understanding the relationship between poverty and children's health

European Economic Review 45 (2001) 1031}1039

Understanding the relationship betweenpoverty and children's health

Robert T. Jensen��*, Kaspar Richter��

�John F. Kennedy School of Government, Harvard University, Cambridge, MA 02138, USA�National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138, USA

�The World Bank, 1818 H Street, NW, Washington, DC 20433, USA�London School of Economics and Political Science, Houghton St., London WC2A 2AE, UK

Abstract

Throughout the world, children from poor households have worse health than chil-dren from wealthier households. There are numerous channels through which suchdisparities may arise. For designing programs to improve children's health, it is impor-tant to identify, for example, whether these disparities arise from di!erences in pre-natalhealth inputs and behaviors (nutrition, alcohol and tobacco use during pregnancy), ordi!erential health inputs following birth. We explore these issues for Russia, wherechildren's health is particularly poor. We "nd that large health disparities between richand poor originate largely from di!erences in pre-natal nutrition and alcohol use.� 2001 Elsevier Science B.V. All rights reserved.

JEL classixcation: I3; I12; J13

Keywords: Health; Poverty; Children

1. Introduction

While it has long been known that great health disparities exist between richand poor countries (see for example, Pritchett and Summers, 1996), there has

*Correspondence address: John F. Kennedy School of Government, Harvard University,Cambridge, MA 02138, USA. Tel.: #1-617-496-1623; fax: #1-617-496-5747.E-mail address: robert}[email protected] (R. T. Jensen).

0014-2921/01/$ - see front matter � 2001 Elsevier Science B.V. All rights reserved.PII: S 0 0 1 4 - 2 9 2 1 ( 0 1 ) 0 0 1 1 0 - 6

�Deaton and Paxson (1999), Feinstein (1993), Fuchs (1982), Marmot et al. (1991) and Smith(1999).

�The survey is coordinated by the Carolina Population Center at the University of NorthCarolina at Chapel Hill. Further description of the data can be found at www.cpc.unc.edu/pro-jects/rlms/rlms}home.html.

recently been increasing interest in the within-country, cross-sectional correla-tion between health and socioeconomic status (SES).� In nearly every countryfor which data are available, individuals with low income or educationalattainment have worse health on average than those who are better-o!. Much ofthis research has focused on adults or the elderly. However, there should be evengreater concern for children's health, given the importance of inputs during the"rst few years of life for later physical, mental and emotional development.Further, temporary shortfalls in health or nutrition can have lasting andirreversible e!ects when they occur during childhood, a period of signi"cantdevelopment. Finally, health investments may be one channel through whichpoverty and disadvantage are transmitted across generations, as children ofpoor parents receive worse health investments, which in turn may reduce futureearnings capacity.

There are many possible channels which could lead to a correlation betweenSES and children's health. The most often suggested is the &health productionfunction' channel, namely that health is produced in part through certain inputs(for example food, medicine, health care and living environment), and lowerincome leads to reduced purchases of these inputs. However, alternative ex-planations are available; for example, there is evidence that maternal healthhabits during pregnancy, especially smoking, can explain much of the di!eren-tial incidence of low birth-weight between rich and poor mothers in the UnitedStates (Meara, 2000). Since alternative channels suggest di!erent correctivepolicy instruments, understanding the connection is extremely important. Forchildren's health, a particularly important distinction is the relative importanceof inputs during pregnancy versus those following birth. For example, if di!eren-tial health habits such as smoking during pregnancy explain much of thedi!erences in children's health, the best policy instruments will include healtheducation programs for pregnant women. As pointed out by Meara (2000),under these circumstances, targeting the majority of public resources towardsprograms subsidizing health care access for the poor will not be as e!ective inimproving the health of poor children. Alternatively, if health di!erences arisemainly from post-natal di!erences in the ability to purchase health inputs,subsidized food and health care programs would be warranted. Since publicresources for programs for the poor are typically "xed, it is essential to under-stand exactly which programs will achieve the greatest improvement in thehealth of children from low-income households.

In this paper, we explore these issues for Russia, applying data from theRussian Longitudinal Monitoring Survey (RLMS),� a nationally representative

1032 R.T. Jensen, K. Richter / European Economic Review 45 (2001) 1031}1039

�Our data come from pooled surveys from the years 1994 to 1998 for which relevant data wereavailable.

� In fact, most measures of health and survival are instead comparable to what is found incountries typically considered low-income; for instance, the Russian male life expectancy of 61 yearsis the same as in Bolivia, Pakistan, Guyana and Guatemala. An especially sharp deteriorationfollowed the transition to a market economy in 1992; see the series of articles in the November 1998edition of World Development for analysis of the trends and patterns. Jensen and Richter (2000)isolate one of the linkages between the economic and health crises by showing that Russia's pensioncrisis lead to worsened nutrition and health status for the elderly.

survey of approximately 4000 households.� The Russian case is important bothbecause overall health status is signi"cantly worse than in countries with similarlevels of industrialization and per capita income,� and because the deteriorationin health that has occurred since the transition to a market economy may haveserious long-term impacts if children were a!ected. While we will not focusspeci"cally on trends over time in children's health and how they relate to theeconomic, political and social changes over the past decade, our analysisprovides important insight into health disparities and their causes, both inRussia and elsewhere more generally.

2. SES and children's health in Russia

2.1. General patterns

One of the most commonly used measures of children's health and nutritionalstatus is height-for-age (HFA) z-score. This measure represents the number ofstandard deviations a child is from the gender- and age-speci"c referencemedians adopted by the World Health Organization to represent healthypopulations. HFA is a summary measure of long-term cumulative nutritionalstatus, or health stock (see Falkner and Tanner, 1986). Low scores re#ectchronic nutritional de"ciency and may adversely a!ect physical and mentaldevelopment. An advantage of this measure over others used in research onhealth is that it is more objective than measures such as self-reported healthstatus.

Table 1 presents HFA and the percentage of children (aged 0}10) who are&stunted' (i.e., &small', a HFA of less than !2). To represent socioeconomicstatus, we group households into quartiles of household income per capita,expenditure per capita, and assets, as well as by parental education and occupa-tional status. Across all measures, boys and girls in the lowest SES groups areconsistently less healthy than those in the highest. In most cases, the incidence ofstunting is over twice as large for the poorest children. For example, boys andgirls living in households in the bottom quartile of income per capita are on

R.T. Jensen, K. Richter / European Economic Review 45 (2001) 1031}1039 1033

Table 1Children's health by socioeconomic status

Boys Girls

HFA % stunted HFA % stunted

Income per capitaBottom quartile !0.64 0.20 !0.23 0.12Top quartile !0.13 0.08 0.30 0.03

Expend. per capitaBottom quartile !0.46 0.16 !0.13 0.11Top quartile !0.17 0.09 0.20 0.07

AssetsBottom quartile !0.85 0.17 !0.22 0.12Top quartile !0.07 0.06 0.04 0.06

Education(High school !0.36 0.13 !0.13 0.09'High school !0.16 0.06 0.14 0.06

OccupationBlue collar !0.59 0.14 !0.32 0.11White collar !0.15 0.08 0.38 0.01

�We use the Epanechnikov kernel because of its relative e$ciency, and choose the bandwidthusing least squares cross-validation.

average 0.5 standard deviations further below the reference median than chil-dren from households in the top quartile, and stunting rates are 2.5 and 4 timesgreater. The level of stunting for the bottom income quartile is extremely high,especially for boys; they are comparable to overall stunting rates found inAlgeria (18%), Iran (19%), Zimbabwe (21%), the Syrian Arab Republic (21%),and Senegal (22%).

Grouping households by income quartiles or coarse educational categoriesmay obscure important detail about the relationship between SES and health.Doing so also relies on an arbitrary de"nition of poor and non-poor in decidingwhere to look for inequalities in health. A question of particular relevance forthe literature on SES and health is whether the relationship is continuous.Alternatively, there may be some non-linearity, such that health improves withincome but only up to some threshold, after which children's health inputs areadequately provided. Such a threshold would be valuable for de"ning incomeguarantees for public programs targeted towards families with children. There-fore, in Fig. 1 we present results from nonparametric regressions (a locallyweighted regression smoother due to Fan (1992))� of the relationship between


Fig. 1. Stunting by (log) income per capita.

�See Efron and Tibshirani (1993). Bootstrap con"dence bands calculated using 100 replications.

the prevalence of stunting and per capita household income, with 95 percentcon"dence bands (calculated through bootstrap estimation).� This techniqueallows us to explore the relationship between the two variables without impos-ing de"nitions or structure, by essentially performing separate (weighted) regres-sions at every point in the income distribution. For both boys and girls, the"gure reveals that increases in income are continuously associated with im-provements in children's health (reduced incidence of stunting). For boys, theslope becomes slightly more #at near the extreme upper-end of the incomedistribution, whereas for girls the relationship is negative throughout. However,overall it is clear that a simple poor/non-poor distinction obscures the conti-nuity of the relationship.

2.2. How does the relationship vary with age?

In Fig. 2, we present nonparametric regressions of the relationship betweenthe prevalence of stunting and age in months, for children living in the poorestand richest households (according to income per capita). The graph con"rmsonce again that children in the bottom quartile are much less healthy than thosein the top. However, the greatest di!erences are found at the youngest ages. Forchildren less than one year old, approximately 20 percent of boys and girls in thepoorest quartile are stunted. However, the incidence of stunting declines mono-tonically with age for the poorest quartile, so that by age 10 the rates are onlyslightly over 10 percent for boys and just under 5 percent for girls. By contrast,stunting is largely unchanged for children from the richest households. Theimprovement of poor children relative to rich children may re#ect both selectivemortality, as the least healthy poor children are more likely to die than the least


Fig. 2. Stunting by age and socioeconomic status.

�However, the voluminous medical literature on growth catch-up yields mixed results, and isplagued with empirical problems which limit inference.

Stress and environment may also in#uence birth-weight, but the medical evidence is less clearand we lack information on these issues in our data set.

healthy rich children, as well as &catch up', whereby children with early nutri-tional de"ciencies grow more rapidly later.� Overall, though, it is clear thatmuch of the di!erence in children's health by SES is manifest in infancy. It istherefore possible that much of the di!erence is determined during pregnancy,since HFA in infancy is highly correlated with birth-weight. While the RLMSdoes not contain information on birth-weight, we can explore maternal healthhabits during pregnancy to probe these issues further.

3. Maternal health habits during pregnancy

Among the behavioral factors known to in#uence fetal development andbirth-weight are diet, smoking and drinking. The RLMS contained self-reportsof smoking and drinking, as well as a 24 hour dietary recall, which was laterconverted into calories and protein. Table 2 explores the health behaviors ofwomen who report being pregnant at the time of the survey, grouped accordingto quartile of income per capita. Average intake of calories and protein amongthe poorest women is signi"cantly lower than that of women in the secondthrough fourth quartiles. While recommended levels for both micronutrientsvary across women, on average pregnant women should consume approxim-ately 70 g of protein per day. De"ciencies can adversely a!ect fetal developmentand lead to low birth-weight (National Academy of Sciences, 1990). The table


Among pregnant women, the poorer individuals tend to be younger on average, and thus requireless protein. However, even after regression-based adjustments for age, the nutritional de"ciencypersists.

Table 2Maternal health habits during pregnancy

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Calories (kcal per day) 1873 2190 2358 2316Protein (g per day) 53.9 72.6 74.1 73.0Takes multi-vitamin? 0.19 0.2 0.39 0.48Take other vitamin? 0.17 0.18 0.17 0.31Drinks? 0.17 0.20 0.21 0.23

Amt. drinks, if '0 1.4 0.99 1.1 0.58Smokes? 0.10 0.12 0.065 0.062

� cigarettes, if '0 9.0 9.0 8.5 11.1Saw doctor about pregnancy? 0.92 0.88 0.85 0.95

shows that women in the poorest quartile average only 54 g, while all otherwomen average over 70 g. Such a dramatic shortfall could explain part of theworse health and birth outcomes for children in poor households. Poor womenare also signi"cantly less likely to take vitamins during pregnancy, which is evenmore harmful since their low food intake suggests they probably receive inad-equate quantities of vitamins from their diet.

Poor women are less likely to drink if they are pregnant; however, conditionalon drinking they report much higher levels of intake. The average woman inthe poorest quartile consumes 1.4 drink-equivalents per day; the amountdeclines steadily with income, to 0.58 drinks per day among the wealthiestwomen. The medical literature unambiguously suggests that excessive alcoholintake during pregnancy adversely a!ects fetal development. However, there isless evidence that light or moderate drinking causes harm. Thus, poor womenare more likely to engage in the type of excessive drinking that could a!ect fetalhealth and development. Poor women are also slightly more likely to smokeduring pregnancy, though they smoke slightly fewer cigarettes per day thanother pregnant women. Other results using panel data (not shown) reveal thatmuch of the di!erential smoking and drinking behavior of rich and poor womenduring pregnancy arises from wealthier (and more educated) women reducing oreliminating these behaviors when they become pregnant, whereas lower SESwomen are less likely to change. Targeted public-education programs designedto encourage low-SES women to cut-back on these behaviors during pregnancycould therefore have bene"cial e!ects.

Finally, there are no di!erences in the likelihood of having seen a doctorduring pregnancy. Given that health care is free (though not necessarily


��Excessive alcohol use is de"ned as a daily intake factor of greater than 2 drink equivalents.

high-quality), the di!erence in the use of pre-natal health services typicallyobserved for other countries does not occur in Russia. Overall, the mostsigni"cant di!erences in health inputs during pregnancy relate to inadequatediet and nutrition, with only small di!erences in drinking and smoking. Theseresults contrast with what is found for the United States, where poor women aresigni"cantly more likely to smoke during pregnancy (Meara, 2000).

In order to link these health behaviors during pregnancy to children's healthoutcomes, we exploit the panel element of the RLMS to match pregnantwomen's health behaviors and SES in one round to her infant's health in thefollowing rounds. The goal is to explore how much of children's growth andphysical stature is determined by inputs during pregnancy versus inputs follow-ing birth. Regressions of HFA in infancy on only income and education showa positive and statistically signi"cant e!ect of these two measures of SES, witha 10 percent increase in household per capita income improving HFA by 0.2standard deviations (results available from the authors). However, when indi-cators for mother's intake of calories, protein and vitamins during pregnancy areadded, the post-natal socioeconomic variables become much smaller and are nolonger statistically signi"cantly di!erent from zero, suggesting they have littleindependent e!ect on children's health. Meanwhile, nutrition has a large impacton infant health; an increase of 100 calories per day would increase HFA by 0.1standard deviations, while an increase of 5 g of protein would increase it by 0.14.Smoking and excessive drinking�� reduce child's HFA by 0.20 and 0.15 standarddeviations respectively, though their inclusion in the regression changes the coe$-cients on the other variables only slightly. Finally, duration of breastfeedingand maternal nutrition during breastfeeding have only small e!ects on infanthealth relative to these other factors.

4. Discussion and conclusions

There are large di!erences in health by SES among children in Russia. In anycontext, it is important to uncover the sources of health disadvantage among thepoor and to design policies best suited to those causes. In the present case, we"nd that most health di!erences among children are evident at the youngestages and then decline.Many of the di!erences in health-related behaviors duringpregnancy between rich and poor women are related to the use of inputs(calories, protein and vitamins) which must be purchased; these observationssupport the production function channel linking health and SES. While it maybe the case that households do not make appropriate expenditure decisions, andwhile encouraging all women to reduce alcohol consumption and smoking


during pregnancy would also achieve gains, the results suggest that nutritionalsupplement programs for pregnant women would be an e!ective way to increasethe health of poor children in Russia. These results are all the more importantbecause of emerging scienti"c evidence that nutritional de"ciencies duringpregnancy, especially during periods of rapid cell division and di!erentiation,may lead to permanent underdevelopment of vital organs and impaired healthin adult life (Barker, 1987).

Acknowledgements

We would like to thank Elizabeth Brainerd, seminar participants at theEuropean Economic Association Conference in Bolzano, Italy, and especiallyHerbert F. Jensen for comments and suggestions.

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