p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4

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Public Health

journal homepage: www.elsevier .com/puhe

Original Research

Income inequality and population health in Islamic countries

A. Esmaeili a,*, S. Mansouri b, M. Moshavash c

aDepartment of Agricultural Economics, Shiraz University, Shiraz, Iranb Shiraz University, Shiraz, IrancShiraz University of Medical Sciences, Shiraz, Iran

a r t i c l e i n f o

Article history:

Received 22 April 2009

Received in revised form

5 February 2011

Accepted 7 June 2011

Keywords:

Population health

Income distribution

Income level

Islamic countries

* Corresponding author. Tel.: þ98 711 228709E-mail address: esmaeili68@yahoo.com (

0033-3506/$ e see front matter ª 2011 The Rdoi:10.1016/j.puhe.2011.06.003

s u m m a r y

Objectives: To undertake a fresh examination of the relationship between income inequality

and population health for a group of Islamic countries using recent information derived

from data resource sites from the World Bank and Islamic countries.

Study design: : Cross-sectional data on different measures of income distribution (pros-

perity, health care, women’s role and environment) and indicators of population health

were used to illuminate this issue.

Methods: The relationship between income inequality and population health for a group of

Islamic countries was tested using recent information derived from data resource sites

from the World Bank and Islamic countries. After consideration of previous studies, seven

dependent variables were determined and tested in six equation formats.

Results: According to the equations, the urban population percentage and gross domestic

product are the most important significant variables that affect life expectancy and the

infant mortality rate in Islamic countries. The income distribution coefficient, regardless of

the type of measure, was almost insignificant in all equations.

Conclusions: In selected Islamic countries, income level has a positive effect on population

health, but the level of income distribution is not significant. Among the other dependent

variables (e.g. different measures of income distribution, health care, role of women and

environment), only environment and education had significant effects. Most of the Islamic

countries studied are considered to be poorly developed.

ª 2011 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Introduction

Despite a large body of research, the extent to which income

inequality in a society is a determinant of the health of the

population remains controversial. Some studies support the

assumption of a reverse association between these two

indices.1�3 Other authors believe that the higher a country’s

per-capita income, themore likely it is that the populationwill

live longer and healthier lives, but this effect tapers off as

3; fax: þ98 7112286082.A. Esmaeili).oyal Society for Public H

income rises. On the other hand, some research indicates that

the influence of income level has a diminishing effect on the

health of a nation.4 These latter authors believe that when

income increases to a threshold, from that point onwards, it is

income inequality (rather than income level) that affects the

health of a population.

The present authors reviewed reports on the relation-

ship between income distribution and measures of pop-

ulation health. Some studies were found to support this

ealth. Published by Elsevier Ltd. All rights reserved.

p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4578

association,3,5�7 whereas others were considered to have

insufficient evidence to prove the relationship.8�11 Table 1

shows the variables, sample sizes, and view of the authors

on the association between income inequality and pop-

ulation health for the studies reviewed.

The share of national income going to the bottom 20th

percentile of the populationwas related tomean age at death in

17 ‘developing’ countries after adjusting for gross domestic

Table 1 e Summary of articles related to population health.

Author Supportive orunsupportive ofan association

between populationhealth and

income inequality

Sample area

Pample and

Pialli (1986)

Unsupportive 18 developed count

Le Grand (1987) Supportive 17 developing count

Waldman (1993) Supportive 41 developing and 1

developed countries

Wennemo (1993) Supportive 11 developing count

Duleep (1995) Supportive 37 developing and

developed countries

Judge (1995) Unsupportive 13 developed count

Saunders(1996) Unsupportive 15 OECD countries

McIsaac and

Wilkinson (1997)

Supportive 13 OECD countries

Judge et al. (1998) Unsupportive Industrialized coun

Gravelle (1998) Unsupportive

Rom (2005) Supportive US data for 2000

and 1990

Wilkinson

and Pickette

(2006)

Supportive Theoretical review

of 168 analysis in

155 papers

Ram (2006) Supportive

GDP, gross domestic product; OECD, Organization for Economic Co-opera

product (GDP) and public and private expenditure on health

care.6 Data for 41 developing countries and 16 ‘developed’

countries were analysed.12 This analysis showed that, after

adjusting for the mean income of the poorest 20% of the pop-

ulation and other variables, the share of income received by the

richest 5% was associated with infant mortality. In a series of

contributions, the authors argue that income distribution is the

most important determinant of differences in life expectancy.

Explanatoryvariable

Population healthproxies

ries Welfare state Infant mortality rate

Health care

Unemployment

Ethnicity

Women’s role

Gini

ries Public and private

expenditure on health

Average age at death

GDP

Percentage of income

going to bottom 20%

of the population

6 Average income of the

poorest 20% of the population

Infant mortality

Average life expectancy

ries Gini Infant mortality

Proportion of households

with an income under 5%

National income available

to the bottom 10%

Infant mortality

ries Proportions of population

receiving 40e60% of income

Life expectancy

Health expenditure

GDP

Female role

Social security

GDP Life expectancy

Health expenditure

Gini

Income distribution Sex and age measures

of mortality rate

tries GDP Infant mortality

Health expenditure Life expectancy

Female role

Social security transfer

Variance of income

across the population

Population mortality

Mean income

Education variables Mortality rate

Race and urbanization

variable

Poverty

Theoretical article Infant mortality

Life expectancy

Income inequality Infant mortality

Illiteracy Life expectancy

Medical care

tion and Development.

p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4 579

An association was found between infant mortality and

measures of income distribution, such as the Gini coefficient,

proportion of households with income <50% of median, and

relative poverty after adjusting for GDP, in 18 industrialized

countries.13 Investigation in 37 developing and ‘developed’

countries showed that the share of national income going to

the bottom 10% only had a significant negative effect on

mortality for the two youngest age groups.5 A significant

relationship has been reported between various causes, sex-

and age-specific measures of mortality, and indicators of

income distribution for 13 Organization for Economic Co-

operation and Development (OECD) countries.14

State-level US data for 2000 and 1990 provide additional

evidence on the roles of income inequality and poverty on

population health.3 This research showed that, contrary to the

suggestion made in several recent studies, the parameter of

income inequality is quite robust and carries statistical

significance in mortality equations estimated from several

observation sets and a fairly wide variety of specific choices.

Second, the evidence does not indicate that the significance of

income inequality is lost if education variables are included.

Third, income inequality shows significance if a race variable

is added, and if terms for race and urbanization are entered.

Fourth, whereas poverty is seen to have some consequence in

increasing mortality, the role of income inequality appears to

be stronger. Fifth, income inequality retains statistical

significance if a quadratic income term is added, and also if

the version of a fairly inclusive model is estimated. It is

therefore suggested that the recent scepticism articulated by

several scholars with regard to the robustness of the income

inequality parameters in mortality equations estimated from

the US data should be reconsidered.

The relationship between the scale of income inequality

and population health in a particular population was

reviewed. Researchers identified 168 analyses in 155 articles,

and classified them according to how far their findings sup-

ported the hypothesis that greater differences in income are

associatedwith lower standards of population health. Seventy

percent of the studies showed that quality of health decreases

as income differences increase in societies. Some researchers

agreed that income inequality, illiteracy and medical care are

determinants of infant mortality in developing coun-

tries.1,7,12,15 They showed that the negative association

between income inequality and good health is replicated, and

that different findings indicated by some scholars may have

been due to unrepresentative samples or inappropriate

models. They showed some support for the proposition that,

even though income may be more important for health in

developing countries, the role of income inequality may be

stronger in developed countries.

The association between infant mortality and income

inequality has been questioned.16 Although an association

between infant mortality and income inequality as measured

by the Gini coefficient was found among 18 developed coun-

tries, researchers showed that this was not significant if they

adjusted for the presence of a welfare state, health care,

unemployment, ethnicity and the role of women.

Wilkinson’s analysis was re-evaluated for a larger number

of countries, including 13 developed countries.17 A significant

association between proxies of income distribution (e.g.

proportions of the population receiving 40%, 50% or 60% of

median income) and life expectancy was not observed. Data

for 15 OECD countries were examined, and evidence of

a significant relationship between income inequality and life

expectancy after adjusting for GDP and health expenditure

was not observed.11 Hypotheses related to absolute and rela-

tive income and health have also been explained.5 The higher

an individual’s income, the better is his/her health (‘absolute

income hypothesis’). The relative income hypothesis states

that an individual’s health is also affected by the distribution

of income within society. Considering this hypothesis,

someone with a given income would have worse health if he/

she lived in a society with greater inequality of income than in

a society in which income is more equally distributed.

Although several studies have suggested that inequalities in

income distributionmay be an important cause of variation in

themean level of population health among rich industrialized

nations, one research team found very little support for the

view that income inequalities are associated with variations

in mean levels of national health.8 What appeared to be

missing from the debate was a systematic review of evidence

about the relationship between different measures of income

distribution and indicators of population health. Their study

aimed to bridge this gap, so they summarized recent literature

on this topic and illustrated themethodological problems that

weaken the inferences that can be derived from it. New

empirical estimates of the relationship between different

measures of income distribution and proxies of population

health (e.g. infant mortality, life expectancy) were therefore

presented.

Islamic countries have different attitudes and policies

towards poverty, life expectancy and infant mortality. There

have been many studies over the last decades that highlight

the importance of religious beliefs in how patients cope with

serious illness and associated suffering.8,18,19 Religious and

cultural beliefs can affect health care decision-making,

particularly at the end of life, and can provide an under-

standing of suffering in one’s life.19

Using the background detailed above, the present authors

tried to answer the following questions:

(1) Is there an association between income distribution and

population health in Islamic countries?

(2) Which of the postulated factors (prosperity, health care,

the role of women, and environment) may affect the

health of the population in Islamic countries?

Fig. 1 shows income levels, and Fig. 2 compares the

income distribution of the Islamic countries evaluated in the

present study.

Malaysia had the highest income level, whereas Nigeria,

Pakistan, Tajikistan and Kazakhstan had the lowest income

levels. Jordan had the lowest Gini coefficient, whereas

Malaysia, Senegal, Mozambique and Guyana had the highest

Gini coefficients among the Islamic countries studied.

The associations between income inequality and infant

mortality rate (IMR) and life expectancy (as proxies of

mean levels of population health) were examined for a group

of Islamic countries using equations proposed by some

researchers.1,3,9,15

0

500

1000

1500

2000

2500

3000

3500

4000

Egypt

Gambia

Guyana

Indonesia

Iran

Jordan

Kazakhstan

Kyrgyzstan

Malaysia

Mali

Mauritania

Morocco

Mozambique

Niger

Nigeria

Uzbekistan

Pakistan

Senegal

Tajikistan

Tunisia

Turkmenistan

Turkey

Uganda

Yemen

Fig. 1 e Comparison of income levels (Per capita GDP in US$) between selected Islamic countries.

p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4580

Methods

Various models were used, as summarized below. The first

model was related to life expectancy and some dependent

variables6:

Hi ¼ a1 þ b1ð1=GDPiÞ þ c1�1=GDP2

i

�þ d1ðGiniiÞ þ Ui (1)

whereHi denotes life expectancy or IMR, GDP is GDPper capita,

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Egypt

Gambia

Guyana

Indonesia

Iran

Jordan

Kazakhstan

Kyrgyzstan

Malaysia

Mali

Mauritania

Morocco

Fig. 2 e Comparison of income distribution (Gin

Gini is the Gini coefficient, and Ui is the stochastic error term.

Rodgers7 explored a wide variety of non-linear specifications

for the income variable, which are all reported in his research.

Themodel of IMRwhich included the logarithmof real GDP

per capita, the Gini coefficient, female literacy rate (Flit) and

the supply of doctors and nurses (which were deleted because

data were not available) resulted in a similar equation1:

LnðIMRÞi¼ a2 þ b2LnðRGDPÞiþc2LnðGiniÞiþd2LnðFlitÞiþUi (2)

Mozambique

Niger

Nigeria

Uzbekistan

Pakistan

Senegal

Tajikistan

Tunisia

Turkmenistan

Turkey

Uganda

Yemen

i index) between selected Islamic countries.

Table 2 e Description of variables and their expected relationship with infant mortality rate and life expectancy.

Group Variables Explanations Expected effecton IMR

Expected effect onlife expectancy

Prosperity GDP GDP per capita in US dollars GDP > 0 GDP > 0

Non-linear form of GDP 1/GDP < 0 1/GDP < 0

1/GDP Non-linear form of GDP 1/GDP2 < 0 1/GDP2 < 0

1/GDP2 Natural logarithmic form of GDP Ln GDP > 0 Ln GDP > 0

Ln GDP

Income

distribution

Gini Gini coefficient Gini > 0 Gini < 0

S6 Share of income available

to the bottom

60th percentile of the income

distribution

S6 < 0 S6 > 0

Ln Gini Natural logarithmic form of the

Gini coefficient

Ln Gini > 0 Ln Gini < 0

Environmental

factor

Urban Percentage of urban population Urban < 0 Urban > 0

Health care Health care Annual health expenditure per person Health care < 0 Health care > 0

Women’s role Female Share of females in working population Female < 0 Female > 0

Education level High Enrolment ratio in high school High < 0 High > 0

Univ Enrolment ratio in university Univ < 0 Univ > 0

Lit Adult literacy rate Lit < 0 Lit > 0

Ln lit Natural logarithmic form of adult

literacy rate

Ln lit < 0 Ln lit > 0

Population

health proxies

Infant mortality

rate

Infant mortality rate per thousand

Life expectancy Average life expectancy at birth

GDP, gross domestic product.

1 The countries studied were Egypt, Gambia, Guyana, Indonesia,Iran, Jordan, Kazakhstan, Kyrgyzstan, Malaysia, Mali, Mauritania,Morocco, Mozambique, Niger, Nigeria, Uzbekistan, Pakistan,Senegal, Tajikistan, Tunisia, Turkmenistan, Turkey, Uganda andYemen.

p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4 581

The following cross-country IMR model, in which school

stands for secondary school enrolment rate and PCY is GDP

per capita, was estimated14:

IMRi ¼ a3 þ b3ðGiniÞiþc3ðPCYÞiþd3ðSchoolÞiþUi (3)

In addition, the two models shown below were used1:

IMRi ¼ a4 þ b4S6i þ c4GDPi þ d4HEi þ e4SSi þ f4TWFi þ Ui (4)

LEi ¼a5þb5ðP90=P10Þiþc5GDPiþd5HEiþe5SSiþ f5TWFiþUi (5)

where LE is life expectancy at birth, S6 is the share of income

going to the bottom 60th percentile of the income distribution,

and P90/P10 is the ratio of the 90th to the 10th percentile of the

income distribution. Health expenditure (HE) represents the

total (private and public) expenditure on health care, SS is the

percentage of GDP spending on the welfare status of people,

and TWF represents female participation in the labour force,

expressed as a percentage of the total work force.

The basic form of the model used by other researchers can

be written as7,10:

DRi ¼a6þb6Giniiþ c6Incomeiþd6HSiþe6Collegeiþ f6Blacki

þg6UrbaniþUi (6)

where DR denotes overall death rate, Gini is the Gini coef-

ficient, income is personal income per capita, HS and college

indicate the percentage of the population with at least

a high school diploma or college degree respectively, Black

denotes the population of Black people (which was deleted

in this study), and urban denotes the urban population

percentage.

These six models were used in the present study to enable

comparison of the results. Some variables were changed or

omitted according to data availability.

According to the information in these six equations,

several groups of variables were re-organized (Table 2).

All these data were derived from data from theWorld Bank

and the Statistical, Economic and Social Research and

Training Centre for Islamic Countries (SESRIC).20�23 Coun-

tries1 were chosen based on their data availability. A mean

value for all available data from 1996 to 2004 was used to

eliminate temporary fluctuation in variables.

Results

The six equations mentioned above were used to estimate

values. The results when IMR and life expectancy were used

as mean population health proxies are summarized in Tables

3 and 4, respectively.

The estimated result of Eq. (1), when IMR is a dependent

variable, shows that the only significant variable was GDP.

The Gini coefficient was not significant; i.e. the GDP level

itself, not inequality in GDP level, affects IMR. A similar result

was observed when life expectancy was used as the depen-

dent variable in Eq. (1), which showed that the higher the GDP

per capita, the higher the life expectancy.

Table 3 e Estimated equations for income inequality and population health, with infant mortality rate as the dependentvariable.

Variable Eq. (1) Eq. (2) Eq. (3) Eq. (4) Eq. (5) Eq. (6)

GDP e e �0.01

(0.67)a�0.01d

(0.003)

�0.01d

(0.001)

�0.01c

(0.003)

Gini 0.57

(1.09)

e �0.36c

(0.003)

e �0.38

(0.70)

�0.34

(0.72)

High e e �0.76d

(0.149)

e e �0.064

(0.39)

Univ e e e e e �0.11

(1.23)

Urban e e e e �0.65c

(�0.16)

�0.43

(0.027)

Health e e e 0.43

(1.33)

0.35

(1.44)

e

Lit e e e �0.034

(0.17)

�0.25

(0.18)

e

Female e e e �0.001

(0.28)

�0.14

(0.23)

e

S6 e e e 3.66

(8.16)

e e

1/GDP 18.2c

(10.78)

e e e e e

1/GDP2 �34

(145)

e e e e e

Ln GDP e �0.11

(0.19)

e e e e

Ln Gini e �1.47

(0.86)

e e e e

Ln lit e �0.74c

(0.31)

e e e e

R2 58 58 76 75 83 78

F 6.68 6.68 15.37 6.05 7.7 14.16

D.W. 2.1 2.1 2.08 2.03 2.2 1.98

a Numbers in parentheses are standard errors.

b S6 is the share of income going to the bottom 60th percentile of the income distribution.

c P ¼ 0.05.

d P ¼ 0.01.

p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4582

Eq. (2) showed that the only significant coefficient among

the other variables that affects IMR and life expectancy is the

ln form of the adult literacy rate. This showed that the higher

the literacy, the lower the IMR and the higher the life

expectancy.

The Gini coefficient and the high school enrolment ratio

were the only significant coefficients in Eq. (3) when IMR and

life expectancy were the dependent variables. The coefficient

sign of high school enrolment in this equation showed that

a higher rate of education will decrease the IMR and increase

life expectancy.

GDP was the only significant variable in Eq. (4). Other

variables such as female share in the working population, rate

of literacy and health expenditure showed no significant

effect on IMR or life expectancy.

Eq. (5) was only estimated if IMR was the dependent vari-

able. GDP and the urban population percentage were the only

significant coefficients. For both, the higher their value, the

lower the IMR.

GDP had a significant negative effect on IMR. The urban

population percentage and the ratio of high school and

university enrolment had a positive effect on life expectancy.

Neither GDP nor the Gini coefficient had a significant effect on

life expectancy when Eq. (6) was used.

When IMR was used as the population health proxy, the

GDP coefficient was significant but the Gini coefficient was not

significant as a proxy for income inequality (linear or non-

linear form). Related to the significance of the coefficients,

a few researchers mentioned that when other controlling

factors (e.g. education, environmental situation and female

role) are included in the model, the Gini coefficient or any

proxy for income inequality can lose its significance.24 In Eq.

(3), the mentioned coefficient is significant. Related coeffi-

cients had the correct signs as expected. This meant that the

higher the GDP, the lower the IMR. A higher rate of education

regardless of the type of proxy used will reduce the IMR. An

increasing urban populationwill decrease the IMR, whichmay

show the effect of facilities and social services available to

people in cities. Education, urban population percentage and

GDP all had significant and positive effects on life expectancy.

This means that, in a particular country, the higher the rate of

education, urbanization and mean income per capita, the

more likely it is that people will live longer and healthier lives.

The highest coefficient of determination (R2) was found for

Table 4 e Estimated equations for income inequality and population health, with life expectancy as the dependentvariable.

Variables Eq. (1) Eq. (2) Eq. (3) Eq. (4) Eq. (5) Eq. (6)

GDP e e �0.8

(0.31)

0.005d

(0.001)

e 0.001

(0.001)

Gini �0.46a

(0.35)

e 0.003d

(0.001)

e e �0.32

(0.30)

High e e 0.33d

(0.08)

e e 0.48c

(0.11)

Univ e e e e e 0.69c

(0.32)

Urban e e e e e 0.36c

(0.15)

Health e e e �0.27

(0.47)

e e

Lit e e e 0.11

(0.06)

e e

Female e e e �0.14

(0.11)

e e

S6 e e e e e e

1/GDP 15.90

(42.41)

e e e e e

1/GDP2 �13c

(66.7)

e e e e e

Ln GDP e �0.11

(0.02)

e e e e

Ln Gini e 0.18

(0.12)

e e e e

Ln lit e 0.52c

(0.05)

e e e e

R2 60 79 58 72 e 82

F 7.18c 19.6 6.68 5.15 e 13.69

D.W. 1.99 2.09 2.1 2.1 e 1.84

a Numbers in parentheses are standard errors.

b S6 is the share of income going to the bottom 60th percentile of the income distribution.

c P ¼ 0.05.

d P ¼ 0.01.

p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4 583

Eq. (5), which was the format offered by other researchers.3

Hence, among all of these equation formats offered by

different scholars, Eq. (5) had the highest power to justify the

relationship between IMR and the dependent variables (envi-

ronment, health care, prosperity, income distribution and the

role of women).

In summary, according to the abovementioned equations,

the urban population percentage and GDP are the most

important significant variables that affect life expectancy and

the IMR in Islamic countries.

Discussion

Different dimensions should be considered when drawing

conclusions on the association between income, equality and

population health. The first dimension is the type and number

of countries involved in the analyses, which means a reason-

ably large number of observations is required. As such, the

authors included as many Islamic countries as possible based

on data availability. Islamic countries were selected because

they have different attitudes and policies towards poverty, life

expectancy and infantmortality thanWestern countries. Data

were collected from the World Bank and SESRIC.

The second dimension that ought to be considered is the

choice of models. In the present study, the authors examined

differentmodels offered by various scholars to cover the areas

mentioned above, along with controlling the association

between independent variables and population health.

Different proxies were offered to measure income distribu-

tion, prosperity, environmental situation, the role of women,

education and population health (Table 2).

Several studies provided firm evidence for a negative asso-

ciation between income inequality and population health

between countries.1,7,12 However, recent studies by some

researchers16,25 suggest that the patterns reported by

others1,7,12 cannot be replicated with recent data, and did not

observe a significant association between income inequality

and population health. In view of the importance of this issue,

themainpurpose of thepresent contributionwas to undertake

a fresh examination of the relationship between income

inequality and population health for a group of Islamic coun-

tries using recent information derived fromdata resource sites

from the World Bank and Islamic countries. After consider-

ation of previous studies, seven independent variables were

determined and tested in six equations.1,3,9,25 The main

conclusion is that, in selected Islamic countries, income level

has a positive effect on population health, but the level of

p u b l i c h e a l t h 1 2 5 ( 2 0 1 1 ) 5 7 7e5 8 4584

income distribution is not significant. Among the other

dependent variables (such as different measures of income

distribution, health care, the role ofwomenand environment),

only environment and education had significant effects. Most

of the Islamic countries studied are considered to be poorly

developed. In collaboration with other studies,6,10,11,17 the

present results support the hypothesis that, in developing

countries, it is the income level or GDP itself rather than the

inequality in GDP level that affects population health.

Ethical approval

Ethical approval was sought for the original collection of data

and the ethical approval was received by Shiraz University.

Funding

None declared.

Competing of interests

None declared.

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