from the same modern consumption goods-producing countries to oil-importing countries, notably China, India, Japan and others in Europe. Some of these oil-importing countries may benefit from supply chain changes aimed at increasing profit margins for countering the fall in marginal returns because of higher energy cost per unit. It is evident that out of the previously mentioned dynamics those that enhance the modern sector labour productivity because of high oil prices are dominant. However, beyond a certain point, rising oil prices will start to weigh on investment and spending by the non-oil sector and households, slowing down the overall growth. Increase of modern sector productivity due to high oil prices may also be a sign of 173 adoption of innovative methods to reduce energy costs per unit, even though such dynamics are expected to operate in the longer term rather than the one-year intervals that are used in Chapter 4. It is recommended to carry out further research to understand the pathways through which oil prices affect innovation and growth in labour productivity in modern sector. Another recommendation is based on the findings in Chapter 2 and the use of a predictive model in Chapter 5 where it is observed that region-wide phenomena affect the productivity growth in the modern sector. The attraction of foreign direct investment from other regions and international firms towards a specific region, the transport and logistics infrastructure and the perception of regional stability are some factors that may affect the growth characteristic of a region. However, we do not disentangle the factors behind the regional characteristics captured by the coefficients of regional dummies. More research needs to be carried out to identify and disentangle the various factors of the regional characteristics that affect growth in the modern sector. Such research should consider including policy variables such as the development of regional and cross-country transportation infrastructure, improving international trade links, strengthening regional security and enhancing the perception of strategic stability and peace, among others. In Chapter 2 the GCC region appears to be performing worse than the reference region (which includes most European countries, the US and Canada – see Appendix 2-A for more details) and most other regions in terms of labour productivity growth in the modern sector. In Chapter 4 it is observed that individual GCC countries are performing no worse than the reference country (US) in terms of the same output variable. One reason behind the apparent contradiction between Chapter 2 and Chapter 4 is related to the difference in the growth period considered. The empirical model in Chapter 2 considers five (5) years growth rates in productivity for three consecutive five (5) year periods from 1998 to 2013. In contrast, the empirical model in Chapter 4 considers annual growth from 1980 to 2015. Also, the model in Chapter 4 accounts for fixed capital investment that may partially cover the foreign direct investment. Foreign direct investment may itself be a factor negatively affecting the regional characteristics of the GCC countries as observed in Chapter 2. Different models, data and 174 time periods may account for why this is observed. More research may be carried out to understand the exact cause of worse regional growth characteristic in the GCC. In the following section, we present a synthesis of the insights from the dissertation and detail the policy implications of our results. 6.4. Integrated insights from the dissertation and policy implications The common theme that emerges from the dissertation is that economic growth and diversification are constrained by the limiting policy or enabler. They are not driven by the best performing determinant in the system. The analysis helps to understand the measures that affect the productivity in the modern sector particularly in oil-rich countries. The results are, however, generalisable to a broader set of developing and natural resource-driven economies. The results of the dissertation can be grouped into three integrated themes. These are presented in the following sub-sections. 6.4.1. Institutional Effectiveness, Productive Efficiency, Human Capital, Education and R&D Findings The importance of institutional effectiveness, productive efficiency, human capital, education and R&D for sustained economic development and diversification is the prevalent outcome in this research. The results of Chapter 2 indicate that improving the efficiency of education and R&D investments can lead to higher productivity growth in the modern sector. Chapter 3 confirms the validity of this result at the country level for three countries of the GCC - Oman, Saudi Arabia and the United Arab Emirates. The limitations in the governance, education and R&D systems of these countries are some of the barriers to the growth in the modern sector productivity, innovation and diversification. The importance of education and productive efficiency of the system is highlighted again in Chapter 4. There it is observed that a higher 175 level of schooling is not only related to higher labour productivity growth in the modern sector but also greater fixed capital formation. In Chapter 5 we observe that the initial conditions of governance, education and R&D are not adequate for Oman and Saudi Arabia to meet their respective 2020, 2025 and 2030 economic diversification targets. Policy Advice The policy recommendations in this regard are based on two lessons. Firstly, education and R&D investments are expected to improve productivity in the modern sector. Secondly, countries can only invest in the modern sector fruitfully based on their productive efficiency, education levels and expertise. Finally, the effectiveness of the policy inputs such as education and R&D spending matters. We also know from the literature that the outcomes of investment in education improve the efficiency of the productive environment. In this regard, the governments of developing countries and natural resource-based economies are recommended to increase their investment in education and R&D. It is also recommended that they focus on improving the overall quality of governance, which would help to minimise inefficiencies in investments arising due to bad governance. This will ensure productivity and innovation output improvements proportional to the investments made and in turn would help meet the development and diversification targets. A route often followed by countries trying to safeguard high levels of human capital is to import highly-skilled labour and knowledge workers to support the development and diversification process. This is one of the policies that has been adopted by some of the GCC countries to varying levels and can be considered in the policy mix. The import of highly skilled knowledge workers is one of the most viable solutions to tackle the limitations in human capital, considering the challenging short-run diversification goals of the GCC countries. However, an appealing environment is required to attract knowledge workers. This forms a major challenge in a country like Saudi Arabia that is often perceived as an unattractive location for foreigners. As such the government of Saudi Arabia must pay special attention to improving the country’s attractiveness for knowledge workers. The need for importing foreign knowledge workers also faces the challenge of the localisation of labour (each GCC country uses a different 176 terminology – such as Omanisation, Saudization and Emiratisation – to define the process of replacement of foreign worker with national labour). The process is mainly bottom-up, that is, localisation is implemented stage-wise starting from the lowest skilled labour and the main target of the labour localisation programs are not primarily knowledge workers. In this context, it is also important for the countries of the region to take into consideration that the development of a broad knowledge-based economy. Keeping in view that the ability to sustain innovativeness in the country is associated with the ability to connect to the international knowledge networks. Such linkages can be established by a regular flux of international knowledge workers. 6.4.2. Natural Resources, Oil, Productivity and Investment Findings In this dissertation, we do not find evidence of the “natural resource curse” in the sense that having natural resources and utilising them for meeting the needs of the population lead to an automatic doom of the economy. Instead, this dissertation shows that natural resources provide a key to prosperity through sound management of the rents. In Chapter 2 the GCC countries are shown to have the worst regional characteristics for modern sector productivity growth when compared to the reference countries and the rest of the world regions (That is, the value of the coefficient for the regional dummy on the GCC countries is observed to be the lowest in the estimation). Further exploration was carried out to find the reasons for the lower regional performance in GCC considering that the broad innovation policy variables, natural resource dependence and agricultural value-added were controlled for. Lack of adequate cross-country regional infrastructure and perceived political and strategic instability may be considered among the many reasons why the GCC region is worse-off in terms of economic growth characteristics. However, the one common characteristic of the countries of the GCC that stands out is their dependence on oil revenues. In Chapter 2, the prediction based on the empirical model shows that the modern sector labour productivity growth in the GCC countries was associated with oil prices. This 177 relationship was not observed in the case of countries like the Netherlands and Norway that were two countries from the reference group. Following the oil dependence reasoning, an analysis of the effect of natural resources on labour productivity growth was carried out in Chapter 4. This showed that natural resources are an important source of revenue that can be used for investment into fixed capital. There is no significant difference between the GCC countries and the reference country the United States (US) in terms of labour productivity growth in the modern sector. One of the results of Chapter 4 indicates that the GCC countries are actively engaged in investing their oil revenues. During high oil price periods, these investments increase disproportionally. Qatar, amongst all countries, has demonstrated the capability to maintain one of the highest labour productivity growths in the modern sector and fixed capital investment for the period 1980 to 2015. Policy Advice The main lesson here is that natural resource-driven countries need to invest in fixed capital, improving governance and institutions, reducing systemic inefficiencies, and developing human capital. The investment as a percent of total GDP is generally higher during high oil price periods in the GCC countries. However, the governments should also target keeping the investment ratio as high as possible during low oil price periods. During high oil price periods, governments may be urged to increase spending on non-productive purposes, such as the creation of employment in the government sector that is not tied to improvements in the effectiveness of the government. Once initiated the need for such spending often persists, for example, to keep unemployment at a low level. The increase in such unproductive expenditures hampers the ability of the government to invest in the development of the modern sector as oil prices fall and the limited revenues have to be mainly used on inefficient and inflexible government spending. In addition to this, the stability of investment should also be targeted along with the stability of output and productivity growth. This is to ensure that the diversification focus of the policy is maintained at all times and inefficient spending is avoided during high oil price period. Both the achievements and failures of GCC countries 178 in diversification highlight the importance of using the natural resource revenues for improving governance, education, R&D and increasing fixed capital. The possibility of natural resource-driven economies to balance their consumption and investments in order to ensure economic stability is one of the most critical policy lessons that can be derived from this dissertation. It is also important for oil-exporting countries to note that new technologies and management techniques are being exploited around the world to offset the profit reducing effect of higher energy cost per unit of production. This among other concerns brings even more urgency to the need for diversification in oil-based economies to ensure sustained economic development and continued well-being of their populations. 6.4.3. Regional Infrastructure, International Trade and Peace Findings Chapter 2 and 5 highlights the importance of regional characteristics. This dissertation does not disentangle all the region-wide factors that affect a country’s labour productivity growth. However, the research is able to show that being rich in natural resources in particular fossil fuels is not responsible for the poor regional characteristics of the GCC countries. Chapter 3 highlights in a comparative analysis of three (3) of the six (6) GCC countries – Oman, Saudi Arabia and the United Arab Emirates – that all three countries face limitation in enablers and policies that are expected to enhance innovation and diversification in their economies. These limitations partly explain why productivity growth in the modern sectors of these economies is constrained. Other limitations may include a lack of cross-country intra-regional infrastructure, and the perception of a lack of political stability, regional peace and security. Policy Advice The GCC region does not have any cross-country rail links. The two most populous GCC countries Saudi Arabia and Oman did not have any direct road connection and all trade traffic has to pass through the United Arab Emirates.42 Saudi Arabia is the only country to 42 Note that as of May 2019 the construction of direct Oman-Saudi road connection through the Empty Quarter is considered completed, however, official opening is not yet announced. 179 have a border with all other GCC countries. However, the 2017 embargo of Qatar by Saudi Arabia (and its partners in the embargo) meant that Saudi Arabia did not have any direct land trade routes to Qatar as well. The Qatar embargo and the Iran-Saudi Arabia conflict also create an image of a region that is unstable. In addition, the GCC is a part of the greater Middle East and North Africa (MENA) region. This means any conflicts, wars or instability in the wider Middle East region such as the Israel-Palestine conflict, the Syrian conflict and even civil/political volatility in farther away countries like Egypt and Libya creates a perception of the GCC being a sub-unit of an unstable region. These perceptions are very likely to negatively impact some economic determinants, such as foreign direct investment in the GCC. Improving regional characteristics by investing in regional and cross-regional infrastructure projects and focusing on regional peace and security is critical for meeting the diversification objective of the GCC countries. It is recommended for the GCC countries to establish a GCC wide rail network for transport of goods and people (with future connection to the countries north and north-west of the Arabian Peninsula including Israel, Turkey and eventually EU). Improving connection to international trade routes by linking to international trade and infrastructure projects such as the Belt and Road initiative is likely to provide a boost to the regional economic characteristics. Several projects addressing the current infrastructure limitations are underway and will contribute to improving the regional characteristics. Saudi Arabia, being the GCC country with the largest population, area and most border countries in the region, is likely to benefit the most from improving regional growth characteristics. It also has a major role to play in peacebuilding with its neighbours (Israel, Iran, Yemen and its fellow GCC country Qatar), and promoting an image of not only the GCC but also the MENA region as a safe and secure region. This is because the destiny of the GCC is tied to the greater MENA region through geography. An alternative would be to brand and market the GCC as a region that is distinct from the greater MENA region. 180 181 7. 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World Development Indicators. [Online] Available at: http://data.worldbank.org/data-catalog/world-development-indicators [Accessed 23 May 2019]. 201 Contents 1. Introduction Appendix 1-A 2. Productivity and Innovation Policy 2.1. Introduction 2.2. Innovation policies and the path towards successful innovation 2.3. Identification Strategy 2.4. Data 2.5. Results 2.5.1. Global 2.5.2. Arabian Gulf countries - A special case? 2.6. Conclusions and Discussion Appendix 2-A Appendix 2-B 3. Policy and Economy in the GCC 3.1. Introduction 3.2. Perspectives on innovation 3.2.1. General 3.2.2. The literature on GCC countries 3.3. The Case of GCC – Policies and Enablers 3.3.1. Section Summary 3.3.2. Development of education systems 3.3.3. Literacy, primary education, secondary education, reforms and performance 3.3.4. Tertiary education and vocational education 3.3.5. R&D 3.3.6. Business and Entrepreneurship 3.3.7. Governance and Infrastructure 3.4. The Outputs of GCC – Indicators of Innovation and Diversification 3.4.1. Section Summary 3.4.2. Patents, Trademarks and Industrial designs 3.4.3. Non-traditional sector - share in the economy and labour productivity 3.5. Connecting Policies, Enablers and Outcomes 3.6. Summary, Discussion and Conclusion Appendix 3-A 4. Natural Resource Abundance: No Evidence of an Oil Curse 4.1. Introduction 4.2. Literature Review 4.3. Modelling the natural resource extraction and capital investment relationship 4.4. Empirical Model 4.5. Data 4.6. Data Reliability 4.7. Results 4.8. Postestimation tests and robustness 4.9. Discussion and Conclusion 5. “Stars in their Eyes?” 5.1. Introduction 5.2. Background and Literature 5.2.1. Diversification 5.2.2. Evaluation of Diversification Strategies 5.2.3. Methodologies for Evaluation 5.2.4. Oman and Saudi Arabia Evaluations 5.3. The Predictive Model 5.4. Review of the Economic Plans of Oman and Saudi Arabia 5.4.1. Oman 5.4.2. Saudi Arabia 5.4.3. Reference Condition and Scenarios 5.5. Results and Discussion 5.6. Summary and Conclusion 6. Conclusion 6.1. Background of the dissertation 6.2. Summary 6.3. Limitations and Suggestions for Future Research 6.4. Integrated insights from the dissertation and policy implications 6.4.1. Institutional Effectiveness, Productive Efficiency, Human Capital, Education and R&D 6.4.2. Natural Resources, Oil, Productivity and Investment 6.4.3. Regional Infrastructure, International Trade and Peace 7. References Innovation and Diversification Policies for Natural Resource Rich Countries Mueid Al Raee UNU MERIT, UM MGSoG Supervisors Professor Jo Ritzen Dr. Denis de Crombrugghe 2 3 Contents 1. Introduction .................................................................................................................... 7 Appendix 1-A ................................................................................................................... 16 2. Productivity and Innovation Policy .............................................................................. 19 2.1. Introduction ........................................................................................................... 20 2.2. Innovation policies and the path towards successful innovation ............................ 24 2.3. Identification Strategy ........................................................................................... 29 2.4. Data ...................................................................................................................... 32 2.5. Results ................................................................................................................... 36 2.5.1. Global ............................................................................................................. 36 2.5.2. Arabian Gulf countries - A special case? ........................................................ 42 2.6. Conclusions and Discussion ................................................................................... 45 Appendix 2-A ................................................................................................................... 49 Appendix 2-B ................................................................................................................... 50 3. Policy and Economy in the GCC .................................................................................. 53 3.1. Introduction ........................................................................................................... 55 3.2. Perspectives on innovation .................................................................................... 59 3.2.1. General ........................................................................................................... 59 3.2.2. The literature on GCC countries .................................................................... 61 3.3. The Case of GCC – Policies and Enablers ............................................................ 68 3.3.1. Section Summary ............................................................................................ 68 3.3.2. Development of education systems ................................................................. 71 3.3.3. Literacy, primary education, secondary education, reforms and performance 72 3.3.4. Tertiary education and vocational education ................................................. 77 3.3.5. R&D ............................................................................................................... 80 4 3.3.6. Business and Entrepreneurship....................................................................... 85 3.3.7. Governance and Infrastructure ....................................................................... 87 3.4. The Outputs of GCC – Indicators of Innovation and Diversification .................... 89 3.4.1. Section Summary ............................................................................................ 89 3.4.2. Patents, Trademarks and Industrial designs .................................................. 91 3.4.3. Non-traditional sector - share in the economy and labour productivity ......... 94 3.5. Connecting Policies, Enablers and Outcomes ........................................................ 96 3.6. Summary, Discussion and Conclusion .................................................................... 98 Appendix 3-A ................................................................................................................. 103 4. Natural Resource Abundance: No Evidence of an Oil Curse ...................................... 107 4.1. Introduction ......................................................................................................... 109 4.2. Literature Review ................................................................................................ 110 4.3. Modelling the natural resource extraction and capital investment relationship ... 113 4.4. Empirical Model .................................................................................................. 113 4.5. Data .................................................................................................................... 121 4.6. Data Reliability ................................................................................................... 124 4.7. Results ................................................................................................................. 127 4.8. Postestimation tests and robustness .................................................................... 133 4.9. Discussion and Conclusion ................................................................................... 134 5. “Stars in their Eyes?” .................................................................................................. 137 5.1. Introduction ......................................................................................................... 138 5.2. Background and Literature .................................................................................. 140 5.2.1. Diversification .............................................................................................. 140 5.2.2. Evaluation of Diversification Strategies ........................................................ 140 5.2.3. Methodologies for Evaluation ....................................................................... 143 5 5.2.4. Oman and Saudi Arabia Evaluations ........................................................... 144 5.3. The Predictive Model .......................................................................................... 146 5.4. Review of the Economic Plans of Oman and Saudi Arabia ................................. 147 5.4.1. Oman ........................................................................................................... 150 5.4.2. Saudi Arabia ................................................................................................ 151 5.4.3. Reference Condition and Scenarios ............................................................... 152 5.5. Results and Discussion ........................................................................................ 154 5.6. Summary and Conclusion .................................................................................... 162 6. Conclusion .................................................................................................................. 165 6.1. Background of the dissertation ............................................................................ 165 6.2. Summary ............................................................................................................. 166 6.3. Limitations and Suggestions for Future Research ................................................ 171 6.4. Integrated insights from the dissertation and policy implications ........................ 174 6.4.1. Institutional Effectiveness, Productive Efficiency, Human Capital, Education and R&D .................................................................................................................... 174 6.4.2. Natural Resources, Oil, Productivity and Investment .................................. 176 6.4.3. Regional Infrastructure, International Trade and Peace .............................. 178 7. References ................................................................................................................... 181 6 7 1. Introduction A challenge that many countries face today is the sustenance of their economic growth in the face of increasing reliance on natural resources. The number of natural resource-driven economies has increased from 58 in 1995 to 82 in 2017. 1 Among these countries, 57% were low- and lower-middle-income countries, while only 13% were high-income countries. The natural resource-driven countries were home to more than two-thirds of all people living in extreme poverty. If low-income natural resource-driven economies engaged in sound policy for effective and efficient utilisation of natural resources, aimed at broad economic development, it is expected that almost half of the world’s poor could be lifted out of poverty by 2030. 2 This number is more than the number of poor people lifted out of poverty due to China’s rapid economic development from 1996 to 2015. In the face of the fast-evolving global demand for natural resources, diversification of the economy offers a path for economic development in low- and middle-income natural resource-driven economies and for sustaining economic growth in the high-income ones. The central aim of this dissertation is to examine the challenge faced by natural resource- driven countries, in particular, the countries in the Arabian Peninsula, to diversify their economies. We investigate what policies can help stimulate innovation and diversification in natural resource-driven economies to ensure sustained development. The research carried out in this dissertation draws upon the evidence of the policies for development in the global context. This part of the dissertation is complemented by research on the state of 1 Natural resource-driven economies are defined as those that qualify under at least one of the criteria: 1. Natural resource rents are higher than 10% of the economic output of the country, and/or, 2. Natural resource rents amount to more than 20% of the fiscal revenue of the country, and/or, 3. Natural resource rents represent more than 20% of the total exports of the country. The natural resource-driven classification is used by various sources (IMF, 2012; Dobbs, et al., 2013; Addison & Roe, 2018). The 2017 classification of natural resource-driven countries is based on the author’s calculations. More details are presented in Appendix 1-A, Table 1.1. 2 See Dobbs, et al. (2013, pp. 5, 135-136) for the methodology used in determining the estimated reduction of poverty due to the effective and efficient utilisation of natural resource rents. The poverty line of 1.90 USD 2011 PPP a day is used to determine the number of people living under extreme poverty. 8 diversification and its relation to the policy measures undertaken in the Gulf Cooperation Council (GCC) 3 and its member countries. The complexity of the problem at hand can be understood by bringing together multiple strands of academic literature. Some of the distinct yet interconnected areas that this dissertation relies upon include economics and policy of innovation, growth economics, studies of natural resource-driven economies and economic diversification. A review of the innovation policy literature at the outset of the research for this dissertation identified a need for more empirical and theoretical research in the area. 4 Addressing this need is expected to push the field further in its current transition from pre-paradigmatic phase towards a defined set of theories of innovation policy. It was observed that research from the broad innovation policy perspective was limited, and more attention was needed for the interaction of policy instruments with systemic conditions and institutional settings.5 Lastly and most importantly, discussions of suitable methodologies were necessary to advance the field and facilitate research that can close the gaps in the innovation policy literature mentioned earlier. One of the propositions has been to understand the policy realm as having two parallel spaces. The first space covers macro-level, systemic and institutional enablers and determinants of innovation, such as governance, education, research and development investment, business environment, fiscal policies, and infrastructure. The second space encompasses the dynamics of the innovation process itself such as knowledge and skills required, the creation of products and services, intellectual property protection, incentives for innovation, production factors, value chains and feedback. The enablers and determinants in 3 The Gulf Cooperation Council (GCC) is the colloquial term used to refer to the Cooperation Council of the Arab States of the Gulf (GCC). We use the abbreviation GCC to refer to the member countries as of 2017 – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates. These are synonymously referred to as the countries of the Arabian Gulf, countries of the Arabian Peninsula, countries of the Persian Gulf and Arab countries of the Gulf. Other countries on the Arabian/Persian Gulf such as Iran and Iraq, and countries in accession talks such as Yemen, Jordan and Morocco are not included. 4 For more information on the evolution of the innovation policy literature landscape refer to Radosevic (2012), Fagerberg (2016) and Borras and Edquist (2019). 5 According to Lundvall (2007) systems of innovation in a narrow sense “leave significant elements of innovation- based economic performance unexplained”. In the “broad” sense the core knowledge-producing and disseminating institutions (such as educational institutes and research units) are embedded in a wider socio-economic system and the relative success of innovation policies is a function of influences and linkages beyond these core institutions (Freeman, 2002). 9 both these spaces form the focus of the innovation policy studies. Therefore, it is frequently recommended to carry out more research that considers the collective effect of these determinants of innovation (Freeman, 2002; Lundvall, 2007; Fagerberg, 2016). The literature of systems of innovation from a growth theory perspective has been criticised for having a narrow conceptual scope. 6 Additionally, the policy relevance of the results in this line of research varies depending on the measures of growth and innovation used.7 The research for innovation policy through the growth theory lens has been carried out using various productivity measures, most prominently Multi-Factor Productivity (MFP). However, its use is challenged because of the imperfect imputation for developing countries and a lack of direct policy implications that can be derived from research using MFP. Other measures also face challenges and their use is context and target-driven. Given this, the use of labour productivity-based measures (such as labour productivity in the modern sector) 8 generally increases the policy relevance of the results and widens the geographical and conceptual scope of the innovation policy studies. In addition to this, innovation studies have also been criticized as being too centred on rich countries. This critique is made plain in the question, “whether innovation systems and policies are only for the rich” (Perez, 2013, p. 90) 9. The substantial focus of innovation studies on innovation in the developed or rich countries has partially contributed to the lack of understanding about the determinants of innovation in a broader country income level context. 6 For more details on the literature of systems of innovation from growth theory perspective see Fagerberg, et al. (2013) and (Soete, et al., 2010). 7 The relationship between productivity and innovation, and a detailed discussion on the pros and cons of the alternative measures of innovation are presented in Mohnen and Hall (2013). 8 In this dissertation the terms non-traditional sector and modern sector exclude the natural resource and agricultural sectors, unless otherwise mentioned. The sectors where the productive efficiency is a function of the knowledge and skills of the labour force is often called the productive sector or modern sector. In the empirical literature, the modern sector has varyingly been defined to exclude either the natural resource sector, or the agricultural sector, or both sectors. This usage is derived from the assumption that the natural resource and agricultural sectors are predominantly non-productive sectors. 9 The question is posed by the editors of “Innovation Studies: Evolution and Future Challenges” Jan Fagerberg, Ben R. Martin and Esben Sloth Andersen to Carlota Perez as a discussion point for her contribution in the book (Fagerberg, et al., 2013). 10 One of the main limitations of the growth literature, in general, is to ignore the importance of natural resources in the economy (Sachs & Warner, 1995; Auty, 2007). The same limitation is also observed in the literature on innovation policy from the growth perspective. Additionally, the innovation policy mix for countries that are close to the technological frontier and for those that are far behind in the catching-up process is likely to be different (Szirmai, et al., 2011; Aghion, et al., 2014). Countries with natural resources form a unique subset of world economies. Among other reasons, the uniqueness originates in the use of natural resource revenues as a facilitator of broader economic growth. Thus, investigations that focus on the policy mix that has been used and can be used in these countries for catch- up, diversification and stimulation of innovation are warranted. The natural resource economics and policy literature for a long time has been focussed on the so-called natural resource curse and the search for its determinants. However, some argue that the outcome of the mismanagement of national revenues for a natural resource-driven economy does not differ from the consequence of the mismanagement of revenues in countries that are not dependent on natural resource revenues (Maloney, 2002). Even without the precise assignment of blame for the economic woes of the countries rich in natural resources, the literature appears to lead towards a probable solution. 10 The general inference is that the diversification of natural resource-driven economies is key for ensuring their long-term economic development. This concept is not dissimilar from the more generalised perspective that countries that provide more diverse products and services are more likely to have higher output growth stability (Krishnaa & Levchenko, 2013; Content & Frenken, 2016). Given the reservations of the neo-classical literature in considering the role of natural resources as a contributor to economic growth, the theoretical contributions in this area have been limited. Also, the determinants of economic growth in developed countries have frequently been used to try to explain the same in low income economies without contextualising the research. Diversification policy has long been a part of the national discourse of natural resource-driven economies. However, policy instruments in some countries 10 For more information on the natural resource economics literature and the natural resource curse debate see Badeeb, et al. (2017) and Papyrakis (2017). 11 have been limited to monetary and fiscal policy instruments, and ineptly chosen targets for economic policy programs make it hard to evaluate the attainment of a positive net stimulus or the lack thereof (World Bank Institue, 2010). Such complications in policy design, evaluation and implementation are symptomatic of the absence of research-based innovation and diversification policymaking. There is a lack of comprehensive theoretical background that adequately applies to a broad set of economies including natural resource-driven and developing economies. This limitation indicates that the complacency of the academics and economic policy consultants is as likely to be blamed as that of the national governments and policymakers for the failure of many countries to have an effective innovation policy. Among the natural resource-driven economies, the GCC countries have attracted the general curiosity of economists. However, the scientific publications aimed at trying to understand the economic structure, systems of innovation and the state of diversification in the GCC countries have been limited. The most frequently given reason is the lack of data. In view of the academic context described in the preceding paragraphs, we have an opportunity to look for the key where it is dark 11. This dissertation presents a conceptual framework that can be used to analyse the interplay of the determinants of innovation and diversification. As a first step, this is used in an empirical exercise to discover the effective relationship between selected determinants and labour productivity growth in the modern sector. A discrepancy among the regions is observed and explored further by mapping the enablers and inputs of innovation and diversification for three GCC countries. The relationship of these determinants is discussed in light of the innovation and labour productivity outputs. These undertakings support theorising in the area of innovation policy studies and ensure that such theorising covers the economic reality and systemic conditions of all countries regardless of their income levels. 11. The streetlight effect, or the drunkard's search principle, is a type of observational bias that occurs when people only search for something where it is easiest to look. A parable featuring the Seljuk Sufi mystic Nasrudin Hodja is considered as the earliest form of the story (Shah, 1964, p. 70). The parable relates him as looking for his key outside his house because there was more light outside, while he knew that the key was lost inside the house. It was popularised in social sciences by Abraham Kaplan in his book “The Conduct of Inquiry: Methodology for Behavioural Science” (Kaplan, 1964). 12 The introduction of the natural resource sector in this dissertation as an important contributor to economic growth as well as to the development of the modern sector follows the same logic as mentioned above. The aim is to ensure that the research contribution has broad academic and policy applicability in terms of economic structure and country income classification. The research places a special emphasis on the GCC countries to illustrate the thesis that important economic questions can be addressed even in a state of data scarcity. Economists should never be wary of research on a seldom studied region. Rather, the opposite should be the norm, and, purposefully and promisingly, it has often been so for the last ten years. The frameworks, tools and models developed in this dissertation can guide policymakers to not only fix realistic targets for economic inputs and outputs but also embed an ex ante and ex post evaluation of the achievements into their programs. Accordingly, the use of an empirical model is presented to highlight the importance of research-based policymaking. The findings in the various chapters of this dissertation illustrate that all the determinants of the innovation system need to be working well in order to stimulate innovation and productivity growth in the modern sector. The highlights, along with the structure of the dissertation, are summarised in the following. Chapter 2, titled “Productivity and Innovation Policy” presents a conceptual framework of innovation policy and its empirical application in developed and developing countries. The condition of the GCC region in terms of labour productivity growth in the modern sector is explored within the context of this model. The results of Chapter 2 underscore the importance of investment into enablers of innovation such as tertiary education, and research and development expenditures. It is observed that not only the level of investment matters but also the effectiveness of the system in which the policy is executed. This observation highlights the importance of improving governance and investing in the development of institutions. Chapter 2 also reveals that the modern sector in the GCC region is performing relatively poorly compared to other regions in terms of labour productivity growth in the modern sector. This is followed by a comprehensive review and comparative analysis of the broad innovation system in three of the six GCC countries in Chapter 3, titled “Policy and Economy in the 13 GCC” in the form of an eclectic comparative case-study. We present a snapshot of the development of enablers and policies for diversification and innovation in Oman, Saudi Arabia and the United Arab Emirates and highlight the limitations of their systems. The low innovation and diversification output in these countries is a consequence of the observed limitations. This observation leads to the notion that the lowest-performing policy area limits the system performance and that it is essential to ensure the robust performance of all the determinants of the system. Chapter 4 of the dissertation, “Natural Resource Abundance: No Evidence of an Oil Curse”, outlines a theoretical model in order to examine the possibility of using natural resource revenues to fund fixed capital investments and develop the modern sector. It also examines empirically, to what extent oil wealth has been used for diversification by the six GCC countries. Chapter 4 shows that the lower performance in the GCC region in terms of labour productivity growth in the modern sector is not due to natural resource rents. The GCC countries have been rather successful in investing their natural resource revenues into fixed capital. The chapter highlights that natural resource rents can be used for the development of the modern sector. This is followed by an analysis of the stated economic diversification policies of Oman and Saudi Arabia contrasted against the predicted outcomes in Chapter 5, titled “Stars in their Eyes?”. It is an ex ante evaluation of national policy programmes based on the empirical model developed in Chapter 2. The challenges and prospects for meeting the desired and declared diversification targets are also discussed. The results of Chapter 5 project that Oman and Saudi Arabia are not likely to meet their 2030 targets for diversification. A research- backed discussion of the policy limitations and possible pathways to meet future targets is thus presented. It is observed that the several enablers that may be improved through investments must all be understood in the national and regional context in order to achieve successful diversification and improvements in the innovation system of the country. In the final Chapter 6, a short review of the background and results of the dissertation are presented. It summarises the chapters of the dissertation, formulates academic research 14 recommendations, acknowledges some of the limitations of this dissertation, proposes an integrated view of the dissertation and outlines the policy implications of the research carried out. The synopsis of this dissertation is that there is no natural resource curse and that there is no one policy or area of investment that drives growth and productivity in the modern sector. A broad and holistic approach to academic enquiry in innovation policy and productivity growth in the modern sector is justified and recommended. The policy relevance of this dissertation, already discussed in the subsection “Integrated insights from the dissertation and policy implications,” is exposed a second time in the “Valorisation Addendum”. The successes and limitations in the policy actions of the GCC countries brought to light in this dissertation support a broad approach to policymaking for diversification. The GCC countries started using their oil revenues for economic and social development while in a position of relative poverty in comparison to the rest of the world (Khalaf & Hammoud, 1987; Pamuk, 2006). 12 They invested in human capital, fixed capital and the improvement of the standards of living of their population. As a result, the GCC countries successfully eradicated extreme poverty in their countries, and by 2017 all the six GCC countries were included in the list of “very high human development countries” based on their Human Development Index (HDI) (UNDP, 2018; GCC-STAT, 2019). Along with using their natural resources for human development, the GCC countries have attempted to stimulate diversification of their economies and ensure sustained economic development. They have been successful to varying degrees and are undertaking policy actions to deepen diversification aimed at securing and increasing the prosperity gains of the last fifty years. The policy lessons from the GCC countries are critical for other natural resource-driven economies. The empirical evidence from this dissertation, on innovation policy, diversification and natural resource-based development, helps illustrate the importance of innovation and diversification policy research. The results demonstrate that low- and low-middle-income natural resource-driven countries 12 By 1970s almost all GCC countries had control over a substantial portion of the oil revenue generated from oil extraction in their countries. 15 can utilise their natural resources effectively and efficiently and aim their policies at diversified production and broad economic development. Such policymaking is expected to support improvements in the state of their human development as it has been accomplished by the GCC countries, and also help eradicate poverty in all forms. 16 Appendix 1-A Table 1.1 – Classification of countries as natural resource-driven economies Natural resource-driven economies are defined as those that qualify under at least one of the criteria: Export: Natural resource rents represent more than 20% of the total exports of the country. Revenue: Natural resource rents amount to more than 20% of the fiscal revenue of the country. Output: Natural resource rents are higher than 10% of the economic output of the country. Based on Addison and Roe’s (2018) definition in the book “Extractive Industries” which is adapted from McKinsey & Company’s report “Reversing the curse” (Dobbs, et al., 2013). The resource dependence classification, country income levels and poverty dynamics were inspired by the mentioned references and are based on the author’s calculations using the World Bank (2019) data published under Creative Commons Attribution 4.0 International License (CC-BY 4.0). Country Name Exports Revenue Output World Bank Income Level Algeria ● ● ● Upper Middle Income Angola ● ● ● Upper Middle Income Armenia ● Upper Middle Income Australia ● ● High Income Azerbaijan ● ● ● Upper Middle Income Bahrain ● High Income Benin ● ● Low Income Bhutan ● Upper Middle Income Bolivia ● Upper Middle Income Brazil ● Upper Middle Income Brunei Darussalam ● ● ● High Income Burkina Faso ● ● ● Low Income Burundi ● Low Income Cameroon ● ● Lower Middle Income Central African Republic ● ● ● Low Income Chad ● ● ● Low Income Chile ● ● ● High Income Colombia ● ● Upper Middle Income Comoros ● Lower Middle Income Congo, Dem. Rep. ● ● ● Low Income Congo, Rep. ● ● ● Lower Middle Income Cote d'Ivoire ● Lower Middle Income Ecuador ● ● Upper Middle Income Egypt, Arab Rep. ● ● Lower Middle Income Equatorial Guinea ● ● ● Upper Middle Income 17 Country Name Exports Revenue Output World Bank Income Level Eritrea ● Low Income Ethiopia ● ● ● Low Income Gabon ● ● ● Upper Middle Income Gambia, The ● Low Income Ghana ● ● ● Lower Middle Income Guinea ● ● ● Low Income Guinea-Bissau ● ● ● Low Income Guyana ● ● ● Upper Middle Income Indonesia ● ● Upper Middle Income Iran, Islamic Rep. ● ● ● Upper Middle Income Iraq ● ● ● Upper Middle Income Kazakhstan ● ● ● Upper Middle Income Korea, Dem. People’s Rep. ● Low Income Kuwait ● ● ● High Income Kyrgyz Republic ● ● Lower Middle Income Lao PDR ● ● Lower Middle Income Liberia ● ● Low Income Libya ● ● ● Upper Middle Income Madagascar ● ● ● Low Income Malawi ● ● Low Income Malaysia ● Upper Middle Income Mali ● ● ● Low Income Mauritania ● ● ● Lower Middle Income Mongolia ● ● ● Upper Middle Income Mozambique ● ● ● Low Income Myanmar ● ● Lower Middle Income New Caledonia ● High Income Niger ● ● Low Income Nigeria ● ● Lower Middle Income Oman ● ● ● High Income Papua New Guinea ● ● ● Lower Middle Income Peru ● ● Upper Middle Income Qatar ● ● ● High Income 18 Country Name Exports Revenue Output World Bank Income Level Russian Federation ● ● ● Upper Middle Income Rwanda ● ● Low Income Saudi Arabia ● ● ● High Income Senegal ● ● Lower Middle Income Sierra Leone ● ● Low Income Solomon Islands ● ● ● Lower Middle Income Somalia ● ● ● Low Income South Africa ● ● Upper Middle Income Sudan ● ● Lower Middle Income Suriname ● ● ● Upper Middle Income Syrian Arab Republic ● ● Low Income Tajikistan ● Low Income Tanzania ● ● Low Income Timor-Leste ● ● ● Lower Middle Income Togo ● ● ● Low Income Trinidad and Tobago ● ● ● High Income Turkmenistan ● ● ● Upper Middle Income Uganda ● ● ● Low Income United Arab Emirates ● ● ● High Income Uzbekistan ● ● ● Lower Middle Income Venezuela, RB ● ● ● Low Income Yemen, Rep. ● ● Low Income Zambia ● ● ● Lower Middle Income Zimbabwe ● ● Lower Middle Income 19 2. Productivity and Innovation Policy Education, Research & Development, Governance, Business, and Productivity Abstract In this chapter, we examine the relationship between “innovation policy” and labour productivity growth in non-traditional sectors, for a cross-section of more than 95 developed and developing countries. We consider that labour productivity growth in non-traditional sectors is in part explained by innovation and catch-up. In developing countries, catch-up is a substantial contributor to productivity increases in addition to new-to-the-world innovations. The ability to catch-up is considered to be dependent on the absorptive capacities of the countries. We term the policies that contribute to improvements in the absorptive capacity as innovation policies. In this chapter, we include investments in tertiary education as a percentage of gross domestic product (GDP), investments in research and development (R&D) as a percentage of GDP, the freedom in the business environment, as well as overall government effectiveness. Our results confirm the convergence of non-traditional sector labour productivity amongst the countries. We could show a significant positive effect of the interaction between, government effectiveness, and, the government expenditures in tertiary education as a percentage of GDP, on labour productivity growth in non-traditional sectors. Also, for developing countries, a positive and significant relationship between the growth variable and effective R&D expenditures was observed. We could not uncover a relationship between other policies considered in this chapter and labour productivity growth in non- traditional sectors. Non-traditional sector labour productivity growth in the oil-rich Arabian Gulf countries was observed to be consistently slower than western countries. We propose that there is the likelihood of higher oil prices crowding-out innovation in oil-rich countries while stimulating innovation in oil-importing countries. Keywords: Innovation policy, labour productivity, catch-up, structural change, government effectiveness, developing countries, Arabian Gulf countries. JEL Classification: O2, O3, O38, O43, O47 20 2.1. Introduction In this chapter, we analyse how individual innovation policies and their interactions influence labour productivity in non-traditional sectors and innovation globally (both in developed as well as in developing countries). We consider the different strategies that may be required to innovate under various conditions of development. We also explore the relationship between labour productivity growth and innovation policies in Arabian Gulf countries, that are characterised by a high share of natural resource rents in the economy. Knowledge, technological change, and innovation have been introduced as drivers of growth in the growth economics literature as it has moved beyond only considering capital and labour as sources of growth 13. It discusses healthy institutions as necessary for technological change and points towards innovation policy to nurture the institutions that promote knowledge production and technological progress. The lumping together of the factors that contribute to human capital, physical capital, and institutional capabilities has been considered in the 1960s and 1970s as a common deficit in the literature. The need for a more in-depth enquiry of complementarities in policies that affect economic activities, capabilities and institutional arrangements has been emphasized by Easterly & Levine (2001), Freeman (2002), Aghion et al. (2009) and notably the winner of the 2018 Nobel Memorial Prize in Economics, Paul Romer (1994). As such, “innovation policy” including, education policy, R&D policy, business policy, and governance is considered in this chapter. The role of education and R&D policy for innovation and modern sector labour productivity growth has been explored in the context of developed and developing countries. This has promoted the need for enquiries on whether higher education and R&D expenditures have dissimilar returns for developed and developing countries (Krueger & Lindahl, 2001; Keller, 2006; Aghion & Durlauf, 2009). An important insight is that countries could be more or less 13 Solow’s works, and studies by Denison, showed that something other than labour and capital was responsible for increasing growth rates in the US (Solow, 1957; Denison, 1963). Romer (1986) incorporated technology as an endogenous factor in constructing a model of increasing returns of technology and knowledge for long-run growth. 21 efficient in the translation of innovation policies to productivity growth and innovation (Griffith, et al., 2004; Ritzen & Soete, 2011; Loukil, 2014). Therefore, we consider the effectiveness of the government in analysing the relationship between labour productivity growth in non-traditional sectors and innovation policy. Considering that innovation-based growth is less likely to affect traditional industries (Becheikh, et al., 2006), we consider the response of innovation policies, for both developed and developing countries, to exhibit in the non-traditional sector labour productivity growth. That is labour productivity growth exclusive of natural resource rents and agricultural value added. Productivity can increase in three ways: through economies of scale, through the utilisation of unused capacity and by innovation. Innovation encompasses creating new products and services, adopting technology that is not available anywhere else, adapting technology that has been available elsewhere and as such moving towards the best practice, improving management techniques, and marketing novelty. Sectors where capital investment is more likely to increase economies of scale in the short-run include traditional natural resource extraction and agricultural sectors (Behrens, et al., 2007; Kislev & Peterson, 1996). As such, labour productivity excluding these traditional sectors is expected to be more closely associated with innovation. We term this part of the economy as the modern sector (interchangeably, non-traditional sector). In the modern sector increasing output by utilising the same input generally requires improving management techniques (for enhancing production), better supply chain (for delivering increased inputs and production outputs), and marketing innovation (for ensuring that production is consumed in the market). Productivity is defined as the index of output over the index of input. Labour productivity is one of the ways to measure productivity. It is the total output over the total amount of labour employed. As such, it does not account for inputs of capital in the denominator. Another approximate measure is the Total Factor Productivity also termed as Multi-Factor Productivity (MFP). It is measured as the Solow residual or using the output of the economy divided by weighted inputs of capital investment and labour. The standard weighting of 0.7 for labour and 0.3 for capital is often employed. A concern with the output measure used for 22 the MFP is that quality improvement is not accounted for within this indicator as price changes do not necessarily reflect the changes in quality. Using output and input deflator is also inaccurate as we may be undervaluing output deflators or overvaluing input deflators and by doing this, we might be reflecting price effects and not necessarily innovation effects. In their work “Innovation and Productivity: An Update”, Mohnen and Hall (2013) provide a brief review of the empirical literature and conclude that innovation leads to better productivity performance. They note that innovation affects productivity increases through both real output and the price. Innovators take advantage of the two effects in order to increase their profitability. Firstly, they are able to sell their products or services at above competitive prices, as in monopolistic competition at least for some time. Secondly, the product or service itself is higher quality and delivers higher value to the consumer – this is called the real innovation output. In terms of the limitation of the productivity measure, Mohnen and Hall find that the innovation price effect and the innovation product effect (real output) are hard to disentangle in the absence of good individual price measures. It is important to note that in this chapter, we are interested in capturing catch-up effects as well as new-to-the-world innovation effects. The innovation price effect and innovation product effect are both important in this context and the disentanglement is not necessitated. Product, process, organisational and marketing innovations all improve productivity through either or both of these effects, in other words, innovation leads to better output per employee performance. Innovation counts are widely used to represent innovation, specifically to account for the innovation product effect. However, innovation counts are not without limitations. In fact, the reason that researchers are unable to find a stronger association between productivity and innovation may be related to the use of imperfect measures to represent innovation (Mohnen & Hall, 2013). It is thus suggested that recording innovation counts in a continuous measure including quality of innovation instead of a binary “yes or no” format would lead to more meaningful and robust estimates on the relationship between productivity and innovation. Patents while promising are controversial as they represent only a small part of innovation. 23 Some patents may never be converted to finished and marketed products or services. At the same time, some innovation may never be patented. Also, new-to-the-country and new-to- the-region innovations may not be reflected in patent data. This type of innovation is vital in developing countries and accounts for the majority of increases in labour productivity through innovation. In this chapter, we are interested in increases in labour productivity associated with innovation policy. As discussed above, we ensure that labour productivity is closely related to innovation by using labour productivity in the modern sector. The issue of selecting the most appropriate measure is however complicated further when considering the availability of data for a broad set of countries and, in particular, for the Arab countries of the Gulf that are the central focus of this dissertation. One commonly quoted disadvantage of productivity data is that of varying recording standards among countries. We find that labour productivity data is most widely available across countries and time. In order to account for the probable variation in recording standards, we use standardised data from a reliable source. Additionally, it is also important for us to use a measure that has direct policy implications. In the construction of MFP, which is derived as the residual of the Cobb-Douglas equation, the unit of MFP does not have a simple economic interpretation and appears to be a modelling artefact. The use of labour productivity in the modern sector ensures the ability to derive simple economic interpretation of the results that can inform policymakers on innovation as well as diversification-concerns. The catch-up process in developing countries is dependent on absorptive capacity. The countries as such can induce catch-up and increases in labour productivity by improving education, R&D, business environment and governance. Innovation policy is a broad term and successful innovation is related to many systemic processes working in sync. As such, we acknowledge that defining the ideal combination of institutions and policies that are important for innovation is a complex task. We consider innovation policies as those directed at improving the innovative and absorptive capacity of the countries. In this chapter innovation policy is characterised by the interaction of expenditures on education with the 24 effectiveness of government, the interaction of expenditures on R&D with the effectiveness of government and business policy. Our identification strategy comprises five-year labour productivity growth rates regressed as a function of initial labour productivity, relevant lagged innovation policy variables, control variables and dummy variables. The literature on innovation, growth and productivity, innovation policies, education, R&D, business, governance and the associated relationships is discussed in Section 2.2. Our identification strategy is drawn out in Section 2.3. The data are presented in Section 2.4. The results related to the effect of innovation policies in the global context, as well as for developed and developing countries are presented in Section 2.5. Finally, we discuss the results, their policy implications, and present our concluding remarks in Section 2.6. 2.2. Innovation policies and the path towards successful innovation The innovation literature is distributed between “narrow” and “broad” focus on innovation policies. In the “narrow” sense, only formal R&D systems and organizations systematically active in knowledge generation and diffusion are the focus. An example of the application of the systems of innovation framework in the former sense is the World Bank Knowledge Assessment Methodology (Chen & Dahlman, 2005). However, systems of innovation in a narrow sense “leave significant elements of innovation-based economic performance unexplained” (Lundvall, 2007). In the “broad” sense the core knowledge-producing and disseminating institutions are embedded in a wider socio-economic system, and the relative success of innovation policies is a function of influences and linkages beyond these core institutions (Freeman, 2002; Soete, et al., 2010). Among the works that discuss new-to-the- world innovation in the latter sense, Furman, Porter and Stern (2002) integrate ideas-driven growth theory, microeconomics-based models of national competitiveness and industrial clusters theory. They consider R&D manpower, knowledge and technology base as important sources of innovation. Archibugi & Coco (2004) define innovation system through patents, publications, ICT, electricity consumption, and education. To formulate a comprehensive narrative, we draw from literature sources focused on individual policy variables, governance, 25 policy interaction, growth, labour productivity, and innovation. This conceptualisation is carried out with the frame of reference that growth is primarily driven by technological change and innovation. The three main factors of growth are generally considered to include MFP, labour input, physical capital and human capital. Technology growth and efficiency are considered as the sub-sections of MFP. Efficiency improvement, in the very least, need management and process innovation. Together we term these components as innovation. This is the origin of the consideration that innovation and technological change is the primary contributor to economic growth (Solow, 1957; Denison, 1963; Romer, 1986). At the same time, Nelson and Winter’s (1982) work on the evolutionary approach views the free-market economic structure as continuously evolving with emphasis on the influence of institutions and government policies on economic activity. Education is one of the policy variables that affect innovation. Aghion et. al (2009) set out to estimate causality of the effect of education on growth, using actual measures of investment in education. They question the adequacy of using lagged spending – in their previous work (Vandenbussche, et al., 2004) – as an instrument to overcomes biases caused by omitted variables such as institutions, especially in the case of small low variation data. They show that there are positive effects of exogenous increases in education expenditure related to four years’ tertiary education in the states of the United States of America (US) that are close to the world frontier (Aghion, et al., 2009). Krueger and Lindahl (2001) find societal returns to schooling in terms of increased growth in cross-country analysis; the relationship is statistically significant and positively associated with subsequent growth for countries with the lowest level of initial education. Faster growing countries in Asia have had higher expenditures on primary education. Keller (2006) does not obtain positive significant results consistently for the effect of such investments on growth. However, it is suggested in the paper that inefficiencies in resource allocation of secondary and tertiary education expenditures may be the reason behind. In other studies, government expenditures on education relate positively to growth in developing countries (Bose, et al., 2007). Also, the inclusion of other policy variables in the studies, such 26 as openness, public spending, and health variables results in a lower estimated impact of education on growth (Benos & Zotou, 2014). From these studies, we conclude that education should be considered as a part of “broad” policies for innovation-based growth. Loayza et al. (2005) find that regulatory burden reduces growth. However, a higher quality of institutional framework leads to the negative effects of excessive regulation on growth to be lessened. In a simple model Djankov et al. (2006) observe the effect of business regulations as represented by the doing business indicators while considering the effect of initial level of growth, and control variables and other determinants of growth that include corruption, law and order, the political system, primary and secondary school enrolment, and civil conflict. They find that going from the worst to the best quartile of business regulation shows a 2.3% increase in annual growth rate. They also observe that the effects of improvement in primary and secondary education from worse to better quartiles of policy or output are significantly lower than the effects of business regulation on growth rate. Hanusch (2012) suggests that regulations related to credit, contract enforcement, costs, time, starting a business, registering property, and protection of investors within the realm of business policies are statistically significantly related to economic growth. Griffith and team (2004) find that R&D, as represented by BERD, is statistically and economically important in the catch-up process as well as for stimulating innovation directly and suggest that the social rate of return of R&D has been underestimated in the literature as many studies only focus on the US. A look into cross-country labour productivity differences due to investment in R&D reveals that R&D investment has a significant positive impact on productivity (Lichtenberg, 1993). Nadiri and Kim (1996) find rates of returns of domestic R&D expenditures to be in the range of 14 to 16% and adding the effect of spill- overs of international R&D spending for six (6) advanced economies showed the returns to be 23 to 26% varying amongst the countries. Hall, Mairesse, and Mohnen (2010) in their review of the econometric literature measuring the private and social returns to R&D find that the literature identifies private returns to R&D as strongly positive, social returns to be greater than private returns and public-funded R&D to be less productive in terms of private 27 returns. In many research avenues, the incentives to invest in R&D is determined on the basis of private returns and not social returns. It is also observed that despite having higher social returns to R&D investments developing countries are not able to achieve the maximum potential in R&D. This may be due to inappropriate or inadequate social policies (Griffith, et al., 2004). Jalilian, Kirkpatrick and Parker (2007) find that there is a strong causal link between government regulation, regulatory quality indices, and economic performance. Other cross- sectional studies also report causal effects of governance on long-run income per capita, using instrumental variables (Kaufmann & Kraay, 2002). Also, the mechanism behind this causal link has been examined and it has been pointed out that one path through which government effectiveness improves economic performance is by creating a better investment environment (Kirkpatrick, et al., 2006). It is likely that government effectiveness translates into high economic growth not only through the path of providing a good investment environment but also by creating a good environment for innovation policies to be effective. The common theme that emerges from the literature is that policies work in coherence with each other and have a combined and complementary effect on growth and productivity. The translation of policy to increased labour productivity growth must go through the governments’ ability to effectively convert inputs of policy into innovative products and services, as well as innovative management, production, and marketing practices. In this respect, non-traditional sector labour productivity is closely associated with innovation, across developed as well as developing countries. Most of the literature referenced here is aimed at determining the relationship between various policies and growth measures such as GDP, Income per Capita and Labour Productivity. In this chapter, we try to study the relationship between selected policies and innovation proxied by labour productivity growth in the modern sector. As such we are delving deeper into the relationship between one of the proximate causes of growth – technological change or innovation and some of the fundamental causes of growth – the policies and institutional settings that drive growth. In this sense in the literature we have discussed is relevant in two ways – one, in pointing out the relationship of 28 fundamental causes of growth to growth itself; and the second, by pointing out that there is a gap in trying to understand the link between the fundamental and proximate causes of growth. Figure 2.1 – Innovation Policy Framework Conditions Figure 2.1 above represents our interpretation of how the flows of knowledge enable an increase in innovation. The innovation eco-system is thus arranged into conditions, linkages, the firms and the market itself. The increase in innovation is linked to increasing labour productivity through innovative management, design, production, and marketing techniques. The change in the state of these conditions is determined through natural transformation and 29 policy. The education condition is affected by government policies, such as government financing of the tertiary education system, policies determining graduate ratios in science and technology fields, alignment to labour demand from the market, university autonomy, and others. Similarly, research and development conditions are impacted by the government expenditure on research and development, type of research grants, targeted scientific field grants, the competitiveness of grants, intellectual property regime, and private-sector research funding, and so forth. Business conditions are related to industrial policies, competition policy, entrepreneurship policy, taxation policy, financial policy, the health of the financial sector, availability of finance, and market access for firms that create new products or services. Infrastructure conditions include the availability of ICTs, Transport, Energy, Standard- Setting, Metrology, Security, among others. Finally, it is considered that without efficient and effective linkages the production of knowledge, as well as transfer of knowledge for the creation of new products and services, would be hampered. For innovation to thrive in the production space it is important that the innovation environment conditions are healthy, governed by sound policy, with effective linkages across various conditions, the production space and the market for consumption of the innovations. 2.3. Identification Strategy We use the framework in Figure 2.1 above to understand policy factors that promote innovation. Consequently, we explore how individual innovation policies and their interactions influence innovation globally, in developed, and developing countries. As such, labour productivity growth in the non-traditional sector is modelled as a function of innovation policy and the effectiveness of innovation policy. We assume the drivers of innovation to be: the initial level of labour productivity, the interaction of government effectiveness with educational expenditures, the interaction of government effectiveness with research and development and a facilitative business environment. We estimate the relationship with an Ordinary Least Squares regression with exogenous variation in explanatory variables of policy. 30 The response variable is defined as the natural log of the ratio of final to initial labour productivity in non-traditional sectors, where final labour productivity is taken to be five years after the initial measure. We use four years averages starting from the initial year to smoothen out one-off effects for the countries. The explanatory variables are lagged one year and include the natural logarithm of initial labour productivity, interaction of government effectiveness and government expenditures in tertiary education as a percentage of GDP (alternatively called effective tertiary education expenditures in this chapter), interaction of government effectiveness and gross expenditures on R&D as a percentage of GDP (alternatively called effective R&D expenditures in this chapter) and index of economic freedom. Innovation is a medium to long term phenomenon, and innovation policies typically take a long time to bear fruit. Using lagged average variables accommodates for long term nature of innovation and also provides a way to exclude reverse causality. The literature provides evidence that initial level of labour productivity is a determinant of labour productivity growth. As such, we account for the initial level of labour productivity in the estimation equation (Barro, 1991). Also, the initial level of education has an impact on how innovation policies influence the role of tertiary education expenditures on innovation itself (Keller, 2006). Natural resources and agricultural endowments also influence the growth path of a country (Lederman & Maloney, 2007). Finally, a country’s regional situation influences its growth trajectory as well (Moreno & Trehan, 1997). We introduce regional dummies, educational attainment in terms of years of education from primary to tertiary level, natural resource rents as a percent of GDP, and agricultural value added as a percent of GDP as additional control variables. Total natural resource rents as a percent of GDP is defined as the sum of oil rents, natural gas rents, coal rents, mineral rents, and forest rents (World Bank, 2014). Agriculture in Agricultural value added corresponds to International Standard Industrial Classification (ISIC) divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. The estimation equation thus takes the form; 31 Equation 2.1: Δ tN-t1ln(labprod) = α0 + βo • labprod t1 + β1 • goveff t1 • edu t1 + β2 • goveff t1 • r&d t1 + β3 • econfreedom t1 + natresrents t1 + agrirents t1 + eduattain t1 + regional dummies + Table 2.1 – Variable Definitions Variable Definition ΔtN-t1 ln(labprod) Productivity Growth Natural log of the ratio of final to initial labour productivity in non- traditional sectors (alternatively including traditional; natural resource and agriculture sectors – see discussion in the main text) labprod t1 Initial Productivity Natural log of initial labour productivity goveff t1 • edu t1 Effective Tertiary Education Interaction of government effectiveness and government expenditures on tertiary education as a percent of GDP - initial goveff t1 • r&d t1 Effective R&D Interaction of government effectiveness and gross expenditures on research and development as a percent of GDP - initial econfreedom t1 Economic Freedom Index of economic freedom - initial natresrents t1 Natural Resource Rents Natural resource rents as a percentage of GDP - initial agrirents t1 Agricultural Value Added Agricultural value added as a percentage of GDP - initial eduattain t1 Educational Attainment Number of years of schooling from primary to tertiary level - initial Note: The subscript “t1” in Equation 2.1 and the reference “initial” in Table 2.1 specifies the magnitude of the variable during the initial year(s) considered. The subscript “tN” in Equation 2.1 and the reference “final” in Table 2.1 specifies the final year. As such the growth is considered between t1 and tN. In this chapter, this period of growth is 5 years and the policy variables are the average of four years, lagged by one year from the final year for which a five-year growth rate is considered. In addition, we evaluate the same equation for labour productivity growth including natural resource rents and agricultural value added. This helps us identify differences in the influence of innovation policies on innovation-based growth versus mixed innovation and traditional sector growth and confirm the robustness of our results. We also estimate the model for developed and developing country groups separately to understand the differences in the influence of innovation policies and analyse the need for varying policies for both the groups. The 15-year data from 1998 to 2013 is regressed in three groups of five years. The results for 32 each period are observed in order to understand period-specific differences. These period- specific differences are controlled-for through a period dummy in the pooled dataset regression that is aimed at generating a larger data set leading to significant and robust coefficients. 2.4. Data Labour productivity is calculated in terms of real GDP per labour force. The labour productivity indicator is constructed by using the GDP from World Bank Development Indicators (World Bank, 2014) and the number of employees’ data from Penn Worlds Table Version 8.1 (Feenstra, et al., 2015). The use of Purchasing Power Parity (PPP) Constant 2010 USD GDP ensures that the data is comparable across time and countries in level and growth rate. Literature suggests that innovation-based growth is less likely to reflect in traditional industries such as those in natural resource and agricultural sectors (Becheikh, et al., 2006). As such the labour productivity growth measure excludes natural resource rents and agricultural value added. Data for government expenditure on tertiary education as a percentage of GDP is acquired from the subset of the UNESCO Institute of Statistics education dataset that is related to financial resources (UIS.STAT, 2016). UIS.STAT receives data on education expenditure from country governments responding to UIS's annual survey on formal education. Tertiary education is considered as one of the most important contributors to innovation. When interpreting this indicator, however, we should keep in mind that in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Educational attainment is based on years of school life expectancy primary to tertiary. The gross domestic expenditure on research and development (GERD) as a percentage of GDP is the total intramural expenditure on R&D performed in a country or region during a given year, expressed as a percentage of GDP of the country or region (UIS.STAT, 2016). The data is used as an indication of research and development policy. The ideal case would be to use GovERD, that is government expenditure on research and development as a 33 percentage of GDP. We use the GERD measure because it captures wider geographical space and time, and is a good representative of what similar higher expenditures can achieve. We use the Index of Economic Freedom as an indicator of government policy towards business. The Index of Economic Freedom is an annual index and ranking created by The Heritage Foundation and The Wall Street Journal in 1995 to measure the degree of economic freedom in the world's nations. The creators of the index took an approach similar to Adam Smith's in The Wealth of Nations, that “basic institutions that protect the liberty of individuals to pursue their own economic interests result in greater prosperity for the larger society" (Heritage Foundation & Wall Street Journal, 2016). The index of economic freedom is based on ten quantitative and qualitative factors. These ten factors are property rights, freedom from corruption, fiscal freedom, government spending, business freedom, labour freedom, monetary freedom, trade freedom, investment freedom, and financial freedom. Each factor is graded on a scale of 0 to 100. A country's overall score is derived by averaging these ten economic freedoms, with equal weight being given to each. It would have been ideal to use Ease of Doing Business data from the World Bank Doing Business Indicators. The ten constitutive measures used in the composite ease of doing business indicator are, starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency (World Bank, 2015). As such ease of doing business accounts for objective as well as subjective measures that are directly related to business policy in the country. However, due to limited time-period availability, we resort to using the Index of Economic Freedom that relates to the business environment in a relative bird-eye manner. Government effectiveness captures, “perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies” (World Bank, 2015). Notably, the indicator is a mix of quality and perception of infrastructure, bureaucratic, state, and policy stability. As such, it is used as a measure of 34 expected effectiveness of innovation policies as related to the enabling conditions that affect linkages amongst various policy conditions and knowledge flow necessary for innovation (See Figure 2.1 – Innovation Policy Framework Conditions). Governance is difficult to account for using any kind of measure. We find it important to touch upon the topic of the selection of Government Effectiveness as an interaction term for the policy measures of expenditures in tertiary education and research and development in more detail. The representative sources for constructing this indicator include quality of bureaucracy, institutional effectiveness, excessive bureaucracy or red tape, infrastructure, quality of primary education, satisfaction with public transportation system, satisfaction with roads and highways, satisfaction with education system, basic health services, drinking water and sanitation, electricity grid, transport infrastructure, maintenance and waste disposal, infrastructure disruption, state failure, and policy instability. The composite is constructed from a weighted average of the individual indicators obtain through an Unobserved Components Model (UCM). The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other. This weighting improves the statistical precision of the aggregate indicators and typically does not affect the ranking of countries much on the aggregate indicators. There are two rationales for using Government Effectiveness. First, it is indicative of the governments’ ability to implement their policies and as such the interactive term represents the efficiency of each dollar spent. Second, the interaction of Government Effectiveness with the expenditures can be looked at with much simpler view that is of representing the policies as related to the governance environment. Both explanations relate well to the definition of Government Effectiveness indicator and its use in the context of this paper and the framework represented graphically in Figure 2.1. The variable, effective government expenditures on tertiary education, is constructed by interacting the index of government effectiveness with the government expenditures on tertiary education as a percent of GDP. The same approach is taken to construct the variable effective GERD as a percent of GDP. The prefix “effective” signifies an interaction with the measure of government effectiveness. Effective expenditure is obtained by the interaction of 35 government effectiveness that runs from 0 to 1 by actual percent expenditures per GDP in the relevant policy areas. As such government effectiveness is translated as the percentage of effectiveness of each dollar spent or simply the interaction of the governance environment with the policy measures. Table 2.2 - Summary Statistics Variable Countries Years Mean Std Dev Min Max Δ yN-y1 ln(labprod) Productivity Growth 150 1998-2013 0.559 0.364 -0.452 1.540 labprod y1 Initial Productivity 157 1998-2013 9.614 1.183 6.884 12.084 goveff.edu Effective Tertiary Education 164 1998-2013 0.483 0.427 0.024 2.198 goveff.r&d Effective R&D 129 1998-2013 0.485 0.716 0.003 3.427 econfreedom Economic Freedom 177 1998-2013 57.740 12.190 8.900 89.060 natresrents Natural Resource Rents 187 1998-2013 6.290 10.560 0 86.170 agrirents Agricultural Value Added 164 1998-2013 16.830 14.510 0 61.800 eduattain Educational Attainment 182 1998-2013 11.790 3.210 3.100 20.230 Note: Description of abbreviations is provided in Table 2.1 The indicator used to represent innovation-based growth is the natural log of the ratio of final to initial labour productivity excluding natural resource rents and agricultural value added. It is noteworthy that the number of countries for which these data points are available varies from 129 for effective GERD as a percent of GDP to 177 for the index of economic freedom for the year between 1998 to 2013. However, in our regression between 95 to 106 countries are represented depending on the time period and extent of the data available. The correlation coefficient of effective tertiary education expenditures and effective research and development expenditures is 0.57. The same for Economic Freedom with effective tertiary education 36 expenditures is 0.47 and with the effective research and development expenditures is 0.51. The pairwise correlation between our explanatory variables of concern is considered moderate and is not expected to have an effect on the coefficients of the estimation. In order to make sure that this is the case, we also regress excluding two of the explanatory variables and compare the results with the original estimation. 2.5. Results 2.5.1. Global Here we present the observed influences of the explanatory variables of concern, on the response variable i.e. labour productivity growth excluding natural resource and agricultural rents. Second, we present the results separately for developed and developing countries. Finally, we glance at how labour productivity growth in the Arabian Gulf countries compares with western countries (See footnote 18 associated with Appendix 2-B). The first result that is observed and presented in Table 2.3 below is that of “beta-convergence”. This term implies that the partial correlation between growth in income or productivity over time, and its initial level is negative. It refers to a process in which poorer regions grow faster than richer ones and therefore catch-up on them. We observe that the initial labour productivity is negatively and statistically significantly correlated to labour productivity growth for pooled data for three periods. An increase of 1% in the country’s initial labour productivity results in the ratio of final to initial labour productivity to be lower by 0.045%. Countries with relatively lower labour productivity are able to grow faster and hence converge to the frontier. In this chapter, we make the observation for innovation represented by labour productivity excluding natural resource rents and agricultural value added. In this context, the results confirm the convergence of labour productivity between countries on the innovative frontier and those away from it. 37 Table 2.3 – Labour Productivity Growth and Policy Variables Response Variable Productivity Growth (Net of natural resource rents and agricultural value added) Period 1 Period 2 Period 3 Pooled Pooled Pooled 1998-2003 2003-2008 2008-2013 Period 1 & 2 Period 2 & 3 Period 1, 2 & 3 Initial Productivity -0.111*** -0.051 0.039 -0.075*** -0.022 -0.045** (0.038) (0.037) (0.028) (0.025) (0.027) (0.021) Effective Tertiary Education 0.024 0.035 0.041* 0.029 0.048* 0.041* (0.040) (0.041) (0.023) (0.028) (0.025) (0.021) Effective R&D -0.005 -0.037 -0.01 -0.023 -0.022 -0.018 (0.029) (0.030) (0.019) (0.020) (0.020) (0.016) Economic Freedom 0.003 0 -0.003 0.002 -0.002 -0.001 (0.002) (0.002) (0.002) (0.001) (0.001) (0.001) Arabian Gulf Dummy -0.118 -0.268** -0.148 -0.215** -0.278*** -0.269*** (0.142) (0.125) (0.114) (0.087) (0.097) (0.078) Period 1 -0.068*** 0.103*** (0.017) (0.019) Period 2 0.149*** 0.147*** (0.018) (0.017) Root Mean Squared Error 0.107 0.126 0.090 0.113 0.126 0.120 Adj. R-squared 0.404 0.446 0.138 0.463 0.342 0.383 N 91 96 100 187 196 287 * p<0.10, ** p<0.05, *** p<0.01 Note: Regional dummies, educational attainment, natural resource rents, and agricultural value added included as control variables We observe that effective expenditures on tertiary education as a percent of GDP have a positive relationship in all periods with the explanatory variables. The pooled data for the three periods shows that there is a statistically significant positive relationship between effective tertiary education spending as a percent of GDP of the country and labour productivity growth. This is statistically significant at the 10% level for two sets of pooled data and for the third period. In this case, the magnitude of the increase is considerable i.e. 38 an increase of 1% in average effective tertiary education expenditure as a percentage of GDP would result in an increase of 4.2% in labour productivity growth. To simplify, a country effectively investing 1% of their GDP in tertiary education will improve their growth rate by 4.2% if they invest an equivalent of 2% of their GDP in tertiary education. Since this variable is represented by an interaction of government effectiveness and tertiary education expenditure as a percent of GDP it is useful to break down this result. A hypothetical country with 1% effective expenditure on tertiary education as a percent of GDP driven by 0.45 government effectiveness, investing 2.2% of GDP as tertiary education expenditure and having an annual labour productivity growth rate of 3% can improve its productivity growth rate to 3.13% (that is an increase by 4.2%) by increasing its effective expenditure to 2% that could be accomplished either by improving government effectiveness to 0.9 or tertiary education expenditure as a percent of GDP to 4.4%. Note that non-traditional sector labour productivity is being discussed here, as it is a measure representing innovation that covers developing countries as well as developed countries. We do not observe positive results for effective R&D expenditure percent of GDP for the complete set of countries. We observe no statistically significant results for the Index of Economic freedom. The magnitudes are small and the pooled data for the three periods show inconsistent correlation for the business policy and labour productivity growth. The estimation is robust to the inclusion of interaction variable’s constituents – government effectiveness, tertiary education expenditure as a percent of GDP, and GERD as a percent of GDP – in the estimation in addition to the interaction variables named effective tertiary education and effective R&D. Also, the signs of the coefficients for effective tertiary education, effective R&D and Economic Freedom do not vary and the magnitudes do not vary by a considerable extent when included individually in the estimation, that is, when the remaining two explanatory policy variables are excluded. This confirms that the moderate pairwise correlation for our explanatory variables discussed in Section 4 has no influence on the results. Table 2.4 shows the results for developed and developing countries. It provides a perspective into differences in the relation of innovation policy to labour productivity between developing 39 countries and developed countries. We observe in Table 2.4 a negative relationship between effective research and development expenditures and labour productivity in the modern sector for developed countries. The results may seem precarious at first. However, the difficulty in finding a relationship between productivity and innovation in the developed countries is well known and termed as the “productivity puzzle” and “Solow paradox”. We can observe for developing countries in Table 2.4 that the effective R&D expenditures variable has a positive effect on labour productivity growth for developing countries14 and the relationship is statistically significant at 10% level for the pooled data for three periods. An increase of 1% in the effective R&D expenditures as a percent of GDP in developing countries would result in the labour productivity growth rate to increase by 27.5% for pooled data of periods 1, 2 and 3. However, we do not find the same for developed economies. The differences between developed countries and developing countries are indicative of different stages of development. The developed countries may be more prominently engaged in new-to-the-world type innovation. At the same time, the developing countries are benefiting from catch-up type innovation and associated R&D. The productivity output of such research is considered to be higher and have lower lag. The lag is higher in the case of new-to-the-world innovation as was observed in the case of the acceleration in productivity growth that started in the technology sector and spread to the overall economy only many years later leading to the rapid productivity growth period of 1995 to 2004. A recession followed this period, as such overall productivity growth was decreasing in the developed countries in our sample set. We discuss these result along with the “productivity puzzle” further in Section 2.6. 14 Note that when resource and agricultural dependency dummies are used instead of actual resource rents and agricultural value added the pooled data for three periods shows significant results at 10% for both effective tertiary education and R&D expenditures. 40 Table 2.4 – Labour Productivity Growth and Policy Variables – High Income OECD and Developing Countries Separately Response Variable Productivity Growth Developed Countries Productivity Growth Developing Countries Pooled Pooled Pooled Pooled Pooled Pooled Period 1 & 2 Period 2 & 3 Period 1, 2 & 3 Period 1 & 2 Period 2 & 3 Period 1, 2 & 3 Initial Productivity -0.001 0.057 0.032 -0.076** -0.024 -0.043* (0.088) (0.083) (0.066) (0.030) (0.033) (0.026) Effective Tertiary Education 0.022 0.023 0.020 0.024 0.038 0.036 (0.038) (0.035) (0.028) (0.041) (0.033) (0.028) Effective R&D -0.006 -0.009 -0.009 0.164 0.165* 0.166* (0.020) (0.019) (0.015) (0.103) (0.097) (0.079) Economic Freedom -0.002 0.002 0.000 0.002 -0.004 -0.002 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Educational Attainment 0.001 0.001 -0.002 0 0.004 0.001 (0.006) (0.007) (0.006) (0.004) (0.007) (0.005) Root Mean Squared Error 0.077 0.069 0.071 0.129 0.143 0.136 Adj. R-squared 0.172 0.632 0.491 0.349 0.326 0.321 N 44 45 67 127 135 196 * p<0.10, ** p<0.05, *** p<0.01, Note: Regional dummies, time dummies, educational attainment, natural resource rents, and agricultural value added included as control variables This result is consistent with Nadiri and Kim (1996) who find the rate of return for domestic R&D spending to be between 23% and 26% varying amongst different countries. The breakdown of government effectiveness and R&D expenditures can be explained in similar terms as effective education expenditures as a percent of GDP. A hypothetical country with 1% effective expenditure on research and development as a percent of GDP driven by 0.45 government effectiveness, investing 2.2% of GDP as research and development expenditure and having an annual growth rate of 3 percent can improve its growth rate to 3.825% (that is an increase by 27.5%) by increasing its effective expenditure to 2%. This increase in effective 41 expenditure could be accomplished either through improving government effectiveness to 0.9 or research and development expenditure as a percent of GDP to 4.4%. As in the case of the regression where all countries are included, we find that the signs of the coefficients for effective tertiary education, effective R&D and Economic Freedom do not vary and the magnitudes do not vary by considerable extent when included individually in the estimation. We find a positive effect of effective tertiary education on labour productivity growth for both developed and developing countries. However, the coefficients are not significant as it was observed in the case of the pooled data for all countries. We observe that effective tertiary education expenditures are important for labour productivity growth in the non-traditional sector in addition to initial educational attainment represented by average years of schooling. Also, the exclusion of the educational attainment represented by average years of schooling does not affect the results. Robustness tests show that the inclusion of natural resource rents and agricultural value added in the regression equation (instead of the resource dependency dummy) does not change the results. We have excluded the possibility of reverse causality. In this chapter, we have accounted for the initial economic state of the country, the initial level of educational attainment in the country region-specific differences, and time-specific differences. Also, the labour productivity growth variable is lagged by a period of five years in order to exclude the possibility of reverse causality. In terms of omitted variable bias, we acknowledge that not accounting for capital investment in the estimation equation may lead to a bias in the estimation. The important question is whether we can assume plausible causality in the case where we observe statistically significant relationships in our empirical outcome or not. We account for lagged labour productivity. The lagged capital investment itself is expected to be associated with lagged labour productivity, which in turn is expected to be associated with labour productivity growth. In this situation, if we are interested in determining the exact magnitude of the effect of lagged labour productivity on labour productivity growth in the modern sector then the omitted variable bias is of serious concern. However, we are trying to determine whether policies such as tertiary education expenditures, research and development 42 expenditures and business environment affect labour productivity in the modern sector. Considering lagged labour productivity in the estimation model we are able to account for much of the omitted variable bias concerning policies as the missing factors would be associated with the lagged labour productivity. As such, it is plausible that the interaction of higher government effectiveness and higher investment in tertiary education as a percent of GDP leads to higher labour productivity growth excluding natural resource and agricultural rents. We also consider that tertiary education expenditures, research and development expenditures and government effectiveness may be associated with other omitted variables. For example, educational attainment can be correlated to tertiary education expenditures and research and development expenditures. Considering that governments with higher educational attainment may be able to invest more in both education and R&D simply because of the availability of educated populace. This leads to a simultaneity problem. Investing in tertiary education and R&D would most probably not be the first choice of the government of a country that has overall lower educational attainment. In this sense, we note that our estimation suffers for the omitted variable bias related to simultaneity concerns. This might be leading to an over-estimation or under-estimation of the relationship between the explanatory variables and our variable of interest labour productivity in the non-traditional sectors. However, we have a choice to make in terms of selecting our variables of interest and are restricted by the coverage of data that we intend to keep geographically wide. 2.5.2. Arabian Gulf countries - A special case? We also present results for Arabian Gulf country dummies in contrast to the reference region (includes North America, Western Europe and Nordic countries - See Appendix 2-B for more details) in Table 2.5 below and compare them to those already seen in Table 2.3 above. We observe that the growth in labour productivity in the non-traditional sector in the Arabain Gulf region is much lower in comparison to the reference group. With rising oil prices from 2003 onwards most of the growth in Arabian Gulf economies appears to have been mostly based on resource rents (Ftiti, et al., 2016). We observe in Table 2.5 below, that, the same regression without excluding natural resource rents and agricultural value added, results in 43 diminished statistical significance for the Arabian Gulf countries’ dummy variable for the pooled sets. This result indicates that the growth in the non-natural resource sector has been slower in comparison with the reference group. It is noteworthy that the coefficient of the Arabian Gulf Dummy is significant for Period 2 in both cases where labour productivity growth excludes and includes natural resources rents. Periods 1 and 3 also corresponds with low oil prices. Table 2.5 – Total Labour Productivity Growth and Policy Variables Response Variable Productivity Growth (Inclusive of natural resource rents and agricultural value added) Period 1 Period 2 Period 3 Pooled Pooled Pooled 1998-2003 2003-2008 2008-2013 Period 1 & 2 Period 2 & 3 Period 1, 2 & 3 Initial Productivity -0.084*** -0.053** 0.009 -0.074*** -0.041 -0.065*** (0.026) (0.026) (0.022) (0.018) (0.027) (0.020) Effective Tertiary Education 0.013 0.036 0.011 0.022 0.013 0.007 (0.028) (0.029) (0.018) (0.020) (0.025) (0.020) Effective R&D -0.014 -0.050** -0.006 -0.030** -0.027 -0.019 (0.020) (0.021) (0.015) (0.015) (0.020) (0.016) Economic Freedom 0.001 -0.002 -0.001 -0.001 -0.003 -0.002* (0.001) (0.002) (0.001) (0.001) (0.002) (0.001) Arabian Gulf Dummy -0.098 -0.164* -0.08 -0.112* -0.081 -0.065 (0.098) (0.088) (0.092) (0.064) (0.097) (0.074) Root Mean Squared Error 0.073 0.089 0.072 0.083 0.127 0.114 Adjusted R-squared 0.617 0.381 0.049 0.521 0.112 0.228 N 91 96 100 187 196 287 * p<0.10, ** p<0.05, *** p<0.01 Note: Regional dummies, time dummies, educational attainment, natural resource rents, and agricultural value added included as control variables As such in the following, we attempt to substantiate the effect of oil price on non-traditional sector labour productivity growth. In Figure 2.2 the predicted labour productivity growth excluding natural resource rents and agricultural value added for two Arabian Gulf countries 44 (Oman and Saudi Arabia) and two reference group countries (Netherlands and Norway) is plotted against the annual growth rate of crude oil price. The predicted labour productivity growth function is computed for each country by using their respective data points and estimation results of pooled data for periods 1, 2 and 3 as shown in Table 2.3. In Figure 2.2 it is observed that lower non-traditional sector labour productivity growth in the Arabian Gulf countries Oman and Saudi Arabia is associated with higher oil prices and vice versa ,, 15 but not for the two countries from the reference group Norway and Netherlands. This provides confirmation that oil prices partly drive the non-traditional sector labour productivity growth and innovative development in the Arabian Gulf countries. Figure 2.2 – Predicted labour productivity growth as a function of the annual growth rate of crude oil prices 15 The years 1999 and 2000 witnessed strong oil price recovery after the oil price crash related to Asian Financial Crises. Excluding 1999 and 2000 would results in an even stronger correlation of oil price growth with labour productivity growth. 45 2.6. Conclusions and Discussion This chapter presents the analyses of the relationship between innovation policy and productivity growth related to innovation and catch-up. It establishes the correlation and plausible causality between innovation policies and labour productivity growth in non- traditional sectors in a cross-sectional evaluation among countries. A selection of innovation policies was chosen based on the literature review and the state-of-the-art “broad” innovation policy approach. Innovation policy in this chapter is represented by indicators of education, research and development, and business. The policy implementation capability and potential of the governments are also analysed. In our results, we observe the convergence between countries with lower labour productivity and those at the innovative frontier. This result is in line with earlier findings of convergence in labour productivity between richer and poorer countries – beta-convergence (Barro, 1991; Barro, 2012). Also, a study by Verspagen (1991) confirms the catching-up of relatively backward countries through technological spill-overs. Further, we observe that there is a significant and positive relationship between the interaction of government effectiveness and government expenditures in tertiary education, and labour productivity in the modern sector. This observation answers one of the questions raised in Keller (2006), where the returns to tertiary education are not found to be consistently positive. Keller (2006) hypothesizes that tertiary education expenditures might be inefficiently allocated. We consider the multiplicative term of government efficiency and tertiary education investment while including tertiary education investment. We found that the interaction of government efficiency and tertiary education expenditures as a percent of GDP were positively and significantly related to labour productivity growth in non-traditional sectors. We could also challenge the notion that primary and secondary investment has priority over tertiary education investment, on the basis of economic returns, by including the initial educational attainment in the form of years of schooling in the explanatory variables. The initial level of educational attainment in the country turns out to be not significantly correlated with labour 46 productivity growth in the modern sector, while tertiary education is. This is important for policymakers as it demonstrates substantial societal returns to tertiary education. When separating developing countries and developed countries, we do not observe a significant effect of effective tertiary education. At the same time, for developing countries, the coefficients of the effective R&D expenditures show a consistently positive and statistically significant effect on labour productivity growth. This relationship contrasts with findings elsewhere, which often highlight the importance of research and development expenditures for developed countries, speculating the opposite for developing countries. For example, Griffith et al. (2004) point out that developing countries are not able to achieve the maximum potential in R&D. They see this as a consequence of inappropriate social policies. Our results indeed highlight that the influence of the interaction between the government effectiveness and R&D expenditures is positive. Through these results, the importance of looking at innovation policies as a complete set within an innovation eco-system rather than only looking at them individually is highlighted further. These results are unique and to the best of our knowledge first of their kind in confirming the interaction of sound governance and innovation policy measures such as expenditures in tertiary education and R&D. In line with the academic literature, we find that near-term lagged effective investments in R&D do not result in increased productivity in developed countries. Following the moderate growth of the 1980s, the developed countries witnessed high productivity growth in the years from 1995 to 2004. This productivity growth episode was associated with the maturity of the technological revolution. The rapid growth in the application of technological advances in productivity-enhancing innovations, and semi-conductor and computer manufacturing lead to rapid labour productivity increases from the mid-1990s (Manyika, et al., 2001). This period was followed by a recession in 2008. There are thus two main reasons we do not find positive returns of effective R&D expenditures for developed countries. Firstly, as observed during the technological revolution, the effects of R&D investment and new-to-the-world innovation take more time to yield productivity increases than we have considered in this chapter. The 47 productivity increases in the first two periods were associated with R&D expenditures that were mainly carried out in the last 10 to 20 years and not during the preceding five years. The findings suggest evidence on the “Solow paradox” or the “productivity paradox” that is found in the manufacturing outside technology-producing sectors (Acemoglu, et al., 2014). There are three other possible explanations of the “Solow Paradox”. One argument is that the current innovations are not as impactful as those of the first and second industrial revolutions, such as the steam engine, electricity, piped water and sanitation, and antimicrobial drugs (Gordon, 2012). The second argument is of secular stagnation, that is, the decline of growth due to the ageing population and lower investments in capital, despite the productivity- inducing innovations (Eggertsson, et al., 2016). Finally, the third argument is related to the mismeasurement of productivity, such as the difficulties in measuring the output of cheaper software and accounting for the benefits of internet-based services (Mokyr, 2013). We do not find any relationship between labour productivity growth and the index of economic freedom. In other words, we cannot demonstrate that a good business environment as defined by the index of economic freedom is conducive to the transformation of knowledge and research into marketed goods and services. The results may be a consequence of the type of indicator we have selected to represent the quality of the business environment. The variable used presents only a bird-eye view of the business environment. Also, our specification fails to catch a potentially shorter response time to business policies. It would be ideal in future research to work with different time lags for business conditions and to work with indicators that objectively represent the business policy and environment in the countries. Overall, Arabian Gulf countries experience lower labour productivity growth in the non- traditional sector as the oil prices increase. For these countries, a crucial policy implication is to devote resources towards tertiary education and R&D, while improving government effectiveness, if they want to grow independent of oil and gas resources. Concerning the limitations, it is important to note that this chapter is not intended to be a part of the extensive literature that tries to explain factors of growth. We acknowledge that 48 a vast amount of literature on the explanations of the productivity growth of countries exist. In this literature, many different and competitive explanatory variables are inserted in the regression for productivity growth. This includes but is not limited to the investment rate, the growth rate of employment and population. Durlauf et al. (2008), for example, find evidence of macroeconomic policy effects and a role for unexplained regional heterogeneity in explaining aggregate growth. The importance of this chapter lies in trying to determine the effect of the interaction of effectiveness measures with policy measures – in this case, tertiary education and R&D expenditures. We were able to expand the geographical coverage of the study and point out the differences that exist between developed and developing countries. This chapter forms an important basis for future research exploring the relationship of innovation policy with innovation, catch-up, diversification and labour productivity growth. It is expected that the increased availability of data for a wider set of countries will provide more insights into the topic. 49 Appendix 2-A Table 2.6 – Data Sources Variable Source GDP PPP World Bank – World Development Indicators (World Bank, 2014) Number of employees Penn Worlds Tables – Version 8.1 (Feenstra, et al., 2015) Natural resource rents World Bank – World Development Indicators (World Bank, 2014) Agricultural value added World Bank – World Development Indicators (World Bank, 2014) Government effectiveness World Bank – World Governance Indicators (World Bank, 2015) Government expenditures on tertiary education as a percent of GDP UNESCO Institute for Statistics (UIS.STAT, 2016) Gross expenditures on research and development as a percent of GDP UNESCO Institute for Statistics (UIS.STAT, 2016) Index of economic freedom Heritage Foundation and Wall Street Journal (Heritage Foundation & Wall Street Journal, 2016) Years of school life expectancy primary to tertiary UNESCO Institute for Statistics (UIS.STAT, 2016) 50 Appendix 2-B Table 2.7 – Regional groups and countries Region Countries East Asia & Pacific Papua New Guinea, Australia, Japan, Korea - Republic, New Zealand, China, Hong Kong, Korea - Democratic People's Republic, Mongolia, Taiwan Eastern Bloc 16 Belarus, Albania, Armenia, Bulgaria, Czech Republic, Estonia, Georgia, Hungary, Kyrgyzstan, Latvia, Lithuania, Moldova, Poland, Romania, Russian Federation, Slovakia, Tajikistan, Ukraine, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, Serbia, Slovenia Gulf Cooperation Council (GCC) Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates Latin America and the Caribbean Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), Suriname, Turks and Caicos Islands, Uruguay, Venezuela, Virgin Islands U.S. Middle East 17 Iran, Iraq, Jordan, Lebanon, Palestine, Syrian Arab Republic, Yemen North Africa Algeria, Djibouti, Egypt, Libya, Morocco, Tunisia North America & Europe 18 (Reference Group) Austria, Belgium, France, Germany, Greece, Ireland, Israel, Italy, Luxembourg, Netherlands, Portugal, Spain, United 16 The name applied to the former communist states of eastern Europe, including Yugoslavia and Albania, as well as the countries of the Warsaw Pact (Hirsch, et al., 2002). 17 Middle Eastern countries on the Asian continent, excluding countries of the Gulf Cooperation Council (GCC), Turkey and Israel 18 Countries of Western Europe, Northern Europe excluding Eastern Bloc, Mediterranean excluding Arab and African countries, North America excluding Latin American countries 51 Kingdom, Cyprus, Switzerland, Turkey, Greenland, Denmark, Finland, Iceland, Norway, Sweden, Canada, United States Small Nations and Islands Bermuda, Åland Islands, American Samoa, Andorra, Anguilla, Antarctica, Bonaire, Sint Eustatius and Saba, Bouvet Island, British Indian Ocean Territory, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Falkland Islands (Malvinas), Fiji, French Guiana, French Polynesia, French Southern Territories, Gibraltar, Guadeloupe, Guam, Guernsey, Heard Island and McDonald Islands, Holy See (Vatican City State), Jersey, Kiribati, Malta, Marshall Islands, Martinique, Mayotte, Micronesia, Montserrat, Nauru, New Caledonia, Niue, Norfolk Island, Northern Mariana Islands, Palau, Pitcairn, Réunion, Saint Barthélemy, Saint Helena, Saint Pierre and Miquelon, Samoa, Solomon Islands, South Georgia and the South Sandwich Islands, Svalbard and Jan Mayen, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Virgin Islands, British, Wallis and Futuna, Western Sahara, Channel Islands, Faroe Islands, Isle of Man, Liechtenstein, Monaco, San Marino South Asia Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka South-East Asia Cambodia, Indonesia, Lao, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, Vietnam Sub-Saharan Africa Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Comoros, Congo - Democratic Republic, Congo - Republic, Côte d'Ivoire, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea- Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, Tanzania, Uganda, Zambia, 52 53 3. Policy and Economy in the GCC Policies, Innovation and Transition in Oman, Saudi Arabia and United Arab Emirates Abstract This paper attempts to assess how policy measures have affected innovation and sectoral labour productivity. We use an eclectic qualitative approach incorporating descriptive statistics from Oman, Saudi Arabia (KSA) and the United Arab Emirates (UAE) for the period 1990 to 2016. The policy measures and outputs are rated on a seven-point scale. The inputs covered include policies of primary, secondary, tertiary and vocational education, research and development (R&D), governance, and business enablers. We use the term ‘enablers’ because these policies are expected to contribute to an environment that boosts innovation and diversification. The innovation outputs and diversification outputs covered include patents, variation in the fields of patents, industrial designs, sectoral labour productivity and share of outputs by sectors. The scores are based on relative expenditures, quality and performance of the enablers and output measures. We find that the state of the ultimate outputs of innovation activity and diversification for the three countries are far from their full potential. We suggest that this is due to the moderate performance of the enablers and relevant policy areas. Oman has limited natural resources and has followed a sequential development of the components of its innovation system. For example, Oman has first focussed on basic education, then expanded expenditures on tertiary education and subsequently increased R&D investments. KSA with more ample oil reserves has approached education, R&D and industrialisation in parallel. It has also engaged in a pull strategy for the setup of R&D units from multinational organisations. The third country under review, the UAE has focused on creating a welcoming business environment. This includes establishing business and logistic free zones and information technology (IT), media, tertiary education and health industry clusters. With education as a weaker element of its innovation system, the UAE has relied on foreign labour. 54 We find that each country’s policy affected innovation and productivity growth as follows: - Oman has limitations in vocational education and R&D enablers. Difficulties in getting credit and resolving insolvency limit the entrepreneurial arena in Oman. Overall, Oman exhibits slow-paced innovation activity and slightly positive progress in the diversification arena. - KSA, despite troubles in the education and governance arena, is engaged in basic research with relatively high patent and design activity. Ease of trading across borders and enforcing contracts has a moderate score in KSA that is uniquely lower than other GCC countries. The labour productivity in certain sectors in KSA has improved while the share of those sectors is declining. - UAE has at least ‘slightly positive’ status on most enablers. However, it appears to be lagging in the development of R&D clusters and R&D funding. The strongest enabler is the development of business clusters. This strength, in addition to the use of the expatriate workforce, appears to reflect positively on the relative innovation output. However, explicit positive trends are not evident in terms of diversification. We observe that the ultimate outputs in GCC countries are constrained by the lowest- performing policy areas that may be termed as the “limiting enablers”. The policy implication of this work is that a piecemeal policy focus is not adequate as all enablers in the system have to function well in order to support innovation and diversification. It is important to note that the relationship between inputs and outputs where expressed is subjective to the rationale discussed in the chapter. Keywords: Innovation policy, innovation, technological change, structural change, government effectiveness, developing countries, Arabian Gulf countries. JEL Classification: O2, O3, O38, O57 3.1. Introduction The countries of the Gulf Cooperation Council (GCC) 19 have long been considered as mainly oil and gas-based economies. Currently, the countries face the challenge of planning for a post-oil and gas economy while facing high population growth. It is expected that the population of the GCC will increase to 66.5 million by 2030 in comparison to 56.5 million at the end of 2018 (World Bank Group, 2019). Meanwhile, the GCC countries have been dependent on foreign labour, and 48% of the current population of the GCC is composed of the expatriate workforce (Abyad, 2018; GCC-Stat, 2019). Among the GCC countries, UAE and Qatar have one of the highest reliance on foreign labour in the world with 94% and 95% of their workforce being composed of expatriates, respectively (GCC-Stat, 2019; Arab News, 2019). This high reliance on the foreign workforce is symptomatic of a lack of human capital development. The GCC countries have enacted varying policies for innovation and diversification. Their endeavours are in the context of population growth, an anticipation of a post-oil and gas economy and the associated need for employment creation. The ultimate aim is the sustenance of a dignified standard of living for their citizens. This aspiration is expected to be accomplished by job creation through economic growth associated with innovation and diversification. These ambitions are also enshrined in the Articles 17, 23, 24, 29 and 30 of the GCC Human Right Declaration (GCC Secretariat-General, 2014). The GCC countries’ governments have invested in the development of indigenous human capital. Also, industrialisation and diversification efforts have led to the emergence of new industries and a revival of old ones. Bahrain, Oman, Qatar and UAE have increased focus on tourism. Trade and logistics sectors have seen development in all the GCC countries. Oman and UAE that have a rich history of shipping and trading, have both invested in the shipping 19 Also referred to as the countries of the Arabian Gulf, countries of the Arabian Peninsula and countries of Persian Gulf and Arab countries of the Gulf. Our preferred terminology in this work is the countries of the Gulf Cooperation Council (GCC) which is colloquial term used to refer to the Cooperation Council of the Arab States of the Gulf (GCC). Geographically, Iraq is also an Arab country on the Arabian/Persian Gulf, Iran is also a country on the Arabian/Persian Gulf. Jordan and Yemen are countries on the Arabian Peninsula that have both been in talks to join the GCC. Finally, Morocco despite being geographically distant from the peninsula and the Gulf has also been in accession talks since 2011. In order to avoid any confusion, we use the term GCC to refer to the GCC members as of 2017 – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. 56 industry (OBG, 2015; Baxter, 2018). Additionally, the GCC countries have set their eyes on moving from a monopolistic and rent-seeking private sector to generating a more dynamic, innovative and productive economy (Hvidt, 2013). KSA has seen growth in its petrochemical conversion industry, and the UAE has invested in setting up various clusters (Seznec, 2011). Despite the GCC having interesting development dynamics, not much academic work has been carried out to analyse the changing economic structures of the GCC countries. The knowledge related to policies for innovation and diversification in the GCC has remained in a black-box, mostly inaccessible or untapped. This inaccessibility may be due to previous limitations in data availability or the wide-spread misunderstanding that there is nothing more to the GCC economies than oil and gas. However, it is evident from the preceding discussion, that the policies undertaken by the GCC countries in order to diversify and develop their innovative capacity are an important subject matter for academic research. A country’s innovative capacity is based not only on its resources and other endowments that offer a natural comparative advantage. The concept of innovation is studied as effective competitive performance in a dynamic context in “National innovation systems: a comparative analysis" (Nelson, 1993). There, it was observed that countries decide on policies, that create a comparative advantage in selected areas (Nelson, 1993). Two features – education and the macroeconomic climate, both subjects of government policy – were identified by Nelson (1993) to affect the ability and incentives of firms to innovate. This was derived from 15 case studies on innovation. In the preceding chapter, we discussed the effect of higher education and R&D policy on innovation in interaction with the quality of governance. In that chapter, innovation is studied as labour productivity in non-traditional sectors, excluding the natural resource and agricultural sectors. It was also observed that the GCC countries are not at par with other countries when considering the features of innovation policy and diversification. Through this approach in the previous chapter we observed that the labour productivity growth in the non-natural resource sector in the GCC group of countries for the years from 1998 to 2013 has been much slower than the group of countries 57 in Western Europe, Northern Europe, European countries of the Mediterranean, USA, and Canada together 20. The observation of much weaker growth in labour productivity in the non-traditional sector for GCC country group in comparison with the reference group becomes important when seen in the light of the other observations as well. With rising oil prices from 2003 onwards, a majority of the growth in Arabian Gulf economies was based on natural resource rents and the development of other sectors was found to be correlated with oil prices. In continuation of this we explored the relationship further and found the oil price and sectoral productivity correlation to be dissimilar for each country – with Oman’s transportation and communication sectors performing better in terms of labour productivity as oil prices increased, UAE’s Manufacturing sector performing better, and no clear relationship for KSA’s sectoral labour productivity performance with oil prices (See Chapter 3 - Appendix 3-A). With these variations in view, we explore the role of policies, enablers and capabilities in driving the non- oil sector and attempt to identify the country-specific conditions that may explain these variations. We compare three GCC countries – Oman, KSA and UAE, both in terms of enablers and innovation.21 The governments in the GCC have a broad sphere of influence in economic structure and workings of their countries. The exports of petroleum remain key to all the three countries Oman, KSA and UAE. Threats such as oil price volatility, depletion of oil resources, climate change, the emerging international consensus to move away from fossil fuels, and a youth bulge in their respective populations, has made it important for these fossil fuel-dependent GCC countries to drive the diversification process and develop highly productive and innovative economies. 20This relationship is scrutinized on country level and the predicted labour productivity growth in non-traditional sectors in Oman and Saudi Arabia is found to be correlated with the annual growth rate of oil prices. In comparison no correlation of non-traditional sector labour productivity growth with oil prices in Netherlands and Norway – two countries from the reference group was found (See Chapter 2) 21 The conceptual scope of innovation in this work is beyond technological innovation and includes management, process, service, product and marketing innovation as well. 58 Oman has a reputation as a young oil-based economy and makes it an ideal case in trying to understand the types of paths countries in early stages of development and limited natural resources can take to develop a sustainable, diversified and innovation-based economy.22 KSA is an oil-rich country with a relatively conservative socio-political setup in comparison to its GCC neighbours. It is one of the twenty largest economies in the world and a member of the G20. KSA remained the largest producer and exporter of oil for a long period. However, from 2002 to 2018 its production numbers were in close proximity to Russia and were overtaken by the US in 2018. From 1987 onwards till now as of March 2019, KSA remains the largest exporter of oil in the world. KSA provides a unique setup to study the invigoration of innovation and diversification through government policies in resource-abundant scenarios. The UAE also has vast oil and natural gas reserves. Its most populous city Dubai occupies the position of an important global city and international trade, shipping, and aviation hub. The UAE's economy is considered to be the most diversified among the six countries of the GCC. This is attributed to the efforts of the federal government and the constituent emirates. A very low indigenous population and high reliance on foreign labour also make it an interesting case for studying innovation and diversification policies. This chapter offers insights from individual countries and is a first of its kind cross-country comparative narrative for the GCC. In the following section, 3.2, we present a review of the literature of innovation studies. In section 3.3, the state of policies and innovation in the GCC countries is explored. Sub-section 3.3.2 addresses the development of the education systems, 3.3.3 focuses on literacy, primary education, secondary education, education reforms and performance, 3.3.4 on tertiary education and vocational training. Sections 3.3.5, 3.3.6 and 3.3.7 cover R&D policy, business and entrepreneurship, and governance and infrastructure, respectively. Section 3.4 covers the outputs of the GCC economies, with sub-sections 3.4.2 focussed on the innovation outputs, and 3.4.3 focussed on the share of individual sectors in the economy and sectoral labour productivity in the GCC countries. The policy inputs and 22 Oman secured control over a substantial portion of the oil revenue generated from oil extraction in the country by early 1970s. 59 associated outcomes are discussed in combination in section 3.5. Finally, section 3.6 contains the summary, closing discussion and conclusion of this chapter. 3.2. Perspectives on innovation 3.2.1. General The innovation literature is distributed between “narrow” and “broad” focus on innovation policies. In the “narrow” sense only formal R&D systems and organisations systematically active in knowledge generation and diffusion are the focus. An example of the application of the systems of innovation framework in the former sense is the World Bank Knowledge Assessment Methodology (Chen & Dahlman, 2005). However, systems of innovation in a narrow sense “leave significant elements of innovation-based economic performance unexplained” (Lundvall, 2007). In the “broad” sense the core knowledge-producing and disseminating institutions are embedded in a wider socio-economic system and the relative success of innovation policies is a function of influences and linkages beyond these core institutions (Freeman, 2002). Among the works that discuss new-to-the-world innovation in the latter sense Furman, Porter and Stern (2002) integrate ideas-driven growth theory, microeconomics-based models of national competitiveness and industrial clusters theory and considers R&D manpower, knowledge and technology base as important sources of innovation, and Archibugi & Coco (2004) define innovation system through patents, publications, ICT, electricity consumption, and education. The common theme that emerges from the literature is that innovation policies work in coherence with each other and have a combined and complementary effect on growth. The translation of policy to growth must go through the governments’ ability to effectively turn inputs of policy into growth. 23 Figure 3.1 below represents our interpretation of how the flows of knowledge enable growth in innovation through an increase in productivity in an economy. The innovation eco-system is thus arranged into conditions, linkages, the firms and the market 23 See Chapter 2 for more details. 60 itself. The change in the state of these conditions is determined through natural transformation and policy. The education condition is affected by government policies as government financing of the tertiary education system, policies determining graduate ratios in science and technology fields, alignment to labour demand from the market, university autonomy, and others. Similarly, research and development conditions are impacted by government expenditure on research and development, type of research grants, targeted scientific field grants, the competitiveness of grants, intellectual property regime, and private- sector research funding, and so forth. Business conditions are related to industrial policies, competition policy, entrepreneurship policy, taxation policy, financial policy, the health of the financial sector, availability of finance, and market access for firms that create new products or services. Infrastructure conditions include the availability of Information and Communication Technologies (ICTs), Transport, Energy, Standard-Setting, Metrology, Security, and so on. Figure 3.1 – Innovation Policy Framework Conditions 61 Finally, it is considered that without efficient and effective linkages, the production of knowledge, as well as transfer of knowledge for the creation of new products and services, would be hampered. For innovation to thrive in the production space it is important that the innovation environment conditions are healthy, governed by sound policy, with effective linkages across various conditions as well as the production space and consequently the market that would consume the innovations and drive the productivity growth. Fagerberg and Srholec (2008) show that “Innovation System” and “Governance” – as understood in the context of the factor analysis carried out in their work – are important for economic development. In this chapter, we bring governance, business policy, education, R&D and infrastructure within the policy sphere of innovation systems. The concept of capabilities or enablers is discussed in conjunction with structural change policies in Rodrik’s (2013b) work where slow growth is explained as an outcome of a high level of capabilities and fundamentals, accompanied with slow structural change. One of the most influential studies for understanding the characteristics of innovation systems looks at innovation systems in the context of technological innovation through fifteen country case-studies (Nelson, 1993). The work illustrates that across the countries there was a variation amongst the most important feature of the country’s innovation system and that is associated to the policies and decision that the country implements to create a comparative advantage (Nelson, 1993). 3.2.2. The literature on GCC countries The GCC countries offer a setting where the government influence spans across more spheres than the countries that have been discussed above in the innovation policy literature. The government holdings in oil and gas production provide GCC countries financial resources to invest in developing human capital, improving R&D enablers, governance and business environments, and to create business and innovation clusters. The GCC countries being relatively young oil-based economies, offer ideal grounds for comparing the pathways that countries with natural resource availability in early stages of development may take to develop 62 a sustainable, diversified and innovative economy. The literature offers very little in the way of understanding the variation in policies between the GCC countries. To the best of our understanding, none of the works discusses the effect of policy inputs on innovation through intermediate outputs and the governments’ ability to implement the policies. A number of works discuss selected specific policy measures in individual GCC countries and a smaller subset offers a comparative narrative. Education policies are perhaps one of the most discussed in the context of GCC countries. The literature discussing primary education policies in the GCC countries, points out that Oman offers a story of success in terms of expanding primary education opportunities and rapid rise in literacy levels, KSA appears to have a problem of low enrolment in primary education, whereas UAE’s primary education system has been plagued with inefficiencies and faces challenges on the supply of teaching staff (Watkins, 2000; Gonzalez, et al., 2008; McMinn, et al., 2015). The secondary education system in GCC is plagued with the so-called middle-child syndrome 24 with secondary school sector being much smaller than the primary school sector, and the majority of secondary school graduates from Oman, KSA and UAE facing a skills and knowledge set mismatch for entering and adjusting to the standards of the job market and tertiary education system (Abouchakra, et al., 2008; Issan & Gomaa, 2010; McGlennon, 2015). The secondary education in GCC also lacks an early transfer of practical skills and knowledge useful for a smooth transition into vocational education programs. Moreover, there is a shortage of adequate vocational and technical training beyond the secondary level (Gonzalez, et al., 2008). Tertiary Education in the GCC countries under discussion has a gap in inputs including the quality of high school graduates and funding (Alyahmadi, 2006; McGlennon, 2015). The quality of both primary and secondary education is not on par with international standards (Gonzalez, et al., 2008). While tertiary education standards in both Oman and KSA are assured by internal as well as external accreditation, UAE has inadequate higher education management systems and inefficient governance that 24 To be neglected because of being in the middle of two others. In this case we mean that primary and tertiary education get more policy attention while policies to strengthen secondary education are weak. However, it may also be that the education expenditure statistics are not accurate in allotting expenditures between primary and secondary education at K-12 public schools. 63 have led to a quality deficit in higher education outputs in UAE (Darandari & Cardew, 2013; McGlennon, 2015). McGlennon (2006) points out that UAE has an open-door policy for universities, leading to the universities offering programs that require minimum investment on their part. The programs have demand mostly from foreign inhabitants in the country. Thus, the majority of tertiary education programs in the UAE are business and IT-related and the actual needs of UAE nationals, local job market alignment and development targets are ignored. The GCC countries’ governments acknowledge the challenges facing the education system and have initiated reforms at various levels. The reforms include; a move from teacher-centred to student-oriented education in the primary and secondary education sector, efforts to match resources to desired outputs, enhancing skills and knowledge at secondary, post-secondary and tertiary level so as to diminish job market mismatch, enhancing linkages, and targeting education design geared towards developing a globalized knowledge economy (The Ministry fo Education, Saudi Arabia, et al., 2004; Alyahmadi, 2006; Abouchakra, et al., 2008; Gonzalez, et al., 2008). Oman, KSA and UAE have faced varied challenges in their pursuit of setting up a world- class R&D system. Lack of coordination within the country and lack of collaboration with foreign researchers is an issue of concern for the Omani R&D system (Thomson Reuters, 2011; UNCTAD, 2014). The literature points towards a lack of a central science and research institute for coordination of research policy up until recently for both Oman and UAE (McGlennon, 2006; Al-Balushi, 2016). Insufficient investment in R&D and the youth of R&D systems are often discussed in the context of GCC countries. 25 UAE has a greater problem of limited availability of tertiary education graduates and their quality. Meanwhile, the limited number of researchers is the main problem in Oman (Al-Balushi, 2016). In contrast to Oman and UAE, the academic R&D research shows a relatively positive picture in KSA. The Saudi government began investing in knowledge basis from the early 2000s with strong government support and alignment of government policy. Shin, et al. (2012) report that research 25 The first formal university in the GCC is the King Saud University, established in 1957 by KSA. In 1966, Kuwait established their first university, followed by Bahrain two years later in 1968, Qatar in 1973, and the UAE in 1976. The last GCC country to open up its first formal university is Oman, where the Sultan Qaboos University was opened in 1986. 64 collaboration in KSA was stable while scientific productivity was increasing dramatically since the late 2000s. They also find that technological development in KSA relies on prior technology i.e. the majority of patents in KSA refer to previously existing foreign patents. The investments into developing a knowledge base in KSA are paying off as they are attracting graduates from around the world and KSA has been able to establish PhD education with the number of PhDs doubling from 2006 to 2010 (Shin, et al., 2012, p. 323). The GCC countries are overall aggressively pushing for stronger R&D systems for transition to a knowledge and innovation-based economy (Al-Hammadi, et al., 2010). Relating foreign direct investment (FDI) and various factors affecting FDI in the GCC countries, Mina (2007) finds that higher trade openness, infrastructure development, and institutional quality have a positive influence on FDI. The study uses trade inflows and outflows to represent trade openness, penetration of telephone and mobile services for representing infrastructure, and rule of law as a proxy for institutional quality. As such it is not clear if the results would hold if alternative definitions for the explanatory variables are used. The GCC countries offer economic stability, political stability, low taxes, legal stability and protection for foreign investors, intellectual property rights, and robust physical infrastructure. Boparikar (2015) argues that the Oman government has pushed entrepreneurship in its development agenda. Shachmurov (2009) also points out that Oman’s economy is one of the most open in the region. In addition to that Al-Ghassani (2010) finds that the education system in Oman has made significant strides to include entrepreneurship education at all levels. While UAE has a strong focus on Free Trade Zones that contribute 80% of the UAE non-oil exports. The free trade zones have been identified in studies as one of the strongest pillars of the country’s diversification strategy and put UAE on the world map as the third most important re-export centre in the world (Shayah & Qifeng, 2015). In “Mapping Entrepreneurship ecosystems of Saudi Arabia” (2010) Khan finds that the business environment in KSA is in early stages of development and does not provide a complete range of support services, financing instruments, institutions and policy actions that have been discussed in the literature to have a positive influence pertaining to big and small businesses, 65 start-ups, and entrepreneurs. The governments of KSA and other GCC countries have taken many steps to develop the SME regime, and the institutionalisation of the policy sphere is expected to deliver positive results in the future (Khan, 2010). The critical question in the context of policy implementation is not only related to the policy itself but also the effectiveness and capacity of the government to implement the policies and providing the essential infrastructure to do so. The works done to study the effectiveness of governance in GCC countries are limited. The economic development in Oman has led to the vast development of infrastructure in Oman. The spending has been focused to establish and improve institutional as well as physical infrastructures, transportation, competitively driven dynamic telecommunication service industry, and power and water sectors (Abdelal, et al., 2008; Rajasekar & Al Raee, 2012; Oxford Business Group, 2014; BMI, 2017). Abdelal et al. (2008) consider that Oman, owing to its geographical, political and economic position in GCC is pulling closer to the international financial system. The move to increase international linkages is accomplished through the development of free-trade zones for manufacturing and services, and the setting up of recreational facilities that attract businesses, highly-skilled labour, knowledge workers and tourists. Oman also has a public-private partnership framework that is considered as one of the more robust ones in the GCC (BMI, 2017). This strength enables Oman to use governance systems to attract private investment (BMI, 2017). Oman is considered to have paid particular attention to governance and regulatory quality in its development path and as such earned the benefits of the development that comes along improved institutional quality (Looney, 2013). The presence of good physical and communication infrastructure in KSA complements the effectiveness of governance and the ability of the Saudi government to implement the policy design (Al Shehry, et al., 2006). However, Al Shehry, et al. (2006) also find that there is a need for enhancing efficiency in terms of cost reduction, fairness, streamlining procedures and reducing fraud and over- crowdedness in KSA’s public services. The participants in the study clearly outline the presence of a gap between planning and implementation. The work also points at “distance” challenges in KSA. First, being the personal distance referring to the limitations in 66 communication between men and women at workplace, public and private sector organisations. The second relates to the physical size of KSA. This natural and social distance among the participant of the KSA’s economy creates inefficiencies in the policy realm (Al Shehry, et al., 2006). Al-Yahya (2010) finds that governance in Oman is constrained not by lack of skills but underutilization of skills, as such effective utilization of talent is an important source of improvement in the quality of governance. The situation in UAE, however, is different with UAE and its constituent Emirates not having a clear government vision, limited technical and entrepreneurial talent, restricted R&D budgets, weak regulatory systems, and inefficiencies in the labour market among others (Schiliro, 2013; Byat & Sultan, 2014; Haouas & Heshmati, 2014). The policies that affect innovation, labour productivity in non-traditional sectors, or sectoral diversity are discussed together in a very limited set of literature. Within this set of literature, it is observed that the level of readiness of GCC countries with respect to key knowledge economy pillars is low. Oman lies amongst the 10th percentile of countries for innovation pillar, KSA lags behind in human resources development and the weakest knowledge economy pillar for UAE is the education pillar (Al-Rahbi, 2008; Ahmed & Alfaki, 2010; Bashehab & Buddhapriya, 2013). In a dedicated chapter on KSA in the Global Innovation Index (GII) Report for 2012, the authors point out at the lack of indigenous engineers and scientists in KSA (Sultan & Zaharnah, 2012). This is in contrast to its GCC neighbour Oman that boasts the highest ratio of science and engineering graduates per capita in the world (Cornell University, INSEAD, and WIPO, 2016). The strengths of the system in UAE according to the literature include; targeted R&D initiatives, sovereign wealth funds, the trust in UAE’s Financial sector despite high exposure and the recent efforts to provide a clear strategy for creating an innovation-based economy. KSA with 40 years of diversification strategy under its belt has succeeded in establishing industrial competitiveness in petrochemicals and downstream products of oil (Seznec, 2011). However, KSA suffers from a lack of entrepreneurial activity and the primary source of transition and innovation in KSA is dependent on large corporations that dominate the industrial landscape in KSA. The 67 discussion on innovation in KSA cannot be complete without discussing the science and technology clusters. Dahran technovalley, KAUST Research Park and Innovation Cluster, and PetroRabigh offer examples of KSA’s efforts to boost R&D and innovation through creating clusters and pulling international organisations to set up their middle east research units in KSA but the success of these units is yet to be observed (Sultan & Zaharnah, 2012). An important observation therein is that the Gulf States have had economic diversification on their agenda for the last half a century (Hvidt, 2013). The work by Hvidt (2013) focuses on the history of diversification and current plans in the countries of the Gulf Cooperation Council. They find that the plans of the GCC countries are targeting a shift towards a production-oriented model where the participants of the economy are encouraged to produce actual goods and services. In another work a comparison of cumulative data for GCC countries KSA, Kuwait and UAE and BRICS region is carried out and the findings point that the development of an effective innovation-based economy is not necessarily associated with expenditures on R&D but rather on efficient allocation of resources and rigorous implementation of a strong innovation policy (Gackstatter, et al., 2014). The above literature provides a good background for our work. There are, however, several points missing. Firstly, there is no explicit empirical evidence about the impact of the composition of education, R&D, business, and governance systems on intermediate and final outputs in the GCC countries in the literature. Secondly, the impact of the combination of the policies that essentially drive the various components of a comprehensive innovation system in GCC is not discussed. These limitations in the literature are not without reason as until recently, data from the countries of the GCC has been sparsely available. In this chapter, inputs, intermediate outputs and the components of the system of innovation in the selected GCC countries are discussed along with the possible impact of the state of these enablers on diversification and innovation in individual countries. An attempt is made to understand the causes of improvements or the absence of innovation and diversification in these countries. However, the eclectic qualitative nature of this work means causation is not established. 68 In the last four decades, the GCC countries have been able to provide a high standard of living for their citizens through economic development based on the exploitation of oil and gas resources. However, as the previous chapter shows, this has not been accompanied by diversification and innovation as was hoped. This fact is alarming in the face of the threats facing the region, including but not limited to, oil price volatility, depletion of oil resources, climate change and the emerging international consensus against the use of fossil fuels. In light of a failure of the policies to deliver sectoral diversification, development of highly productive and innovative sectors; the GCC countries will not be able to maintain economic stability and the high standard of living for their citizens. The high rate of population growth in the region – Oman 2%, KSA 1.5% and UAE 2.5% per year during the period 2016 - make the challenge of sustaining per capita incomes even greater. This work provides a critical comparative study for Oman, KSA and UAE on the policies, enablers, and the outcomes of these on the share of value added of individual sectors in the economy, sectoral labour productivity, and innovation indicators. In the following, we study the innovation policies and their influence on innovation, labour productivity and diversification in the three GCC countries Oman, KSA and UAE. 3.3. The Case of GCC – Policies and Enablers 3.3.1. Section Summary Table 3.1 below highlights the overall strength of policy measures. In terms of education, Oman and the UAE perform equally well in the primary and secondary education policy and outcomes. However, overall government spending in primary education is comparatively lower in Oman and the UAE than it is in KSA. KSA with relatively higher spending in primary education has not been able to achieve positive outcomes in primary education students’ Math and Science results as well as in primary to secondary transition and secondary education students’ results. Oman faces shortcoming in vocational and technical education policy and outcomes, as well as R&D spending, number of scientific researchers and number of research staff. KSA and UAE perform relatively well in vocational and technical education, and R&D 69 spending and R&D staff, while facing challenges in terms of the total number of scientists active in research. The development of R&D clusters and enactment of R&D reforms and coordinated policy appear to be a positive development in Oman’s R&D policy component and relatively stronger in KSA. UAE lags in both of these elements in terms of their R&D policy. Oman appears to be the only one out of the three countries providing targeted research funding programmes through a central R&D administration. In terms of the business environment, all three countries perform low on the ease of resolving insolvency and ease of getting credit indicators. KSA has been slower in the Free Zone implementation policy with the announced free-zones in KSA only reaching operational maturity from 2015 onwards. UAE has used the Free Zone model as a way to boost individual sectors through clustering. Meanwhile, Oman has three active and mature free trade zones focussed on industry, manufacturing and logistics, with the first one established in 2002. While there is a scope for improvement in governance in all three countries, the UAE scores relatively higher in government effectiveness and control of corruption. Oman has all governance indicators positive, with voice and accountability at the mean value. KSA performs positively only on the rule of law component of governance. 70 Table 3.1 – Comparing Innovation Policies and Enablers Country  Innovation Enablers  Oman KSA UAE Primary and Secondary Education Primary Education Spending – – + – – Primary Education Enrolment and Literacy +++ +++ +++ Primary Education Results (TIMMS) + – + Secondary Education Enrolment (Primary to Secondary Transition) ++ – – + Secondary Education Results (TIMMS) + – + Teaching Quality +++ + +++ Primary & Secondary Education Reforms ++ + ++ Summary ++ – + Vocation, Technical and Tertiary Education Vocational and Technical Education – – – + – Tertiary Education Enrolment (Upper Secondary to Tertiary Transition) + ++ + The ratio of Science, Engineering and Technology Graduates +++ + o Summary o + o Research and Development R&D Spending – – + + Number of Researchers – – – Total R&D Staff – ++ + Development of R&D Clusters + ++ – R&D Reforms & Coordinated Policy + ++ o Research Funding Programmes + – – Summary – + – Business Environment Starting a business +++ ++ +++ Getting credit – o – Paying Taxes +++ +++ +++ Trading across borders ++ o ++ Enforcing contracts + o + Resolving insolvency – – – – – Free Trade Zones + o +++ Summary + – + Governance and Infrastructure Government Effectiveness + o ++ Voice and Accountability o – – – Political Stability – – + Regulatory Quality + o + Rule of Law + + + Control of Corruption + o ++ Summary + – ++ Note: Description of the quality of or level of success in policy, enables or outcomes – – – Extremely Negative – – Negative – Slightly Negative o Moderate +++ Highly Positive ++ Positive + Slightly Positive 71 3.3.2. Development of education systems Oman: The first university in Oman was established in 1985. By the early 1990s, the baby- boom population following early development in Oman’s economy was reaching post- secondary age. In 1995 providing education and job opportunities to the young became an important part of the government's agenda. Oman developed its Vision 2020 that focussed on the development of a diversified economy. However, there was no evidence of an increased focus on tertiary education immediately after 1995. The changes in spending trends in tertiary education appear much later. Since 2001 Oman witnessed steady drops in primary education spending as a percent of GDP, that went down from their peak of 2.0% in 2001 to 1.4% in 2011. Similarly, secondary education expenditures as a percent of GDP reduced from 2.1% in 1998 to 1.6% in 2013. We observe that a major portion of this spending was moved towards tertiary education expenditures. This change was especially visible as the expenditure as a percent of GDP on tertiary education was on a growth trajectory increasing from a dip of 0.25% in 2005 to 1.2% in 2009. There was an apparent move from investing in primary and secondary education towards the development of higher education capacity. As basic education infrastructure reached stability and primary education needs of the population were being met, the government had a policy shift to spend more towards higher education. This shift was also in line with a stated effort to strengthen knowledge capabilities for a diversified and innovative economy. KSA has one of the most wide-ranging education systems in GCC countries, mainly due to its early focus on vocational education in addition to tertiary education. It offers universal primary education, secondary education with tracking that includes, science, social and vocational education, and tertiary education. The social track is synonymous to the Islamic education track and occupies a similar position in the Saudi education system as the Secondary Modern Schools occupied in the Tripartite Education System in the United Kingdom from 1945 to 1970. UAE: Data from UAE reveals that the expenditure on education as a percent of GDP in 1998 was 1.2% and was distributed between pre-primary, primary, lower-secondary and upper- 72 secondary education in ratios of 6.6%, 47%, 24% and 21.6% respectively. Expenditure on tertiary education was not included in the overall expenditure. Primary and secondary education, both received close to 50% of the overall education spending. As such, it appears that the government has had the objective to ensure the development of both primary and secondary education. The tertiary education system in UAE, however, lacks a coordinated policy. 3.3.3. Literacy, primary education, secondary education, reforms and performance Literacy and primary education: The three countries of the GCC under discussion – Oman, KSA and United Arab Emirates (UAE) – have performed exceptionally well in terms of youth literacy despite not having compulsory basic education. Starting with modern education in 1970s, Oman had achieved above 97% literacy by early 1990s. As such Oman was the earliest amongst the three, while KSA and UAE were still below 95% in terms of literacy till the early 2000s. By 2015 the youth literacy rates for Oman, KSA and UAE were 99.1%, 99.5% and 99.3% respectively, the gross primary school enrolment was above 100% for the three countries and the primary school enrolment gender parity index was 1.035, 0.995, and 1.03 respectively. The net enrolment rates were 94.5%, 97.6% and 93.4% respectively. The adult literacy at the same time was 93%, 94% and 93% respectively. In Oman, from 1997 to 2001 the education expenditures per primary student have stayed between USD 4000 and USD 5000 per student, thereon we see a marked increase from 2002 onwards, and the expenditures range between USD 6000 and USD 7000 per student. 73 Figure 3.2 – Net enrolment rate, by education level, both sexes (%) – Primary and secondary education (Selected years for which data for all three countries is available) Secondary education: In Oman, the secondary education expenditures have varied overall without any pattern that may be helpful in extracting any links to the quality of education as well as economic development. The one thing that appears from the fluctuating finance at the secondary education level (between USD 5,000 to 10,000 per student on average) is that Oman lacks a clear coordinated secondary education strategy. From 2010 to 2014 we observe a drop in enrolment at the secondary school level that follows a related drop in primary school enrolment, and by 2015 Oman achieved 95% gross (85% net) enrolment in secondary education. For UAE, whereas primary education survival rates are as high as 100%, the completion situation in secondary education is of concern. Firstly, between 2008 and 2012 between 77% to 80% lower secondary school-age children were enrolled for lower secondary school education. This ratio has improved to 88% and 92% in the years 2013 and 2014. Secondly, the gross intake ratio to the last grade of lower secondary school between 2008 and 2014 has been as low as 66% to 71% for both sexes. For KSA, we notice that the enrolment in secondary education (2015) is roughly 20% lower than that of the enrolment in primary school six (6) years earlier (2009). This drop out rate represents a daunting figure with no clear indication of how the children who drop out of education at the age of 12 contribute to their personal development, the society and the economy. Note that the same figure is 7% and 4% for Oman and UAE respectively (See Figure 3.2 above). 65 70 75 80 85 90 95 100 1999 2000 2009 2011 2014 2015N et e nr ol m en t ra te , b y ed uc at io n le ve l, bo th s ex es (% ) Primary Oman Secondary Oman Primary Saudi Arabia Secondary Saudi Arabia Primary United Arab Emirates Secondary United Arab Emirates 74 Figure 3.3 – Net intake ratio to grade 1 of education level, both sexes (%) – Primary and secondary education (Selected years for which data for all three countries is available) Teachers: In Oman, the pupil-teacher ratio at primary and secondary level was 20 and 17, respectively. The same in KSA was close to 10 for both primary and secondary school. While in UAE, the pupil-teacher ratio, at the primary and secondary levels of education, stands at 23 and 16 respectively in 2015. The job satisfaction of science and math teachers at primary education level was very high, with Oman ranked at number three (3) for grade four (4) and number eight (8) for grade eight (8) teachers. 73-74% of grade four (4) and 61-64% grade eight (8) students in Oman were being taught by teachers who were very satisfied with their working conditions. KSA performs a little lower in teachers’ job satisfaction with 55-61% of the students being taught by teachers who were very satisfied with their job. The job satisfaction of science and math teachers at primary education level in UAE is one of the top five (5) in the world with 64-70% students taught by teachers reporting that they were very satisfied and only between 3% to 5% (depending on grade level and subject) of students being taught by teachers who reported less than satisfied on job satisfaction in the year 2015. In Oman, for grade 4 between 58-64% of pupils are taught by math and science teachers who have majored in both the concerned subject and education. For the remaining students, around 33% are taught by teachers who have majored only in the subject concerned or only in education. For grade eight (8) between 36-40% of pupils are taught by math and science teachers who have majored in both the concerned subjects and education, and around 50-60% are taught by teachers who have majored only in the subject concerned or only in education. 80 85 90 95 100 105 110 115 2010 2011 2012 2013 2014 2015 N et in ta ke r at io to g ra de 1 o f ed uc at io n le ve l, bo th s ex es (% ) Primary Oman Secondary Oman Primary Saudi Arabia Secondary Saudi Arabia Primary United Arab Emirates Secondary United Arab Emirates 75 In KSA the percentage of students taught by teachers with both the concerned subjects (Math or Science) and Education qualification was lower and between 17-34%. In the UAE, between 25 to 26% of students are taught by math and science teachers who have majored in both the concerned subject and education. For the remaining students, around 50 to 60% are taught by teachers who have majored only in the subject concerned. Figure 3.4 – Job satisfaction of school teachers as reported in TIMMS 2015 Reforms: In Oman, an important reform – the Basic Education System – was introduced in 1997. This system was to replace all the General Education System gradually and the first schools started with the Basic Education System in 1998. The degree of prevalence of Basic Education System in Oman is not determined at the time of writing this paper. The system promises to be student-oriented rather than the previous teacher-oriented design. The Basic Education Reforms may be the second part of the story of the marked increase in results of Mathematics and Science by 2015. UAE has followed suit in both primary and secondary education. Whereas primary and secondary education reform have been recommended for 0 10 20 30 40 50 60 70 80 Grade 4 Science Grade 4 Math Grade 8 Science Grade 8 Math Jo b sa tis fa tio n of te ac he rs a s pe rc en ta ge o f s tu de nt s ta ug ht b y te ac he rs w ho r ep or t b ei ng " ve ry s at is fie d" (% ) Oman Saudi Arabia United Arab Emirates 76 KSA for some time by experts, any concrete action towards a comprehensive education reform has yet to be seen. Performance: The resource allocation changes and education reforms may be partially responsible for the improved TIMMS results of primary education and secondary education in 2015 in Oman. In terms of the translation of primary education spending to the quality of education, the scenario in Oman has been encouraging. The TIMMS scores for students of 4th Grade age were a low of 385 for Mathematics and 377 for Science in 2011. Similarly, the 8th Grade scores for Mathematics and science were 372 and 423 in 2007, respectively. The increase is visible from 2011 to 2015 where Oman made to the top of TIMMS greatest improvers list showing an increase of 4th Grade score from 385 to 425 for Mathematics and from 377 to 431 in Science. The 8th Grade scores in Mathematics increased from 366 to 403, and those in Science increased from 420 to 455 during the same period. Between 2011 and 2015 KSA has witnessed a fall in the Trends in International Mathematics and Science Scores (TIMMS) Math and Science scores for 4th and 8th Grade students. The scores for 4th Grade Math results went from 410 to 383 and the science result dropped from 429 to 390 respectively. Similarly, the results for 8th Grade Math and Science dropped from 394 to 368 and 436 to 396. It is hard to say what forces are behind this drop as the data related to total and per capita education expenditures for the years 2011 to 2015 was not available. However, the last data point available shows that 2.2% of GDP was spent on primary education. It is also clear from the TIMMS analysis that roughly 90% of that students were taught in a condition whereby they faced shortages in resources for Math and Science teaching for both 4th and 8th grade. The TIMMS scores in the United Arab Emirates showed an improvement from 434 to 452 and 456 to 465 for 4th Grade and 8th Grade Maths scores between 2011 to 2015. The science scores improved from 428 to 451 and 465 to 477 in the same period for grades 4th and 8th, respectively. To put things in perspective the students in the highest-scoring countries scored around 620, and in European countries and Anglo-Saxon countries, the students scored around 500 to 550. One reason for lower scores in the GCC countries may be that students were starting education in primary level at a later age than their peers in other countries 77 Table 3.2 – TIMMS Score for Grade 4 and 8 students in Science and Mathematics Score Country 2011 2015 Trend Grade 4 Science Oman 377 431 + Saudi Arabia 429 390 – United Arab Emirates 428 451 + Grade 4 Math Oman 385 425 + Saudi Arabia 410 383 – United Arab Emirates 434 483 + Grade 8 Science Oman 420 455 + Saudi Arabia 436 396 – United Arab Emirates 465 477 + Grade 8 Math Oman 366 403 + Saudi Arabia 394 368 – United Arab Emirates 456 465 + 3.3.4. Tertiary education and vocational education Tertiary education: In 1997, the total number of students enrolled in tertiary education in and outside Oman was hardly approaching 10,000. By 2005 the number of tertiary education students had increased to 48,500 with government spending a meagre 0.25% of the total GDP on tertiary education. This spending accounted for roughly 290 million USD constant 2011 PPP total expenditure on tertiary education or 6,000 USD constant 2011 PPP per student. It was a drop from the 1997 per student expenditure of roughly 20,000 USD constant 2011 PPP. However, by 2005 onwards the spending increased to 25,000 USD constant 2011 PPP per student, with 127,000 students enrolled in tertiary education leading to a total expenditure of roughly 3.2 billion USD constant 2011 PPP per student or 2.0% of total GDP. Tertiary education expenditures in Oman have been an average of USD 20,000 per from 1997 onwards. 78 Even though the expenditures per student have remained somewhat constant on average the overall expenditure has increased since 2005 from 0.25% of the GDP to nearly 2.0% of GDP by 2015. This increase is reflected in a larger number of Omanis graduating from tertiary education institutes in Oman and foreign countries. Also, there was a marked increase in natural science, mathematics, information technology, statistics and engineering graduates. The share of graduates in these fields of education increased from 27% of total graduates in 2007 to 45% in 2015. The number of higher education graduates in sciences has been acknowledged as the greatest strength of Oman’s young innovation system as Oman has the highest ratio of higher education science and engineering graduates when compared to any other country in the world. This success is rather recent, yet it is one that is bound to reflect positively in building innovation capacity in Oman. Figure 3.5 - Percentage of graduates from tertiary education graduating from various tertiary education (by programme type) In KSA, the transition from upper secondary to tertiary education has a ratio close to one (1). KSA has at least 35 universities and around 70 plus tertiary education institutes. 35% of 47.3% 68.4% 70.7% 42.5% 23.9% 21.8% 6.9% 5.7% 6.3% 3.0% 1.8% 1.0% 0.3% 0.2% 0.2% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2015 2015 2015 Agriculture, Forestry, Fisheries and Vetrinary Services Health & Welfare Natural Sciences, Mathematics & Statistics, Information & Communication Technologies, Engineering, Manufacturing & Construction Education, Arts & Humanities, Social Science Journalism & Law 79 the tertiary education graduates are from tertiary education pertain from engineering, manufacturing, construction, natural science, mathematics, statistics, information and communication technology, and health and welfare programs. Another 15% of graduates are from the education field. Whereas around 50% finish their tertiary education in social science, law, business, and service sector related programmes. In terms of tertiary education, we observe that UAE has two universities ranked at 51-60 in the QS University 50 under 50 ranking. Overall, for tertiary education, we also observe that business, administration, and law programmes form close to 50% of the total graduates in the UAE. This share may be related to the strong focus on trade in the UAE economy. The UAE is the largest host of international branch campuses in the world. It has done very well in increasing the intake of secondary school graduates into tertiary education. In 1993 only 25% of the students that were enrolled in the upper-secondary education two years earlier from were enrolled in tertiary education. This ratio has increased to roughly 80% by 2015. The attrition rate at every level of education is a cause of concern. The technical or vocational education in the UAE is highly focused on business and IT as such it is not clear how the country aims to include the national population in the process of setting up a diversified and innovative economy. Another cause of concern is the type of programmes offered by the UAE’s education system. The branch campuses have become a highlight of the UAE education policy. However, most of them offer the same easy to set up, low-cost, high return programs, such as business administration and information technology. The biggest of these private universities have lower than 15% enrolment of national students. Since there is a large foreigner population, there is currently no mechanism through which the objectives of these universities can be aligned with the labour force, social, and economic growth objectives of the United Arab Emirates. The open and free-market style education system results in branch campuses popping up since 2005 target profitability and foreign student demand rather than future labour market requirements. The result is that the UAE has less than 15% of tertiary education graduates completing natural science, statistics, mathematics, technology, engineering, or construction programmes. 80 Gonzalez et al. (2008) have pointed out for Oman, that secondary school graduates who do not make it to tertiary education or higher education are not ready for vocational and service jobs and there is not adequate post-secondary vocational, technical and service education available thereon. The data for KSA does not show the numbers related to vocational secondary education. The problem of getting the youth ready for the labour market in UAE is compounded by the fact that there is no coordinated policy for vocational education. Some vocational training institutes operate in the private and public sector, however, since UAE has a very high expatriate population, the education provided by the institutes is not always aimed at the requirements of UAE rather the monetary objectives that may be greatly driven by the educational demands of the foreigners living in the UAE. 3.3.5. R&D The condition of the R&D policy sphere is essentially different from the education sphere for GCC countries. Whereas Oman performs relatively well in education outcomes, KSA has a clear advantage on the R&D inputs side of the picture. Oman spends the least in proportion to its GDP on R&D followed by UAE with KSA having the highest spending on R&D as a percent of its GDP. The number of researchers in Oman is also lower than its GCC neighbours. KSA has more high-ranking universities and higher rating on research capabilities of these universities. UAE performs better than Oman, whereas Oman lags in the research arena. The development of R&D parks and sovereign funds focussed on R&D and Innovation has also entered the agendas of all three countries however these agendas will take time to show results. Expenditure and Inputs: The gross expenditure on research and development as a percent of GDP for Oman has hovered between 0.14% to 0.25% during the period 2011 to 2016. The headcount number of researchers per million population has dropped gradually from 450 to 129 during the same period. The research expenditure has shifted from higher education institutes to the government between 2011 and 2012. The total GERD has ranged between USD 150 million and 250 million per year (constant USD 2005 PPP). The contribution of business enterprises towards R&D has been in the range of 24 to 29% in the recent year. 81 KSA, in contrast, has been spending between 0.8 to 0.9% as a percent of its GDP on R&D. The UAE spent only 0.5% of its GDP on Research and Development (R&D) in 2011 with 0.2% spent by the public sector, another 0.14% by higher education institutes, and only 0.14% by business enterprises. The Gross Expenditure on Research and Development (GERD) in the UAE increased to 0.7% by 2014 with 0.5% carried out by the government and 0.2% by private enterprises. The GERD as a percent of GDP in the UAE was 0.96% in 2016. Figure 3.6 - GERD as a percentage of GDP - (Selected years for which data for all three countries is available) Whereas Oman lags in most inputs that contribute to research and development, it boasts the highest ratio of science and engineering graduates as a portion of its total graduates when compared to rest of the world, as discussed earlier. The headcount of researchers shows that the number of researchers active in research was 1446, 1301 and 1235 in 2011, 2012 and 2013 respectively. The total R&D personnel in the UAE numbered at 11400 in 2011 that is roughly 1300 per million inhabitants. The same data was not available for KSA however is expected to be higher than both Oman and UAE. 0 0.2 0.4 0.6 0.8 1 1.2 2009 2010 2011 2012 2013 2014 2015 2016 G ER D a s a pe rc en ta ge o f G D P (% ) Oman Saudi Arabia United Arab Emirates 82 Figure 3.7 - R&D personnel, headcount basis, per million inhabitants (in the year 2015, breakdown for UAE not available) Performance: The average QS Universities Ranking for top three universities in Oman has hovered around 9 (0 being the lowest and 100 being the highest). However, it must be noted that Oman with 3 million inhabitants has only one university ranked in QS top 700. It is noteworthy that this university is ranked 61 in universities under 50 years old. The top 10 universities in UAE showed a rating of medium to high in terms of research, with the highest rating possible being very high. Only two universities the United Arab Emirates University and Khalifa University performed at the level of high ranking in research according to QS University Ranking 2016. Oman scores a meagre 4.2 (0=weakest 100=strongest) on the R&D in the Innovation Input sub-index of the Global Innovation Index 2017 report. This performance followed a gradual drop from 9.0 in the previous 5 years. KSA performed very well in its R&D in the Innovation Input sub-index of the Global Innovation Index, having a score of 41.2 and ranked 22 in the world. UAE was not doing as well as it would appear and had a score of 18.2 on the R&D sub-index. Reforms, Coordinated Policy and Central Research Institution: To improve the situation of research and development and drive the research and innovation ecosystem in Oman towards a positive direction the Research Council (TRC) with it research and innovation section was set up in 2005. Since then, the research council has launched many initiatives. The UAE lacks 0 1000 2000 3000 4000 5000 6000 7000 8000 Oman Saudi Arabia United Arab Emirates R & D s ta ff , h ea d co u n t b a si s, p er m il li o n i n h a b it a n ts Total Researchers Technicians Other Supporting Staff 83 a central authority and a central policy for directing Research and Development in the country. The main weaknesses observed include a weak intellectual property regime, non- clarity of the government in terms in terms of the R&D priorities that the industry and academia is to follow, and absence of an overreaching regulatory body that can communicate the governments R&D priority areas with the ability to facilitate and/or enforce partnerships between national and international stakeholders. Whereas Oman is the first one to have a central research coordination institution, a research agenda has only been recently outlined. However, it seems that KSA does quite well without a central research institution. KSA does not have a coordinated research policy or a dedicated central research institution and in UAE research policy is handled by the Ministry of Higher Education and Research. In the same regard, the UAE has also tried to improve its R&D footprint by trying to establish a central policy and induce a coordinated R&D effort. Research Funding: Oman has the Open Research Grants Program that allocates funds to small and medium-sized research in areas highlighted as priorities in the National Research Strategy26. In 2011, the fifth cycle of the Open Research Grants Program set 13 million constant 2011 USD PPP for the funding of 20 Research Proposals that included funding towards 12 PhD and 20 Masters students associated with the projects. In 2010 the Industrial Innovation Centre (IIC) took place of the Industrial Innovation assistance program that existed before. Between 2009 and 2013 this program invested 7.8 million constant 2011 USD PPP for 31 projects of which 2.6 million USD were contributed by the IIC and the remaining from the Industrial Partners in the Projects. The TRC also had the Strategic Research Program since 2012, and the Faculty Mentored Undergraduate Research Award Program (FURAP) since 2013 that sponsored 249 projects by 2016. The annual spending of the FURAP program is roughly half a million USD. The TRC also established the publication & awareness department to create a learning culture using media. Their initiatives included TRC youth science and innovation show, print media coverage, 26 The areas of research highlighted as priorities in the National Research Strategy include; Culture & Basic Sciences, Energy & Industry, Environment & Biological Resources, Education & Human Resources, Health & Social Services, Information & Communication Technology 84 social media coverage, television coverage, and showcasing research activities in exhibitions. Since there are no central research institutes in KSA and UAE it is hard to estimate the research grant and funding of research project in these countries R&D and Innovation Clusters and Sovereign Funds: In 2016 The Research Council (TRC) in Oman launched Ibtikar, an innovation-investment company with a 360 million USD constant 2011 PPP equivalent initial capital to invest in start-ups and also to attract international companies that will utilise Oman's natural and human capital and benefit Oman directly with knowledge transfer and development. In 2016, the TRC finalised the development of its Innovation Park Muscat (IPM) with 560,000 square meters of area research, innovation, incubation integrated facilities available for research and development activities. The program has an estimated 8 to 10 years maturation period, and after that it is expected to generate jobs, provide direct revenue and establish a flow of companies to Oman while increasing foreign direct investment. KSA has established at least four (4) major research clusters that include the KAUST Research and Technology Park, Riyadh Valley Makkah Park, and Dahran TechnoValley (DTV). DTV is particularly interesting as it started with four (4) major international business and two (2) Saudi corporates setting up their research centres in 2011. By 2017 there are now 12 research centres belonging to major and international businesses and four (4) for Saudi firms in DTV. There are plans of a 2 trillion USD sovereign fund for R&D and innovation projects that will be accompanied by a national transformation plan. UAE does not have dedicated research and innovation cluster or sovereign fund for setting up technology, innovation, and research related institutes and organisation, however, three significant clusters – Dubai Science Park, Dubai Internet City and Dubai Silicon Oasis – are established for businesses with the expectation of R&D and innovation spill-overs. 85 3.3.6. Business and Entrepreneurship Index of Economic Freedom: Oman has maintained relatively stable scores on the overall Index of Economic Freedom. The overall score is composed of many different indices amongst these we have selected business freedom, trade, investment, and monetary indices as they directly relate with government policies to boost businesses (100 representing the frontier). We observe that Oman scores high on business freedom and trade conditions with the scores being in the range of 70 to 80 during most of the period from 1995 to 2015. During the same period, monetary and investment conditions have improved continuously with recent scores on the sub-indices being 60 and 65, respectively. The same is true for KSA with the scores on business freedom and trade freedom, improving regularly between 2005 and 2016 from 50 and 60 to 80 and 70, respectively. However, the scores on the index for business freedom are currently lower than those observed in 1995 where KSA scored 85 on the business freedom sub-index. Fiscal freedom has remained at 100 while investment freedom has been below 50 and hovered from 30 to 40 for most of the period 1995 to 2016. The UAE has a long tradition of business and entrepreneurship and in particular international trade. From 1996 to 2002 we observe that business freedom was at 85 out of 100 on the business freedom sub-index of the Index of Economic Freedom (IOEF). From 2002 to 2008, we observe a steady drop in the business freedom sub-index, and after that by 2016, it increases to 80. Trade freedom sub- index has maintained around 80 points from 1995 to 2016. Fiscal freedom has been steady at 100 or close to 100 on the IOEF index. However, investment freedom appears to be on the lower side and has been below 50 on the index and in most years closer to 30. Doing Business: The Doing Business indicators present a more detailed picture of the business policies and the business environment. Figure 3.8 below, shows distance from the frontier (DTF) for various business-related policies in Oman. The measure DTF is ordered from 0 being the furthest from the frontier and 100 being at the frontier. We notice that since 2008 Oman has made great strides in improving the Starting a Business measure, going from 49.22 in 2008 to 92.85 in 2017. This improvement has been achieved by reducing the number of days, procedures, and capital required to start a business. For example, the paid-in minimum 86 capital was reduced to 0% of the income per capita. It is apparent that policies to boost entrepreneurship have been put in place in Oman. Paying taxes and Trading across borders measures maintained close to the frontier in the last 13 years and were at 90.6 and 80.17 in 2017, respectively. Enforcing contracts measure has seen modest improvement in from 2014 to 2015 and was steady at 61.55 in 2017. The areas where Oman is far behind the frontier includes that of Resolving insolvency and Getting credit at 42.65 and 35 during 2017. Figure 3.8 – Distance to frontier (DTF) on Doing Business indicators – Oman, KSA and UAE (100 being the frontier) – 2004 to 1017 KSA has been close to the frontier with the value of Paying Taxes DTF close to 100 between 2013 to 2015 owing to low tax requirements and ease of tax payment systems, however, in the last two years 2016 and 2016 the Paying Taxes DTF has fallen to 80. The ease of starting a business DTF saw an improvement in 2008 and jumped from 30 to 70 and since then has slowly hovered close to a value of 80 still below the leader in ease of starting a business among the GCC countries that is Oman. The situation regarding ease of resolving insolvency is alarming as it appears that KSA is furthest from the frontier. The ease of resolving insolvency can be paramount in terms of attracting businesses and in this case could be a potential 0 10 20 30 40 50 60 70 80 90 100 Starting a Business - DTF Getting Credit - DTF Paying Taxes - DTF Trading across Borders - DTF Enforcing Contracts - DTF Resolving Insolvency - DTF  Oman  Saudi Arabia  United Arab Emirates 87 inhibitor of risk-taking behaviour among entrepreneurs leading to slow down of innovation efforts. United Arab Emirate has a low tax regime, and this appears in the Doing Business indicators with UAE close to the frontier in terms of Ease of Paying Taxes. Trading across borders had always been one of the strong points of the UAE economy however, during 2015 and 2016 the UAE has witnessed movements away from the frontier. The ease of starting a business that is critical for entrepreneurial activity has steadily improved going from 63 to 91 from 2004 to 2016. Finally, resolving insolvency, getting credit and enforcing contracts remain areas where the UAE needs further efforts and policy to get closer to the frontier. Trade Zones: There are at least 24 free trade zones in the UAE, with most of them concentrated in the Emirate of Dubai. The companies that set up in free trade zones in the UAE can benefit from 100% foreign ownership, full repatriation of capital and profits, no requirement for minimum capital investment, quick approval procedures for setup and no corporate or personal income taxes. However, a free trade-zone company in UAE cannot trade directly with the UAE or broader GCC market. Such trade can be made only through locally appointed distributors that need to have appropriate trade licenses and 51% ownership by UAE National. A customs duty of 5% is applicable when the free zone company sells its products or services in the local market. Through the establishment and effective operation of trade zones, UAE has been able to accomplish two direct goals. Firstly, the country profits from real estate development, and secondly, it can make products and services from all around the world to be available at its doorsteps. Whereas the target of diversification of the economy is clearly helped through free trade zone, it is not very clear how the policies related to activities in the free trade-zones impact the aim of inducing innovation in the UAE. 3.3.7. Governance and Infrastructure The worldwide governance indicators (WGI) from World Bank show that Oman has improved in the rule of law and control of corruption indices from 1995 up to the early 2000s and there 88 on seen a drop. Regulatory quality and government effectiveness data show a regular drop from 1995 to 2013. The situation is not alarming as Oman is above the threshold of 50 points for all governance indices indicating reasonable governance structure and confidence in the public authorities. However, it can be observed that there is a lot of room for improvements. The countries with the best quality and perceptions of governance exhibit scores of above 90 points in terms of government effectiveness, regulatory quality, rule of law and control of corruption. Figure 3.9 – World Governance Indicators – Oman, KSA, UAE – 1996-2015 The main improvements are apparent in the Regulator Quality in Oman that went up from 50.4 to 64.2 between the years 2000 and 2002. Other indicators including Government Effectiveness, Political Stability, Rule of Law, and Control of Corruption have stayed somewhat stable between 1996 and 2017. The indicator that seems to show the worst performance is that of Voice and Accountability. The was a steady drop from 39.76 to 27.61 points between 1996 and 2006 and 2007 onwards the indicator has been stable around 30 points. 0 10 20 30 40 50 60 70 80 90 100 Voice and Accountability Political Stability Government Effectiveness Regulatory Quality Rule of Law Control of Corruption  Oman  Saudi Arabia  United Arab Emirates 89 The WGI show that KSA has the lowest levels for all Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality and Control of Corruption. Political Stability dropped from 50 to 40, while control of corruption has improved from 40 to 50 from 2000 to 2015. Regulatory quality and rule of law have remained relatively steady at approximately 50 and 55, respectively. Government effectiveness has steadily improved from 45 to 55 from 1996 to 2015. Finally, voice and accountability measure is low as expected for GCC countries. The World Governance Indicator shows that the UAE has been steadily improving its governance. Government effectiveness has improved from 63.75 to 80.74 from in the last 20 years, from 1996 to 2015. Political Stability and Rule of Law appear to be in a stable and healthy position since at a level around 65 out 100 on the indices. Regulatory quality had seen a dip from 1996 to 2010 and thereafter improvements have been observed. The regulatory quality from 2010 to 2015 has improved from 57 points to 72.5 points on the index. As is often the case in the GCC region, Voice and Accountability is seen to be low and dropping from 40 to 30 points from 1996 to 2015. 3.4. The Outputs of GCC – Indicators of Innovation and Diversification 3.4.1. Section Summary Table 3.3 below shows that patent activity in Oman is lower than both KSA and UAE. Meanwhile, industrial design activity is relatively higher in Oman and UAE than KSA on a per capita basis. In terms of sectoral diversification, Oman and UAE have explicit positive trends in tourism and trade sectors respectively, and KSA has explicit positive trends in all sectors other than industry and manufacturing sector. An increase in the share of value added of industry and manufacturing sector in the economy of Oman was observed between 1990 and 2013. While the same was true for UAE, yet the overall increase was observed to go through cycles of fluctuation during the period. While the trade, financial and business service and public sector were shrinking in Oman the overall productivity in the sectors was increasing. The industry and manufacturing sector witnessed a decrease in labour 90 productivity. The UAE also witnessed a similar trend in industry and manufacturing, while the opposite was true in KSA where the overall share of manufacturing reduced as the labour productivity in these sectors grew. The overall trend shows that Oman improved in the tourism sector with the value added from the tourism sector as a percent of total GDP being stable with increasing labour productivity. KSA performing well in trade sector with both the share of value added of the trade sector and labour productivity of the trade sector improving. KSA’s share in value added for both tourism, and, finance and business-related service sector was stable with increasing labour productivity, while the public, education and health sectors increased in size with stable labour productivity. UAE has a clear positive trend in only trade sector where the share of value added increased with labour productivity being stable. Table 3.3 - Comparing Innovation and Diversification Outputs Country  Innovation and Diversification Outputs  Oman KSA UAE Innovation Patent Applications – – + o Variation in Patents by Fields of Technology – – o + Industrial Design ++ + ++ Summary - o + Share of Value Added (VA) & Labour Productivity (LP) by Sector Industry – Chemical & Metals (VA) ++ – + Industry – Machinery, Equipment and Other Manufacturing (VA) ++ – + Trade – Wholesale and Retail (VA) – ++ + Service – Tourism Related (VA) o o o Service – Finance and Business (VA) – o o Public Sector, Education and Health (VA) – + – Manufacturing (LP) – + – Trade (LP) ++ + o Service – Tourism Related (LP) ++ + o Service – Finance and Business (LP) ++ + – Public, Education, and Health (LP) ++ o + Summary ++ o - Note: Description of the quality of or level of success in policy, enablers or outcomes – – – Extremely Negative – – Negative – Slightly Negative o Moderate +++ Highly Positive ++ Positive + Slightly Positive 91 3.4.2. Patents, Trademarks and Industrial designs The patent, trademark and industrial design data show that Oman is slowly progressing in a positive direction. The number of patent applications abroad had risen to 24 by the year 2012 coming back to around 10 in the year 2015. Resident Patent application has also seen a positive trend. It is noteworthy that a major proportion of the patent applications is related to Civil Engineering. Non-Resident Industrial Design Registrations have tripled from 2007 to 2015. Oman’s innovation output is dwarfed in comparison to KSA that has developed its R&D and innovation systems for a longer period than Oman. Additionally, Oman’s pace in producing patent and industrial design applications and grants is much slower than the UAE which appears to be active in the arena since 2001 onwards (See Figure 3.10 and Figure 3.12). Figure 3.10 – Patent, Trademark & Industrial Design Data – Oman (WIPO Statistics Database) 92 KSA appears to have benefited from the conversion industry set up during the early phases of its development, and by 2015 it had 200 resident patent grants from those filed previously while filing 1000 resident patent applications during the year. The top five fields in which patents are filed in KSA are associated with chemical and material industry, and this association clarifies the significant role of the chemical process industry in development in KSA. Industrial design applications in both Oman and KSA seem to have picked up pace since 2009. KSA also shows a higher number of patent applications, perhaps, due to having set up a local patent office as well as having the GCC patent office located in its borders. Figure 3.11 – Patent, Trademark & Industrial Design Data – KSA (WIPO Statistics Database) The patent, trademark and industrial design data for UAE shoes that patents application in UAE from 2001 and 2015 are from in many different areas. Civil engineering patents are related to the real 93 estate development in UAE, whereas computer technology, engines and machine patent application, information technology and medical technology patent application are associated with the various free zones that have been instated to develop the economy and push local innovation. Figure 3.12 – Patent, Trademark & Industrial Design Data - UAE (WIPO Statistics Database) Overall trends exhibit that patenting, design and trademark registration activity when considered on a per capita basis is roughly twenty (20) times higher in KSA than Oman, and ten (10) times higher in UAE than Oman. Meanwhile, Oman and UAE have similar levels of activity in terms of industrial design applications and registrations on a per capita basis that is roughly ten (10) times higher than the activity in this area in KSA. 94 3.4.3. Non-traditional sector - share in the economy and labour productivity In terms of share of value added of sectors as a percent of total output in Oman, we observe that value added in the mining and quarrying sector increased from 7.0% to 12.0%. In addition to that, we observe increases in Chemical Manufacturing, Metal Manufacturing and Machinery and transport Manufacturing in the range of 1.0% to 2.5%. One exception to this apparent growth in the share of sectors in addition to the Mining and Quarrying sector is that of the Hotel and Restaurant sector within the Services broad category. Despite having a strong policy to promote the tourism sector in Oman, the sector has not grown in absolute terms. We observe in Figure 3.13 that the share of Hotels, Restaurants, and Transport services sector in the economy has remained virtually stagnant in the past few years. KSA, on the other hand, observed a drop in the share of value added as a percent of total output from the Mining and Quarrying sector along with Petroleum, Chemical and Non-Metallic manufacturing sectors, Metal products sector. Wholesale and Retail Trade is the leading sector in private industry that appears to be improving from 2002 onwards. UAE’s Mining and Quarrying Sector has also reduced in the share of value added from 2002 onward while Electrical and Machinery, Transport Equipment and Other Manufacturing have seen a rise in their share of value added as a percent of total output in the UAE economy. 0% 5% 10% 15% 20% 25% 30% S h a re o f V a lu e A d d ed in t o ta l O u tp u t (% )  Oman  Saudi Arabia  United Arab Emirates 95 Figure 3.13 – Share of value added by sector as a percent of total output for Oman, KSA and United Arab Emirates (1990 to 2013) Figure 3.14 – Labour Productivity by sector, KSA and United Arab Emirates (1990 to 2013) The labour productivity changes in the various sector provide additional information on sectoral diversification in the three countries. Figure 3.14 above shows that labour productivity in all sectors except Mining and Quarrying, Manufacturing, and Electricity, Gas & Water saw a steady increase between 1998 and 2008. Consequently, Manufacturing in Oman has dropped from being the most productive to the second most productive sectors in Oman. The productivity increase in the transport and communication sector may be related to two events. In the transport sector Oman Air a government-owned airline that was formed in 1993 became a part of IATA and expanded its operations in 1998. Also, in the transport sector, Oman Shipping Company, a government-owned enterprise was incorporated in 2003 and started operations. Oman also deregulated the telecommunications sector in 2004, privatised part of the government-owned telecommunication company, and opened the licensing market for an additional private player in the market. These three events coincide 100 200 300 400 500 600 700 800 900 1,000 L a b o u r P ro d u ct iv it y (U S D p er e m p lo y ee )  Oman  Saudi Arabia  United Arab Emirates 96 with the first and second increase in labour productivity in the transportation and communication sectors. KSA’s labour productivity in all sectors appears to be stagnant from 2007 onwards. This levelling followed the rise and fall of labour productivity in all sectors except Mining and Quarrying from 1994 to 2007 with the peak productivity observed in most sectors in the years 2001 and 2002. The mining sector has retained productivity levels around to USD 200,000 per employee with a slightly increasing trend. In the UAE, the mining and quarrying sector and manufacturing sector that altogether contribute less than 20% of the value-added in the economy have become the most productive sectors in the economy. It is interesting to note that the trade sector while being the largest employer in the economy is amongst the lowest productive sectors in the economy of the UAE. It contributes approximately 10% of value- added in the overall economy of the UAE. 3.5. Connecting Policies, Enablers and Outcomes The comparative analysis of the three countries discusses indicates that all three of them have had very little success in terms of innovative activity and diversification in the economy away from natural resources such as oil and gas. This result indicates that the level of success achieved by the countries in their intention to diversify is closely matched to the quality and level of policy inputs and condition of enablers in the country. The pattern indicates that it is often the lowest ranking policy measure or enabler that decides the highest possible level of success in innovation and diversification. Leading to the understanding that the condition of all inputs needs to be strong in order for innovation activity as well as diversification to prosper. Oman’s failure to have substantial progress in innovation activity and only achieving moderate results is coincident with moderate policy measures in the research and development arena. Similarly, KSA’s failure to have a strong industrial and sectoral development policy associated with an excellent business environment has led to moderate success in diversification. Not only that, but the relatively moderate conditions of the primary and 97 secondary education policy and quality expresses in vocational, tertiary graduates and overall labour quality. Even though primary and secondary education quality may be considered as indirectly related to innovation and diversification efforts of a country, yet there is a direct correlation observed between the highest quality of education in the country to the highest level of success achieved in the ultimate outputs of innovative activity and diversification. United Arab Emirates is another example of the same trend where vocational, technical and tertiary education and the condition of the R&D policy and enablers are leading to moderated levels of diversification despite the relatively stronger business environment and governance regime in comparison to the other two countries. It is important to note that the slightly positive output trends for Oman’s diversification arena and those for KSA and UAE in the area of innovation activity are just that – slightly positive. The innovation and diversification goals of these countries are far from their full potential and constrained by the lowest- performing policy areas. Table 3.4 – Summary of policy and enablers condition to innovation and diversification output in the three GCC countries Oman, KSA and United Arab Emirate Country Input Trend and Overall Rating Output Trend Oman Primary & secondary education ++ Innovation (Patenting activity and industrial design) – Vocation, technical & tertiary education o Research & development – Business environment and industrial/sectoral development policy + Diversification (sectoral value added and labour productivity) ++ Governance & infrastructure + KSA Primary & secondary education – Innovation (Patenting activity and industrial design) + Vocation, technical & tertiary education + Research & development + Business environment and industrial/sectoral development policy – Diversification (sectoral value added and labour productivity) o Governance & infrastructure – UAE Primary & secondary education + Innovation (Patenting activity and industrial design) + Vocation, technical & tertiary education o Research & development – Business environment and industrial/sectoral development policy + Diversification (sectoral value added and labour productivity) – Governance & infrastructure ++ Note: Description of the quality of or level of success in policy, enablers or outcomes – – – Extremely Negative – – Negative – Slightly Negative 98 o Moderate +++ Highly Positive ++ Positive + Slightly Positive The summary of these policy inputs and enablers has been discussed in Section 3.3 on page 68 and the detail of these have been presented in the sub-sections. Similarly, the summary and details of the innovation and diversification outputs have been discussed in Section 3.4 on page 89. 3.6. Summary, Discussion and Conclusion The overall conclusion for the three GCC countries discussed is that the innovation and diversification goals of these countries are constrained by the comparatively lowest-performing policy area and the lowest quality enablers. The governments of the GCC countries have focussed in varying degrees on the required policies for improving, education, R&D, governance and business environment. However, we find that as long as all policy areas and enabler are not improved adequately, the diversification and innovation output in these countries will remain far from their maximum potential. The cases of Oman, KSA and UAE show that no one policy alone leads to improvements in innovation and diversification outputs. Overall, the “limiting enabler” for both Oman and UAE are research and development policy. The implementation of the policy set in Oman and UAE generates differing result in the two countries. Oman is most constrained in terms of innovation output and UAE in terms of diversification output. The limiting enablers in the policy set of KSA are primary and secondary education policy, business environment, industrial and sectoral policy, and governance and infrastructure. KSA performs moderately on both innovation and diversification outputs. On the whole, the three countries are far from their full potential in term of the pace of diversification and innovative activity in the economy. The systems of education in the GCC countries developed distinctly. Oman delayed the development of a tertiary education system in subsequence to successes in primary and secondary education in order to provide an adequate base for the next level. In contrast, KSA developed its vocational and tertiary education in parallel to primary and secondary education systems. The UAE seems to have focused equally on the development of primary and 99 secondary education. However, the UAE lacks a specific policy in terms of tertiary and vocational education. The literacy and primary education performance has been excellent for Oman, KSA and UAE. Secondary education seems to lack a clear policy for both Oman and KSA. Oman has the highest enrolment rate in secondary education, whereas KSA and UAE do not perform very well in primary to secondary transfer. The job satisfaction of primary school teachers in Oman and UAE is among the best in the world while KSA lags slightly behind. In terms of qualifications of teachers, Oman does relatively better than KSA and UAE, and a higher proportion of students in Oman are taught by teachers who have both concerned subjects (Math or Science) qualification and education as their major. Oman has been the first in the region to phase-out primary education reforms moving towards a student-oriented approach. UAE has followed suit in both primary and secondary education. Whereas primary and secondary education reform have been recommended for KSA for some time by experts, any firm action towards comprehensive education reform has yet to be seen. The performance improvement in both Oman and UAE has been observed following reforms. The TIMMS scores for maths and science improved in Oman and UAE, while KSA witnessed a drop in TIMMS scores leading to concerns in terms of system performance. In Oman, it is also apparent that the government has put in efforts to increase enrolment in tertiary education. It is estimated that the government funds roughly half of the students in tertiary education. While not all secondary school graduates in Oman enter tertiary education yet the tertiary education science, technology and engineering graduates’ ratio to the total graduates is 45%, that is highest in the world. The transition ratio from upper secondary to tertiary in KSA is close to 1 and in UAE is as low as 0.25. The ratio of natural science, technology and engineering graduates to the total number of graduates in KSA is also high being close to 35%. In the UAE it is only 15%, and most of the tertiary education graduates pertain from the law, social science and business programs. All three countries appear to suffer from lack of technical training, vocational education programs and coordinated education policy 100 The condition of the R&D policy sphere is substantially different from the education sphere for the individual GCC countries. KSA has a clear advantage on the R&D inputs side of the picture. Oman spends the least in proportion to its GDP on R&D followed by UAE with KSA having the highest spending on R&D as a percent of its GDP. The number of researchers in Oman is also lower than its GCC neighbours. KSA has more high-ranking universities and higher rating on research capabilities of these universities. UAE performs better than Oman whereas Oman lags in the research arena. The Development of R&D parks and sovereign funds focussed on R&D and innovation has also entered the agendas of all three countries however these agendas will take time to show results. In understanding the business policies and environment through the lens of Index of Economic Freedom, we observe that Oman does the best among the three countries in terms of providing investment freedom with a score of 70 (out of 100). The scores for investments freedom in both KSA and UAE have remained below 50 (out of 100). The three countries perform similar to each other in terms of business freedom, trade freedom and fiscal freedom with scores close to 70, 80 and 100, respectively. When we look at the business environment through the lens of Doing Business indicators we find that UAE and Oman have the most attractive tax regimes, ease of starting a business and ease of trading across borders with KSA lagging relatively behind. In terms of getting credit and enforcing contracts, the three countries seem to perform equally moderate. KSA has no system installed to ensure smooth insolvency resolution while Oman and UAE perform moderately in this aspect. In terms of governance, Oman and UAE perform well with most scores being in the range of 50 to 70. Oman has seen improvements in regulatory quality and UAE in control of corruption from 2002 onwards. KSA lags with scores ranging from 40 to 55 in all aspect of governance. Voice and accountability is a critical area for attention in order to develop and improve the overall standard of governance in the GCC countries. Both KSA and UAE are performing better than Oman in terms of innovation output. KSA’s patent output seems to be concentrated on chemical and conversion industry. Whereas, UAE seems to have patent activity in a variety of areas. The development of R&D systems in 101 parallel with education systems, development of the conversion industry and pulling innovation and R&D centres of multinational firms seems to be paying off for KSA in terms of high patent and design activity. UAE has also benefited from its free zone and clusters model triggering innovative activity in the particular areas that have dedicated market clusters in the country. Despite having a stronger education base, Oman lacks the relevant market, business environment and industrial setup for the high paced and diversified innovative activity. These sets of policy instruments and conditions act as enablers for the market to innovate and the economy to diversify. The limitations in inputs, policy and enablers that are common for Oman, KSA, and UAE are vocational education, R&D systems and provision of financial instruments to boost entrepreneurship. The different limitations for the three countries in relation to each other are R&D for Oman, primary and secondary education for KSA, and tertiary education in science and engineering disciplines for UAE. In this chapter, we are able to isolate and discuss the performance of policy measures and enablers. We observe that there are limitations in the policy area. Similarly, we have also been able to identify the gaps and relative performance differences in innovation and diversification for the three countries. We suggest that these performance differences are correlated to limitations in the input policies enablers and intermediate outputs of the input policies. However, due to this chapter being a qualitative study, we are unable to confirm the causal inferences. It is recommended that a quantitative study is carried out to relate the lowest-performing policy areas to innovation and diversification output. The main challenge in such a study would be to normalise differing policy and enabler on a standard scale. The seven-point scale established in this work offers a starting point for such investigations. In conclusion, the innovation and diversification outputs of the GCC countries are much below the ‘highly positive’ level. It is expected that the most competitive countries and regions in the world are performing at or striving to perform at the highest level of output. In the context of a highly competitive global economy, the GCC countries are facing a systemic risk of falling behind rather than catching up. It is evident that some of the policy measures and 102 enablers in place are not at the highest performance levels. In comparison to each other UAE performs better in terms of innovation and Oman perform better in terms of diversification. Yet, these “better” performances are far from full potential. The fact that the countries are not investing in the full spectrum of policies is leading to the diversification efforts being hindered and innovation being slow-paced. 103 Appendix 3-A Sectoral Labour Productivity vs. Oil Price Y-Axis – Labour Productivity, Current USD per employee, X-Axis – Oil Price, USD Figure 3.15 - Labour Productivity by Sector vs. Oil Price – Oman y = -976.94x + 329351 0 100000 200000 300000 400000 500000 0 20 40 60 80 100 120 Mining and Quarrying y = 163.91x + 3395.7 0 5000 10000 15000 20000 25000 0 20 40 60 80 100 120 Agriculture y = 7268.8x + 88726 0 200000 400000 600000 800000 1000000 0 20 40 60 80 100 120 Transport and communications y = 214.55x - 260.79 0 5000 10000 15000 20000 25000 30000 0 20 40 60 80 100 120 Fishing y = 557.45x + 1126.5 0 10000 20000 30000 40000 50000 60000 70000 0 20 40 60 80 100 120 Education, Health and Other Services y = -2174x + 533028 0 100000 200000 300000 400000 500000 600000 700000 0 20 40 60 80 100 120 Manufacturing 104 Y-Axis – Labour Productivity, Current USD per employee, X-Axis – Oil Price, USD Figure 3.16 - Labour Productivity by Sector vs. Oil Price – KSA y = 63.093x + 176556 0 50000 100000 150000 200000 250000 0 20 40 60 80 100 120 Mining and Quarrying y = 68.642x + 7026.8 0 5000 10000 15000 20000 0 20 40 60 80 100 120 Agriculture y = 378.4x + 48618 0 50000 100000 150000 200000 250000 0 20 40 60 80 100 120 Transport and communications y = 61.761x + 9208.80 5000 10000 15000 20000 25000 0 20 40 60 80 100 120 Fishing y = 81.587x + 13821 0 5000 10000 15000 20000 25000 30000 35000 0 20 40 60 80 100 120 Education, Health and Other Services y = 1259.9x + 82058 0 200000 400000 600000 800000 1000000 0 20 40 60 80 100 120 Manufacturing 105 Y-Axis – Labour Productivity, Current USD per employee, X-Axis – Oil Price, USD Figure 3.17 - Labour Productivity by Sector vs. Oil Price – United Arab Emirates y = 2794.8x + 69310 0 50000 100000 150000 200000 250000 300000 350000 400000 0 20 40 60 80 100 120 Mining and Quarrying y = 291.14x + 14650 0 10000 20000 30000 40000 50000 0 20 40 60 80 100 120 Agriculture y = 85.586x + 110720 0 20000 40000 60000 80000 100000 120000 140000 160000 0 20 40 60 80 100 120 Transport and communications y = 501.54x + 3885.4 0 10000 20000 30000 40000 50000 60000 0 20 40 60 80 100 120 Fishing y = 128.89x + 114050 0 50000 100000 150000 200000 0 20 40 60 80 100 120 Education, Health and Other Services y = 5810.3x + 97959 0 100000 200000 300000 400000 500000 600000 700000 800000 0 20 40 60 80 100 120 Manufacturing 106 107 4. Natural Resource Abundance: No Evidence of an Oil Curse Abstract This chapter examines the relationship between labour productivity, capital formation, and natural resource extraction in countries with natural resource reserves. We develop a theoretical two-sector model for a closed economy, that maximises consumption over time, and examine how the control variables - natural resource extraction and the savings rate – determine fixed capital investment. We find that in a closed economy, the overall labour productivity is a positive function of capital investment per labour. That is in turn related to the externally given natural resource price, natural resource reserves and the resource extraction ratio. High natural resource prices and extraction rates provide opportunities to increase the overall investment in fixed capital and thus boost labour productivity. We empirically test this model for oil as a natural resource. The data covers 36 years from 1980 to 2015 and includes 149 countries. Eighty-five (85) of these countries possessed commercially recoverable oil reserves in at least a part of the period covered. We are able to exploit the panel and carry out the estimation using two-way fixed effects. We observe that oil price has an overall positive impact on labour productivity growth in the modern sector. The savings rate and schooling are positively correlated to labour productivity growth as well as fixed capital formation per capita. We find that the oil sector variables – oil reserves and oil extraction ratio – do not contribute to labour productivity growth directly, rather through increased capital formation per capita. An important finding is that higher oil prices lead to increased fixed capital formation per capita. We also observe, while accounting for time period fixed effects and country-level heterogeneities, that countries with larger oil extraction ratios have higher fixed capital formation per capita. We do not observe a statistically significant relationship between higher oil reserves ratio and fixed capital formation. However, we find that countries with larger oil reserves are found to have higher fixed capital formation during higher oil price periods. 108 Simply stating, higher oil revenues are related to greater investments into fixed capital. There are other mechanisms for higher fixed capital formation in oil extracting countries during high oil price periods that have not been considered here. One such mechanism may be that high oil prices increase the desirability of oil-rich countries as a destination for foreign direct investments (FDI). The determination of the mechanism for the impact of high oil prices on non-oil economies is beyond the scope of this chapter and thesis. However, for non-oil economies, a positive relationship may be driven by FDI from oil-rich countries, increased exports of capital goods and consumption goods (and services) to oil-rich countries, and/or innovation in non-oil economies to counter the effects of reduced marginal returns because of increased energy costs per unit produced. We find that the oil-rich countries of the Gulf Cooperation Council (GCC) are able to exploit high oil prices and invest into fixed capital owing to their relatively large oil reserves per capita. Qatar is the most successful among the GCC countries in diverting its natural resource wealth towards fixed capital. The results indicate that natural resources by themselves do not constitute a curse. Countries with large natural resource reserves per capita can use their natural resource outputs to increase overall fixed capital formation. Investment into fixed capital, in turn, leads to higher labour productivity growth. We find that increasing the efficiency of the production systems through improved schooling can also lead to higher fixed capital formation and labour productivity growth in the modern sector. Thus, smart management of natural resources can support the diversification of the economy. The so- called “natural resource curse” originates not in “simply having” natural resources. It is instead the mismanagement of natural resource rents that leads to lower productivity in the modern sector. There is no evidence that natural resource-rich countries are more or less prone to mismanage their rents. Keywords: structural change, natural resource curse, GCC, theoretical modelling, empirical application, capital formation JEL Classification: E21, E24, O13, O47, Q32 109 4.1. Introduction Countries rich in natural resources have been concerned about the extractive nature of their economies. The most important assets of these countries – natural resources – have not always been valued highly at world markets. After the 1970s and 1980s, the world witnessed periods of high natural resource prices, but the volatility has remained high. For example, for a hundred years before 1973, oil had stayed at around 20 USD per barrel in constant 2010 prices. It then went as high as 100 USD a barrel by 1980 and as low as 30 USD by 1989. By 1999 the oil prices had fallen to 15 USD per barrel. The 2000s saw oil prices as high as 130 USD and as low as 30 USD per barrel. The other natural resources followed similar patterns. The policymakers in the natural resource-rich countries have long desired to diversify and move away from their heavy dependence on natural resources. Diversification has been on their agenda for three to four decades. Despite this effort, many natural resource-rich countries have failed to diversify (van der Ploeg, 2011). In this chapter, we explore if this failure to diversify provides evidence of a natural resource curse. In recent years, the focus on the “natural resource curse” research has led to the consensus that the curse is highly context-specific. The curse is best described as a lowering of labour productivity with an increase in revenues from the extraction of natural resources. The literature emphasises the complexities and conditionalities of the curse. This has led to a notion that the curse is not given but is rather a result of the specific policies and the conditions of the ecosystem in which the institutions exist. The presence and intensity of the resource curse depend on the types of resources, socio-political institutions and the linkages with the rest of the economy (Papyrakis, 2017). In this chapter, we focus on the relation between natural resource extraction and productivity. Richard M. Auty (2007) argues that the models formulated in the mid-twentieth century, like the Cobb-Douglas and Harrod-Domar models, did not adequately account for the role of natural resources in the economy. We aim to estimate the dynamics of capital formation and the decision factors in oil extraction and investment. We start from the following premise: If 110 the use of non-renewable extractive natural resources, such as oil, gas, and minerals, does not lead to the accumulation of other forms of productive capital, but instead is used to support only consumption, there will be no income-generating assets to replace it when it is exhausted (Canuto & Cavallari, 2012). We develop a two-sector closed economy theoretical model with the natural resource sector and modern sector being the only two sectors in the economy. The decision variable (control) for the natural resource sector is limited to the natural resource extraction ratio. The second control variable is the savings ratio: the percentage savings from the income generated by the modern sector and the natural resource sector, leaving the rest for consumption. These savings are assumed to be invested into fixed capital. Next, we explore the empirical application of the theoretical model with oil as a natural resource, using data covering a period of 36 years and 149 countries. We impute the productivity and investment estimations using time period and country fixed effect. Accounting for the time period effects as well as individual country level heterogeneities enables us to comment on the causal mechanisms related to labour productivity growth in the modern sector and fixed capital formation. Also, we are able to make additional inferences about the productivity and fixed capital formation in the individual Gulf Cooperation Council (GCC) countries. The chapter is structured as follows. In section 4.2, we present a brief review of the relevant literature. In section 4.3, we develop the theoretical model. Then, we describe the empirical model, data, data reliability, results and postestimation tests in sections 4.4 to 4.8, respectively. Finally, we summarise our main findings, discuss the limitations of this chapter and propose an outlook into future research avenues in section 4.9. 4.2. Literature Review Academics studying the natural resource-based economies have argued varyingly whether these countries are cursed or blessed (van der Ploeg, 2011). The resource curse thesis “interprets a mineral boom as a net economic loss, where the present value of the positive effects of the boom is more than offset by the present value of the negative effects.” (Davis, 111 1995, p. 4). It is also argued that the wealth resulting from natural resources should lead to prosperity as rents from natural resources can lead to government investment in public goods, infrastructure, and other development project expenditures. Such investment would not have been possible if these resources would not have been available. The welfare improving nature of resource-based development is often recognized as a consequence of a new equilibrium with higher incomes and higher consumption. Albeit so, to a great extent the increase in consumption of goods and services is based upon increased imports. There is historical evidence that the development in several European countries and the United States of America (US) followed a similar natural resource-based trajectory (Lederman & Maloney, 2008). This is represented in the work of Alan Gelb and Associates as, “there is evidence that, at least in some cases, high-rent activities… have provided an important stimulus to growth” (Gelb & Associates, 1988, p. 33). The discussion in the literature shows that the short-term gains in welfare are often at the expense of long-term growth. Jeffery Sachs and Andrew Warner (1995) have presented a model for this discrepancy between long term and short term gains of natural resource richness based on the concept that manufacturing and non-resource tradable goods and services are better for growth due to their characteristic learning by doing effects and positive technological spillovers. However, Lederman and Maloney (2007) not only question the robustness of the resource curse finding on econometric grounds but also question the validity of the argument that the natural resource sector is inferior to manufacturing in its growth- enhancing characteristics. Many scholars have been critical of an idea of resource-based development, including Prebisch (1959), Singer (1950), Richard M. Auty (2001), and Jeffrey Sachs and Andrew Warner (2001). However, data from Maddison (1994) shows that from 1913 to 1950, resource-rich countries grew faster than the industrialized countries (Ferranti, et al., 2002). Also, Maloney et al. (2002) and Stijns (2005) find no negative association between resource abundance and growth. The argument on whether resources are an actual curse to nations’ development has come around to a point where it is considered that the curse essentially lies in the management or rather mismanagement of the resource revenues 112 (Amuzegar, 1982, pp. 817-821; Levy, 1978; Davis, 1995, pp. 1773-1776), and thus can be avoided by learning and sound policy implementation (Stijns, 2005). A review of the natural resource curse studies by Papyrakis (2017) concludes that the presence or absence of the resource curse is dependent on the type of resources, socio-political institutions and linkages with the rest of the economy. Good institutions that ensure property rights protection can discourage rent-seeking behaviour in mineral-rich contexts and hence prevent the resource curse phenomena and stimulate economic development (Boschini, et al., 2007; Sarmidi, et al., 2014). The central message is that good institutional setup, in the form of secure property rights, efficient bureaucracies and low levels of corruption, ensures better natural resource sector output management and can turn the curse into a blessing (Anshasy & Katsaiti, 2013; Mehlum, et al., 2006). The inability of the neoclassical growth models to account for the role that natural resources play in a country’s path towards economic growth and stability has been highlighted, among others, by Sachs & Warner (1995) and Auty (2007). In an attempt to introduce non-renewable resources in addition to labour and capital in the neo-classical model, Henry Thompson does not find evidence of natural resource rent contribution to maintaining a stable per capita income (Thompson, 2012). Even though the majority of the countries in the world rely on natural resources for meeting their economic objectives to a considerable extent, we also find that theoretical models accounting for the natural resource sector are rare. Keeping in mind this critical limitation in the literature, we have attempted to construct a theoretical model that accounts for natural resource output in the total economic output that is either consumed or saved for investment in fixed capital. In addition to that, we explore the empirical application of the theoretical model that we develop. In order to do this, we build upon the literature that focuses on the mechanisms and policies that create a robust economy through investments from the natural resource sector. In the following section, we present the model accommodating the decision factors in oil extraction and investment into fixed capital. 113 4.3. Modelling the natural resource extraction and capital investment relationship We consider a two-sector economy where the total output is the sum of the output of the natural resource sector and the non-natural resource sector. The output of the non-natural resource sector (Barro & Salai-i-Martin, 2004) is given by, 𝑄𝑄 = 𝐾𝐾 𝛼𝛼𝐴𝐴𝐴𝐴1−𝛼𝛼 4.1 Where Q is the output of the non-oil sector, K is the total capital in the non-oil sector, L is the labour force in the economy. Assuming constant growth of labour, �̇�𝐴 = 𝛽𝛽𝐴𝐴 4.2 A is the efficiency parameter and is often considered to be the absorptive capacity of the economy. It is considered exogenous in this equation and represents the efficiency with which the capital and labour are converted to output. The output elasticity of the capital is represented by α. It is the responsiveness of output to a change in the level of capital used in production, ceteris paribus. For example, if α = 0.75, a 1% increase in capital usage would approximately lead to a 0.75% increase in output. The output elasticity of labour is 1 - α. The growth rate of labour is β. Dividing Equation 4.1, 𝑄𝑄 = 𝐴𝐴𝐾𝐾𝛼𝛼𝐴𝐴1−𝛼𝛼, by L on both sides we get: 𝑄𝑄 𝐴𝐴 = 𝐴𝐴� 𝐾𝐾 𝐴𝐴 � 𝛼𝛼 4.3 Let 𝑞𝑞 be defined as total output per labour and 𝑘𝑘 be defined as total capital per labour in the economy, 𝑞𝑞 = 𝑄𝑄 𝐴𝐴 = 𝐴𝐴𝑘𝑘𝛼𝛼 4.4 Where, 𝑘𝑘 = 𝐾𝐾 𝐴𝐴 4.5 114 Let us consider the example of the oil sector as the only natural resource sector in the economy. R represents the producible oil reserves in constant price equivalent of barrels. In such a case, the change in the natural resource reserves is given by the total reserves extracted (Hoel, 2015). 𝑂𝑂 = −�̇�𝑅 4.6 Where O is the oil extraction expressed as the total number of barrels produced (evaluated in constant prices; note that in case of other natural resources, this is simply the total units of the natural resource produced, evaluated at constant prices). With an externally given price of oil Poil and the total producible reserves of oil in barrels Rbbl, the producible oil reserves in constant price equivalent of barrels are given as 𝑅𝑅 = 𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙. 𝑅𝑅𝑏𝑏𝑏𝑏𝑙𝑙. Also, the extracted reserves are a fraction of the total reserves. This fraction is given by ‘o’ and is called the natural resource extraction ratio 𝑂𝑂 = 𝑜𝑜𝑅𝑅 4.7 Dividing both sides with L we get, 𝑂𝑂 𝐴𝐴 = 𝑜𝑜𝑅𝑅 𝐴𝐴 4.8 Let qoil be defined as the total output of the oil sector per labour and r be defined as total producible oil reserves per labour in the economy, 𝑞𝑞𝑜𝑜𝑜𝑜𝑙𝑙 = 𝑂𝑂 𝐴𝐴 = 𝑜𝑜𝑅𝑅 𝐴𝐴 = 𝑜𝑜𝑜𝑜 4.9 Where, 𝑜𝑜 = 𝑅𝑅 𝐴𝐴 4.10 Taking the derivative with respect to time, we obtain the change in reserves per labour as follows: �̇�𝑜 = �̇�𝑅 𝐴𝐴 − 𝛽𝛽𝑜𝑜 = −(𝑜𝑜 + 𝛽𝛽)𝑜𝑜 4.11 115 Combining equation 4.4 and 4.9, the total output of the economy per labour is given by: 𝑦𝑦 = 𝑞𝑞 + 𝑞𝑞𝑜𝑜𝑜𝑜𝑙𝑙 = 𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑜𝑜 4.12 The change in the capital stock in the economy is driven by the depreciation of the capital stock and new investment. Assuming δ is the depreciation rate of capital and ‘I’ is the total new investment into the non-oil sector of the economy, �̇�𝐾 = 𝐼𝐼 − 𝛿𝛿𝐾𝐾 4.13 ‘I’ is a function of the total output and the savings rate for the economy, as given by, 𝑠𝑠 = 𝑆𝑆 𝑌𝑌� 4.14 where S is total saving and S=I 𝐼𝐼 = 𝑠𝑠𝑌𝑌 4.15 Dividing by L on both sides gives us, 𝑖𝑖 = 𝑠𝑠𝑦𝑦 4.16 Where, 𝑖𝑖 = 𝐼𝐼 𝐴𝐴 4.17 And, 𝑦𝑦 = 𝑌𝑌 𝐴𝐴 4.18 Given Equation 4.12 and Equation 4.16: 𝑖𝑖 = 𝑠𝑠(𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜) 4.19 With the total output of the economy given as Y = Q + O �̇�𝐾 = 𝑠𝑠𝐴𝐴𝑘𝑘 𝛼𝛼𝐴𝐴 − 𝑠𝑠𝑜𝑜𝑅𝑅 − 𝛿𝛿𝑘𝑘𝐴𝐴 4.20 Also, taking the derivative of 𝑘𝑘 = 𝐾𝐾 𝐿𝐿 with respect to time: 116 �̇�𝑘 = 𝑠𝑠𝐴𝐴𝑘𝑘 𝛼𝛼 − 𝑠𝑠𝑜𝑜𝑜𝑜 − (𝛿𝛿 + 𝛽𝛽)𝑘𝑘 4.21 Let consumption of the population be given by c, that is the total output of the economy excluding investment per labour, 𝑐𝑐 = 𝑦𝑦 − 𝑖𝑖 4.22 Putting y and i from Equations 4.12 and 4.19 in this equation we get: 𝑐𝑐 = 𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜 − 𝑠𝑠(𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑜𝑜) 4.23 𝑐𝑐 = (1 − 𝑠𝑠)(𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜) 4.24 Presenting Equation 4.11 and 4.21, �̇�𝑜 = −(𝑜𝑜 + 𝛽𝛽)𝑜𝑜 �̇�𝑘 = 𝑠𝑠𝐴𝐴𝑘𝑘 𝛼𝛼 − 𝑠𝑠𝑜𝑜𝑜𝑜 − (𝛿𝛿 + 𝛽𝛽)𝑘𝑘 The Hamiltonian function can be set up as follows to maximise consumption per capita accounting for per capita changes in capital and oil reserves (Barro & Salai-i-Martin, 2004): 𝐻𝐻 = 𝑐𝑐 + 𝜆𝜆1�̇�𝑘 + 𝜆𝜆2�̇�𝑜 4.25 𝐻𝐻 = (1 − 𝑠𝑠)(𝐴𝐴𝑘𝑘 𝛼𝛼 + 𝑜𝑜𝑜𝑜) + 𝜆𝜆1[𝑠𝑠𝐴𝐴𝑘𝑘𝛼𝛼 − 𝑠𝑠𝑜𝑜𝑜𝑜 − 𝛿𝛿(𝑘𝑘 + 𝛽𝛽)𝑘𝑘] − 𝜆𝜆2(𝑜𝑜 + 𝛽𝛽)𝑜𝑜 4.26 Since the technological change is exogenous, oil price 𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙 is given externally, and total reserves in barrels per effective labour are given as 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙. Substituting for 𝑜𝑜 = 𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙. 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙, we have four equations by taking partial derivative of the Hamiltonian with respect to the control and state variables: 𝜕𝜕𝐻𝐻 𝜕𝜕𝑠𝑠 = −(𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙) + 𝜆𝜆1(𝐴𝐴𝑘𝑘𝛼𝛼 + 𝑜𝑜𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙) 4.27 𝜕𝜕𝐻𝐻 𝜕𝜕𝑜𝑜 = (1 − 𝑠𝑠)𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 − 𝜆𝜆1𝑠𝑠𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 − 𝜆𝜆2𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 4.28 117 𝜕𝜕𝐻𝐻 𝜕𝜕𝑘𝑘 = (1 − 𝑠𝑠)𝐴𝐴𝐴𝐴𝑘𝑘𝛼𝛼−1 + 𝜆𝜆1(𝑠𝑠𝐴𝐴𝐴𝐴𝑘𝑘𝛼𝛼−1 − 𝛿𝛿 − 𝛽𝛽) 4.29 𝜕𝜕𝐻𝐻 𝜕𝜕𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙 = (1 − 𝑠𝑠)𝑜𝑜𝑃𝑃𝑜𝑜𝑜𝑜𝑙𝑙 + 𝜆𝜆1𝑠𝑠𝑜𝑜 − 𝜆𝜆2(𝑜𝑜 + 𝛽𝛽) 4.30 There is no analytical solution to this utility maximisation problem involving a fixed capital- driven sector and the extractive natural resource-based sector. Our purpose here is to understand the nature of the relationship between oil sector variables and productivity growth in the modern sector through savings invested in fixed capital. Computational solution for parameters can be carried out to explain the dynamics of the economy through sound reasoning. Given the time limitations, unfortunately, such an exercise remains out of scope for this dissertation. A useful exercise is to have separate controls for savings in the extractive sector and the fixed capital-driven sector where the oil sectors saving rate is connected to either the technological state of the economy or the level of fixed capital. This recommendation is based on two reasons. Firstly, the oil extraction decision is not a yes or no decision, but, it is related to the technological state of the economy. In reality, technological advances such as “Enhanced Oil Recovery” 27 can be used to increase the extraction ratios as well as the total extractable oil reserves. Secondly, investments in the modern sector are expected to be more productive in economies with higher absorptive capacity or technological state. Countries are more likely to invest more of their natural resource sector rents into the modern sector when they have higher absorptive capacity. Without such absorptive capacity their capital investments are likely to be inefficient. In equation 4.27, we observe that the change in consumption/savings per unit of output is a function of the productive efficiency of the economy (A), the fixed capital per labour (k), oil extraction ratio (o), the price of oil (Poil) and the oil reserves per labour (rbbl). In equation 4.29, we observe that the change in fixed capital per labour is a function of the productive efficiency of the economy (A), the fixed capital per labour (k) and the savings per unit of output (s). Equations 4.28 and 4.30 show that the oil reserves per capita (rbbl) and oil 27 Enhanced oil recovery (EOR), also called tertiary recovery, is the extraction of crude oil from an oil field that cannot be extracted without using EOR advancements. EOR can extract 30% to 60% or more of a reservoir's oil, compared to 20% to 40% using primary and secondary recovery. 118 extraction ratio (o) are endogenous and related to the savings rate per unit of output (s) and the exogenously given price of oil (Poil). The change in the stock of fixed capital per labour is as such a function of the initial fixed capital stock (ko), the efficiency of the economy (A), the extraction ratio of oil (o), the total oil reserves per labour (rbbl), the price of oil (Poil) and, the savings rate (s). The savings rate (s) determines the total contribution of the output of the economy towards the fixed capital formation. The savings per unit of output are determined after the consumption decision is made. The output of the economy used for consumption originates from both the modern sector as well as the natural resource sector. The functions for labour productivity growth in the modern sector and the fixed capital investment that we adapt in section 4.4 for the empirical application of our theoretical model are given as follows, ∆𝑞𝑞 𝑞𝑞𝑜𝑜 = 𝑓𝑓(𝐴𝐴, 𝑞𝑞𝑜𝑜, 𝑘𝑘𝑜𝑜, 𝑠𝑠, 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙, 𝑃𝑃, 𝑜𝑜) 4.31 ∆𝑘𝑘 = 𝑓𝑓�𝑘𝑘𝑜𝑜,, 𝐴𝐴, 𝑠𝑠, 𝑜𝑜𝑏𝑏𝑏𝑏𝑙𝑙, 𝑃𝑃, 𝑜𝑜� 4.32 4.4. Empirical Model We derive the functions of labour productivity growth in the modern sector and fixed capital formation. The first equation explains the one year growth rate of labour productivity in the modern sector as a function of initial labour productivity, initial stock of fixed capital in the economy, initial efficiency of the production system proxied by school life expectancy from primary to tertiary level, the savings rate lagged by one year, the oil price lagged by one year, the rate of extraction of the total oil reserves lagged by one year and the total oil reserves per capita as a ratio of the world oil reserves per capita lagged by one year. 119 Productivity Equation ∆𝑙𝑙𝑜𝑜𝑙𝑙(𝑙𝑙𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑝𝑝𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦 𝑖𝑖𝑖𝑖 𝑚𝑚𝑜𝑜𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖 𝑠𝑠𝑚𝑚𝑐𝑐𝑝𝑝𝑜𝑜𝑜𝑜)𝑡𝑡, 𝑡𝑡−1 = 𝐴𝐴𝑜𝑜 + 𝛽𝛽1 𝑙𝑙𝑜𝑜𝑙𝑙(𝑙𝑙𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑝𝑝𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦)𝑡𝑡−1 + 𝛽𝛽2 𝑙𝑙𝑜𝑜𝑙𝑙(𝑓𝑓𝑖𝑖𝑓𝑓𝑚𝑚𝑝𝑝 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑙𝑙 𝑠𝑠𝑝𝑝𝑜𝑜𝑐𝑐𝑘𝑘 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙)𝑡𝑡−1 + 𝛽𝛽3(𝑠𝑠𝑐𝑐ℎ𝑜𝑜𝑜𝑜𝑙𝑙𝑖𝑖𝑖𝑖𝑙𝑙)𝑡𝑡−1 + 𝛽𝛽4(𝑠𝑠𝑙𝑙𝑖𝑖𝑖𝑖𝑖𝑖𝑙𝑙𝑠𝑠 𝑜𝑜𝑙𝑙𝑝𝑝𝑚𝑚)𝑡𝑡−1 + 𝛽𝛽5 𝑙𝑙𝑜𝑜𝑙𝑙(𝑜𝑜𝑖𝑖𝑙𝑙 𝑝𝑝𝑜𝑜𝑖𝑖𝑐𝑐𝑚𝑚)𝑡𝑡−1 + 𝛽𝛽6 � 𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑜𝑜𝑖𝑖𝑙𝑙 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 𝑤𝑤𝑜𝑜𝑜𝑜𝑙𝑙𝑝𝑝 𝑙𝑙𝑖𝑖𝑚𝑚𝑜𝑜𝑙𝑙𝑙𝑙𝑚𝑚 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 � 𝑡𝑡−1 + 𝛽𝛽7(𝑜𝑜𝑖𝑖𝑙𝑙 𝑚𝑚𝑓𝑓𝑝𝑝𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝑜𝑜𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜)𝑡𝑡−1 + �𝛾𝛾𝑜𝑜(𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 149 𝑜𝑜=1 + �𝛾𝛾𝑗𝑗(𝑝𝑝𝑖𝑖𝑚𝑚𝑚𝑚 𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑜𝑜𝑝𝑝 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 8 𝑗𝑗=1 + 𝜖𝜖 4.33 The investment equation explains the change in gross fixed capital stock per capita (not including depreciation of existing assets) over one year by initial stock of fixed capital per capita, initial school life expectancy from primary to tertiary, the savings rate lagged by one year, one-year growth of GDP lagged by one year, oil price lagged by one year, the oil reserves per capita as a ratio of world oil reserves per capita lagged by one year and the oil extraction as a ratio of total reserves lagged by one year. 120 Investment Equation 𝑙𝑙𝑜𝑜𝑙𝑙(𝑙𝑙𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠 𝑓𝑓𝑖𝑖𝑓𝑓𝑚𝑚𝑝𝑝 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑙𝑙 𝑓𝑓𝑜𝑜𝑜𝑜𝑚𝑚𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙)𝑡𝑡 = 𝐴𝐴1 + 𝛾𝛾1 𝑙𝑙𝑜𝑜𝑙𝑙(𝑓𝑓𝑖𝑖𝑓𝑓𝑚𝑚𝑝𝑝 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑙𝑙 𝑠𝑠𝑝𝑝𝑜𝑜𝑐𝑐𝑘𝑘 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙)𝑡𝑡−1 + 𝛾𝛾2(𝑠𝑠𝑐𝑐ℎ𝑜𝑜𝑜𝑜𝑙𝑙𝑖𝑖𝑖𝑖𝑙𝑙)𝑡𝑡−1 + 𝛾𝛾3(𝑠𝑠𝑙𝑙𝑖𝑖𝑖𝑖𝑖𝑖𝑙𝑙𝑠𝑠 𝑜𝑜𝑙𝑙𝑝𝑝𝑚𝑚)𝑡𝑡−1 + 𝛾𝛾4(𝑙𝑙𝑝𝑝𝑝𝑝 𝑙𝑙𝑜𝑜𝑜𝑜𝑤𝑤𝑝𝑝ℎ)𝑡𝑡−1, 𝑡𝑡−2 + 𝛾𝛾5 𝑙𝑙𝑜𝑜𝑙𝑙(𝑜𝑜𝑖𝑖𝑙𝑙 𝑝𝑝𝑜𝑜𝑖𝑖𝑐𝑐𝑚𝑚)𝑡𝑡−1 + 𝛾𝛾6 � 𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑜𝑜𝑖𝑖𝑙𝑙 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 𝑤𝑤𝑜𝑜𝑜𝑜𝑙𝑙𝑝𝑝 𝑙𝑙𝑖𝑖𝑚𝑚𝑜𝑜𝑙𝑙𝑙𝑙𝑚𝑚 𝑜𝑜𝑚𝑚𝑠𝑠𝑚𝑚𝑜𝑜𝑖𝑖𝑚𝑚𝑠𝑠 𝑝𝑝𝑚𝑚𝑜𝑜 𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙 � 𝑡𝑡−1 + 𝛾𝛾7(𝑜𝑜𝑖𝑖𝑙𝑙 𝑚𝑚𝑓𝑓𝑝𝑝𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝑜𝑜𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜)𝑡𝑡−1 + �𝛾𝛾𝑜𝑜(𝑐𝑐𝑜𝑜𝑙𝑙𝑖𝑖𝑝𝑝𝑜𝑜𝑦𝑦 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 149 𝑜𝑜=1 + �𝛾𝛾𝑗𝑗(𝑝𝑝𝑖𝑖𝑚𝑚𝑚𝑚 𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑜𝑜𝑝𝑝 𝑝𝑝𝑙𝑙𝑚𝑚𝑚𝑚𝑖𝑖𝑚𝑚𝑠𝑠)𝑡𝑡 8 𝑗𝑗=1 + 𝜖𝜖 4.34 Where, t = 1982, 1983, 1984, … 2015, and, 𝛽𝛽 and 𝛾𝛾 are the coefficients of the explanatory variables in equations 4.33 and 4.34, respectively. In Table 4.1 we present the names and definitions of the variables used in the estimation models. We will refer to the variables by their short names describes in hereon. 121 Table 4.1 – Variable names and definitions Variable Definition Productivity Growth log of the ratio of final to initial labour productivity (one-year growth rate of labour productivity) Fixed Capital Formation log of gross fixed capital formation per labour in the economy (not including depreciation of existing stocks) Log of Initial Labour Productivity log of labour productivity lagged by one year Fixed Capital Stock log of fixed capital stock per capita lagged by one year Schooling school life expectancy primary to tertiary lagged by one year Lagged GDP Growth log of the ratio final GDP to initial GDP lagged by one year (one-year growth rate of GDP lagged by one year) Savings Rate savings rate lagged by one year Oil Price log of the price of oil lagged by one year Oil Reserves Ratio average of the ratio of country oil reserves per capita to the average world oil reserves per capita lagged by one year Oil Extraction Ratio oil extraction ratio lagged by one year 4.5. Data The data for labour productivity in the modern sector is calculated as real GDP in constant 2011 USD per number of employees excluding the share of natural resource and agricultural sector. The number of employees’ data is sourced from the Penn Worlds Table (PWT) Version 9.1 (Feenstra, et al., 2015). The share of natural resource rents and agricultural value added is sourced from World Bank Development Indicators dataset that is agglomerated from national accounts data (World Bank, 2019). Where, total natural resource rents as a 122 percentage of GDP the as the sum of; oil rents, natural gas rents, coal rents, mineral rents, and forest rents, and Agriculture in Agricultural value added corresponds to International Standard Industrial Classification (ISIC) divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production (World Bank, 2019). The real GDP in constant 2011 USD, the fixed capital stock data and the gross fixed capital formation data is sourced from “IMF Investment and Capital Stock Data, 2017” and is in constant 2011 USD (Gupta, et al., 2006; Kamps, 2004). According to the description of the IMF Investment and Capital Stock Data, “Information on public and private investment and GDP comes from three main sources: the OECD Analytical Database (August 2016 version) for OECD countries, and a combination of the National Accounts of the Penn World Tables and the IMF World Economic Outlook for non-OECD countries. Information on PPP investment comes from two main sources: The World Bank Private Participation in Infrastructure Database and European Investment Bank (EIB) data sourced from the European PPP Expertise Centre (EPEC) at the EIB. Information on country income groupings used in depreciation rates' assumptions is from the World Bank World Development Indicators.” The world bank definition of fixed capital includes “land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchase; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings” (World Bank, 2018). Moreover, it consists of resident producers’ investments, deducting disposals, in fixed assets during a given period. It also includes certain additions to the value of non-produced assets realised by producers or institutional units. Fixed assets are tangible or intangible assets produced as outputs from production processes that are used repeatedly, or continuously, for more than one year. PWT data on capital formation covers, “nine asset types: residential buildings, other structures, information technology, communication technology, other machinery, transport 123 equipment, software, other intellectual property products and cultivated assets (such as livestock for breeding and vineyards). These investment data are drawn from country National Accounts data, supplemented by estimates based on the total supply of investment goods (import plus production minus exports) and data on spending on information technology. Note that coverage is limited to assets currently covered in the System of National Accounts. This means that land and inventories are omitted, as well as other forms of intangible capital – such as from product design or organisation capital – and subsoil assets – such as oil or copper.” The Savings Rate is measured using the Final Consumption Expenditure as a percent of GDP that is sourced from the World Bank Development Indicators (World Bank, 2019). The Savings Rate is calculated as one (1) minus the Final Consumption Expenditure as a percent of GDP. The efficiency of the economy is proxied by the school life expectancy from primary to tertiary level (UNESCO, 2018). It is calculated as the sum of the age-specific enrolment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates. A relatively high school life expectancy indicates greater probability for children to spend more years in education and higher overall retention within the education system. The oil extraction ratio is proxied by the total oil extraction by a country in a given year divided by total oil reserves. The data for oil extraction is based on total petroleum and other liquids production in barrels, and the data for oil reserves is based on proven crude oil reserves in barrels from Energy Information Administration (EIA, 2019). The data for total proven reserves of oil are taken to be those quantities that geological and engineering information indicates with reasonable certainty can be recovered in the future from known reservoirs under existing economic and geological conditions. Market-linked pricing is the main method for pricing crude oil in international trade. The current reference, or pricing markers, are Brent, West Texas Intermediate (WTI), and Dubai crudes. Note that the price variation of 124 Brent, WTI and Dubai crude resembles each other as such using any one of them for pricing serves the purpose well. However, in order to ensure representative world crude oil price in this chapter we have used the average price of oil indicated in World Bank Commodity Price Data. The world bank defines the price as the equally-weighted average spot price of Brent, Dubai and WTI crude oil (GEM Commodities, World Bank Group, 2019). In the case where we consider the time period and country fixed effects, the reference time period is period number eight (8) that runs from 2010 to 2015 and the reference country is the US. The time periods are based on the US business cycles reported by Nation Bureau of Economic Research (NBER). The economic “Peak” and “Trough” announcements provided by NBER since 1980 form the start and end year of each period (NBER, 2019). The only exception is the addition of period number five (5) where 1997 is considered as the Trough Year instead of 2001 March which was the first recession in the US following 1991 Peak according to NBER. The added period considers the Asian Financial Crises (1997), Russian Financial Crises (1998) and the oil price crash of 1999 and runs from 1997 to 2001. 4.6. Data Reliability The first concern that we address is the objection to the use of labour productivity in capital driven sectors defined to exclude the natural resource and agricultural sector. It is argued that modern agricultural technology, modern mineral recovery technology and enhanced oil recovery (EOR) are examples of change in productivity through fixed capital investments. In our theoretical background, such examples may be considered as part of the capital-driven sector. In terms of our estimation excluding the natural resource and agricultural output (as such the modern, fixed capital-driven part of this output) from the labour productivity is justified as the portion and usage of modern mineral recovery technology and EORs is much smaller in comparison to the usage of traditional recovery. The same is true for agricultural technology albeit the usage is higher. However, it is much more important for us to exclude the traditional productivity and productivity of the extractive sector in contrast to capturing 125 all capital-driven productivity. Owing to the data limitations, we find that our technique is the most suitable way to account for labour productivity in the modern sector. Another uncertainty that we face is that different countries and companies from which the oil data is agglomerated from have varying abilities to precisely estimate the recoverable oil reserves. We consider that our source EIA makes every effort to come up with a consistent series for reserves based on a common definition. However, the use of different methodologies is apparent in the data. An example in case is that of Bangladesh, Equatorial Guinea, Morocco, South Africa where the EIA data shows oil extraction exceeding oil reserves, or oil extraction for two subsequent years at a level very close to the total reserves. This by definition is impossible, as such the data was excluded from the set. The excluded data consists of data points for Bangladesh, Equatorial Guinea, Morocco and South Africa. We also observed that oil extracting countries that are facing war or sanctions tend to have very low oil extraction ratios. In addition to that, Canada shows very low oil extraction ratio because large quantities of difficult to extract and heavy tar sands distort the total recoverable oil reserves measure. Note that we also tested our models by excluding this set of countries and the results are discussed in the postestimation tests and reliability section. We also checked the pairwise correlation between our explanatory variables. The correlation is considered low and is not expected to have an effect on the coefficients of the estimation. Another important concern is that a small set of data points may affect the coefficients of the regression disproportionately. In this regard, we carry out an influence analysis for the regression with respect to the explanatory variable. We use dfbeta values that measure the difference between the regression coefficient calculated for all of the data and the regression coefficient calculated with the observation deleted, scaled by the standard error calculated with the observation deleted. This value can then be used to limit the variance in the estimations by using a rule of thumb. For this case, we tested for where n is the total number of observations. In the following, we term the data points with dfbeta higher than as outliers. We will go through the implications in our postestimation test and reliability section. The 126 data was also visually inspected in correlation matrix graphs and the data was confirmed to be free from outliers. The time range of the data runs from 1980 to 2015. As such the total number of years is 36. The minimum number of years for a country was four (4) years, and the maximum was 36 years. The explained variables for investment estimation include the growth of GDP over one year lagged by one year. As such, the first set of observations is from the year 1982. The average number of years per country is 23 years. The total number of countries in the data for both the productivity and investment estimations is 149. Out of these, 85 had an oil sector for at least part of the time period in consideration. We carry out ordinary least squares regression with estimates efficient for homoskedasticity and standard errors robust to heteroskedasticity and autocorrelation (HAC) using robust and Newey-West estimation with a lag of one (Newey & West, 1987). The summary statistics for the data used in productivity estimation and investment estimation are presented in Table 4.2. Table 4.2 – Summary statistics for productivity estimation Variable Countries Years Obs. Mean Std. Dev. Min Max Productivity Growth 149 1982-2015 3337 0.019 0.105 -1.415 1.475 Fixed Capital Formation 149 1982-2015 3337 8.026 1.505 2.915 11.352 Initial Labour Productivity 149 1982-2015 3337 9.536 1.397 4.713 12.017 Fixed Capital Stock 149 1982-2015 3337 10.471 1.378 5.212 12.960 Savings Rate 149 1982-2015 3337 0.193 0.156 -1.420 0.884 Schooling 149 1982-2015 3337 11.115 3.651 1.437 23.282 GDP Growth 149 1982-2015 3337 0.036 0.046 -0.670 0.296 Oil Price 149 1982-2015 3337 3.691 0.595 2.766 4.557 Oil Reserves Ratio 149 1982-2015 3337 0.847 4.391 0 60.027 Oil Extraction Ratio 149 1982-2015 3337 0.048 0.066 0 0.811 127 The mean and standard deviation of productivity growth in the modern sector over one year was 0.019 and 0.105, respectively. The fixed capital formation had a mean of 8.026 with a standard deviation of 1.505. The mean of log of initial labour productivity was observed to be 9.536 while the standard deviation was 1.397. The log of the initial stock of fixed capital had a mean of 10.471 with a standard deviation of 1.378. The mean of savings rate was 0.193 with a standard deviation of 0.156. The mean of school life expectancy from primary to tertiary (schooling) was 11.115, and the standard deviation was 3.651. The explanatory variables, lagged log of oil price, lagged oil reserves to world oil reserves ratio and lagged oil extraction ratio had means of 3.691, 0.847 and 0.048, respectively. Their respective standard deviations were observed to be 0.595, 4.391 and 0.066. 4.7. Results Here we present the observed influences of the explanatory variables of concern, on the explained variable that is labour productivity growth excluding natural resource and agricultural rents. Second, we present the results of the estimation for gross fixed capital formation explained by initial fixed capital stock, schooling, savings rate, GDP growth and oil sector variables including oil price, oil reserves and oil extraction ratio. See Equations 4.33 and 4.34, and Tables 4.1 and 4.2 for more details on the estimation and definitions of the variables. 128 Table 4.3 – Regression results – Productivity Equation Dependent Variable Productivity Growth (Net of natural resource rents and agricultural value added) A-1 A-2 A-3 A-4 A-5 A-6 A-7 A-7 Continued INT#Country Labour Productivity -0.029*** -0.028*** -0.028*** -0.177*** -0.177*** -0.177*** -0.202*** (0.010) (0.010) (0.010) (0.034) (0.034) (0.035) (0.036) Fixed Capital Stock 0.009 0.009 0.009 0.061** 0.062** 0.062** 0.070*** (0.007) (0.007) (0.007) (0.024) (0.024) (0.024) (0.023) Savings Rate 0.042** 0.041* 0.040* 0.036 0.025 0.028 0.037 (0.021) (0.021) (0.021) (0.038) (0.038) (0.040) (0.047) Schooling 0.006*** 0.006*** 0.005*** 0.011*** 0.009*** 0.009*** 0.011*** (0.001) (0.001) (0.001) (0.003) (0.003) (0.003) (0.003) Oil Price (OP) 0.004 0.023*** 0.011*** 0.023*** 0.023*** 0.018*** (0.004) (0.006) (0.004) (0.006) (0.006) (0.006) Oil Reserves Ratio (OR) 0.000 0.000 0.003 0.002 0.003 0.004 (0.001) (0.001) (0.003) (0.003) (0.004) (0.004) Oil Extraction Ratio (OE) 0.013 0.012 -0.001 -0.006 -0.007 0.035 (0.017) (0.017) (0.029) (0.029) (0.029) (0.036) OP#OR (INT) 0.000 0.005 (0.001) (0.020) Bahrain -0.040** -0.034* -0.035* -0.032 -0.013 (0.019) (0.019) (0.020) (0.031) (0.044) Kuwait -0.167 -0.134 -0.102 -0.456 -0.004 (0.149) (0.148) (0.159) (0.394) (0.020) Oman -0.023 -0.027 -0.026 -0.032 -0.005 (0.031) (0.031) (0.031) (0.108) (0.022) Qatar -0.002 0.006 0.014 0.181* -0.009 (0.053) (0.053) (0.056) (0.105) (0.020) KSA -0.090 -0.075 -0.065 -0.070 -0.006 (0.058) (0.057) (0.059) (0.138) (0.020) UAE -0.035 -0.024 -0.009 0.204 -0.008 (0.063) (0.063) (0.073) (0.212) (0.020) Time period fixed effects No No Yes No Yes Yes ... Yes Country Fixed Effect No No No Yes Yes Yes ... Yes Root Mean Square Error 0.104 0.104 0.103 0.099 0.099 0.099 ... 0.097 Adj.R-squared 0.024 0.023 0.029 0.114 0.120 0.119 ... 0.153 Countries 149 149 149 149 149 149 149 Years 23 23 23 23 23 23 23 Observations 3337 3337 3337 3337 3337 3337 ... 3337 * p<0.10, ** p<0.05, *** p<0.01 129 In Table 4.3: A-1 includes only the base explanatory variables, A-2 introduces oil price (OP), reserves ratio (OR) and extraction ratio (OE), A-3 adds only time period fixed effects and A- 4 only country fixed effects. A-5 includes both time and country fixed effects. A-6 introduces the interaction between oil price and oil reserves ratio (INT). Finally, A-7 adds the interaction of country dummies with INT. Note that we first discuss the results of productivity estimation model A-5 indicated in Table 4.3 based on equation 4.33. The first result that we observe in above Table 4.3 is that initial labour productivity is negatively correlated with growth in labour productivity in the modern sector. The coefficient is statistically significant, with a confidence level of 99%. The standard error is 0.034. A 1% higher initial labour productivity is observed to produce a 0.177 ± 0.034% lower labour productivity growth in the modern sector. The initial capital in the economy is statistically significantly correlated with the growth in labour productivity in the modern sector. Higher initial capital stocks lead to higher growth of labour productivity in the modern sector. The coefficient is statistically significant with a confidence level of 95%. A 1% increase in initial fixed capital stocks in the economy is observed to increase the labour productivity growth rate in the modern sector by 0.062 ± 0.024%. We observe that a year’s increase in schooling is expected to increase labour productivity growth in the modern sector by 0.009 ± 0.003% (99% confidence). We observe that the coefficient of the oil price variable is positive and statistically significant, with a confidence of 99%. An average oil price increase of 1% lead to a 0.023% higher modern sector labour productivity growth rate with a standard error of 0.006. The oil reserves ratio and the oil extraction ratio do not explain the change in labour productivity in the modern sector. The coefficient for all the GCC country dummies except that of Qatar is negative. Excluding Bahrain, the labour productivity growth in the modern sectors of the GCC countries is not statistically different from the reference country (US). Bahrain has the lowest growth in labour productivity in the modern sector among the GCC countries. The root mean squared error of the estimation is 0.099. The mean of the estimated labour productivity growth in the modern sector is 0.019% with 0.102 as the standard deviation of 130 the residuals. In comparison to this, the real mean of the explained variable is 0.019% with a standard deviation of 0.105. We observe in A-2 and A-3 that an increase in savings rate leads to higher labour productivity growth in the modern sector. In A-2 (without fixed effects) and A-3 (with time period fixed effects but without country fixed effects), a 1% change in savings rate is expected to yield 0.041 and 0.040 ± 0.021% increase in labour productivity growth in the modern sector, respectively. The relationship is statistically significant with confidence at a level of 90%. However, the relationship between the savings rate and labour productivity growth has lower confidence and a higher standard error when considering country fixed effects. Therefore, cross-country differences are important in explaining the effect of savings rate on labour productivity growth in the modern sector. In A-6 and A-7 we do not observe any statistically significant effect of the interaction (INT) of oil price with oil reserves ratio or the interaction of the country dummies with INT, on the labour productivity in the modern sector. However, in A-7, we observe that including the interaction of INT with country dummies makes the coefficient of the country dummy for Qatar larger and statistically significant with a confidence level of 90%. This observation is indicative that increased labour productivity in the modern sector in Qatar is not driven directly through returns from oil extraction. We will analyse this relationship further in the interpretation of the investment estimation in this section, and the conclusion and discussion section (Section 4.9). According to our theoretical model and the associated empirical interpretation, we expect that the effect of oil sector variables on labour productivity is driven through investments in fixed capital. In the following, the regression output of the empirical model explaining gross fixed capital formation through initial fixed capital stock, savings rate, GDP growth, schooling and oil sector variables is presented. 131 Table 4.4 – Regression results – Investment Equation Dependent Variable Fixed Capital Formation B-1 B-2 B-3 B-4 B-5 B-6 B-7 B-7 Continued INT#Country Fixed Capital Stock 0.902*** 0.915*** 0.923*** 0.734*** 0.731*** 0.749*** 0.739*** (0.011) (0.011) (0.011) (0.030) (0.029) (0.030) (0.037) Savings Rate 0.858*** 0.825*** 0.832*** 0.595*** 0.562*** 0.457*** 0.509** (0.075) (0.075) (0.074) (0.171) (0.168) (0.176) (0.213) Schooling 0.050*** 0.041*** 0.036*** 0.039*** 0.012** 0.013** 0.014** (0.003) (0.003) (0.004) (0.005) (0.006) (0.006) (0.006) GDP Growth 3.300*** 3.251*** 3.276*** 2.050*** 2.059*** 2.097*** 1.934*** (0.296) (0.294) (0.295) (0.192) (0.189) (0.186) (0.182) Oil Price (OP) 0.116*** 0.051* 0.138*** 0.084*** 0.077*** 0.063*** (0.012) (0.027) (0.012) (0.019) (0.019) (0.019) Oil Reserves Ratio (OR) 0.001 0.001 -0.001 -0.003 -0.026*** -0.023*** (0.001) (0.001) (0.006) (0.005) (0.008) (0.007) Oil Extraction Ratio (OE) 0.091 0.090 0.245* 0.256* 0.276** 0.453** (0.101) (0.101) (0.138) (0.133) (0.133) (0.200) OP#OR (INT) 0.011*** -0.597*** (0.003) (0.160) Bahrain -0.114 -0.124 -0.104 -0.585*** 0.864*** (0.081) (0.085) (0.085) (0.118) (0.211) Kuwait -0.011 0.043 -0.809** 0.732 0.597*** (0.279) (0.263) (0.326) (0.472) (0.160) Oman 0.108 0.010 -0.021 -0.667*** 0.641*** (0.068) (0.069) (0.067) (0.159) (0.161) Qatar 0.634*** 0.584*** 0.360*** 0.225 0.605*** (0.132) (0.128) (0.126) (0.195) (0.160) KSA -0.073 -0.050 -0.336** -1.233*** 0.617*** (0.128) (0.123) (0.133) (0.156) (0.160) UAE 0.273** 0.240* -0.184 -1.253*** 0.618*** (0.133) (0.127) (0.156) (0.315) (0.160) Time Period Effects No No Yes No Yes Yes ... Yes Country Fixed Effect No No No Yes Yes Yes ... Yes Root Mean Square Error 0.410 0.405 0.403 0.273 0.269 0.269 ... 0.253 Adj.R-squared 0.926 0.928 0.928 0.967 0.968 0.968 ... 0.972 Countries 149 149 149 149 149 149 149 Years 149 23 23 23 23 23 23 Observations 149 3337 3337 3337 3337 3337 ... 3337 * p<0.10, ** p<0.05, *** p<0.01 In Table 4.4: B-1 includes only the base explanatory variables, B-2 introduces oil price (OP), reserves ratio (OR) and extraction ratio (OE), B-3 adds only time period fixed effects and B- 4 only country fixed effects. B-5 includes both time period and country fixed effects. B-6 introduces the interaction between oil price and oil reserves ratio (INT). Finally, B-7 adds the interaction of country dummies with INT. Here we discuss the results of the investment estimation model B-5 based on Equation 4.34 as presented in Table 4.4. We observe that capital formation in the previous period is positively correlated with new capital formation. A 1% higher initial capital stock leads to 0.731% increase in fixed capital formation over a year. The standard error of the coefficient is 0.029, and the result is statistically significant, with a confidence of 99%. We also find that the savings rate, schooling and GDP growth are positively and statistically significantly related gross fixed capital formation. A 1% higher savings rate results in a 0.562% higher gross fixed capital formation. The coefficient is statistically significant at a confidence level of 99%, and the robust standard error is 0.168. A year’s increase in initial schooling is expected to increase the fixed capital formation in the final year by 0.012 ± 0.006%. The coefficient is statistically significant at the confidence level of 95%. We observe that Qatar is able to accumulate the highest fixed capital per capita in comparison to its neighbouring GCC countries. The coefficient of the country dummy for Qatar is positive and statistically significant, with a confidence level of 99%. The coefficient of the country dummy for UAE is also positive and statistically significant (confidence level 90%) showing higher fixed capital formation in comparison to the reference country (US). The fixed capital formation per capita in the remaining GCC countries is not statistically different from the reference country. Bahrain is the lowest-performing among the GCC countries in terms of the fixed capital formation in the country while Saudi Arabia is the second from the last. We observe that the coefficient of the oil price variable is positive and statistically significant, with a confidence level of 99%. An average oil price increase of 1% leads to a 0.084% higher capital formation. The standard error is 0.019. We observe that a higher oil extraction ratio 133 is expected to have a positive effect on fixed capital investment. The coefficient is observed to be 0.256 with a standard error of 0.133 and a confidence level of 90%. The root mean squared error of the estimation is 0.269. The mean of the estimated fixed capital formation is 8.039 with 0.387 as the standard deviation of the residuals. In comparison to this, the real mean of the explained variable is 8.026, with a standard deviation of 1.505. In order to delve further into the mechanism through which oil reserves and oil prices affect the capital formation, we test the interaction of oil price and oil reserves ratio (INT) in B-6. Also, we interact all country dummies with the interaction INT reported as INT#Country and discuss the results for the six GCC countries in B-7. In B-6 we observe that INT is positively correlated with the fixed capital formation with a coefficient of 0.011 and standard error of 0.003 (confidence level 99%). In B-7 we observe through the interaction of country dummies with INT that all GCC countries are able to exploit high oil prices for increased capital formation, owing to their large oil reserves ratio (See column for B-7 Continued INT#Country in Table 4.4). It is noteworthy that the coefficient of the country dummy of Qatar has a smaller magnitude in B-7 in comparison to B-6. We discussed a similar comparison for Qatar between A-7 and A-6 for the productivity estimation. There, we observed that the revenues generated from oil extraction in terms of labour productivity are not the direct drivers of Qatar’s modern sector labour productivity growth. In the investment estimation, it becomes clear that the revenues from oil extraction have a positive impact on the fixed capital formation in Qatar. This trend is most clearly observed from the coefficients for Qatar in both estimations, and the relationship is the same for all the GCC countries except Kuwait. All in all, the ability of all GCC countries to divert their natural resource profits to higher fixed capital formation is evident. 4.8. Postestimation tests and robustness In relation to the countries with very low oil extraction ratios (discussed in section presenting the Data Reliability), we repeated the estimation of both equation 4.33 and 4.34, excluding 134 the country group. The exclusion of countries with very low oil extraction ratios did not affect the outcome of the estimations with the coefficients and standard errors not changed to any considerable extent. The estimation results in Table 4.3 and 4.4 include these countries. The regressions result for equation 4.33 and 4.34 were also estimated excluding the outliers identified by calculating dfbetas. The standard error, significance and coefficients of the variables in the estimation model based on equations 4.33 and 4.34 were not meaningfully different when estimated excluding the outliers identified. The estimation results in Table 4.3 and 4.4 do not exclude the outliers identified by calculating the dfbetas. We find that the standard errors of the oil price variable are robust to changes in the selection of time dummy periods as well as when no time dummies are used. The oil extraction ratio standard errors are robust to selection of time dummy periods and also when no time dummies are used. Note that we have selected the time dummy periods to be based on the business cycles as reported by NBER with the addition of the Asian Financial Crises. However, we tested by using random six years periods and did not observe any substantial changes in the results of the estimation. The oil reserves per capita as a ratio of world oil reserves per capita coefficient and the oil extraction ratio are robust to these changes. 4.9. Discussion and Conclusion Our theoretical model predicts that the change in fixed capital formation per capita is a function of the initial stock of fixed capital, the savings rate, the productive efficiency of the economy, and output of the oil sector. The output of the oil sector is a function of the oil sector variables – oil price, oil reserves per capita, and the ratio of oil reserves extracted from the total oil reserves. The change in the productivity of the modern sector is a function of the initial labour productivity, initial fixed capital stock, productive efficiency of the economy and gross fixed capital formation. In our estimation, we test the theoretical model for 149 countries, including 85 countries with commercially extractable oil reserves and do no find any evidence of an “Oil Curse”. We find that higher oil prices affect labour productivity in the modern sector as well as fixed capital 135 formation. The oil reserves and the oil extraction ratio do not affect labour productivity growth directly. Also, we find that the oil reserves variable on its own does not have a statistically significant effect on fixed capital formation. However, countries with high oil reserves benefit from high oil prices in order to form new fixed capital. The oil extraction ratio has a positive effect on fixed capital formation. The mechanism for the increase of labour productivity in the modern sectors of countries with and without an active oil sector can be assumed to be different to a certain extent. The similarity between the two types of countries may be related to foreign direct investments (FDI). Countries without an oil sector may be benefiting from FDIs from the sovereign wealth funds of oil-rich nations. Firms in rich countries may be offsetting higher energy costs by moving production to lower-income countries for reducing overall costs of production, thus increasing productivity in the modern sectors and fixed capital investment there. Oil-rich countries may also become more attractive FDI destinations for international firms during high oil price periods because of liquidity and availability of finance. For countries without an active oil sector, the increase in labour productivity in the modern sector may also be driven by demand for capital and consumption goods (and services) from oil-rich countries that have surplus wealth available to consume as well as to invest during high oil price periods. Another possibility is that higher oil prices reduce the marginal returns for producing consumption goods due to high cost of energy per unit, this leads to innovation that is aimed at improving profitability, that in turn leads to higher productivity in the modern sector of non-oil-extracting countries. However, we expect that a mechanism whereby higher energy prices drive innovation would exhibit itself in the longer run, rather than one-year periods that we use in this chapter. It is recommended that further research is undertaken to further disentangle the mechanisms of the relationship between labour productivity growth in the modern sectors of countries with and without an oil sector. We observe that all the GCC Countries are able to generate higher amounts of fixed capital during high oil price periods owing to their large oil reserves per capita. We observe that Qatar has been able to generate higher fixed capital per capita in comparison to the reference 136 country (US), as well as in comparison to all other GCC countries. The UAE’s investments in fixed capital are higher in comparison to the reference country and other GCC countries, but lower than Qatar. We find that for Saudi Arabia and UAE, the formation of fixed capital is highly associated with their ability to benefit from high oil prices owing to their high oil reserves. A similar effect is observed for Oman and Bahrain, but the association is of a lesser magnitude than that of Saudi Arabia and UAE. Unlike the other GCC countries, Kuwait and Qatar appear to have a positive relationship of their individual country dummies with fixed capital formation even when the oil price, oil reserves and country dummies interaction is introduced. As such, it appears that oil income during high oil price periods is not the only source for fixed capital formation in these two countries. As far as the effect of the oil variables is concerned, it may well be that oil reserves offer countries a type of collateral or warranty inducing financial influx through financial intermediation or as mentioned earlier through FDIs. These can lead to an increase in fixed capital investments. However, future research may be carried out to focus on this aspect of the oil sector’s contribution to overall fixed capital formation. We observe that schooling has a positive effect on labour productivity growth in the modern sector as well as fixed capital formation. The relationship between labour productivity on the one hand and productive efficiency or education on the other is well known. We propose that the relationship between schooling and fixed capital formation is driven by the countries’ ability to invest in modern technology because of having a higher skill level of the population that is required for such investment. Simply stated, improving capital stocks without relevant education and skill is not expected to have analogous returns. As such countries only invest in fixed capital that is matching the productive efficiency level of the economy proxied here by schooling. Further research into the relationship between productive efficiency and/or schooling and fixed capital formation is recommended. All in all, we find that natural resources offer an important means for investing in the modern sector, diversify the economy and ensure economic sustainability. This is evident in the case of GCC countries investing in fixed capital using their oil wealth. 137 5. “Stars in their Eyes?” Evaluating the Development Visions of Oman and Saudi Arabia Abstract Oman and Saudi Arabia are two Gulf Cooperation Council (GCC) countries with ambitious development plans. In this chapter, we evaluate the quantitative targets set in these plans, using the model developed in Chapter 2. The model assumes relations between, on the one hand, modern sector labour productivity, and, on the other initial labour productivity, government effectiveness, tertiary education expenditure as a percent of GDP, research and development (R&D) expenditure as a percent of GDP, regional characteristics and time period status. The coefficients have been estimated using pooled OLS regression for 75 developing countries covering three consecutive 5 years periods from 1998 to 2013. We find that in the most probable scenario, by 2020, the modern sector labour productivity in both Oman and Saudi Arabia will be close to their respective targets. However, by 2030 both countries will fall substantially behind their targets. We consider two additional policy scenarios. In the first, Oman and Saudi Arabia do not improve tertiary education, R&D, governance and regional characteristics. In the second scenario, these are improved. The results show that a delay in improving these determinants will lead to a failure in achieving the 2030 targets. Swift policy action can help avoid this and may even facilitate the two countries to surpass their targets. Factors associated with regional characteristics are not disentangled in the model. However, we discuss the likely constraints that require policy action. These include the lack of direct cross-country transportation infrastructure, regional and international trade integration, peace, and security in the region. Finally, we suggest that upgrading human capital by employing knowledge workers from other countries while nurturing local expertise can fast-track development. Keywords: structural change, GCC (Gulf Cooperation Council), ex ante evaluation, complicated and complex policies, economic development JEL Classification: E21, E24, O13, O53 138 5.1. Introduction The oil glut of 1980s that culminated in the 1986 plunge (Koepp, 1986), the oil demand drop associated with the Asian financial crises of 1997-1999 (Manning, 1998), the oil price shock of 2014-2016 (Grigoli, et al., 2017), the promise of green technology affecting fossil fuel demand (Gerlagh, 2011), and the apparent limits to technically and commercially exploitable oil have all been strongly imprinted as an existential threat over the consciousness of the GCC 28 policymakers. The governments of the region have devised many strategic plans to encounter this urgent need to diversify away from fossil fuels as the main share in the output of their economies. Here we analyse the probability of success of the strategies and plans developed by Oman and Saudi Arabia for diversification away from oil rents as the main share in the total output of these economies. The government of Oman announced the Vision 2020 in 1995, which is culminating with the 9th five-year development plan (Oman Vision 2020) (Supreme Council for Planning, Oman, 2019). In addition to this, the National Program for Enhancing Economic Diversification of Oman (تنفیذ - Tanfeedh 29) is an action-oriented program derived from Oman’s five-year development plan (Supreme Council for Planning, Oman, 2017). This program is being followed by Oman Vision 2040 (Oman Vision 2040), the preliminary document for which has been published on 25th July 2019 (Oman Vision 2040, 2019). The latest plans and visions in Oman are complemented by The National Innovation Strategy (The Research Council, Oman, 2017). Similarly, the government of Saudi Arabia has presented its National Transformation Program (Saudi NTP) 30 highlighting its development policies up to 2020 and 28 The Gulf Cooperation Council (GCC) is the colloquial term used to refer to the Cooperation Council of the Arab States of the Gulf (GCC). We use the abbreviation GCC to refer to the member countries as of 2017 – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. These are synonymously referred to as the countries of the Arabian Gulf, countries of the Arabian Peninsula, countries of Persian Gulf and Arab countries of the Gulf. Countries on the Arabian/Persian Gulf such as Iran and Iraq, and countries in accession talks such as Yemen, Jordan and Morocco are not included. 29 Tanfeedh is the official short name of Oman’s National Plan for Enhancing Economic Diversification. It is an Arabic term that means achievement or accomplishment. 30 Saudi Arabia’s National Transformation Plan 2020 is presented in lieu of the 10th five-year plan 139 followed it with the ambitious Saudi Arabia Vision 2030 (Saudi Vision 2030) 31. This is complemented by the National Science Technology and Innovation Plan (معرفة – Ma’rifah 32) (Alsodais, 2013). To the best of our knowledge, there has not been any research into the success or failure of the plans, strategies and visions implemented, except for qualitative introspection and statistical reviews. We attempt to fill this gap. We first assess the quantifiable inputs of these strategies. They include primary inputs such as initial labour productivity, government effectiveness, education expenditure as a percent of Gross Domestic Product (GDP), research and development (R&D) expenditure as a percent of GDP, regional characteristics. These inputs are subsequently inserted in a quantitative model of economic diversification and innovation. Our last step is to compare the outcomes of this model in terms of labour productivity in the non-oil and gas-based sectors with the targets. We follow this with a discussion on the context, including, cross-country competitive pressures, regional cooperation, strategic development, trade and foreign direct investment. In Section 5.2 we present the relevant literature, the background for considering diversification for Oman and Saudi Arabia that is the reduction of oil dependency, as well as the model used for evaluating. Subsequently, in Section 5.3 we discuss the predictive model for assessing the performance of the modern sector. In Section 5.4 we take the first step to extract the inputs and the targets for Saudi Arabia and Oman from the available national strategy documents. In section 5.5 we present the output of the model and contrast it with the intended targets. In Section 5.5.1, we suggest options for realising the intended targets. Finally, we present the conclusion, limitations and recommendations on future research in Section 5.6. 31 The official website of Saudi Vision 2030 “https://vision2030.gov.sa” provides access to documents and information related to both the Saudi NTP and Saudi Vision 2030 (Saudi Vision 2030, 2016) 32 Ma’rifah is the official short name for Saudi Arabia’s National Science Technology and Innovation Plan. It is an Arabic word that means knowledge and awareness 140 5.2. Background and Literature 5.2.1. Diversification Our subject is the evaluation of diversification strategies of two countries: Oman and Saudi Arabia. Much of the recent literature has indeed focussed on diversification rather than specialisation as holding the key to economic stability (Rodrik, 2005; Hidalgo & Hausmann, 2009). In the past decades across the world, governments have set their eyes on industrialisation and innovation policy to facilitate diversification and innovation while limiting obstacles to the restructuring goals (Rodrik, 2005). Public policy addresses deficiencies in capabilities, behaviours, institutions and framework conditions that negatively affect the performance of the system (Arnold, 2004; Rodrik, 2005). This is in addition to or complementary with import substitution, planning, state ownership, economic liberalisation and the application of taxes and subsidies to correct market failures. The focus on diversification makes sense: countries that are more diversified in terms of capabilities, products, sectoral production and employment are more likely to have a higher income (Imbs & Wacziarg, 2003; Klinger & Lederman, 2004; Hidalgo & Hausmann, 2009). 5.2.2. Evaluation of Diversification Strategies In the innovation and diversification literature, we find many works that evaluate the success and failure of government programs. However, there is very little literature on ex post evaluation of government policy at the national level and none that evaluate national level policies ex ante by quantitative means using a model with interactions. Roessner (1989) analyses the evaluation strategies of four programs in the US. One of these was the state technical services (STS) program 1965 aimed at industrialisation, use of advanced technology, increasing employment and enhancing the competitive position of the US products. For this program, he finds that the public evaluation committee was not formulated and equipped to perform a systematic evaluation. At later stages, Arthur D. Little Inc. (ADL) was employed to evaluate STS (Roessner, 1989). With no mechanism set up to collect baseline and intermediate data, ADL collected the data and used “lower bound” methodology. The overall 141 lessons from STS and the other three programs studied by Roessner (1989) were that program evaluations should be designed early to help achieve program objective, program designs should not be rigid, and the credibility of evaluations should be maximised. In the new approaches of national industrial and innovation policy, a package of related measures is involved, with multiple instruments or multiple levels of governance. The OECD report on “Evaluation of industrial policy: Methodological issues and policy lessons” classifies this type of policy as “complicated” and in cases of high uncertainty “complicated/complex” (Warwick & Nolan, 2014). While such policies are proliferating 33, the evaluation of such policies through research is limited. Some evaluations that have been presented in the academic literature include those of South Africa, Namibia, Tanzania, Rwanda, Malaysia and Australia’s respective Visions (Qwabe, 2013; Marope, 2005; Mallya, 2000; Islam, 2010; Mason, et al., 2011). A qualitative assessment of South Africa’s Vision 2030 determines that the realisation of the targets is dependent on building the capability of the state (Qwabe, 2013). An extensive study into the adequacy of Namibia’s education system for meeting the goals of Namibia Vision 2030 finds that the education system has poor learning outcomes at all levels, inefficiencies, inequalities, lack of relevance to the economy, poor capacity for knowledge creation and innovation, and, inadequate and declining resources (Marope, 2005). As such the system cannot achieve the national development goals and the transformation as envisioned under Namibia’s Vision 2030. This is backed up by Amukugo et. al. (2010) who find that education enrolment in Namibia meets the targets. However, this is at the cost of quality. The understanding among the researchers is that without extensive investment to improve the conditions in schools and to provide teachers with appropriate training, the goals of Namibia’s Vision 2030 are at jeopardy (Marope, 2005; Amukugo, et al., 2010). A review of 33 To name a few in no particular order: Malaysia Vision 2020, Kenya Vison 2030, Oman Visions 2020 and 2040, Namibia Vision 2030, South Africa National Development Plan – Vision 2030, Australia Vision 2040, , Uganda Vision 2040, Qatar National Vision 2030, Tanzania Development Vision 2025, Rwanda Vision 2020, Bangladesh Vision 2021, Bahrain Vision 2030, UAE Vision 2021, Vision Kuwait 2035, Vision 2030 - Jamaica National Development Plan, Brunei Vision 2035, Cameroon Vision 2035 and so on are examples of such that can be classified as “complicated” policies and in many cases due to national, regional and international uncertainties labelled as “complicated/complex”. 142 Tanzania’s Development Vision 2025 finds that the goals are too many and over-ambitious. The goals do not relate to the capability of Tanzania’s government and economy in real terms, and as such, the goals are inappropriate for the selected time-frame (Mallya, 2000) An encouraging picture emerges from Rwanda in an article that presents a mid-term review of Rwanda’s Vision 2020. The country had up until 2010 met or exceeded targets on child mortality, school enrolment, government capacity and overall economic growth. However, this comes with a caveat – the benefits of the growth are concentrated at the top segment of the society, the policies are not farmer focussed, small scale landowner farmers have had difficulties in enduring as the income gap is becoming larger (Ansoms & Rostagno, 2012). A study evaluating success factors of Malaysia Vision 2020 applies an analytical hierarchy process and outlines nine main challenges that include creating a mature democratic government, pushing science and technology, and increasing economic prosperity, among other social and economic challenges (Islam, 2010). Another study finds that rather than catching-up Malaysia’s gap with other advanced knowledge economies is increasing in terms of most common knowledge economy indicators such as researchers per capita and investment into research and development (R&D) (Evers, 2003) An interesting policy programme is Australia Vision 2040 – A vision for Australia’s mineral future (Mason, et al., 2011). Its uniqueness is that it is developed for a natural resource-driven economy that is also a high-income OECD country. In addition to using mineral wealth for economic development, the Vision also focuses on the environment and society as such, creating targets using nested or embedded notion of sustainability. Some of the works delve into evaluating only one input, output, pillar or goal of such policies, such as Evers (2003) for Malaysia, Amukugo, et al. (2010) for Namibia and Qwabe (2013) for South Africa. Others try to present a broader picture trying to relate the results of multiple inputs individually to the desired goal such as Mallya (2000) for Tanzania, Islam (2010) for Malaysia and Ansoms & Rostagno (2012) for Rwanda. No work has – to the best of our knowledge – yet been able to deal with the “complicated/complex” nature of the policy sets, 143 as is our aim. Such an evaluation is critical in understanding the implications of the targets selected in these “complicated/complex” policies. In addition to that, it provides a clearer picture to the stakeholders for retrenching in order to achieve the intertwined objectives without losing the focus on the ultimate goal(s). 5.2.3. Methodologies for Evaluation Here we try to offer a literature review of the applicability of methodologies that have been attempted for evaluating “complicated/complex” policies. We also explore the best- recommended practice. In their paper O’Sullivan et. al. (2013) compare the “new” industrial policies of four industrialised countries United Kingdom, United States, Germany, and Japan. They present a matrix approach consisting of interlinking “intervention levels” and the “factor inputs” of the national manufacturing system and contrasting with the global manufacturing systems and market. They use it to analyse policy variations within and across the countries. The policy matrix approach is interesting to isolate policy goals. An important takeaway from their work is that the policies made in the context of national manufacturing systems involve complex networks integrated to varying degrees with the national innovation systems (O’Sullivan, et al., 2013). In Patton’s (2006) view developmental evaluations differ in design and learning goals from traditional evaluations. In terms of design, the evaluations are required to capture the dynamics of the system, the interdependencies and the emergent connections instead of linear cause and effect models. Meanwhile, the learning objectives are targeted at producing context-specific understanding that informs further policy development rather than to produce findings that are generalisable across space and time. In this context, Warwick & Nolan (2014) present a synopsis on White’s (2013) and Rogers’ (2008; 2011) works and their application in the arena of complex industrial and innovation policy. In scenarios where a package of measures is being evaluated in an uncertain and complex situation, counterfactuals may not be possible. The first approach in this regard is to construct a model for the policy or program, ensuring that all elements of the policy and the interrelationships are recognised and presented (Rogers, 2008). For complicated and complex polices with 144 multiple policy areas, inputs, determinants, and outcomes it is recommended to focus on the components to be evaluated quantitatively and exclude the areas that do no form the core of the policy evaluation. Thereon qualitative measures or discussions can be used to describe the interactions and systemic effects of the policy areas that have not been evaluated quantitatively with the policy outcome that is considered in the Quantitative evaluation (Rogers, 2008; 2011; Warwick & Nolan, 2014). This ensures that interactions within the broader program are accounted for even if the quantitative evaluation is limited to a small sub-set of the complicated or complex policy with many facets. 5.2.4. Oman and Saudi Arabia Evaluations Here we review the research that has already been conducted in terms of forecasting the economic situation in Oman and Saudi Arabia. Oman Al-Mawali et. al. (2016) carry out macro-economic modelling to estimate the dependence of the Omani economy on the oil sector revenues. They forecast that by 2020, the share of the oil sector in the total GDP of Oman will be 40.50%. They do not contrast this result against the targets of Oman’s 9th Five Year Plan 2015-2020 and Omani Vision 2020. It is important to mention that Vision 2020 that was presented in 1995 targeted the oil and gas sector share in GDP to be 9.0%. The Vision 2020 was however not rigid and the five (5) year plans introduced flexibility in the Vision’s goals. Oman’s 9th Five Year Plan 2015-2020 and Oman’s National Program for Enhancing Economic Diversification (Tanfeedh) put the same number at 32.4% by the end of the year 2020. It is apparent that the goals of Vision 2020 in their original shape are not expected to be met as the current share of the oil and gas sector in the total GDP of Oman is 43.0%. The referred study finds results that are pessimistic in comparison to Vision 2020 however not far from the target of Oman’s 9th Five Year Plan 2015-2020 and Tanfeedh. We find that the study is restricted in its forecasting ability as it takes only a linear approach to a complex problem. The variables in the model are limited to sectoral GDPs and ignore 145 the systemic nature of economic development as well as the “complicated/complex” nature of the development plans. This systemic nature requires us to include changes to the education, R&D, business, government and infrastructure arena among others. The complex nature of the policies requires us to discuss the alternative economic regimes under which the plans are expected to unfold so as to adequately account for the uncertainty. Saudi Arabia In an assessment of Saudi Vision 2030 by means of documental analysis Nurunnabi (2017) finds that the policy agenda in Saudi Arabia can only be effective if it includes further development of human capital, increased expenditure on R&D with a push for applied research, tackling unemployment and increasing the labour force participation of Saudis, particularly that of university-educated females. Albassam (2015) carries out a descriptive statistical review of the Saudi government’s five-year development plans from 1970 to 2015. The author finds that despite having diversification as the main focus of all the development plans, there has been no success in achieving economic diversification. The poor quality of institutions and the inability of the Saudi government to induce the private sector into actively contributing towards the diversification goal are pointed out as the main challenges to achieving it (Albassam, 2015). 146 5.3. The Predictive Model We use the output of the estimation model presented in Chapter 2 for predicting the state of the non-oil sector in Oman and Saudi Arabia The predictive model is given below: 𝐹𝐹𝑖𝑖𝑖𝑖𝑙𝑙𝑙𝑙 𝐴𝐴𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑃𝑃𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦 𝐺𝐺𝑜𝑜𝑜𝑜𝑤𝑤𝑝𝑝ℎ 𝑖𝑖𝑖𝑖 𝑚𝑚𝑜𝑜𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖 𝑆𝑆𝑚𝑚𝑐𝑐𝑝𝑝𝑜𝑜𝑜𝑜 = 𝐴𝐴 + 𝛽𝛽1 log(𝐼𝐼𝑖𝑖𝑖𝑖𝑝𝑝𝑖𝑖𝑙𝑙𝑙𝑙 𝐴𝐴𝑙𝑙𝑙𝑙𝑜𝑜𝑙𝑙𝑜𝑜 𝑃𝑃𝑜𝑜𝑜𝑜𝑝𝑝𝑙𝑙𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑦𝑦 𝑖𝑖𝑖𝑖 𝑝𝑝ℎ𝑚𝑚 𝑚𝑚𝑐𝑐𝑜𝑜𝑖𝑖𝑜𝑜𝑚𝑚𝑦𝑦) + 𝛽𝛽2(𝐼𝐼𝑖𝑖𝑖𝑖𝑝𝑝𝑖𝑖𝑙𝑙𝑙𝑙 𝐸𝐸𝑓𝑓𝑓𝑓𝑚𝑚𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑚𝑚 𝑇𝑇𝑚𝑚𝑜𝑜𝑝𝑝𝑖𝑖𝑙𝑙𝑜𝑜𝑦𝑦 𝐸𝐸𝑝𝑝𝑙𝑙𝑐𝑐𝑙𝑙𝑝𝑝𝑖𝑖𝑜𝑜𝑖𝑖 𝐸𝐸𝑓𝑓𝑝𝑝𝑚𝑚𝑖𝑖𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑜𝑜𝑚𝑚𝑠𝑠 𝑙𝑙𝑠𝑠 𝑙𝑙 𝑝𝑝𝑚𝑚𝑜𝑜𝑐𝑐𝑚𝑚𝑖𝑖𝑝𝑝 𝑜𝑜𝑓𝑓 𝐺𝐺𝐺𝐺𝑃𝑃) + 𝛽𝛽3(𝐼𝐼𝑖𝑖𝑖𝑖𝑝𝑝𝑖𝑖𝑙𝑙𝑙𝑙 𝐸𝐸𝑓𝑓𝑓𝑓𝑚𝑚𝑐𝑐𝑝𝑝𝑖𝑖𝑖𝑖𝑚𝑚 𝑅𝑅𝑚𝑚𝑠𝑠𝑚𝑚𝑙𝑙𝑜𝑜𝑐𝑐ℎ & 𝐺𝐺𝑚𝑚𝑖𝑖𝑚𝑚𝑙𝑙𝑜𝑜𝑝𝑝𝑚𝑚𝑚𝑚𝑖𝑖𝑝𝑝 𝐸𝐸𝑓𝑓𝑝𝑝𝑚𝑚𝑖𝑖𝑝𝑝𝑖𝑖𝑝𝑝𝑙𝑙𝑜𝑜𝑚𝑚𝑠𝑠 𝑙𝑙𝑠𝑠 𝑙𝑙 𝑝𝑝𝑚𝑚𝑜𝑜𝑐𝑐𝑚𝑚𝑖𝑖𝑝𝑝 𝑜𝑜𝑓𝑓 𝐺𝐺𝐺𝐺𝑃𝑃) + 𝛽𝛽4(𝑅𝑅𝑚𝑚𝑙𝑙𝑖𝑖𝑜𝑜𝑖𝑖𝑙𝑙𝑙𝑙 𝐶𝐶ℎ𝑙𝑙𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑠𝑠𝑝𝑝𝑖𝑖𝑐𝑐𝑠𝑠) + 𝛽𝛽5(𝐺𝐺𝑜𝑜𝑜𝑜𝑤𝑤𝑝𝑝ℎ 𝑃𝑃𝑚𝑚𝑜𝑜𝑖𝑖𝑜𝑜𝑝𝑝 𝐶𝐶ℎ𝑙𝑙𝑜𝑜𝑙𝑙𝑐𝑐𝑝𝑝𝑚𝑚𝑜𝑜𝑖𝑖𝑠𝑠𝑝𝑝𝑖𝑖𝑐𝑐𝑠𝑠) + 𝜖𝜖̂ The values of the respective coefficients are based on the developing countries model. The regional and growth period characteristic coefficients are based on the lowest and highest coefficients of the dummy variables in the estimated model for developing countries. The lowest coefficient for regional characteristics is based on the coefficient of the GCC region dummy, and the highest value of the coefficient are based on the South East Asia dummy coefficient. It reflects that the GCC region has had the worst regional characteristics that are conducive for labour productivity growth in the modern sector. At the same time, South East Asia perform best in terms of region-wide economic characteristics. We assume these to be reflective of regional transport and trade infrastructure connectivity, market access, regional peace and security. The lowest coefficient for growth period characteristics reflects a global economic downturn, corresponding to the time period dummy of the five-year period from 2008 to 2013 in the estimation. The highest coefficient reflects an economic upturn. This is based on the time period dummy of the five-year period from 2003 to 2008. The values are given in the following table. 147 Table 5.1 – Value of respective coefficients, their standard errors and root mean square error (RMSE) of the estimation Coefficient Standard Errors 𝜶𝜶 0.668 0.262 𝜷𝜷𝟏𝟏 -0.043 0.026 𝜷𝜷𝟐𝟐 0.036 0.028 𝜷𝜷𝟑𝟑 0.166 0.079 𝜷𝜷𝟒𝟒 Low -0.225 0.102 Medium 0 High +0.225 𝜷𝜷𝟓𝟓 Low -0.150 0.025 Medium 0 High +0.150 RMSE 0.136 Number of Observations: 196, Number of Countries: 75, Time: 1998-2013 (Three periods of five years), Adjusted R2 = 0.363, Method of Estimation: Pooled Ordinary Least Squares (OLS), Panel: Unbalanced. In the original model, we consider a time period of five years for the policies to reflect on labour productivity in the modern sector. The initial values of labour productivity, educational expenditures as a percent of GDP, R&D expenditures as a percent of GDP and government effectiveness are lagged by one year from the reference year as per the specification of the original empirical model. In the next section, we present an overview of the relevant economic plans of Oman and Saudi Arabia, isolate the inputs and targets of the Visions, and detail the reference condition based on the isolated inputs. 5.4. Review of the Economic Plans of Oman and Saudi Arabia We find that the targets of the Visions, Programs and Plans outlined in the respective documents for Oman and Saudi Arabia are often presented in a form that makes the accountability for the targets difficult or unfeasible34. An example of this in the case of Oman Vision 2020 is that the diversification target is stated in terms of the share of the non-oil sector in the economy. This target is dependent on the prevailing oil price in the year of 34 Note that an important exception to this is the preliminary release (Oman Vision 2040, 2019) of the Oman Vision 2040 where all targets are referenced with the source and both the baseline and targets for all indicators are defined explicitly. 148 evaluation. A low oil price would automatically make the relative share of the non-oil sector in the economy appear larger even though the actual size of the non-oil sector may remain unchanged. In the case of Saudi Arabia, the Saudi National Transformation Plan (Saudi NTP 2020) and Saudi Vision 2030 are both quiet about precise targets for the expansion of the non-oil sector. The original Saudi NTP 2020, in particular, shows the “Non-oil GDP as a percentage of GDP” target to be “under study”. The November 2018 version of Saudi NTP 2020 has no mention of a target for “Non-oil GDP as a percent of GDP”. The original document is mainly a combination of individual ministry targets with some effort dedicated to bringing together the targets at the national level. We find relevant 2020 and 2030 targets for our study through the 2020 targets of Vision 2030 whereby Saudi Arabia commits to be the regional leader in terms of having a diversified economy by 2020 and to be a global leader in terms of having a diversified economy by 2030. While Oman Vision 2020 and 9th Five Year Plan are mainly focused on outputs, the Saudi Vision 2030 and Saudi NTP 2020 are stronger in defining targets for some inputs that need to be met. We recommend that evaluation methods should be built-in, and both input and output targets should be explicit and balanced in the policy, vision and program designs. Such an approach would lead to addressing the national systemic limitation as well as ensuring accountability of the programs or policies. We include the strategies that complement the main policy or vision as a part of the same programme. As such when we mention the Economic Development Plan of Oman or Vision 2020, we mean to include Oman’s National Program for Enhancing Economic Diversification (Tanfeedh), the 9th Five Year Plan 2015-2020 in Oman and Oman Innovation Strategy, unless mentioned otherwise. Similarly, when we mention the Economic Development Plan of Saudi Arabia or Saudi Vision 2030, we mean to include the Saudi National Transformation Plan 2020, unless specified differently. In Figure 5.1 we present the conceptual scheme of the Oman Vision 2020 and Saudi Arabia Vision 2030. 149 In the following sub-sections, we discuss three (3) areas namely economic output, education and research & development, and, governance and business environment of the respective Visions. These are also highlighted in the conceptual scheme presented in Figure 5.1. Figure 5.1 – The conceptual scheme of the main elements of Oman Vision 2020 and Saudi Arabia Vision 2030 - highlighted to show the focus area of the ex ante quantitative evaluation carried out in this chapter. 35 35 Our construction of the policy model uses the visual format of Collaborative Institute for Research, Consulting and Learning Evaluation (CIRCLE, 2006) and the design is adapted from the recommendation of Rogers (2008) for the design of the logic model of complicated/complex policies. Education R&D Governance Economic Economic Output Overall Sectoral Sustainable Economic Development - Based on a mix of non-fossil fuel-based sources with considerable diversification away from oil & gas Varied and ubiquitous products Environmental and Social Sustainability Application of capacity to address challenges and seize opportunities FDI Misc. Identification of Capacity Development of Capacity Identification of Opportunity Development of Opportunity Greater Awareness and Participation by Stakeholders Innovation Capacity Capability & Capacity Other Opportunities Labour Participation & Employment High mobility and low inequality 150 5.4.1. Oman Target: Economic Output Oman has witnessed an average annual growth rate of GDP 4.5% from the commencement point of Oman’s Vision 2020 in 1990 till 2017. During the same period GDP per capita has dropped from 18,700 to 16,150 constant 2010 USD making the average annual GDP growth rate a negative of -0.8%. The average of the GDP per capita over the last 17 years was observed to be 18,000 USD. As such we can conclude that accounting for or excluding the economic and oil price cycles the GDP per capita in Oman has remained stagnant for the previous 17 years. The per capita economic growth target of Oman Vision 2020 was 3.8% and for the Oman 9th Five Year Development Plan was 2.8%. The total investment rate has grown from 10.3% in 2000 to 31.2% in 2015 against the target of 34% for 2020 under Oman Vision 2020. This is a remarkable achievement. The target for 2020 for the investment rate of the economy in Oman set in 2014 under the 9th Five Year Development Plan’s was 28%. However, this was already 32% in the year the target was set. The average annual growth rate of GDP in the non-oil sector has been 5.85% from 2000 to 2013. In order to meet the target of Oman Vision 2020, the labour productivity in the modern sector in Oman has to increase from 9,912 Constant 2010 USD to 13,250 Constant 2010 USD from 2015 to 2020. That represents an annual growth rate of 5.0%. Input: Education, Research & Development and Innovation Oman aims to be among the top 40 most innovative countries by 2020 and among the top 20 most innovative countries by 2040. This is in comparison to its position in 2016 of rank 73 on the GII. The current total education expenditure as a percent of GDP in 2017 was 6.7% and the current expenditure into tertiary education as a percent of GDP in Oman is at 1.9%. At the same time, Oman only invested 0.25% of its GDP into R&D. There are no explicit input targets for future education and R&D spending mentioned in any of the available relevant documents. 151 For the evaluation of the development policies we derive the inputs on education and R&D from the top 40 countries in the GII, so that the inputs devoted to education and research to reach the economic targets in 2020 are: 2.0% for tertiary education expenditure as a percent of GDP and 1.3% for R&D expenditures as a percent of GDP. Input: Governance and Business Environment Oman’s government effectiveness index was observed to be 0.3 on a scale of -2.5 to +2.5. The top 40 countries on the GII have an average of government effectiveness index of 1.35. Oman has a score of 76.3 out of 100 on the Index of Economic Freedom (IOEF) – Business Freedom subcategory. This is in comparison to the average of 78.3 for top 40 countries on the GII. We identify Oman’s target for government effectiveness and business environment to be scores of 1.35 and 78.3 for government effectiveness index and the business freedom sub-category of IOEF, respectively. 5.4.2. Saudi Arabia Target: Economic Output The Saudi Vision 2030 was announced in 2016. In the follow-up, the Kingdom of Saudi Arabia rebranded the 10th five-year plan to the Saudi National Transformation Plan (Saudi NTP 2020) and launched it in June of 2016 as an integral part of the Saudi Vision 2030. GDP per capita in Saudi Arabia in 2015 stood at 21,500 USD. The Saudi NTP 2020 does not set a target for the share of the non-oil sector. Saudi Vision 2030 sets the target for 2030 to be at the level of the 2016 regional benchmark for the share of non-oil GDP in the economy. This was 69%, while the share in Saudi Arabia was 58% in 2016. As the government of Saudi Arabia envisions no changes in the output of the oil sector, the entire growth of this share is expected to come from the non-oil sector. Input: Education, Research & Development and Innovation The government of Saudi Arabia had in 2016 the Gross Expenditure on R&D equivalent to 0.82% of the GDP. Total expenditure on tertiary education was around 1.6 % as a percent of 152 GDP. Unfortunately, Saudi NTP 2020, as well as the various programs under Saudi Vision 2030, do not mention expenditures the intended tertiary education and R&D expenditures 36. We use the average of the top 40 most innovative countries on the Global Innovation Index (GII) as the target for Saudi Arabia in 2020 and consider 2.0% as an input for the best policy scenarios. Input: Governance and Business Environment Saudi Arabia outlines a baseline of rank 26 and a target of 10 by 2030 for the Social Capital Index. However, there is no mention of the reference for the value of Social Capital. We were unable to find this value in World Economic Forum’s (WEF) Global Competitiveness Index (WEF, 2019) or in the Global Sustainable Competitiveness Index (SolAbility, 2019) or the Social Capital Pillar of Legatum Prosperity Index (Legatum, 2019). Saudi Arabia was indeed ranked as 26th in various years between 2013 to 2017 in relation to judicial independence, institutions, social institutions, firm ethics, flexibility, infrastructure quality or some other component of these indices. However, the reference is not explicit. One of the targets set by the Saudi Vision 2030 is that of reaching the rank of 10th country in the world on the Global Competitiveness Index. In addition to that, it highlights the aim for Saudi Arabia to go from the 80th rank on Government Effectiveness to the 20th rank by 2030. This would imply a target for government effectiveness index of 1.0 by 2020 and 1.8 by 2030. Both these targets appear difficult to achieve as it was only 0.25 in 2017. 5.4.3. Reference Condition and Scenarios Here we present the references initial condition for both the countries for most probable, inadequate policy measures and best practice policy action scenarios. The worst scenarios are based on maintaining the status quo on policy and input measures. The best practice scenarios are based on average economic growth and high improvements in regional characteristics for 36 Note that the Saudi Vision 2030 is an evolving plan. Some of the programs under the Saudi Vision 2030 like the Human Capital Development program was not published by the time this chapter was published (Saudi Vision 2030, 2019) 153 the GCC region. The governance, education and R&D expenditures for the best practice scenarios are based on the targets identified as best practice in the Visions of Oman and Saudi Arabia. We acknowledge that some of these choices are arbitrary. The purpose is to show the difference of outcome between what we have defined as inadequate, most probable and best practice policy. As such, we believe that our assignment of inputs for demonstrating the difference in output based on varying level and quality of policy action is justified. Table 5.2 – Reference Oman’s and Saudi Arabia’s respective Visions Oman 2020 Prediction Initial Reference Conditions (2014) Oman 2025 Prediction Initial Reference Conditions (2019) Oman 2030 Prediction Initial Reference Conditions (2024) Saudi 2020 Prediction Initial Reference Conditions (2014) Saudi 2025 Prediction Initial Reference Conditions (2019) Saudi 2030 Prediction Initial Reference Conditions (2024) G D P p er c a p it a C o n st a n t 2 0 1 0 U S D p e r C a p it a Most Probable 16,800 16,636 19,999 21,500 20,581 24,917 Inadequate Policy Not Applicable 17,080 16,687 Not Applicable 21,138 23,191 Best Practice Not Applicable 16,409 21,285 Not Applicable 20,298 16,604 L a b o u r P ro d u c ti v it y C o n st a n t 2 0 1 0 U S D p e r C a p it a Most Probable 9,912 12,044 15,799 12,147.50 15,168 19,684 Inadequate Policy Not Applicable 12,367 13,183 Not Applicable 15,579 17,092 Best Practice Not Applicable 11,880 16,185 Not Applicable 14,959 21,017 S h a re o f n o n -o il (m o d er n ) se ct o r a s a p er ce n t o f G D P Most Probable 59.0% 72.4% 79.0% 56.5% 73.7% 79.0% Inadequate Policy Not Applicable 72.4% 79.0% Not Applicable 73.7% 79.0% Best Practice Not Applicable 72.4% 79.0% Not Applicable 73.7% 79.0% E x p en d it u re o n R & D a s a p er ce n t o f G D P Most Probable 0.25% 1.30% 2.00% 0.82% 1.30% 2.00% Inadequate Policy Not Applicable 0.22% 0.22% Not Applicable 0.82% 0.82% Best Practice Not Applicable 1.3% 2.00% Not Applicable 2.00% 2.00% E x p en d it u r e o n te rt ia ry ed u ca ti o n a f Most Probable 1.90% 1.92% 2.00% 1.60% 1.60% 2.00% Inadequate Policy Not Applicable 1.92% 1.92% Not Applicable 1.60% 1.60% 154 Oman 2020 Prediction Initial Reference Conditions (2014) Oman 2025 Prediction Initial Reference Conditions (2019) Oman 2030 Prediction Initial Reference Conditions (2024) Saudi 2020 Prediction Initial Reference Conditions (2014) Saudi 2025 Prediction Initial Reference Conditions (2019) Saudi 2030 Prediction Initial Reference Conditions (2024) Best Practice Not Applicable 2.00% 2.00% Not Applicable 2.00% 2.00% G o v er n m en t E ff ec ti v en es s Most Probable 0.28 0.21 0.80 0.21 0.25 0.8 Inadequate Policy Not Applicable 0.00 0.00 Not Applicable 0.00 0.00 Best Practice Not Applicable 0.50 1.8 Not Applicable 1.8 1.8 R eg io n a l G ro w th C h a ra ct er is ti cs (C o ef fi ci en t) Most Probable Low (-0.225) Low (-0.225) Low (-0.225) Low (-0.225) Low (-0.225) Low (-0.225) Inadequate Policy Not Applicable Low (-0.225) Low (-0.225) Not Applicable Low (-0.225) Low (-0.225) Best Practice Not Applicable High (0.225) High (0.225) Not Applicable High (0.225) High (0.225) T im e P e ri o d C h a ra ct er is ti cs (C o ef fi ci en t) Most Probable Upturn (0.15) Upturn (0.15) Upturn (0.15) Upturn (0.15) Upturn (0.15) Upturn (0.15) Inadequate Policy Not Applicable Neutral (0.00) Neutral (0.00) Not Applicable Neutral (0.00) Neutral (0.00) Best Practice Not Applicable Neutral (0.00) Neutral (0.00) Not Applicable Neutral (0.00) Neutral (0.00) The population in Oman in 2020, 2025 and 2030 is projected to be 5.15, 5.57, 5.90 million people, and in Saudi Arabia it is projected to be 34.7, 37.3, 39.5 million people, respectively (UN DESA, 2019) 5.5. Results and Discussion The model of Table 5.1, together with the input data of Table 5.2 is used to compute the outcomes of the development strategies. These outcomes are compared with the 2020 and 2030 targets for Oman and Saudi Arabia. Confidence intervals are calculated using the root mean standard errors as these approximate the standard error of the predictions. 155 Table 5.3 – Most probable outcome of Oman and Saudi Arabia’s non-oil sector labour productivity Oman Saudi Arabia Target Actual Predicted Target Actual Predicted 2020 Labour Productivity in Modern Sector Constant 2010 USD 13,250 12,662* 12,547 Confidence Interval 10,952 to 14,375 18,595 15,836** 15,897 Confidence Interval 13,876 to 18,213 Annual Growth in Labour Productivity of Modern Sector (2015 to 2020) 5.0% 4.2% 4.0% Confidence Interval 1.7% to 6.4% 7.4% 4.5% 4.6% Confidence Interval 2.2% to 7.0% *Based on actual 2018 figure of 12,364 Constant 2010 USD and 1.2% annual growth expected as per April 2019 World Bank Economic Update for Oman. **Based on actual 2018 figure of 15,311 Constant 2010 USD and 1.7% annual growth expected as per April 2019 World Bank Economic Update for Saudi Arabia. In Table 5.3 we observe that Oman and Saudi Arabia are very likely to achieve their respective 2020 target for diversification. However, it must be noted that when compared to the original plans these targets were highly diluted in the last five-years plan. This is especially true for Oman where the Oman Vision 2020 documented the aim to increase the share of the non-oil sector in the economy to 91% of the total GDP by 2020. However, considering no changes in the size of the oil sector, the share of the non-natural resource sector in the economy is predicted to be between 65% and 75% (this range depends on the price of oil). The labour productivity in the modern sector target as per the original Oman Vision 2020 was 17,700 Constant 2010 USD. While Oman is successful in achieving the target of its 9th five-year development plan, it has failed in accomplishing Oman Vision 2020. The situation of Saudi Arabia is similar. The desire to completely diversify away from crude oil exports was implicit in Saudi policy for the last 20 years, however, Saudi Arabia at the time did not make it explicit through a formal long-term plan such as Oman Vision 2020 or its own Saudi Vision 2030. The first version of Saudi Arabia’s National Transformation Plan 2020 stated explicit targets, however, these were scrapped in the revised version that has officially replaced the original version. The only diversification target for 2030 that can be used to extrapolate intermediate 156 2025 targets is the global benchmark of 84% for non-oil sector contribution to GDP. One of the objectives of Saudi Arabia is to match global benchmarks by 2030. Another important target for Saudi Arabia in Saudi Vision 2030 is to be amongst the 15 largest economies in the world by 2030. We consider the target for Saudi Arabia to be equivalent to an estimated GDP of 1.7 trillion constant 2010 USD with a projected population of 39.5 million (UN DESA, 2019). With 83% of the output of the economy to be generated by the non-oil sector, this translates to an output per capita of 35,700 Constant 2010 USD for the non-oil sector. The preliminary version for Oman Vision 2040 released on 25th of July 2019 does not define the target for the output of the knowledge economy as a percent of total GDP for 2030 or 2040 and marks it as under development. For the purpose of comparison with the 2030 predicted outcome, we consider 35,700 constant 2010 USD as the labour productivity target for the non-oil sector for both Oman and Saudi Arabia for 2030. The following figure shows a comparison of the predicted productivity in the non-oil sector in Oman and Saudi Arabia with the intermediate targets and final target for 2030. Note that here we assume that by 2025 Oman and Saudi Arabia would have met their respective targets for the effectiveness of governance, investment in tertiary education and R&D. Here we present the comparison of the target for 2020, 2025 and 2030 with the most probable prediction. 157 Figure 5.2 – Predicted and Targeted Labour Productivity in the Modern Sector We also look into the best and worst policy scenarios in terms of tertiary education expenditures, research and development expenditures, government effectiveness, regional growth characteristic (regional connectedness, transport and trade infrastructure, regional peace and security and international trade, et cetera). 5000 10000 15000 20000 25000 30000 35000 40000 45000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Oman Target Most Probable Scenario 10000 15000 20000 25000 30000 35000 40000 45000 50000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Saudi Arabia Target Most Probable Scenario ⌶ ± 1 RMSE ⌶ ± 1 RMSE 158 Figure 5.3 – Labour productivity in the modern sector in varying policy conditions We observe that Oman and Saudi Arabia will achieve their respective 2020 targets. However, the differential between the target and the most probable scenario is going to increase with time. It appears that without deliberate policy action for improving the determinants of labour productivity in the modern sector the governments of Oman and Saudi Arabia will face stagnation of labour productivity growth in the modern sector. 5000 10000 15000 20000 25000 30000 35000 40000 45000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Oman Most Probable Scenario No Improvement Scenario Best Scenario ⌶ ± 1 RMSE 10000 15000 20000 25000 30000 35000 40000 45000 50000 2015 2020 2025 2030 L a b o u r P ro d u ct iv it y i n t h e M o d er n S ec to r C o n st a n t 2 0 1 0 U S D p er c a p it a Year Saudi Arabia Most Probable Scenario No Improvement Scenario Best Scenario ⌶ ± 1 RMSE 159 The predictions in Figures 5.2 and 5.3 have to be understood within the context of the model used, which supports plausible causal links between policies and productivity growth in the modern sector. While individual countries do not have any control over the global business cycle, they do, however, have some ability to affect their own regional growth characteristics. Efforts to increase regional integration and cohesion, regional peace and stability, enacting trade agreements, launching regional development programs, opening up trade routes that connect other important countries, and improving regional transportation networks are all examples of regional characteristics where individual countries can enact policies to improve their growth prospects. In the following, we present a discussion on what Oman and Saudi Arabia can do to reach their respective diversification and economic growth targets. Oman Currently, Oman spends 0.25% of its GDP on R&D and 1.90% of its GDP on tertiary education. The government effectiveness in Oman has fallen from its high point of 0.5 to the current level of 0.28. The apparent decline of the quality of governance in Oman is a cause of alarm. Looking at its level of R&D expenditures it is easy to recommend the government of Oman to stimulate an increase in the expenditure on R&D. However, it is most important to improve the quality of governance. Without a highly effective government, other policy measures are likely to be negatively affected by the inefficiencies in the system. Based on the trends of government effectiveness in Oman, a scenario where the quality of governance in Oman declines further is plausible. In such a scenario, the annual growth in the non-oil sector in Oman is estimated to be approximately 2.6%. This is not adequate to ensure the replacement of oil as the main driver of the economy by 2030. Oman is already taking measures to improve its connectivity to international trade routes. Oman’s new port in the open Arabian Sea (Indian Ocean) features prominently on the Chinese Belt and Road Initiative (BRI) and holds the promise to be one of the most important transport and trade hubs in the region. Oman has also held a neutral position in international strategic disputes and facilitated negotiations to dispel conflict in the region. Oman is engaged in expanding its 160 road and air networks and at the same time has commissioned the construction of a railway network through all major cities. It is planned to be eventually connected to the proposed GCC railways network. Oman’s contribution to improving regional characteristics through regional transportation integration, international trade links and maintaining peace and security is important to achieve the diversification goal. It is recommended that Oman should increase each of its R&D and tertiary education spending to 2.0% of GDP and improve its governance (for example, to the level of Denmark which had the 10th most effective governance system in the world in 2017). In the best case policy scenario, Oman could achieve labour productivity in the modern sector of 37,900 Constant 2010 USD by 2030. This is above the target of 35,700 Constant 2010 USD and much above 30,000 Constant 2010 USD that is more likely under the most probable scenario considered in this chapter. It must be noted that Oman’s military expenditure as a percent of GDP in 2016 was 15.3% (The International Institute for Strategic Studies (IISS), 2017). It is obvious that the suggested increases in spending are completely feasible in the case of Oman. Saudi Arabia What can Saudi Arabia do to ensure that 2020 targets are met? Government effectiveness and expenditure on R&D are also the areas where Saudi Arabia can make tremendous improvements. Regional growth trends will have a great influence on Saudi Arabia’s ability to achieve its targets. Saudi Arabia is the country that holds the key to improving many regional characteristics such as the perception of peace, trade links and regional cohesion. Two regional limitations impeding the economic development of the GCC are poor infrastructure and geopolitical tensions. Saudi Arabia can play a major role in overcoming these in favour of greater growth in the modern sector. The regional infrastructure can be improved by creating rail, air, road and maritime connections and improving trade networks with all middle eastern countries including Israel, Syria and Iran. Lasting peace could be achieved in the Arabian Peninsula by including Yemen, reintegrating and increasing the cohesion of the GCC, which includes ending the trade embargo of Qatar, and improving relationships with Iran. These measures are expected to improve the regional growth 161 characteristics that are key to the growth of Saudi Arabia’s modern sector itself. In their ambitions to diversify and improve economic and social stability the countries of the GCC can benefit from regional peace and stability, the Chabahar Port, the Belt and Road Initiative and the GCC wide rail link extending to the West. Saudi Arabia consistently ranks among the top military spenders in the past few years. By diverting resource towards governance, tertiary education, R&D, and improving regional characteristics, it should be feasible to achieve best practice policy scenario. This would result in labour productivity in the modern sector of Saudi Arabia equivalent to 47,800 Constant 2010 USD by 2030. In this scenario, Saudi Arabia could stand among the top thirty most productive economies in the world with its modern sector productivity at a level comparable to that of advanced western economies. Positive Regional Developments in GCC Duqm has a special place in the global and GCC trade. Unlike most of the ports in the region that are prone to access concerns, the Duqm port is in the open Arabian Sea and provides quick access to the Indian Ocean trade routes. The government of China through the Wan Fang Consortium has allocated 10.7 billion current 2017 USD to development of a transit- oriented industrial city named China-Oman Industrial Park in Duqm, Oman. China promises to complete 30% of the project within 5 years. The Chabahar Port in Iran is an Indian sponsored port on the Gulf of Oman that sisters with the Sohar Port of Oman. Meanwhile, Saudi Arabia is envisioning a road link between the Gwadar port in Pakistan and Oman and has agreed to a 10 billion grant investment in the China-Pakistan Economic Corridor (CPEC) that is a critical part of the Belt and Road Initiative. The GCC rail project appears to have been delayed, however, individual countries in the GCC are constructing their individual rail networks and Israel has shown interest to join the network. Oman and Saudi Arabia have the first land link in the form of a construction marvel road link through the world’s largest contiguous sand desert - the Empty Quarter. All signs indicate that the growth characteristics of GCC region for modern and non-oil and gas sector are going to move from “low growth characteristics” to “high growth characteristics” in the medium to long term. 162 5.6. Summary and Conclusion We evaluated ex ante the development strategies of Saudi Arabia and Oman, aimed at less oil dependency for the years 2020 through 2030. For that purpose, we used a predictive quantitative model assessing the targets under the respective policies, based on the econometric analysis carried out in Chapter 2. As a first step, we sought to extract inputs and quantifiable targets from the strategy documents of both countries. Wherever the inputs or targets by year were not explicitly mentioned, we extrapolated them from high-level targets (such as being top 40 in innovation by 2040 or top 10 in GDP by 2030). For Saudi Arabia, we had to deduce the target for the share of the non-oil sector in the economy, while for Oman, these targets are clearly stated for 2020. Since the share of the non-oil sector is dependent on the oil price, we have focused on the annual growth in labour productivity of the modern sector and the final level. In the most probable scenario, Oman and Saudi Arabia will both not be able to meet their targets for diversification away from an oil-based economy by 2030. One potential reason for this expected failure to meet the growth targets for the modern sector is related to not having the required systemic conditions, enablers and inputs. Another part of it comes from limitations in baseline analysis and simply having unrealistic targets. An example of this is the Oman Vision 2020 target for the non-oil and gas sector to contribute 91.0% of the total GDP by 2020. This is the “putting stars in their eyes” effect, as some of the targets presented were overly ambitious at best or ambiguous at worst. Poor policy action on the determinant discussed will mean that the countries will face stagnation in the growth of their respective modern sectors. In order to ensure that the long-term targets for their respective visions are met, it becomes imperative for the governments of Oman and Saudi Arabia to focus on all systemic conditions. Investment in education, R&D, improving government effectiveness, business environment and regional characteristics is crucial for ensuring successful diversification and sustainable economic development. 163 We observed in Chapter 2 that the regional growth characteristics in the GCC were worse than in other regions. In their ambitions to diversify and improve economic and social stability, the GCC and its countries can benefit from improving the regional growth characteristics in the coming 5 to 10 year. This can be achieved through a focus on regional peace and stability and investing in major regional infrastructure projects such as the Chabahar Port, connections to the Belt and Road Initiative and the GCC wide rail link extending to the West. The governments of Oman and Saudi Arabia will be able to maximise growth opportunities by opening the highly-qualified employment sector to the international job market in the short term, especially in the area of R&D and modern sector. At the same time plans should be drawn to settle Omani and Saudi citizens in highly-qualified employment position especially in the R&D arena as the modern sector expands in the long term. The governments of Oman and Saudi Arabia are interested in increasing the productivity of the country in general, and at the same time, they are strongly interested in employment creation. Therefore, expansion of the modern sector is the only key to both higher productivity as well as job creation. Note that higher productivity can also be achieved through increased efficiency. However, the population of Oman and Saudi Arabia is young, increasing and well educated. As such expansion and efficiency have to go hand in hand. Oman has the highest ratio of science and engineering graduates in the world and Saudi Arabia is also performing well in this measure. However, Oman and Saudi Arabia both perform very low in terms of R&D personnel per capita. While increasing investments in the R&D sector will help mitigate the current situation, the fastest and most plausible solution in order to fast track growth of the modern sector is to ease the job market participation of foreign R&D personnel in both Oman and Saudi Arabia. In the long run, it is expected that employment opportunities for science and engineering graduates in the expanding modern sector, entrepreneurship and R&D arena will be created through focussed programs and investment into the relevant areas. This research provides us with important avenues for future research, such as looking deeper into the regional characteristics that affect economic growth. Another area of research is to 164 study government policies for brain-import (hiring knowledge workers and highly skilled labour from other countries) versus local development of knowledge workers for creating a modern knowledge-based sector and ensure sustainable growth through diversification. We recommend that evaluation methods should be inbuilt, and both input and output targets should be explicit and balanced in the policy, vision and program designs. Such an approach would lead to addressing the national systemic limitation as well as ensuring accountability of the programs or policies. 165 6. Conclusion Background, Summary, Synthesis and Recommendations 6.1. Background of the dissertation Economic diversification and innovation are considered to positively influence sustained economic development (Fagerberg, 1994; Frenken, et al., 2007; Content & Frenken, 2016). Industrial specialisation and natural resource specialisation provide some growth benefits for countries (Krugman, et al., 2012). However, such strategies may make the economy susceptible to volatility and cyclical risks (Koren & Tenreyro, 2007). Economic diversification is expected to help counter the effects of downturns that affect particular industries or extractive sectors (Ramey & Ramey, 1995; Osakwe, 2007). Countries that offer a more diversified set of products and services are known to have higher levels of development in comparison to countries that do not (Krishnaa & Levchenko, 2013). Additionally, economies with higher skill levels, well-developed institutions and strong systems of innovation have been shown to be more robust in the face of shifting demand and evolving market structure (Chor, 2010). Effective utilisation of knowledge and streamlining of the adoption of transferrable skills are considered crucial for transition and diversification (Blanchard & Kremer, 1997). Strong institutions support this process and help guide the economy and its participants for transition (Costinot, 2009). The relevance of diversification, innovation, and investment in the modern sector 37 is amplified in scenarios where countries are highly dependent on natural resource extraction. Many countries with natural resource rents as the main share of their total output have to confront natural resource price volatility, risks associated with anticipated depletion of the natural resources, as well as associated strategic risks. It is not surprising that these countries have had diversification on their agendas for a considerable time. However, reports of outright success in creating a diversified economy are rare and those of failure are abundant. The literature highlights that institutional settings, education, research and development (R&D), governance, business environment, sound industrial and clusters policy, and increased capital investment into the modern sector are 37 non-natural resource and non-agricultural sectors 166 related to successful diversification and stimulation of innovation in the economy. The overarching gaps in the literature are highlighted in the Introduction to this dissertation and a detailed discussion on the relevant literature is presented in the literature review sections in Chapters 2 through 5. In the context outlined above, this dissertation aims to understand the causes of successful diversification and growth in labour productivity in the modern sector of an economy. The question at the heart of this dissertation is whether natural resources hamper the growth in the modern sector. Additionally, we explore the systematic strengths (or limitations) that lead to improved (or restricted) growth in the modern sector of a natural resource-rich economy. These enquiries are complemented by an effort to understand the state of “broad innovation policies” 38, the role of enablers39 and their relationship with diversification and innovation in the member states of the Gulf Cooperation Council (GCC) 40. Finally, the dissertation attempts to find out whether the GCC countries are on the path towards successful diversification. This is determined in terms of the predicted outcome of their diversification efforts contrasted against their stated targets. 6.2. Summary In this dissertation, several issues concerning policy for diversification, innovation and growth of labour productivity in the modern sector have been addressed. These are termed the enablers. They include government effectiveness, investment in education, R&D expenditures 38 According to Lundvall (2007) systems of innovation in a narrow sense “leave significant elements of innovation- based economic performance unexplained”. In the “broad” sense the core knowledge producing and disseminating institutions are embedded in a wider socio-economic system and the relative success of innovation policies is a function of influences and linkages beyond these core institutions (Freeman, 2002) 39 The ‘enablers’ include primary, secondary, tertiary and vocational education, research and development (R&D), quality and efficiency of governance, and business environment. The policies affecting these areas such as investment into education and R&D and regulation improving the governance and business environment are also covered under the term ‘enablers’ as they ultimately contribute to boosting innovation and diversification. It is synonymous to the term determinants. 40 The Gulf Cooperation Council (GCC) is the colloquial term used to refer to the Cooperation Council of the Arab States of the Gulf (GCC). We use the abbreviation GCC to refer to the member countries as of 2017 – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. These are synonymously referred to as the countries of the Arabian Gulf, countries of the Arabian Peninsula, countries of Persian Gulf and Arab countries of the Gulf. Countries on the Arabian/Persian Gulf such as Iran and Iraq, and countries in accession talks such as Yemen, Jordan and Morocco are not included. 167 and the state of the business environment. Furthermore, the growth of the modern sector is explored taking into account the role of fixed capital investments, the amount of commercially extractable oil reserves, the oil extraction ratio as a share of total oil reserves and the oil price. The main focus is on natural resource-based economies. The wider global perspective along with the policy consideration for non-high-income economies in contrast to high-income economies is also presented. The diversification and innovation policy in the case of GCC countries is explored in greater depth. Chapter 1 presents an introduction and summarises the rationale behind the research carried out for this dissertation. Chapter 2 explores whether “broad innovation policies” affect labour productivity in the modern sector. The question was addressed through an econometric model based on an innovation policy framework. The innovation policy framework is a conceptual framework that identifies factors influencing innovation in a market space. The policy focus in this chapter was limited to government effectiveness, expenditures on tertiary education, expenditures on R&D, and the condition of the business environment. Data from different sources were used to carry out pooled OLS regressions for a cross-section of 95 countries covering the time period of 1998 to 2013, and regional effects were considered. The policy measures were lagged by 5 years. The results indicate that government effectiveness played an important role in labour productivity growth in the modern sector. It was estimated that the interaction of government effectiveness and tertiary education expenditures has a statistically significant and positive association with labour productivity growth in the modern sector for the global data set. The positive association persisted for the set of non- OECD countries that includes non-high-income countries and GCC countries. Furthermore, for non-OECD countries, it was observed, that the effective R&D expenditure as a percent of GDP was important for labour productivity growth in the modern sector. We could not identify the effect of a good business environment (as defined by the index of economic freedom) on growth in labour productivity in the modern sector. The GCC region is lagging behind all other regions in terms of regional characteristics that positively affect labour productivity growth in the modern sector. In Chapter 2 we also explore the probability that oil prices affect the ability of the GCC countries to sustain the development of their modern 168 sector. The chapter highlights the importance of the interactions among various systemic conditions and enablers for stimulation of innovation and diversification. Overall, the results lead us to the exploration of the relationship of innovation and diversification output in the GCC countries with the systemic conditions and enablers as well as oil sector variables such as extraction ratio, reserves and oil prices. In Chapter 3, the analysis focussed on the performance of the GCC countries in terms of policies affecting diversification and innovation. The analysis was guided by an eclectic qualitative approach for the cases of Oman, Saudi Arabia and the United Arab Emirates. A qualitative discussion of the data, relating to several policy measures, enablers and outputs, was presented. The results revealed the state of each policy or enabler, and output on a seven- step scale ranging from “extremely negative” (---) to “extremely positive” (+++). The linkage between the state of the policies or enablers and the state of outputs was explored. Drawing upon this connection, the conclusions were presented. The discussed policies and enablers included seven (7) measures of primary and secondary education, four (4) measures of vocational, technical and tertiary education, six (6) measures of R&D, seven (7) measures of the business environment, and six (6) measures related to governance and infrastructure. In terms of outputs, three (3) innovation outputs and eleven (11) diversification outputs were discussed and graded on the scale. The assessment of the state of each policy, enabler and output by itself forms an important outcome of this chapter as it provides a snapshot of the innovation system in these countries. It was observed that Oman lags behind in innovation output (-) with relatively better performance on the diversification front (++). Saudi Arabia and the United Arab Emirates were performing slightly positively (+) on innovation outputs in relative terms. The diversification output for Saudi Arabia was rated as moderate (o) and that of the United Arab Emirates as slightly negative (-). Furthermore, the relationship between the inputs and outputs was discussed. The state of the outputs relates to a system of policies and enablers in the five overarching input areas where the three countries have different strengths and weaknesses. In order to improve the outputs, these countries have to strengthen all of the discussed policies and enablers. Additionally, there should be a special focus on the weakest areas. In the case of Oman, these are vocational education and R&D. 169 For Saudi Arabia, primary and secondary education is the first observed limitation within the scope of Chapter 3. The states of the business environment, governance and infrastructure represent additional weaknesses in the Saudi Arabian economic and innovation system. Finally, for the United Arab Emirates, the limiting enablers include vocational education, science and engineering education, and the lack of a coordinated R&D policy. Chapter 4 focuses on the oil industry variables – oil price, oil reserves, and oil extraction as a ratio of total oil reserves – in improving modern sector labour productivity and fixed capital formation. In order to explore this research question, we develop a theoretical model for a closed economy with a modern sector (in this case capital-driven) and a traditional sector (natural resource-based where the price of the natural resource is externally given). The theoretical model provides a background for the empirical model. The Hamiltonian maximisation for consumption indicates that investment in fixed capital is a function of the productive efficiency of the economy, the savings rate, the oil reserves, oil prices and oil extraction. Based on the interpretation of the theoretical model, given that the labour productivity growth in the modern sector is related to investment in fixed capital, natural resource extracting countries can utilise the rents from resource extraction to diversify effectively. The theoretical model was tested in an econometric specification with a panel of 149 countries for the period of 36 years from 1980 to 2015. The data set included 85 countries that had oil reserves in part of the time period considered. It was observed that countries with high levels of oil reserves per capita (as a ratio of world oil reserves per capita) benefited from high oil prices to generate higher fixed capital. In addition to this, the initial level of capital, the savings rate, schooling and GDP growth rate had positive relationships with fixed capital formation. The relationship of labour productivity growth in the modern sector with oil reserves and the oil extraction ratio was not found to be statistically significant. Higher oil prices were observed to have a positive relationship with labour productivity growth in the modern sector. The GCC countries demonstrated an ability to invest their oil wealth into fixed capital. The investment trend was positively related to a higher total value of the oil reserves for all the six GCC countries. That is, the interaction of oil reserves with the oil price 170 and country dummies for the individual GCC countries was positive and statistically significant. Qatar’s performance in terms of fixed capital investments was the best among the GCC countries, while Bahrain and Saudi Arabia were the worst performers among the GCC countries in terms of fixed capital formation. Chapter 4 shows that improving the productive efficiency of the country through better education not only has led to high labour productivity growth in the modern sector but also has improved the ability of the country to invest in fixed capital. All in all, according to our analysis, natural resources do not constitute a curse, rather, they offer countries the opportunities to grow their modern sector and diversify. In the final research chapter – Chapter 5, an evaluation of the economic diversification plans of Oman and Saudi Arabia was presented. The main question in this regard is whether the targets of these plans are realistic and achievable. The question was addressed through an ex ante evaluation of the policies of both countries using the predictive model constructed on the basis of the econometric analysis of Chapter 2. The results were computed for three states of global economic development and three states of regional characteristics. The most probable scenario was discussed in detail and it was observed that between 2015 and 2020 Oman and Saudi Arabia were predicted to achieve 4.2% and 4.5% annual growth in modern sector labour productivity. This is in comparison to the targeted 5.0% and 7.4% annual growth rates, respectively. It is estimated that in the most probable scenario the gaps between predicted and targeted labour productivity in the modern sectors are going to widen further by 2030. In addition to this, the worst-case scenario shows a likelihood of stagnation if special attention is not given to the state of enablers, and systemic conditions are not improved. However, the countries can improve governance, increase expenditure on education and research and development, and improve regional characteristics (such as cross-country transportation infrastructure, trade links and perception of peace and security, among others). This will result in Oman and Saudi Arabia meeting their desired diversification goals. Given the right policy response, it is not only possible to stimulate growth in the modern sector and diversify away from dependence on natural resources, but also to join the ranks of some of the most developed nations by 2030. 171 6.3. Limitations and Suggestions for Future Research The research carried out for this dissertation identifies and recommends pathways for successful diversification and increased innovation for natural resource-driven economies. At the same time, it also opens up many avenues for future research. In Chapter 2 the use of innovation policy framework conditions was limited to a few enablers and policies – namely government effectiveness, education policy, research and development policy and business environment. A limitation encountered there is a lack of appropriate indicators for proxying the state of the business environment. 41 The indicator used – Index of Economic Freedom – does not show a relationship with the modern sector labour productivity. The empirical model may be refined further, and a better indicator may be selected or constructed to identify the effect of the ease of doing business independent from other variables. An important element of Chapter 3 is the use of the seven-point scale for enablers and policies that affect innovation, diversification and economic growth in general. One of the limitations there is that the ranking on this scale is relative and the scores are based on the expertise and judgment of the authors. It is recommended to repeat the ranking of inputs and enablers on a similar scale for the same countries at different points of time, increase the number of expert reviewers for assigning scores/points on the scale and add more countries to the comparison. Additionally, the scale may be normalised and the relationship of the worst- performing input against an economic output may be explored quantitatively. This will provide a way to analyse the relationship of a “limiting policy” with innovation, economic growth and/or diversification. In the current discourse in economics, the discussion often leans more towards identifying what causes the output to be achieved rather than what limits it. In the system approach, all the enablers and inputs must be performing healthily, and the system’s level of performance may be considered as a function of the worst-performing input. Appropriate normalisation techniques need to be developed in order to ensure that the indicators of the various components of the innovation system are comparable. 41 The World Bank’s Doing Business indicators did not cover the full time period under observation. 172 The key contribution of Chapter 4 is the introduction of the natural resource sector as an important contributor in a two-sector economic development model. The observation that the natural resource rents can be successfully invested in fixed capital formation and contribute to the growth in the modern sector is not surprising and he relationship between fixed capital and economic output is generally accepted in the economic discourse. However, Chapter 4 also raises a queston about the efficiency of the new fixed capital in natural resource-rich economies, which is of great interest to the academic discourse. While our findings indicate that there is no “natural resource curse”, we must ask – Is the efficiency of the fixed capital investments in some countries lower than others? This question may be answered by disentangling the components of fixed capital investments. The role of the relatively shorter- term high return fixed assets (such as fixed asset software) confounds the analysis of economic growth based on fixed capital investments further and opens up new avenues for research in this direction. The positive effect of oil prices on the growth of labour productivity growth in the modern sector is an interesting result encountered in Chapter 4. Rising oil prices contribute to the acceleration of business investment and activity in the oil sector. This acceleration, in turn, is likely to positively affect the business activity and investment along the supply chain where the oil sector connects through to the modern sector. Thus, high oil prices may help in boosting the overall economic expansion. Countries that produce modern consumption goods, such as the US and some European countries, benefit during high oil price periods from increased exports to oil-exporting countries in the GCC and other regions. However, high oil prices also tend to limit the growth of exports from the same modern consumption goods-producing countries to oil-importing countries, notably China, India, Japan and others in Europe. Some of these oil-importing countries may benefit from supply chain changes aimed at increasing profit margins for countering the fall in marginal returns because of higher energy cost per unit. It is evident that out of the previously mentioned dynamics those that enhance the modern sector labour productivity because of high oil prices are dominant. However, beyond a certain point, rising oil prices will start to weigh on investment and spending by the non-oil sector and households, slowing down the overall growth. Increase of modern sector productivity due to high oil prices may also be a sign of 173 adoption of innovative methods to reduce energy costs per unit, even though such dynamics are expected to operate in the longer term rather than the one-year intervals that are used in Chapter 4. It is recommended to carry out further research to understand the pathways through which oil prices affect innovation and growth in labour productivity in modern sector. Another recommendation is based on the findings in Chapter 2 and the use of a predictive model in Chapter 5 where it is observed that region-wide phenomena affect the productivity growth in the modern sector. The attraction of foreign direct investment from other regions and international firms towards a specific region, the transport and logistics infrastructure and the perception of regional stability are some factors that may affect the growth characteristic of a region. However, we do not disentangle the factors behind the regional characteristics captured by the coefficients of regional dummies. More research needs to be carried out to identify and disentangle the various factors of the regional characteristics that affect growth in the modern sector. Such research should consider including policy variables such as the development of regional and cross-country transportation infrastructure, improving international trade links, strengthening regional security and enhancing the perception of strategic stability and peace, among others. In Chapter 2 the GCC region appears to be performing worse than the reference region (which includes most European countries, the US and Canada – see Appendix 2-A for more details) and most other regions in terms of labour productivity growth in the modern sector. In Chapter 4 it is observed that individual GCC countries are performing no worse than the reference country (US) in terms of the same output variable. One reason behind the apparent contradiction between Chapter 2 and Chapter 4 is related to the difference in the growth period considered. The empirical model in Chapter 2 considers five (5) years growth rates in productivity for three consecutive five (5) year periods from 1998 to 2013. In contrast, the empirical model in Chapter 4 considers annual growth from 1980 to 2015. Also, the model in Chapter 4 accounts for fixed capital investment that may partially cover the foreign direct investment. Foreign direct investment may itself be a factor negatively affecting the regional characteristics of the GCC countries as observed in Chapter 2. Different models, data and 174 time periods may account for why this is observed. More research may be carried out to understand the exact cause of worse regional growth characteristic in the GCC. In the following section, we present a synthesis of the insights from the dissertation and detail the policy implications of our results. 6.4. Integrated insights from the dissertation and policy implications The common theme that emerges from the dissertation is that economic growth and diversification are constrained by the limiting policy or enabler. They are not driven by the best performing determinant in the system. The analysis helps to understand the measures that affect the productivity in the modern sector particularly in oil-rich countries. The results are, however, generalisable to a broader set of developing and natural resource-driven economies. The results of the dissertation can be grouped into three integrated themes. These are presented in the following sub-sections. 6.4.1. Institutional Effectiveness, Productive Efficiency, Human Capital, Education and R&D Findings The importance of institutional effectiveness, productive efficiency, human capital, education and R&D for sustained economic development and diversification is the prevalent outcome in this research. The results of Chapter 2 indicate that improving the efficiency of education and R&D investments can lead to higher productivity growth in the modern sector. Chapter 3 confirms the validity of this result at the country level for three countries of the GCC - Oman, Saudi Arabia and the United Arab Emirates. The limitations in the governance, education and R&D systems of these countries are some of the barriers to the growth in the modern sector productivity, innovation and diversification. The importance of education and productive efficiency of the system is highlighted again in Chapter 4. There it is observed that a higher 175 level of schooling is not only related to higher labour productivity growth in the modern sector but also greater fixed capital formation. In Chapter 5 we observe that the initial conditions of governance, education and R&D are not adequate for Oman and Saudi Arabia to meet their respective 2020, 2025 and 2030 economic diversification targets. Policy Advice The policy recommendations in this regard are based on two lessons. Firstly, education and R&D investments are expected to improve productivity in the modern sector. Secondly, countries can only invest in the modern sector fruitfully based on their productive efficiency, education levels and expertise. Finally, the effectiveness of the policy inputs such as education and R&D spending matters. We also know from the literature that the outcomes of investment in education improve the efficiency of the productive environment. In this regard, the governments of developing countries and natural resource-based economies are recommended to increase their investment in education and R&D. It is also recommended that they focus on improving the overall quality of governance, which would help to minimise inefficiencies in investments arising due to bad governance. This will ensure productivity and innovation output improvements proportional to the investments made and in turn would help meet the development and diversification targets. A route often followed by countries trying to safeguard high levels of human capital is to import highly-skilled labour and knowledge workers to support the development and diversification process. This is one of the policies that has been adopted by some of the GCC countries to varying levels and can be considered in the policy mix. The import of highly skilled knowledge workers is one of the most viable solutions to tackle the limitations in human capital, considering the challenging short-run diversification goals of the GCC countries. However, an appealing environment is required to attract knowledge workers. This forms a major challenge in a country like Saudi Arabia that is often perceived as an unattractive location for foreigners. As such the government of Saudi Arabia must pay special attention to improving the country’s attractiveness for knowledge workers. The need for importing foreign knowledge workers also faces the challenge of the localisation of labour (each GCC country uses a different 176 terminology – such as Omanisation, Saudization and Emiratisation – to define the process of replacement of foreign worker with national labour). The process is mainly bottom-up, that is, localisation is implemented stage-wise starting from the lowest skilled labour and the main target of the labour localisation programs are not primarily knowledge workers. In this context, it is also important for the countries of the region to take into consideration that the development of a broad knowledge-based economy. Keeping in view that the ability to sustain innovativeness in the country is associated with the ability to connect to the international knowledge networks. Such linkages can be established by a regular flux of international knowledge workers. 6.4.2. Natural Resources, Oil, Productivity and Investment Findings In this dissertation, we do not find evidence of the “natural resource curse” in the sense that having natural resources and utilising them for meeting the needs of the population lead to an automatic doom of the economy. Instead, this dissertation shows that natural resources provide a key to prosperity through sound management of the rents. In Chapter 2 the GCC countries are shown to have the worst regional characteristics for modern sector productivity growth when compared to the reference countries and the rest of the world regions (That is, the value of the coefficient for the regional dummy on the GCC countries is observed to be the lowest in the estimation). Further exploration was carried out to find the reasons for the lower regional performance in GCC considering that the broad innovation policy variables, natural resource dependence and agricultural value-added were controlled for. Lack of adequate cross-country regional infrastructure and perceived political and strategic instability may be considered among the many reasons why the GCC region is worse-off in terms of economic growth characteristics. However, the one common characteristic of the countries of the GCC that stands out is their dependence on oil revenues. In Chapter 2, the prediction based on the empirical model shows that the modern sector labour productivity growth in the GCC countries was associated with oil prices. This 177 relationship was not observed in the case of countries like the Netherlands and Norway that were two countries from the reference group. Following the oil dependence reasoning, an analysis of the effect of natural resources on labour productivity growth was carried out in Chapter 4. This showed that natural resources are an important source of revenue that can be used for investment into fixed capital. There is no significant difference between the GCC countries and the reference country the United States (US) in terms of labour productivity growth in the modern sector. One of the results of Chapter 4 indicates that the GCC countries are actively engaged in investing their oil revenues. During high oil price periods, these investments increase disproportionally. Qatar, amongst all countries, has demonstrated the capability to maintain one of the highest labour productivity growths in the modern sector and fixed capital investment for the period 1980 to 2015. Policy Advice The main lesson here is that natural resource-driven countries need to invest in fixed capital, improving governance and institutions, reducing systemic inefficiencies, and developing human capital. The investment as a percent of total GDP is generally higher during high oil price periods in the GCC countries. However, the governments should also target keeping the investment ratio as high as possible during low oil price periods. During high oil price periods, governments may be urged to increase spending on non-productive purposes, such as the creation of employment in the government sector that is not tied to improvements in the effectiveness of the government. Once initiated the need for such spending often persists, for example, to keep unemployment at a low level. The increase in such unproductive expenditures hampers the ability of the government to invest in the development of the modern sector as oil prices fall and the limited revenues have to be mainly used on inefficient and inflexible government spending. In addition to this, the stability of investment should also be targeted along with the stability of output and productivity growth. This is to ensure that the diversification focus of the policy is maintained at all times and inefficient spending is avoided during high oil price period. Both the achievements and failures of GCC countries 178 in diversification highlight the importance of using the natural resource revenues for improving governance, education, R&D and increasing fixed capital. The possibility of natural resource-driven economies to balance their consumption and investments in order to ensure economic stability is one of the most critical policy lessons that can be derived from this dissertation. It is also important for oil-exporting countries to note that new technologies and management techniques are being exploited around the world to offset the profit reducing effect of higher energy cost per unit of production. This among other concerns brings even more urgency to the need for diversification in oil-based economies to ensure sustained economic development and continued well-being of their populations. 6.4.3. Regional Infrastructure, International Trade and Peace Findings Chapter 2 and 5 highlights the importance of regional characteristics. This dissertation does not disentangle all the region-wide factors that affect a country’s labour productivity growth. However, the research is able to show that being rich in natural resources in particular fossil fuels is not responsible for the poor regional characteristics of the GCC countries. Chapter 3 highlights in a comparative analysis of three (3) of the six (6) GCC countries – Oman, Saudi Arabia and the United Arab Emirates – that all three countries face limitation in enablers and policies that are expected to enhance innovation and diversification in their economies. These limitations partly explain why productivity growth in the modern sectors of these economies is constrained. Other limitations may include a lack of cross-country intra-regional infrastructure, and the perception of a lack of political stability, regional peace and security. Policy Advice The GCC region does not have any cross-country rail links. The two most populous GCC countries Saudi Arabia and Oman did not have any direct road connection and all trade traffic has to pass through the United Arab Emirates.42 Saudi Arabia is the only country to 42 Note that as of May 2019 the construction of direct Oman-Saudi road connection through the Empty Quarter is considered completed, however, official opening is not yet announced. 179 have a border with all other GCC countries. However, the 2017 embargo of Qatar by Saudi Arabia (and its partners in the embargo) meant that Saudi Arabia did not have any direct land trade routes to Qatar as well. The Qatar embargo and the Iran-Saudi Arabia conflict also create an image of a region that is unstable. In addition, the GCC is a part of the greater Middle East and North Africa (MENA) region. This means any conflicts, wars or instability in the wider Middle East region such as the Israel-Palestine conflict, the Syrian conflict and even civil/political volatility in farther away countries like Egypt and Libya creates a perception of the GCC being a sub-unit of an unstable region. These perceptions are very likely to negatively impact some economic determinants, such as foreign direct investment in the GCC. Improving regional characteristics by investing in regional and cross-regional infrastructure projects and focusing on regional peace and security is critical for meeting the diversification objective of the GCC countries. It is recommended for the GCC countries to establish a GCC wide rail network for transport of goods and people (with future connection to the countries north and north-west of the Arabian Peninsula including Israel, Turkey and eventually EU). Improving connection to international trade routes by linking to international trade and infrastructure projects such as the Belt and Road initiative is likely to provide a boost to the regional economic characteristics. Several projects addressing the current infrastructure limitations are underway and will contribute to improving the regional characteristics. Saudi Arabia, being the GCC country with the largest population, area and most border countries in the region, is likely to benefit the most from improving regional growth characteristics. It also has a major role to play in peacebuilding with its neighbours (Israel, Iran, Yemen and its fellow GCC country Qatar), and promoting an image of not only the GCC but also the MENA region as a safe and secure region. This is because the destiny of the GCC is tied to the greater MENA region through geography. An alternative would be to brand and market the GCC as a region that is distinct from the greater MENA region. 180 181 7. 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World Development Indicators. [Online] Available at: http://data.worldbank.org/data-catalog/world-development-indicators [Accessed 23 May 2019]. 201 Contents 1. Introduction Appendix 1-A 2. Productivity and Innovation Policy 2.1. Introduction 2.2. Innovation policies and the path towards successful innovation 2.3. Identification Strategy 2.4. Data 2.5. Results 2.5.1. Global 2.5.2. Arabian Gulf countries - A special case? 2.6. Conclusions and Discussion Appendix 2-A Appendix 2-B 3. Policy and Economy in the GCC 3.1. Introduction 3.2. Perspectives on innovation 3.2.1. General 3.2.2. The literature on GCC countries 3.3. The Case of GCC – Policies and Enablers 3.3.1. Section Summary 3.3.2. Development of education systems 3.3.3. Literacy, primary education, secondary education, reforms and performance 3.3.4. Tertiary education and vocational education 3.3.5. R&D 3.3.6. Business and Entrepreneurship 3.3.7. Governance and Infrastructure 3.4. The Outputs of GCC – Indicators of Innovation and Diversification 3.4.1. Section Summary 3.4.2. Patents, Trademarks and Industrial designs 3.4.3. Non-traditional sector - share in the economy and labour productivity 3.5. Connecting Policies, Enablers and Outcomes 3.6. Summary, Discussion and Conclusion Appendix 3-A 4. Natural Resource Abundance: No Evidence of an Oil Curse 4.1. Introduction 4.2. Literature Review 4.3. Modelling the natural resource extraction and capital investment relationship 4.4. Empirical Model 4.5. Data 4.6. Data Reliability 4.7. Results 4.8. Postestimation tests and robustness 4.9. Discussion and Conclusion 5. “Stars in their Eyes?” 5.1. Introduction 5.2. Background and Literature 5.2.1. Diversification 5.2.2. Evaluation of Diversification Strategies 5.2.3. Methodologies for Evaluation 5.2.4. Oman and Saudi Arabia Evaluations 5.3. The Predictive Model 5.4. Review of the Economic Plans of Oman and Saudi Arabia 5.4.1. Oman 5.4.2. Saudi Arabia 5.4.3. Reference Condition and Scenarios 5.5. Results and Discussion 5.6. Summary and Conclusion 6. Conclusion 6.1. Background of the dissertation 6.2. Summary 6.3. Limitations and Suggestions for Future Research 6.4. Integrated insights from the dissertation and policy implications 6.4.1. Institutional Effectiveness, Productive Efficiency, Human Capital, Education and R&D 6.4.2. Natural Resources, Oil, Productivity and Investment 6.4.3. Regional Infrastructure, International Trade and Peace 7. References

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