6210 Week 8 Discussion - Paper Answers

Discussion – Week 8

Top of Form

Discussion: Sociocultural Differences in Perspectives on Aging

Some cultures view death not as an endpoint but as a beginning, or not as a distinct separation but merely a thin veil or doorway through which a person passes. Other cultures esteem the older generations and honor them for their wisdom, whereas others regard elders as incapable of contributing to society. How might these distinct views on death and older people influence perspectives on the aging process? What other cultural differences might impact perceptions of aging?

This week, you explore different cultures’ views of aging and consider how these differences might influence social work. You also think cross-culturally about how you could integrate another culture’s perspective in your practice.

To Prepare:

Review the Learning Resources on sociological aspects of later adulthood.
Using the Walden Library, research two cultures different from your own and examine their perspectives on aging.
An example search in the library databases is social conditions or sociocultural AND aging.

By 01/19/2022

Post a comparison of your culture’s( I’m from Dominican republic/Hispanic ) perspective on aging to the perspectives of the two cultures you researched. Explain why you think these differences exist. Also, explain how different perspectives on aging might impact social work practice.

Bottom of Form

Required Readings

Zastrow, C. H., Kirst-Ashman, K. K., & Hessenauer, S. L. (2019). Understanding human behavior and the social environment (11th ed.). Cengage Learning.

· Chapter 16, “Sociological Aspects of Later Adulthood” (pp. 703–728)

Quach, L. T., Primack, J., Bozzay, M., Madrigal, C., Erqou, S., & Rudolph, J. L. (2021). The intersection of physical and social frailty in older adults. Rhode Island Medical Journal, 104(4), 16–19.

Teater, B., Chonody, J. M., & Davis, N. (2021). Risk and protective factors of loneliness among older adults: The significance of social isolation and quality and type of contact. Social Work in Public Health, 36(2), 128–141. https://doi.org/10.1080/19371918.2020.1866140

U.S. Department of Health and Human Services. (n.d.). Aging: Resources near you. https://www.hhs.gov/aging/state-resources/index.html

Follow Rubric

Initial Posting: Content

14.85 (49.5%) – 16.5 (55%)

Initial posting thoroughly responds to all parts of the Discussion prompt. Posting demonstrates excellent understanding of the material presented in the Learning Resources, as well as ability to apply the material. Posting demonstrates exemplary critical thinking and reflection, as well as analysis of the weekly Learning Resources. Specific and relevant examples and evidence from at least two of the Learning Resources and other scholarly sources are used to substantiate the argument or viewpoint.

Follow-Up Response Postings: Content

6.75 (22.5%) – 7.5 (25%)

Student thoroughly addresses all parts of the response prompt. Student responds to at least two colleagues in a meaningful, respectful manner that promotes further inquiry and extends the conversation. Response presents original ideas not already discussed, asks stimulating questions, and further supports with evidence from assigned readings. Post is substantive in both length (75–100 words) and depth of ideas presented.

Readability of Postings

5.4 (18%) – 6 (20%)

Initial and response posts are clear and coherent. Few if any (less than 2) writing errors are made. Student writes with exemplary grammar, sentence structure, and punctuation to convey their message.

Discussion- Week 8

Top of Form

Discussion: Sociocultural Differences in Perspectives on Aging

To Prepare:

· Review the Learning Resources on sociological aspects of later adulthood.

· Using the Walden Library, research two cultures different from your own and examine their perspectives on aging.

· An example search in the library databases is social conditions or sociocultural AND aging.

By 01/19/2022
Post a comparison of your culture’s( I’m from Dominican republic/Hispanic ) perspective on aging to the perspectives of the two cultures you researched. Explain why you think these differences exist. Also, explain how different perspectives on aging might impact social work practice.

Bottom of Form

Required Readings

Zastrow, C. H., Kirst-Ashman, K. K., & Hessenauer, S. L. (2019). Understanding human behavior and the social environment (11th ed.). Cengage Learning.

· Chapter 16, “Sociological Aspects of Later Adulthood” (pp. 703–728)

U.S. Department of Health and Human Services. (n.d.). Aging: Resources near you. https://www.hhs.gov/aging/state-resources/index.html

Follow Rubric

Initial Posting: Content

14.85 (49.5%) – 16.5 (55%)

Follow-Up Response Postings: Content

6.75 (22.5%) – 7.5 (25%)

Readability of Postings

5.4 (18%) – 6 (20%)

AGING, HUMAN CAPITAL, AND PRODUCTIVITY IN FRANCE:

A GENERATIONAL ACCOUNTING PERSPECTIVE

by Xavier Chojnicki* AND Paul Eliot Rabesandratana

LEM–CNRS (UMR 9221), University of Lille

The aim of this paper is to highlight the potential productivity gains resulting from improvements in
the (i) educational attainment and (ii) health status of the working-age population. For that purpose,
we develop a Generational Accounting Model applied to the French economy. Using the conventional
methodology of generational accounting, we first estimate the adjustments that will be necessary to
ensure the sustainability of French fiscal policy in the long term under the assumption that individual
taxes and transfers grow at the same rate as labor productivity. However, this assumption does not
account for the explicit determinants of individual productivity. Therefore, we then explain how pro-
ductivity growth is partly due to the French population�s skill level and its health level, which is
approximated by the survival rate of adults. We estimate that the increased educational attainment and
improved adult survival rate in France generate potentially important productivity gains that could
significantly challenge the weight of the burden induced by aging. Therefore, we estimate that this
change could reduce the tax burden bequeathed to future generations by 79 percent. Our results are
robust to the main assumptions.

JEL Codes: E62, H51, I1

Keywords: generational accounting, health status, education, productivity, aging

1. Introduction

The French population is engaged in an aging process that, far from being
specific to France, affects virtually all populations worldwide. Although the
French population has aged considerably over the past 35 years—the median age
increased from 31.7 to 40 years—the mass retirement of the baby boomers is con-
siderably amplifying the effects of the aging process. According to INSEE1 (Blan-
pain and Chardon, 2010), the total French population should increase by
approximately 20 percent between 2007 and 2060, although the French labor
force is expected to remain relatively stable. During the same period, the share of
the population aged 65 and over should nearly double. These demographic evolu-
tions would induce a significant increase in the old-age dependency ratio—that is,
the ratio of the population aged 65 and over to the population aged 15–64

Note: This paper benefited from the valuable comments of H. d�Albis and L. Ragot. We thank
the participants of the 2nd Lille–Ghent Workshop in Economics (Lille, 2013), the International Pen-
sion Workshop (Paris, 2013), the Forum for Economists International (Amsterdam, 2013), the con-
gress of the AFSE (Aix-en-Provence, 2013), and the internal seminar of the University of Lille for
helpful comments and discussions. The usual disclaimer applies.

*Correspondence to: Xavier Chojnicki, LEM–CNRS (UMR 9221), Universit�e Lille 3, Maison
de la recherche, Domaine, Universitaire du Pont de Bois, B.P. 60149, 59653 Villeneuve d�Ascq,
France (xavier.chojnicki@univ-lille3.fr).

1The National Institute of Statistics and Economic Studies.

Review of Income and Wealth

DOI: 10.1111/roiw.12306

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years—in the coming decades. Having been approximately 25 percent in 2007,
this ratio could be as high as 47 percent in 2060.

The impact of these developments on public finances is a source of con-
cern.2 Based on current policies, public spending directly related to age could
increase by approximately 3.1 percentage points of gross domestic product
(GDP) according to the European Commission (2012). Most of this increase
would come from spending on pensions (10.5 point), health (11.4 point), and
long-term care (12.1 points). Potential savings in expenditures targeting
younger segments of the population (spending on education and unemploy-
ment in particular) would be very small (–0.4 for the former and 20.6 for the
latter). Unfortunately, the slow growth of the French working-age population
will not be able to absorb this increase in social spending. Therefore, the
implicit public debt—that is, the present value of future deficits arising from
population aging due to the rising costs of pensions, healthcare, and other age-
related government expenditures—will substantially exceed explicit public
debt.3

The use of the implicit public debt is called for because of the increasing diffi-
culty of using the budget deficit as a good indicator of economic policy. Although
it is widely used, the deficit exhibits the double disadvantage of being easily
manipulated and, especially, static, thereby encouraging short-term policies. Use
of the deficit would account for some receipts without integrating their counter-
parts in terms of future commitments. To incorporate long-term public commit-
ments, Auerbach et al. (1991) developed generational accounting (GA) as an
alternative to the budget deficit to assess the sustainability of fiscal policy in the
long term. This approach builds on an intertemporal treatment of the government
budget constraint: at any date, the present value of government purchases must
be covered by the current net public wealth, the present value of the net taxes that
will be paid by future generations over their entire lives. This technique makes it
possible to evaluate, for the base year, the actual value of the net payments that
current generations (i.e. those of which one or more members are still alive today)
will pay to the state until the end of their lives. Based on the state�s long-term
budget constraint, the technique then compares the net burden carried by those
born in the base year (the only generation to be followed across its entire lifespan)
with the net burden to be carried by the generations to come (those born after the
base year).

Using balanced growth assumptions, the classical methodology of GA
defines the generationally balanced growth policy as a situation in which individ-
ual taxes and transfers increase at the same rate as labor productivity. However,
existing GA exercises rely on very simple assumptions regarding the changes in
labor productivity across generations. These changes are usually related to exoge-
nous growth rates that have no explicit link to the human capital level held by
each generation. However, the role of human capital in productivity is now well

2It should be noted here that we will only focus on the fiscal impact of aging. Thus, the
non-monetary costs of aging are outside the scope of our study.

3The implicit public debt could range between 253 percent of GDP (Bovenberg and Van Ewijk,
2011) and 295 percent (IMF, 2012).

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established (Mankiw et al., 1992). Not accounting for increases in human capital
inevitably leads to an overestimation of the negative effects of aging on productiv-
ity and the public budget (Chojnicki and Docquier, 2007). The purpose of this
paper is to revisit the standard GA methodology by introducing skill heterogene-
ity and productivity gains induced by the continuous increase in French life
expectancy.

The concept of human capital, which was first highlighted by Schultz (1961)
and Becker (1964), is the productive capacity acquired through the individual
accumulation of general and specific knowledge and skills. It primarily consists of
the education and health of workers. Increased educational attainment in succes-
sive cohorts affects the growth rate of labor productivity and influences the aver-
age age profile of taxpayers and recipients of transfers over time. Indeed, ceteris
paribus, a higher educational level implies higher labor productivity, higher labor
income and, at least, higher net tax payments. This is why the age profile of net
taxes is highly dependent on educational attainment. Thus, by disaggregating the
generational account of each generation by schooling level, Chojnicki and Doc-
quier (2007) manage to highlight the positive effects of the first component of
human capital, namely, education.

Health is the second component of human capital that positively affects
labor productivity. Nelson and Phelps (1966) and Grossman (1972) were the first
to demonstrate theoretically how better health improves labor efficiency and, con-
sequently, labor productivity. Subsequent contributions by Bloom and Canning
(2005), Weil (2007), Aghion et al. (2012), Barro (2013), and O�Mahony and
Samek (2016) empirically validate these theoretical findings. In addition, regard-
ing the effects of health on productivity in the long term, Ashraf et al. (2008)
undertake quantitative simulations to assess the potential productivity gains gen-
erated by future health improvements. They estimate the effects of exogenous
health improvements on output per capita in developing countries and report that
the effects are slightly positive.

In this paper, we demonstrate that it is crucial to include workers� skill heteroge-
neity and health status when evaluating fiscal sustainability for three main reasons.
First, the age profile of taxes and transfers is highly dependent on educational attain-
ment. According to our estimates, the present value of taxes paid by an individual
over his or her entire life amounts to e91,126 for low-skill, e136,958 for medium-skill,
and e210,392 for high-skill workers. Second, the skill composition of the population
has changed dramatically over time and is likely to change further in the future. For
example, in 2008, 72 percent of the 80-year-olds in France had educational attainment
below a baccalaureate4 level, 13 percent had educational attainment between the bac-
calaureate level and a university undergraduate degree, and only 15 percent had a
higher qualification. For the cohort aged 30, these figures were 35 percent, 24 percent,
and 41 percent. Even if one assumes stability in the level of education of future young
cohorts, the average education level of the population will continue to grow due to the
gradual rise of young and educated people in the age pyramid. Third, if the increase
in life expectancy is likely to substantially affect the financing of social protection

4The baccalaureate is a French academic qualification, which is usually obtained at the end of
high school.

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systems, these gains will also affect the working-age population and, potentially, posi-
tively affect worker productivity. For example, Bloom and Canning (2005) demon-
strate that a 1-percentage-point increase in adult survival rates translates into a 2.8
percent increase in labor productivity. This is why we regard improved health as a
determinant of the evolution of the growth of labor productivity in this paper.

The remainder of this paper is organized as follows. Section 2 provides the
mathematical tools for GA with heterogeneous skill and health status. Section 3
discusses data issues and the calibration of net tax profiles by age and education.
Section 4 presents the results of the conventional GA methodology (Auerbach
et al., 1991) with a single representative agent within each generation and where
the labor productivity growth rate is considered exogenous. This conventional
methodology yields our baseline scenario. We find that aging could generate an
intertemporal public liability (IPL) of e2,260 billion (129 percent of 2010 GDP).
The French government should increase all taxes by 13.5 percent or decrease all
benefits by 14.5 percent to ensure the sustainability of French fiscal policy in the
long term. Section 5 considers methodological issues affecting GA with skill het-
erogeneity and health status. First, compared with the baseline, we show that the
total burden left for future generations is reduced by 62 percent when accounting
for the future change in the skill structure of the French population. Second, we
observe that the productivity gains generated by improving the health of the
French population could reduce IPLs by 16 percent relative to the baseline. Fur-
thermore, we find that the simultaneous improvement of the skill structure and
the health status of the French population should generate productivity gains
which can reduce IPLs by 79 percent relative to the baseline.

A sensitivity analysis is presented in Section 6. Our results are quite robust to
discounting assumptions, skill premium forecasts, the educational level of future
cohorts, and the elasticity of health with respect to productivity. Section 7 sum-
marizes and concludes.

2. The GA Model with Human Capital

2.1. Basic Features

Generational accounting (GA) was first introduced by Auerbach et al.
(1991) and is a meaningful way to evaluate the sustainability of a fiscal policy.
The GA method relies on the notion of an intertemporal budget constraint that
requires that all public expenditures must be financed by taxes. For the base year,
this can be expressed as follows:

PVLt1PVFt5PVGt2Wt:(1)

According to equation (1), the French Government Budget is balanced in the long
term when the present value of government purchases, PVGt, less public net wealth,
Wt, equals the sum of the present value of net tax payments by living generations
over the rest of their lives, PVLt, and the present value of net tax payments by future
generations over the rest of their lives, PVFt. Wt constitutes the only directly

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observable element. Traditionally, it is considered equal to the opposite of the
national debt, leaving aside the government�s wealth, particularly government assets.

The present value of government purchases, PVGt, is the discounted sum of
public expenditures:

PVGt5
X1
s5t

Gs
ð11iÞs2t

;(2)

where Gs measures public consumption that is not age specific in year s and i is
the interest rate. Total public expenditures, Gs, is assumed to evolve under the
double influence of population growth and productivity growth, which is equiva-
lent to requiring expenditures to evolve according to productivity:

Gs
ps

5ð11cÞs2t
Gt
pt
;(3)

where c is the rate of productivity growth and pt is the total population size in
year t.

2.2. Introducing Skill Heterogeneity

The present value of net tax payments by living generations can be obtained
by summing the present value of the net taxes that these generations will pay to
the government over the rest of their lives; that is, by summing the generational
accounts of living cohorts. We distinguish three educational levels (L 5 low skills,
M 5 medium skills, and H 5 high skills) and assume that each individual lives for
a maximum of D years. The present value of payments by living generations,
PVLt, can be written as follows:

PVLt5

j50

nLj;tp
L
j;t1n

M
j;t p

M
j;t 1n

H
j;tp

H
j;t

� �
;(4)

where pXj;t is the size of type X population (X 5L; M; H) of age j at time t and n
X
j;t

measures the generational account of these agents.
The generational account sums the value of net taxes to be paid by each type

of individual over the rest of his or her life:

nXj;t

5
1

pXj;t

k5j

hXk;t1k2j p
X
k;t1k2j

ð11iÞk2j
; j50; . . . ; D; X 5L; M; H;(5)

where hXk;t1k2j is the net tax payment by an agent of type X and age k at time
t1k2j. In practice, pXk;t1k2j can be projected using demographic forecasts (includ-
ing mortality and net immigration flows), data on schooling levels per age, and
estimates of the educational attainment of young living generations after the com-
pletion of their education.

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Assume that there exist q types of taxes and transfers in the economy with
q51; . . . ; Q, and let s be a tax if s > 0 and a transfer if s < 0. Then, we have

hXk;t1k2j 5
XQ

q51

sX;qk;t1k2j; X 5L; M; H;(6)

in which sX;qk;t1k2j describes the tax (resp. transfer) profile for a tax (resp. transfer)
of type q of an agent who belongs to cohort k at time t1k2j and has skill type X.

2.3. Health Status and Productivity

Most existing GA models assumes time invariance of the age distribution of
taxes and transfers except for the rate of technical progress:

sX;qk;t1k2j115ð11cÞ3s
X;q
k;t1k2j; X 5L; M; H:(7)

The mechanical application of a uniform growth rate means that economic
growth does not change the age profiles of taxes and transfers, and that the fruits
of growth are shared equally among the different cohorts. However, an increase in
the average endowment of human capital per worker is potentially able to gener-
ate substantial fiscal gains. According to human capital theory, explicit and par-
ticularly important determinants of individual productivity are education and
health.

By introducing skill heterogeneity in GA (Section 2.2), we are able to
account for the effects of education on the evolution of labor productivity and,
therefore, on the evolution of the present and future generational accounts. To be
able to consider the second explicit determinant of individual productivity, we
assume a simple theoretical framework in line with Aghion et al. (2012). In
period t1k2j, workers with skill X produce Y Xt1k2j due to the production tech-
nology FðAt1k2j; H Xt1k2jÞ as follows:

F At1k2j; H

X
t1k2j

� �
5At1k2j H

X
t1k2j

� �q
:(8)

The production of workers with skill X then depends on the total factor produc-
tivity, At1k2j , and the health of workers, H Xt1k2j . Intuitively, a higher level of
health makes labor more productive and therefore increases the amount of effi-
ciency labor in the economy. Assuming that FðAt1k2j; H Xt1k2jÞ is a Cobb–Douglas
production function, q describes the rate of return of H Xt1k2j in equation (8). Let
cXt1k2j; g

A
t1k2j and g

h;X
t1k2j denote the growth rates of Y

X
t1k2j; At1k2j and H

X
t1k2j ,

respectively. Using a Solow growth decomposition (Solow, 1957), we obtain

cXt1k2j 5g
A
t1k2j 1 q3g

h;X
t1k2j

� �
:(9)

According to equation (9), the rate of productivity growth for workers with skill
X at t1k2j is simply the sum of the rate of total factor productivity growth,

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(gAt1k2j ), and the health improvement of the worker of type X, ðg
h;X
t1k2jÞ, weighted

by q. Inserting equation (9) into equation (7), we introduce an explicit link
between individual productivity and worker health.

2.4. An Assessment of the Sustainability of Fiscal Policy

Adding equation (9) into our GA framework, we can then assess the finan-
cial sustainability of public policies. Given the present value of payments by living
generations, PVLt, the present value of government purchases, PVGt, and the net
public wealth, Wt, we can easily determine the present value of net contributions
of future generations, PVFt, as the residual of the intertemporal budget constraint
(equation (1)). However, if this indicator can be read as a burden/surplus
bequeathed to future generations, it appears difficult to imagine carrying forward
the adjustment only for future generations (i.e. those born after the reference
year). One way to proceed is to compute the hypothetical generational accounts
of future cohorts under current fiscal policy. Using the same reasoning as in equa-
tions (4) and (5) above, we write:

PVF�t 5
X1

s5t11

XMin s2t21;D½ �

j50

hLj;sp
L
j;s1h

M
j;s p

M
j;s 1h

H
j;sp

H
j;s

ð11iÞs2t
;(10)

where PVF�t is the present value of net payments by future generations if current
fiscal policy is unchanged. This hypothetical value can then be compared with the
residual value, PVFt, computed from equation (1):

� if PVF�t 5PVFt, the policy is sustainable and there is no need to make a
fiscal adjustment;

� if PVF�t > PVFt, the government budget is in surplus and benefits could
be increased without increasing taxes; and

� if PVF�t < PVFt, the current policy is not sustainable: this implies that current policy must be adjusted to restore sustainability.

As in Raffelh€uschen (1999), the sustainability of fiscal policy can then be esti-
mated using the concept of intertemporal public liabilities (the “Sustainability
Gap” in Auerbach et al., 1991) to obtain a measure of generational imbalances
arising from following current fiscal policy for an infinite horizon. This indicator,
IPL, is the residual of the intertemporal budget constraint, if all generations, pres-
ent and future, receive the same tax treatment:

IPL5PVGt2Wt2PVLt2PVF
�
t :(11)

This indicator assesses the extent of reforms designed to restore balance (IPL50).
If the present policy is unsustainable, the obvious strategy is to adjust taxes and/
or transfers at some future date. In this paper, we use an adjustment method that
covers all members of all generations. If a gap has to be financed (to cover a defi-
cit), we compute the proportional adjustment in all taxes (or all transfers)
required to balance the budget.

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Let us decompose the net taxes on all generations into two basic components,
taxes and benefits: hXj;s5h

X
T;j;s2h

X
B;j;s. A time-invariant adjustment factor can be

applied to each of these components (gT for taxes and gB for benefits) to restore
sustainability. We then apply these proportional changes to both living genera-
tions (over the rest of their lifetimes) and future generations to balance the budget
constraint. Our adjustment rule is summarized by the following set of equations:

PVLadjt 5
XD
j50

XD
k5j

X
X 5L;M;H

hXT;k;t1k2jð11gTÞ2h
X
B;k;t1k2jð12gBÞ

h i
pXk;t1k2j

ð11iÞk2j
;

PVF adjt 5
X1

s5t11
XMin s2t21;D½ �
j50
X
X 5L;M;H

hXT;j;sð11gTÞ2h
X
B;j;sð12gBÞ

h i
pXj;s

ð11iÞs2t
;

PVGt5PVL
adj
t 1PVF

adj
t 1Wt:

There is a continuum of pairs (gT; gB) restoring the balance. Two specific pairs
are usually considered, one with gT 50 if the balance is achieved through cuts in
transfers and one with gB50 if the balance is achieved through tax increases.

3. Data Issues and Assumptions

The collection of data is the preliminary stage of any longitudinal exercise.

3.1. Population Forecast

Our baseline scenario uses the intermediate population projection of INSEE
provided by Blanpain and Chardon (2010). We retain the intermediate population
projection for the period 2007–60:

� the life expectancy at birth rises from 77.2 and 84.2 years for men and
women in 2007 to 86.0 and 91.1 years, respectively, in 2060;
� the fertility rate remains at 1.98 children per woman between 2007 and

2015 and decreases to 1.95 children per woman after this period;
� the net number of persons immigrating to France is equal to 100,000 per

year until 2060, which corresponds to the average net annual migration
flows observed since the mid-2000s.

This scenario includes a sharp increase in the old-age dependency ratio (i.e. the
number of people aged 651 as a percentage of the number of people aged 15–64).
The ratio was 25.3 percent in 2007 and is expected to reach approximately 47 per-
cent in 2060 (see Table 1).

GA requires population projections to a very distant horizon. This is neces-
sary to evaluate the net payment from living generations until the end of their
lives, the value of public expenditures, and the generational accounts of future
generations indefinitely. The INSEE forecasts are thus extended until 2110,
assuming that the mortality, fertility, and migration rates remain at their 2060

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levels. Nonetheless, GA attributes little weight to the net payment of generations
to a fairly distant horizon due to discounting effects.

3.2. Educational Attainment

Three educational levels are distinguished: low-skill workers are those who
have an education level below a baccalaureate (LS); medium-skill workers are
formed by those who have completed at most two years of education above the
baccalaureate level (MS), and high-skill workers are those who have completed
more than two years of study above the baccalaureate level (HS). We use data on
the skill composition of living French cohorts in 2008 taken from the French pop-
ulation census (INSEE, 2008) for the population aged 30 and over. We assume
that the skill structure of future cohorts (i.e. those aged 30 after 2008) to be con-
stant. Thus, the resulting projection can be considered relatively pessimistic.5

The evolution of the skill structure of the French population over time is
described by Figure 1. In 2008, 42.8 percent of the population aged 30 had a high
skill level, compared with 22.1 percent who had a medium skill level, and 35.1
percent who did not even have a baccalaureate. Among those aged 60, these
shares were 19.7 percent, 13.5 percent, and 66.8 percent, respectively. The main
changes in educational attainment (as measure by the share of each educational
group in the total French population) occurred before 2008. However, even
assuming that there is no more progress in education attainment for younger
cohorts (those aged 30 after 2008), the skill structure of the population is none-
theless affected even in the future due to the rise of the younger cohorts, which
have a high educational level, in the age structure of the population.

3.3. Tax and Transfer Profiles by Age and Educational Attainment

Estimating the age profiles of taxes and benefits for a reference year is the basis
for any longitudinal calculation. We consider the six main branches of social security
expenditures, corresponding to the different risks defined by social security account-
ing: (1) retirement, (2) health, (3) family, (4) unemployment, (5) housing, and (6) pov-
erty/exclusion. To these social security expenditures, we add education expenses, which
also correspond to a form of transfer to a well-defined age group. On the income side,
we retain six categories of taxes: labor income taxes, capital income taxes, consumption
taxes, local taxes, GSC (generalized social contribution)/NDRC (national debt

TABLE 1

The Evolution of the French Population between 2007 and 2060

2007 2020 2060

Total population (in thousands) 61,795 65,962 73,558
Population aged 15–64 years (in thousands) 40,266 40,704 41,831
Population aged 65 years and over (in thousands) 10,208 13,453 19,643
Old-age dependency ratio (651/15–64) 0.2535 0.3305 0.4696

Source: Blanpain and Chardon (2010).

5We estimate simulations based on a more optimistic assumption in Section 5.2.

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repayment contribution), and social contributions. For the majority of profiles, we
make use of the 2010–11 French household survey (INSEE, 2011a)6 provided by the
French institute for public statistics. Our sample consists of 30,416 individuals. To
obtain sufficiently rich blocks of comparable sizes, we divide our database into five-
year age slices. For each type of tax and transfer, the FHS2011 database thus allows us
to determine the distribution by age and educational attainment of the various mone-
tary flows considered. Some resources and expenditures are clearly individualized in
the study, such as retirement, unemployment, and minimum income, but many others
are only relevant at the household level and thus require certain assumptions to enable
their individualization. Consequently, we attribute these monetary flows to the differ-
ent members of the household proportionally to the revenues of each member of the
household.7 The majority of the taxes and transfers are reported directly in the
FHS2011 database. The social contributions and the GSC–NDRC are calculated by
reconstructing the gross revenues from activity and then by applying employee and
employer social contribution rates as a function of the income level and the type of
employment. The calculation of consumption taxes follows from the application of the
different rates to the consumption expenses appearing in the FHS2011 database. With
the exception of the GSC–NDRC, the taxes paid on capital income do not appear in
the study, and hence we adopt the assumption that the profile of capital taxes is the
same as that of capital incomes.

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1
9

4
5

1
9

5
5

1
9

6
5

1
9

7
5

1
9

8
5

1
9

9
5

2
0

0
5

2
0

1
5

2
0

2
5

2
0

3
5

2
0
4
5
2
0
5
5
2
0
6
5
2
0
7
5
2
0
8
5
2
0
9
5

LS MS HS

Figure 1. Population Shares by Educational Attainment (Percentage of the Total Population)

Source: INSEE (2008) and authors� calculations. [Colour figure can be viewed at wileyonlineli-
brary.com]

6FHS2011 thereafter.
7Our results are not sensitive to the way in which the resources are individualized within the

household. On the other hand, this question becomes more important when generational accounts are
disaggregated according to gender (for a discussion see, Raffelh€uschen, 1999).

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To obtain the distribution of healthcare expenditures by age and educational
attainment, we use the 2010 healthcare study from the Institute for Research and
Information in Health Economics (IRDES) provided by Dourgnon et al. (2012),
which considers a sample of 15,973 individuals. As with the FHS2011 study, we
consider five-year age slices and group total health expenditures to evaluate the
total cost of healthcare.

For educational expenses, we evaluate the average cost by age by applying
the enrollment rates by age, derived from the population census of 2008, to the
average expenditure per graduate derived from the statistics of the national Minis-
try of Education.

Figures 2–4 give the decomposition by schooling level of total taxes, total
benefits, and net taxes for each age group. The black bold lines represent the total
taxes, benefits, and net taxes paid/received by a representative individual in each
age cohort in 2010. The primary impact of educational attainment is on taxes: at
age 50, the taxes paid by a high-skill individual are 2.6 times as great as those
paid by a low-skill individual. Smaller differences are observed in benefit profiles.
In terms of net taxes, low-skill agents are obviously the main beneficiaries of fiscal
policy, while medium- and high-skill agents are net contributors. At age 50, the
ratio of net taxes between a high- and a medium-skill individual is approximately
1.8 to 1. Hence, it is indisputable that changes in the educational structure will
strongly affect the sustainability of current fiscal policy.

Each of the aggregates reconstituted from the profiles shown in Figures 2
and 3 differs from those given by the national accounts (Table 2). We therefore
rescale them uniformly over these aggregates with the help of the national
accounting report (INSEE, 2011b).

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

0 5

1
0

1
5
2
0
2
5

3
0

3
5

4
0

4
5

5
0

5
5

6
0

6
5

7
0

7
5

8
0

8
5

9
0

9
5
1
0
0

HS MS LS Average

Figure 2. The Tax Profile by Age and Educational Attainment (in Euros)

Source: INSEE (2011a) and authors� calculations.

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Finally, the assumptions made for the construction of our reference scenario
include a discount rate of 3 percent. In the baseline scenario based on the stand-
ard GA methodology, the growth rate of individual taxes and transfers8 is set at
1.3 percent, corresponding to the medium variant of the official projections gen-
erated by the pension advisory council (Conseil d�Orientation des Retraites,
2012). Traditionally, only the financial wealth of the public administration, which
totaled e1,109 billion in 2010, is retained.9

3.4. Health Improvement and Labor Productivity Growth

Following equation (9), the productivity growth rate is given by the sum of total
productivity growth and the improvement in health weighted by its rate of return.

Measuring the Health Improvement

According to equation (9), we must estimate the annual health improvement
experienced by the French population to evaluate the labor productivity for each
skill and for each year. However, there is no consensus on an appropriate indica-
tor that provides a good measure of health improvement at the macroeconomic
level. On the one hand, authors such as Sachs and Warner (1997), Bloom and
Williamson (1998), Bloom et al. (2004), Acemoglu and Johnson (2007), Cervellati
and Sunde (2011), Aghion et al. (2012), and Barro (2013) assess the health
improvement of a given population through its increase in life expectancy at birth.

0
10,000
20,000
30,000
40,000

50,000

60,000

0 5
1
0
1
5
2
0
2
5
3
0
3
5
4
0
4
5
5
0
5
5
6
0
6
5
7
0
7
5
8
0
8
5
9
0
9
5
1
0
0
HS MS LS Average

Figure 3. The Transfer Profile by Age and Educational Attainment (in Euros)

Source: INSEE (2011a) and authors� calculations.

8With the exception of pensions, which according to the law, keep pace with inflation.
9In 2010, the gross debt of the social security institutions accounted for 11 percent of total govern-

ment debt.

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The increase in life expectancy at birth is retained because it is assumed implicitly
that improved health allows for an extended life. On the other hand, authors such
as Bhargava et al. (2001), Bloom and Canning (2005), Weil (2007), and Ashraf
et al. (2008) measure the health improvement of a given population by the evolu-
tion of its average survival rate (ASR). The insight is that the ASR is a positive
function of health improvement.

In this paper, we employ the survival rate rather than life expectancy as a
health indicator. An increase in life expectancy primarily captures the increase in
lifespan induced by the aging process that appears after one�s working-age life.

-40,000

-30,000

-20,000

-10,000

0
10,000
20,000
30,000
40,000
0 5
1
0
1
5
2
0
2
5
3
0
3
5
4
0
4
5
5
0
5
5
6
0
6
5
7
0
7
5
8
0
8
5
9
0
9
5
1
0
0
HS MS LS Average

Figure 4. The Net Tax Profile by Age and Educational Attainment (in Euros)

Source: INSEE (2011a) and authors� calculations.

TABLE 2

Public Taxes and Spending in 2010 (in Millions of Euros)

Taxes
Millions of

Euros % of GDP Transfers
Millions of

Euros % of GDP

Labor income taxes 43,026 2.43 Pension 253,579 14.31
Capital income taxes 34,674 1.96 Housing 14,960 0.84
Excise taxes 157,535 8.89 RMI 13,206 0.74
Council taxes 42,603 2.40 Unemployment 35,024 1.98
GSC–NDRC 80,866 4.56 Family 50,327 2.84
Social contributions 330,376 18.64 Health 169,460 9.56
Other taxes 188,009 10.61 Education 124,085 7.00

Other spendings 302,004 17.04
Interest 40,137 2.26

Total 877,090 49.48 Total 1,002,782 56.57
Deficit 125,692 27.09

Sources: French national account, INSEE; French social security account, Drees.
Notes: GSC, generalized social contribution; NDRC, national debt repayment contribution;

RMI, minimum income.

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Thus, if we assume that the life expectancy is a good health proxy, the increase in
life expectancy primarily reflects the improvement in the health of the retired pop-
ulation. However, the aim of this paper is to highlight the productivity gains in
the workforce from an improvement in health induced by the aging process.

Blanpain and Chardon (2010) assess data on the evolution of the survival
rate for each generation in the French population for each age and for each year
between 2007 and 2060. We disaggregate the survival rates by skill using the esti-
mates of Mejer (2004). Beyond 2060, we assume that the survival rate for each
skill and for each age remain at their 2060 levels. It is important to note that the
data on survival rates are strictly the same as those used to undertake the popula-
tion forecast. By using the INSEE data, we can approximate the health improve-
ment among the population aged 15–64 years for each cohort and for each skill.
The annual growth rate of the average survival rate (ASR) of the working-age
population gives the numerical value of the variable gh;Xt1k2j in equation (9). The
evolution of gh;Xt1k2j over time is then shown in Figure 5.

First, the growth rate of the ASR for each year and for each skill is relatively low.
Between 2010 and 2060, the growth rate of the ASR is below 0.5 percent per year. In
other words, the health improvement of the French workforce induced by the aging
process is not very important over time. However, we will subsequently show that this
small health improvement can nevertheless produce significant productivity gains (see
Section 4.3). Second, we note that the growth rate of the ASR decreases each year.
Thus by assuming that the ASR is a good health proxy, the INSEE forecasts show
that the health improvement induced by the French population aging declines over
time. Third, on average, the health improvement of the French workforce as a whole
derives primarily from LS and MS health improvement.

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

2
0

1
2

2
0

1
7

2
0

2
2

2
0

2
7

2
0

3
2

2
0

3
7

2
0

4
2

2
0

4
7

2
0

5
2

2
0

5
7

Average LS MS HS

Figure 5. The Growth Rate of the Average Survival Rate for Each Year

Source: INSEE (2008) and authors� calculations.

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Estimating the Evolution of Productivity Growth

The impact of health on labor productivity is measured by the value of q.
The estimation of this parameter is far beyond the scope of this study, and we pre-
fer to use the results of Bloom and Canning (2005). Indeed, by assuming that the
ASR reflects the health of the workforce, they estimate q using a panel of 21
OECD countries observed every five years from 1960 through 1995. They report
that an increase in the ASR by 0.01 could increase productivity by 2.8 percent.
The retained elasticity of Bloom and Canning (2005) corresponds to an interme-
diate value between the findings of Weil (2007) and Barro (2013). By using the
same proxy for health as Bloom and Canning (2005), Weil (2007) finds that
q50:0653. By contrast, Barro (2013) uses life expectancy at birth to approximate
the health of the population and finds that q50:014. In Section 5, we will test the
sensitivity of our results to these different values of q.

Finally, the value of the total factor productivity (TFP) growth rate is
assumed to be constant over time and independent of skill level. In other words,
gAt1k2j 5g

A. To provide a numerical value for gA, we refer to Cabannes et al.
(2013). These authors estimate the evolution of TFP in France between 1979 and
2010 and obtain that gA51:3 percent during this period. We retain this value and
assume that the average past trend in the evolution of TFP in France persists in
the long term.

Figure 6 depicts the evolution of labor productivity for our GA exercise.
Health enhancement generates significant productivity gains that vanish over
time. On average, productivity growth decreases to 1.57 percent in 2060 and sta-
bilizes at 1.3 percent after 2060. Thus, beyond 2060, the productivity growth of

1.30%

1.40%

1.50%

1.60%

1.70%

1.80%

1.90%

2.00%

2.10%

2
0

1
1

2
0

1
6

2
0

2
1

2
0

2
6

2
0

3
1

2
0

3
6

2
0

4
1

2
0

4
6

2
0
5
1
2
0

5
6

Average LS MS HS

Figure 6. Labor Productivity Growth with Productivity Gains from Health

Source: INSEE (2008) and authors� calculations.

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the French workforce reaches its long-term value and the economy evolves along
a balanced growth path. After 2060, productivity growth is the same for each skill
and for each cohort, and remains at 1.3 percent.

4. A Better Assessment of Human Capital Effects on Labor Productivity

For each scenario, we proceed as follows to obtain generational accounts for
living and future generations. The use of assumptions regarding the time path of
government purchases and individual amounts of taxes and transfers for living
generations enables us to derive the present value of government purchases and
payments made by living generations. It is also possible to compute the hypotheti-
cal generational accounts of future cohorts under the same fiscal policy as that
used for living cohorts. Combining these amounts in the government�s intertem-
poral budget constraint, we compute the fiscal adjustment required to balance
the budget. This adjustment affects taxes and/or transfers from 2010 onwards.
Hence, it affects the situation of both living and future generations. Depending
on the type of adjustment we opt for, we compute the “adjusted” generational
accounts of living and future generations that are sustainable with respect to the
budget.

We present four different scenarios. In the first (Section 4.1), we use the tradi-
tional GA methodology. In the second (scenario EA in Section 4.2), we disaggre-
gate generational accounts only by schooling level. The comparison of the first
and second scenarios allows us to measure the fiscal gains induced by changes in
the skill structure of the French population. In the third scenario (scenario HI in
Section 4.3), we apply the revised GA model described in Section (2) and isolate
the fiscal gains generated by improvements in health. Finally, our fourth scenario
(scenario EA 1 HI in Section 4.4) combines the future change in the skill structure
and the productivity gains from improved health.

4.1. Baseline Results

Our baseline results are obtained using the conventional GA methodology
(Auerbach et al., 1991). The generational accounts of living generations in 2010
are summarized in the first part of Table 3. These accounts give the net payment
(total taxes paid minus total transfers received) of each generation alive in 2010
until the end of their lives. We recover fairly standard results: these accounts
increase in the first year of life and peak at approximately age 25. Then, they
decrease due to the reduction in the time remaining in an individual�s active work-
ing life and the lesser discounting of expenses tied to old age (retirement, health-
care, and disability). They become negative at approximately age 50, reach their
minimum at approximately age 70, and then increase again due to the decrease in
the time left to live.

To evaluate the sustainability of fiscal policy in France over the long term,
we calculate the total of the intertemporal financial obligations, the IPL, which
corresponds to the difference between the nominal value of the national debt for
the year 2010 and the actualized value aggregated from the net payments of living
and future generations (Table 4). This is determined by adding to the net debt

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observed in 2010 the sum of the generational accounts of present and future gen-
erations, multiplied by the respective sizes of the cohorts according to our popula-
tion projections, as well as public consumption. In a case in which fiscal policy is
not changed (such as the rights to retirement benefits that will have to be

TABLE 3

Generational Accounts of Living Generations

Present
Value

of Taxes

Present
Value

of Benefits
Generational

Accounts

Baseline scenario
0 136,160 2169,311 233,152
20 316,066 2157,581 158,485
30 368,435 2174,487 193,948
40 326,851 2197,295 129,556
50 251,563 2227,915 23,648
60 146,206 2281,375 2135,169
70 97,261 2249,417 2152,156
100 6,261 226,473 220,212
GA with educational attainment (weighted average)
0 150,518 2169,542 219,024
20 350,205 2164,598 185,607
30 429,982 2181,813 248,169
40 373,254 2208,746 164,508
50 269,172 2238,824 30,348
60 159,301 2295,463 2136,162
70 100,956 2260,498 2159,543
100 6,238 226,916 220,678
Low skill
0 91,126 217,1041 279,915
20 210,137 2165,436 44,700
30 234,784 2188,893 45,892
40 213,695 2196,872 16,823
50 166,032 2214,892 248,860
60 97,720 2255,669 2157,949
70 70,947 2225,139 2154,193
100 4,544 223,579 219,034
Medium skill
0 136,958 2163,037 226,079
20 318,257 2151,014 167,242
30 379,073 2175,858 203,216
40 362,409 2209,835 152,575
50 294,725 2265,226 29,499
60 178,983 2348,901 2169,918
70 128,208 2324,091 2195,883
100 8,015 233,861 225,846
High skill
0 210,392 2172,151 38,241
20 491,391 2172,043 319,348
30 617,990 2179,087 438,903
40 601,338 2224,750 376,588
50 517,772 2282,909 234,863
60 347,381 2389,350 241,969
70 246,507 2403,625 2157,118
100 17,717 246,476 228,759

Source: Authors� calculations.

Note: Present value in 2010 euros.

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honored), this net intertemporal debt, based simultaneously on the actual debt
and future revenues and obligations of public administrations, would be on the
order of 130 percent of 2010 GDP. The current fiscal policy is thus not sustainable
in the long term because the net current and future payments are negative and
will further increase the current level of the national debt.

For a newborn in 2010, the prospective net payments over his or her life cycle
are negative (on average, a newborn in 2010 will thus receive more over his or her
life cycle than he contributes). Because the discounted value of the net payments
of present and future generations is unable to cover the total public consumption
and the current national debt, adjustments to the fiscal policy are clearly neces-
sary. For that purpose, we use an adjustment method that concerns all members
of all generations. In a first step, we compute the present value of payments by
future generations under the current fiscal policy. Comparing this amount to the
residual burden given by the budget constraint, we obtain the gap to be financed
by all living and future generations. In a second step, we compute the propor-
tional adjustment in all taxes (or in all transfers) required to balance the budget.
Finally, given the “adjusted” fiscal policy, we derive the new generational
accounts. Our adjustment calculations rely on the counterfactual assumption that
all changes begin in 2010. Thus, a proportional increase in the tax rate by 13.5
percent or a decrease in all transfers by 14.6 percent for the generations alive in
2010, as well as for future generations, would make the budget viable over the
long term. Such a policy would significantly increase the net contribution of a
newborn in 2010 (to approximately e18,322 in the case of a tax adjustment and
e21,357 in the case of a transfer adjustment).

4.2. Generational Accounts and Educational Attainment

The conventional GA methodology overstates the tax burden generated by
aging because it does not account for the productivity gains from the change in
the skill structure of the French population (Chojnicki and Docquier, 2007).
Thus, we now compute generational accounts by explicitly accounting for changes
in the skill structure of the French population as reported in Figure 1. The

TABLE 4

Intertemporal Budget Constraint Equilibrium: Baseline Scenario

Newborns� generational account 233,152
Implicit debt (in % of 2010 GDP) 66
Explicit net debt in 2010 (in % of 2010 GDP) 63.6
IPL (in % of 2010 GDP) 129.6

Tax adjustment (%) 13.46
Adjusted newborns� generational account 214,830

Transfer adjustment (%) 214.55
Adjusted newborns� generational account 28,512

Tax and transfer adjustment (%) 6.99
Adjusted newborns� generational account 211,795

Source: Authors� calculations.

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generational accounts by skill level of living generations in 2010 are summarized
in the second part of Table 3.

It is evident that generational accounts vary with respect to skill level. Thus, over
their entire lifetime, unskilled and medium-skill agents have negative generational
accounts and are expected to receive more in transfers than they pay in taxes to the gov-
ernment. Specifically, the average generational account of a low-skill newborn is three
times less than that of a medium-skill newborn. Conversely, the generational account
of high-skill individuals is positive. It is worth noting that generational accounts of low-
skill agents are negative under 16 years of age and after 45, while this is true before age
7 and after 52 for the medium-skill and only after 58 for the high-skill agents.

Given the changes in the skill composition of these living cohorts, our aver-
age generational accounts per cohort are not identical to those given in our base-
line scenario using the standard GA methodology (Table 3 and Figure 7). The
differences are small for old cohorts but quite large for younger cohorts. If we
extrapolate the future taxes and transfers of newborns on the basis of the present
profile alone, the classical method underestimates newborns� average account by
74 percent (–e33,152 compared to –e19,024).

An examination of the sustainability of fiscal policy requires computation of
the present value of payments by future generations under the current policy and
the employment of some assumptions concerning forecasts of the educational
structure of future generations. Here, we adopt the most consensual assumption
of maintaining the level of education of future generations on the basis of those
who left the school system in 2010. Despite the remarkable rise in educational
attainment as a result of the spread of education among younger generations,

–

200,000

–

150,000

–

100,000

-50,000

0
50,000
100,000
150,000
200,000

250,000

300,000

0
1
0
2
0
3
0
4
0
5
0
6
0
7
0
8
0
9
0
1
0
0

Baseline Educa�onal a�ainment

Figure 7. The Average Generational Account per Living Cohort: Baseline versus Educational
Attainment (Present Value in 2010 Euros)

Source: Author�s calculations.

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maintaining the current policy for future generations generates a present value of
payments lower than the total burden left by living generations. In other words,
the current fiscal policy is still unsustainable: the tax burden bequeathed to future
generations decreases by 62 percent relative to the baseline (Table 5).

Restoring the balance through tax adjustment induces an increase in all taxes by
4.5 percent (Table 6). The newborns� generational accounts then become –e75,792,
–e19,881, and e47,762 for low-, medium-, and high-skill agents, respectively. It should
be noted here that tax adjustment falls much more heavily on the skilled (MS and
HS), while the unskilled would be relatively spared. Restoring the balance through
transfer adjustment induces a cut in all transfers by 5.2 percent. The newborns� gen-
erational accounts then become –e70,880, –e17,466, and e47,335. These results con-
trast with the traditional method developed in the baseline scenario, which predicts
that generational balance requires increasing all taxes by 13.5 percent or reducing all
benefits by 14.6 percent. This demonstrates the huge potential impact of the rise in
educational attainment in evaluating the long-run sustainability of fiscal policies.

4.3. Generational Accounts and Health

In addition to education, health is another important determinant of the
evolution of individual productivity. As we explained in Section 3.4, we

TABLE 5

Intertemporal Public Liabilities and Fiscal Adjustments in the Different Scenarios

IPL
(% of 2010 GDP)

Tax
Change (%)

Transfer
Change (%)

Tax and Transfer
Change (%)

Baseline scenario 129.64 13.46 214.55 6.99
Educational attainment (EA) 48.53 4.53 25.28 2.44
Health improvement (HI) 108.22 10.35 211.52 5.45
EA&HI 26.76 2.34 22.79 1.27

Source: Authors� calculations.

TABLE 6

Generational Imbalance: Educational Attainment Scenario

Present Value of Taxes Present Value of Benefits Generational Accounts

Newborns� generational account

LS 91,126 2171,041 279,915
MS 136,958 2163,037 226,079
HS 210,392 2172,151 38,241
Restoring the balance through tax adjustment (14.53%)
LS 95,249 2171,041 275,792
MS 143,156 2163,037 219,881
HS 219,913 2172,151 47,762
Restoring the balance through transfer adjustment (–5.28%)
LS 91,126 2162,006 270,880
MS 136,958 2154,425 217,466
HS 210,392 2163,057 47,335

Source: Authors� calculations.
Note: Present value in 2010 euros.

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assume that the average survival rate is a good proxy for the health of the
working-age population. Here, we will simply introduce equation (9) in our
GA model to include the effect of health improvement on individual produc-
tivity. In a first step, we do not consider skill heterogeneity to isolate only the
effect on productivity due to improvements in the health status of the
working-age population.

Does the introduction of the evolution of productivity due to the health
status of the population fundamentally change the evaluation of our genera-
tional accounts? Table 7 gives the generational accounts of living generations
and the required adjustments. Treating health status as a determinant of indi-
vidual productivity increases the newborns� account: the difference from the
baseline scenario amounts to e8,341. This number measures the average
improvement in health for a newborn in 2010 (in present value at birth). Natu-
rally, the changes relative to the baseline are more important for young indi-
viduals. Extrapolating the future taxes and transfers of newborns on the basis
of an exogenous productivity growth rate, the classical GA method thus
underestimates newborns� average account by approximately 33.5 percent. The
effect on generational accounts is less pronounced here than when the gains
from education are included, but is not negligible. In this scenario, the current
fiscal policy is naturally still unsustainable and generates a long-run budgetary
deficit (IPL) that is reduced by 16.5 percent relative to the baseline (Table 5).
However, the necessary adjustments of taxes or transfers are not similar to
those required in the baseline scenario. In this scenario, the government
should increase all taxes by only 10.4 percent (113.5 percent in the baseline
scenario) or should cut all benefits by only 11.52 percent (–14.6 percent in the
baseline scenario).

4.4. Generational Accounts and Human Capital

Finally, let us combine the effects of an increase in the population�s educa-
tion level with the improvement of its health on the evolution of individual pro-
ductivity. This scenario goes beyond a simple linear combination of the two
previous scenarios. Indeed, as changes in survival probabilities differ with respect
to skill level, here the changes in individual productivity will vary according to

TABLE 7

Generational Imbalance: Health Improvement Scenario

Present Value of Taxes
Present Value

of Benefits
Generational
Accounts

Newborns
157,415 – 182,226 – 24,811

Restoring the balance through tax adjustment (110.35%)
173,712 2182,226 28,514

Restoring the balance through transfer adjustment (–11.52%)
157,415 2161,234 23,819

Source: Authors� calculations.
Note: Present value in 2010 euros.

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our three skill levels.10 As reported in Figure 6, the LS workers have higher labor
productivity growth than the MS and HS workers between 2010 and 2060. For
example, the productivity growth of LS, MS, and HS workers amounts to 2.03
percent, 1.84 percent, and 1.74 percent, respectively, in 2011.

The results in terms of the long-term imbalance (Table 5), measured by the
intertemporal public liability (IPL), are less troubling than those resulting from
the simple consideration of the impact of educational attainment on individual
productivity presented in Section 4.2: the IPL is reduced by 21.7 points of 2010
GDP (26.8 percent vs. 48.5 percent) against 19.58 points without considering
skill heterogeneity (108.2 percent vs. 129.6 percent). Thus, the required fiscal
policy adjustments induce an increase in all taxes of approximately 2.3 percent
(vs. 4.5 percent in the educational attainment scenario) and a cut in all transfers
of approximately 2.8 percent (vs. 5.3 percent in the educational attainment
scenario).

5. Sensitivity Analysis

In this section, we analyse the sensitivity of our calculations to alternative
assumptions regarding the difference in net taxes across education groups (Sec-
tion 5.1), about the educational attainment of future cohorts (Section 5.2), and
about the discount rate and the rate of return of health on labor productivity
(Section 5.3).

TABLE 8

Generational Imbalance: Educational Attainment and Health Improve-
ment Scenarios

Newborns� Generational Account

EA EA&HI

LS 279,915 279,236
MS 226,079 218,616
HS 38,241 51,326

Restoring the balance Restoring the balance
through tax through tax

adjustment (14.53%) adjustment (12.34%)
LS 275,792 276,755
MS 219,881 215,021
HS 47,762 56,739

Restoring the balance Restoring the balance
through transfer through transfer

adjustment (–5.28%) adjustment (–2.79%)
LS 270,880 274,066
MS 217,466 213,807
HS 47,335 56,353

Source: Authors� calculations.
Note: Present value in 2010 euros.

10Our strategy of combining the effects of education and health status as components of human
capital is very simple. In this respect, there is a fairly wide literature on the relationship between health
and education. See, for example, Cutler and Lleras-Muney (2010) or Clark and Royer (2013).

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5.1. Skill Premium Forecasts

Our results show that if the structure of taxes and transfers is held constant,
changes in the skill composition of the population will yield increases in future
tax payments and decreases in future transfer payments. Are these fiscal gains
robust to the increasing supply of skills? Are these results robust to possible
changes in the structure of returns to skill? Most advances in educational attain-
ment have occurred in recent decades. Over the past 20 years, skill-biased techni-
cal changes have increased the skill premium despite a remarkable rise in
educational attainment. Indeed, according to the French labor force survey, the
average wage gap between a high-skill and low-skill worker has increased from
1.25 in the early 1990s to 1.8 just 20 years later. Will this persist in future decades?

As Acemoglu (2002) argues, the rise in educational attainment could, alternatively,
drive the skill premium upwards, because an increase in the supply of skills can incentiv-
ize firms to invest in skill-biased technologies. Nevertheless, it is also possible that edu-
cational increases will generate a slight compression in the wage distribution. A
complete model of the labor market would be required to assess the impact of the sup-
ply of skills on relative wages. Such a task goes beyond our purely accounting frame-
work. Here, we consider a simple scenario in which (i) the tax profile of medium-skill
workers is held constant, (ii) the low-skill relative to medium-skill gap in the tax profile
increases by 5 percent, and (iii) the high-skill relative to medium-skill gap in the tax pro-
file decreases by 5 percent. Hence, the high-skill to low-skill gap decreases by 10 percent.
The assumption of a larger change would be rather inconsistent with our assumption
concerning the schooling decisions of future cohorts. These changes are introduced pro-
gressively and linearly. The total reduction in wage inequality is obtained in 2060.

The results are presented in Table 9. The low-skill newborns� generational
account and the high-skill newborns� account do not change with variation in the

TABLE 9

The Sensitivity of GA and Budgetary Adjustments to Skill Structure

EA&HI
Lower Skill

premium & HI
Better

Skill & HI

Newborns� generational account
LS 279,236 279,236 278,749
MS 218,616 218,616 218,145
HS 51,326 51,326 51,820
Restoring the balance (%)
Through tax change 2.34 3.06 21.52
Through transfer change 22.79 23.63 1.89
Newborns� generational account after policy adjustment
Taxes
LS 276,755 271,905 280,365
MS 215,021 213,905 220,486
HS 56,739 48,937 48,295
Transfer
LS 274,066 268,552 282,242
MS 213,807 212,360 221,392
HS 56,353 48,666 48,426

Source: Authors� calculations.

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skill premium. In terms of generational imbalance, the deficit increases. Taxes
need to be increased by 3.1 percent (instead of 2.34 percent in the scenario com-
bining both an increase in the population�s education level and the improvement
of health on the evolution of individual productivity) or transfers have to be
reduced by 3.6 percent (instead of 2.79 percent). These policy changes are still
much smaller than those required when using the conventional GA methodology
(see Section 3). We could thus conclude that our findings are quite robust to
assumptions concerning skill premium forecasts.

5.2. The Educational Level of Future Cohorts

Let us now examine the sensitivity of our results to the assumptions concern-
ing the educational attainment of young and future generations. Our benchmark
educational attainment scenario is relatively pessimistic. It assumes the stability
of the educational structure of the cohort aged 30 years in 2010 for all future gen-
erations. After 2010, we simply assume that the skill structure of future cohorts
(aged 30 after 2010) will be stationary. For individuals who have just completed
schooling in 2010, the percentages of low-, medium-, and high-skill workers are
35 percent, 22 percent, and 43 percent. Here, we simulate a more optimistic alter-
native forecast. The EU�s Lisbon Strategy includes the objective of 50 percent of
a generation having completed at least licensure. Thus, we adopt here a more opti-
mistic assumption regarding future educational attainment: in the long run, we
assume that the proportion of high-skill workers aged 30 will reach 50 percent in
2020, against 17.5 percent for low-skill workers and 32.5 percent for medium-skill
workers.

Table 9 presents the results. Clearly, newborns� generational accounts are
quite stable. In terms of generational balance, the higher-skill scenario has a sub-
stantial impact on the long-run-deficit and generates a fiscal surplus in the long
term. Taxes could be reduced by 1.5 percent (vs. 12.3 percent under our conven-
tional assumption regarding future educational attainment) or transfers could be
increased by 1.9 percent (instead of 22.8 percent). Of course, achieving the better
skill variant�s educational attainment would likely require highly expansionary
education policy. If the marginal cost of education increases, the discounted cost
of such a policy could exceed the discounted gains. In some sense, the baseline
scenario appears more reliable because it is based on current state commitments.
Nevertheless, our sensitivity analysis demonstrates the crucial role of education
policies in the debate on aging and public finance.

5.3. Interest Rates and the Influence of Health on Productivity

The generational accounts also depend on uncertain assumptions regarding
the discount rate and the influence of health on individual productivity. As noted
above, the reference case uses a real discount rate of 6 percent. The choice of the
discount rates may be debatable. The choice of discount rate is crucial because it
determines the relative weight given to future net payments relative to current
ones (Diamond, 1996).

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Table 10 allows us to verify the extent to which our results are sensitive to the
choice of discount rate. Thus, different discount rates significantly change the
generational accounts of newborns, which is not surprising. By contrast, the dif-
ferences between the baseline scenario and the EA&HI scenario, resulting from
the inclusion of the increase in average human capital, are extremely stable The
same conclusion can be drawn concerning the necessary economic policy adjust-
ments: the inclusion of human capital substantially improves the assessment of
the sustainability of fiscal policy.

Concerning the impact of heath on productivity, we retain in our benchmark
simulation the intermediate value from the study of Bloom and Canning (2005)
(q52:8 percent). We now test the sensitivity of our results to alternative values of
q by using the estimations of Weil (2007) (q56:53 percent) and Barro (2013)
(q51:4 percent). As we have demonstrated that improving the health of workers
has a significant impact on their productivity, here we naturally observe substan-
tial variation in our results in response to variations in q (Table 10). For a given
interest rate, the required increase in taxes changes by approximately 4 percentage
points across the different values of q. However, such changes do not affect the

TABLE 10

The Sensitivity of the EA&HI Scenario to Discount Rate and Influence of Health on
Productivity (q)

Newborns� generational account

i 5 0.05 i 5 0.06 i 5 0.07
(Baseline)

213,490 233,152 244,813
Tax change (baseline) (%)

10.99 13.46 15.48
Transfer change (baseline) (%)

212.03 214.55 216.62

LS (EA&HI)
q50:014 281,642 279,655 276,990
q50:028 280,080 279,236 277,247
q50:0653 274,400 277,210 277,400
MS (EA&HI)
q50:014 2,657 222,494 237,608
q50:028 9,293 218,616 235,426
q50:0653 29,508 26,695 228,610
HS (EA&HI)
q50:014 109,291 44,598 5,326
q50:028 120,461 51,326 9,361
q50:0653 153,394 71,220 21,346

Tax change (EA&HI) (%)
q50:014 0.08 3.42 6.21
q50:028 21.05 2.34 5.19
q50:0653 23.87 20.40 2.59

Transfer change (EA&HI) (%)
q50:014 20.10 24.03 27.22
q50:028 1.28 22.79 26.09
q50:0653 4.87 0.49 23.11

Source: Authors� calculations.
Note: Present value in 2010 euros.

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significance of the positive effect arising from improvements in individual health
status in the assessment of the sustainability of fiscal policy. This leads to the
question of which type of health expenditure to implement to increase workforce
productivity.

6. Conclusion

The usual argument is that expected demographic changes threaten the sus-
tainability of fiscal policies. However, the assessment of the economic costs of
population aging is difficult for two main reasons. First, an important share of
the economic effects of aging remain in the future and have to be evaluated under
conditions of considerable uncertainty. Second, many of the mechanisms that
transmit demographic changes in the economic sphere are complex and do not
allow us to conclude with certainty in favor of a positive or negative effect. Gen-
erational accounting is generally considered as a meaningful approach to evaluat-
ing fiscal policy. All studies using this tool reveal a large generational imbalance
that demands profound reform of budgetary or fiscal policy.

However, these studies do not account for the positive effects of aging in
terms of human capital accumulation. This change is important from the budget-
ary perspective because it substantially modifies the fiscal means and needs of
successive cohorts. Thus, a rise in the educational attainment of successive genera-
tions affects the growth rate of labor productivity and thus influences the average
age profile of taxpayers and recipients of transfers over time. In parallel, while
improving the health status of each age class of the population contributes to the
aging of the population, this improvement can also be an asset to the economy.
Indeed, longer life can be likened to a rejuvenation of the labor force, that can
then improve the productivity of individual workers.

In this paper, we have shown that including future changes in the skill struc-
ture of the population and the future improvements in the health of the popula-
tion substantially affects the results of GA. Therefore, we estimate that the future
change in the skill structure of the French population and the improvement in its
health in the long term could reduce the tax burden bequeathed to future genera-
tions by 79 percent. These results are quite robust to our assumptions regarding
future returns to skill, interest rates, and the impact of improving the health of
the workforce on productivity. However, our results are more sensitive to assump-
tions concerning the educational structure of future cohorts.

Is this a sufficient reason to abandon the very negative view of the impact of
aging on public finances? We should remain cautious regarding the generalizabil-
ity of our results. We should bear in mind that GA is a purely mechanical tool. It
does not account for all of the interdependencies between demography and the
economy. In reality, there are multiple economic impacts of aging, which operate
through many different mechanisms that are not accounted for by our model of
partial equilibrium. Thus, the main contribution of this study is to show the
extent to which the long-term effects of aging on fiscal policy are sensitive to
the assumptions made regarding the accumulation of human capital. Therefore,
the integration of generational accounts, human capital, and fiscal policy within

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the framework of a general equilibrium model is obviously a promising subject,
but one that we must leave for future research.

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S O C I A l d E T E R M I N A N T S O F h E A lT h

The Intersection of Physical and Social Frailty in Older Adults
Lien t. QuacH, phD, MD; JenniFeR pRiMack, phD; MeLanie Bozzay, phD; caRoLine MaDRiGaL, phD, Rn;

SeBHat eRQou, MD, phD; JaMeS L. RuDoLpH, MD, SM

A b S T R A C T
Frailty, a vulnerability to stressors, has been increasing-
ly woven into the clinical understanding of older people
who are unable to respond to the impact of diseases, dis-
ability, and age-related decline. While the literature has
focused on physical frailty, social frailty has been con-
ceptualized within the domains of social needs (social
and emotional support, loneliness), resources (income,
food, housing, medical care, etc), social fulfillment (en-
gagement in work and activities), and self-management
(cognitive function, mental health, advance planning).
This review outlines the assessment of the four domains
of social frailty within the structure of clinical visits,
particularly annual wellness and advance care planning.
Increasing connectivity with the community, health sys-
tem, and government support is the primary recommend-
ed intervention. On a policy level, expanding opportuni-
ties to connect socially frail people with resources may
help mitigate the vulnerability of physical frailty.

K E Y W O R d S : frailty, physical frailty, social frailty

I N T R O d u C T I O N

Frailty decreases resiliency and reserves, which renders peo-
ple vulnerable to the stress of disease, disability, or social
change. Physical frailty has dominated medical literature for
the past 20 years. With prevalence estimates of up to 45%
among adults 85 years or older,1 physical frailty increases the
risk of low functional status, hospitalization, and mortality.2
Despite its high prevalence, physical frailty is not a normal
process of aging, and many have postulated that frailty can
be prevented or treated.3

Over the past two decades, physical frailty measurements
have emerged: 1) the clinically favorable frailty phenotype
and 2) the data-focused frailty index (accumulation of defi-
cits). The phenotype of frailty by Fried et al. (2001) includes
five criteria: weight loss, reduced activities, grip strength,
gait speed, and exhaustion. Clinically, the objective mea-
surement of the frailty phenotype is possible within the
context of an office visit and is billable, starting in 2021,
with the R54 ICD-10 code. In contrast, the frailty index
presents a model of deficit accumulation.4 With the breadth
of comorbidities, disabilities, and age-related decline, each

additional deficit results in the patient being less able to
rebound from stressors. For example, a patient with many
comorbidities, including dementia, is going to be less able
to rebound from the stress of acute hospitalization. Frailty
indexes incorporate clinical information, such as that from
an assessment of function, cognition, depression, physical
ability, and comorbidities. For clinicians with access to elec-
tronic medical record data, the frailty index can be calculated
with fields completed in the course of clinical care.

The social frailty gap
In examining the fundamental definition of frailty – a
decrease in resiliency and reserves, clinicians invariably
recognize that numerous social factors beyond those con-
tributing to the phenotype of frailty index definitions play a
substantial role in patient function. For example, if a person
lacks financial resources for food (a socially-anchored pro-
cess), solely capturing strength loss in the physical frailty
phenotype does not account for social factors that may be
largely responsible for frailty in nonphysical domain. Thus,
there is a gap in the narrow definitions of physical frailty
that does not include the broader perspective of social frailty
– a gap that has clear ramifications for improving patient
care, and even potentially mitigating negative outcomes.
Therefore, social frailty should be considered in concert with
broader frailty definition. Social frailty has been defined as
a progressive loss of resources, activities, or the ability to
participate in social activities to fulfill basic social needs.5

Social frailty often manifests with clinical stressors
such as the response to a new diagnosis or acute hospital-
ization, when the system supporting the patient may get
overwhelmed or break down. Other symptoms of social
frailty include limited social support, a smaller social net-
work, poor living conditions, fewer socially-oriented leisure
activities, and risk of losing resources.5 Other features may
include unhealthy social behaviors (lack of physical exer-
cise, poor diet, alcohol use, and smoking), social isolation,
and loneliness.5 Social frailty is a broad but highly medically
relevant construct. Yet, clinical tools for identifying social
frailty remain elusive.

The purpose of this article is to describe the intersec-
tion of physical frailty and social frailty and utilize existing
social frailty literature to describe a framework for building
a clinical checklist of social frailty.

1 6M A Y 2 0 2 1 R h o d e i s l a n d m e d i c a l j o u R n a l R i M J a R c H i V e S | M a y i S S u e w e B p a G e | R i M S

The social frailty framework: measurement
and integration into care and treatment
Figure 1 highlights the intersection of physical and social
frailty. This intersection is influenced by biological, psycho-
logical, social, and environmental factors. Prior systematic
reviews of social frailty have developed a framework of four
social frailty domains5 including 1) social needs; 2) general
resources; 3) social fulfillment; and 4) self-management to
provide a more comprehensive view of the system support-
ing people living with frailty. Social needs encompass social
and emotional support. General resources include life essen-
tials such as housing, food, water, air, and income. Social
fulfillment describes a person’s ability to interact and engage
in activities that allow survival and thriving. Self-manage-
ment is the autonomous component of social frailty that
includes self-determination and motivation necessary to
achieve equilibrium among the other social frailty domains
and potentially avoid physical frailty.

This conceptual framework of social frailty is based on
a combination of different theories including: 1) Loneliness
Theory,6 which refers to an individuals’ social network and
relationships being less satisfactory than expected; 2) The
convoy theory of social relations,7 which refers to individu-
als receiving social support throughout their life by members
of their cohort; 3) Self Determination Theory,8 which refers
to the status of motivation or autonomy and control, and
4) Social Production Functions Theory,9 which refers to indi-
viduals who maximize their psychological and environmen-
tal factors or resources for physical and social well-being.

Table 1 describes the relationship between social frailty
domains and physical frailty. An analysis conducted by Woo
et al. in 2005 found that increasing social support was asso-
ciated with lower frailty.10 Weight loss from physical frailty

Figure 1. Frailty and social frailty framework

phenotype, has been associated with the resource domain
of social frailty (occupation, race, gender, and educational
level, neighborhood deprivation, and individual socioeco-
nomic status).5,11 The social fulfillment domain highlights
that components of frailty such as exhaustion can be associ-
ated with depression and slow gait speed leading to reduced
social engagement.5,10 Similarly, the self-management
domain has a strong relationship with cognitive function
and can be associated with weakness, resulting from reduced
exercise and poor disease management among people with
cognitive impairment.5,11

Clinical recommendations for integrating frailty
and social frailty into treatment
Incorporation of yet another assessment into an already
busy clinical practice has potential to benefit patients with
physical frailty, but should be accomplished with an eye
toward minimizing additional clinical burden. There are
components of social frailty that could be built into pre-visit
assessments, annual wellness visits, advance care planning,
or pre-procedure shared decision making. The purpose would
be to facilitate clinical responses when stressors affect the
social infrastructure of a patient, rather than simply rote
completion of assessment fields. This approach emphasizes
that medicine is within the control of the provider.

Table 2 presents a framework for a social frailty checklist
with example measures based on a multi-component model
of social frailty that includes social isolation, loneliness
(social needs), social exercise and participation (social fulfill-
ment), housing, food (resources), behavior, and motivation
(self-management). The checklist identifies key elements of
social frailty (but is not comprehensive), assessments of the
element, and clinical opportunities to complete the assess-
ment. This checklist may assist providers and multidisci-
plinary teams in coordinating evaluation at the early stages
of frailty or addressing frailty in older adults.

Social Frailty
Domains

Connection to
Physical Frailty
Domains

Clinical Example(s)

Social needs weakness/decreased
grip strength

Lack of emotional and social
support for daily activities

General
Resources

weight loss Food insecurity results in food
vs. housing decision, with
housing taking precedence

Social
Fulfillment

exhaustion
Slow gait speed

Depression leads to reduced
social engagement and social
participation

Self-
management

physical activities cognitive impairment results
in reduced exercise and
disease management leading
to further sarcopenia

Table 1. the relationship between social frailty and physical frailty

domains, along with clinical examples.

S O C I A l d E T E R M I N A N T S O F h E A lT h

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Clinical research directions
While the physical frailty phenotype has dominated the
medical literature, the study of social frailty is less devel-
oped.4 The demonstrated association of physical frailty and
adverse health outcomes with biological underpinnings
strongly suggests that the conceptualizations of physical
frailty are appropriate. However, the lack of incorporation
of social domains suggests that the overall concept of frailty
needs reconsideration. Recent research has systematically
examined the association of physical frailty with elements
of social frailty domains.11 Additional work is needed to tar-
get interventions in social frailty domains using existing
infrastructure (e.g., meals on wheels, home, and commu-
nity-based services, etc.) to determine if modifying social
frailty can impact physical frailty. While pharmaceuticals
may address biological deficits, larger-scale interventions
are necessary to influence social determinants. Fortunately,
social support programs could permit or encourage such
interventions (e.g., Meals on Wheels, Program of All-Inclu-
sive Care of the Elderly, VA Homeless Programs, State Med-
icaid home, and community-based services, etc.). Finally,
the breadth of social frailty is beyond the ability of a single
provider to overcome all aspects. As a result, physicians, pro-
viders, researchers, and policymakers should collaborate to
find innovations to social frailty that span health systems,
social support agencies, and government services.

C O N C l u S I O N S

Social frailty contributes to reduced resiliency and ability to
maintain independence. using a literature-based conceptual
model of social frailty, this manuscript identifies potential
opportunities to assess social frailty. Because there is clear
overlap between physical and social frailty, integrating a
broader and socially-sensitive view of frailty into medical
practice may be useful to identify factors that could impact
frailty (both physical and social) and maybe amenable to
interventions to improve patient outcomes.

Domain Element Assessment Clinical Assessment Timing

Social needs Social Supports perceived support when needed12 Demographic information

Loneliness ucLa loneliness13 annual wellness

General
Resources

Food Security not able to afford the food in household in the last 12 months14 annual wellness

Housing Security Have any housing problems11 annual wellness

elder abuse neglect, physical abuse, psychological abuse, financial abuse15 annual wellness

Discrimination perceived Discrimination Scale16 annual wellness

Social Fulfillment Leisure time activities iaDLs17

internet accessibility
introduction to Medicare visit/
annual wellness

Mental Health pHQ-918 annual wellness

Self-management cognitive Function See aa cog screening19 annual wellness

care planning physical exercise and physical activity Scale for the elderly (paSe)20

Health care proxy; and instruction directives21
(types of treatment do not want if facing a medical crisis)

advance care planning

Table 2. Social frailty checklist

References
1. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults:

evidence for a phenotype. J Gerontol A Biol Sci Med Sci.
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2. Khandelwal D, Goel A, Kumar u, Gulati V, Narang R, Dey AB.
Frailty is associated with longer hospital stay and increased
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3. Ahmed N, Mandel R, Fain MJ. Frailty: an emerging geriatric
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4. Rockwood K, Song X, MacKnight C, et al. A global clinical
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5. Bunt S, Steverink N, Olthof J, van der Schans CP, Hobbelen JSM.
Social frailty in older adults: a scoping review. Eur J Ageing.
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6. Dykstra PA. Older adult loneliness: myths and realities. Eur J
Ageing. 2009;6(2):91-100.

7. Antonucci, Birditt KS, Akiyama H. Convoys of social relations:
an interdisciplinary approach. In: Bengtson VS, Gans, D., Put-
ney, N.M., & Silverstein, M, ed. Handbook of theories of aging.
New York: Springer Publishing Company, 2009.

8. Ryan RM, Deci EL. Self-determination theory and the facilita-
tion of intrinsic motivation, social development, and well-be-
ing. Am Psychol. 2000;55(1):68-78.

9. Ormel J, Lindenberg S, Steverink N, Vonkorff M. Quality of life
and social production functions: a framework for understanding
health effects. Soc Sci Med. 1997;45(7):1051-1063.

10. Woo J, Goggins W, Sham A, Ho SC. Social determinants of frail-
ty. Gerontology. 2005;51(6):402-408.

11. Bessa B, Ribeiro O, Coelho T. Assessing the social dimension of
frailty in old age: A systematic review. Arch Gerontol Geriatr.
2018;78:101-113.

12. Lubben J, Blozik E, Gillmann G, et al. Performance of an abbre-
viated version of the Lubben Social Network Scale among three
European community-dwelling older adult populations. Geron-
tologist. 2006;46(4):503-513.

13. Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. A Short Scale
for Measuring Loneliness in Large Surveys: Results From Two
Population-Based Studies. Res Aging. 2004;26(6):655-672.

14. Bishop NJ, Wang K. Food insecurity, comorbidity, and mobility
limitations among older u.S. adults: Findings from the Health
and Retirement Study and Health Care and Nutrition Study.
Prev Med. 2018;114:180-187.

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15. Fulmer T. Elder abuse and neglect assessment. J Gerontol Nurs.
2003;29(1):8-9.

16. Williams, Yu, Jackson, Anderson. Perceiced Discrmination Scale.
http://sparqtools.org/mobility-measure/perceived-discrimi-
nation-scale/. Published 1997. Accessed.

17. Graf C. The Lawton Instrumental Activities of Daily Living
(IADL) Scale. Medsurg Nurs. 2009;18(5):315-316.

18. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a
brief depression severity measure. J Gen Intern Med. 2001;16(9):
606-613.

19. Association As. Conitive Assessment Toolkit. https://www.
maineddc.org/images/PDFs/Cognitive-Assessment-Toolkit .
Published 2013. Accessed.

20. Washburn RA, Smith KW, Jette AM, Janney CA. The Physical
Activity Scale for the Elderly (PASE): development and evalua-
tion. J Clin Epidemiol. 1993;46(2):153-162.

21. https://www.nia.nih.gov/health/advance-care-planning-health-
care-directives

Acknowledgments
Dr. LQ was supported in part by grant The National Institutes of
Health #1, under grant P2CHD065702, and the National Insti-
tutes on Aging #2, the Boston Claude D. Pepper Older Americans
Independence Center, under grant P30-AG031679. This work was
supported by the VA Health Services Research and Development
Center of Innovation in Long Term Services and Supports (CIN 13-
419 and C19 20-213), the VA QuERI-Geriatrics and Extended Care
Partnered Evaluation Center for Community Nursing Homes (PEC
15-465). The statements and opinions expressed are those of the au-
thors and do not represent the official policy or procedures of the
united States Government or the Department of Veterans Affairs.

Disclaimer
The funding agencies and employers played no role in the content
of this paper.

Financial disclosure
The authors declare that there is no conflict of interest.

Authors
Lien T. Quach, PhD, MD, VA Center of Innovation in Long

Term Services, Providence VA Medical Center, Providence,
RI; The university of Massachusetts Boston, Department
of Gerontology, Boston, MA; Massachusetts Veterans
Epidemiology Research and Information Center, VA Boston
Healthcare System, Boston, MA.

Jennifer Primack, PhD, VA Center of Innovation in Long Term
Services, Providence VA Medical Center, Providence, RI;
Department of Psychiatry & Human Behavior, Alpert Medical
School of Brown university, Providence, RI.

Melanie Bozzay, PhD, Department of Psychiatry & Human
Behavior, Alpert Medical School of Brown university,
Providence, RI; VA RR&D Center for Neurorestoration
and Neurotechnology, Providence VA Medical Center,
Providence, RI.

Caroline Madrigal, PhD, RN,VA Center of Innovation in Long
Term Services, Providence VA Medical Center, Providence, RI.

Sebhat Erqou, MD, PhD, Department of Medicine, Alpert Medical
School of Brown university and Providence VA Medical
Center, Providence, RI.

James L. Rudolph, MD, SM,VA Center of Innovation in Long
Term Services, Providence VA Medical Center, Providence, RI;
Division of Geriatrics and Palliative Medicine, Warren Alpert
Medical School of Brown university, Providence, RI; Center for
Gerontology and Health Services Research, Brown university
School of Public Health, Providence RI.

Correspondence
Lien T. Quach, PhD, MD
The Center of Innovation in Long Term Services and Supports
Providence VA Medical Center
830 Chalkstone Boulevard
Providence, RI 02908
401-273 7100, ext: 16502
Fax 401-457-3305
Lien.Quach@va.gov or quach.hrca@gmail.com

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Social Work in Public Health

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Risk and Protective Factors of Loneliness among
Older Adults: The Significance of Social Isolation
and Quality and Type of Contact

Barbra Teater, Jill M. Chonody & Nadia Davis

To cite this article: Barbra Teater, Jill M. Chonody & Nadia Davis (2021) Risk and
Protective Factors of Loneliness among Older Adults: The Significance of Social Isolation
and Quality and Type of Contact, Social Work in Public Health, 36:2, 128-141, DOI:
10.1080/19371918.2020.1866140

To link to this article: https://doi.org/10.1080/19371918.2020.1866140

Published online: 28 Dec 2020.

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Citing articles: 2 View citing articles

Risk and Protective Factors of Loneliness among Older Adults: The
Significance of Social Isolation and Quality and Type of Contact
Barbra Teatera,b, Jill M. Chonodyc, and Nadia Davisa

aDepartment of Social Work, College of Staten Island, New York, New York, USA; bThe Graduate Center, City University
of New York, New York, New York, USA; cSchool of Social Work, Boise State University, Boise, Idaho, USA

ABSTRACT
Loneliness has a significant impact on the health and well-being of older
people, including an increased risk of mortality. This cross-sectional study
explored possible risk and protective factors that can help explain loneliness
and emotional and social loneliness in a sample of community-dwelling
older adults (N = 477). The survey incorporated a standardized scale of
loneliness and items to assess type and quality of contact with others,
community support, social isolation, physical health, cognitive health, and
functional ability. Bivariate and multivariate analyses explored the factors
that contributed to loneliness, emotional loneliness, and social loneliness.
Results indicated overall quality of contact with others, use of phone contact,
and social isolation was significant in all three regressions; other significant
variables were different for each analysis. The findings support social work
and public health recommendations for addressing loneliness, particularly
within the current climate of “social distancing” under the COVID-19
pandemic.

KEYWORDS
Loneliness; emotional
loneliness; social loneliness;
social isolation; older adults;
public health emergency

Nearly 33% of adults aged 50 and older in the United States (US) reported being lonely (Wilson &
Moulton, 2010), with over 42 million older adults having experienced chronic loneliness (Holt-Lunstad,
2017). Loneliness can be experienced at any age and has been found to progress nonlinearly across
middle and old age with the highest prevalence of loneliness found among adults over 80 years of age
(Demakakos, Nunn, & Nazroo, 2006; Dykstra, 2009; Wilson & Moulton, 2010). The effects of loneliness
on older adults found in the literature indicate that loneliness predicts depression and psychological
distress (Ng & Lee, 2019) and premature mortality (Cacioppo & Cacioppo, 2018; Valtorta & Hanratty,
2012). Social connections are viewed as both a preventative factor of loneliness and outcome of loneliness
in that individuals who have strong social relationships are able to maintain independence, and
maintaining independence allows one to engage in social relationships that prevent loneliness (Ten
Bruggencate, Luijkx, & Sturm, 2019). This study builds on prior research by exploring the possible risk
and protective factors associated with loneliness in older adults by examining how different types of
social contact, quality of social interaction, a supportive community environment, social isolation,
physical and cognitive health, and functional ability are associated with loneliness and emotional and
social loneliness among community-dwelling older adults (aged 55 and older) residing in the US.

Literature review

Loneliness

Loneliness has both social and emotional components. Social loneliness is the loss or lack of a wider
social network or circle of friends, family, people in the neighborhood, and/or acquaintances that

CONTACT Barbra Teater barbra.teater@csi.cuny.edu 2800 Victory Blvd Staten Island, NY 10314.

SOCIAL WORK IN PUBLIC HEALTH
2021, VOL. 36, NO. 2, 128–141
https://doi.org/10.1080/19371918.2020.1866140

provide meaningful companionship and a sense of belonging, whereas emotional loneliness is the lack
of an attachment figure who provides a more intimate relationship and emotional support, such as
a partner (Weiss, 1973). Thus, a decrease in quantity of social networks may lead to social loneliness,
and a decrease in quality of social networks may lead to emotional loneliness, which could include
feelings of emptiness, abandonment, desolation, and insecurity (Olawa & Idemudia, 2019; Weiss,
1973). Moreover, loneliness is viewed as crucial for emotional and social well-being (Holt-Lunstad,
2017) and is viewed as the result of “situations in which the number of existing relationships is smaller
than is considered desirable or admissible, as well as situations where the intimacy one wishes for has
not been realized” (De Jong Gierveld, 1987, p. 120). Dykstra (2009) highlights that loneliness includes
two elements: a subjective experience and a negative affect.

It is important to note the distinction between loneliness and social isolation although they both
refer to a lack of social connection (Holt-Lunstad, 2017). Whereas loneliness is a negative, subjective
experience, social isolation is more objective and refers to the absence of relationships or ties with
other people (De Jong Gierveld & Van Tilburg, 2006; Dykstra, 2009). As Cohen-Mansfield and Perach
(2015) report, “loneliness has been contrasted with belonging, whereas social isolation contrasts with
social participation” (p. 109). According to the AARP Foundation (2020), an estimated 8 million
adults age 50 and over are affected by social isolation with prolonged isolation and a lack of social
connections having health risks equivalent to smoking 15 cigarettes a day (Holt-Lunstad, Smith, &
Layton, 2010). Although they are two distinct concepts, examining their relationship with one another
is important.

Prevalence and impact of loneliness on older adults

A study by Wilson and Moulton (2010) found 33% of a nationally representative sample of older
adults (aged 50+) in the US to report being lonely, with age, income, and marital status significantly
related to loneliness. Older adults, individuals with higher income, and those who were married
reported less loneliness, yet no differences were found based on gender, education, or race/
ethnicity. In particular, a perceived lack of social support and a reduced network of friends were
associated with loneliness, and individuals who were lonely were less likely to be engaged in social
activities, such as attending religious services, volunteering, or participating in a community
organization.

Many detrimental physical and psychological effects for older adults are linked to loneliness. For
example, individuals who are lonely have an increased risk for mortality (Cacioppo & Cacioppo, 2018;
Valtorta & Hanratty, 2012), have greater numbers of chronic illnesses and medical conditions
(Theeke, 2010; Thurston & Kubzansky, 2009; Wilson & Moulton, 2010;), and have reduced ratings
of subjective health (Cornwell & Waite, 2009; Theeke, 2010). A meta-analysis including 70 indepen-
dent studies with over 3.4 million individuals reported social isolation, loneliness, and living alone
have a significant and equivalent effect on risk for morality (Holt-Lunstad, Smith, Baker, Harris, &
Stephenson, 2015). Loneliness has been found to impair activities of daily living, which in turn
influences the individual’s functional status (Shanker, McMunn, Demakakos, Hamer, & Steptoe,
2017) and can lead to functional disability (Theeke, 2010). Additionally, loneliness has been linked
to mental health in that it is associated with depression and lower levels of well-being (Golden et al.,
2009; Routasalo, Savikko, Tilvis, Strandberg, & Pitkala, 2006; Tiikkainen & Heikkinen, 2005) and
a predictor of suicide among older adults aged 65 and older (Waern, Rubenowitz, & Wilhelmson,
2003). Ten Bruggencate and colleagues conclude, “social needs do not change much with aging. Only
the resources a person has seem to change during a lifetime” (p. 1845), with reduced resources being
caused by health problems, reduced mobility, death of individuals in their social networks, and
finances.

SOCIAL WORK IN PUBLIC HEALTH 129

Risk and protective factors of loneliness in older adults

Researchers have attempted to gain a better understanding of the risk and protective factors of
loneliness in older adults to support ways in which to prevent and treat loneliness. One identified
risk factor is living alone, particularly for older adults who have lost a spouse or partner, or is
geographically distant from families who could provide social contact and support to cope with age-
related difficulties (Holt-Lunstad, 2017; Zebhauser et al., 2015). Additionally, research has repeatedly
established an association between depression and loneliness among older adults (Bath, Yang, &
Nicholls, 2018; Courtin & Knapp, 2017; Domènech-Abella et al., 2017; Martinez, Baron, Largaespada,
Ceruto, & Chaves, 2019; Zebhauser et al., 2015), but the causal direction of the relationship is unclear.
For example, a cross-sectional study by Martinez et al. (2019) of community-dwelling older adults
found lower social supports, lower social functioning, and higher loneliness were associated with
higher levels of depression after controlling for chronic diseases and mobility difficulty, and a study
including older adults who live alone found non-depressed participants were slightly over three times
less likely to feel lonely when compared to depressed participants (Zebhauser et al., 2015). Finally,
research has highlighted an association between physical limitations and health and loneliness. Wilson
and Moulton (2010) found 55% of individuals who reported poor health also reported high levels of
loneliness compared to 25% of individuals who reported excellent health, and Theeke (2010) found
fewer age-related physical limitations were associated with less loneliness given that mobility and good
health allowed the individuals to engage and maintain their social activities and social contacts.

Protective factors rest on a stable social network, meeting social needs, and the increasing use of
social technology. A study including older adults who live alone found those participants who reported
a stable social network were four times more likely not to report feeling lonely when compared to
participants who reported a poor social network. When comparing the influence of social networks
alongside other risk and protective factors, this study also identified social networks as the key factor
while other factors, such as higher income, educational level, and being in good physical and mental
health, were less likely to influence loneliness (Zebhauser et al., 2015). Olawa and Idemudia (2019)
argue that a reduced social network can result in older adults experiencing both social and emotional
loneliness, which, coupled with findings from the Pew Research Center (2009) that found the majority
of adults in the US do not participate in any kind of social group, means that a significant number of
older adults may be at risk for social isolation and loneliness. Continuing to examine the possible risk
and protective factors of loneliness, as well as social and emotional loneliness, among older adults is
crucial in helping to establish social work and public health strategies to address this public health
concern. Therefore, this study aims to answer the following research question:

(1) To what extent do different types of social contact, quality of social interaction, a supportive
community environment, social isolation, physical and cognitive health, and functional ability
contribute to (a) loneliness; (b) emotional loneliness; and (c) social loneliness?

Method

Setting and sample

The data for this cross-sectional, exploratory study were drawn from an online survey of older adults
in 2019 through Mechanical Turk (MTurk), an Amazon supported survey participant strategy that
allows completion of tasks for monetary reward. This platform is popular for social scientists who in
the past have relied on college students to complete surveys and experiments, and due to the large pool
of potential respondents, researchers can target specific groups that are relevant to their study (e.g.,
older people) allowing larger groups to be reached (Paolacci & Chandler, 2014). Researchers are called
“requesters” in this platform, the survey is called a “hit,” and the respondents are referred to as
“workers” although the “work” in this case is small monetary payments for survey completion rather
than actually being employed by Amazon. The use of MTurk is quite common in the empirical

130 B. TEATER ET AL.

literature (Paolacci & Chandler, 2014), and this platform has been found to be as reliable as traditional
survey methods in terms of a demographically diverse population and reliability of data outcomes
(Buhrmester, Kwang, & Gosling, 2011).

The recruitment of participants is conducted by Amazon MTurk based on the study inclusion and
exclusion criteria, which are specified by the researcher. The inclusion criteria in this study were older
adults aged 55+ who resided in the US and denoted as “master workers,” which, according to
Amazon’s MTurk website, are those workers who have been identified by Amazon’s built technology
that monitors workers and identifies those who “demonstrated excellence across a wide range of
tasks.” Following best practices, when designing the hit, US residents only were selected and respon-
dents were required to enter a code at the end of the survey, which prevents fake workers. “Master
workers” were part of the inclusion criteria, and a monetary reward ($0.75 US) was deemed sufficient
(Kees, Berry, Burton, & Sheehan, 2017). The study ceases when the requested sample size is reached at
which point the data are transferred back to the researcher in an SPSS file. No identifying information
(e.g., name, contact details) were recorded during survey completion. Ethical approval for the study
was approved by the relevant Institutional Review Board. The participants were provided with a cover
letter at the beginning of the survey, which described the purpose of the study, the confidential and
voluntary nature of this study, and the contact information of the researcher. Completion of the online
survey served as consent for participation. A total of 477 US residents 55+ completed the survey
through the online MTurk platform.

Instrumentation

The author-constructed questionnaire consisted of 22 items that measured loneliness, types of social
contact, quality of social interaction, the extent to which a community supports aging, social isolation,
subjective physical health, subjective cognitive health, and functional ability. An additional six ques-
tions were asked to capture the participants’ sociodemographics of identified gender, age, race/
ethnicity, sexuality, relationship status, and living arrangement.

Dependent variables
The loneliness scale (De Jong Gierveld & Kamphuis, 1985; De Jong Gierveld & Van Tilbrug, 1999)
measures the level of loneliness through two sub-scales of social loneliness and emotional loneliness,
which served as the dependent variables in this study. The 11-item measure consists of five statements
on social loneliness (e.g., “I miss having people around”) and six statements on emotional loneliness
(e.g., “There are many people that I can trust completely”). The scale asks participants to indicate the
extent to which they agree with the statements (1 = Strongly disagree – 6 = Strongly agree). After
reverse scoring the five social loneliness items, the response to each item is summed to produce an
overall loneliness score (11– 66) with higher scores indicating higher levels of loneliness. Additionally,
the six items on social loneliness can be summed to produce an overall social loneliness score (6– 36),
and the five items on emotional loneliness can be summed to produce an overall emotional loneliness
score (5– 30). If a participant scored one or more missing values, the particular participant was deleted
from the analysis. Cronbach’s alpha indicated a high level of internal consistency for the social
loneliness subscale (α =.92), the emotional loneliness subscale (α = .90), and the overall loneliness
scale (α = .93) in this study.

Independent variables
Four different types of social contact were assessed individually by asking the following questions:
“On average, how many days per week do you have face-to-face contact with others (family,
friends)?”; “On average, how many days per week do you have phone contact (including
FaceTime or Skype) with others (family, friends)?”; “On average, how many days per week do
you text with others (family, friends)?” and “On average, how often do you have contact with groups
or organizations (hobby groups, church) per week?” The participants were asked to rate the quality

SOCIAL WORK IN PUBLIC HEALTH 131

of their social contacts through the following question, “As a whole, how would you rate the quality
of your interaction (spending time together, talking, and/or texting) with others?” (1 = Very poor –
6 = Excellent). The extent to which a community supports aging was measured by asking partici-
pants to indicate their level of agreement (1 = Strongly disagree – 6 = Strongly agree) with the
following statement, “My city/community supports me as I age (for example, places to rest in public,
sidewalks, access to transportation)”. Social isolation was measured through a single-item indicator
that asked participants to respond to the statement, “I feel socially isolated” (1 = Never – 7 =
Always).

Additional independent variables related to health and functional ability were included as they
have consistently been found to serve as both predictive and outcome factors to loneliness
(Cornwell & Waite, 2009; Shanker et al., 2017; Theeke, 2010; Thurston & Kubzansky, 2009;
Wilson & Moulton, 2010). Therefore, this study aimed to explore the extent to which health and
functional ability contributed to loneliness and social and emotional loneliness alongside social
isolation, community supports, and quality and type of contact. Physical health and cognitive health
were measured through self-report by completing the following two statements: “My physical health
is . . . ” and “My cognitive health (i.e., brain functioning) is . . . ” (1 = Very poor – 6 = Excellent).
Finally, functional ability was asked through two questions that garnered respondents’ ability to
engage in activities of daily living (ADLs), and instrumental activities of daily living (IADLs). ADLs
were measured by the following question, “To what extent are you able to complete your activities
such as cutting your toenails, completing your own bath, and going up and down the stairs?”, and
IADLs were measured by the following question, “To what extent are you able to complete your
activities such as grocery shopping, housework, and meal preparation?” (1 = I’m able to complete
these activities without any difficulty – 4 = I have significant difficulty with these activities and need
quite a bit of help”).

Data analysis

The data were analyzed in IBM SPSS, version 24, software using descriptive statistics to determine
percentages, frequencies, and measures of central tendency for the sociodemographic variables and the
items measuring loneliness, types and quality of social contact, community supports, social isolation,
physical and cognitive health, and functional ability. Bivariate analyses were run to explore the
relationship between two variables, for example, the relationship between loneliness and living
arrangement, or the relationship between functional ability and loneliness. Before conducting
ANOVAs, descriptive statistics were examined to ensure at least five cases within each category; if
there were less than five cases in a specific category, then the specific categories of the variable were
combined to create an “other” category. Post-hoc comparisons using the LSD test were preformed to
assess specific differences between groups.

The variables found to be significant at the bivariate level were included in three separate ordinary
least squares (OLS) regressions analyses to determine which factors contributed to overall loneliness,
emotional loneliness, and social loneliness. Gender (0 = men; 1 = women) and age were included as
control variables. The predictor variables were relationship status (1 = single, never married; 0 = all
other relationship types), living arrangement (1 = lived alone; 0 = all other arrangements), face-to-face
contact, phone contact, text contact, group/organization contact, quality of contact, supportive
community environment for aging, social isolation, physical health, cognitive health, ADLs, and
IADLs. The outcome variables were overall loneliness, emotional loneliness, and social loneliness.
The control and predictor variables were entered simultaneously. Missing data were addressed
through listwise deletion. Alpha was set at .05.

132 B. TEATER ET AL.

Results

Sample sociodemographics

A total of 468 respondents were included in the data analysis after removing nine cases due to missing
items on the loneliness scale. The mean age of the respondents was 63.52 years with a range from 55 to
81 years. Over 68% identified as female, nearly 90% identified as White (non-Hispanic), and 96%
identified as straight or heterosexual. Over 49% of respondents reported their relationship status as
“married/partnered” with 20.30% reporting their relationship status as “divorced.” Finally, over 52%
reported living with their spouse/partner and 27.30% reported living alone. Table 1 reports the
sociodemographics of the sample.

Loneliness and potential risk and protective factors for loneliness

Table 2 reports the descriptive statistics for the loneliness, emotional loneliness, and social loneliness
scales as well as other potential risk and protective factors for loneliness. The respondents had an

Table 1. Sample sociodemographics (N = 468).

Variable (n) M (SD) % (f)

Age (466) 63.52 (4.78)
55– 59 23.18% (108)
60– 64 39.27% (183)
65– 69 25.75% (120)
70– 74 8.80% (41)
75– 81 3.00% (14)
Gender
Female 68.60% (321)
Male 31.40% (147)
Race/Ethnicity
African American/Black 4.90% (23)
American Indian/Native American 0.20% (1)
Asian American/Asian 0.90% (4)
Biracial/Multiracial 0.90% (4)
Chicano/Mexican-American 1.10% (5)
Puerto Rican 0.20% (1)
White (non-Hispanic) 89.70% (420)
Another Racial/Ethnic Identity 2.10% (10)
Sexuality
Asexual 0.20% (1)
Bisexual 1.30% (6)
Lesbian, Gay, or Homosexual 2.40% (11)
Straight or Heterosexual 95.90% (449)
Don’t Know 0.20% (1)
Relationship Status
Divorced 20.30% (95)
Divorced and Dating 2.10% (10)
Divorced and Remarried/Partnered 4.10% (19)
Married/Partnered 49.70% (232)
Single, Never Married 11.80% (55)
Single and Dating 0.90% (4)
Widowed 7.10% (33)
Widowed and Remarried/Partnered 1.10% (5)
Other 3.00% (14)
Living Arrangement
Live alone 27.30% (127)
Adult child 8.60% (40)
Family member other than child 4.90% (23)
Friend 0.20% (1)
Roommate 1.30% (6)
Spouse/Partner 52.60% (245)
Other 5.20% (24)

SOCIAL WORK IN PUBLIC HEALTH 133

overall level of loneliness, emotional loneliness, and social loneliness that fell within the mid-range of
the possible scores on these scales. Respondents reported spending more days per week having face-to-
face contact, followed by text contact, phone contact, and group/organization contact, with a reported
quality of contact as generally good to very good. The respondents tended to agree that their
community supported them as they age, and had a lower level of social isolation. Overall physical
and cognitive health was reported as good to very good, and respondents reported generally being able
to complete ADLs and IADLs.

Relationship between sociodemographics, risk and protective factors, and loneliness

Demographics
Bivariate analyses indicated that no relationship existed between age, race, or sexuality and overall
loneliness, emotional loneliness, or social loneliness. Additionally, no difference in overall loneliness
or emotional loneliness was found based on gender, but men were found to have a higher level of social
loneliness (M = 14.96; SD = 6.51) compared to women (M = 13.57; SD = 6.81), t(466) = 2.08, p = .038.

Relationship status
Table 3 reports the results from the bivariate analyses for risk and protective factors and overall
loneliness, emotional loneliness, and social loneliness. There was a difference in relationship status and
loneliness and post-hoc comparisons revealed: (a) single, never married respondents had higher levels
of loneliness compared to respondents who were divorced and dating (p = .008), divorced and
remarried/partnered (p = .002), and married/partnered (p < .001); and (b) divorced respondents had higher levels of loneliness compared to respondents who were divorced and dating (p = .035), divorced and remarried/partnered (p < .001), and married/partnered (p < .001). Likewise, there was a relationship between relationship status and emotional loneliness, and post-hoc comparisons revealed: (a) respondents who were single, never married had higher levels of emotional loneliness compared to respondents who were divorced and dating (p = .015), divorced and remarried/partnered (p = .009), and married/partnered (p < .001); (b) respondents who were divorced had higher levels of emotional loneliness compared to respondents who were divorced and dating (p = .036), divorced and remarried/partnered (p = .026), and married/partnered (p = .001); and (c) respondents who were widowed had higher levels of emotional loneliness compared to respondents who were married and partnered (p = .001). Finally, there was a relationship between relationship status and social loneliness, and post-hoc comparisons revealed: (a) respondents who were single, never married had higher levels of social loneliness compared to respondents who were divorced and dating (p = .020), divorced and remarried/partnered (p = .002), married/partnered (p < .001), widowed (p = .010), and widowed and remarried/partnered (p = .030); and (b) respondents who were divorced had higher levels of social

Table 2. Loneliness and potential risk and protective factors for loneliness (N = 468).

Variable (n) M (SD) Theoretical Range

Loneliness (468) 30.53 (13.10) 11– 66
Emotional Loneliness (468) 16.52 (7.66) 6– 36
Social Loneliness (468) 14.01 (6.74) 5– 30
Face-to-Face Contact (467) 5.79 (2.03) 0– 7
Phone Contact (465) 4.00 (2.54) 0– 7
Text Contact (464) 4.41 (2.67) 0– 7
Group/Organization Contact (467) 1.59 (1.92) 0– 7
Quality of Contact (466) 4.38 (1.21) 1– 6
Community Supports Aging (467) 4.05 (1.42) 1– 6
Social Isolation (467) 2.72 (1.66) 1– 7
Physical Health (466) 4.11 (1.11) 1– 6
Cognitive Health (467) 5.08 (0.89) 1– 6
ADLs (467) 1.19 (0.59) 1– 4
IADLs (468) 1.18 (0.59) 1– 4

134 B. TEATER ET AL.

loneliness compared to respondents who were divorced and remarried/partnered (p = .024), and
married/partnered (p = .001).

Living arrangements
Table 3 also reports the details of the respondents’ living arrangements and the relationship with
overall loneliness, emotional loneliness, and social loneliness. There was a relationship between living
arrangement and overall loneliness, and post-hoc comparisons indicated respondents who lived alone
had higher levels of loneliness compared to respondents who lived with an adult child (p = .004), with
a spouse/partner (p <.001), and those living in an “other” arrangement (p = .004). Likewise, there was a relationship between living arrangement and emotional loneliness, and post-hoc comparisons indicated respondents who lived alone had higher levels of emotional loneliness compared to

Table 3. Bivariate analyses: Risk and protective factors and loneliness, emotional loneliness, and social loneliness (N = 455).

Loneliness Emotional Loneliness Social Loneliness

Variable M (SD) F/r df p M (SD) F/r df p M (SD) F/r df p

Relationship
Status

4.66 7, 459 <.001 3.62 7, 459 .001 4.38 7, 459 <.001

Single,
never
married

36.58 (13.51) 19.31 (7.65) 17.27 (6.64)

Divorced 33.92 (14.27) 18.24 (8.48) 15.67 (7.84)
Widowed 31.73 (13.31) 18.18 (8.01) 13.55 (6.21)
Married/

partnered
28.16 (11.96) 15.25 (7.00) 12.91 (6.11)

Widowed and
remarried/

partnered 26.20 (9.31) 15.60 (6.35) 10.60 (4.10)
Divorced and

remarried/
partnered 26.00 (9.72) 14.05 (6.82) 11.95 (5.21)
Divorced and

dating
25.00 (9.72) 13.00 (6.91) 12.00 (6.27)

Other 29.72 (12.88) 16.22 (7.63) 13.50 (6.64)
Living

Arrangement
6.69 5, 460 <.001 6.47 5, 460 <.001 4.53 5, 460 <.001

Lived alone 35.81 (13.52) 19.53 (8.03) 16.28 (7.19)
Family member

(other)
32.48 (14.15) 18.04 (8.10) 14.43 (7.14)

Friend/
Roommate

31.00 (11.31) 16.86 (7.69) 14.14 (6.07)

Adult child 29.10 (12.41) 15.55 (6.96) 13.55 (6.74)
Spouse/partner 28.09 (11.92) 15.19 (6.97) 12.90 (6.13)
Other

arrangements
14.66 (2.99) 14.38 (8.41) 13.29 (7.15)

Face-to-Face
Contact

−.40 <.001 −.37 <.001 −.36 <.001

Phone Contact −.46 <.001 −.38 <.001 −.46 <.001 Text Contact −.41 <.001 −.33 <.001 −.43 <.001 Group/

Organization
Contact

−.31 <.001 −.28 <.001 −.28 <.001

Quality of
Contact

−.69 <.001 −.58 <.001 −.68 <.001

Community
Supports
Aging

−.32 <.001 −.20 <.001 −.39 <.001

Social Isolation .75 <.001 .74 <.001 .61 <.001 Physical Health −.41 <.001 −.34 <.001 −.41 <.001 Cognitive Health −.33 <.001 −.33 <.001 −.27 <.001 ADLs .20 <.001 .17 <.001 .19 <.001 IADLs .23 <.001 .20 <.001 .22 <.001

SOCIAL WORK IN PUBLIC HEALTH 135

respondents who lived with an adult child (p = .003), with a spouse/partner (p <.001), and those living in an “other” arrangement (p = .002). Finally, there was a relationship between living arrangement and social loneliness, and post-hoc comparisons indicated respondents who lived alone had higher levels of social loneliness compared to respondents who lived with an adult child (p = .002), with a spouse/ partner (p <.001), and those living in an “other” arrangement (p = .042).

Type and quality of contact, and social and physical health
The correlations among the social interaction, physical health, and social health variables are described
in Table 3. The analyses revealed statistically significant relationships between all the potential risk and
protective factors and loneliness, emotional loneliness, and social loneliness at p < .001. In particular, face-to-face contact, phone contact, text contact, group/organization contact, quality of contact, a supportive community, physical health, and cognitive health were negatively associated with lone- liness, emotional loneliness, and social loneliness. Social isolation, difficulty with ADLs, and difficulty with IALs were positively associated with loneliness, emotional loneliness, and social loneliness.

Risk and protective factors contributing to loneliness

Loneliness
The results of the regression analysis for loneliness indicated that seven variables explained 70% of the
variance. As Table 4 reports, being single/never married, living alone, less phone contact, lower quality
of interaction, less community supports, higher levels of social isolation, and poorer subjective
physical health were associated with higher levels of loneliness. Collinearity diagnostic tests indicated
no problems with multicollinearity in this model (Durbin-Watson = 2.09, tolerance >.2, variance
inflation factor <10; Field, 2013).

Emotional loneliness
The results of the regression analysis indicated that four variables explained 60% of the variance. As
Table 4 reports, living alone, less phone contact, and lower quality of interaction, and higher levels of
social isolation were associated with higher levels of emotional loneliness. Collinearity diagnostic tests
indicated no problems with multicollinearity in this model (Durbin-Watson = 2.00, tolerance >.2,
variance inflation factor <10; Field, 2013).

Social loneliness
The results of the regression analysis indicated that seven variables explained 61% of the variance. As
Table 4 reports, being single/never married, less phone contact, lower quality of interaction, less
community supports, higher levels of social isolation, poorer subjective physical health, and higher
subjective cognitive health were associated with higher levels of social loneliness. Collinearity diag-
nostic tests indicated no problems with multicollinearity in this model (Durbin-Watson = 2.06,
tolerance >.2, variance inflation factor <10; Field, 2013).

Discussion

Results of this study support previous research regarding loneliness. First, this sample was moderately
lonely overall, which is reflective of previous findings that found around 33% of older people are lonely
(Wilson & Moulton, 2010). Subjective physical health was significant in the multivariate analyses for
overall loneliness and social loneliness, which has been found in previous studies (e.g., Wilson &
Moulton, 2010); however, social isolation, quality of contacts, and phone contact had greater impact in
all three regression analyses based on their effect size. Nonetheless, subjective physical health played
a larger role in explaining the variance for social loneliness, which echoes previous research that
suggested that those who have fewer age-related physical impairments report less loneliness (Theeke,
2010). Relatedly, ability to complete ADLs and IADLs, while not significant in the regressions, were

136 B. TEATER ET AL.

reported to be high among this sample, which further supports this past research and points to the role
of health and mobility as protective factors for loneliness.

Relationship status, particularly being single/never married, played a small role in the explanation
of overall loneliness and social loneliness, which was also found in past research (Dahlberg,
Andersson, McKee, & Lennartsson, 2015; Wilson & Moulton, 2010). Socializing both with a partner

Table 4. Factors contributing to loneliness, emotional loneliness, and social loneliness (N = 450).

Variable B SE B ß t p

Loneliness
Gender −.19 .79 −.01 −.23 .815
Age .06 .08 .02 .81 .418
Relationship Status 2.51 1.14 .06 2.20 .028
Living Arrangement 2.01 .97 .07 2.07 .030
Face-to-Face Contact −.19 .22 −.03 −.89 .375
Phone Contact −.58 .17 −.11 −3.43 .001
Text Contact −.13 .17 −.03 −.77 .445
Group Contact −.01 .19 −.00 −.03 .974
Quality Contact −2.89 .43 −.27 −6.79 <.001 Community Supports −.61 .27 −.07 −2.27 .024 Social Isolation 3.86 .27 .49 14.21 <.001 Physical Health −.92 .41 −.08 −2.27 .024 Cognitive Health .28 .45 .02 .62 .533 ADLs −1.19 1.18 −.05 −1.01 .313 IADLs 1.47 1.17 −.07 1.26 .208 Adjusted R2 .67 F 66.60* Emotional Loneliness Gender .12 .53 .01 .23 .818 Age .04 .05 .26 .83 .409 Relationship Status 1.13 .77 .05 1.47 .141 Living Arrangement 1.30 .66 .08 1.98 .048 Face-to-Face Contact −.06 .15 −.02 −.41 .681 Phone Contact −.23 .11 −.08 −2.03 .043 Text Contact −.03 .12 −.01 −.27 .790 Group Contact −.09 .13 −.02 −.73 .464 Quality Contact −1.05 .29 −.16 −3.65 <.001 Community Supports .14 .18 .03 .80 .424 Social Isolation 2.66 .18 .57 14.49 <.001 Physical Health −.23 .27 −.03 −.84 .400 Cognitive Health −.28 .31 −.03 −.93 .356 ADLs −.54 .79 −.04 −.67 .501 IADLs .58 .79 .04 .04 .464 Adjusted R2 .59 F 43.43* Social Loneliness Gender −.31 .46 −.02 −.67 .506 Age .02 .04 .01 .43 .665 Relationship Status 1.38 .67 .07 2.07 .040 Living Arrangement .71 .57 .05 1.25 .213 Face-to-Face Contact −.14 .13 −.04 −1.04 .297 Phone Contact −.35 .09 −.13 −3.52 <.001 Text Contact −.10 .10 −.04 −1.00 .318 Group Contact .09 .12 .03 .79 .431 Quality Contact −1.84 −.25 −.33 −7.40 <.001 Community Supports −.75 .16 −.16 −4.79 <.001 Social Isolation 1.21 .16 .29 7.57 <.001 Physical Health −.69 .24 −.11 −2.91 .004 Cognitive Health .56 .27 .08 2.13 .034 ADLs −.66 .69 −.06 −.95 .343 IADLs .89 .69 .08 1.31 .191 Adjusted R2 .59 F 44.72*

B = unstandardized beta; SE B = standard error for the unstandardized beta.
*p <.001

SOCIAL WORK IN PUBLIC HEALTH 137

and also as a couple may help prevent feelings of loneliness and also decrease the likelihood of social
isolation. Likewise, living alone played a small role in the explanation for overall loneliness and
emotional loneliness. When living alone, socializing can seem more daunting as these activities have
to be endeavored solo, which may increase social isolation in an older person. At the bivariate level,
single/never married participants had the greatest level of loneliness whereas those participants who
were married or dating had the lowest levels of loneliness. The importance of relationships in older
adulthood has been studied extensively, and research suggests that relationship quality has positive
impact on both health (Umberson, Williams, Powers, Liu, & Needham, 2006) and well-being
(Sherwood, Kneale, & Bloomfield, 2014). In a qualitative study of more than 1,500 participants 55
and older in an ongoing relationship, companionship and laughter were amongst the “best liked”
elements of the relationship (Chonody & Gabb, 2019).

In addition to the quality of the contacts, which had a moderate effect size in the overall regression
analyses, frequency of contact also played a small role, which has been found in other studies where
frequency of contact with social networks was important in feelings of loneliness (Domènech-
Abella et al., 2017; Rico-Uribe et al., 2016). Surprisingly, phone contact was significant in the multi-
variate analyses, but face-to-face contact was not. Similarly, contact via texting and contact that occurs
in groups and organizations were not significant either. Perhaps phone contact helps when there is
a physical issue or a mobility problem, and emotional support can be garnered through phone contact
with friends and loved ones. This type of contact also bridges geographical gaps, which in our
increasingly mobile society could be a significant issue in older adulthood, not just in the case of
adult children who have moved away, but also close friends. Equally, phone contact can be a way to
reach adults who are at risk of experiencing loneliness in the time of public health crises, such as the
novel corona disease 19 (COVID-19) pandemic that is taking place at the time of writing this
manuscript. When the public is practicing “social distancing” during a pandemic, and older adults
are being urged to socially isolate, the use of phone contact may serve as a protective factor to lessen
the feelings of loneliness both by family and friends, but also volunteers through befriending services,
or organized peer-support groups.

Limitations

The results of this study should be considered within the context of its limitations. First our sample
was not representative of the older adult population, particularly in terms of race/ethnicity. The
findings from this predominately White, heterosexual sample cannot be generalized to other groups.
This may in part be explained by the research that indicates African American/Black and Latinx
workers are underrepresented on MTurk (Berinsky, Huber, & Lenz, 2012). Relatedly, use of MTurk
workers limits generalizability given that they may be more tech-savvy, which could have implications
for negotiating loneliness in more modern ways, such as the use of social media and apps (e.g., Marco
Polo, Groupme). Additionally, past studies have indicated that MTurk workers are likely to be in
better health, have more education, and more likely to be unemployed (Goodman, Cryder, & Cheema,
2013). Nonetheless, these samples “are more representative of the US population than in-person
convenience samples” (Berinsky et al., 2012, p. 351). Replication with a more diverse group of older
people, including those who are not internet users, would be important to our understanding of risk
and protective factors for loneliness.

Secondly, income was not included in the survey, which has been found to be associated with
loneliness (Cohen-Mansfield, Hazan, Lerman, & Shalom, 2016; Niedzwiedz et al., 2016; Wilson &
Moulton, 2010). Financial resources may play a key role in different types of loneliness given that it can
increase accessibility, and future research should explore its role in this complex interplay of factors.
Third, the measures for loneliness and social isolation may have influenced the significance of the risk
and protective factors. Expanding the measurement strategy would be important for replication
studies and would allow a greater assessment of these interrelated concepts. Similarly, while the
explained variance was quite high across the three analyses, additional risk and protective factors

138 B. TEATER ET AL.

were not included in this study. Other variables, such as income and stressors, could provide
additional insight into these predictors. Despite the limitations, this study has contributed to the
knowledge base by highlighting the importance of type and quality of contact in potentially serving as
protective factors to loneliness among older adults.

Implications for practice

The findings of this study have replicated findings from previous studies, but also add to this
substantive literature base, particularly when considering them in the time of a public health
emergency. The three significant variables common across the three regression analyses indicate
social isolation explains loneliness, yet the use of phone contact and the quality of contact can serve
as protective factors for loneliness. These findings, along with the significance of community
supports for loneliness and social loneliness, provide the foundation for the following recommen-
dations for future emergency preparedness that specifically recognizes the health needs of older
adults in the time of a public health emergency. First, community supports explained a small but
significant effect size in explaining loneliness, particularly social loneliness. This highlights the need
for supportive and age-friendly communities that provide ways to increase the ability for older
adults to leave their home and use the physical space of their community, particularly in times of
social distancing when interacting with others is not a possibility. Participating in walks outside of
their home or places to sit in the outdoors, even alone, may assist with physical and mental health,
which in turn may serve as protective factors to loneliness. Second, there is a need to enhance the
use of telehealth and video conferencing to meet the physical and mental health needs of older
adults, which will also serve as a preventative measure to reduce social isolation which has been
found to be highly correlated with loneliness. This study found phone contact to serve as
a protective factor for loneliness, therefore, this type of contact in addition to the availability of
telehealth and video conferencing need to become a usual way of reaching and communicating with
older adults. This will require increasing the education and training of social work and public
health workers in the use of telehealth, embedding telehealth into routine service delivery, and
ensuring financial reimbursement for such services (Smith et al., 2020). Finally, older adults who
are socially isolating during a public health emergency should be identified and contacted six
months after release from isolation (Torales, O’Higgins, Castaldelli-Maia, & Ventriglio, 2020) to
assess loneliness. Gardner, States, and Bagley (2020) argue that “isolation and loneliness is
a medical problem in its own right” (p. 5), and understanding the risk and protective factors can
serve as a starting point for prevention and intervention strategies to address this public health
concern.

As we experience this public health emergency, fresh insights into how people fought feelings of
loneliness will add to our understanding of how to mobilize both new and old connections in
creative ways. Using apps, such as Marco Polo, or setting up dinners on Zoom are some of the ways
that people are continuing to connect, and our research suggests that contact does not need to take
place face-to-face to have an impact on loneliness. Programming for older adults, both those who
are aging in place and those who may be living in a facility, can also take advantage of this
technology to help keep social connections alive. For example, a global campaign emerged from
the United Kingdom to “adopt a grandparent” to foster intergenerational friendships and cross-
cultural learning experiences for older people living in care facilities (see https://chdliving.co.uk/
adopt-grandparent). Innovation coupled with technology can help combat the growing social
disconnection that many older people feel.

Disclosure statement

No potential conflict of interest was reported by the authors.

SOCIAL WORK IN PUBLIC HEALTH 139

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SOCIAL WORK IN PUBLIC HEALTH 141

Indian Journal of Gerontology

2015, Vol. 29, No. 3, pp. 259–282

Promoting Active Ageing Through the Use of
ICT: From Global and Indian Perspective

Soumyadeep Chakrabarti, Sohom Karmakar and

*Somprakash Bandyopadhyay

Department of Electronics and Telecom Jadavpur University.

*Management Information Systems, Indian Institute of Management,

Calcutta

ABSTRACT

With the advent of science especially in the areas of medicine and
physiology the average life expectancy has been on the rise since the
past few decades. This, along with decreasing infant mortality rate,
has led to an increase in the elderly population all over the globe.
With a thriving elderly population the concept of active ageing has
gained traction in the last few years and modern society has found
widespread application in this area. Not surprisingly, active ageing
has benefitted largely from use of Information and Communi-
cation Technologies (ICT). It has profound implications in
educational institutions, labour markets, social justice, medical
care, long term care and relationship between generations. With
the ever growing popularity of nuclear families, the condition of
the elderly population seems to have taken a backseat in recent
years. With children moving away for the sake of careers the older
generation finds itself under the care of professional agencies which
provide a kind of social security but do not really provide any sense
of “activity” to nourish the mind. To address this problem, the
theory of active ageing aims to include better opportunities for
people to continue working as they grow old and contribute to
society in some way or the other. Active ageing has found many

advocates whose policies tend to improve individual quality of life.
This paper presents the current situation of market in Europe and
United States where active ageing through ICT is already an estab-
lished concept. Further, a brief overview of the market situation in
India has been discussed along with further scope of implication in
this sector.

Key words: Active Ageing, Quality of Life, Telecare

From its very inception, studies on ageing have not only provided
description and mechanisms of ageing phenomenon, but have also
enhanced the reservoir of existing knowledge required for the change
in living situation of the old which would positively affect their ageing
process. They have influenced policy decisions of both the private as
well as the government sectors since the first world assembly on ageing
in 1982 to the first global consensus on providing dignified care of the
elderly in the form of the Madrid International Plan of Action on
Ageing (United Nations, 2008) in 2002. The concept of “active ageing”
refers to the method of ageing by which people maintain a high quality
of life as they age, ensuring that they not only receive passive help
from the society but can also engage in its activities. One of the basic
challenges of research on ageing concerns the question whether active
ageing (Tesch-Roeme, 2012), is possible and if so, which factors enable
individuals, social groups, and societies to grow older healthily and
actively. Three highly important domains on quality of life need to be
considered regarding any discussion on active ageing: health, social
integration, and participation. Active ageing is normally synonymous
to successful ageing. Successful ageing in general includes three main
components: low probability of disease and disease-related disability,
high cognitive and physical functional capacity, and active engagement
with life. For successful implementation of active ageing the following
basic requirements have to be fulfilled.

Early Awareness of Active Ageing

Active ageing should incorporate diverse aspects of life (even
before seniority is attained) such as volunteering in childhood and
adolescence and education and healthy behaviour. Of these, education
has the greatest effect visible in old age.

260 Indian Journal of Gerontology

Offer Opportunities for Active Ageing Also Later in the Life Course

Lack of energising social integrand and stimulating volunteer
activities are prime examples of vanishing active ageing investment
even in middle and late adulthood. Even though studies show that
changes in health and participation are possible up to late adulthood,
the changes are practically growing obsolete. Moreover, efficiency of
interventions decreases as one grows older. It is therefore the responsi-
bility of the respective authorities to provide life-long health education
for the aged along with sustainable environment for everyone,
irrespective of their age.

Improve Societal Frameworks for Active Ageing

Active ageing needs a secure base. Health and participation in late
life can be fostered by societal frameworks. Results from comparative
surveys (United Nations, 2005), show that the extent of welfare state
support – through social security systems like unemployment
allowance, pension and prolonged elderly and medical care system –
seems to be connected to opportunities for active ageing. Although the
instruments for building social security differ between societies,
governments may provide regulation for the combined effects of
different stakeholders. Highly relevant is the prevention of poverty, as
poverty bears the high risk of social exclusion. Combing poverty will
also help to reduce health inequalities and increase the chances to take
an active part in society.

Pay Attention to Images of Ageing

Societal and individual conceptions of ageing influence develop-
mental trajectories over the life span. The societal images of ageing
have a profound impact on proper utilization of the potentials of
active ageing dealing with the restrictions of frailty and dependency in
old age. Inflicting new “images of ageing” into the consciousness of the
general public might show that older people are a potential societal
resource. It should be noted, however, that purely positive images of
ageing do not do justice to frail, old people in need of care. Hence,
images of ageing should be inclusive and embrace both potentials and
risks of old age.

Promoting Active Ageing Through the Use of ICT 261

With the recent developments and breakthroughs in portable
communication technology and computing systems, Information and
Communication Technologies (ICT) has been given a central role to
play in the advancement of active ageing. Due to varying levels of
importance attached to the development of these new technologies by
the policy-making bodies of different countries, ICT has faced
different challenges and achieved different levels of penetration as we
will see in the following section.

Situation of the Market in Europe and Beyond

The general background to this study was derived from the trend
towards an ever increasing ageing population (United Nations, 2012)
and this has been observed across Europe and beyond for some time
already. For Europe and many other countries around the world, the
on-going demographic development has significant socio-economic
implications: in the future, there will be more older people both in
numbers as well as in percentage of the population. The very-old
section will particularly experience a boom, there will be a decrease in
their family support system, and there will be a smaller productive
workforce to contribute to the creation of economic wealth as well as
to the financing of health and social services in particular.

During recent years, the social and economic challenges
connected to these developments have received increasing policy
attention. In this regard, the potential offered by Information and
Communication Technologies (ICT) is of paramount importance in
order to cope with them in an efficient manner. Recently, the
European Commission has adopted an Action Plan on Information
and Communications Technology for Ageing where it is highlighted
that better utilization of the potential provided by ICT for
independent living in an ageing society represents both a social
necessity and an economic opportunity. More specifically, it is
emphasised that ICT holds the key for more efficient management and
delivery of health and social care for the aged population thereby facil-
itating active ageing (Organisation for Economic Cooperation
Development, 2007).

262 Indian Journal of Gerontology

Advancement in Telecare

This section focuses on telecare services, one of the most
important examples of ICT. Telecare is defined for current purposes
mainly in the form of ICT-supported remote social care services. It is
the “continuous, automatic and remote monitoring of real time
emergencies and lifestyle changes over time in order to manage the
risks associated with independent living” as defined by Tunstall, the
leading telecare developer in the world.

Telecare systems essentially perform two basic functions:

1. Detect and Record Emergencies: These systems control processors
which process the signals from sensors and detect events such as
major falls or heart-attacks, in which case, carers are immediately
notified. These systems can also compute the time variation of
minor events monitored over a long time and this data in the
form of graphs, etc. is useful for caregivers to determine any
change necessary in the course of treatment.

2. Reduce chances of an emergency: As an illustration we can consider
a sound-producing device attached to asthma inhalers for the
elderly which can be remotely activated in order to aid in finding
them easily in case of an impending asthma attack.

Telecare includes social alarm services, also known as first gener-
ation telecare, and more advanced telecare services involving
additional sensors and other variants. Figure 1 represents the use of

Promoting Active Ageing Through the Use of ICT 263

Figure 1

Age -related utilisation of social alarms among the 50+ population in five

EU countries with the age groups listed on the vertical axis

(Kubitschke and Cullen, 2010)

social alarms among 50+ populations in the four European countries
mentioned below. United States of America is the only non-European
example included in the following list.

Germany

Social alarm services have been provided in Germany for more
than 25 years and are available throughout the country. Nearly 90 per
cent of the social alarm services are provided by six large social welfare
organisations. The rest of the market is made up by commercial
providers, such as Recontrol, Tunstall, Vitaphone, HausNotruf
Service GmbH and Bosch (Kubitschke and Cullen, 2010). In addition,
an increasing number of housing organisations are providing social
alarm services, e.g. the housing societies in Wuppertal or in
Gelsenkirchen within the framework of SOPHIA. Some of the service
providers also offer mobile alarms alongwith GPS localisation. Mobile
alarms are not widely used, nor is, since reimbursement in these types
of services within the existing framework of the long term care
insurance possible yet. The social welfare organisations that are
providing the social alarm services often have their own call centres.
There are around 180 call centres run by welfare as well as commercial
organisations in Germany. While some forms of telecare are widely
available in the form of enhancements to basic social alarms (e.g.
smoke detectors, gas detectors, fall detectors or movement detectors),
in practice there is rather little usage of anything other than basic
alarms. Some social alarm providers offer additional services such as
organisation of home- and outpatient services, and reminder calls
(partly automated), although the latter appear not to be much in use.
Apart from social-alarm based telecare, there are only a few other
telecare services up-and-running in the marketplace. One example is
the SOPHIA service which is a commercial picture-based care and
communication service for old people, operated as a regional franchise
company which seeks to extend operations nationwide. The service
model is for a new standard for safety and security, communication,
comfort, telemedicine, multimedia and facility management. It is
currently the only picture communication service. Several other
efforts to establish comparable services on the German senior market
failed. Telecare devices and services are yet not listed in the eligibility
catalogues of insurers, which means that costs are not reimbursed

264 Indian Journal of Gerontology

under the insurance systems and have to be paid for out of pocket. The
government here has also helped in setting up the research programme
on Ambient Assisted Living (AAL), jointly organised by different
countries across Europe.

France

Social alarm services are widely available throughout the country
and are provided at the level of counties and municipalities. Service
operation may include various players such as local fire departments,
commercial organisations and insurance companies. Uptake of social
alarms is estimated at about 3 per cent of the population aged 65 and
above. Existence of considerable variation in end user charges across
the country has been reported. It is estimated that the average monthly
service charge ranged between 25 and 35 Euro (Kubitschke and Cullen,
2010). Beyond this, sometimes an initial installation charge may be
imposed on the end user, which may amount to about 50 Euro. Social
funding is estimated to range between 30 per cent and 50 per cent of
monthly costs, while in some parts of the country the service has been
reported to be provided free of charge. Users who are eligible to
receive support under the social benefit scheme can receive full cost
reimbursement.

United Kingdom

The UK has a well-developed infrastructure of community alarm
services provided by local housing authorities, social service organiza-
tions and voluntary and private sectors. Social alarm services are
provided to both section of people, those who are living in sheltered
housing and those in ordinary housing in the community. There is
also a significant private subscriber market. Overall, there are an
estimated 1.5–1.6 million people using some form of social alarm in
the UK, representing about 15 per cent of those aged 65 years or older
(Ibid). Most local authorities run an alarm scheme, either directly
provided by themselves or with outsourcing to a private supplier. In
general, it seems that outside the sheltered housing context, family
carers are typically the main responders once the call centre has been
alerted, although in some areas the social care services also provide a
mobile response team in addition to the nominated informal carer
response. The charging/reimbursement situation varies across local

Promoting Active Ageing Through the Use of ICT 265

authorities. As a general rule, it seems that equipment is provided free
of charge to those with an assessed need and users pay a monthly usage
charge unless they are eligible for a waiver on the basis of low income.
User costs may vary from 10 to 25 euro per month, depending on
location and provider. In recent years, social care authorities have been
putting into place telecare sensor services (e.g. smoke, heat, flood
detectors) and the UK is on the verge of taking telecare into the
mainstream. This has been driven by policy and funding, including the
Preventative Technology Grant in England and other programmes on
telecare in Scotland, Wales and Northern Ireland. It has been reported
there were nearly 1,50,000 new telecare users in England in 2006/7,
and a further 1,61,000 in 2007/8. This approximately amounts to
about 3 per cent of the population aged 65 years or older who are
receiving ‘telecare’. Provision and charging approaches vary consid-
erably across local authorities. In general, the most common approach
of telecare sensor services seems to be similar to that of social alarms
although sometimes at a higher level because of the additional extras
provided. Preventative Technology Grant funding is given to councils
in England with expectation that they will work with volunteers and
government authorities in housing to establish new services. Some
local authorities/primary care trusts have recently claimed to be
providing mainstream telecare services. It would appear that telecare is
now embedded in government health and social care policy but it is
yet to be fully embedded in mainstream services. The Scottish
government have been promoting telecare service provision through a
Telecare Development Programme since 2006. Regional care
providers have started providing practical and implementable
solutions tailored to the local environment. The Welsh Telecare
strategy which was launched in 2005 gives grants to local authorities.
A Telecare capital grant of £9 million has been made available (with a
policy target of providing 10,000 homes with telecare equipment),
together with additional money to support the development of
telecare strategies. All 22 Welsh local authorities have now produced
telecare strategies, which in many cases are very ambitious. Based on
monitoring reports it is expected that by the end of the grant period
some 45,000 people will be using a telecare service other than a
community alarm (this would be about 7 per cent of the population

266 Indian Journal of Gerontology

aged 65 years and older). The Minister for Health, Social Services and
Public Safety in Northern Ireland announced a grant of £1.5 million in
January 2008 for pilot projects to promote the development of new
technologies to assist people to live at home over the next two years.
The European Centre for Connected Health was established at the
same time to promote improvements in patient care through the use of
technology and to fast track new products and innovation in health
and social services. Substantial investment was planned to use remote
tele-monitoring to improve care for people with chronic conditions.

Italy

Social alarm services are widely available, although many local
service offerings seem to have emerged only during recent years.
Today, the major municipalities in Italy seem to have initiated social
alarm schemes and in some cases such schemes have been initiated by
the Provinces. Uptake is estimated between 1 per cent and 2 per cent
of the overall population aged 65 years and above (Kubitschke and
Cullen, 2010). In many cases the technical infrastructure, notably
alarm centres, and the service itself are operated by commercial service
providers or third sector organisations. This accords with the general
situation in Italy where social and welfare service frameworks are
determined on local or regional administrative levels and are often
complemented by services provided by commercial and/or voluntary
organisations. There seems to be no general charging model that
applies across the whole country. Individual examples suggest that
users tend to be charged a monthly service fee of about 20–40 Euros.

Promoting Active Ageing Through the Use of ICT 267

Figure 2

Sector-wise utilisation for social alarms in the European countries

(Kubitschke and Cullen, 2010)

Under certain circumstances users may be eligible to use the service
free of charge.

Figure 2 illustrates the utilisation rate of telecare in different
countries of Europe.

United States

Social alarms are known and used as personal emergency response
systems (PERS) throughout USA. There are both national and local
providers, including private companies, hospitals and social service
agencies. It has been estimated that about 2.3 per cent of the
population aged 65 years and older use social alarms (Ibid). The main
forms of provision are either linked to healthcare facilities or private
companies. In the former case, the response may often be provided by
staff employed by the healthcare facility; in the latter case, response
would normally be by local, user-nominated contacts. Historically,
the focus seems to have been especially on provision by hospitals or
other healthcare facilities with a view to reducing bed-occupancy and
other costs. There also has been provision by religious/charities as a
more social welfare oriented service, and by manufacturers and
security companies. Most PERS are purchased out of pocket by the
individual or their family members. Purchase prices range from $200
to more than $1,500. There are additional charges for installation and
monthly monitoring ranging from $10–$30.

In America, there has been an overall increase in interest in
telecare, with the emphasis apparently more on healthcare than social
care in a wider sense. Such ‘telecare’ services are provided by a range of
providers including medical practice sites, hospitals and social service
providers, both public and private. The availability of services varies
from state to state with little or no coherence in application or utili-
zation. The extent of take-up varies hugely across the country and
there is no data available on the extent of take-up. To date, the
Veterans Administration healthcare system seems to be the main
provider of telecare services with an independent living focus, even
though the main focus of its remote support monitoring is telehealth.
Some of the services have been mainstreamed. In Florida, for example,
the Low ADL Monitoring Program (LAMP) is a Community Care

268 Indian Journal of Gerontology

Coordination Service (CCCS) program designed to address the needs
of veterans with activities of daily living (ADL).

Summary of Benefits Obtained and Preliminary
Identification of Barriers

A successful telecare application is seen to have certain established
benefits:

1. The most important benefit is the improvement in patient
prognosis, including both the number of emergency hospital
admissions and mortality rate.

2. The old will also be able to live a more independent life, taking
care of themselves with their dignity intact.

3. Also, the respective governments benefit from the decrease in
monetary benefits (given to people with disability) and higher tax
returns which in turn leads to more spendable income.

4. Finally, ICT in the form of telecare has been a boon to unpaid
caregivers as it allows them to pursue paid employment in
addition to the care-giving job and also gives further assurance
about the security and well-being of the elders.

The extent of mainstreaming of home telehealth is very limited to
date and in many countries no major drivers can yet be discerned. In
general, increased attention being given to more effective management
of chronic diseases and increase in importance of this with population
of ageing provides the most important underlying driver, even if this is
not leading to a lot of mainstream telehealth yet (Figueras, et al., 2008).

In relation to first generation telecare, the key factors of influence
seem to vary considerably across countries. In fact, some countries
may already be at ‘saturation’ point to a certain degree (Solow, 1956)
and thus have no concrete barriers, as such, to the achievement of
higher penetration levels. Underlying this may be some important
variability in perceptions of the role of social alarms in social care, and
of where it fits in the spectrum of human and other services that are
needed. More generally, where they exist, the main barriers appear to
be limited public provision and lack of public funding and disparities
in geographical availability in some countries. It also seems that
technology and, especially, technological change may be a limiting

Promoting Active Ageing Through the Use of ICT 269

factor in some countries, for example upgrading old systems to work
with new digital telecommunications networks and providing services
to IP telephony user.

Role of ICT In Ageing: An Overview of the Situation in India

Ageing of population is a major aspect of the process of
demographic transition. The developed regions of the world being
ahead of the developing countries with respect to demographic
transition have already experienced its consequences and the devel-
oping world is currently facing the consequences. Even though the
relative number of elderly in some developed countries seems to be on
the lower side, the sheer population size of these countries signifi-
cantly increases the absolute numbers (Chen, 1998). There has been a
spurt in the studies focused on developing countries’ elderly
population: this can be understood to be the result of the deteriorating
living conditions of the elderly in these countries. Natural
demographic change account for the increasing numbers while the
shift in traditional family structure due to modernisation and
migration of younger family members is to blame for the
socio-economic degradation of the elderly.

Projected increases in both the absolute and the relative sizes of
the elderly population in many third world countries are a subject of
growing concern for public policy. Such increases in the elderly
population are the result of changing fertility and mortality regimes
over the past 40 to 50 years. The combination of high fertility and
declining mortality during the twentieth century has resulted in large
and rapid increases in elderly populations as successively larger cohorts
step into old age. Further, the sharp decline in fertility experienced in
recent times is bound to lead to an increase in the population of the
elderly in the future. Besides, given that these demographic changes
have been accompanied by rapid and profound socio-economic
changes, cohorts might differ in their experience as they join the ranks
of the elderly. Against this backdrop, we may now preface our
discussion with an account of the structure and size of the elderly
population. The number of elderly in the developing countries has
been growing at a phenomenal rate; in 1990 the population of persons
aged 60 years and above in the developing countries exceeded that of

270 Indian Journal of Gerontology

the developed countries. According to present indications, most of this
trend of growth would take place in developing countries and over
half of this would be in Asia. Obviously, the two major population
giants of Asia, namely India and China would contribute a significant
proportion to the growth of the elderly.

In India, the 2011 census has shown that the elderly population
consisting of 28 states and 7 Union Territories accounted for 97
million. In 1961, the elderly population had been only 24 million; it
increased to 43 million in 1981 and to 57 million in 1991. The
proportion of elderly persons in India has increased from 5.63 per cent
in 1961 to 6.58 per cent in 1991 and to 8 per cent in 2011. Within the
elderly population, persons aged 70 and above have also grown
rapidly; from a mere 8 million in 1961 to 21 million in 1991 and to 40
million in 2001. The growth rates among the different groups of the
elderly, namely 60 years plus, 70 years plus and 80 years plus during
the decade 1991–2001, were much higher than that of the general
population growth rate of 2 per cent per annum (Bose and
Shankardass, 2004), a trend continuing to this day. Available findings
on ageing suggest that fertility as compared to mortality has played a
predominant role in the ageing process. As far as India is concerned,
there has been a substantial reduction in mortality compared to
fertility since 1950. For instance, while the crude birth rate declined by
52 per cent from 47.3 during 1951–61 to 22.8 in 1999, the crude death
rate fell more steeply by 70 per cent from 28.5 to 8.4 during the same
period (Chakraborti, et al., 2004). Logically, therefore, India is
expected to undergo a more rapid decline in fertility in the immediate
future than mortality because mortality has already fallen to an
extremely low level. The ageing process in India is expected to be,
therefore, faster in the years to come than in other developing
countries. Moreover, the transition from high to low levels of fertility
is expected to narrow down the age structure at its base and broaden it
at the top (D’Souza, 1989). In addition, improvement in life expec-
tancy at all ages would allow more old people to survive thus
intensifying the ageing process. In this context, an examination of the
rising trends in life expectancy indicates that the gain is going to be
shared more and more by elderly people, a process which would make
them live even longer (Clark, et al., 1997). The size of India’s elderly

Promoting Active Ageing Through the Use of ICT 271

population aged 60 and above is expected to increase from 77 million
in 2001 to 179 million in 2031 and further to 301 million in 2051. The
proportion is likely to reach 12 per cent of the population in 2031 and
17 per cent in 2051. The number of elderly persons above 70 years of
age (old-old) is likely to increase more sharply than those of 60 years
and above. The old-old are projected to increase five-fold during
2001–2051 – from 29 million in 2001 to 132 million in 2051 (Bordia
and Bhardwaj, 2003). Their proportion is expected to rise from 2.9 per
cent to 7.6 per cent.

Health Concerns of the Old in India

Health care of the elderly is a major concern of a society as old
people are more prone to morbidity than young age groups. Ageing is
invariably accompanied by multiple physical ailments, but the less
publicly acknowledged fact is that the aged are more prone to mental
ailments as well, which arises from nervous system disorders, old-age
and perceived quality of life including comfort and independence.

272 Indian Journal of Gerontology

Figure 3

Dependency Status among the Elderly

(Irudaya Rajan, et al., 2003)

Preliminary studies by government and private organisations point to
the deplorable health status of the Indian elderly population.

The proportion of the sick and the bedridden among the elderly
is found to increase with age; the major physical disability consists of
blindness and deafness. A study of urban elderly in Gujarat found
deteriorating physical conditions among two-thirds of the elderly,
such as poor vision, impairment of hearing, arthritis and loss of
memory. An interesting observation made in this study relates to the
sick elderly’s preference for treatment by private doctors. Besides
physical ailments, psychiatric morbidity is also prevalent among a
large proportion of the elderly. An enquiry in this direction provides
evidence of psychiatric morbidity (Darshan, et al., 1987) among the
elderly. A sharp distinction between the functional and organic aspects
of ailments is suggested by a large number of studies. Functional
disorder strikes first and gradually develops into organic disorders
around the age of seventy. Another rural survey reported that around
5 per cent of the elderly were bedridden and another 18.5 per cent had
only limited mobility. Given the prevalence of ill health and disability
among the elderly, it was also found that dissatisfaction existed among
the elderly with regard to the provision of medical aid. The sick
elderly lacked proper familial care and that public health services were

Promoting Active Ageing Through the Use of ICT 273

Figure 4

Health Service by Elderly (Irudaya Rajan, et al., 2003)

insufficient to meet the health care needs of the elderly. The uptake of
healthcare from different sources is illustrated in Figure 4.

Among the elderly, 80 per cent died at home and only 17 per cent
died in hospitals (9 per cent in government hospitals compared to 8 per
cent in private hospitals). Similarly, close to 30 per cent of the elderly
had not received any medical attention before death (D’Souza, 1989).
A few had been examined by medical practitioners. One in three was
reported to have died of old age. More than 5 per cent of the elderly
died due to causes such as disorders related to the lungs, blood circu-
lation and digestion.

Approximately 50 per cent of all elderly Indians are under
lifelong medication for at least one chronic disease and this trend is
stronger among the urban population. The Eastern region led all the
other regions in India in the matter. The percentage of elderly (two
out of three) suffering from at least one chronic disease was the highest
in this region. It was followed by the South; the lowest proportions
were in the North and North-Western regions of India. Similarly, one
out of every five elderly reported suffering from two chronic diseases
canvassed in the NSS; from Figure 5, we can see that close to three per
cent suffered from three chronic diseases.

274 Indian Journal of Gerontology

Figure 5

Reported Chronic Diseases in Old Age (Irudaya Rajan, et al., 2003)

Five types of disabilities of the elderly were probed by the NSS:
visual impairment, hearing problem, difficulty in walking (locomotor
problem), problems in speech and senility. The prevailing disability
demography in India (Ibid) is illustrated in Figure 6 and Figure 7.

Promoting Active Ageing Through the Use of ICT 275

Figure 6

Number of Disabled per 1 Lakh Elderly Persons for Different Types of

Disability (Irudaya Rajan, et al., 2003)

Figure 7

Percentage of Differently Abled Old Age Population

(Irudaya Rajan, et al., 2003)

Twenty-five per cent of the elderly in India suffered from visual
impairment, followed by hearing difficulties (14%) and locomotor
disability and senility (each 11%). The prevalence rates of all the five
disabilities were higher in rural than in urban areas (James, 1994).
Except in respect of visual impairment, women were ahead of males in
respect of the disabilities. Though the elderly in India tend to suffer
from many ailments, particularly the old-old and the oldest old, they

276 Indian Journal of Gerontology

Figure 8

Percentage Distribution of Elderly Men of Various Age Group by State of

Physical Mobility (Ibid)

Figure 9

Per cent of Elderly Women of Various Age Groups by State of Physical

Mobility (Irudaya Rajan, et al., 2003)

do not undergo proper medical treatment due to absence of a compre-
hensive health insurance scheme; this is particularly true in the case of
the poorer elderly (Gulati and Irudaya Rajan, 1999). One such
disability is the lack of physical mobility which affects a large
population of India as can be seen from Figure 8 and Figure 9.

Dependency among the elderly population in India is illustrated
in Figure 3 which shows high degree of dependence across the
rural-urban divide. This dependence is not only of economic origin
but is also associated with first-hand care, as can be seen from the
demographically differentiated graph in Figure 10.

Daily Life Assistance: An Illustration

Consider a retired octogenarian who is living all by his own in
the outskirts of the city. In spite of his age related physical limitations
he seems perfectly at ease largely due to a well organised and holistic
ICT network which caters to his everyday needs. A system installed in
his house provides a proactive environment with a range of intercon-
nected sensors, devices and smart appliances working together to
provide a safe and secure place to live. These appliances are easy to use
due to their customized interfaces and are connected to the neigh-
bourhood care centre. This allows, when necessary, remote operation
by authorized personnel. As part of the system infrastructure, the
smart phones of his children also interact with his home during times

Promoting Active Ageing Through the Use of ICT 277

Figure 10

Percentage of Elderly Persons by State of Economic Independence
(Irudaya Rajan, et al., 2003)

of emergency. Several video cameras distributed along the house allow
observing his daily routines (by authorized people) and, at the same
time, maintain his privacy. The system analyses the situation from the
captured images and decides on the best course of assistance, which
varies from helping in cooking to interacting with the care-providers.
The installed system is also able to react to the most common domestic
accidents that are recurrent to people living alone. If it sees him
suffering a potential injury, like falling on the floor or cutting himself,
the system inquires him to make sure he is well. This interaction is
done via spoken natural language. If there is no reply, an alert is
immediately sent to his children and the care centre.

Thus with proper application of ICT technology these short-
comings which are largely prevalent among the aged community at
present, can be successfully curtailed and an overall upliftment is
definitely possible.

Existent Organizations In India Supporting Active Ageing

In India, HelpAge and Agewell are organizations working towards
creating awareness of the problems and needs of older persons in
society and government. But, they do not provide any specific
platform for interaction between volunteers or emergency assistance
to older people. Heritage Health Care, which is based out of
Hyderabad and has 18 years of experience in treating senior citizens
has diversified from a geriatric hospital to providing care at home and
personalized old age home. But unlike the European and American
counterparts, there has been no such noticeable progress in the field of
application of ICT for helping the aged population (Knodel and
Debavalya, 1997). As a result, there are several areas in the healthcare
services which can be developed by using ICT, so as to include old
people within the perimeter of advanced telehealth and telecare
programmes (as in developed countries), for improved and prompt
medicare.

India being a developing country, specific case of telecare may
actually work to her advantage. India can use the scientific knowledge
and intellectual resources already available due to the extensive R&D
investments done by developed countries. In fact, a joint survey by
Georgia State University and Apollo Telenet working Foundation

278 Indian Journal of Gerontology

shows that Indians are quickly becoming conversant with the concept
of telecare: 55 per cent of rural and 72 per cent of urban population is
aware about and open to using telecare services. In fact the Indian
government has recently planned to install 1,00,000 computer centres
in rural areas, which will further increase awareness about telecare.

Moreover the “Smart City” plan of the Government of India also
includes provisions for use of telecare to create a holistic automated
environment. Rs 7,060 crore has already been provisioned as seed
money for this project, which is to be utilised for information
technology to provide the most efficient and comfortable living
standard for the bulging neo-middle class in the Indian society.

Fields of Improvement

Old people value their independence, and thus there is a need of
an effective proactive environment which will function remotely and
will consist of a group of professionally trained and dedicated volun-
teers, who can be available to old people as and when needed during
emergency situation. A large section of the aged community of our
country is in need of assistance but the present market fails to cater to
their needs. Some of the NGOs, in spite of aiming to work for the
upliftment of the aged population, largely fail to deliver as per the
requirement. Figure 11 and Figure 12 depict the current scenario of
Kolkata, one of the major metro cities of our country (Liebig, et al.,
2003). So various functioning units of public healthcare need to be

Promoting Active Ageing Through the Use of ICT 279

Figure 11

Need for Support (Liebig, et al., 2003)

integrated to form an efficient network to function effectively in
tandem.

One serious problem is obviously, lack of professional caregivers
which often proves to be detrimental in this respect.

On the other hand, a user friendly technology is required, in the
form of radio-alarms and effective social networking so that old people
can connect to the health-centres when they feel the need of any sort
of medical assistance. This also helps older people overcome isolation
and loneliness, and increases possibilities for keeping in contact with
friends and also extending their social involvement (Subrahmanya and
Jhabvala, 2000). Thus, a person with movement disability can use an
alarm if (s)he has any difficulty in movement, so that a trained
caregiver is available for immediate assistance. Obviously it requires
prompt service, so efficient management and monitoring of the entire
telehealth facility is of immense importance. Technology can assist in
normal daily life activities, like tasks at home, mobility, safety, etc.
Main developments under this perspective are focused on assistance at
home, namely for elderly people living alone, which can be further
expanded into developing smart homes. It includes services such as
living status monitoring, with connection to care providers in case of
any emergency, companion and service robots, integration of intel-
ligent home appliances, etc. Support outside home, namely in terms of
mobility assistance, shopping assistance, and other daily life activities,
is also considered (Schafer, 1999).

280 Indian Journal of Gerontology

Figure 12

Effectiveness of NGOs (Liebig, et al., 2003)

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282 Indian Journal of Gerontology

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COMMENTARY

Minority Group Status and Healthful Aging:
Social Structure Still Matters

During the last 4 decades,
a rapid increase has oc-
curred in the number of sur-
vey-based and epidemio-
logical studies of the health
profiles of adults in general
and of the causes of dispar-
ities between majority and
minority Americans in par-
ticular. According to these
studies, healthful aging con-
sists of the absence of dis-
ease, or at least of the most
serious preventable diseases
and their consequences, and
findings consistently reveal
serious African American
and Hispanic disadvantages
in terms of healthful aging.

We (1) briefly review con-
ceptual and operational def-
initions of race and Hispanic
ethnicity, (2) summarize how
ethnicity-based differentials
in health are related to social
structures, and (3) empha-
size the importance of atten-
tion to the economic, politi-
cal, and institutional factors
that perpetuate poverty and
undermine healthful aging
among certain groups. {Am
J Public Health. 2006;96:
1152-1159. doi:10.2105/AJPH.
2006.085530)

Jacqueline L Angel, PhD, and Ronald J. Angel. PhD

ALTHOUGH THE SUPREME

Courl outlawed the principle of
sepajate but equal in 1954 with
its famous Brown versus Bom-d
of Education decision, many mi-
nority y^mericans luul that they
are still separate and unequal.
Despite a century of impressive
innovations in medical science
and improvements in public
health, poverty continues to un-
dermine the pliysical and emo-
tional health of a large number
of Americans, and serious ra-
cial/ethnic health disparities
persist'”^ Low-income families
have inadequate healtli care
coverage,”‘^ and individuals who
lack adequate insurance are
more likely to die from cancer
and other serious diseases be-
cause of late diagnoses and defi-
cient care.^”” Perhaps the most
basic question is wliether health
disadvantages among minority
Americans are the direct and
almost complete resuit of pov-
erty and its correlates. Well-
documented correlates include
low educationai levels, labor
force disadvantages, and resi-
dential segregation iii ghettos
and barrios, where individuals
are exposed to environmental
and social health risks such as
drugs. \’io!ence. and fainily
disruption.'”^” ̂ ”

Radal/ethnic disparities in mor-
bidity and mortality are so glaring
that the federal govemment has
been forced to respond, and a
large body of research has exam-
ined tlie role socioeconomic status
(SES) and ailture play in these
disparities.’̂ The ultimate goal Ls
to identiiy the sodal stuictural

causes of inequities in health so
that genera] population health can
be impn)ved. We will present ap-
proaches to studying radal/etlinic
health disparities hy (1) reviewing
operational definitions of race and
ethnicity and tlie research tools
tliat estimate difierential disease
burdens and health au’e use,
(2) assessing jast how far the field
has come in understanding healtli.
and (3) |iro]X)sing a future re-
search agenda that examines the
sodal, economic, and [xilidcal
foires tliat peipetuatc health
vulnerabilities.

GROUP CLASSIFICATION

Duriiig the past 2 decades, we
have witnessed an increasing
appreciation for the conceptual
complexity of gi-oup dassification
and its potential for intiTxiudng
bias into studies of comparative
health levels.’̂ Individuals can be
of mixed race/ethnidt>’, tliey can
intermarry and identify with an
adopted group, and they can even
ivject a group clas.sification. pai-
ticularly if that identity is imposed
by others. Individuals who strug-
gle e^aiiist the sodal stigma asso-
dated with group dassification
often embrace that identity as a
political statement and a sign of
defiance. Standard classifications
of race/ethiiidty do not overlap
with spedfic genetic profiles or at-
tributes.’^ To a large extent, sudi
classifications are political cate-
gories defined by history ajid the
sodal vulnerabilities imposed on
minority gi-oups by the dominant
majority.'” A political basis of
gi-oup classification does not

translate directly into useful sden-
tific or intellectual classification.””
We no longer differentiate among
non-llispanic White nationality
groups, because distinguishing
whidi nation an individual’s an-
cestors came from is no longer
relevant. According lo Richard
Alba, Americans of all European
ancestries have come to be viewed
and to view themselves as ethni-
cally American.”” Therefore, tlie
radal/ethnic disdndions that re-
main reflect enduring sodoeco-
nomic viilnerabiiitiGs.

Because of the compiex social
basis of radal/ethnic classifica-
tions and identities, David Wil-
liams proposed that Hispanic be
included with African American
and the various .̂ sian nati

1152 I Commentary | Peer Reviewed I Angel and Angel American Journal of Public Health I July 2006, Vol 96. No. 7

COMMENTARY

whose ancestors arrived with the
Conquest.* ‘̂

Census categories, even as
they become more detailed aiid
provide more choices, gloss over
a great deal of heterogeneity
ihat is of immediate importance
Lo health and heaiUi service
use?” The reali^ is most health
survey and census data use re-
spondents’ seli-ieported race, but
only provide a limited number
of choices. Biracial individuals or
individuals who consider tliem-
selves to be something other
ihan White. Afiican American,
Hispanic, or any of the other
available categories answer
questions about radal/ethnic
j{i-oup classification in ways that
are not yet understood.

Some data systems, such as
the National Vital Statistics Sys-
tem, do not even collect informa-
tion on the race/ethnicity of the
decedent, and data on mortality
r-isks come from different and po-
lentially contradictory sources.
Data on the number of deaths.
lor example, come from death
certificates completed by fiineral
directors or medical personnel
on the basis of information from
;m infonnant, usually a family
member.’̂ “‘ In other systems, such
as those in which data are de-
rived from hospital/patient care
records, it is often unclear who
made the racial/ethnic determi-
nation. The different sources of
radal/ethnic classification create
a potential confounding factor
when recording deaths.” ‘̂’ Infor-
mation about the population at
risk comes from survey data.^’
!-;ach of these data sources intro-
duces different possibilities for
undercoujiLs or racial/ethnic mis-
classification.

Additional reporting problems,
such as the census undercount
of minority group membei-s, af-
fect population estimates. As a

consequence of the combined ef-
fect of numerator and denomina-
tor biases, it has been estimated
that death rates are overstated by
about 1 “/o for the White popula-
tion and by about 5% for tlie Af-
rican American popuiation. Such
biases lead to underestimates of
mortality for other groups, per-
haps up to 21 % for the Ameri-
can Indian or Alaska Native pop-
ulations, up to 11% for Asian/
Pacific Islanders, up to 2% for
Hispanics as a group,^” and up to
6% for Mexican Americans.^”

In addition to gaining a better
understanding of problems with
administrative classification, re-
seai’chei:s have become more
aware of tlie potentially serious
measurement biases that are in-
hereiit when self-reported healUi
data are used. Understanding the
effect of these SES, cultural, and
linguistic factors on the interpre-
tation and response to questions
about health is imperative if in-
vestigators want to reduce poten-
tial bias in the collection of data
from survey and clinical respon-
dents.’*” The group differences
in cognitive schemas and world
views that ethnographic studies
of local and culturally based be-
lief systems—including those that
address disease and its causes-
take as their objects of investiga-
tion are methodological nui-
sances for siurey reseai-chers
and epidemiologists who want
to deveiop valid and universal
[jrobes that can be translated
from one language to another for
comparative use,” Unfortunately.
the figurative and impredse na-
ture of language makes such an
objective elusive.’^

Although researchers are
aware of the potential confound-
ijig of outcomes and predictors in
comparative studies of the health
of different groups, this potential
confounding presents serious

pi’oblems to researchers who are
only working with 1 cultural
group. Individuals who have
chronic conditions (e.g., diabetes)
that have never been diagnosed
by a doctor wiU answer nega-
tively to a question about
whetlier a doctor has ever told
them they had the disease.”
Such confounding means that
prevalence estimates for groups
that have very different health
care experiences, such as African
Americans and non-Hispanic
Whites, may vary gi^atly in their
validity. In the absence of some
objective criterion or other inde-
pendent data about a respon-
dent’s actual condition, survey-
based prevalence estimates must
be inteipreted cautiously.

The ways in which individuals
structure their responses to gen-
eral health questions or to ques-
tions about symptoms are poorly
undei-stood.” To make progress
in measurement, researchers
must have a much more sophisti-
cated understanding of the im-
pact of culture, language, SES,
and other group-related factors
on the complex response task. It
is clear that reference group fac-
tors affect how individuals evalu-
ate their own healtli. Otlier cul-
tui’ally based appraisals and
valuations also may affect re-
sponses. For example, it is possi-
ble that in some cultures the fear
of appearing arrogant leads indi-
viduals to report their health as
fair ratlier tlian as very good or
excellent.””’ One useful character-
istic of comparative research is
that it does not allow researehers
to ignore the problems of compa-
rability that probably affe(-t all
data collecLJon efforts, even
within the same cultural group.

We believe traditional epi-
demiological approaches a:id re-
search instruments, particularly
those that elidt self-reports of

subjective states, should be com-
plemented whenever possible by
other techniques and should in-
clude qualitative assessments of
how respondents inteipret ques-
tions and structuix’ responses.^^
A multimethod approach may
lead to a more sophisticated im-
derstanding of subjective re-
sponses spedficaily and the in-
terview response task more
generally.

Understanding social struc-
tures and theii- impact on health
requires an emphasis on both the
cognitive aspects of culture and
the social and material resources
tliat individuals have at their dis-
posal. ‘̂ The combination of tra-
ditional epidemiological methods
and ethnographic tecliniqiies is
more effective for assessing the
terminology that individuals use
to talk about disease and the
meaning it has for them. Com-
bining qualitative techniques
with surveys and even more ob-
jective physiological data and
[performance assessments will
gi-eatly improve our knowledge
of real comparative health levels
among different populations and
subgroups.

A CULTURE OF POVERTY?

The existence of minority
group disadvantages in health
indicators have led many to
speculate about how poverly
might cieate and |ierpetuate
health disparities. Some theorists
have suggested variations of the
culture of poverty explanation
(i.e., that chronic poverty leads
individuals to develop a set of
orientations and behaviors that
are incompatible with sodal mo-
bility and economic success or
effective Involvement with social
organizations) forwarded by
Oscar Lewis several decades
ago.*” Susan Mayer, for example.

July 2006, Vol 96. No. 7 | American Journal of Public Health Angel and Angel \ Peer Reviewed I Commentary I 1153

COMMENTARY

argiied that poverty is a product
of the loanied present orienta-
tion ol” tliose who grow up in
poverty.”” Individuals who never
witness a payofT to effective
long-term pianning do not leam
the niiddle-dass ability to delay
gratific:ation and thus do not
leam to plan lor their own fu-
tures. From this perspective, tlie
social environmenls in which
such individuals grow up do not
foster a strong work ethic, nor
do Ihey encourage the resistance
of immediate gî atification. Indi-
viduals who have been social-
ized in tliis way are unlikely to
respond to educational opportu-
nities or interventions for chang-
ing their hehavior or reducing
their health risks.

Blocked Opportunities
More structural explanations

focus on the limited opportuni-
ties available to individuals be-
cause of their racial/ethnic chai’-
acteinstics. From this perspective.
Ihe deleterious heaith conse-
quences of poverty are the result
of exploitation and structural vul-
nerabilities. Piven and Qoward,
fttr example, explained higli rates
of poverty among African Ameri-
cans as Ihe result of institutional
racism, which refers to the sys-
tematic differential allocation of
rewards (jn the basis of race. ‘**
Institutional racism and discrimi-
nation perpetuate poverty and its
resultant individual-level healtli
damage through unsafe and
unhealtlifijl envii-onmenls, low
educational levels, inadequate
medical care, and feelings of
helplessness and hopelessness.” ~

Our reseairh and that of otiiers
show that the fundamental nature
of the laboi’ market that places
African /Xmericans and Ilispanics
at a disadvantage in terms of
health insurance also under-
mines heath and well-being.’ ‘*’

Historically, African Americans
and Hispanics have been dispro-
portionately confined to the low-
wage service sector or to casual
and informal jobs, where pay-
ment is made in casli and where
their ability to accumulate wealth
is impaired. Discriminatory prac-
tices iji the real estate market
have confined many members of
these groups to unsafe neighbor-
hoods that liave few local em-
ployment opportunities or com-
munity rcsf)urces and inferior
schools.” Such confinement, and
the inescapable poverty associ-
ated with it. create chronically
high levels of physical and social
stress that increase the risk for
poor health and vitality.””̂ Indi-
viduals who live in tliese situa-
tions lack adequate social capital
and thus have few resources that
might improve their lots.

Poverty and deprivation can
undermine a people’s sense of
control and roh them of the opti-
mism needed for a healthy life.
Individuals who experience pov-
erty, relative deprivation, and
stress early in life become vul-
nei-able to a variety of stressors
throughout adulthood, which
increases their risk for demoral-
ization and depression late in
life.”” ‘̂ Older poor women, for
example, are exposed to more
social disruption in their lives
compared with more af^uent in-
dividuals, and these women’s
lives are often punctuated by a
series of negative life events that
are difficult to manage. At the
same time, they are exposed to
elevated levels of stress and have
fewer resources for coping with
life’s hardships.”^

Disparities in Health Care
Access

Among tbe reasons for the
large differentials in health be-
tween majority and minority

Americans ai’e the large differ-
ences in adequacy of health care
coverage, amount and quality of
care, and access to long-tenn
care.'”‘~”^ Institutional racism
that is rooted in ailturally insen-
sitive and discriminatoiy prac-
tices may explain the tendency
for older minorities to receive
fewer and lower-quality acute
and chronic health care services.^
Those who sjiend their lives in
low-wage service sector jobs are
unable to save for retirement
and the employers for whom
they work rarely offer healtli or
retirement benefits.̂ ‘̂ ‘

Even after contixil for SES dif-
ferences, older African Ameri-
cans perceive more discrimina-
tion, personal rejection, and
unfair treatment compared with
non-Hispanic Wliites. and self-
reported discrimination has been
shown to inci”ease reports of de-
pressive symptoms.^’ In other
cases, older minorities are sys-
tematically excluded from pub-
licly limded programs. Medicaid,
for example, potentially penalizes
poor elderly Mexican Americans
and others who have lai”ge and
complex families and want to
care for frail parents. Under Med-
icaid waiver programs, some
states restrict eligibility to indi-
viduals who have serious disabili-
ties and are unable to function
and who do not have access to
other community-based services
or family support. Although thii
exclusion limits participation to
those who have no other alterna-
tives, it clearly discriminates
against those who aie most de-
pendent on their families. Rather
than aiding family caregivers of
elderly parents, this program may
discourage their involvement^^

Immigration and Health Levels
In addition to SES. nativity has

an important impact on health

outcomes. Studies on racial/
ethnic change in the United
States have shown the increas-
ingly important role nativity
plays in determining the position
of immigrants within the social
structure.^’ A generation of so-
cial stratification has drawn atten-
tion to ttie serious disadvantages
immigrants may face in Ameri-
can society. ̂ ”~ ‘̂’ Although a se-
lection effect may me-dn that im-
migrants are healthier than those
who remain behind or even indi-
viduals who were bom in the
United States,”” immigrants often
suffer economic hardships and
experience other strains as pail
of the migration experience itself,
which can undemiine their men-
tal health and impede their social

As a result of inadequate health
care in their aiuntry of origin,
many immigrants may not be in
optimal health when they arrive
in the United States. In addition
to the system-level barriers that
may place the health of immi-
grants at risk, disadvantages im-
migrants face in the labor market
also may place their health at
risk. Many HLspanic elderly im-
migrants have spent the majority
of their lives outside the United
States toiling in often harsh and
dangerous conditions for very
low pay. Many have been ex-
posed to dangerous materials
and have had inadequate preven-
tive health care. Dangerous or
difficult work and tlie lack of
regular health care can result in
serious health problems later in
life, and a lifetime of low pay
means that the financial re-
sources necessary for maintain-
ing a liealtliy independence can-
not be accumulated.^” When
these individuals become ill or
incapacitated, they often have
no recourse but to rely on family
members for support.*”

1 1 5 4 j Commentary J Peer Reviewed | Anget and Angel American Joumai of Public Health i July 2006, Vol 96, No. 7

One reason why individuals
kick health insuraiice is the em-
ployment-based system of group
health care coverage in the
Uniled States. Few service sector
jobs offer health insurance, and
when they do, the premium that
the employee is required to pay-
particularly for family coverage-
is prohibitive. Needless to say,
jote that do not ofTer group cov-
erage are unlikely to provide
wages that allow employees to
purchase private insurance. In
the absence of a universal
health care system in the United
States, minority groups and re-
i:ent immigrants are often con-
fined to working in the low-wage
service sector, which makes it
difficult to obtain the care neces-
.sary for maintaining optimal
healtli with dignity.

Linguistic and Cultural
arriers to Care

Racial/ethnic classifications
say little about an individual s
biological or genetic makeup. In
Llie same vein, although such
classifications indicate an individ-
ual’s origin, they say little about
the individual’s level of accultur-
ation or cultural orientation.
Broad census categories, such
as Asian or Hispanic, combine
various groups that have differ-
ent cultures, belief systems, and
histories. Specific nation-of origin
groups also have very different
immigration histories: they
came to the United States at dif-
ferent times in history, and they
came for different reasons {e.g.,
economic opportunities vs polit-
ical asylum).

Immigrants also have dilTer-
ent levels of English proficiency
and social competency, because
of the age at which they immi-
grated and other individual,
family, and community factors.^”
Although immigrant children

quickly leam the language and
customs nf the host society,
older individuals and those who
migrate to the United States late
in life face particular problems
in becoming fluent or proficient
with the English language,”^
and many never do. Individuals
who migi-ate during midlife or
later often find the experience
to be traumatic, because they
are uprooted from familiar sur-
roundings and are thrust into a
new culture where they must
leam a new language, new cus-
toms, and a new set of social
institutions. This can lead to
mental health problems, such
as depression.*”‘

Cultural and Neighborhood
Protective Factors

Although poverty and a lack
of assets increase health risks
among older minorities, other
factors associated with culture
potentially neutralize tliese
health lisk factors and act in a
protective manner. Cultural iden-
tity and social Incorporation into
a group that provides positive
social involvement can improve
health in and of itself, and group
involvement can foster or en-
courage positive health behav-
iors.'””* Therefore, cultural factors
that reduce the risk for social
isolation are potentially health
protective or enhancing.

Strong social institutions, such
as family and church, can pro-
vide similar support that pro-
motes health and well-being.'”^’^
Hvidence suggests tliat religious
involvement protects health gen-
erally and plays an important
role in minimizing the negative
consequences of chronic condi-
tions.”’ Older Mexican American
Catholics benefit fi-om frequent
church attendance and report
that it provides them with com-
fort during times of trouble.”^

Church members can assist older
inlinn members with daily tasks,
which allows the older membei-s
to remain in the community.*”*

Recent findings showed that
residents who lived in high-
density Mexican American and
Cuban American neighborhoods
were in better health than those
who lived in lower-density neigh-
borhoods.^” Although the data
show a strong con-elation be-
tween ghetto or barrio residence
and poverty, other aspects of
racial/ethnic enclaves may well
protect health, possibly because
of an enhanced sense of belong-
ing, positive social interactions
where the native language is spo-
ken, and the availability of instru-
mental social support.

DOES THE
EPIDEMIOLOGICAL
APPROACH MINIMIZE
STRUCTURED
INEQUALITIES?

Much progress in understand-
ing health risks for individuals of
all ages has been made in recent
decades. Yet, it is dear that much
remains to be understood if dis-
parities in health are to be elimi-
nated or even reduced and if
everyone in the population is to
enjoy optimal healtb at every age.
To that end. we s u r e s t future re-
search should improve our un-
derstanding of how social policy
and organizational structures and
practices affect the opportunities
available to minority Americans
in ways that directly and iiidi-
rectly affect group health levels.

The structured and institu-
tional inequalities that have im-
peded minority Americans’ eco-
nomic and social progress in tlie
past and that continue to operate
today—often in subtle ways-
have their basis m a history of
racism and systematic exclusion

from opportunities for economic
and social advancement. Among
African Americans and Hispan-
ics. almost every aspect of social
service delivery, educational op-
portimities. and employment op-
portunities have been infiucnced
by race/ethnicity.””^ Data show
that the health levels of entire
groups are directly influenced by
the fact that political and eco-
nomic power are determined by
both hi.story and the specific so-
cial policies that perpetuate the
social exclusion of specific groups
of people.

Afiican Americans and His-
panics lag far behind non4-lis-
panic Whites in personal and col-
lective wealth and political
power. Lack of resources limits
their ability to help their children
and grandchildren buy houses
and continue their education, and
it translates into diminished eco-
nomic and political power for the
community’ as a wbole. Although
income and wealth do not guar-
antee a good and virtuous life,
poverty certainly does not guar-
antee it either. Tbe intentional or
unintentional exclusion of groups
from sources of economic and
political power is a major public
health problem. We must develop
a better understanding of the
pathways to disadvantage and
how health vulnerabilities are
perpetuated Irom one generation
to tlie next as the result of fonnal
policies and institutional barriers
to social mobili^.

Individuals choose to take ad-
vantage of opportunities for
economic and social advance-
ment, and they make pei’sonal
choices that affect their health.
If opportunities for pei-sonal ad-
vancement do not exist, or if
they are blocked on the basis
of group classification, members
of that group find it difficult or
impossible to avail themselves

July 2006, Vol 96, No. 7 American Journaf of Public Health Angel and Angel Peer Reviewed Commentary ‘ 1155

of potential avenues for social
and economic advancement.
Tliese blocked opportunities
may result in frustrated hopes,
demoralization, and deleterious
health behaviors.

The complex association be-
tween race/ethnicity and health
has heen well doaunented at the
individual level. African Ameri-
cans suffer from more and more
serious illnesses and die at
higher rates compared witb non-
Hispanic Whites. Although sur-
vey-based studies tbat examine
individuals and their vulnerabili-
ties continue to provide useful
information about health risks,
Lheir failure to directly focus on
the problems of institutionalized
racism and exclusion is a serious
sbortcoming. Studies tJiat ob-
serve and analyze the individual
have, for the most pail, not been
accompanied by significant at-
tempts to understand the role
lai-ger social stmctures play in
perpetuating racial/ethnic strati-
fication and contributing to less
favorable individual family, and
community health profiles.

There are many reasons for
Ihis i-elative neglect of structural
and political factors. After World
War II. the rapid development
of survey researdi and the intro-
duction of sophisticated analytic
techniques pushed researchers in
the direction of survey-based epi-
demiological and health studies.
Funding agencies, including the
federal government tended to
shy away from politically sensi-
tive topics and instead focused on
individual risk profiles. Tliis locus
promised to inform public policy
with educational and individual-
level public health interventions.
The power of individual-level
biological approaches has mani-
fested itself in the recent impe-
tus to fund researdi projects that
examine genetics and biology,’^

Yet the heath profiles of com-
munities and groups are influ-
enced by factors well above the
level of the cell or the individual.
Tliey are alTected by the ade-
quacy of public healtti initiatives.
federai and state health cai-e poli-
cies, and other sodal policies. Be-
yond that, bealtli levels are af-
fected directly and indirectly by
education, poverty, housing,
physical and social environmen-
tal stressors, and social exclusion
and discrimination. These are
emergent phenomena that can-
not be undei-stood solely on the
basis of individual-level studies.

New Directions in Research

Future investigations should
build upon and add to the bio-
medical mode! of disease and
illness and should include a
broader definition of health.̂ *’ A
more comprehensive and useful
conceptual model of healthful
aging might well hegin with a
definition that includes not only
the absence of disease and physi-
cal infirmity at ils coi-e hut also
tlie institutional and structural
components and factors—such as
educational opportunities, good
housing, and sale neighborhoods—
that have been shewn to affect
health. The health of poor and
minority Americans is under-
mined by what has been termed
tiie new morbidity. i.e.. threats to
health from domestic violence,
drug abuse, crime, and the perva-
sive sense of inferiority that is the
result of discrimination.

New studies on healthfiil aging
should examine the underlying
determinajits of illness within the
community and develop better
conceptual motiels and methods
for assessing the stiiictured and in-
stitutionalized stresses that minor-
ity- Americans experience.̂ ^ We
need to understand how these
stresses affect individual-level

behaviors, patterns of sodal
interaction, risk for \’idimization,
aime. poverty, and other factors
tliat inlluence health and func-
tioning at all ages. Again, this
approach should avoid purely
individual- or family-level attribu-
tions and should seaidi for the
lai^er contextual factors that re-
sult in structui’ed inequalities and
disadvantage.

Ironically, the “diseases of af-
fluence” in the United States-
obesity, heart disease, cancer,
and diabetes—take tiieir greatest
toll on the least allluent, Tlie
prevalence of these chronic dis-
eases is affected by diet and
other lifestyle factors and thus is
influenced by SES. Almost one
half (49.6%) of all African Amer-
ican women anti more tliaii one
third (38.9%) of Mexican Ameri-
can women are obese.’*’ To im-
prove the health status of minor-
ity women, newer and more
aggressive efforts that educate
medical care providers, extend
community outreach, and im-
prove compliajice with treatment
regimens are necessary. Because
ofthe pervasiveness of the struc-
tui-al disadvantages minority
.Vnericans face in the labor
force, the entrenched poverty
characteristic of urban ghettos
and barrios and continuing dis-
crimination efforts focused solely
on individual health-related be-
haviors aî e unlikely to be suc-
cessful in improving population
health levels.

In current practice, institu-
tional and structural factors enter
individual-level statistical models
indirectly through controls for
health insurance (private, Medic-
aid. Medicare, or other coverage)
and controls for income and edu-
cation. Certain hierarchical tedi-
niques indude ecological and
larger geographical chai-acteris-
tics. but these do not address

how institutionalized discrimina-
tion, specific organizational
structures, or fonnal aspects of
public polides influence the
health of spedfic groups. Because
level of education, income, and
wealth are determined by boUi
opportunity structures and per-
sonal choice, understanding how
those structtires are maintained
and how they operate to influ-
ence health risks is necessary for
understanding radal/etlinic
health disparities.

The ina-easing awareness of
tlie need to target research specif-
ically at the unique health ml-
nerabilides of poor and minority
Americans is a welcome develop-
ment Poverty, low educational
levels, and other social disadvan-
tages are the underlying causes
of poor health generally, but
these economic ajid .sodal disati-
vantages are not randomly dis-
tributed throughout the popula-
tion and are greatest among
Alrican Americans and 1 lispan-
ics. Both groups will comprise a
large proportion of the working-
age population of the future, and
they will comprise a growing pro-
portion of the retired population.
The capadty of tJie young to be
productive and the general health
levels and quality of life of Oie el-
derly are both affected by factoi’s
closely associated with race, His-
panic ethnicity, and inequality.

The Data Archive
Attempting to better under-

stand tliese social vulnerabilities
is an important research agenda.
This effort will require the imagi-
native use of existing data sets
and an enhancement of san^)Ies
to include larger oversamples of
minority ,’\mericans. New data
collection initiatives will be diffi-
ailt during what is likely to be a
period of retrenchment for majoi”
funding agendes. Nonetheless,

1156 Commentafy F^er Reviewed Angel and Angel American Joumai of Public Health i Juiy 2006. Vol 96. No, 7

COMMENTARY

new and specialized data that
examine spedfic vulnerabilities
among groups that live and work
in specific ecological and social
niches will be necessary if we are
to make progi’ess. During the
past decade, the National Insti-
tutes of Health have recognized
the need for specially focused
surveys that show the health sta-
tus and functioning of minority
elderly groups. ITie National
Center for Health Stadstic’s sup-
plement to the National Health
Interview Survey—the Longitu*
dinal Study of Aging (LSOA)-
presented new opportunities for
documenting trends and cohort
changes in the health and fiinc-
tioning of a representative sam-
ple of aging African Americans.’^
Data from several sources en-
riched the LSOA data set and
made the analysis of age-graded
sodal processes possible.

Other existing and ongoing
data sets include the National
Health Interview Survey, the
1984 Health Insurance Supple-
ment, the 1984 baseline Survey
on Aging, the follow-up LSOA in-
terviews. Medicare records, tlie
National Death Index, and multi-
ple cause-of-death files. Research-
ers are usijig these data sets to
examine patterns of health ser-
vice access and use, including the
impact of medical insurance, fam-
ily dructure. housing, fomial and
informal sources of care, employ-
ment history, transpoi-tation, and
sodal networks. There ai’e many
unexplored possibilities for the in-
novative and informative use of
these data. The availability of lon-
gitudinal data makes it possible to
examine (1) the sequence and
the consequences of morbidity
and health care access on func-
tional independence and
dependence, as well tis death,
within the community, and
(2) the risk for institutionalization.

The possibility of new mod-
ules in ongoing efforts provides
new opportunities for under-
standing the needs of spedfic
groups. For example, although
most survey questions were iden-
tical in the first 2 waves in ttie
LSOA series, new infomiation
was gathered on individual risk
behaviors, induding health opin-
ions, during the third wave. In
addition to interviews with sur-
vivors, additional information
was collected about decedents’
hospitalization and nursing fadl-
ity admission from their named
next-of-kin contact. As part of the
series, the Family Resources Sup-
plement replaced the Health In-
surance Supplement and pro-
vided in-depth infonnation about
caregiving, care receiver needs,
unmet care needs, and reasons
that needs were not met.

The Third Health and Nutri-
tion Examination Survey
(1988-1994) is a particularly
useful source of information
about the incidence and the
prevalence of type 2 diabetes
among the elderly. The sample
had no preset upper-age limit
and included individuals older
than 85 years. This study in-
cluded a medical examination of
respondents and is one of the
few data sets that provides both
objective clinical observations
and infonnation about the sub-
jective experience of having dia-
betes.’^ The life spans of older
African Americans and Hispan-
ics who have chronic conditions
will prohably increase in the fu-
ture as disease management im-
proves. Yet, without substantial
improvement to the economic
and social situations of these
groups, they will continue to
fare worse than non-Hispanic
Whites. Understanding all
aspects of the assodation be-
tween sodal factors, genetics.

and chronic illness is a higb-
priority research objective.

The Hispanic Established
Populations for the Epidemiologi-
cal Stiidies of the Elderly is an
important example of a spedal-
ized study that is focused on a
single group. This 10-year longi-
tudinal study is ongoing and is
sponsored by the National Insti-
tute on Aging. It examines Mexi-
can-origin individuals who tive in
the Southwest and who were
aged 65 years and older at the
beginning of the study, its results
are providing much needed infor-
mation about tlie dynamics of
aging throughout the life course.
Studies of this sort are expensive
and may gamer little political
support if they focus on power-
less groups. However, without
such focused efforts, our tmder-
standing of the physical and
mental health and the health care
needs of the minority elderly will
remain superficial. National Insti-
tute on Aging initiatives that are
aimed at understanding and re-
dudng health disparities among
older persons and populations
will foster these efforts.

In addition to important re-
search on the health of older mi-
nority Americans, special data
makes it possible to investigate
the impact of individuals’ pre-
retirement economic situations
on welfare and healtli during
their postretirement years. The
Health and Retirement Study
and the Study of Assets and
Health Dynamics among the
Oldest Old. for example, provide
a better understanding ofthe
complex interactions of race/
ethnicity, health, economics, and
other social factors on aging
processes for different groups.
These data show serious income
and aSxSet deficits among African
Americans and Hispanics as
they approach retirement, when

many of these individuals will
lack resources for needed pre-
ventive, acute, or long-term
care and resources for living
the most fulfilling life possible,
including the possibility of help-
ing their children.

Conclusions
As we progress into the 21st

century, new and important med-
ical innovations will increase life
spans and will improve the qual-
ity of those additional years.
Much of that progress will no
doubt result from a better under-
standing of the genetic contribu-
tion to disease. However, social
structural factors that place cer-
tain groups at a liigh risk for ill-
ness and that impede their access
to the highest quality health care
continue to plague our sodety. As
documented by the Institute of
Medicine, institutionalized disad-
vantages that manifest themselves
most obviously as occupational,
ijicome, and asset disadvantages
across the life coui-se translate di-
rectly into impaired health care
access and poorer healtli among
minority Americans.̂ ^ This fact
makes it imperative that we con-
tinue to examine sodal factors in
health service, epideniiological,
and health poficy research. The
necessities of a healthful living
and healthful aging are dear, but
they ai’e out of tlie reach ol” many
minority Americans. •

About the Authors
]acquelme L Angel is with ttie Schoot of
Puhtic Affairs ami Depurtment ofSodot-
Hgy. and Ronald J. Anget ts icith the De-
partmettt of Sodotogy. Vmversity of Texas,
Austin. Both authors are with the Popula-
tion Hesearch Center. University of Texas,
.’histin.

Requests for reprints should he sent to
Jacqueline L. Angel. LBJ School of Public
.•Iffairs. University of Texas at Austin. PO
Box Y. Austin. TX 78713-8925 (e-mait:
jangel@mail. utexas.edu).

This articte was accepted February 19.
2006.

July 2006, Vol 96. No. 7 | American Journal of Public Health Angel and Angel ‘ Peer Reviewed | Commentary | 1157

Acknowledgments
The aulhors Uiank David WilliBms for
hiK valuable comments.

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