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Social Science & Medicine 72 (2011) 617e624

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Social Science & Medicine

journal homepage: www.elsevier .com/locate/socscimed

World health inequality: Convergence, divergence, and development

Rob Clark*

Department of Sociology, University of Oklahoma, Kaufman Hall 331, 780 Van Vleet Oval, Norman, OK 73019, USA

a r t i c l e i n f o

Article history:Available online 16 December 2010

Keywords:Cross-nationalDevelopmentHealth inequalityLife expectancyInfant mortality

* Tel.: þ1 405 325 2566.E-mail address: robclark@ou.edu.

0277-9536/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.socscimed.2010.12.008

a b s t r a c t

Recent studies characterize the last half of the twentieth century as an era of cross-national healthconvergence, with some attributing welfare gains in the developing world to economic growth. In thisstudy, I examine the extent to which welfare outcomes have actually converged and the extent to whicheconomic development is responsible for the observed trends. Drawing from estimates covering 195nations during the 1955e2005 period, I find that life expectancy averages converged during this time,but that infant mortality rates continuously diverged. I develop a narrative that implicates economicdevelopment in these contrasting trends, suggesting that health outcomes follow a “welfare Kuznetscurve.” Among poor countries, economic development improves life expectancy more than it reducesinfant mortality, whereas the situation is reversed among wealthier nations. In this way, developmenthas contributed to both convergence in life expectancy and divergence in infant mortality. Drawing from674 observations across 163 countries during the 1980e2005 period, I find that the positive effect of GDPPC on life expectancy attenuates at higher levels of development, while the negative effect of GDP PC oninfant mortality grows stronger.

� 2010 Elsevier Ltd. All rights reserved.

Introduction

The last half of the twentieth century has been characterized asan era of cross-national health convergence (Neumayer, 2003;Wilson, 2001), as well as an era featuring rapid economic growthfor a number of developing nations (Firebaugh, 2003). However, theextent to which these two trends are connected, and whethereconomic development is responsible for welfare gains in thedevelopingworld, are hotly debated topics (Brady, Kaya, & Beckfield,2007; Firebaugh & Beck, 1994; Wimberley & Bello, 1992). Someresearch celebrates the “centrality” of development as a primarymechanism for improving human welfare among less developedcountries (Firebaugh&Beck,1994). From this perspective, economicdevelopment is thought to introduce a higher standard of living viabetter wages (Firebaugh & Beck, 1994), more advanced medicaltechnology (Shen & Williamson, 2001), and the purchase of goodsthat directly or indirectly improve health (Pritchett & Summers,1996), thereby reducing mortality. The benefits of developmentare also thought to “trickle down,” improving welfare via a numberof intermediary mechanisms, such as domestic investment andformal education (Jenkins & Scanlan, 2001).

Previous studies have found strong associations betweena country’s level of economic development (or its rate of growth

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over time) and human welfare outcomes. Development has beenfound to positively affect life expectancy and negatively affectinfant/child mortality, even among poorer countries (Babones,2008; Scanlan, 2010; Wickrama & Mulford, 1996). Economicdevelopment has been shown to improve a nation’s “physicalquality of life,” a composite measure consisting of items such as lifeexpectancy and infant mortality (London & Williams, 1990). Simi-larly, economic growth has been shown to reduce infant mortality,while boosting life expectancy, among developing nations(Firebaugh & Beck, 1994; Shandra, Shandra, & London, 2010). Insum, previous studies generally conclude that a nation’s level ofeconomic development and/or its growth rate are positively asso-ciated with improvements in human welfare, particularly amongless developed countries.

However, other work suggests that economic growth may nothave played a large role in mortality improvements during thetwentieth century (Preston, 1975). Scholars have drawn attention,anecdotally, to notable discrepancies between a country’s level ofhealth and its level of development (Sen, 1999; Shen &Williamson,2001). Other research shows that economic growth produces onlymodest welfare benefits in the developing world (Wimberley &Bello, 1992). Indeed, even when economic development has beenfound to significantly improve a country’s infant survival rate (Shen&Williamson, 2001), child survival rate (Shen &Williamson, 1997),or life expectancy (Brady et al., 2007) among developing countries,it oftentimes produces effects that are smaller than other predic-tors, such as education or gender-related measures.

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Amartya Sen and others have concluded that a single-mindedpursuit of economic development is far too narrow and restrictive.“It is as important to recognize the crucial role of wealth in deter-mining living conditions and the quality of life as it is to understandthe qualified and contingent nature of this relationship.Withoutignoring the importance of economic growth, we must look wellbeyond it” (Sen, 1999: 14). In particular, it is important to considerthe distribution of income and wealth within nations (Stiglitz, Sen,& Fitoussi, 2010). While economic growth may suggest thata country’s living standards have risen, the welfare of vulnerablesegments of the populationmay not improvewhen the distributionof benefits is highly skewed. For example, as Jenkins and Scanlan(2001: 719) note, economic development may increase a coun-try’s aggregate food supply, but the poor and disadvantaged remainhungry when resources are unequally controlled or sectoraldisparities (urban vs. rural; industrial vs. agricultural) favor somesegments of the population over others. “If inequality increasesenough relative to the increase in average per capita GDP, mostpeople can be worse off even though average income is increasing”(Stiglitz et al., 2010: 3). Thus, inequality may reduce the healthbenefits of economic development by distorting the gains achievedfrom growth. In sum, while a number of previous studies have beenable to trace welfare improvements to economic development,others suggest that this relationship is weak or conditional acrosswelfare outcomes. Consequently, it is uncertain whether the cross-national health convergence observed by some during the twen-tieth century can be attributed to economic development.

In this study, I offer new evidence regarding (a) inequalitytrends in life expectancy and infant mortality, as well as (b) the rolethat economic development may have played in producing thesetrends. I first examine whether life expectancy averages and infantmortality rates have indeed converged across 195 countries duringthe 1955e2005 period. Consistent with prior work, I find thatcross-national inequality in life expectancy declined during the1955e2005 period, but that this convergence stalls during the post-1990 era. Moreover, and contrary to previous work, I find thatinfant mortality rates diverged continuously across the sampleperiod. I then develop a narrative to explain these contrastingtrends, suggesting that cross-national health outcomes followa “welfare Kuznets curve.” According to this model, economicdevelopment improves life expectancy more than it reduces infantmortality among poor countries, whereas the situation is reversedamong wealthier nations.

To test this thesis, I use random effects models to estimate theimpact of gross domestic product per capita (GDP PC) on lifeexpectancy and infant mortality, drawing from 674 observationsacross 163 countries during the 1980e2005 period. In thesemodels,GDP PC significantly raises life expectancy and reduces infantmortality, producing massive effects that are larger than any othersubstantive predictor under investigation. However, the results alsoshow that GDP PC’s positive effect on life expectancy weakens athigher levels of development (thereby contributing to cross-nationalconvergence), while its negative effect on infant mortality becomesstronger (thereby contributing to cross-national divergence). In sum,I argue that economic development has contributed to bothconvergence in life expectancy and divergence in infant mortality.

Methods

Dependent variables

Life expectancyLife expectancy at birth indicates the number of years

a newborn infant would live if prevailing patterns of mortality atthe time of its birth were to stay the same throughout its life.

Infant mortalityInfant mortality refers to the number of infants dying before

reaching one year of age, per 1000 live births in a given year. Datafor both measures come from the World Population Prospects: The2006 Revision (United Nations, Department of Economic and SocialAffairs, Population Division, 2007). The data are reported in five-year intervals across the 1955e2005 period.

Independent variables

Unless otherwise noted, all predictors come from the WorldDevelopment Indicators (World Bank, 2010). I log several variables toreduce skew, as noted below.

GDP PC (PPP) (log)Following previous studies (Babones, 2008; Brady et al., 2007;

Scanlan, 2010; Shandra et al., 2010; Shen & Williamson, 2001), Imeasure economic development with each country’s GDP PC basedon purchasing power parity. Data are in constant 2005 internationaldollars. An international dollar has the same purchasing power overGDP as the U.S. dollar has in the United States. In the models pre-sented below, I also include a second-order term for GDP PC to testwhether the impactof economicdevelopmenton life expectancyandinfant mortality weakens/strengthens among wealthier nations.

Time periodIn order to control for the substantial rise in life expectancy

averages and decline in infant mortality rates, I include a timeperiod indicator in the subsequent analyses (1 ¼ 1985; 2 ¼ 1990;3 ¼ 1995; 4 ¼ 2000; 5 ¼ 2005).

World regionI also control for the considerable cross-sectional variation in

welfare by including world region as a predictor in the analyses. Iclassify states as belonging to one of the following six worldregions: (1) Europe and theWest (the excluded reference category),(2) Latin America and the Caribbean, (3) Central and Sub-SaharanAfrica, (4) North Africa and the Middle East, (5) East Asia and thePacific, and (6) Eastern Europe and Central Asia.

HIV prevalencePrevious work suggests that the AIDS crisis stalled the long-

term convergence trend in life expectancy (Goesling & Firebaugh,2004; Neumayer, 2004) and has had a significant impact on childmortality among less developed countries (Scanlan, 2010).Accordingly, I include a measure of each country’s HIV prevalencerate, which refers to the percentage of people ages 15e49 who areinfected with HIV. Because the AIDS crisis emerged during the1990s, the World Development Indicators does not report HIVprevalence rates during the early portion of my sample period.Thus, to preserve this period for my analyses, all pre-1990 values forthis measure are coded as zero.

Following Brady et al. (2007), I also include controls for schoolenrollment, fertility rate, and urbanization, each of which werefound to be significant predictors in their models of female/malelife expectancy and infant/child mortality.

School enrollmentSchool enrollment refers to the ratio of secondary school

enrollment to the population of the age group that officiallycorresponds to the secondary level.

Fertility rate (log)Fertility rate represents the number of children that would be

born to a woman if she were to live to the end of her childbearing

R. Clark / Social Science & Medicine 72 (2011) 617e624 619

years and bear children in accordance with current age-specificfertility rates.

UrbanizationUrbanization refers to the proportion of the total population

living in urban areas.In addition, I control for a country’s level of democracy and

domestic investment.

DemocratizationPrior studies suggest the importance of democracy for improving

a country’s life expectancy and infant mortality (Wickrama &Mulford, 1996), as well as its overall “physical quality of life”(London&Williams,1990). Democracy scores come from Freedom inthe World 2010 (Freedom House, 2010). Freedom House’s annualsurvey measures freedom according to two broad categories:political rights and civil liberties. Each country is rated on a seven-point scale in both categories,with 1 representing themost free, and7 representing the least free. I calculated each country’s averagescore across both categories and inverted this value so that highernumbers represent greater levels of democracy.

Capital formationI measure domestic investment with each state’s level of capital

formation, calculated as a share of GDP. Capital formation considersadditions to the fixed assets of the economy, including landimprovements (e.g., fences, ditches, drains), plant, machinery, andequipment purchases, as well as the construction of roads, railways,schools, offices, hospitals, private residential dwellings, andcommercial and industrial buildings. Previous work has found thatdomestic investment reduces infant mortality and positively affectshealth spending, the latter two of which are significantly associatedwith one another in the developing world (Wimberley, 1990).

Sample

My sample includes 674 observations across 163 countries overfive waves during the 1980e2005 period. For the dependent vari-ables, the five waves cover the years 1985, 1990, 1995, 2000, and2005. For the predictors, each wave represents a prior five-yearperiod (1980e1984, 1985e1989, 1990e1994, 1995e1999, and2000e2004), where each data point for each measure representsa state’s average value across the entire wave. The pooled data areunbalanced with some states contributing more observations thanothers. However, almost all states (157) are present across two ormore waves. In separate diagnostics, I re-estimated models withthe full set of predictors for life expectancy and infant mortalitywhen (a) restricting my sample to those 157 countries contributingtwo or more observations (N ¼ 668), and (b) using a balancedsample comprised of those 100 countries that contributed obser-vations across all five waves (N ¼ 500). In both sets of replications,the main effect of GDP PC remains statistically significant (p< .001)as a positive predictor of life expectancy and negative predictor ofinfant mortality. Moreover, GDP PC’s second-order term continuesto reduce the positive effect of life expectancy (p< .05) and increasethe negative effect of infant mortality (p < .001).

I also considered the impact of influential observations byremoving outliers formally identified in those models featuring thefull set of predictors using the Hadi procedure available in Stata 11(Stata Corporation, 2009). The procedure identifies multipleoutliers in multivariate data using ordinary least squares (OLS)regression. In the life expectancy model, I detected 13 outliers(Botswana, wave 5; Equatorial Guinea, waves 4e5; Lesotho, waves3e4; Mongolia, waves 1e2; Rwanda, wave 3; United Arab Emirates,wave 1; Zimbabwe, waves 1e3 and 5). In the infant mortality

model, I detected seven outliers (Equatorial Guinea, waves 4e5;Lesotho, waves 3e4; Mongolia, waves 1e2; United Arab Emirates,wave 1). When re-estimating my models without these cases, themain effect of GDP PC remains statistically significant (p < .001) asa positive predictor of life expectancy and negative predictor ofinfant mortality. Moreover, GDP PC’s second-order term continuesto reduce the positive effect of life expectancy (p< .05) and increasethe negative effect of infant mortality (p < .001).

Analysis

Mydataset containsmultiple observations for different countriesacross time. Such a panel structure allows me to use estimationtechniques that deal with potential heterogeneity bias (the con-founding effect of unmeasured time-invariant variables), which canseriously affect OLS coefficient estimates. The random effects model(REM) and the fixed effects model (FEM) represent two estimationstrategies designed to correct for the problem of heterogeneity bias.Both procedures “simulate” the unmeasured time-invariant factorsas country-specific intercepts (Nielsen & Alderson, 1995: 685).

I present results using a generalized least squares estimator ofthe REM that includes a first-order autocorrelation correction. TheREM represents the matrix weighted average of the estimatesproduced by the FEM (focusing on variation within cases) and thebetween effects model (BEM) (focusing on variation between cases).Thus, while the FEM ignores all cross-sectional variation in itsestimates by subtracting all variables from their case-specificmeans, and the BEM ignores all temporal variation by using thecase-specific means as predictors, the REM incorporates both thefixed and between effects components, thereby making the REMadvantageous for capturing both cross-sectional and longitudinalvariation. In short, REMs are useful because it is important toexamine how economic development predicts variation in welfareoutcomes across countries, as well as how economic growthpredicts variation in welfare outcomes within individual countriesover time. Moreover, because much of the variation in life expec-tancy and infant mortality is cross-sectional (see Table 2), FEMswould crucially ignore much of the variation that exists in the data.And because the number of states (N¼ 163) far exceeds the numberof waves (T ¼ 5), it is important to make efficient use of the cross-sectional variation. In fact, it has been shown that FEMs producebiased results when N far exceeds T (Nickell, 1981), as is the case inthe present study. FEMs also consume a degree of freedom for everyadditional N, making them less efficient than REMs (Greene, 2000).

Results

Inequality trends

In the first stage of analysis, I examine inequality trends in lifeexpectancy and infant mortality based on data from 195 nationsduring the 1955e2005 period. Previously, Neumayer (2004)reports inequality trends in life expectancy and infant mortalityusing the standard deviation, keeping both healthmeasures in theiroriginal form. As he notes, keeping measures in their original formcan be problematic when examining mean-changing variables, asthe standard deviation will rise (if the mean rises) or fall (if themean falls) even if the distributional pattern remains constant.According to Neumayer (2004), there are a few remedies to thisproblem, including (1) logging the variables prior to calculating thestandard deviation so that the results are not sensitive to changes inthe mean, or (2) dividing the standard deviation by the mean (i.e.,calculating the coefficient of variation). However, Neumayer (2004:733) dismisses these remedies, noting that “it makes little differ-ence for the analysis of convergence trends whether variables are

Table 1Cross-national health inequality (1955e2005), N ¼ 195.

Year SDOR Mean SDLG COV Gini Theil SDOR Mean SDLG COV Gini Theil

Life expectancy (population-unweighted) Life expectancy (population-weighted)1955 11.746 50.572 .239 .232 .133 .027 12.547 49.183 .246 .255 .138 .0311960 11.811 53.059 .231 .223 .128 .025 12.069 51.785 .227 .233 .127 .0261965 11.614 55.243 .220 .210 .121 .023 11.142 54.314 .202 .205 .113 .0211970 11.194 57.222 .207 .196 .112 .020 9.947 57.786 .177 .172 .097 .0151975 10.830 59.101 .195 .183 .104 .017 9.322 59.867 .162 .156 .088 .0121980 10.688 60.896 .191 .176 .099 .016 8.791 61.698 .150 .142 .080 .0101985 10.114 62.758 .174 .161 .091 .014 8.156 62.866 .135 .130 .072 .0091990 9.870 64.361 .166 .153 .085 .012 7.899 64.387 .129 .123 .068 .0081995 10.268 65.276 .179 .157 .086 .013 7.977 65.428 .132 .122 .066 .0082000 10.486 66.168 .175 .158 .087 .013 8.382 66.516 .137 .126 .068 .0082005 11.082 66.985 .183 .165 .091 .015 8.902 67.441 .144 .132 .071 .009

Infant mortality (population-unweighted) Infant mortality (population-weighted)1955 60.018 128.360 .587 .468 .267 .117 62.904 136.944 .657 .459 .255 .1241960 59.246 114.230 .653 .519 .297 .144 61.021 122.993 .706 .496 .274 .1451965 57.014 101.673 .699 .561 .321 .167 51.820 102.286 .707 .507 .282 .1491970 54.305 90.336 .744 .601 .342 .189 47.382 86.806 .725 .546 .308 .1641975 51.828 80.507 .786 .644 .365 .214 44.653 76.418 .754 .584 .330 .1831980 51.556 71.425 .852 .722 .398 .256 41.719 68.396 .796 .610 .341 .1971985 46.508 61.576 .914 .755 .419 .284 38.933 59.480 .852 .655 .366 .2251990 44.186 54.623 .960 .809 .443 .319 36.349 53.100 .880 .685 .380 .2411995 43.421 49.665 1.016 .874 .470 .360 34.300 48.958 .894 .701 .384 .2472000 40.599 44.940 1.063 .903 .485 .384 32.814 45.136 .920 .727 .393 .2602005 38.542 40.759 1.101 .946 .502 .415 31.522 41.326 .945 .763 .407 .280

Note: SDOR¼ Standard deviation when the variables are kept in their original form; SDLG ¼ Standard deviation when the variables are logged; COV ¼ Coefficient of variation.

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held in log or in level form. Hence any potential bias is too small toaffect the analysis here,” and that the coefficient of variation “isapproximately equal to the standard deviation of the naturallogarithm of the variable.”

In Table 1, I examine thematter empirically, reporting inequalitytrends in life expectancy and infant mortality using (1) the standarddeviationwhen the variables are kept in their original form (SDOR),(2) the standard deviation when the variables are logged (SDLG),(3) the coefficient of variation (COV), as well as two popular indexmeasures, (4) the Gini, and (5) the Theil, both of which are scale-invariant (i.e., inequality is not affected by the mean). The top panelshows the life expectancy trends (both population-unweighted andpopulation-weighted). In this case, even though the mean risessomewhat across time (31%e37%, depending on whether countriesare population-weighted), it matters little which measure is used.According to all five measures, and regardless of whether I weightcountries based on their population size, inequality in life expec-tancy generally declines from 1955 to 1990 and increases from 1990to 2005. This is consistent with prior work noting the overallconvergence in life expectancy, but featuring a reversal at the endof the period due to the AIDS crisis in Africa (Goesling & Firebaugh,2004; Neumayer, 2004).

However, the inequality trends in infant mortality (shown in thebottom panel) reveal quite different results. According to SDOR,inequality declines between 1955 and 2005. However, the meandrops dramatically, as well (68%e70%, depending on whethercountries are population-weighted), suggesting that this mayaccount for the drop in SDOR. Not surprisingly, when adoptingeither remedy to the changing mean (SDLG or COV), or whenrelying on the Gini or the Theil, cross-national inequality in infantmortality increases steadily throughout the sample period. More-over, these results hold whether I weight countries by their pop-ulation size or not. In sum, the overall trend for life expectancy isone of convergence (notwithstanding the post-1990 reversal),while the inequality trend in infant mortality is quite the opposite,diverging steadily across the sample period.

In order to illustrate how these contrasting trends have man-ifested, I summarize life expectancy averages and infant mortality

rates across six world regions in Table 2. The first two columns inTable 2 show the 1955 and 2005 means, respectively, for lifeexpectancy. In 1955, life expectancy was highest in the West(68.18), followed by Eastern Europe (60.53). As the third columnindicates, the average life expectancy increased in every region ofthe world during the sample period. However, the growth rate inlife expectancywas lowest in the two regions where life expectancywas highest in 1955: the West (16.04%) and Eastern Europe(17.69%). Every other world region increased their life expectancyby at least twice as much, with the Middle East (56.49%) and EastAsia (50.83%) leading the way. Thus, life expectancy averagesconverged because the healthiest countries (i.e., the West andEastern Europe) experienced the least improvement.

However, the final three columns tell a very different story forinfant mortality trends. In 1955, infant mortality was lowest in theWest (39.74), while it was highest in Africa (180.03). As the lastcolumn indicates, infant mortality rates dropped dramatically inevery region of the world between 1955 and 2005. However, thereduction in infantmortality rateswasgreatest in theWest (�86.02%)and lowest inAfrica (�50.32%),while all otherworld regions reducedinfant mortality by more or less the same rate (�77.02% to�79.14%).Thus, infant mortality rates diverged during the sample periodbecause the healthiest (i.e., the West) and unhealthiest (i.e., Africa)regions continued to diverge from one another.

The Welfare Kuznets Curve

In this section, I develop a narrative to help explain the con-trasting trends in cross-national health inequality that haveoccurred during the last half of the twentieth century: convergencein life expectancy averages and divergence in infant mortality rates.In doing so, I suggest that economic development may have playeda role in producing both trends, capitalizing on the idea from priorresearch that the impact of economic development does not tend tobe uniform across outcomes or samples. As Brady et al. (2007: 5)suggest, GDP PC “might not have consistent effects across allmeasures of well-being.” In a prior health study, Pritchett andSummers (1996: 845) divide their cross-national sample into four

Table 2Regional trends and patterns, life expectancy and infant mortality (1955e2005), N ¼ 195.

Life expectancy Infant mortality

Mean (1955) Mean (2005) Growth rate Mean (1955) Mean (2005) Growth rate

All states (N ¼ 195) 50.57 66.98 35.77% 128.36 40.76 �72.44%Europe & the West (N ¼ 23) 68.18 78.97 16.04% 39.74 4.84 �86.02%Latin America & the Caribbean (N ¼ 37) 53.84 72.41 36.42% 108.71 22.68 �78.79%Central & Sub-Saharan Africa (N ¼ 47) 38.93 51.72 34.15% 180.03 90.43 �50.32%North Africa & the Middle East (N ¼ 25) 45.14 69.43 56.49% 168.32 37.28 �79.14%East Asia & the Pacific (N ¼ 34) 46.70 68.92 50.83% 133.80 33.35 �77.02%Eastern Europe & Central Asia (N ¼ 29) 60.53 70.93 17.69% 99.13 23.50 �78.27%

Note: Europe & the West ¼ Australia, Austria, Belgium, Canada, Channel Islands, Denmark, Finland, France, Germany, Iceland, Ireland, Italy, Luxembourg, Malta, Netherlands,New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States; Latin America & the Caribbean ¼ Argentina, Aruba, Bahamas, Barbados, Belize,Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica,Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Lucia, Saint Vincent-Grenadines, Suriname, Trinidad-Tobago, Uruguay,Venezuela, Virgin Islands (USA); Central & Sub-Saharan Africa ¼ Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad,Comoros, Congo (DR), Congo (R), Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Ivory Coast, Kenya, Lesotho, Liberia, Madagascar,Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Sao Tome-Principe, Senegal, Sierra Leone, Somalia, South Africa, Swaziland,Tanzania, Togo, Uganda, Zambia, Zimbabwe; North Africa & the Middle East ¼ Afghanistan, Algeria, Bahrain, Cyprus, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya,Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, Turkey, United Arab Emirates, Western Sahara, Yemen; East Asia & the Pacific ¼ Bangladesh,Bhutan, Brunei, Cambodia, China, East Timor, Fiji, French Polynesia, Guam, Hong Kong, India, Indonesia, Japan, Laos, Macao, Malaysia, Maldives, Micronesia, Mongolia,Myanmar, Nepal, New Caledonia, North Korea, Papua New Guinea, Philippines, Samoa, Singapore, Solomon Islands, South Korea, Sri Lanka, Thailand, Tonga, Vanuatu, Vietnam;Eastern Europe & Central Asia¼ Albania, Armenia, Azerbaijan, Belarus, Bosnia-Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Greece, Hungary, Kazakhstan,Kyrgyzstan, Latvia, Lithuania, Macedonia, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan.

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income quartiles. And while they do not draw much attention to it,their results show that the wealthiest two quartiles increased theirlife expectancy by lower rates, but reduced their infant mortality byhigher rates, than the poorest two quartiles during the 1960e1990period. Thus, poor countries had been outperforming rich countriesin improving life expectancy, but lagging behind in their reductionof infant mortality. This suggests, first, that indicators of economicdevelopment may not perform optimally across all welfaremeasures in the developing world. Indeed, using a sample of lessdeveloped countries during the 1980e2003 period, Brady et al.(2007) find that GDP PC positively affects male and female lifeexpectancy, but that it does not significantly impact infant or childsurvival. In short, the above findings suggest that the link betweendevelopment and life expectancy may be stronger among poorernations, whereas the link between development and infantmortality may be stronger among wealthier nations.

To pursue this idea further, consider the classic Kuznets curve,a phenomenon in which income inequality is lowest within poorand wealthy nations and greatest within middle-income nations.As poor nations begin to develop and industrialize, inequalitywithin these nations begins to rise for two reasons: (1) industrialwages are higher than agricultural wages, and (2) there is greatereconomic inequality within the industrial sector than the agricul-tural sector (Kuznets, 1955). Thus, the initial shift to industrialproduction is accompanied by rising inequality, as a small (butgrowing) number of people begin entering into a higher-payingsector whose wages show greater dispersion around the mean. It isonly until advanced industrialization that income inequality peaksand begins to dissipate, where agricultural workers constitutea small (and shrinking) portion of the workforce, thereby corre-sponding to the falling portion of the Kuznets curve. Although thepattern is typically detected in a cross-section of countries, thecurve is presumably operative within individual countries overtime, as well.

In the same vein, consider what I call a “welfare Kuznets curve”that models the complex relationship between economic devel-opment and health outcomes. As the urban/industrial transitioncommences, the bulk of the population (which disproportionatelyincludes the impoverished) remains excluded. Improvements in lifeexpectancy occur more quickly than reductions in infant mortality,given that infant and child mortality rates are more accurate

welfare gauges of the broader population (and of the poor, inparticular) (Brady et al., 2007: 5) and are less sensitive to welfaregains made disproportionately by the wealthy (Brady et al., 2007:10). As countries continue to develop and join other nations thatreside closer to the middle of the income distribution, the disparitybetween life expectancy (which is steadily improving) and infantmortality (which is improving at a slower pace) reaches its peak. Bycontrast, during the downward phase of the curve, as the agricul-tural/rural segment of the population begins to constitute a rela-tively small (and dwindling) percentage of the total, furtherincreases in development begin to yield diminished returns for lifeexpectancy. On the other hand, improvements in infant/childmortality start to accelerate, as the broader population becomesincreasingly situated within a context of elevated living standards.Consequently, the welfare Kuznets curve offers an explanation forboth (a) the long-term convergence in life expectancy, as most ofthese improvements occur early in development, and (b) the long-term divergence in infant mortality, as most of these reductionsoccur later in development.

Is this model consistent with the observed data? In order toillustrate the welfare Kuznets curve empirically, I first created a setof “world health residuals” by regressing life expectancy on infantsurvival (i.e., the inverse of infant mortality). I then plotted theresiduals against a nation’s GDP PC among all 674 cases in mysample. Fig. 1 shows the resulting scatter plot along with a fittedquadratic line. While there is considerable scatter, the observationstend to cluster in a curvilinear fashion. GDP PC is positively asso-ciated with the residuals among the poorest countries (r ¼ .495before the inflection point of the curve), but negatively associatedwith the residuals among wealthier nations (r ¼ �.657 after theinflection point of the curve). That is, economic developmentimproves life expectancy more than infant survival among poornations, whereas the situation reverses among wealthier nations.

One way to appreciate the practical significance of this curvi-linear association is to divide the 674 observations in my sampleinto one of two groups based on their location relative to theinflection point in the curve (GDP PC ¼ $2530) and compare theirwelfare trends during the 1980e2005 period. Table 3 reports theresults of this comparison. Group A features 243 observations(comprising 36% of the sample) located before the curve (GDPPC < $2530), with a large majority of these observations coming

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1

Wor

ld H

ealth

Res

idua

ls

-2 -1 0 1 2

GDP PC (PPP) (Log) (Standardized)

Fig. 1. The Welfare Kuznets Curve (1980e2005), N ¼ 673a. aOne observation (Rwanda, wave 3) not pictured in order to preserve scale. Note: “World Health Residuals” produced byregressing life expectancy on infant survival. Positive residuals indicate a higher life expectancy than expected given that country’s infant survival rate. Negative residuals indicatea lower life expectancy than expected given that country’s infant survival rate.

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from Africa (62.14%) and East Asia (22.63%). Group B features 431observations (comprising 64% of the sample) located after the curve(GDP PC > $2530), with a majority of these observations comingfrom Latin America (26.22%) and the West (25.06%). The overallcomparison featured in the first row indicates that states belongingto observations in Group A improved their life expectancy by12.84% during the 1980e2005 period, while states belonging toobservations in Group B only improved their life expectancy by10.14%. However, countries in Group A only reduced their infantmortality by 34.84%, while those in Group B cut their infantmortality by 58.79% (note that some countries may have crossedthe inflection point during the sample period and would, therefore,be contributing observations to both groups). Thus, countries foundbefore the inflection point increased their life expectancy toa greater extent, but decreased their infant mortality to a lesserextent, than countries found after the inflection point. Moreover, as

Table 3Change in life expectancy and infant mortality (1980e2005), N ¼ 674.

Life expectancy(growth rate)

Infantmortality (growthrate)

Group A Group B Group A Group B

All states 12.84% 10.14% �34.84% �58.79%(N ¼ 243) (N ¼ 431) (N ¼ 243) (N ¼ 431)

Europe &the West

e 7.62% e �61.52%(N ¼ 0) (N ¼ 108) (N ¼ 0) (N ¼ 108)

Latin America &the Caribbean

13.65% 12.55% �48.01% �57.00%(N ¼ 11) (N ¼ 113) (N ¼ 11) (N ¼ 113)

Central &Sub-Saharan Africa

7.51% �2.90% �25.38% �29.25%(N ¼ 151) (N ¼ 36) (N ¼ 151) (N ¼ 36)

North Africa &the Middle East

20.75% 18.69% �41.76% �69.81%(N ¼ 15) (N ¼ 75) (N ¼ 15) (N ¼ 75)

East Asia &the Pacific

26.95% 13.47% �55.38% �62.31%(N ¼ 55) (N ¼ 47) (N ¼ 55) (N ¼ 47)

Eastern Europe &Central Asia

3.87% 3.85% �39.42% �58.35%(N ¼ 11) (N ¼ 52) (N ¼ 11) (N ¼ 52)

Notes: Group A consists of 243 observations located before the inflection point (GDPPC < $2530) of the curve featured in Fig. 1. Group B consists of 431 observationslocated after the inflection point (GDP PC > $2530) of the curve featured in Fig. 1.

the subsequent rows indicate, this pattern holds even whencomparing countries within the same world region. Consequently,in the following analyses, I hypothesize that economic develop-ment will significantly improve a country’s life expectancy averageand infant mortality rate. However, I anticipate that GDP PC’spositive effect on life expectancy is weaker at higher levels ofdevelopment, while its negative effect on infant mortality isstronger at higher levels of development.

Analyses

Table 4 presents results from random effects models of lifeexpectancy (models 1e4) and infant mortality (models 5e8). Eachcell reports the z-score, with the standardized coefficient in bold. Inmodel 1, GDP PC is a positive predictor of life expectancy (p< .001),while its second-order term exerts a negative effect (p < .01),indicating that the impact of GDP PC on life expectancy becomessignificantly weaker at higher levels of development. These twomeasures by themselves explain over 70% of the variation in lifeexpectancy (R2 Overall ¼ .713). In model 2, I add the temporal andregional controls. As expected, time period is positively signed andhighly significant (p < .001), indicating the longitudinal increase inlife expectancy across the sample period. Also, all of the regionalmeasures are negatively signed and significant, indicating that allfive regions exhibit lower life expectancy averages than the West.Nevertheless, the main effect of GDP PC (B ¼ .41; p < .001) and itssecond-order term (B ¼ �.06; p < .01) remain highly significant. Inmodel 3, I add the substantive controls, all of which are significantat the .05 level or higher, and in the expected directions. Again,though, GDP PC (B ¼ .21; p < .001) and its second-order term(B ¼ �.05; p < .01) remain significant. Moreover, the main effect ofGDP PC exerts the largest impact among the substantive predictors,followed by fertility rate (B ¼ �.18; p < .001), urbanization (B ¼ .12;p < .001), HIV prevalence (B ¼ �.11; p < .001), and school enroll-ment (B ¼ .10; p < .001). Democratization (B ¼ .05; p < .01) andcapital formation (B ¼ .02; p < .05) also achieve significance aspositive predictors, but their effects are smaller. In sum, GDP PC

Table 4Random effects models of life expectancy and infant mortality (1980e2005)

Life expectancy Infant mortality

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

GDP PC 22.06d 11.77d 5.93d 3.78d �26.79d �15.17d �12.33d �9.46d

.67 .41 .21 .20 �.86 L.49 L.39 L.50GDP PC2 �2.99c �2.73c �2.78c �4.56d �2.51b �5.30d

L.07 L.06 L.05 �.11 L.05 L.09Time period 9.16d 6.30d 1.46 �22.86d �9.18d �3.97d

.22 .16 .06 L.38 L.20 L.15Latin America �2.78c �0.23 0.88 7.76d 4.32d 2.24b

L.12 L.01 .04 .36 .16 .11Africa �10.01d �4.53d �2.94c 12.09d 5.68d 2.13b

L.51 �.22 L.19 .64 .27 .14Middle East �4.58d �0.84 0.50 9.73d 5.04d 3.28c

L.16 L.03 .02 .38 .17 .15East Asia �3.47d �0.70 �0.42 6.72d 2.80c 1.34

L.15 L.03 L.02 .32 .11 .07Eastern Europe �2.14b �2.75c �1.80a 4.11d 4.17d 3.93d

L.09 L.09 L.07 .18 .14 .15HIV prevalence �18.18d �10.66d 8.08d 3.39d

L.11 L.09 .04 .03School enrollment 3.75d 4.09d �4.64d �3.36d

.10 .17 L.10 L.13Fertility rate �5.53d �3.60d 10.32d 6.45d

L.18 L.19 .30 .31Urbanization 3.36d 0.98 �1.53 0.06

.12 .04 L.05 .00Democratization 3.09c 2.14b �2.59c �1.79a

.05 .05 L.03 L.04Capital formation 2.33b 3.33d 0.20 0.10

.02 .06 .00 .00Gini index 1.42 2.65c

.03 .06GDP PC � Gini index �0.85 2.76c

L.02 .06Observations 674 674 674 287 674 674 674 287States 163 163 163 128 163 163 163 128R2 Within .139 .294 .660 .646 .359 .727 .799 .815R2 Between .734 .837 .863 .899 .775 .844 .906 .924R2 Overall .713 .816 .863 .884 .772 .852 .907 .890

Notes: All models include a first-order autocorrelation correction. Each cell reports the z-score with the standardized coefficient in bold.a p < .1b p < .05c p < .01d p < .001(two-tailed tests)

R. Clark / Social Science & Medicine 72 (2011) 617e624 623

exerts large positive effects on a country’s life expectancy average,but it more effectively improves life expectancy among poorernations than it does among wealthier nations.

In models 5e7, I turn to infant mortality. In model 5, both themain effect (p< .001) and the second-order term (p< .001) for GDPPC are highly significant as negative predictors of infant mortality,indicating that the negative effect of GDP is strengthened substan-tially among wealthier nations. Impressively, these two measuresby themselves explain over 77% of the variation in life expectancy(R2 Overall ¼ .772). In model 6, I add the temporal and regionalcontrols. Again, as expected, time period is negatively signed andhighly significant (p < .001), indicating the longitudinal decline ininfant mortality across the sample period. Meanwhile, all of theregional measures are positively signed and reach the highest levelof significance (p < .001), indicating that all five regions exhibithigher infant mortality rates than the West. Nevertheless, theaddition of these controls does not explain away the curvilineareffect of GDP PC, as both the main effect (B ¼ �.49; p < .001) andsecond-order term (B ¼ �.05; p < .05) remain significant. In model7, I introduce the substantive predictors. Net of these measures,GDP PC’s main effect (B ¼ �.39; p < .001) and second-order term(B ¼ �.09; p < .001) remain significant. In fact, GDP PC again exertsthe strongest effect among all the substantive predictors, followed

by fertility rate (B¼ .30; p < .001) and school enrollment (B¼�.10;p< .001). Collectively, the findings from Table 4 are consistent withthe thesis that economic development had a major impact onhuman welfare over the past several decades, but that its differ-ential performance across samples and health outcomes hasproduced contrasting trends, helping life expectancy averages toconverge and infant mortality rates to diverge.

Finally, I examine the impact of income inequality on the rela-tionship between economic development and health. Theoretically,inequality may exert a harmful moderating effect by concentratingthe health benefits associated with economic development into thehands of the privileged classes. However, because infant mortalityis a more accurate welfare gauge of the poor than life expectancy,inequality should reduce the negative association between devel-opment and infant mortality more than it reduces the positiveassociation between development and life expectancy. I examinethis proposition in models 4 and 8, where I estimate the direct andmoderating impact of income inequality using a reduced sample ofcountries for which inequality estimates are available. I use the Giniindex to measure inequality (World Bank, 2010), which indicatesthe extent to which the distribution of income within a countrydeviates from a perfectly equal distribution. Thus, higher scoresindicate greater inequality. I also include an interaction term (GDP

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PC � Gini Index) in these models to test whether the benefits ofeconomic development are significantly reduced in those countrieswith greater income inequality. In model 4, the interaction term isnegatively signed, but not significant, indicating that incomeinequality does not significantly reduce the impact of developmenton life expectancy. In model 8, however, the Gini index is highlysignificant as a positive predictor of infant mortality (B ¼ .06;p < .01). Moreover, the interaction term indicates that inequalitysignificantly reduces the negative effect of development on infantmortality (B ¼ .06; p < .01). This latter finding provides support fordevelopment critics who claim that it is important to take intoaccount the distribution of benefits within a country whenconsidering the impact of development on health.

Discussion

Cross-national inequality trends in life expectancy and infantmortality have followed different trajectories over the past halfcentury. Life expectancy averages have demonstrated long-termconvergence since the mid-twentieth century, while infantmortality rates have continuously diverged during this same timeperiod. In 1955, Western nations enjoyed the longest life spans andlowest infant mortality rates. Since that time, these countriesexperienced slower growth in life expectancy, but faster improve-ment in infant mortality, than other world regions. In this study, Ifind that economic development helps account for both cross-national convergence in life expectancy, as well as divergence ininfant mortality, via its differential impact on these two welfareoutcomes. In generating a narrative to explain this process, Iidentify a “welfare Kuznets curve,” in which economic develop-ment yields more immediate benefits to life expectancy at earlystages of growth, with reductions in infant mortality laggingbehind. In other words, development improves life expectancymore than it reduces infant mortality among poor nations.However, during the latter stages of development, this dynamicreverses, such that further economic growth yields diminishingreturns to life expectancy, while reductions in infant mortalitybegin to escalate.

Drawing from a sample of 674 observations during the1980e2005 period, I show that countries located before theinflection point of the welfare Kuznets curve (GDP < $2530)improved their life expectancy at a higher rate, but reduced theirinfant mortality at a lower rate, than countries located after theinflection point (GDP > $2530). Finally, I estimate the differentialimpact of economic development on life expectancy and infantmortality. I find that economic development boosts life expectancyand reduces infant mortality to a greater extent than any othersubstantive predictor under investigation. However, GDP PC’spositive effect on life expectancy is significantly reduced at higherlevels of development, whereas its negative effect on infantmortality is notably enhanced.

In general, the findings from this study support a developmentagenda. Economic growth is quite important for improving welfareoutcomes among both rich and poor societies. Nevertheless, it isimportant to understand the limitations of development asawelfare tool. It is not a question ofwhether development improveshealth, but under which circumstances its effects will be maxi-mized. Moreover, a number of factors may reduce the impact ofdevelopment. For example, the results from this study indicate thatthe pursuit of economic development is a less effective strategy forcombating infant mortality in those countries featuring high levelsof income inequality. More generally, policy prescriptions in this

realm must be made cautiously. The relationship betweeneconomic development and welfare is complex. The promotion ofgrowth policies must first consider which welfare outcomes arebeing targeted, as well as where such policies are to be imple-mented. While economic development offers clear benefits in mostcontexts, there may be circumstances in which preventing thespread of HIV or reducing fertility or income inequality should takepriority. Likewise, future studies examining the relationshipbetween economic development and human welfare should care-fully consider both setting and health outcome.

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