Source2argumentativeessay Source1argumentativeessay
The countries with the highest levels of corruption.
Who care
s
about corruption?
Alvaro Cuervo-Cazu
rra
Moore School of Business, University of South
Carolina, Columbia, South Carolina, USA
Correspondence: A Cuervo-Cazurra, Sonoco
International Business Department, Moore
School of Business, University of South
Carolina, 1705 College Street, Columbia,
SC 29208, USA.
Tel: þ 1 803 777 0314;
Fax: þ 1 803 777 3609;
E-mail: acuervo@moore.sc.edu
Received: 24 August 200
5
Revised: 15 April 2006
Accepted: 17 April 2006
Online publication date: 14 September 2006
Abstract
This paper examines the impact of corruption on foreign direct investment
(FDI). It argues that corruption results not only in a reduction in FDI, but also in
a change in the composition of country of origin of FDI. It presents two key
findings. First, corruption results in relatively lower FDI from countries that have
signed the Organization for Economic Cooperation and Development
Convention on Combating Bribery of Foreign Public Officials in International
Business Transactions. This suggests that laws against bribery abroad may act as
a deterrent against engaging in corruption in foreign countries. Second,
corruption results in relatively higher FDI from countries with high levels of
corruption. This suggests that investors who have been exposed to bribery at
home may not be deterred by corruption abroad, but instead seek countries
where corruption is prevalent.
Journal of International Business Studies
(2006) 37, 807–822.
doi:10.1057/palgrave.jibs.8400223
Keywords: corruption; foreign direct investment; international management
Introduction
Host country corruption discourages foreign direct investment
(FDI). Corruption, the abuse of public power for private gain,
creates uncertainty regarding the costs of operation in the country.
It acts as an irregular tax on business, increasing costs, and
distorting incentives to invest (Shleifer and Vishny, 1993; Mauro,
1995; Wei, 2000a) Many empirical studies provide support for this
idea, as they find that corruption in the host country is negatively
related to FDI (e.g., Wei, 2000a, b; Habib and Zurawicki, 2002;
Lambsdorff, 2003).
However, some scholars have argued that corruption can have a
positive impact on investment by facilitating transactions in
countries with excessive regulation (Huntington, 1968; Leff,
1989). Investors who greatly value their access to a certain asset,
for example a permit, will pay for this access (Lui, 1985). Some
empirical studies do not find a negative relationship between
corruption and FDI, and some even report a positive relationship
(e.g., Wheeler and Mody, 1992; Henisz, 2000). Moreover, some
countries with high levels of corruption, such as China or Nigeria,
are the recipients of a great deal of FDI. Corruption does not keep
FDI out of very corrupt countries. This fact begs the question of just
how corruption affects FDI.
In this paper, we argue that corruption results not only in a
reduction in FDI, but also in a change in the composition of
country of origin of FDI. We suggest that not all foreign investors
care about corruption in the host country. Although corruption has
a negative impact on FDI because of the additional uncertainty and
Journal of International Business Studies (2006) 37, 807–822
& 2006 Academy of International Business All rights reserved 0047-2506 $30.00
www.jibs.net
costs, such costs vary depending on the country of
origin of the FDI. We discuss two such cases: (1)
FDI
from countries that have signed the Organization
for Economic Cooperation and Development
(OECD) Convention on Combating Bribery of
Foreign Public Officials in International Business
Transactions and (2) FDI from countries
with high
levels of corruption. We analyze the impact of
corruption on FDI from these two sets of countries
in comparison with FDI from a third set of
countries: those that have low levels of
corruption
and have not signed the OECD Convention.
Our results show that the relationship between
corruption and FDI is modified by the country of
origin of the FDI. Corruption in the host country
results in relatively less FDI from countries that
have signed the OECD Convention, but in rela-
tively more FDI from countries with high levels of
corruption. The outcome of these two effects is that
countries with high corruption receive less FDI
from countries with laws against bribery abroad,
which are the largest sources of FDI, and more FDI
from other countries with high corruption levels.
Two views of corruption
Corruption refers to the exercise of public power for
private gain. We focus on public corruption or
corruption in government, whereby a public
employee, elected or not, uses his or her position
in government in order to obtain private benefits.
The existence of corruption indicates a lack of
respect for the rules and regulations that govern
economic interactions in a given society. It repre-
sents the need to make additional, irregular
payments to get things done (Kaufmann et al.,
2003).
There are incentives for corruption whenever an
official has discretion over the distribution of a
good or the ‘avoidance of a bad’ to the private
sector (Rose-Ackerman, 1999). The official has an
incentive to ask for a bribe to increase his or her
income in exchange for a good that has little cost to
him or her (Shleifer and Vishny, 1993). The firm has
an incentive to offer a bribe and obtain benefits to
which it would not otherwise have access, such as
being granted a contract without competitive
tender.
There are two views of corruption, one positive
and the other negative. Although corruption is
rarely justified on ethical grounds, some scholars
view corruption in positive terms as ‘grease in the
wheels of commerce’. Corruption is seen as facil-
itating transactions and speeding up procedures
that would otherwise occur with more difficulty, if
at all (Huntington, 1968; Leff, 1989). Corruption is
a way to bring market procedures into an environ-
ment of excessive or misguided regulation, intro-
ducing competition into what is otherwise a
monopolistic setting (Leff, 1989). Corruption
enables free markets to emerge in situations of
limited freedom. Investors who value time or access
to an input more than others will pay more for it
(Lui, 1985).
However, many scholars have a negative view of
corruption, because it is rarely restricted to areas
where it may increase welfare. These scholars see
corruption as ‘sand in the wheels of commerce’,
indicating that corruption results in the wasteful
use of resources devoted to corruption as well as to
fighting it. These resources could be invested more
profitably in other ways (Kaufmann, 1997). More-
over, the payment of a bribe does not ensure that
the promised goods are delivered. Investors do not
have recourse in the courts to demand fulfillment
of the agreement, as bribery is illegal. Even when
the bribe results in fulfillment of the promise, the
firm faces increased costs (Shleifer and Vishny,
1993). The official can withhold approval of a
permit until a bribe is paid, thus increasing the cost
to the firm. Moreover, government officials have an
incentive to create additional regulations with the
sole purpose of generating an opportunity for more
bribes (De Soto, 1989). Corruption also results in
the inefficient allocation of resources towards areas
that are more prone to bribe payment (Mauro,
1998).
Impact of host-country corruption
on FDI
A great deal of research on the relationship between
host-country corruption and FDI has found a
negative relationship between the two. Mauro’s
(1995) analysis of the institutional characteristics of
67 countries found that corruption reduced overall
investment in the country. Wei (2000a) analyzed
bilateral FDI from 12 developed countries to 45
destination countries and found that corruption
had a negative impact on FDI. Wei (2000b)
confirmed the negative relationship between cor-
ruption in the host country and FDI after taking
into account government policies towards FDI.
Smarzynska and Wei (2000) found that corruption
had a negative impact on foreign investment in 22
Eastern European countries. Habib and Zurawicki
(2002) analyzed bilateral FDI flows from seven
developed countries to 89 countries and found that
both the level of corruption in the host country and
Who cares about corruption? Alvaro Cuervo-Cazurra
808
Journal of International Business Studies
the absolute difference between the level of corrup-
tion in the host country and in the home country
had a negative impact on FDI. Voyer and Beamish’s
(2004) analysis of Japanese FDI found that corrup-
tion had a negative impact on FDI per capita,
especially in developing nations.
However, not all empirical studies have observed
a negative relationship. For example, Wheeler and
Mody (1992) found no relationship between risk,
which includes corruption, and foreign investment
by US firms. Hines (1995) analyzed US FDI, and his
results showed that corruption in the host country
did not affect the level of total inward FDI,
although it had a negative impact on the growth
of FDI after the passage of the Foreign Corrupt
Practices Act (FCPA) of 1977. Henisz (2000) found
that, for US firms, corruption tends not to affect
their investments, and in some cases it increases
the probability of investing in the foreign country.
Variation on the sensitivity of FDI to host-country
corruption
We argue that not all investors are equal. The
sensitivity of FDI to host-country corruption is
likely to vary with the country of origin of the FDI.
This line of thinking has yet to be thoroughly
explored in the literature. Below we discuss how the
characteristics of the
country of origin of FDI
influence the cost of engaging, and incentives to
engage, in bribery and, as a result, the sensitivity of
FDI to host-country corruption. Figure 1 illustrates
the relationships among the key constructs. We
discuss two characteristics of the home country
that affect this sensitivity: the existence of laws
against bribery abroad, and the existence of high
levels of corruption.
Sensitivity of FDI from countries
with laws against
bribery abroad to host-country corruption
Some countries have implemented laws against
bribery abroad in order to limit the supply of bribes
by foreign investors. Such legislation is likely to
increase the cost of engaging in bribery abroad for
investors from these countries. The benefits of
paying a bribe to a foreign official may not be
worth the cost when the foreign investor takes into
account not only the cost of the bribe but also the
cost of the penalties, and the cost of the damage to
its image. Such a cost may alter the investor’s
perception that it is appropriate to bribe foreign
officials to secure contracts. At the same time,
managers can use the existence of such legislation
as a signal that their hands are tied, reducing the
demand for bribes from foreign officials (Elliot,
1997: 205). As a result, these investors may reduce
their FDI into countries with high corruption,
although they may not avoid these countries
altogether.
The first country to have such laws was the US. In
an effort to clean up the image of US firms and their
use of bribery, the FCPA was passed in 1977. This
law requires strict accountability of payments,
making it possible to prosecute US firms and
individuals for bribing government officials abroad
(Kaikati and Label, 1980; Hines, 1995). The FCPA
established three main requisites: accurate record-
keeping; effective internal accounting control sys-
tems; and prohibition of corrupt payment to
foreign officials, politicians, and political candi-
dates. The Act provides for penalties of up to US$1
million for a firm, and a US$10,000 fine as well as a
5-year prison term for an employee (US Congress,
1977). It made illegal not only direct payments to
foreign officials or politicians, but also payments to
other individuals – facilitating agents – who bribe
on the firm’s behalf. However, the FCPA was
explicit in not penalizing ‘grease payments’, or
payments to foreign officials to expedite processes
that would otherwise occur without a bribe, albeit
more slowly.
It is not clear that the FCPA has been effective in
deterring US investments in corrupt countries.
Hines (1995) found support for this deterrence
effect, as US firms reduced the growth of invest-
ments in FDI, capital–labor ratios, joint venture
activity, and aircraft exports to countries with high
corruption after the passage of the Act. However,
Wei (2000a) found no such support: his data
suggested that US investors did not have lower
FDI stocks in corrupt countries than investors from
other developed countries; all of them were
negatively affected by corruption in the same way.
There are two possible explanations for these
conflicting results (Tanzi, 1998). One is that US
Host country
corruption
FDI
Home country with
laws against bribery
abroad
Home country with
high corruption
Hypothesis 1
Hypothesis 2
Figure 1 Theoretical framework.
Who cares about corruption? Alvaro Cuervo-Cazurra
809
Journal of International Business Studies
investors had an incentive to bypass the FCPA to
avoid losing competitiveness in the allocation of
contracts abroad to competitors from other foreign
countries. Another is that the US government was
not forceful in prosecuting bribery abroad, espe-
cially in ‘friendly’ countries. This changed in the
mid-1990s thanks to the convergence of several
trends (Tanzi, 1998). First, the end of the Cold War
reduced the need to turn a blind eye to corruption
in friendly countries. Second, the spread of democ-
racy and freedom of the press exposed bribery that
used to be hidden, especially in centralized econo-
mies. Third, non-governmental institutions, such
as Transparency International, took an active role
in denouncing corruption. Fourth, international
institutions, such as the World Bank and Interna-
tional Monetary Fund, started demanding better
governance in development projects. Finally, inter-
national organizations, such as the OECD, took an
active role in promoting the reduction of bribery.
We explore this last point in more detail.
On 21 November 1997 the 30 members of the
OECD, and an additional five non-members, signed
the Convention on Combating Bribery of Foreign
Public Officials in International Business Transac-
tions. The Convention, which came into effect on
15 February 1999, established a general framework
that criminalizes bribery of foreign officials to
provide the firm with an improper advantage
(OECD, 1997). The Convention prohibits bribing
not only of government officials but also of officials
of public international organizations. It requires
signatory countries to modify their laws to make
illegal the bribery of foreign officials, to provide
mutual legal assistance in investigations, and to
allow for extraditions. Additionally, the Conven-
tion requires stricter accounting standards, external
auditing, and internal controls in national laws. A
companion agreement disqualifies bribes from
being tax-deductible business expenses (OECD,
1996). The Convention establishes a systematic
mechanism for monitoring of the implementation
of the Convention’s standards by each signatory
country. The OECD’s Working Group on Bribery in
International Business Transactions periodically
evaluates and publicizes progress made in the
adaptation of national laws towards the standards
set by the Convention and in the enforcement of
such laws. This mutual monitoring mechanism
addresses some of the limitations of the FCPA. It
establishes the same ethical requirements of con-
duct for all foreign investors, thus leveling the
playing field among competitors and reducing
incentives to bypass the legislation. Additionally,
the periodic evaluation of the progress in the
application of the Convention may create social
sanctions that improve enforcement. Governments
that do not make adequate progress towards the
prosecution of corruption may be pressured by
governments that fulfill their obligations.
Therefore we argue that corruption may further
discourage FDI from countries that have signed the
OECD Convention and have developed laws
against bribery abroad. The Convention increases
not only the potential costs of bribing foreign
officials by creating penalties, but also the effective
costs by increasing the probability of detection
through the mutual monitoring mechanism. Such
an increase in costs may alter the incentives to
invest in countries with corruption where investors
will be asked for bribes. Therefore we hypothesize
that:
Hypothesis 1: In comparison with FDI from
other countries, the relationship between host-
country corruption and FDI is negative for FDI
from countries with laws against bribery
abroad.
Sensitivity of FDI from countries with high
corruption to host-country corruption
Some FDI comes from countries with high levels of
corruption. These investors operate in countries
where the payment of bribes is a normal way of
doing business. As a result, they are likely to have
developed experience on how best to engage in
bribery to be able to operate in their home country.
Thus, when these investors internationalize, they
may not be deterred by host-country corruption,
unlike other investors, and they may even be
attracted by it for two reasons. First, they would
face lower costs of doing business abroad than
dealing with corruption in the host country
represents. Second, they may even select countries
with high levels of corruption because of the
similarities in institutional conditions to their
country of origin.
Internationalization requires dealing with addi-
tional costs of doing business abroad (Hymer, 1976)
or a liability of foreignness (Zaheer, 1995). Some of
these costs involve dealing with corruption in the
host country (Calhoun, 2002). We can distinguish
two sets of such costs: the costs of changing
attitudes regarding the use of bribes abroad, and
the costs of knowing how to bribe abroad. First,
corruption requires managers to alter their assump-
tions regarding the way in which one establishes
Who cares about corruption? Alvaro Cuervo-Cazurra
810
Journal of International Business Studies
and maintains business transactions. Managers
must change their belief in contracts and the
rule of law as the accepted way and instead
accept illegal payments as the way to conduct
business abroad. However, it is difficult to change
such deep-seated attitudes and beliefs about the
ways in which business is conducted (Prahalad and
Bettis, 1986), particularly when the firm expands
abroad (e.g., Johanson and Vahlne, 1977; Eriksson
et al., 1997).
Second, it is costly to develop expertise on how to
best deal with bribery in the host country because
there are no visible guides or consulting firms that
can provide knowledge about how to bribe success-
fully; its illegal nature precludes such services.
Engaging in corruption requires an understanding
of the subtleties involved in offering a bribe owing
to the illegal nature of the offer; simply offering a
bribe, not just the payment thereof, is a criminal
offence. In addition to being illegal, corruption is
opaque: it requires secrecy to be effective (Shleifer
and Vishny, 1993). Thus, engaging in bribery
requires an understanding of the subtleties
involved in payment of bribes in the host country.
In some cases, it may be difficult to separate the
cultural norms of gift exchange from bribery
(Donaldson, 1996).
A company may use joint ventures or managers
with experience in bribery to deal with the
payment of bribes abroad. These actions would
reduce the costs of learning how to bribe in the
country. However, the firm will still, and first, have
to incur the cost of changing the assumptions of
managers at headquarters about corruption, and
accept bribery as a valid way of doing business.
Additionally, the firm will have to incur the
additional costs of finding and monitoring the
local partner or manager so that they do not extract
rents from the company while they bribe others.
In contrast, those investors who have experi-
enced corruption at home are likely to have already
altered their beliefs about bribery as an accepted
way of doing business and to have mastered the
subtleties of how to deal with bribery. As a result,
when they enter other countries with high levels of
corruption, the costs of engaging in bribery are
lower. For firms whose managers have already
learned through experience, the cost of internatio-
nalization is lower (Eriksson et al., 1997). These
investors are accustomed to paying bribes in order
to secure permits and win contracts at home (Ades
and Di Tella, 1997): thus they may be undeterred by
the illegality, opacity and uncertainty in the bribery
process, because they are likely to already know
how best to deal with it.
FDI from countries with high corruption may not
only be undeterred by host-country corruption, but
may even be attracted by it. Investors from
countries with high corruption face lower costs of
doing business abroad when they enter other
countries with high corruption. The similarities in
the conditions of the institutional environment
induce these investors to focus their FDI there. This
argument builds on the ideas discussed in the
incremental internationalization process or
Uppsala model (Johanson and Wiedersheim-Paul,
1975; Johanson and Vahlne, 1977). This model
explains the selection of countries in which to
internationalize based on the concept of ‘psychic
distance’ between the home and host countries
(Johanson and Wiedersheim-Paul, 1975). Psychic
distance is the difference between countries in
terms of language, culture, education, business
practices, industrial development, and regulations,
all of which may limit the transfer of information.
This distance reduces the ability of the firm, and
particularly of its managers, to understand foreign
information. As a result, the firm first expands into
countries that are close to the host country in terms
of psychic distance, and only later enters countries
that are more distant. The current paper focuses not
on the order of investment, but rather on the idea
that investors from countries with high corruption
will seek other countries with corruption. Hence we
hypothesize that:
Hypothesis 2: In comparison with FDI from
other countries, the relationship between host-
country corruption and FDI is positive for FDI
from countries with high corruption.
Research design
We test the hypotheses using data on bilateral FDI
inflows from 183 home economies to 106 host
economies. By including such a large number of
home countries, we are able to analyze how the
relationship between corruption and FDI varies
depending on the characteristics of the country of
origin of FDI. Previous studies of the impact of
corruption on FDI have analyzed FDI either from
one country only, or from a limited number of
home countries, usually developed or OECD coun-
tries, which tend to have low corruption. In
contrast to these studies, we use a large number of
host countries to be able to compare countries with
laws against bribery abroad and countries with high
Who cares about corruption? Alvaro Cuervo-Cazurra
811
Journal of International Business Studies
levels of corruption with other countries that do
not have such characteristics.
The bulk of FDI data comes from the United
Nations Conference on Trade and Development
(UNCTAD) country profiles (UNCTAD, 2005). This
database provides the widest coverage of bilateral
FDI inflows available. We complemented this
database with information on FDI from the OECD
(2004), which has been a common source of data in
other studies. We included all the countries for
which we have data. The list of countries appears in
the Appendix.
Variables and measures
Table 1 provides a summary of the variables,
measures, and sources of data. The dependent
variable is the natural log of bilateral FDI inflows
from a home country to a host country, measured
in US$ using the average foreign exchange rate for
the year.
The independent variable of interest is host-
country corruption. We used the indicator ‘control
of corruption’ provided in Kaufmann et al. (2003).
Corruption is illegal and, as such, difficult to
measure with any degree of precision. Studies rely
on subjective measures of corruption: for a discus-
sion of the alternative measures of corruption and
how they generate similar results, see Wei (2000a).
Kaufmann et al. (2003) created a composite mea-
sure that integrates 31 indicators from 14 different
sources using an unobserved components model,
weighting indicators by their precision. This
reduces the noise of single indicators. This compo-
site indicator uses not only polls of experts but also
surveys of businesspeople or citizens in the country.
Of all measures of corruption available, it has the
widest coverage, 184 economies, which we need to
avoid constraining the database. The indicator
measures control of corruption within an interval
of �2.5 (low control of corruption) to 2.5 (high
control of corruption). To simplify the interpreta-
tion of the coefficients, we rescaled the indicator by
subtracting the original index from 2.5, such that a
higher number indicates higher corruption and a
lower number indicates lower corruption.
To test Hypotheses 1 and 2 we used interaction
terms. We are interested in understanding how the
relationship between levels of corruption in the
host country and FDI varies depending on the
characteristics of the country of origin of the FDI.
Therefore we first introduced an indicator of host-
country corruption in the analysis. This captures
the general impact of host-country corruption on
FDI. We then introduced interaction terms between
host-country corruption and dummy indicators of
the type of country of origin of the FDI (countries
with laws against bribery abroad, and countries
with high corruption levels). These interactions
capture the additional influence of corruption on
FDI associated with countries with such character-
istics in relationship to countries that do not have
them: that is, countries that have low corruption
and do not have laws against bribery abroad. To test
these hypotheses, we then analyzed the sign and
significance of the coefficient of each product. We
also controlled for the country of origin of FDI with
a dummy variable to capture other influences that
the country of origin has on FDI. Wei (2000a, 8)
provides a detailed explanation of this procedure,
which he used to test whether US investors are
more sensitive to corruption in the host country
than are investors from other developed countries.
Specifically, to test Hypothesis 1, we first mea-
sured
countries that have laws against bribery
abroad using a dummy indicator that the country
has
signed the OECD Convention on Combating
Bribery of Foreign Officials in International Busi-
ness Transactions. The countries that have done so
are the 30 members of the OECD, as well as
Argentina, Brazil, Bulgaria, Chile, Estonia, and
Slovenia. We multiplied this indicator by the
measure of host-country corruption. A negative
and statistically significant coefficient of this
product can be taken to provide support for
Hypothesis 1.
To test Hypothesis 2, we first measured countries
with high corruption using a dummy indicator that
the level of corruption in the home country is
above the average for all countries. Excluded from
this indicator were countries that have signed the
OECD Convention but that have high levels of
corruption: Argentina, Bulgaria, Mexico, and Tur-
key. This was done in order to avoid counting these
countries twice. We then multiplied the indicator
by host-country corruption. A positive and statisti-
cally significant coefficient of this product can be
taken to provide support for Hypothesis 2. We used
alternative indicators of high corruption (corrup-
tion above the average and half standard deviation,
corruption above the median, corruption in the top
third, corruption in the top sixth) to check for the
robustness of this measure. The results do not
change in sign or significance.
We controlled for other variables that may affect
the relationship between corruption and FDI
following a gravity model. The gravity model has
Who cares about corruption? Alvaro Cuervo-Cazurra
812
Journal of International Business Studies
been applied to the study of the determinants of
FDI flows (e.g., Bevan and Estrin, 2004), and in
particular to the impact of corruption on FDI (e.g.,
Wei, 2000a). The theoretical basis is the proximity-
concentration hypothesis (Horstmann and
Markusen, 1992; Brainard, 1993). This idea extends
the ownership, location, and internationalization
paradigm of international production (Dunning,
1977) to highlight the challenges inherent in the
expansion of multinational enterprises (MNEs)
across borders, specifically the balancing of costs
or barriers against the benefits of scale economies.
Table 1 Variables, measures, and sources of data
Variable Measure Source
Dependent variable Ln FDI inflows Natural log of FDI inflows into the country in
the year in US$
UNCTAD (2005) or OECD (2004)
Independent
variable of interest
Host country
corruption
Indicator on the level of corruption in the host
country, from 0 (low) to 5 (high) (2.5 minus
the original score for control of corruption)
Constructed using data from aggregate
governance indicators database,
Kaufmann et al. (2003)
Home country
with laws against
bribery abroad
Dummy indicator that the home country has
signed the OECD Convention on Combating
Bribery of Foreign Officials in International
Business Transactions, 1 or 0
Constructed using the list of signatory
countries of the OECD Convention on
Combating Bribery of Foreign Officials
in International Business Transactions
from OECD
(2005)
Home country
with high
corruption
Dummy indicator that the level of corruption
in the home country is above the average for
all countries (2.5), 1 or 0. We exclude
countries that have laws against bribery
abroad.
Constructed using data from aggregate
governance indicators database,
Kaufmann et al. (2003)
Control variables Ln GDP Natural log of gross domestic product in
power purchasing parity in US$
Data from world development
indicators database, World Bank (2005)
Population Number of inhabitants in the country, in
millions
Data from world development
indicators database, World Bank (2005)
Ln Distance Natural log of the greater circle distance
between the centers of the home and host
country in miles
Computed using data on geographic
coordinates from
CIA (2005)
Landlocked Indicator that the none, one, or both home
and host countries are landlocked, 0, 1, or 2
Computed using data on coastline from
CIA (2005)
Island Indicator that the none, one, or both home
and host countries are island nations, 0, 1, or 2
Computed using data on land
boundaries from CIA (2005)
Common border Dummy indicator of the existence of a
common border between the home and host
country, 1 or 0
Computed using data on land
boundaries from CIA (2005)
Common language Dummy indicator of the existence of a
common language between the home and
host country, 1 or 0
Computed using data on languages
from CIA (2005) and from Gordon
(2005)
Common colony Dummy indicator that the home and host
country were colonies of the same colonial
power after 1945, 1 or 0
Computed using data on independence
from CIA (2005) and on history from
Encyclopedia Britannica (2005)
Ever colonial link Dummy indicator that the home and host
country were ever under a colonial
relationship, 1 or 0
Computed using data on independence
from CIA (2005) and on history from
Encyclopedia Britannica (2005)
Restrictions on
trade
Indicators of trade policy, from 1 (very low
barriers to trade) to 5 (very high barriers to
trade)
Heritage Foundation (2005)
Restrictions on FDI Indicators of capital flows and foreign direct
investment, from 1 (very low barriers to
foreign investment) to 5 (very high barriers to
foreign investment)
Heritage Foundation (2005)
Who cares about corruption? Alvaro Cuervo-Cazurra
813
Journal of International Business Studies
The gravity model has demonstrated its usefulness
in explaining bilateral FDI (e.g., Eaton and Tamura,
1995; Brenton et al., 1999; Wei, 2000a, b; Wei and
Wu, 2001; Bevan and Estrin, 2004) and in generat-
ing new theoretical insights on the distances that
firms face as they move abroad (e.g., Ghemawat,
2001).
The base gravity model explains FDI based on
indicators of the host country’s size (GDP and
population) and the geographic distance between
home and host countries (Linneman, 1966). There-
fore, we controlled for the country’s economic size
using indicators of gross domestic product and
population. Larger countries are more likely to
attract FDI, because MNEs can achieve the necessary
economies of scale in the country (e.g., Linneman,
1966). We controlled for geographic distance
between countries using an indicator of the great
circle distance, which measures distance on the
surface of the earth using longitude and latitude
coordinates. Distance indicates the existence of
transportation costs that would discourage trade
and favor FDI (e.g., Linneman, 1966; Wei, 2000a).
This distance measure is traditionally complemen-
ted by indicators of whether the country is land-
locked or an island, as these characteristics affect
the difficulty in transporting products, and there-
fore the likelihood of undertaking FDI (e.g., Frankel
and Rose, 2002). A final complement to distance is
the existence of a common land border between
the countries (e.g., Feenstra et al., 2001).
To these, we added controls of common political
and cultural backgrounds. Similarities in political
and cultural backgrounds facilitate FDI, because
investors benefit from a reduced psychic distance
between home and host countries (Johanson and
Vahlne, 1977; Ghemawat, 2001). Cultural similari-
ties were captured using an indicator of the
existence of a common language between home
and host country, which facilitates the transfer of
information across borders and a reduction of
psychic distance (Johanson and Vahlne, 1977;
Feenstra et al., 2001). Commonalities in administra-
tion were measured using indicators of the existence
of a colonial relationship, and the existence of a
common colonizer (e.g., Frankel and Rose, 2002).
Colonial powers traditionally imposed their admin-
istrative traditions, such as the legal system, upon
their colonies (La Porta et al., 1998).
We also controlled for restrictions to FDI and to
trade (e.g., Wei, 2000b). Restrictions to FDI are
likely to have a negative impact on FDI because the
government actively blocks it. We measure these
with the indicator freedom of FDI and capital
flows of the Heritage Foundation. Restrictions to
trade are likely to have a positive impact on FDI
because firms are forced to serve the country with
domestic production rather than with exports. We
measure these with the indicator of freedom in
trade policy of the Heritage Foundation. These two
indicators take values from 0 (low barriers) to 5
(high barriers).
Method of analysis
Following Wei (2000a), we used a double-log model
with quasi-fixed-effects and one-year lag to analyze
the data. In the double-log model, we applied
natural logs to the dependent variable (FDI) and the
independent variable (GDP, distance) to ensure the
homoscedasticity of the error term (Wei, 2000a, 4).
We used a quasi-fixed-effects specification whereby
we controlled for the home country using a dummy
indicator for each country. These home country
dummies were designed to capture characteristics
of the home country that may affect its FDI abroad,
including its economic size and level of develop-
ment. We did not include dummies for host
countries because doing so would eliminate the
possibility of estimating the impact of corruption
on FDI. The dependent variable was measured at
the end of 1999, because this is after the legislation
barring bribery abroad was signed (November 1997)
and came into effect (February 1999). The inde-
pendent variables were measured one year earlier
(1998) in order to account for the time lag that
occurs between the decision to invest and the
actual FDI. Finally, because the log of FDI takes
positive values, we used a Tobit specification
(Tobin, 1958; Maddala, 1983). We used a modified
Tobit specification because the log of zero is
undefined (for a discussion of this model see Eaton
and Tamura, 1995; Wei, 2000a). Therefore the
specification of the empirical model we used is
the following:
Ln FDIijt ¼ g1Host country corruptionjt�1
þ g2Home country with high corruptionit�1
�Host country corruptionjt�1
þ g3Home country with laws against
bribery abroadit�1
�Host country corruptionjt�1þXijt�1bþeij
ð1Þ
where Xijt�1 is a vector of the control variables; g1,
g2 and g3 are the parameters of interest; b is a vector
of other parameters; and e is the error.
Who cares about corruption? Alvaro Cuervo-Cazurra
814
Journal of International Business Studies
Table 2 Summary statistics and correlation matrix
Variable Mean Std. dev. 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Ln FDI inflows 2.230 3.424 1
2. Host-country
corruption
2.500 1.000 �0.327*** 1
3. Home country
with high
corruption
0.252 0.434 �0.334*** 0.018 1
4. Home country
with laws against
bribery abroad
0.546 0.497 0.350*** �0.037+ �0.628*** 1
5. Ln GDP 18.628 1.928 0.437*** �0.480*** 0.025+ �0.040+ 1
6. Population 32.676 52.456 0.291*** �0.161*** 0.030+ �0.048** 0.733*** 1
7. Ln Distance 7.858 0.997 �0.108*** 0.068*** �0.042* �0.053** 0.060** 0.188*** 1
8. Landlocked 0.328 0.531 �0.200*** 0.215*** 0.011 0.040* �0.325*** �0.193*** �0.205*** 1
9. Island 0.253 0.476 �0.033 �0.146*** �0.080*** �0.136*** 0.059** 0.080*** 0.276*** �0.174*** 1
10. Common
border
0.059 0.236 0.092*** 0.010 0.115*** �0.004 0.010 �0.005 �0.440*** 0.092*** �0.130*** 1
11. Common
language
0.155 0.362 0.060* 0.064** 0.108*** �0.083*** �0.135*** �0.057** �0.134*** �0.010 0.003 0.220*** 1
12. Common
colony
0.046 0.210 �0.053* 0.131*** 0.140*** �0.116*** �0.164*** �0.085*** �0.105 0.097*** �0.050** 0.125*** 0.440*** 1
13. Ever colonial link 0.042 0.200 0.124*** �0.030 �0.025 0.079*** 0.004 0.003 �0.037+ �0.062** �0.044* 0.145*** 0.320*** 0.336***
14. Restrictions
on FDI
2.389 0.689 �0.252*** 0.450*** 0.017 �0.041* �0.158*** 0.081*** 0.090*** 0.229*** 0.026 0.008 �0.021 0.095*** 0.0008 1
15. Restrictions
on trade
2.681 1.138 �0.139*** 0.496*** �0.079*** 0.047* �0.201*** 0.007 0.063** 0.033+ �0.087*** 0.028 0.065*** 0.105*** 0.015 0.528***
Significance levels: +Po0.1, *Po0.05, **Po0.01, ***Po0.001.
W
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Results
Table 2 presents the summary statistics and correla-
tion matrix. The average bilateral FDI inflow is
US$508 million, with a maximum of US$116,605
million from the UK to the US. Of the 183 countries
for which we have FDI data, 36 countries are
classified as having laws against bribery abroad
and 117 are classified as having high corruption.
Although some of the variables show high correla-
tion coefficients, the analyses are not subject to
multicollinearity. The variance inflation matrix
suggested not using natural logs for the population
measure in order to reduce its multicollinearity
with the GDP measure.
The results of the analysis appear in Table 3.
Model 1 shows the analysis with only the control
variables. Models 2 and 3 show the partial analyses.
Model 4 shows the full analysis. We discuss the
results of the full analysis. The results support
Hypothesis 1 and Hypothesis 2. First, the coeffi-
cient of the product of the indicator of countries
with laws against bribery abroad and host-country
corruption is negative and statistically significant
(Po0.05), supporting Hypothesis 1. In other words,
in comparison with FDI from other countries, FDI
from countries with laws against bribery is further
reduced as a result of host-country corruption.
Second, the coefficient of the product of the
indicator of home country with high corruption
and host-country corruption is positive and statis-
tically significant (Po0.05), supporting Hypothesis
2. In other words, in comparison with FDI from
other countries, FDI from countries with high
corruption is less discouraged by host-country
corruption.
Alternative explanations
We argued that not all investors care about
corruption: while FDI from countries with laws
against bribery abroad is deterred by host-country
corruption, FDI from countries with high levels of
corruption is attracted by host-country corruption.
However, there do exist some plausible alternative
explanations. We analyze four that warrant discus-
sion, and show that none appear to be supported.
The first alternative explanation is the idea that
the majority of FDI of OECD countries, which
represent the world’s highest-income nations, goes
to other OECD nations, and that the majority
of FDI from low-income countries goes to other
Table 3 Results of the analyses of the change in the relationship between corruption and FDI depending on the characteristics of the
country of origin of FDI
Dependent variable: Ln FDI inflows
Model 1 Model 2 Model 3 Model 4
Home country with laws against bribery
abroad � host-country corruption
— �0.575*** (0.129) — �0.323* (0.162)
Home county with high
corruption � host-country corruption
— — 0.707*** (0.181) 0.460* (0.219)
Host-country corruption �0.345*** (0.081) 0.078 (0.124) �0.443*** (0.084) �0.170 (0.160)
Ln GDP 0.480*** (0.067) 0.493*** (0.066) 0.464*** (0.066) 0.473*** (0.066)
Population 0.008*** (0.001) 0.008*** (0.001) 0.009*** (0.001) 0.008*** (0.001)
Ln Distance �0.833*** (0.089) �0.725*** (0.091) �0.799*** (0.090) �0.756*** (0.092)
Landlocked �0.039 (0.190) �0.089 (0.188) �0.088 (0.189) �0.112 (0.189)
Island �0.479w (0.250) �0.545* (0.247) �0.488* (0.249) �0.515* (0.249)
Common border 0.709* (0.292) 0.623* (0.289) 0.529w (0.294) 0.543w (0.293)
Common language 0.542* (0.226) 0.602** (0.223) 0.581* (0.226) 0.602** (0.226)
Common colony �0.197 (0.510) �0.261 (0.503) �0.307 (0.507) �0.281 (0.506)
Ever colonial link 0.714* (0.320) 0.752* (0.316) 0.672* (0.321) 0.676* (0.320)
Restrictions on FDI �0.218w (0.125) �0.209w (0.124) �0.253* (0.125) �0.244w (0.125)
Restrictions on trade 0.027 (0.072) 0.031 (0.071) 0.031 (0.072) 0.030 (0.072)
Intercept 4.843*** (1.515) 4.037** (1.506) 5.144*** (1.510) 4.719** (1.519)
w2 1073.09*** 1092.42*** 1079.68*** 1083.64***
Pseudo R2 0.228 0.232 0.232 0.233
Log likelihood �1881.762 �1802.099 �1778.227 �1776.249
Significance levels: wPo0.1, *Po0.05, **Po0.01, ***Po0.001.
The analyses have source country dummies that are not reported here.
Who cares about corruption? Alvaro Cuervo-Cazurra
816
Journal of International Business Studies
low-income countries. This idea does not appear to
be supported. First, in the analysis we addressed this
through controls. We controlled for GDP and
population of the host country in order to account
for the attractiveness of richer, more populous
nations. Both controls have positive coefficients
that are statistically significant at Po0.001. We also
controlled for the characteristics of the country of
origin using a dummy variable for each source
country.
Second, the analysis of the distribution of FDI
flows does not support this alternative explanation.
To maintain consistency with the previous ana-
lyses, we separated countries into two groups: those
that signed the OECD Convention, and those that
did not. First, countries that signed the Convention
are the largest sources of FDI in the world,
regardless of the characteristics of the destination
country. We observe that the majority of FDI flows
going into countries that signed the Convention
originate in other countries that signed the Con-
vention (98.4%), but a similar pattern of behavior
appears in countries that did not sign the Conven-
tion. The majority of FDI flowing into countries
that did not sign the Convention originates in
countries that signed the Convention (88.4%).
Second, countries that did not sign the Convention
do not concentrate their investments in other
countries that did not sign the Convention.
Instead, the majority of FDI originating in countries
that did not sign the Convention goes to countries
that signed the Convention (68.9%). We conducted
an additional check of the distribution of FDI flows
because not all the countries that signed the OECD
Convention are usually considered high-income.
The Group of 7 (G7) countries (Canada, France,
Germany, Italy, Japan, UK, and USA) are commonly
considered the most developed in the world.
Therefore, we identify high-income nations as G7
countries, and classify the remainder as low
income. Our analysis using this alternative classifi-
cation confirms the previous findings. G7 countries
are the source of the majority of FDI flows world-
wide, regardless of the country of destination. The
majority of FDI flowing into G7 countries comes
from other G7 countries (73.7%). Similarly, the
majority of FDI flowing into non-G7 countries
comes from G7 countries (61.5%). Non-G7 coun-
tries concentrate their investments in G7 countries.
The majority of FDI from non-G7 countries goes to
G7 countries (56.7%).
Third, in this paper we do not separate countries
by level of income or development. We use other
characteristics: having laws against bribery abroad
or having high levels of corruption. Although many
of the countries that have laws against bribery
abroad are high income, others are not. Similarly,
although many of the countries with high levels of
corruption are low income, others are not. The
analysis of the sensitivity of FDI from countries
with different levels of income to corruption in the
host country would require a separate study.
A second alternative explanation is that there are
capital controls biased towards some nations,
particularly high-income nations. We controlled
for capital controls in our data analysis. We found
that the existence of restrictions to FDI, a measure
that includes capital controls, has a negative
coefficient that is statistically significant in most
models.
It is unlikely that capital controls that are biased
towards all high-income nations are widespread
enough to affect the results. This would require that
a large number of countries limit FDI from all high-
income nations, while allowing FDI from low-
income nations. The reality is that limitations to
FDI from one high-income nation tend to result in
FDI coming from another high-income nation. For
example, limitations to FDI from US oil firms in
some Middle Eastern nations have resulted in large
FDI by French firms. Moreover, low-income nations
may also face discriminatory controls when invest-
ing in high-income nations. For example, in 2005
the US Congress prevented the acquisition of the
US oil firm Unocal by the Chinese oil firm CNOOC.
Even in the case of regional trade and investment
agreements that favor some countries over others,
this discrimination applies only to countries not in
the agreement, regardless of their level of income.
Finally, capital controls that discriminate against a
particular nation are not permitted under the WTO.
Temporary capital controls are allowed, but dis-
crimination according to country of origin is
prohibited under the most-favored-nation clause
in the General Agreement on Trade in Goods, in the
Agreement on Trade-Related Investment Measures,
and in the General Agreement on Trade in Services.
The WTO has 149 member countries and 32
observer countries, or countries in the accession
process. Only a reduced number of very small
countries in our list of host economies are not
members or observers of the WTO. The amount of
FDI they receive is not large enough to bias the
results.
A third alternative explanation is that the OECD
Convention on Combating Bribery of Foreign
Who cares about corruption? Alvaro Cuervo-Cazurra
817
Journal of International Business Studies
Public Officials in International Business Transac-
tions does not have the strong impact suggested,
and that the variable instead captures the concen-
tration of FDI from OECD countries into other
OECD countries. As we indicated above, we control
for home country and for the size of the host
country. The distribution of FDI inflows does
not support the notion that OECD countries invest
only in other OECD countries. They are the largest
investors not only in other OECD countries,
but also in non-OECD countries.
We conducted additional tests to analyze whether
the Convention has been effective in altering the
sensitivity of FDI from countries that signed it to
host-country corruption. To explore this, we ran an
additional test (available upon request) comparing
the sign and statistical significance of the coeffi-
cient of the interaction between the indicator that
the country has signed the OECD Convention and
the level of host-country corruption before and
after the Convention came into force. This analysis
is inspired by Hines’s (1995) study of the effective-
ness of the FCPA. We observe that the coefficient
is not statistically different from zero in the
time period before the Convention came into
force (years 1997 and 1998), but it becomes
negative and statistically significant (Po0.001)
after it came into force (years 1999 and 2000). We
conducted an additional analysis that separates the
G7, all of which signed the Convention, from other
countries that signed the OECD Convention to
check that the previous results are not driven by
FDI from the largest source countries, which are
also low-corruption countries. We find that the
coefficient of the interaction between the indicator
that the country is in the G7 and the level of host-
country corruption is not statistically different
from zero in 1997, it becomes negative and weakly
statistically significant in 1998 (Po0.1), and
becomes statistically significant (Po0.001) in
1999 and 2000. We also find that the coefficient
of the interaction between the indicator that the
country signed the Convention but is not in the G7
and the level of host-country corruption is positive
and statistically significant in 1997, becomes not
statistically different from zero in 1998, and
becomes negative and statistically significant in
1999 (Po0.05) and in 2000 (Po0.01). We interpret
these results as support for the idea that the OECD
Convention has altered the sensitivity to host-
country corruption of investors from countries that
signed the Convention. Nevertheless, we acknowl-
edge that there are variations in the effectiveness of
the implementation of the Convention across
countries, and that more detailed analyses of the
specific laws in each country are necessary.
A fourth alternative explanation is that the
indicator of home country with high corruption
captures the influence of the difference in corrup-
tion scores on FDI discussed by Habib and
Zurawicki (2002). Their and our studies differ
markedly. First, the analysis of Habib and Zurawicki
includes only developed, relatively low-corruption
countries among the seven home countries they
analyze (Germany, Italy, Japan, Korea, Spain, UK,
and the USA); it is a matter of empirical test to
confirm that their results apply to home countries
that truly have high corruption. Second, the
variables used in each study capture different
constructs. The variable used in our study – home
country with high corruption – is an absolute
variable. A home country is either classified as
having high levels of corruption, or it is not. The
variable used in their study – differences in
corruption scores – is measured relative to
other countries. A country is classified as having
higher or lower levels of corruption than another.
These two variables produce different classifications
of countries, which are likely to result in different
insights.
Discussion and conclusions
In the present paper, we examined the effect of
corruption on FDI. Corruption creates challenges
for investors, because it increases the cost of
operating abroad, as well as the uncertainty and
risk involved. Previous studies have argued that
corruption discourages FDI. However, we argue that
corruption does not impact on all foreign investors
equally, because there is variability in the cost of
engaging in bribery abroad. Investors from coun-
tries that have laws against bribery abroad are likely
to further limit their FDI in countries with high
levels of corruption. These laws increase the cost of
engaging in bribery abroad. In contrast, investors
from countries with high levels of corruption
appear not to limit their FDI in other countries
that also have high levels of corruption. They have
experienced corruption at home. As a result, they
are apparently not deterred by corruption as much
as other investors. They may even seek countries
with high corruption.
These are important findings that add depth to
our understanding of the impact of corruption on
FDI. Scholars need to be aware that FDI from
different countries is affected differently by
Who cares about corruption? Alvaro Cuervo-Cazurra
818
Journal of International Business Studies
host-country corruption. Corruption apparently
further discourages FDI from countries that
have signed the OECD Convention, whereas it
does not deter FDI from countries with
high corruption. The implication of these two
findings is that corruption in the host country
not only reduces FDI, but also changes the
composition of FDI. Consequently, a government
that confronts and reduces corruption in the
country is likely to be rewarded not only with
more FDI, but also with more FDI from countries
that actively discourage bribery and with less FDI
from countries that have high levels of corruption.
This will reinforce the efforts of the government in
combating corruption.
There are several limitations to this study that
arise from the nature of the data presented here
that can be addressed in future research. First, we
do not have disaggregated data at the firm level.
Future research can analyze firm-level data to study
differences among investors (e.g., Hakkala et al.,
2004). Second, we have assumed a degree of
homogeneity in the industries of operation because
we do not have disaggregated data at the industry
level. Future research can explore how the char-
acteristics of the industry affect the impact of
corruption on FDI. Third, we have used available
measures indicating the level of perceived
corruption in the country. However, corruption
has various dimensions, each of which may
have a differential impact on the investment
decisions of firms, as argued by Rodriguez et al.
(2005). Fourth, we analyzed FDI flows captured
in national accounts. The insights generated
may not generalize to investors who do not
engage in FDI abroad but use instead other
methods of internationalization, such as interna-
tional trade or contractual relationships. Future
research can compare differences in the effect of
corruption on the behavior of firms that use
alternative internationalization methods, and how
these differences vary according to the country of
origin of the investors.
Overall, the present paper contributes to two
streams of research, one that studies the relation-
ship between corruption on FDI, and another that
analyzes the influence of the country of origin on
the behavior of MNEs. With respect to the first
line of inquiry, the paper provides a better under-
standing of the impact of corruption on FDI by
highlighting differences in the sensitivity to host-
country corruption among FDI from different
home countries. Future studies should be explicit
about the sensitivity of the country of origin of FDI
to host-country corruption in their analyses of the
relationship between host-country corruption and
FDI. This paper also hints at the usefulness of laws
against bribery abroad. Although it is necessary to
prosecute the demand for bribes with legislation at
home to reduce distortions and enable growth
(Shleifer and Vishny, 1993; Mauro, 1995), prosecut-
ing the supply of bribes with laws against bribery
abroad may also help. However, to be effective,
these laws require a multilateral approach. When
investors from multiple countries are subject to
these legal constraints, government officials face
difficulties in extracting bribes from foreign firms.
Otherwise, when investors from only one country
are legally constrained not to pay bribes, govern-
ment officials will simply allocate contracts and
extract bribes from firms coming from countries
that do not have these legal constraints, continuing
the vicious circle of corruption.
With respect to the second line of research, this
paper highlights the importance of understanding
the characteristics of the country of origin when
studying the internationalization of firms. This
contributes to a better understanding of the impact
of location on internationalization, an area that
has been neglected relative to the related areas
of ownership and internalization advantages
(Dunning, 1998). Future studies should take into
account not only the benefits but also the costs of
coming from a particular location if we are to better
understand the selection of countries and entry
strategies.
Acknowledgements
The paper benefited from suggestions by the Guest
Editor Peter Rodriguez, three anonymous reviewers,
the discussant Marty Meznar, Chuck Kwok, Kendall
Roth, Annique Un, and the audience at the JIBS
Focused Issue Workshop in Phoenix, Arizona. The
School of Global Management and Leadership at
Arizona State University at the West campus, Lally
School of Management and Technology at Rensselaer
Polytechnic Institute, the Department of Economics at
Rensselaer Polytechnic Institute, and the Center for
International Business Education and Research at
Thunderbird, The Garvin School of International
Management, provided financial support for the
Workshop. Funding from the Center for International
Business Education and Research at the University of
South Carolina is gratefully acknowledged. All errors
remain mine.
Who cares about corruption? Alvaro Cuervo-Cazurra
819
Journal of International Business Studies
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Appendix
Home and host countries
Host countries
Algeria, Angola, Anguilla, Argentina, Armenia,
Aruba, Australia, Austria, Azerbaijan, Bahamas,
Barbados, Belgium/Luxembourg, Belize, Benin,
Bermuda, Bolivia, Brazil, Brunei, Bulgaria,
Burkina Faso, Burundi, Cambodia, Cameroon,
Canada, Cape Verde, Central African Republic,
Chad, Chile, Colombia, Comoros, Costa Rica,
Cuba, Czech Republic, Denmark, Djibouti,
Dominican Republic, Ecuador, El Salvador,
Eritrea, Estonia, Ethiopia, Finland, France,
Gambia, Germany, Greece, Guatemala, Guyana,
Haiti, Honduras, Hungary, Iceland, Ireland, Italy,
Jamaica, Japan, Kazakhstan, Korea, Kyrgyzstan,
Latvia, Lithuania, Macau, Macedonia, Malawi,
Mali, Mauritius, Mexico, Moldova, Mongolia,
Morocco, Mozambique, Myanmar, Netherlands,
Netherlands Antilles, New Zealand, Nicaragua,
Norway, Panama, Paraguay, Peru, Poland,
Portugal, Russia, Rwanda, Saint Kitts Nevis, Saint
Lucia, Sierra Leone, Slovakia, Slovenia, Somalia,
Spain, Suriname, Sweden, Switzerland, Tanzania,
Trinidad Tobago, Tunisia, Turkey, Uganda, UK,
Uruguay, US, Uzbekistan, Venezuela, Zambia,
Zimbabwe.
Home countries
Afghanistan, Albania, Algeria, Andorra, Angola,
Anguilla, Antigua Barbuda, Argentina, Armenia,
Aruba, Australia, Austria, Azerbaijan, Bahamas,
Bahrain, Bangladesh, Barbados, Belarus, Belgium/
Luxembourg, Belize, Bermuda, Bhutan, Bolivia,
Bosnia Herzegovina, Botswana, Brazil, British
Virgin Islands, Brunei, Bulgaria, Cameroon,
Canada, Cape Verde, Cayman Islands, Chad,
Channel Islands, Chile, China, Colombia,
Congo, Cook Islands, Costa Rica, Croatia, Cuba,
Cyprus, Czech Republic, Denmark, Dominica,
Dominican Republic, Ecuador, Egypt, El Salvador,
Estonia, Faeroe Islands, Fiji, Finland, France,
French Polynesia, Gambia, Georgia, Germany,
Gibraltar, Greece, Grenada, Guatemala,
Guernsey, Guinea-Bissau, Guyana, Honduras,
Hong Kong, Hungary, Iceland, India, Indonesia,
Iran, Iraq, Ireland, Isle of Man, Israel, Italy, Ivory
Coast, Jamaica, Japan, Jersey, Jordan, Kazakhstan,
Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon,
Liberia, Libya, Liechtenstein, Lithuania, Macau,
Macedonia, Malawi, Malaysia, Mali, Malta,
Marshall Islands, Mauritania, Mauritius, Mexico,
Moldova, Monaco, Mongolia, Morocco,
Myanmar, Nauru, Nepal, Netherlands,
Netherlands Antilles, New Caledonia, New
Zealand, Nicaragua, Nigeria, Niue, North Korea,
Northern Marianas, Norway, Oman, Pakistan,
Palau, Panama, Papua New Guinea, Paraguay,
Peru, Philippines, Poland, Portugal, Puerto Rico,
Qatar, Reunion, Romania, Russia, Saint Kitts
Nevis, Saint Vincent Grenadines, San Marino,
Saudi Arabia, Serbia Montenegro, Seychelles,
Sierra Leone, Singapore, Slovakia, Solomon
Islands, South Africa, South Korea, Spain, Sri
Lanka, Sudan, Suriname, Swaziland, Sweden,
Switzerland, Syria, Taiwan, Tajikistan, Tanzania,
Thailand, Trinidad and Tobago, Tunisia, Turkey,
Turkmenistan, Turks and Caicos, Uganda, UK,
Ukraine, United Arab Emirates, Uruguay, US
Virgin Islands, US, Uzbekistan, Vanuatu, Vatican,
Venezuela, Viet Nam, Wallis and Futuna, Western
Samoa, Yemen, Zambia, Zimbabwe.
Home countries that do not appear in the list of host
countries
Afghanistan, Albania, Andorra, Antigua Barbuda,
Bahrain, Bangladesh, Barbados, Belarus, Bhutan,
Bosnia Herzegovina, Botswana, British Virgin
Islands, Cayman Islands, Channel Islands,
China, Congo, Cook Islands, Croatia, Cyprus,
Dominica, Egypt, Faeroe Islands, Fiji, French
Polynesia, Georgia, Gibraltar, Grenada, Guernsey,
Who cares about corruption? Alvaro Cuervo-Cazurra
821
Journal of International Business Studies
Guinea-Bissau, Hong Kong, India, Indonesia, Iran,
Iraq, Isle of Man, Israel, Ivory Coast, Jersey, Jordan,
Kenya, Kuwait, Laos, Lebanon, Liberia, Libya,
Liechtenstein, Malaysia, Malta, Marshall Islands,
Mauritania, Monaco, Nauru, Nepal, New
Caledonia, Nigeria, Niue, North Korea, Northern
Marianas, Oman, Pakistan, Palau, Papua New
Guinea, Philippines, Puerto Rico, Qatar, Reunion,
Romania, Saint Vincent Grenadines, San Marino,
Saudi Arabia, Serbia Montenegro, Seychelles,
Singapore, Solomon Islands, South Africa, Sri
Lanka, Sudan, Swaziland, Syria, Taiwan,
Tajikistan, Thailand, Turkmenistan, Turks and
Caicos, Ukraine, United Arab Emirates, US Virgin
Islands, Vanuatu, Vatican, Viet Nam, Wallis and
Futuna, Western Samoa, Yemen.
About the author
Alvaro Cuervo-Cazurra is an Assistant Professor of
International Business at the Moore School of
Business, University of South Carolina. His primary
research interest is understanding how firms devel-
op resources to become competitive and how they
then become international. He is also interested in
governance issues. He has started a long-term
project to analyze the emergence and success of
developing-country multinationals. Professor Cuer-
vo-Cazurra holds a Ph.D. from MIT and a Ph.D.
from Salamanca University in Spain. Before joining
the University of South Carolina, he was an
assistant professor at the University of Minnesota
and a visiting assistant professor at Cornell
University.
Accepted by Lorraine Eden, Army Hillman, Peter Rodriguez, Donald Siegel and Peter Rodriguez, Guest Editors, 17 April 2006. This paper has been with the
author for two revisions.
Who cares about corruption? Alvaro Cuervo-Cazurra
822
Journal of International Business Studies
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
ORIGINAL PAPER
Corruption and Its Effects on FDI: Analysing the Interaction
Between the Corruption Levels of the Home and Host Countries
and Its Effects at the Decision-Making Level
Jose Godinez1 • Ling Liu2
Received: 30 November 2015 / Accepted: 6 November 2016 / Published online: 17 November 2016
� Springer Science+Business Media Dordrecht 2016
Abstract This study furthers our understanding of how
corruption affects the decision-making process of allocat-
ing foreign direct investment. Drawing on the responses of
28 managers in charge of establishing operations in a
highly corrupt host country, we argue that those firms
based in home countries with low levels of corruption are
more proactive in preparing to face corruption abroad than
those based in countries with high corruption levels. This
means that firms from less corrupt home countries have
strategies in place to deal with high corruption abroad. This
finding is based on the fact that these firms have stronger
pressures to not engage in corruption from their home
stakeholders. Also, these firms might not have the experi-
ence of dealing with corruption at home, which hinders
their potential to deal with corruption abroad. On the other
hand, those firms based in highly corrupt home countries
do not have clear strategies to deal with
corruption
abroad.
This assertion is based on the fact that these firms might
have familiarity in dealing with corruption and thus, might
not see it as an obstacle to operating abroad.
Keywords Corruption � Foreign direct investment
allocation � Firm-level analysis
Introduction
Corruption, defined as the abuse of public power for per-
sonal gain (Collins et al. 2008), is an important problem
that affects all countries (Petrou and Thanos 2014). The
issue of corruption has been intensified by the fall of bar-
riers to commerce, since firms that were not used to dealing
with high corruption levels at home might be encountering
them abroad. Corruption exists at some degree in all
countries; however, it is more prevalent in developing ones
(Hellman et al. 2000). Accordingly, all firms deciding to
start operations abroad should take the level of
corruption
of the host country into account when developing their
strategies for internationalisation, especially if such loca-
tion is considered developing. However, despite the fact
that current literature acknowledges that the effects of
corruption on businesses is very important (Collins et al.
2008), most studies analysing how corruption affects the
allocation of foreign direct investment (FDI) to a highly
corrupt host country have focused on whether or not cor-
ruption deters FDI inflows (i.e. Cuervo-Cazurra 2006; Doh
et al. 2003; Habib and Zurawicki 2002), without paying
enough attention to how the decision-making process of
FDI allocation at the firm level is affected by high levels of
corruption. Therefore, this study takes into account the
heterogeneity of the home country of multinationals and
sheds light in this regard by proposing a framework that
illustrates the decision-making process of allocating FDI to
a highly corrupt foreign location depending on the level of
corruption of the home country compared to the corruption
level of the host
location.
Until recently, very few studies had actually analysed
how firms took corruption into consideration when decid-
ing to invest in a highly corrupt foreign location (Ro-
driguez et al. 2005). To close this gap in the literature,
& Jose Godinez
godinezj@merrimack.edu
Ling Liu
ling.liu@ed.ac.uk
1
Merrimack College, 315 Turnpike St. O’Reilly Hall 418,
North Andover, MA 01845, USA
2
University of Edinburgh Business School, 29 Buccleuch Pl,
Room 2.25, Edinburgh EH8 9JS, UK
123
J Bus Ethics (2018) 147:705–719
https://doi.org/10.1007/s10551-016-3380-7
http://crossmark.crossref.org/dialog/?doi=10.1007/s10551-016-3380-7&domain=pdf
http://crossmark.crossref.org/dialog/?doi=10.1007/s10551-016-3380-7&domain=pdf
https://doi.org/10.1007/s10551-016-3380-7
Goodspeed et al. (2011) analysed FDI flows to developing
and developed countries, concluding that the
uncertainty
generated by the level of corruption of the host country has
a direct negative effect on firms investing in the former but
not the latter. Furthering this line of research, Goodspeed
et al. (2013) proposed that the main deterrent of FDI in a
developing country is the level of uncertainty created by
corruption as opposed to high taxation. Nevertheless, these
studies have mainly focused on if the level of corruption of
the host country affects the attraction of FDI rather than
how.
Addressing this dearth of research is important, because
even though many businesses declare that corruption
affects them negatively, they still engage in it. Building on
the premise that not all foreign investors perceive and react
to corruption in the same manner (Cuervo-Cazurra 2006;
Goodspeed et al. 2013), we propose that foreign investors
craft strategies differently depending on the level of cor-
ruption of their home country as compared to that of the
host country. To do so, we follow Godinez and Liu (2015)
and divide foreign investors into two categories: those
based in home countries with lower corruption levels than
the host country and those based in countries with higher
corruption levels. The rationale for this categorisation is to
understand whether those foreign investors familiar with
dealing with corruption at home react differently to cor-
ruption abroad than those investors without such experi-
ence. In this sense, we argue that those firms headquartered
in home countries with low corruption levels will have in
place a plan to deal with corruption abroad. This is justified
because of their need to be perceived ethically in their
home country, as well as the lack of experience in oper-
ating in highly corrupt locations. On the other hand, those
firms headquartered in home countries with high levels of
corruption do not devise plans to deal with corruption
abroad because they might not see this phenomenon as an
impediment for conducting operations. The next section of
the study presents a literature review on corruption and
FDI, followed by the methodology, results and discussion,
and conclusions.
Literature Review
Corruption
To study corruption one has to define it first. For this study,
we define corruption as the abuse of public power for
personal gain (Collins et al. 2008). Even though many
other definitions have been used in different studies [see,
Judge et al. (2011)], this definition is appropriate for our
study since it encompasses transactional and institutional
activities between governments and private individuals.
From this definition, scholars have identified two kinds of
corruption: public and organisational. Public corruption is
the abuse of public power for personal gain (Luiz and
Stewart 2014). Organisational corruption is the conscious
violation of legal rules of the organisation for personal
gain, possibly at the organisation’s detriment (Hodgson and
Jiang 2007). While corruption can occur exclusively in the
private sector, analysing this phenomenon is beyond the
scope of our study. Also, we focus on public corruption
since the private sector should be regulated by the public
sector of any given location. Therefore, if the public sector
is highly corrupt, we can assume that the private sector can
also be experiencing a very similar condition.
Corruption has its roots in the economic and institutional
conditions of a nation (Ufere et al. 2010) and reflects the
legal, political, economic, and cultural institutions of a
country (Svensson 2005). Corruption thrives on a weak
institutional system (Rose-Ackerman and Coolidge 1997).
This means that even though corruption is an important
problem in any society, regardless of their development
level (Collins et al. 2008), this problem is more prevalent in
developing countries (Powpaka 2002) since they are
characterised by challenging institutional environments
that include low standards of living, weak administration
capacity, underdeveloped industrial base, and low Human
Development Indices (HDI) (Collins et al. 2008; Cuervo-
Cazurra 2006; Pajunen 2008; United Nations
2014).
Therefore, when analysing how firms react and implement
strategies to operate in foreign locations characterised by
high levels of corruption, emerging markets can be con-
sidered the obvious setting.
Effects of Corruption
Shleifer and Vishny (1993) state that if bureaucrats are
self-interested and have monopolistic powers to manage
public properties, they might exploit such powers for per-
sonal benefit to the detriment of public interests. Therefore,
corruption is believed to have negative effects on economic
growth since it allows the misallocation of productive
resources, leading to sub-optimal growth rates (Halkos and
Tzeremes 2010). Jain (2001) argues that corruption violates
a country’s legislation, which undermines a nation’s
sovereignty. Corruption also has direct effects on firms
since it may act as a tax, even though participating in more
distortionary and costly corrupt activities may cause higher
transaction costs than taxes (Besley and McLaren 1993;
Shleifer and Vishny 1993). The idea of corruption
increasing costs more than taxes is developed by Shleifer
and Vishny (1993) who argue that firms engaging in cor-
rupt deals should devote human and financial resources to
manage corruption, and that these resources could have
been used more productively in other activities.
706 J. Godinez, L. Liu
123
In addition to rising costs, high corruption in a given
location also increases uncertainty, especially for busi-
nesses expanding their operations internationally (Curevo-
Cazurra 2008). The result of the uncertainty generated by
corruption may include a reduction on FDI, or a reduction
in the quality of such investment. For example, Wei (1997)
argued that corruption in a host country would negatively
affect FDI despite government policies to prevent this from
happening. Lambsdorff (1998) concluded that corruption
had a negative impact on FDI flows to developing nations
due to the difficulty of navigating their institutional envi-
ronment. Cuervo-Cazurra (2006) argued that foreign
investors from countries who signed the OECD Convention
on Combating Bribery of Foreign Public Officials in
International Transactions were also deterred by corruption
abroad. Goodspeed et al. (2013) investigated the relation-
ship between corruption, taxes, and FDI concluding that
taxes and corruption are substitutes and that the impact of
taxes on FDI will be lessened when corruption is higher.
Since corruption tends to be more prevalent and tax
administration weaker in developing countries, so the level
of development of the host country has a direct effect on
FDI flows to such location. On the other hand, Goodspeed
et al. (2013) point out that if the host country is considered
developed, corporate taxes are an important predictor for
the attraction of FDI, but not the level of corruption
because of the stability provided by the institutional envi-
ronment in those countries.
Recent scholarship has attempted to shed light on this
topic by arguing that the level of corruption of the home
and host country should be taken into consideration when
studying how corruption affects the attraction of FDI. In
this regard, Habib and Zurawicki (2002) demonstrated that
it was not only corruption what might negatively affect the
attraction of FDI but the distance in corruption levels
between home and host countries. Building on this pre-
mise, Godinez and Liu (2015) proposed that what deters
FDI is not the level of corruption of the host country but the
uncertainty created by the distance and direction of cor-
ruption levels between the home and host countries. The
authors argued that the more distance between a home
country with lower corruption levels than the host country,
the more FDI will be negatively affected. On the other
hand, if such distance is between a home country with
higher corruption levels than an already highly corrupt host
country, the corruption distance between these two coun-
tries does not affect the attraction of FDI.
Despite the wealth of studies analysing how corruption
might affect the allocation of FDI, this subject is still not
yet well understood. Even though the topic has remained
popular in the management research agenda, this problem
is elusive to study due to its secretive nature. Another
reason why corruption and its effects on FDI are not yet
fully understood may be because of the macroeconomic
method usually employed to analyse this
phenomenon.
According to Yackee (2010), generally, research analysing
corruption and its effects on FDI share a similar design.
Such design includes an independent variable comprised
by an index measuring the perception of corruption. Then,
this independent variable is regressed against data mea-
suring country-level FDI flows. Regrettably, Yackee
(2010) points out that the results of these complex statis-
tical methodologies are inconsistent due to their depen-
dence on secondary data. Therefore, the return to the less
cutting-edge, but probably more informative methodolo-
gies of interviews and surveys is advised to analyse this
phenomenon.
Firms Responses to Corruption
Even though most studies analyse how corruption affects
the decision-making process of allocating FDI to a foreign
location at the macroeconomic level, there are others
looking at this phenomenon from the firm-level perspec-
tive. For instance, Collins et al. (2008, p. 101) demon-
strated that ‘‘the personal relationships of top managers
with public officials are significant predictors of engage-
ment in corruption’’. These authors also argued that support
of political activities, from top managers, have a strong
relationship with the willingness of such managers to
engage in corrupt activities. Luiz and Stewart (2014) also
studied how firms reacted to corruption when investing
abroad and proposed that managers think of themselves as
‘‘institution takers’’ and that they only respond to the
institutional conditions of the foreign locations. Nonethe-
less, the authors prove that firms can actually be proactive
in changing the institutional environment of the location
where they operate, specifically when instigating
corruption.
Understanding corruption at the firm level and how
likely managers are to engage in it has not yet been fully
understood, as most studies dealing with entering new
foreign locations only deal with issues of whether or not to
engage in corruption activities (Doh et al. 2003; Galang
2012). Although some companies are being proactive in
how they deal with corruption abroad, there is a dearth of
research dealing with specific approaches that can be used
to create strategies to deal with this problem. Therefore, we
propose to analyse how managers react to high corruption
abroad when deciding whether or not to invest in such
location. In order to do so, we decided to analyse how
managers from two different home locations (either more
or less corrupt than the host country) rationalise their plans
to enter a foreign location that is characterised by high
levels of corruption. The rationale for this design is to
study whether the strength of the home country
Corruption and Its Effects on FDI: Analysing the Interaction Between the Corruption Levels… 707
123
institutional environment has an effect on engagement in
cor
ruption abroad.
FDI responds differently to various public policies if the
host country is considered developed or developing
(Goodspeed et al. 2013; Godinez and Garita 2016).
Separating home countries based on their quality of insti-
tutions can provide a valuable insight into how firms create
strategies to deal with corruption abroad. Based on this
premise, we propose that the level of corruption of the
home country might also play an important role when
allocating FDI in a highly corrupt host country. Such dif-
ferences might steam from knowledge developed at home
about dealing with corruption and pressures from home
constituencies regarding engaging in corrupt activities
abroad.
Research Design, Data, and Methods
In order to analyse how the corruption level of the host
country affects the decision-making process of FDI allo-
cation, this research utilised a qualitative approach. The
qualitative methodology was appropriate to generate
inferences from the respondents in an inductive manner.
Furthermore, a qualitative approach was more appropriate
for this study since our aim was to analyse the perception
of corruption and its effects on the decision making process
of allocating FDI rather than a quantification of such
effects. Therefore, to generate the data needed for this
study, we utilised semi-structured interviews. This format
was preferred because it allowed us an informal setting for
questioning to suit the needs of each participant. Also, the
semi-structured interviews allowed us the flexibility to
clarify responses or pursue emergent issues (Bryman
2008).
Setting
Guatemala was chosen to conduct this study. The reason
for using Guatemala as the setting for our study is because
of its high corruption levels (Transparency International
has ranked Guatemala in the 123rd place in the world, out
of 177 countries) (Transparency International 2014). In
fact, several members of the Guatemalan government have
been facing allegations of corruption, which resulted in the
resignation and incarceration of the country’s former
president and vice-president (Malkin 2015). Despite of
Guatemala’s high corruption levels, the country is the
second largest recipient of FDI in Central America with a
GDP of $53.80 billion in 2014 and receives the largest
amount of FDI in the Central American region, which
accounted for $1.308 billion during the same year (World
Bank 2014).
The high levels of corruption in Guatemala are
explained by its levels of inequality and its transition from
a lengthy civil war. According to Rose-Ackerman (2008),
inequality within a nation is a predictor of high corruption.
In the case of Guatemala, the United Nations Development
Programme ranks it as one of the 20 most unequal coun-
tries in the world (UNDP 2009). Also, scholars analysing
corruption point out that nations transitioning from an
internal conflict are usually characterised by weak institu-
tions, which are fertile grounds for corruption (Rose-Ack-
erman 1997, 2008). Thus, due to its levels of corruption
and FDI inflows, Guatemala is an adequate location to
analyse how the corruption level of the host country affects
the decision-making process of allocating FDI to a highly
corrupt foreign location.
Data Collection
Data collection took place in two phases: from June to
August 2012, and from June to July 2014. Data were col-
lected by contacting all foreign firms that had invested in
Guatemala since 2007. The reason for contacting these
firms was to be able to talk to those individuals who had an
active participation in the decision-making process of
investing in the country. Once all firms were contacted, 28
firms agreed to allow us to interview managers who were
involved in the decision regarding investment in a highly
corrupt foreign location. The interviews were semi-struc-
tured in order to increase the focus and depth of the issue at
hand. The interviewees had the choice of being interviewed
in English or Spanish by the lead author. Those interviews
conducted in Spanish were translated to English and then
back to Spanish to corroborate their accuracy. Each inter-
view lasted between 60 and 90 min and followed standard
protocols to capture emerging themes in field research
(Strauss and Corbin 1994), and was recorded and
transcribed.
After the first round of interviews (six interviews), we
decided to focus our data collection on a small number of
steps used in the process of allocating FDI to a highly
corrupt host location. To do this, a theoretical sampling
was applied (Denzin 1989), which is recommended for
analytical induction (Bansal and Roth 2000) in order to
identify such steps. This approach was taken in order to
capture a broad set of beliefs and practices during the
decision-making process of allocating FDI to a corrupt
location within the sample. In this manner, we followed
Mair et al. (2012) who propose to use the cases to arrange
and stimulate data analysis, instead of using them as a
method to expose variance.
After analysing the preliminary results of the first six
interviews, five main steps were identified as the main
process used by managers to invest in a highly corrupt
708 J. Godinez, L. Liu
123
foreign location. The investor’s characteristics depending
on the corruption level of their home country (more or less
corrupt than the host country); previous knowledge of
dealing with corruption; perception of corruption; uncer-
tainty created by corruption distance; and strategy to deal
with corruption. Once these areas of interest were identi-
fied, 22 additional interviews were conducted. Together
with the first round of interviews, the data gathered was
then used in a repetitive manner to compare information
across informants and to analyse important points of syn-
ergy (Glaser and Strauss 1967).
Analysis
The data were analysed in two phases in order to go back
and forth between emerging theoretical arguments and the
data, following Mair et al. (2012). Before any analysis, a
narrative account of findings was arranged in a chrono-
logical manner. The historical account revealed a method
to arrange the data around the steps utilised to invest in a
highly corrupt host location. The qualitative analysis was
carried out with the help of NVivo 9.0. The first six
interviews included three from investors located in home
countries with higher corruption levels than the host
country and three from less corrupt host countries. In this
manner, we developed a code system that allowed us to
understand how the level of corruption of the home country
affected the process of investing in a highly corrupt host
location. After the data were collected, it was subjected to a
manifest analysis of the condensed narratives, following
Berg (2004). The analysis was carried out by identifying
phrases commonly employed by the respondents, then by
identifying justified descriptions. Based on these analyses,
we were able to examine how the difference in corruption
levels of the home and host country affected the decision-
making process of allocating FDI to a highly corrupt host
country.
The first stage of formal analysis consisted of corrobo-
rating whether there were differences between how inter-
viewees saw and responded to corruption based on the
corruption level of their home country as compared to that
of the host location with open coding. In order to carry out
this stage, we relied on institutional theory that proposes
that firms will seek legitimacy by conforming to their
social context (Glynn and Abzug 2002). Nevertheless,
gaining legitimacy is a difficult process due to the different
institutional environments in which such firms operate
(Kostova and Zaheer 1999). However, the data corrobo-
rated that those firms headquartered in countries with high
levels of corruption were able to achieve legitimacy in a
highly corrupt host country since such institutional envi-
ronment resembled that of their home country. On the
contrary, those firms located in countries with low
corruption levels had a more difficult time adapting to the
institutional environment of a host country characterised by
high corruption levels.
The second stage of formal data analysis centred on
investigating how managers rationalised their investment
decision in a highly corrupt host country in relation to the
two identified home countries, either more or less corrupt
than the host country. The emphasis was placed on the
remaining four steps of allocating FDI, even though it
would have been possible to identify a greater number of
activities. The rationale for focusing on these steps is
because during the analysis, it became apparent that these
areas were consistently emerging from all the interviews.
Therefore, the decision was made to carry an extensive
analysis of these steps. The analysis in this stage was
carried out by creating provisional categories and first-code
orders (Van Maanen 1979). With the help of NVivo 9.0, it
was possible to maintain a record of emerging categories
and to see comparable coded texts concurrently, which was
useful to manage the large dataset. Following Miles and
Huberman (1994), the first categorical codes offered labels
for different activities. The codes were created to mirror
the words used by the interviewees, which included, for
example, ‘‘being used to dealing with corruption’’, ‘‘prob-
lems associated with corruption’’, and ‘‘being prepared to
deal with corruption’’.
After analysing the first order of codes, an axial coding
was utilised (Strauss and Corbin 1994). The second order
of codes was used to provide meaning to how respondents
rationalised their process of understanding corruption and
creating a process to allocate FDI in a highly corrupt host
country. In this procedure, an inductive process was uti-
lised to identify a more abstract and theory rich construct of
the data. In this stage, the data gathered and analysed was
used to understand how managers allocated FDI to a highly
corrupt host location and to provide the theoretical impli-
cations of this process.
Results and Discussion
This study explores how corruption affects the decision-
making process of FDI allocation. The analysis is com-
prised by the experiences and views of managers who had a
direct role in deciding whether or not to invest in a highly
corrupt foreign location, their previous experience and
preconceived conceptions of corruption, and the conceptual
and practical steps involved in minimising the effects of
corruption when investing abroad, as presented in Fig. 1.
This section presents an analysis and decision-making
model of allocating FDI in a highly corrupt host country.
The results in this section emphasise the importance of the
corruption level of the country where firms are
Corruption and Its Effects on FDI: Analysing the Interaction Between the Corruption Levels… 709
123
headquartered when creating strategies to deal with cor-
ruption abroad.
Investors Characteristics
The characteristics of respondents for this analysis are
presented in Table 1. The names of respondents and MNEs
are not shared to protect the identity of the participants due
to the sensitive nature of this research. All respondents
represent MNEs operating in the services industry. In other
words, all the investments made in the host country can be
classified as market-seeking. The respondents were iden-
tified as members of an MNE that had recently (within the
last 5 years) established operations in Guatemala.
Respondents also had to have actively participated in the
decision-making process of investing and subsequent
operations in the host country. As previously mentioned,
the respondents were divided into two groups: MNEs
headquartered in countries with lower levels of corruption
than the host country and MNEs headquartered in countries
with higher corruption levels than the host country. This
distinction was made in order to compare how corruption
abroad affects those foreign investors that are familiar with
corruption at home compared to those without such expe-
rience. Also, the corruption distance and direction between
the home and host country are provided, as well as the
number of other subsidiaries in Latin America.
Previous Knowledge of Dealing with Corruption
After conducting all the interviews with managers of firms
with higher levels of corruption than the host country, it
was evident that all of them had extensive experience with
dealing with corruption. This experience was developed
when operating in their home country as well as in other
countries that had corruption levels similar to that of the
home country. However, not all of them acquired such
experience in the same manner. For instance, Investor 1
argued that ‘‘knowledge of how to deal with corrupt offi-
cials can be acquired at home’’. This view was supported
by investors 2, 4, 7, 8, and 14 and supports previous claims
that firms can learn to operate in challenging environments
and deploy such knowledge abroad, as proposed by
Buckley et al. (2007) and Cuervo-Cazurra and Genc
(2008).
All investors from home countries with higher corrup-
tion levels than the host country agreed that they acquired
knowledge of how to deal with corruption at home. How-
ever, some of them did so implicitly (See Table 2 for a
summary of illustrative comments from respondents). An
example of this is Investor 10, who stated that ‘‘nothing can
fully prepare you to deal with corruption abroad; but, after
having operations in the country you learn what to expect’’.
Investor 3 had a similar view to Investor 10, claiming that
‘‘no one can totally learn how to deal with corruption at
home or abroad. But, the experience at home taught us
where we should expect illegal claims’’. In a similar view,
Investor 8 argued that ‘‘there is no need to know how to
deal with corruption. You should learn to deal with people
of different customs and that experience can be acquired at
home’’.
Table 3 presents a summary of responses from man-
agers headquartered in
countries with lower corruption
levels than the host country when asked about acquiring
knowledge to deal with corruption with varied responses.
In this sense, many foreign investors argued that corruption
is an important problem that is severely punished at home,
even if the corrupt act is committed abroad. Thus,
according to these investors (Investors, 18, 20, 23, and 26),
they did not have the opportunity to learn how to deal with
corruption either at home or in the other foreign locations
where they operate. On the other hand, other investors from
countries with lower corruption levels than Guatemala
(Investors 15, 19, 22, 24, and 25) argued that corruption
Investor’s Previous knowledge Perception of Uncertainty Strategy to deal
characteristics of Dealing with Corruption Created by with Corruption
Corruption Distance Abroad
Host
country
corruption
More corrupt
than host
country
Less corrupt than
host country
Yes
No
Part of business
culture
Important
problem
Will not increase
with (+) corrup�on
distance
Increases with (-)
corrup�on
distance
No
Yes
Foreign
Direct
Investment
Fig. 1 Corruption and how it affects the decision-making process of allocating FDI
710 J. Godinez, L. Liu
123
although bad, can be seen as a ‘‘means to an end’’. It is
plausible that these investors argue for a more benign
nature of corruption, since the corruption levels of their
home countries are close to those of Guatemala, according
to Transparency International (Transparency International
2014).
From an institutional theory point of view, these
findings mean that the level of corruption of the host
country, as compared to that of the home country, mat-
ters when analysing FDI flows to a highly corrupt host
location. This finding validates that at the firm level with
a qualitative approach, the results proposed by Good-
speed et al. (2013). Also, these results suggest that
managers can develop the capacity to operate in a par-
ticular institutional environment (Johanson and Vahlne
1977). Managers, in this sense, generate assumptions that
influence how their firms operate in an external envi-
ronment and are able to deploy them in similar foreign
environments. In the case of MNEs headquartered in
highly corrupt countries, this means that a foreign envi-
ronment that can be considered as ‘challenging’ by some
managers, cannot be considered as such if a manager had
the opportunity to learn how to operate in it at home. On
the other hand, firms located in home countries with low
corruption levels might face considerable problems
achieving legitimacy in a foreign environment charac-
terised by high corruption. The difficulties to achieve
legitimacy arise from the complexity of the different
institutional environments in which an MNE operates
(Kostova and Zaheer 1999). When talking about a firm
operating in a highly corrupt host country, this means
that MNEs without knowledge of how to operate in such
conditions might have difficulties assessing their critical
constituencies (Scott 1995) and might face strong pres-
sures at home to not engage in
corruption abroad
(Cuervo-Cazurra 2006).
Table 1 Profile of respondents
Investor Home country Corruption distance
and direction
Amount invested in past
5 years (US$)
No. of subsidiaries in
Latin America
I1 Honduras -3 Up to 1 million 2
I2 Vietnam -1 Between 10 and 20 million 6
I3 Guyana -2 Between 5 and 10 million 4
I4 Honduras -3 Up to 1 million 3
I5 Vietnam -1 Between 5 and 10 million 4
I6 Venezuela -13 Between 5 and 10 million 5
I7 Nicaragua -4 Between 10 and 20 million 8
I8 Honduras -3 Between 5 and 10 million 4
I9 Russia -5 Up to 1 million 3
I10 Paraguay -8 Between 5 and 10 million 4
I11 Russia -5 Between 1 and 5 million 3
I12 Turkmenistan -15 Between 10 and 20 million 6
I13 Nicaragua -4 Up to 1 million 2
I14 Nicaragua -4 Between 1 and 5 million 3
I15 China 4 Between 10 and 20 million 5
I16 Canada 49 Between 10 and 20 million 7
I17 Mexico 3 Between 20 and 30 million 9
I18 USA 42 Between 30 and 40 million 7
I19 Mexico 3 Between 5 and 10 million 5
I20 Canada 49 Between 20 and 30 million 8
I21 Germany 47 Between 10 and 20 million 6
I22 China 4 Between 30 and 40 million 10
I23 USA 42 Between 5 and 10 million 4
I24 China 4 Between 20 and 30 million 6
I25 Spain 28 Between 10 and 20 million 6
I26 USA 42 Between 30 and 40 million 7
I27 Germany 47 Between 10 and 20 million 6
I28 Spain 28 Between 5 and 10 million 4
Corruption and Its Effects on FDI: Analysing the Interaction Between the Corruption Levels… 711
123
Perception of Corruption and Engagement
in Corruption Abroad
Responses from MNEs headquartered in home countries
with higher levels of corruption than the host country
agreed that corruption was a problem. For example,
Investor 1 declared that ‘‘corruption is morally wrong’’.
Other respondents argued that corruption is one of the most
important problems that they have to face when deciding to
start operations in a foreign location and to continue their
activities in such places (Investors 3, 5, 6, 8, 9, 10, and 12).
These responses are in line with the general view that
corruption has a negative effect on businesses (Doh et al.
2003; Wei 1997). Nonetheless, twelve respondents, out of
fourteen, justified the existence of corruption. In this sense,
respondents argued that even though corruption is detri-
mental for their businesses, they had to comply with it to
remain in businesses. For instance, Investor 9 argued that
‘‘corruption is wrong but if I do not comply, we go out of
business and we cannot offer employment’’. This view was
echoed by all respondents as summarised in Table 4,
except Investors 2 and 7, who declared that ‘‘corruption
should not exist’’ and that ‘‘firms should not engage in it’’.
Despite their negative views on corruption, all fourteen
respondents from MNEs based on countries with higher
levels of corruption than the host country admitted to have
had participated in corrupt deals abroad. In fact, to some
degree, all respondents agreed that corruption was just part
Table 2 Knowledge of dealing with corruption from managers headquartered in countries with higher corruption
levels than the host country
Investor Illustrative quotes
I1 Of course knowledge of how to deal with corrupt officials can be acquired at home. We learned what corrupt officials want and how
we can provide it. In this manner we ensure that we can remain in business
I2 If you think of it, Vietnam and Guatemala aren’t that different. Businesses want to operate and government workers want to
supplement their income. In this sense, we have plenty of practice at home of how to help both parties reach their goals
I3 Corruption is different in different places. This means that no one can totally learn how to deal with corruption at home or abroad. But,
the experience at home taught us where we should expect illegal claims and how to deal with them
I4 At home [Honduras] we have been dealing with corrupt officials since we started our business. That experience has definitely helped
us operating abroad. We learned very early that there are certain expectations for businesses to be able to secure contracts and
acquire licenses, for example
I7 Yes, we have had to deal with many corrupt public officials at home. This experience gave us enough practice to know how to
maneuver corrupt officials abroad, especially since our countries are very similar
I8 Government officials are the same everywhere. We had the chance to learn to deal with them at home and we realized that officials
abroad aren’t that different
I9 Companies should not be too worried about learning to deal with corruption. Instead, they should learn how to deal with people of
different customs and that experience can be acquired at home
I10 Nothing can fully prepare you to deal with corruption abroad; but, after having operations in the country, you learn what to expect and
how to deal with different requests
I14 We have had to learn to collaborate with corrupt officials at home. They have a goal and we have ours. It is the same in other countries
Table 3 Knowledge of dealing with corruption from managers headquartered in countries with lower corruption levels than the host country
Investor Illustrative quotes
I15 We had adapted to co-exist with corruption. Sometimes we have to comply but we try not to
I18 Absolutely not. Of course there is corruption at home but nothing even close than Guatemala
I19 We try to avoid doing corrupt deals as much as we can even though sometimes it is difficult, especially with low level officials asking
for small bribes
I20 I am sure there is corruption at home but we have a zero tolerance policy at home and abroad
I22 Corruption at home is not uncommon. However, due to cultural differences I do not believe that corruption at home helped cope with
corruption in Guatemala
I23 Not interested in learning
I24 We had learned to maneuver in conditions considered less than ideal
I25 You could learn to deal with corruption if you wanted to. Either learn to engage on it or how to avoid it
I26 No, at home it is known that there is corruption in government contracts but we do not participate in those
712 J. Godinez, L. Liu
123
of doing business in Guatemala. Investor 4 is a good
example of this finding by declaring that ‘‘if we wanted to
be awarded contracts, we had to participate in corrupt
deals’’. Investor 13 also said that ‘‘we had no choice but to
comply with the local customs’’. This result might confirm
that those foreign investors based in highly corrupt home
countries might internalise knowledge of how to deal with
corruption at home and deploy it abroad. Also, this finding
shows that foreign investors from countries with high
levels of corruption are aware that corruption is wrong but
they choose to engage in it nevertheless, as illustrated in
Table 4. Thus, this result may indicate that those MNEs
headquartered in home countries with high levels of cor-
ruption might not face pressures from stakeholders at home
to not engage in corrupt deals abroad.
When talking to respondents from MNEs headquartered
in
countries with lower levels of corruption than the host
country, it was evident that they also saw corruption as an
important problem. Out of fourteen respondents, nine
argued that corruption was detrimental for their businesses
and for society in general. However, these investors dis-
agreed on how much of a problem corruption really was.
For example, Investor 20 declared that ‘‘corruption is a
cancer of a society’’. Investor 16, on the other hand, argued
that ‘‘corruption is a major impediment for conducting
business abroad but it is not the only factor to be consid-
ered’’. Nevertheless, all respondents declared that corrup-
tion was detrimental for their businesses and that it even
decreased the amount of investment allocated in a foreign
location.
The main difference between MNEs headquartered in
countries with lower level of corruption than the host
country, and those with higher corruption levels, was that
those MNEs with low levels of corruption at home were
less likely to engage in corruption abroad, as presented in
Table 5. According to investors 18, 20, 21, 23, and 26, they
not only have regulations at home preventing them from
engaging in corruption overseas but also internal policies to
minimise the risk of engaging on corrupt deals abroad.
Also, investors 15, 16, 19, 22, 24, 25, and 27 declared that
they too ‘‘try to avoid’’ corrupt deals abroad when possible.
An example of how foreign investors try to avoid corrup-
tion abroad was provided by Investor 27 who said that
when it is necessary, they rely on their local partners to
carry out certain operations that might not be perceived as
totally ethical. This finding confirms the long held idea that
corruption has a negative effect on MNEs. However, the
negative effect of corruption on a business might be even
more detrimental if the MNE is headquartered in a country
with lower levels of corruption than the host country.
The results of the perception of corruption depending on
the corruption level of the home country as compared to
that of the host country, means that the location where a
manager spent his/her formative years has a direct influ-
ence on how such manager views corruption. Institutional
theory has been utilised to understand how organisational
structures and processes become institutionalised over time
(Oliver 1991). The fundamental premise of this theory is
that MNEs have tendencies to conform to predominant
norms and traditions of the location in which they operate
(Meyer and Rowan 1977). In turn, such tendencies lead to a
homogeneity amongst the structures of an MNE and its
activities that is shaped by social pressures (Oliver 1997).
In the case of corruption, it means that a manager’s per-
ception of corruption is directly linked to the corruption
level of their home country.
Uncertainty Created by Corruption Distance
Corruption can be seen as a tax on MNEs (Mauro 1995),
but the greatest challenge it poses on firms might not be the
costs themselves, but the uncertainty regarding the actual
Table 4 Perception of corruption and engagement in corruption abroad from managers headquartered in countries with higher corruption levels
than the host country
Investor Illustrative quotes
I1 Corruption is morally wrong. However, sometimes it is necessary to continue in business
I2 Corruption is deplorable and should not exist and firms should not engage in it.
I3 Corruption is wrong, but it is understandable since some public officials need to complement their wages
I4 I do not believe that there is such thing as corruption. People only have different business cultures and it is our job to figure out how to
operate in each one
I5 Corruption is wrong but if I do not comply, I do not have a business and cannot offer employment
I6 It should not be acceptable but it is the only way to do business sometimes
I7 Corruption is wrong. It should not be acceptable
I8 Corruption is part of doing business anywhere
I9 Corruption should not exist but it does. If we do not participate we will go out of business
I11 Corruption is wrong but that is how business is conducted
Corruption and Its Effects on FDI: Analysing the Interaction Between the Corruption Levels… 713
123
costs that the firm will need to pay to a corrupt foreign
official (Wei 1997). While previous studies agree that the
uncertainty created by corruption might be more detri-
mental to foreign investors than the actual corruption level
(Curevo-Cazurra 2008; Fisman and Miguel 2007), little is
known about how such uncertainty affects the decision-
making process of allocating FDI to a highly corrupt host
country. In this research, the majority of respondents of
MNEs based in countries with high levels of corruption
agreed that uncertainty was not a characteristic of the
Guatemalan market. According to these investors, they had
previous knowledge of the corruption levels of the host
country and how to manoeuvre through it. When asked a
question regarding uncertainty, Investor 3 replied that ‘‘no
one is surprised about the levels of corruption in Guate-
mala’’. To further this point, Investor 8 argued that its firm
‘‘knows how to do business where they decide to operate’’.
This sentiment was shared by investors 5, 6, 7, 9, 10, and
11. However, even though most investors based in home
countries with high levels of corruption declared that the
uncertainty created by corruption was minimal in Guate-
mala, they agreed that corruption had detrimental conse-
quences. According to Investor 7, ‘‘the uncertainty created
by corruption was minimal but it did increase their oper-
ating costs’’. This finding, illustrated in Table 6, is in line
with the conclusions reached by Cuervo-Cazurra (2008)
and Uhlenbruck et al. (2006) who argued that the levels of
arbitrariness of corruption are more detrimental to foreign
investors than its levels of pervasiveness.
In general, respondents from MNEs headquartered in
countries with lower levels of corruption than the host
country declared that they had knowledge that Guatemala’s
was considered highly corrupt. Nevertheless, the uncer-
tainty it created when deciding how to operate in the
country was a great impediment for doing businesses there
(Collins et al. 2008), as presented in Table 7. In this study,
nine respondents (out of fourteen) argued that uncertainty
generated by corruption was a concern when deciding to
start operations in Guatemala. These respondents also
voiced that uncertainty was an obstacle to operate in a
highly corrupt host country. In fact, respondents 15, 17, 19,
and 20 argued that local officials in Guatemala can request
unlimited bribes. Other respondents, such as Investors 16,
17, 25, and 28, agreed that the costs associated with cor-
ruption in Guatemala were predictable, and hence, uncer-
tainty generated by corruption was minimal.
Corruption is distinct in different countries in both the
reach throughout the economy (Uhlenbruck et al. 2006),
and the uncertainty it creates (Chakrabarty and Bass 2013).
A host locations characterised by high uncertainty derived
from corruption might require several ineffectual corrupt
transactions from foreign firms. Moreover, under a weak
administrative governance, government officials might be
willing to change the set of necessary approvals without
giving notice to receive maximal bribes (Shleifer and
Vishny 1993). Therefore, firms without previous knowl-
edge of how to deal with corruption might suffer negative
effects when operating in a foreign location characterised
by high uncertainty created by corruption. However, this
study finds that the uncertainty created by high corruption
has detrimental effects on foreign investors when such
investors are located in countries with low levels of
corruption.
Furthermore, such negative effects are exacerbated with
a higher corruption distance between the home and host
countries. On the other hand, corruption distance appears to
not have an effect on firms located in a home country that
has higher corruption levels than an already highly corrupt
host location. This finding is explained because such firms
have been equipped with developed knowledge regarding
operating in highly corrupt countries and are able to deploy
such knowledge to operate abroad. Also, this finding can be
explained because firms located in a highly corrupt home
country might not face strong pressures to not engage in
corruption abroad from home country stakeholders.
Strategic Measures to Deal with Corruption Abroad
Most studies analysing corruption and FDI agree that
corruption has a negative effect to various degrees on
foreign investment (Cuervo-Cazurra 2006; Doh et al. 2003;
Habib and Zurawicki 2002). In addition to market
Table 5 Perception of
corruption and engagement in
corruption abroad from
managers headquartered in
countries with lower corruption
levels than the host country
Investor Illustrative quotes
I18 Corruption is wrong and should be combatted
I19 Corruption is wrong and delays processes
I20 Corruption is a cancer of a society
I21 Corruption is morally wrong and affects an entire society
I22 Corruption increases prices and diminishes reputations
I24 Corruption should not exist and business should be transparent. That is not always the case
I26 Corruption is illegal at home and anywhere we operate
I28 Corruption is wrong and should not happen, but it does
714 J. Godinez, L. Liu
123
avoidance, mutational enterprises have made great efforts
to explore different ways to lessen the sensitivity of the
affects and to engage in FDI activities (for example, trad-
ing favours). However, fewer studies have analysed
strategic responses of MNEs when the decision of investing
abroad has been made. To address this gap in the literature,
Luiz and Stewart (2014) argue that some firms have created
anti-corruption policies with strict anti-corruption partici-
pation mandates even though the authors argue that for
some companies, sometimes participating in corrupt deals
is unavoidable. Building on their work, this study shows
that there is a great difference between MNEs based in
countries with low levels of corruption and MNEs based in
highly corrupt countries, when talking about strategies to
deal with corruption abroad. In general, MNEs based in
home countries with high corruption levels do not have
formal strategies to deal with corruption abroad. Of the
fourteen MNEs studied, only four declared to have a
strategy to deal with corruption abroad (Investors 1, 2, 5,
and 6). Nevertheless, their strategy was to allocate
resources to their budget to pay for bribes. Therefore, while
these investors describe themselves as proactive, they are
still participating in corrupt deals abroad, as exemplified in
Table 8.
The responses from MNEs based in countries with lower
levels of corruption than the host country showed that in
general these firms have strategies to deal with corruption
abroad (Table 2). In line with Luiz and Stewart (2014),
Investors 15, 16, 18, and 20 declared that their main
strategy to deal with corruption was avoidance. These
investors argued that they avoided conducting business
with the local government when possible. Other respon-
dents went further and declared that their organisational
structure was designed to be as transparent as possible
(Investors 21, 23, 24, and 25). These investors declared that
they had the mandate from headquarters to adhere to a
strict code of conduct, as Table 9 illustrates. Such code of
conduct required that all negotiations with local govern-
ment officials and/or business partners be recorded and
attended by at least two members of the MNE. Moreover,
these investors argued that they had clear
transparency
procedures in place to hire suppliers. However, there were
investors that declared not to have strategic measures in
place to deal with corruption abroad (Investors 17, 22, and
28), which might be because their levels of corruption,
although lower than Guatemala, can still be considered
high.
Another approach used by firms from less corrupt
countries when investing in Guatemala was to have a
decentralised approach. While some investors declared that
their strategy to deal with corruption was to avoid it, others
argued that they preferred their strategy to have some
Table 6 Uncertainty created by corruption from managers headquartered in countries with higher corruption levels than the host country
Investor Illustrative quotes
I2 When investing in Guatemala we had knowledge of the problem in the country. Uncertainty was minimal
I3 There is no uncertainty due to corruption. No one is surprised by corruption in Guatemala
I4 There is no uncertainty if you know what you will encounter
I6 There is no uncertainty when you know what to expect
I8 Not uncertain, we know how to do business where we operate
I9 Corruption increases prices but we know how to deal with it
I13 Corruption expedites processes but increases prices
Table 7 Uncertainty created by corruption from managers headquartered in countries with lower corruption levels than the host country
Investor Illustrative quotes
I16 Corruption occurs more often when doing business with the government. We try to avoid that
I17 Corruption in Guatemala is very predictable as well as its costs
I19 Uncertainty can be high but once officials see how we do business they do not bother us
I20 We do whatever we can to avoid engaging in corruption
I23 Corruption erodes an organization’s image, increases costs, increases poverty
I25 Increases costs and sometimes prevents us from doing business with the government (when we refuse to pay)
I26 Corruption worsens poverty, promotes inequality and increases costs to everyone
I28 Corruption makes access to permits and contracts more difficult. Those with connections have an advantage
Corruption and Its Effects on FDI: Analysing the Interaction Between the Corruption Levels… 715
123
‘‘room for interpretation’’ to account for variations in the
host countries where they operate. This approach was put
in place to adhere to the host country’s local norms, such as
the acceptance of hospitality. Nevertheless, these firms also
had a high transparency requirement that made it necessary
for managers to record and report all gifts received when
conducting businesses there. Table 10 presents a summary
of the main findings of this study.
Conclusions
This study analysed how the corruption level of the host
country affects the decision-making process of allocating
FDI to a highly corrupt foreign location. Based on insti-
tutional theory, this study analysed two kinds of corruption,
public and organisational (Luiz and Stewart 2014), and
how such corruption affects MNEs investing in a highly
corrupt foreign location. In order to answer the question, 28
interviews were carried out with managers of MNEs that
had recently invested in Guatemala. Following the work of
Godinez and Liu (2015), we divided the respondents into
two groups: MNEs headquartered in countries with higher
corruption levels than the host country, and MNEs head-
quartered in countries with lower corruption levels than the
host country. The classification of home countries as either
more or less corrupt than the host country was made to
compare and contrast reactions to corruption abroad based
on the experience foreign investors had at home regarding
how to deal with corruption and if such experience could
be utilised abroad (Goodspeed et al. 2013). The results
show that firms based in home countries with lower levels
of corruption than Guatemala were negatively affected by
corruption in the host country. On the other hand, those
MNEs based in home countries with higher levels of cor-
ruption than the host country were not as negatively
affected when investing in a foreign country characterised
by high levels of corruption.
Corruption is believed to have negative effects on eco-
nomic growth (Halkos and Tzeremes 2010). Corruption
acts like a tax. Firms devote resources to manage corrup-
tion but those resources could be better allocated (Shleifer
and Vishny 1993). Corruption is also believed to have a
negative effect on foreign investors (Godinez and Garita
2015). However, building on the foundation that not all
foreign investors are equal (Cuervo-Cazurra 2006), we
argue that those foreign investors based in home countries
with lower levels of corruption than the host country might
not be as likely to engage in corruption abroad. On the
other hand, those investors from MNEs based in countries
Table 8 Strategic measures to deal with corruption abroad from managers headquartered in countries with higher corruption levels than the host
country
Investor Illustrative quotes
I1 On our business plan we allocated money for payments to local officials
I2 We already knew that several requests were going to be made. We had to account for those expenses when devising our plan
I3 Our institution does not have a plan to deal with corruption abroad, as far as I am concerned
I7 We know how to operate in these kinds of countries. There is no need to create formal strategies to do what we already know how to
do
I13 This company does not have formal norms about dealing with corrupt
government officials
Table 9 Strategic measures to deal with corruption abroad from managers headquartered in countries with lower corruption levels than the host
country
Investor Illustrative quotes
I15 Our main strategy is to avoid doing business with local government. If necessary, we hire a local company to do deal with them for us
I17 Our company does not have clear strategic measures to deal with corruption abroad. We just try to do the right thing
I18 Avoid doing business with local government. Clear code of conduct applicable to all of our employees
I20 I am not sure if this is considered a strategy, but we have a clear code of conduct applicable to all of our employees which is zero
tolerance to corrupt behavior
I24 If my memory serves me well, we have a petty cash budget to deal with small requests mainly from low level bureaucrats
I26 We follow the code of conduct when doing business abroad in place by our firm. We also exercise our discretion always acting
ethically
716 J. Godinez, L. Liu
123
with high levels of corruption might be more likely to
participate in corrupt deals in foreign locations. While
studies analysing corruption and its effects on FDI have
seen MNEs as institutional takers (Luiz and Stewart 2014),
we argue that MNEs can actually be more influential in the
institutional arrangement of a foreign location than previ-
ously thought. We base this conclusion on the fact that
those MNEs familiar with dealing with corruption at home
can actually seek out other corrupt countries to establish
new operations. On the other hand, those firms based in
countries with low levels of corruption might try to avoid
engaging in corruption abroad and actually have policies to
ensure they operate corruption free abroad.
Finally, while firms should have strategies to deal with
corruption (Luiz and Stewart 2014), this study found,
however, that those MNEs from countries with higher
levels of corruption than the host country did not have
strategies in place to deal with corruption abroad.
Respondents from these firms argued that corruption was
just a part of doing business and that no specific strategies
were needed. On the other hand, those firms located in
countries with lower corruption levels than Guatemala
attributed their success in the country to their strategies to
deal with the country’s corruption. These investors argued
that their strategies to deal with corruption included a clear
policy to avoid doing businesses with the local govern-
ment, as well as a well-designed organisational structure
that allowed transparency.
Limitations and Future Research
This study has many limitations. Due to the qualitative
nature of this analysis, we were able to study in depth how
corruption affects the decision-making process of allocat-
ing FDI to a corrupt foreign location. However, qualitative
studies rely on a restricted number of respondents. For this
reason, future studies should analyse this issue in a quan-
titative manner and hopefully with more than one host
country. This approach is necessary to develop
testable hypotheses that can further our knowledge of the
subject. Also, due to the large levels of corruption present
in Guatemala, the number of home countries with higher
corruption levels than the host country is limited.
Lastly, even though this study argues that those MNEs
headquartered in countries with high corruption levels
might not be negatively affected by corruption when
investing and operating abroad, it is important to note that
corruption is still a problem that affects them. As stated by
Shleifer and Vishny (1993), resources allocated to dealing
and complying with corruption could be more effectively
and efficiently deployed to more productive operations.
Firms from highly corrupt home countries should follow
the lead of their counterparts from less corrupt countries
and think proactively about this problem and how to avoid
it. If they do so, they would not only utilise their resources
more wisely, but they could also gain more goodwill from
customers that demand higher standards from MNEs.
Acknowledgements We would like to thank the editor and the two
anonymous reviewers for their useful suggestions for improvement of
this manuscript. We would also like to thank Dr. Rick Woodward and
Dr. Mark Cordano for their helpful comments at different stages of
this project.
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Characteristics Previous knowledge of dealing with
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- Corruption and Its Effects on FDI: Analysing the Interaction Between the Corruption Levels of the Home and Host Countries and Its Effects at the Decision-Making Level
Abstract
Introduction
Literature Review
Corruption
Effects of Corruption
Firms Responses to Corruption
Research Design, Data, and Methods
Setting
Data Collection
Analysis
Results and Discussion
Investors Characteristics
Previous Knowledge of Dealing with Corruption
Perception of Corruption and Engagement in Corruption Abroad
Uncertainty Created by Corruption Distance
Strategic Measures to Deal with Corruption Abroad
Conclusions
Limitations and Future Research
Acknowledgements
References
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