Embed Size (px)
Citation preview
ARTICLE IN PRESS
0277-9536/$ - se
doi:10.1016/j.so
�Tel.: +1 309
E-mail addr
Social Science & Medicine 62 (2006) 779–791
www.elsevier.com/locate/socscimed
Further examination of the cross-country association betweenincome inequality and population health
Rati Ram�
Economics Department, Illinois State University, Normal, IL 61790-4200, USA
Available online 26 July 2005
Abstract
Several scholars have put forward the view that the estimates by Rodgers [(1979). Income and inequality as
determinants of mortality: An international cross-section analysis. Population Studies, 33 (2), 343–351], Flegg [(1982).
Inequality of income, illiteracy and medical care as determinants of infant mortality in underdeveloped countries.
Population Studies, 36 (3), 441–458] and Waldmann [(1992). Income distribution and infant mortality. Quarterly
Journal of Economics, 107 (4), 1283–1302] showing a negative cross-country association between income inequality and
population health, cannot be replicated from recent data. In view of the importance of this matter, the present study
further examines the issue from the most recent, and probably more accurate, data for the largest cross-country sample
used in this line of research. The main conclusion is that the negative cross-country association between income
inequality and good health, reported by Rodgers, Flegg, and Waldmann, is replicated very well. The different findings
indicated by some scholars may have been due to their samples or the models being unusual. Therefore, the recent
skepticism about the existence of such a negative association needs to be reconsidered. Several additional points are also
noted. First, income inequality shows significance even after an index of ethnic heterogeneity is included. Second, ethnic
heterogeneity itself has a negative association with population health. Third, income inequality retains significance in
the presence of a measure of social capital. Fourth, however, the association between the measure of social capital and
population health appears weak. Fifth, a simple analysis does not support the view that the positive association between
income inequality and infant mortality in less developed countries (LDCs) may just be a reflection of the role of
poverty. Finally, there is some support for the proposition that while income may be relatively more important for
health in LDCs, the role of income inequality may be stronger in developed economies.
r 2005 Elsevier Ltd. All rights reserved.
Keywords: Income inequality; Population health; Cross-country data; Poverty
Introduction
As many scholars, including Deaton (2003), Lynch
et al. (2004), Subramanian and Kawachi (2004), and
Wagstaff and Doorslaer (2000), have indicated, relation-
ship between income inequality and population health
e front matter r 2005 Elsevier Ltd. All rights reserve
cscimed.2005.06.034
438 7101; fax: +1 309 438 5228.
ess: [email protected].
has lately been subjected to immense scholarly research.
In addition to the estimates reported for the United
States and a few other countries, cross-country evidence
on the relationship has been an important component of
this research. As Ellison (2002, p. 561) noted, a
compelling body of ecological research had found
statistical association between various measures of
inequality and average health status at the population
level. In particular, Rodgers (1979), Flegg (1982),
d.
ARTICLE IN PRESSR. Ram / Social Science & Medicine 62 (2006) 779–791780
Waldmann (1992), and Wilkinson (1992, 1996) found a
highly significant association between income inequality
and population health in several types of cross-country
data and models. Ellison (2002, p. 562) further observed
that the remarkable consensus between such a wide
variety of different studies led Wilkinson (1996, p. 105)
to conclude that ‘‘the income distribution relationship is
now firmly established’’, and to propose the ‘‘relative
income hypothesis’’, which says that an individual’s
health is affected by the distribution of income within
society.
Apart from the legitimate view that aggregate data do
not provide a good basis for an inference about the role
of income inequality in health at the individual level,
some recent research has questioned the very existence
of a negative association between income inequality and
population health in cross-country data. For example,
Baumbusch (1995) indicated that, contrary to Wald-
mann’s (1992) estimates, an increase in the income share
of the top 5% lowers infant mortality. Similarly, Judge,
Mulligan, and Benzeval (1998) considered the best
available data for 15 high-income countries and found
insignificant parameters for Gini in regression models of
life expectancy. More recently, based on 1990 data for
47 countries, Mellor and Milyo (2001, p. 499) indicated
that inclusion of average income and secondary-school
enrollment causes the coefficients of Gini to have a
perverse sign in models of life expectancy and infant
mortality. Similarly, Gravelle, Wildman, and Sutton
(2002) estimated Rodgers’s (1979) life-expectancy model
with more recent data and found (p. 580) that the
income-inequality parameter lacks statistical significance
at any meaningful level, and their extensive explorations
in terms of alternative specifications (p. 581), structural
changes with respect to time (p. 582), and variations
across high- and low-income levels did not indicate a
significantly negative parameter for income inequality.
They concluded (p. 587) ‘‘we have found, using a wide of
variety of specifications, that the association (between
income distribution and life expectancy) in our data
setyis never statistically significant’’. Wildman, Grav-
elle and Sutton (2003) stated that they could not
replicate Waldmann’s (1992) results with more recent
data. The studies claiming lack of a significant associa-
tion between income inequality and population health in
cross-country data seem to have been highly influential
and, for example, led Mackenbach (2002, p. 1) to state
that ‘‘Evidence favouring a negative correlation between
income inequality and life expectancy has disappeared’’.
Even more tellingly, in a recent discussion on Rodgers’s
(1979) paper, Deaton (2002, p. 548) stated that the
‘‘cross-country relationships for wealthy countries esti-
mated by Rodgers and Wilkinson do not show up in the
LIS (Luxembourg Income Study) data, nor can Rodgers
original regressions be replicated on the international
data from the World Bank’’, and that ‘‘As of the time of
writing, little appears to remain of the whole enterprise’’.
In the same discussion, Lynch and Smith (2002, p. 549)
wondered whether there was the ‘‘end of the story’’ in
regard to the relation between income inequality and
health. In the same vein, Deaton (2003, p. 139) stated
that ‘‘Later research has cast considerable doubt on the
robustness and reliability of many of these (cross-
country) findings’’ (of a negative relation between
income inequality and good health).
The motivation for this study is provided by the
consideration that, as noted above, while Rodgers
(1979), Flegg (1982), and Waldmann (1992) reported a
significant negative cross-country association between
income inequality and population health, several scho-
lars have suggested that such a negative association is
not found in the studies that tried to replicate the earlier
results from more recent data. In view of the obvious
importance of the point, the primary objective of this
study is to re-examine the cross-country association
between income inequality and population health by
using the most recent and the best available data for the
largest possible cross-country sample. The dataset used
in the present paper has the following desirable
characteristics that make it appropriate for the purpose:
1.
It covers a cross-section of 108 countries and is thuslarger than that used in almost any previous work.
2.
The variables pertain to the year 2000 or the late1990s, and the data are thus more recent by 10 to 25
years than those used in earlier studies.
3.
The information on income inequality (and incomeshares) reported in World Development Report 2003
(World Bank, 2003) is very good, and should be a
considerable improvement over that used in pre-
vious studies. As noted by World Bank (2003,
pp. 246–247), besides the LIS database for high-
income countries, information from nationally re-
presentative household surveys has been used, and an
effort has been made to ensure that the data on
income distribution are as comparable as possible.
Moreover, since the Gini coefficients (and income
shares) are based on income for some countries and
consumption for others, the present work makes an
adjustment so as to enhance cross-country compar-
ability of the numbers.
4.
Data on per-person GDP are in PPP (international)dollars which have much greater cross-country
comparability than those used by Rodgers (1979)
and Flegg (1982).
5.
World Bank’s current information on infant mortal-ity is likely to be better than that available 10 to 25
years ago.
6.
A lag of about 5 years is allowed between the healthindicators, which are for the year 2000, and each of
the regressors which typically pertain to mid- to late
ARTICLE IN PRESSR. Ram / Social Science & Medicine 62 (2006) 779–791 781
1990s. This makes the regressors econometrically
predetermined, and attenuates the ‘‘simultaneity’’
problem mentioned in the literature.
Besides the focus on a re-estimation of the basic
models used in the studies by Rodgers (1979), Flegg
(1982), Waldmann (1992), and some variants of these,
several related points are also studied. These include (a)
judging the robustness of the income inequality para-
meter in the presence of an index of ethnic hetero-
geneity, which might be an ‘‘omitted variable’’, (b)
considering the role of social capital, which might be a
channel for the effect of income inequality on health, (c)
shedding some light on Deaton’s (2003, p. 151) thesis
that the association between income inequality and
infant mortality in less developed countries (LDCs)
might be a reflection of the effect of poverty, and (d)
conducting a preliminary exploration of the view
expressed by Wilkinson (1996) and Deaton (2003,
p. 121) that while income might be relatively more
important for population health in LDCs, income
inequality is likely to be relatively more important in
developed countries (DCs).
Despite the usefulness of a comprehensive and
updated investigation to see whether the negative
association between income inequality and population
health reported by Rodgers, Flegg, and Waldmann can
be replicated from the most recent data, a major
shortcoming of all such studies that work with aggregate
data should be noted. One important issue addressed by
studies in this line of research is whether income
inequality at the aggregate level has an independent
‘‘contextual’’ or ‘‘ecological’’ effect on an individual’s
health. A proper investigation of that question requires a
multilevel research design in which income inequality at
the aggregate or community level is entered along with
individual characteristics in a model of individual health.
Such multilevel studies have been conducted by many
scholars, including Subramanian and Kawachi
(2003a, b), and have been ably reviewed by Subramanian
and Kawachi (2004). Aggregate models of the kind used
in the present study, and those which it replicates, cannot
shed any direct light on the role of income inequality in
individual health. The main point addressed in the
present study is to judge afresh the presence or absence
of a negative association between income inequality and
population health in cross-country data.
Models and data
Models
The models for which final estimates were provided by
Rodgers (1979, Table 6, p. 349) are used in this
replication. These may be written as
Hi ¼ a1 þ b1ð1=PCYÞi þ c1ð1=PCY2Þi
þ d1ðGINIÞi þ ui1 ð1Þ
and
Hi ¼ a2 þ b2½1= lnðPCYÞ�i þ d2ðGINIÞi þ ui2, (2)
where Hi denotes life expectancy at birth (LIFE) or
infant mortality rate (IMR) for country i, PCY is GDP
per capita, PCY2 is the square of PCY, GINI is the Gini
coefficient, ln denotes natural logarithm of the variable,
and u’s are stochastic error terms. As indicated by
Rodgers (1979), he explored a wide variety of nonlinear
specifications for the income variable, and reported in
his Table 6 (p. 349) the results that were ‘‘the best or
most interesting’’.
Flegg’s (1982, p. 445) model of infant mortality
included logarithm of real GDP per capita, Gini
coefficient, female illiteracy rate, supply of nurses, and
supply of doctors. However, data on nurses are not
available in the sources used for this study, and Flegg
did not report estimates with the exclusion of nurse-
supply alone. Therefore, the following model, for which
he reported estimates (Flegg, 1982, p. 445, Table 1,
second line) and for which data are now available, is
estimated so as to facilitate comparisons:
lnðIMRÞi ¼ a3 þ b3 lnðPCYÞi þ c3 lnðGINIÞi
þ d3 lnðFILITÞi þ ui3, ð3Þ
where FILIT denotes female illiteracy rate. Since he
worked with a sample of LDCs, this study also focuses
on that group relative to the above model.
Waldmann’s (1992, pp. 1283, 1287) basic model has
the following form:
lnðIMRÞi ¼ a4 þ b4 lnðPCYPOORÞi þ c4 lnðPCYMIDÞ
þ d4ðSHARE5Þi þ ui4. ð4Þ
where PCYPOOR is per-person income of bottom 20%,
PCYMID denotes per capita income of the middle
group (between bottom 20% and top 5%), and
SHARE5 is the income-share of top 5%. Three
variations over Waldmann’s model are adopted in this
study. First, World Bank data on the share of bottom
20% pertain to income for some countries and
consumption for others, and it is not possible to make
a reasonable estimate of per capita income of bottom
20% from the information on their share. Also, the
poor-income variable lacked significance in his full
regressions at any meaningful level. Therefore, that
variable is not included here. Second, due to the shares
being a mix of consumption and income, it is also not
possible to get good estimates of per capita income of
the ‘‘middle’’ group lying between bottom 20% and top
5%. Therefore, per capita income of the entire popula-
tion (PCY) is used, which is fairly close to his variable.
ARTICLE IN PRESSR. Ram / Social Science & Medicine 62 (2006) 779–791782
Third, information on the share of top 10% (SHARE10)
is directly available from World Development Report
2003, and that is used in place of the share of top 5%.
However, since the share-information is based on
income for some countries and on consumption for
others, an intercept-dummy has been included to make
allowance for this characteristic of the data on shares.
The dummy variable (DCON) takes the value one for
countries where the distribution data are based on
consumption and zero for others. Thus the following
variant of Waldmann’s model is estimated:
lnðIMRÞi ¼ a04 þ b04 lnðPCYÞi þ c04ðSHARE10Þi
þ d 04ðDCONÞi þ u0i5. ð40Þ
This has a fair resemblance to Waldmann’s specification.
Also, since SHARE10 is a well-known measure of
income inequality, the modified model can be perceived
as a format that contains average income along with
income inequality, but inclusion of the share of top 10%
along with the logarithm of mean income may attenuate
the ‘‘aggregation problem’’.
In addition to estimation of models of the kind
specified by Rodgers (1979), Flegg (1982) and Wald-
mann (1992), an effort is made to estimate the following
cross-country IMR model reported by Mellor and Milyo
(2001, p. 499) whose estimates show a dramatic
difference from those of Rodgers (1979).
IMRi ¼ a5 þ b5ðGINIÞi þ c5ðPCYÞi
þ d5ðSCHOOLÞi þ ui5, ð5Þ
where SCHOOL stands for secondary school enrollment
rate. They reported the estimates for the full sample, and
that is done for the replications also.
Data
The information on income inequality is taken
from World Development Report 2003 (World Bank,
2003, pp. 236–237). Every country for which data on
Gini index and share of top 10% was available has
been included, leading to a cross-section of 108
countries. Most information is from mid- to late
1990s. The Report (pp. 234–235) also provides informa-
tion on life expectancy at birth for the year 2000, which
allows a lag of a few years between the dependent
variable (health indicator) and Gini index. Data on IMR
are taken from the 2002 edition of World Development
Indicators on CD-ROM (World Bank, 2002), and, like
life expectancy, are for the year 2000. Information on
income, female illiteracy rate, and secondary school
enrollment is also taken from the CD-ROM and is for
1995 (or a year closest to that), allowing a 5-year lag
relative to the dependent variables. GDP per person in
current-price PPP (international) dollars is the proxy for
income.
It is perhaps of some importance to note again that
the Gini index is based on income for some countries
and on consumption for others. Following Deininger
and Squire (1996, p. 582), Gini coefficients based on
consumption have been adjusted upward by 6.6 so as to
make this important variable more comparable across
countries. It may also be noted that both Rodgers (1979)
and Flegg (1982) used income measures in conventional
US dollars since PPP measures were not easily available
at that time.
The Appendix lists the 108 countries and also
identifies the OECD members. However, since avail-
ability of data for the variables differs, the sample size
varies across the models.
Main results
Tables 1 and 2 provide a preliminary view of the data.
Table 1 indicates descriptive statistics for the sample
and Table 2 shows simple correlations. Besides the
variables discussed above, the tables include three others
that are introduced later. One is an index of ethnic
heterogeneity (ETHNIC). Another is generalized trust
(TRUST), which is a measure of social capital. The third
is the poverty index (POOR), which is the percent of
population whose income is below the international
poverty line of one PPP dollar per day (per person). The
tables indicate several points. First, cross-country
dispersion in infant mortality, income, and female
illiteracy (and poverty) seems much greater than in life
expectancy, Gini coefficient, and share of the top 10%.
Second, life expectancy and IMR have fairly high
correlations of the expected kind with both income
and Gini, and the correlations with female illiteracy and
school enrollments are also high and have the expected
signs. Third, correlations between life expectancy and
IMR and between GINI and share of top 10% are
expectedly very high (�0.94 and 0.94). Moreover,
correlation between female illiteracy and school enroll-
ments is high, and so is that between GINI and school
enrollments. Fourth, correlations associated with the
three other variables (ETHNIC, TRUST and POOR)
are of the expected kind, and, as discussed later, seem
informative.
Table 3 contains the main results. Estimation is
done by the ordinary least-squares (OLS) procedure,
but, due to the diversity of the sample, the t-statistics
are based on White’s (1980) heteroscedasticity-consis-
tent standard errors. Besides the models specified in
Eqs. (1)–(5), a modified version of Mellor and Milyo
(2001) is also estimated so as to permit a nonlinear
relation between income and infant mortality. The
modification is that their linear IMR equation is
changed to the Flegg-type log-log form, but the
ARTICLE IN PRESS
Table 1
Descriptive statistics for the main variables
Mean Std. dev. Min. Max. N
LIFE (life expectancy at birth, years, for year 2000) 65.59 12.20 38.00 81.00 108
IMR (infant mortality rate, per thousand live births, 2000) 38.62 37.13 3.40 153.60 108
PCY (GDP per capita, PPP dollars, around 1995) 7106 7569 474 28284 108
GINI (adjusted Gini coefficient, percent, mostly late 1990s) 43.38 10.94 19.50 69.50 108
TOP10 (income/consumption share of top 10%, percent, mostly
1990s)
31.13 7.48 18.20 48.80 108
FILIT (female illiteracy rate, percent, around year 1995) 24.18 27.41 1.00 93.40 101
SCHOOL (secondary school enrollment rate, percent, around 1995) 56.45 30.92 4.90 98.60 66
POOR (percent of population that is below the international poverty
line of PPP $1 per day, mostly 1990s)
18.52 19.57 2.00 72.80 77
TRUST (proxy for social capital, 2000 or mid-1990s) 26.72 14.74 2.83 66.53 68
ETHNIC (index of ethnic fractionalization, around 1990s) 0.42 0.25 0.002 0.93 107
Note: Data on GINI, TOP10, POOR and LIFE are from World Development Report 2003 (World Bank, 2003, pp. 234–237).
Information on IMR, PCY, FILIT, and SCHOOL is from the 2002 edition of World Development Indicators on CD-ROM (World
Bank, 2002). For most OECD countries, the female illiteracy rate is zero or nearly zero. To enable a logarithmic transformation for
Flegg’s (1982) model, a value of 1 has been used in these cases. Information on ETHNIC is taken from Alesina, Devleeschauwer,
Easterly, Kurlat, and Wacziarg (2003, pp. 184–189) and most data are for the 1990s. TRUST is taken from Bjornskov (2006,
Appendix Table 1), and most numbers are based on 4th wave of World Values Survey and the reference year is around 2000.
Table 2
Simple correlations between the main variables
LIFE IMR PCY TOP10 FILIT SCHOOL POOR ETHNIC TRUST
IMR �0.94*
PCY 0.65* �0.62*
TOP10 �0.45* 0.42* �0.46*
FILIT �0.73* 0.83* �0.57* 0.32*
SCHOOL 0.77* �0.81* 0.78* �0.68* �0.72*
POOR �0.79* 0.82* �0.60* 0.49* 0.64* �0.77*
ETHNIC �0.61* 0.59* �0.48* 0.37* 0.48* �0.48* 0.54*
TRUST 0.40* �0.31* 0.57* �0.51* �0.18 0.51* 0.03 �0.34*
GINI �0.57* 0.55* �0.59* 0.94* 0.42* �0.75* 0.57* 0.46* �0.55*
Note: See Table 1 for variable definitions. An asterisk (*) indicates significance at least at the 5% level, based on the p-values generated
by SAS for Windows (version 8), which was used for all computations.
R. Ram / Social Science & Medicine 62 (2006) 779–791 783
variables are kept unchanged. The following points are
indicated by Table 3:
1.
1Rodgers (1979, p. 349) compared the full sample estimates
with those for the LDCs. Full sample estimates are reported in
Table 3. LDC estimates, which are very similar, are available
from the author. For example, LDC parameters for GINI in
models of life expectancy and IMR have White-consistent t-
statistics that range from 2.96 to 3.98 in absolute value.
Rodgers’s (1979, p. 349) models are remarkably well
replicated despite (a) the much larger cross-country
sample, (b) the data being more recent by at least 25
years, and (c) the income variable being in PPP
dollars. In every case, estimated parameter for GINI
is highly significant in both LIFE and IMR
equations. In particular, the estimates for LIFE are
a stark contrast from the replications reported by
Gravelle et al. (2002, p. 580, column 6) in regard to
the significance of the parameter for GINI. Part (ii)
in section A of the table provides a quick comparison
of the GINI parameter estimated in this study with
those reported by Rodgers (1979, p. 349) and
Gravelle et al. (2002, p. 580). The comparison
indicates the estimate reported by Gravelle et al. to
be ‘‘unusual’’.
Some differences from Rodgers’s estimates might
also be of interest.1 Besides the greater explanatory
power (R2 being 0.76–0.81, as compared with
ARTICLE IN PRESS
Table 3
Relation between income inequality and population health: life expectancy (LIFE) and/or infant mortality rate (IMR) equations
A. Rodgers (1979, p. 349) models
C 1/PCY 1/PCY2 1/ln(PCY) GINI R2 N
(i) Full sample estimates from this study
LIFE 87.184* �30.104* 6.565* �0.254* 0.78 108
(33.85) (�6.06) (2.63) (�3.10)
141.201* �548.368* �0.190* 0.75 108
(40.76) (�11.29) (�2.14)
IMR �23.078* 98.205* �22.050* 0.634* 0.81 108
(�3.87) (6.06) (�2.34) (3.32)
�194.203* 1734.173* 0.456* 0.76 108
(�15.64) (12.50) (2.12)
(ii) Comparison of GINI coefficients in LIFE equations from Rodgers (1979, p. 349), Gravelle et al. (2002, p. 580) and the present study for the common model
Rodgers (1979, p. 349, row iv) �36.47 (t-statistic �3.76)
Gravelle et al.(2002, p. 580, col. 6) �5.39 (t-statistic �0.97)
Present study (first row above, GINI on 0–1 scale) �25.42 (t-statistic �3.10)
B. Flegg’s (1982, p. 445) model: Dependent variable is ln(IMR): LDC sample
ln(Y) ln(GINI) ln(FILIT) R2 N
Flegg’s estimates �0.299* 0.774* 0.278* 0.66 46
(1982, p. 445, 2nd row) (�2.93) (3.03) (3.50)
Present study estimates �0.569* 0.626* 0.250* 0.84 74
(�7.62) (2.57) (5.97)
C. Models like those of Waldmann (1992, p. 1287): Dep. variable is ln(IMR)
C l(Ypoor) l(Ymid) Rich-share Year-dummy R2 N
Waldmann’s estimates 11.92 0.07 �0.80* 2.48* 0.06 0.79 57
(1992, p. 1287, col. 1) (13.2) (0.45) (�5.57) (2.82) (0.44)
Wildman, Gravelle, 10.07 �0.27 �0.65** �0.51 �0.19 0.77 71
Sutton (2003, p. 1002, column 4) (14.48) (�0.71) (�1.79) (�0.32) (�1.62)
Present study C ln(PCY) TOP10 DCON R2 N
7.33* �0.67* 3.52* 0.45* 0.88 108
(11.18) (�12.45) (5.31) (3.92)
D. Mellor and Milyo (2001, p. 499) IMR models: Dep. variable is IMR
GINI PCY SCHOOL R2 N
Mellor and Milyo (2001, p. 499, col. 3) estimates �0.364 �0.001 �98.15 0.72 47
(�0.99) (�0.73) (�4.32)
Present study �0.467 �0.076 �105.450* 0.66 66
(�1.09) (�0.24) (3.90)
Adaptation of Mellor–Milyo IMR model to log-log form: Dep. variable is ln(IMR)
ln(GINI) ln(PCY) ln(SCHOOL) R2 N
1.048* �0.664* �0.175** 0.91 66
(3.70) (�8.60) (�1.76)
Note: The models are explained more fully in the text. The variables are defined in Table 1. ‘‘ln’’ denotes natural logarithm of the
variable. DCON in Waldmann’s models is an intercept dummy variable which is included to take account of the fact that TOP10
represents income-share for some countries and consumption-share for others.
Relevant t-statistics are in parentheses. For the present study, these are based on White’s (1980) heteroscedasticity-consistent standard
errors. All computations are done on SAS for Windows (version 8).
*Statistically significant at least at the 5% level.
**Significant at the 10% level
R. Ram / Social Science & Medicine 62 (2006) 779–791784
ARTICLE IN PRESS
2
rep
equ3
cou
Ro
and
its
the
pan
esti
sec
R. Ram / Social Science & Medicine 62 (2006) 779–791 785
Rodgers’s 0.56–0.77), every income term in Table 3
(section A) is statistically significant at least at the
5% level, which increases the plausibility of the
estimates. Also, significance of the GINI parameter is
much stronger in the present study for IMR models.
The difference may reflect larger sample size and the
better income variable used in the present study. The
differences in the magnitude of the estimates are
probably due to different units. In the present study,
income is in thousand (1995) PPP dollars, and GINI
is on a 0–100 scale instead of the traditional 0–1
scale.
2.
Flegg’s (1982) model is replicated very well despite a25-year difference in the timing of the data, improved
variable values, and a considerably larger sample
size. The difference in the size of the income
parameters probably reflects the better (PPP) income
measure used in the present study.
3.
Section C in the table reflects faithfully the mainpoints of Waldmann’s (1992) basic argument. De-
spite the differences in the models, there is a
remarkable similarity between the two sets of
estimates. The parameter for per capita income of
the entire population [ln(PCY)] is close to Wald-
mann’s for per capita income of the middle 75%
group [l(Ymid)]. More importantly, the coefficient
for the share of top 10% is close to his number for
the top 5% (rich-share). It is clear that given average
income of the population, income inequality proxied
by the income share of top 10% is associated with a
substantial increase in IMR.2
A comparison of the present study’s and Wald-
mann’s estimates with those of Wildman et al. (2003,
p. 1002) is also provided in section C of Table 3. The
comparison suggests that their parameter for the
rich-share is likely to reflect some unusual character-
istics of the sample or the data.
4.
The basic cross-country estimates of Mellor andMilyo’s (2001, p. 499) IMR model are replicated well
in section D of Table 3. As shown in their full
regressions (p. 499, column 3), neither income nor
Gini has a significant association with IMR; only
secondary-school enrollment matters.3
Mellor–Milyo models that enter income linearly in
Following Waldmann, estimates for the IMR model are
orted. The results from the corresponding life expectancy
ation are similar.
Two points about the Mellor and Milyo (2001) cross-
ntry estimates may be noted. First, perhaps following
dgers (1979), they reported estimates for both life expectancy
IMR. Table 3 reports the IMR equation so as to facilitate
reformulation into the Flegg-type logarithmic form. Second,
y also reported (2001, Table 3, p. 501) some results from
el cross-country data. However, their Table 2 (p. 499)
mates are more relevant to the present study that uses cross-
tion data.
health regressions are unusual in the literature, and
a flavor of the consequence of entering income
nonlinearly in their specification is provided in the
last part of section D of Table 3. It is done by
estimating a Flegg-type log-log model of infant
mortality with the same variables as were used
by Mellor and Milyo, which really amounts to
replacing Flegg’s female illiteracy rate by secon-
dary school enrollment. The estimates show that
the change in going from Mellor–Milyo linear
form to Flegg-type log-log form is rather dramatic.
The explanatory power of the model rises to 0.91,
income shows a highly significant negative sign,
and GINI has a highly significant positive associa-
tion with infant mortality. The schooling variable
is also marginally significant and has the expected
sign. These estimates seem much more plausible than
those of Mellor and Milyo and the corresponding
replication in the present study. It would be
appropriate to note here that there are obviously
many ways in which the models can be made
nonlinear in income, and the example given here is
somewhat arbitrary in choosing Flegg’s specification.
However, unreported estimates from an alternative
model (of logarithm of IMR), in which only PCY is
entered logarithmically while GINI and SCHOOL
enter linearly, also show GINI to be significant at
better than the five percent level with a White-
consistent t-statistic of 2.03.
5.
The overall picture suggested by Table 3 is that theestimates reported by Rodgers (1979), Flegg (1982)
and Waldmann (1992), which all show income
inequality to be significantly negatively associated
with good health, are replicated well despite the
substantially larger sample size, much more recent
data, and considerably better variables. In particular,
the comparison in section A of Table 3 of the life-
expectancy models shows the stark contrast between
Rodgers’s (1979) and this study’s results from those
reported by Gravelle et al. (2002, p. 580). Similarly,
replication of the modified Waldmann (1992) model
in section C of Table 3 is a contrast from Wildman
et al. (2003). Moreover, Mellor and Milyo (2001,
p. 499, columns 3 and 6) estimates, which indicate
neither income nor inequality to have any signifi-
cant effect on life expectancy or infant mortality,
appear implausible in suggesting school-enrollment
to be the only driver of cross-country variations
in life expectancy and infant mortality, and it is
shown that the estimates undergo a dramatic change
when a Flegg-type nonlinear model of infant
mortality is used. It is, therefore, fair to conclude
that predominance of the cross-country evidence
supports the view that income inequality has a
significant negative association with the health status
of the population.
ARTICLE IN PRESS
4Samples for Rodgers’s models in Tables 3 and 4 differ
slightly due to missing data on ETHNIC for Yemen.
R. Ram / Social Science & Medicine 62 (2006) 779–791786
Discussion
The estimates in Table 3 can be interpreted as belying
the skepticism articulated by Deaton (2002, 2003),
Gravelle et al. (2002), Lynch and Smith (2002),
Mackenbach (2002), Mellor and Milyo (2001), Wildman
et al. (2003), and other scholars, about the replicability
of the studies by Rodgers (1979), Flegg (1982) and
Waldmann (1992) and the existence of a negative
association between income inequality and good health
in cross-country data. However, it seems useful to
consider some questions that have a bearing on the
topic, although these apply as much to the earlier studies
as to the present one. The point about the difficulty of
using aggregate data to shed light on the relation
between income inequality and individual health has
already been noted in the introductory section. The
difficulty of judging the concavity of the individual
income–health relation from aggregate data is also
evident.
An additional concern about using income inequality
in aggregate health models is that some relevant
variables may have been omitted and the effect of these
variables may be reflected in income inequality para-
meters. One such variable is ethnic or social hetero-
geneity. Table 2 indeed shows that the index of ethnic
heterogeneity (ETHNIC) has a sizable positive correla-
tion with GINI and a large negative correlation with
measures of good health. Therefore, absence of ethnic
heterogeneity in health models may bias the coefficient
of income inequality. Such a possibility is explored by
including index of ethnic heterogeneity along with GINI
in Rodgers’s (1979) specification and the modified
Waldmann model, for which the sample size is the
largest. The index of ethnic fractionalization is taken
from Alesina et al. (2003, pp. 184–189), and, following
the standard practice, has been computed by them as
one minus the Herfindahl index of ethnic groups’
population shares. Although the reference years differ,
most numbers are for the 1990s and are temporally
congruent with other variables in this study. Section A
in Table 4 shows the estimates with inequality (GINI
or TOP10) alone, with both inequality and ETHNIC,
and with ETHNIC replacing inequality. Six points
may be noted here. First, parameters of income
inequality (GINI or TOP10) are very similar whether
or not ETHNIC is added to the model. There is thus
no indication of any bias in the income inequality
parameters due to the omission of ETHNIC. Second,
significance of the income inequality variables is
hardly altered after ETHNIC is added to the models.
Third, ethnic heterogeneity has a negative associa-
tion with good population health even in the presence
of an inequality term. Fourth, as would be expected,
ETHNIC has a sizable negative association with
population health when income inequality is not
included and the association is stronger than when
income inequality is included. Fifth, explanatory
power of the models is somewhat weaker when
ETHNIC replaces income inequality, suggesting that
the latter may be more important. Last, in all cases, the
estimates bear a remarkable similarity with the corre-
sponding rows in Table 3, indicating robustness of the
estimates.4
Another important point concerns the mechanisms
through which income inequality may affect population
health adversely. Several well-known hypotheses have
been proposed to explain the association between
income inequality and health. As Wagstaff and Door-
slaer (2000) have shown, it is difficult to judge from
aggregate data relative merits of these hypotheses.
However, omission of a major mechanism from
aggregate health models could lead to a bias in the
income inequality parameter in much the same way as
the omission of another relevant variable, and it is
desirable to make an effort to shed some light on the role
of a possible mechanism. One important hypothesis
about a possible mechanism suggested by Wilkinson
(1996) and other scholars is that income inequality
lowers social cohesion or social capital and thus
adversely affects individual and population health.
Although social capital seems to have several dimen-
sions, ‘‘generalized trust’’ is considered to be a major
component, and it is possible to do an exploratory
analysis to see how a measure of generalized trust
correlates with income inequality and whether addition
of such a measure erodes the income inequality
parameter. As Bjornskov (2006) and other scholars
have noted, World Values Survey (WVS) provides a
good source for country-level information on general-
ized trust (TRUST). The numbers used in the present
study are taken from Bjornskov (2006, Appendix Table
1). He has explained that the data are based largely on
the fourth wave of WVS and most are for the year 2000.
The number for each country is the percentage of
population that answered ‘‘yes’’ to the question, ‘‘In
general, do you think that most people can be trusted, or
can’t you be too careful?’’
In terms of simple correlations, Table 2 shows that
TRUST has large negative correlations with income
inequality (GINI and TOP10) that are highly significant.
Thus, there seems some merit in Wilkinson’s hypothesis
about income inequality eroding social capital. More-
over, TRUST has sizable positive correlations with
measures of good health. Therefore, it is possible that
exclusion of TRUST from aggregate health regressions
might bias the income inequality parameters. Part B in
Table 4 reports estimates of Rodgers’s and modified
Waldmann models with income inequality alone, with
ARTICLE IN PRESS
Table 4
Selected tests of sensitivity of income inequality parameters
A. Sensitivity to inclusion of ethnic heterogeneity index (ETHNIC)
Rodgers (1979, p. 349) models
C 1/PCY 1/PCY2 GINI ETHNIC R2 N
LIFE
With GINI but 86.792* �30.542* 6.473* �0.242* 0.79 107
Without ETHNIC (33.47) (�6.00) (2.52) (�2.92)
With both GINI 87.130* �27.725* 5.647* �0.208* �6.389* 0.80 107
And ETHNIC (35.08) (�5.27) (2.20) (�2.60) (�2.17)
With ETHNIC but 80.357* �32.739* 7.888* �8.697* 0.78 107
Without GINI (104.80) (�6.41) (3.06) (�2.63)
IMR
With GINI but �22.074* 99.323* �21.814* 0.603* 0.82 107
Without ETHNIC (�3.70) (5.98) (�2.22) (3.16)
With both GINI �22.815* 93.156* �20.006* 0.528* 13.987** 0.82 107
And ETHNIC (�3.86) (5.50) (�2.02) (2.81) (1.74)
With ETHNIC but �5.616* 105.887* �25.697* 19.846* 0.81 107
Without GINI (�2.47) (6.79) (�2.74) (2.37)
Modified Waldmann (1992, p. 1287) model: Dep. variable is ln(IMR)
C ln(PCY) TOP10 ETHNIC DCON R2 N
With TOP10 but 7.324* �0.671* 3.519* 0.445* 0.88 107
Without ETHNIC (10.74) (�11.98) (5.16) (3.90)
With both TOP10 6.819* �0.623* 3.285* 0.449* 0.435* 0.89 107
And ETHNIC (9.56) (�10.38) (5.10) (2.33) (4.03)
With ETHNIC but 8.695* �0.727* 0.631* 0.325* 0.85 107
Without TOP10 (12.60) (�10.72) (2.82) (2.32)
B. Sensitivity to the inclusion of social capital (TRUST)
Rodgers (1979, p. 349) models:
C 1/PCY 1/PCY2 GINI TRUST R2 N
LIFE
With GINI but 87.044* �25.795* 5.054** �0.274* 0.67 68
Without TRUST (24.08) (�3.90) (1.76) (�2.39)
With both 84.783* �25.763* 5.132** �0.244** 0.039 0.67 68
GINI and TRUST (18.28) (�3.95) (1.79) (�1.94) (1.03)
With TRUST but 73.905* �30.884* 7.398* 0.121* 0.63 68
Without GINI (37.94) (�5.37) (2.77) (2.62)
IMR
With GINI but �20.276* 78.172* �17.961* 0.661* 0.74 68
Without TRUST (�3.32) (4.45) (�2.22) (3.21)
With both GINI �26.813* 78.263* �17.734* 0.748* 0.114 0.75 68
And TRUST (�2.88) (4.45) (�2.21) (3.24) (1.20)
With TRUST but 6.523 93.956* �24.677* �0.136 0.68 68
Without GINI (1.35) (5.80) (�3.27) (�1.31)
Modified Waldmann (1992, p. 1287) model: Dep. variable is ln(IMR)
C ln(PCY) TOP10 TRUST DCON R2 N
With TOP10 but 6.114* �0.574* 4.645* 0.526* 0.86 68
Without TRUST (8.47) (�9.41) (5.73) (4.17)
With both TOP10 6.103* �0.581* 4.753* 0.001 0.524* 0.86 68
And TRUST (8.43) (�8.80) (5.28) (0.41) (4.16)
With TRUST but 8.613* �0.676* �0.008* 0.439* 0.78 68
Without TOP10 (10.81) (�7.47) (�2.01) (2.38)
Note. See the text for meanings and sources of ETHNIC and TRUST. Notes in other tables apply here also; in particular, t-statistics
are based on White’s consistent standard errors.
R. Ram / Social Science & Medicine 62 (2006) 779–791 787
ARTICLE IN PRESSR. Ram / Social Science & Medicine 62 (2006) 779–791788
both TRUST and income inequality, and with TRUST
replacing income inequality. These estimates also
suggest six points. First, parameters for income inequal-
ity variables (GINI and TOP10) are almost identical
whether or not TRUST is added to the models. In fact,
the income inequality parameters become larger with the
addition of TRUST in Waldmann-type and Rodgers’s
IMR models. Second, income inequality retains high
statistical significance in almost all cases when TRUST
is added. Third, despite the sizable correlations of
TRUST with IMR and LIFE in Table 2, when income
inequality and TRUST are included in the models,
coefficients of TRUST lack statistical significance at any
meaningful level and have perverse signs in both IMR
equations. Fourth, when TRUST replaces income
inequality, it has the expected sign and shows signifi-
cance in two of the three cases. Fifth, the explanatory
power of the models is considerably weaker when
TRUST replaces the income inequality variables. There-
fore, while erosion of trust (social capital) might
certainly be one channel through which income inequal-
ity affects health, the role of income inequality seems
more important and diverse. Last, despite the very
different and much smaller sample, estimates in part B
of Table 4 are similar to the corresponding rows in Table
3 and part A of Table 4, once again reaffirming
robustness of the estimates.
As the earlier discussion explains, while this study’s
estimates in Table 3 are remarkably similar to those of
Rodgers (1979), Flegg (1982) and Waldmann (1992) in
showing a negative association between income inequal-
ity and good health, there are important differences
from the patterns reported by several influential studies,
and it is useful to consider further possible reasons for
these differences.
The difference between the estimates reported by
Rodgers (1979), that are well replicated in this study,
and the results shown by Mellor and Milyo (2001) may
be due to the very different specification used by Mellor
and Milyo. Besides the addition of the schooling
variable, they entered income linearly although, follow-
ing Preston (1975), Rodgers (1979) explicitly and care-
fully explored several nonlinear forms of the income
variable. The work by Mellor and Milyo (2001) seems to
be the only instance in the literature where income is
entered linearly in a model of aggregate health. As
already observed, section D in Table 3 shows that if
Mellor–Milyo linear specification is changed to (Flegg-
type) log-log form, income inequality shows a highly
significant positive coefficient in the IMR equation,
which is a sharp contrast from Mellor–Milyo estimates
in the linear form. Estimates from the log-log form seem
much more plausible than those of Mellor and Milyo
which indicate school enrollment to be the only
significant driver of cross-country variations in health
and suggest that even average income does not matter.
Considerable effort was made to see why Gravelle
et al. (2002) were unable to replicate Rodgers’s (1979)
finding of a significant negative coefficient on income
inequality in models of life expectancy. It was noted that
their study differed from Rodgers’s in several ways.
Besides the more recent periods, a different (and
apparently smaller) set of countries, and pooling of
observations for two periods, they used male life
expectancy instead of overall life expectancy. A quick
ancillary project was undertaken to try to replicate the
Gravelle et al. results with the kind of data used by
them. Following their procedure, the years 1981 and
1989 (or close to these) were taken, income was obtained
from an update (PWT 6.2) of Summers and Heston
(1991), Gini was derived from the high-quality compo-
nent of Deininger and Squire (1996) dataset, and male
life expectancy was obtained from United Nations
(2001, Table A.30), which is an authentic and original
source. The sample size in that project was 110 for the
cases in which data for 1981 or 1989 (or both) were
available. However, coefficient estimates for Gini
corresponding to their columns 5 and 6 (p. 580) were
found to be significant at the one percent level, and the
White-consistent t-statistics were �3.43 and �2.85,
respectively. These estimates portray a picture that is
very different from theirs, but is very similar to that in
Table 3. It is possible that they just happened to get
atypical observations in their relatively limited cross-
country sample.
It may also be noted that while subjecting the life-
expectancy models to an extensive examination, Grav-
elle et al. (2002) did not report any estimates for
Rodgers’s infant-mortality equations. It is possible that
Gini coefficients were significant for infant-mortality
models even in their sample.
It is difficult to say why Wildman et al. (2003) were
unable to replicate from their data Waldmann’s (1992)
result that share of top 5% is associated with increased
infant mortality. A much larger sample and more recent
and better data in the present study show a pattern that
is strikingly similar to Waldmann’s despite several
differences in the models. It is possible that there was
something unusual or special about the Wildman et al.
(2003) sample or data.
One aspect related to the main exploration undertaken
in the present study is Deaton’s (2003, p. 151) suggestion
that infant (and child) mortality in less developed
countries is primarily a consequence of poverty and
that, conditional on average income, income inequality is
important only because it is effectively a measure of
poverty. While there are well-known difficulties in
comparing poverty incidence across countries, a simple
test was conducted by estimating for the LDCs the IMR
equations reported in Table 3 with the addition of a
poverty variable (POOR), which is the percent of
population below the international income-poverty line
ARTICLE IN PRESS
Table 5
Comparing roles of income inequality (GINI) and poverty (POOR) relative to infant mortality (IMR) in less-developed countries
(LDCs)
Rodgers’s model: Dep. variable is IMR
C 1/PCY 1/PCY2 GINI POOR R2 N
�17.976 85.156* �20.050** 0.450** 0.475* 0.82 69
(�1.52) (3.48) (�1.81) (1.65) (2.09)
Flegg’s (1982) model: Dep. variable is ln(IMR)
C ln(PCY) ln(GINI) ln(POOR) ln(FILIT) R2 N
4.384* �0.448* 0.474** 0.091** 0.249* 0.87 64
(3.76) (�5.60) (1.88) (1.90) (5.89)
Modified Waldmann model: Dep variable is ln(IMR)
C ln(PCY) TOP10 POOR DCON R2 N
6.774* �0.588* 2.801* 0.007 0.474* 0.79 69
(8.27) (�6.24) (2.91) (1.44) (3.61)
Mellor–Milyo (2001) model in log-log form: Dep variable is ln(IMR)
C ln(PCY) ln(GINI) ln(POOR) ln(SCHOOL) R2 N
5.431* �0.751* 1.016* 0.077 �0.007 0.81 37
(2.79) (�6.19) (2.44) (0.73) (�0.05)
Note: These are some of the IMR models of sections A, B, C and D of Table 3 with the addition of the poverty variable, and are
estimated for the LDC sample, which is obtained by excluding OECD countries from the full sample. Notes for that table are relevant
here also. In particular, t-statistics in parentheses are based on White’s (1980) heteroscedasticity-consistent standard errors, and an
asterisk indicates statistical significance at least at the 5% level, while ** indicates significance at the 10% level.
5Additional information is available from the author.
R. Ram / Social Science & Medicine 62 (2006) 779–791 789
of one PPP dollar per day per person. Despite its
weaknesses, the measure has some usefulness for cross-
country comparisons because, as World Bank (2003,
p. 246) noted, it holds ‘‘the real value of poverty line
constant across countries’’. Based on World Bank’s
(2003, pp. 236–237) data on poverty, Table 5 contains
estimates of the augmented IMR models. In two of the
four equations, POOR lacks significance at the 10% level
while income inequality variables remain significant at
better than the 5% level. In Flegg’s model, both income
inequality and poverty are significant at the 10% level. In
Rodgers’s model, while poverty is significant at 5%,
income inequality is significant at the 10% level. Based
on this preliminary evidence, it is difficult to say that the
association of income inequality with infant mortality in
LDCs is just a reflection of the effect of poverty. While
poverty does seem important, income inequality appears
to have a significance of its own.
Another related aspect is the proposition articulated
by Wilkinson (1996) and Deaton (2003, p. 121) that,
relative to income inequality, average income should
have a stronger association with population health in
less-developed countries, but income inequality should
be relatively more important in developed economies. A
preliminary exploration of that proposition was done by
comparing the implied marginal ‘‘effects’’ (parameters)
of GINI and per capita income on IMR from Flegg’s
model for the full sample and the LDCs. The
comparison is indirect since LDC estimates are com-
pared with those from the full sample and not for the
DCs which is a relatively small group, defined to consist
of the OECD countries. Based on sample means, the
implied marginal effects of GINI and per capita income
(in thousand PPP dollars) are of the following order:
Full
sample
LDCs
Ratioof
LDCs
to full
sample
GINI
0.56 0.65 1.16Income per capita (thousand
PPP dollars)
�3.08
�8.12 2.64It is thus seen that while both income and GINI have
larger effects in the LDCs, the relative effect of GINI is
stronger in the full sample (and therefore in the DCs),
while that of income is stronger in the LDCs.5 Estimated
marginal parameters at the sample means from a simple
version of Rodgers’s model (that includes reciprocal of
ARTICLE IN PRESSR. Ram / Social Science & Medicine 62 (2006) 779–791790
income and GINI) also indicate a similar pattern. These
estimates should, however, be interpreted with caution
since the aggregate relation between health and income
may not faithfully reflect the concavity of the income–-
health relation at the individual level.
Concluding remarks
Careful research by Rodgers (1979), Flegg (1982) and
Waldmann (1992) provided solid evidence of a negative
association between income inequality and population
health in broad cross-country data. However, recent
studies by some scholars, including Mellor and Milyo
(2001), Gravelle et al. (2002), and Wildman et al. (2003),
suggest that the patterns reported by Rodgers, Flegg,
and Waldmann cannot be replicated from recent data
which indicate no significant association between
income inequality and population health in multi-
country contexts. In view of the importance of the issue,
the main purpose of the present study is to undertake a
fresh examination of the cross-country relation between
income inequality and population health by using the
largest possible sample and the most recent information
with a high level of data accuracy. In particular, it
considers whether it is true that the estimates reported
by Rodgers, Flegg, and Waldmann, indicating a
negative association between income inequality and
population health, can or cannot be replicated. The
main conclusion is that, using the largest cross-country
sample and the most recent and accurate data, this study
is able to replicate remarkably well estimates of the kind
reported by the three scholars, and there is consistent
evidence of a negative cross-country association between
income inequality and population health. Possible
reasons for deviations from the patterns reported by
Mellor and Milyo (2001), Gravelle et al. (2002) and
Wildman et al. (2003) were considered at some length.
The most likely reasons seem to be (a) unusual nature of
the Mellor–Milyo model in which income was entered
linearly, (since a Flegg-type log-log model with Mel-
lor–Milyo variables yields a very different scenario), and
(b) samples and data used by Gravelle et al. (2002) and
Wildman et al. (2003) being probably unusual or
atypical. Therefore, the widespread recent skepticism
about the presence of a negative association between
income inequality and population health, articulated by
many scholars, including Lynch and Smith (2002),
Mackenbach (2002), and Deaton (2002, 2003), needs
to be reconsidered. Besides the observation that such
aggregate studies are not well suited to shed light on the
effect of income inequality on health at the individual
level, and that concavity of the income–health relation
at the individual level may not be faithfully reflected in
aggregate data, this study notes seven additional points.
First, when ethnic heterogeneity, which may be
perceived as an omitted variable in such models of
population health, is added, there is very little change in
the income inequality parameters which retain high
statistical significance in the presence of the ethnic-
heterogeneity term. Second, ethnic heterogeneity also
has an adverse association with population health.
Third, when an index of social capital (TRUST), which
may be regarded as a channel through which income
inequality affects health, is included in the models,
income inequality parameters remain almost unchanged,
and retain high statistical significance in almost all
equations. Fourth, despite the sizable simple correla-
tions, the association of TRUST with population health
in the regression models appears weak. Fifth, the
regression patterns observed after the addition of ethnic
heterogeneity or TRUST indicate the estimates to be
quite robust. Sixth, a simple test does not support the
view that the (positive) association between income
inequality and infant mortality in the LDCs is simply a
reflection of the effect of poverty. Last, although there is
an obvious difficulty in capturing in aggregate data
concavity of the individual income–health relation, a
simple indirect test indicates that while both average
income and inequality have stronger marginal para-
meters in health models for the LDCs, the marginal
parameter for income is relatively stronger in the LDCs
while that for income inequality is likely to be relatively
stronger in the DCs.
Acknowledgements
Extremely useful comments on an earlier version from
two anonymous referees are gratefully acknowledged. V.
Cristina Iliuta provided helpful research assistance. The
usual disclaimer applies.
Appendix. List of sample countries (an asterisk indicates
OECD member, and all others are treated as LDCs)
Algeria, Armenia, Australia*, Austria*, Azerbaijan,
Bangladesh, Belarus, Belgium*, Bolivia, Brazil, Bulgar-
ia, Burkina Faso, Burundi, Cambodia, Cameroon,
Canada*, Central African Republic, Chile, China,
Colombia, Costa Rica, Cote d’Ivoire, Croatia, Czech
Republic*, Denmark*, Dominican Republic, Ecuador,
Egypt, El Salvador, Estonia, Ethiopia, Finland*,
France*, Georgia, Germany*, Ghana, Greece*, Guate-
mala, Guinea, Honduras, Hungary*, India, Indonesia,
Ireland*, Israel, Italy*, Jamaica, Japan*, Jordan,
Kazakhstan, Kenya, Korea (Rep.)*, Kyrgyz Republic,
Latvia, Lesotho, Lithuania, Madagascar, Malaysia,
Mali, Mauritania, Mexico*, Moldova, Mongolia, Mor-
occo, Mozambique, Nepal, Netherlands*, Nicaragua,
Niger, Nigeria, Norway*, Pakistan, Panama, Papua
ARTICLE IN PRESSR. Ram / Social Science & Medicine 62 (2006) 779–791 791
New Guinea, Paraguay, Peru, Philippines, Poland*,
Portugal*, Romania, Russian Federation, Rwanda,
Senegal, Sierra Leone, Slovak Republic*, Slovenia,
South Africa, Spain*, Sri Lanka, Sweden*, Switzer-
land*, Tajikistan, Tanzania, Thailand, Tunisia, Tur-
key*, Turkmenistan, Uganda, Ukraine, United
Kingdom*, United States*, Uruguay, Uzbekistan, Ve-
nezuela, Vietnam, Yemen (Rep.), Zambia, Zimbabwe.
References
Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., &
Wacziarg, R. (2003). Fractionalization. Journal of Economic
Growth, 8, 155–194.
Bjornskov, C. (2006). The multiple facets of social capital.
European Journal of Political Economy, forthcoming.
Baumbusch, A. P. (1995). Income inequality and infant
mortality. Junior independent work, Princeton University,
Princeton, NJ.
Deaton, A. (2002). Commentary: The convoluted story of
international studies of inequalities and health. International
Journal of Epidemiology, 31, 546–549.
Deaton, A. (2003). Health, inequality, and economic develop-
ment. Journal of Economic Literature, 41, 113–158.
Deininger, K., & Squire, L. (1996). A new data set measuring
income inequality. World Bank Economic Review, 10,
565–591.
Ellison, G. T. H. (2002). Letting the Gini out of the bottle?
Challenges facing the relative income hypothesis. Social
Science & Medicine, 54, 561–576.
Flegg, A. T. (1982). Inequality of income, illiteracy and medical
care as determinants of infant mortality in underdeveloped
countries. Population Studies, 36(3), 441–458.
Gravelle, H., Wildman, J., & Sutton, M. (2002). Income,
income inequality and health: What can we learn from
aggregate data? Social Science & Medicine, 54, 577–589.
Judge, K., Mulligan, J., & Benzeval, M. (1998). Income
inequality and population health. Social Science & Medi-
cine, 46, 567–579.
Lynch, J., & Smith, G. D. (2002). Commentary: Income
inequality and health: the end of the story? International
Journal of Epidemiology, 31, 549–551.
Lynch, J., Smith, G. D., Harper, S., Hillemeir, M., Ross, N.,
Kaplan, G. A., et al. (2004). Is income inequality a
determinant of population health? Part 1. A systematic
review. The Milbank Quarterly, 82, 5–99.
Mackenbach, J. P. (2002). Income inequality and population
health. British Medical Journal, 324, 1–2.
Mellor, J. M., & Milyo, J. (2001). Reexamining the evidence of
an ecological association between income inequality and
health. Journal of Health Politics, Policy and Law, 26,
487–522.
Preston, S. H. (1975). The changing relation between mortality
and level of economic development. Population Studies,
29(2), 231–248.
Rodgers, G. B. (1979). Income and inequality as determinants
of mortality: An international cross-section analysis. Popu-
lation Studies, 33(2), 343–351.
Subramanian, S. V., & Kawachi, I. (2003a). The association
between state income inequality and worse health is not
confounded by race. International Journal of Epidemiology,
32, 1022–1028.
Subramanian, S. V., & Kawachi, I. (2003b). Response: In
defense of the income inequality hypothesis. International
Journal of Epidemiology, 32, 1037–1040.
Subramanian, S. V., & Kawachi, I. (2004). Income inequality
and health: What have we learned so far? Epidemiologic
Reviews, 26, 78–91.
Summers, R., & Heston, A. (1991). The Penn World Table
(Mark 5): An expanded set of international compar-
isons, 1950–1988. Quarterly Journal of Economics, 106,
327–368.
United Nations. 2001. World population prospects: The 2000
revision (Vol. 1). New York.
Wagstaff, A., & Doorslaer, E. (2000). Income inequality and
health: What does the literature tell us? Annual Review of
Public Health, 21, 543–567.
Waldmann, R. J. (1992). Income distribution and infant
mortality. Quarterly Journal of Economics, 107(4),
1283–1302.
White, H. (1980). A heteroskedasticity-consistent covariance
matrix estimator and a direct test for heteroskedasticity.
Econometrica, 48(4), 817–838.
Wildman, J., Gravelle, H., & Sutton, M. (2003). Health and
income inequality: Attempting to avoid the aggregation
problem. Applied Economics, 35, 999–1004.
Wilkinson, R. G. (1992). Income distribution and life ex-
pectancy. British Medical Journal, 304, 165–168.
Wilkinson, R. G. (1996). Unhealthy societies: The afflictions of
inequality. London: Routledge.
World Bank (2002). World development indicators on CD-ROM.
Washington, DC.
World Bank (2003). World development report 2003. Washing-
ton, DC.
