|Explaining Child Malnutrition in Developing Countries: A Cross-Country Analysis - Research Report 111 (IFPRI, 2000, 126 p.)|
A number of cross-country studies of the determinants of child malnutrition and related health outcomes have been carried out over the last five years. In this chapter, three that address the determinants of health (an immediate determinant) and six that directly address child malnutrition are reviewed. The goal is to give a broad overview of these studies' findings on the causes of child malnutrition and to identify limitations that can be overcome in the present analysis. The studies and their findings are summarized in the Appendix, Tables 22 and 23. Before moving on, it is useful to consider the merits and demerits of cross-country studies.
General Issues Concerning Cross-Country Studies
Cross-country studies are a useful complement to within-country case studies mainly because they exploit the fact that some variables that might be important determinants of child nutrition, such as democracy and women's status, may vary more between countries than within them. Other variables may only be observed at a national level, for example, national food supplies and incomes. In addition, the use of cross-country data for multivariate analysis identifies weaknesses in data series that might not be identified through the casual observation of trends and two-way tables, thus generating a demand for improvement in data quality. Finally, cross-country analysis can provide a basis for establishing policy priorities on a regional and global basis.
Several concerns regarding cross-country studies have been raised.2 First, the quality and comparability of the data themselves have been questioned. Data on different variables may come from different agencies, each of which has its own quality standard and sampling frame. Moreover, variable definitions may not be uniform across countries. For example, the definition of "access to safe water" may be different between Egypt and Ghana. A second concern is that data availability problems are more pronounced at the national level than they are for household-level analysis. Studies must often employ available data as proxies for variables for which one would like to employ a more direct measure.
2 Many of the concerns expressed about cross-country studies are concerns that will plague any study employing cross-sectional data. These include: (1) the lack of a theory that is specific enough to determine which variables belong in the regression equation (Sala-I-Martin 1997), (2) general problems with making inferences from cross-section data in that the counterfactual is never observed (Przeworski and Limongi 1993), and (3) the diminished ability to control for confounding variables (Pritchett and Summers 1996). Fortunately, in the area of malnutrition good conceptual models are available to minimize problems related to model specification. In addition, econometric techniques - the techniques used in this study - are available to account for problems of confounding variables. The issue of drawing inferences from cross-sectional data is a profound one and is a limitation that the authors of this report, along with all other researchers who use cross-sectional data, have to acknowledge and respect.
A third concern regarding cross-country studies relates to their subnational applicability. Child malnutrition is inherently an individual and household-level phenomenon. Can cross-country data be used to make inferences about household and individual behavior? An implicit assumption is that a country represents a "representative citizen." But the use of average data can be misleading if distribution is important and differs across countries (Behrman and Deolalikar 1988). Similarly, results arrived at through the use of cross-national data may not be applicable to individual countries' situations, yet it is at the country (and subnational) levels that many policy decisions are made. That all countries are given equal weight in a cross-country regression analysis may exacerbate this policy, yet many countries' populations are hundreds of times smaller than, for instance, China's and have populations that vary widely in their characteristics and behaviors.
Finally, some variables that are exogenous at a household level must be treated as endogenous at the national level since they reflect choices of national policymakers. Therefore, addressing endogeneity concerns is particularly crucial in cross-national studies (Behrman and Deolalikar 1988). Because of the scarcity of data, however, it is particularly difficult to do so and often not done.
The quality of the data employed in this study is discussed in Chapter 4. Care has been taken to use only the best data available to construct variables that as far as possible measure the key variables in the conceptual framework. In Chapter 4 the issue of using different regression weights for countries based on their population size is also discussed, as well as the steps taken to address endogeneity issues. Concerning sub-national applicability, it can only be said that cross-country studies, while often based on aggregated household-level data, are intended to capture broad global and (for some studies) regional trends. Readers must keep in mind that at the household level there may be wide variation within countries; policies and programs targeted at a sub-national level will have to be formulated with these differences in mind. The same can be said of the concern about the applicability of the results to individual countries.
Past Studies of the Determinants of Health and Child Malnutrition
The main determinants examined in the cross-country health determinants literature are national incomes, poverty, education, and the state of countries' health environments, including the availability of health services. The outcome variables of interest are measures of life expectancy and premature mortality.
In a 1993 journal article, Anand and Ravallion seek to answer the question of how health is affected by per capita national income levels, poverty, and the public provision of social services. National income is measured as gross domestic product (GDP) per capita, and poverty as the proportion of a country's population consuming less than one dollar a day. Both measures are reported in U.S. dollars arrived at through exchange rates adjusted to purchasing power parity (PPP) to improve cross-country comparability. The public provision of social services is measured as public health spending per capita. The authors find a strong simple correlation between national income and life expectancy for 86 developing countries in 1985. Using ordinary least squares (OLS) regression techniques for a subsample of 22 countries for which they have comparable data, they add poverty incidence and public health spending per person as explanatory variables. They find that the significant, positive relationship between life expectancy and national income vanishes entirely once poverty and public health spending are controlled for. Poverty has a significant negative effect on life expectancy; public health spending has a significant positive effect. A similar result is found by the authors for infant mortality. The authors conclude that "average income matters, but only insofar as it reduces poverty and finances key social services" (Anand and Ravallion 1993, 144).3 They find that one-third of national incomes' effect on life expectancy is through poverty reduction and two-thirds through increased public health spending.
3 In a later study, Bidani and Ravallion (1997) use data on 35 developing countries to estimate a random coefficients model of life expectancy and infant/perinatal mortality rates on the distribution of income (breaking countries' populations into groups of "poor" and "nonpoor"), public health spending, and primary schooling. They find poverty to be an important determinant of health and they find that public health spending and primary school enrollment matter, but more for the poor than the nonpoor.
Subbarao and Raney (1995) focus on the role of female education using a sample of 72 developing countries and data over the period 1970 to 1985. Using OLS regression, they regress infant mortality rates in 1985 on female and male gross secondary school enrollment ratios lagged 5 and 10 years, GDP per capita (PPP-adjusted), rates of urbanization, a family planning services score,4 and a proxy variable for health service availability, population per physician. They find that female education has a very strong influence on infant mortality rates. Per capita national income, family planning services, and population per physician are statistically significant, but are not as powerful as female education. The authors estimate that, for a typical poor country, a doubling of female education in 1975 would have reduced infant mortality rates in 1985 from 105 to 78. In comparison, halving the number of people per physician would have reduced it by only 4 points (from 85 to 81) and a doubling of national income per capita would have lowered it by only 3 (from 102 to 99).
4 This score is based on several factors including community-based distribution of family planning services, social marketing, and number of home-visiting workers.
Neither of these two studies test for the possibility that a country's income itself may be affected by the health of its citizens. The OLS regression technique employed also does not account for any omitted country-specific effects that may influence health outcomes and be correlated with the explanatory variables included. They thus risk identifying a merely associative rather than causative relationship between the dependent and explanatory variables of interest.
Pritchett and Summers (1996) take the income question a step further by applying econometric techniques that detect and account for any possible spurious association or reverse causation between health and income.5 Using data from 1960 to 1985 for between 58 and 111 countries (depending on the estimation technique employed), they examine the impact of GDP per capita ($PPP) and education levels on infant mortality, child mortality, and life expectancy. They eliminate possibly spurious correlation by controlling for country-specific, time-invariant factors (such as climate and culture) using a first-difference approach. They control for possible reverse causation between income and the outcome variables by employing instrumental variables techniques, using a variety of instruments for income, for example, countries' terms-of-trade and investment rates. For all regressions the authors find a significant and negative impact of income on infant mortality. The results are similar for other dependent variables such as child mortality rates, but weaker for life expectancy. They conclude that "increases in a country's income will tend to raise health status" (p. 865), estimating a short-run (5-year) income elasticity of -0.2 and a long-run (30-year) income elasticity of -0.4. Education was also found to be a significant factor in improving health status.
5 Reverse causation between two variables means that the first variable affects the second and the second in turn affects the first.
Most of the explanatory variables considered in cross-national studies of child malnutrition are the same as for health outcome studies: per capita national incomes, female education, and variables proxying for health service provisioning. Almost all studies also include food available for human consumption as an explanatory variable, measured as daily per capita dietary energy supply (DES) derived from food balance sheets. The dependent or outcome variables employed are the prevalence of underweight or stunted children under age five.6
6 A child under age five is considered stunted if the child falls below an anthropometric cut-off of -2 standard deviations below the median height-for-age Z-score of the United States' National Center for Health Statistics/World Health Organization international reference.
A study undertaken by the United Nations Administrative Committee on Coordination's Sub-Committee on Nutrition (ACC/SCN) (1993) examines the determinants of underweight prevalence for 66 developing countries from 1975 to 1992.7 The study includes several countries for which data are available for more than one point in time, giving a total number of observations of 100. Applying OLS regression, it finds that DES (especially for South Asia), female secondary school enrollment, and government expenditures on social services (health, education, and social security) are all negatively and significantly associated with underweight prevalence. Regional effects, accounted for by using dummy variables, are found to be statistically significant and especially large for South Asia. This suggests that factors specific to South Asia that are not accounted for in the analysis are partly responsible for its high prevalence of malnutrition.
7 Note that this study was undertaken with the primary aim of developing an estimating equation with the best predictive value. Nevertheless, the estimation results identify variables, some of which may be causal factors, that are statistically associated with child underweight rates.
A 1994 ACC/SCN update focuses specifically on the role of per capita income growth in determining annual changes in underweight prevalences for 42 developing countries from 1975 to 1993. The study finds a statistically significant relationship between GDP per capita growth and changes in underweight prevalence,8 with a one point increase in the growth rate of the former leading in general to a 0.24 percentage point decrease in the underweight prevalence annually. Given an average annual reduction in the underweight prevalence rate during the study period (estimated from the reported regional averages) of 1.5, this is a fairly large effect. The study concludes, however, that "although economic growth is a likely factor in nutritional improvement, the deviation from the rate expected is substantial and important" (p. 4), suggesting that other factors are important as well. Gillespie, Mason, and Martorell (1996) extend the ACC/SCN analysis to include consideration of a role for public expenditures on social services and food availabilities. Using a subset of 35 countries in the original data set, they find that levels of public health and education expenditures (measured as a share of total government budgets) are significant determinants of changes in underweight prevalences, but that both levels and changes in food availability are not.9
8 It is not clear whether the GDP growth rates utilized are estimated using PPP-adjusted exchange rates or using data generated by the traditional World Bank atlas method.
9 Note that the "quasi" first differences approach, in which the dependent variable is expressed in changes over time but some or all of the independent variables are not, does not account for country-specific (time invariant) factors as would a pure first differences approach.
Rosegrant, Agcaoili-Sombilla, and Perez (1995)10 use data from 61 developing countries to regress underweight prevalences on DES, percentage of public expenditures devoted to social services (health, education, and social security), female secondary education, and as a proxy for sanitation, the percentage of countries' populations with access to safe water. The data employed are predicted underweight rates for 1980, 1985, and 1990 generated by the ACC/SCN (1993) study. The data over these time periods were pooled and OLS regression techniques were applied. The study found DES and social expenditures to be significantly (negatively) associated with underweight rates, but female education and access to safe water were statistically insignificant determinants.
10 The estimations in this study were also undertaken with the primary aim of developing an estimating equation with the best predictive value rather than identifying causal relationships.
Osmani (1997) attempts to explain the "South Asian puzzle," that is, why South Asia's child malnutrition rate is so much higher than Sub-Saharan Africa's, despite almost equal poverty rates, higher food availability in South Asia, and comparable levels of public provision of health and sanitation services. The study employs OLS regression to explore the determinants of child stunting for 66 developing countries in the early 1990s. The initial explanatory variables are per capita GDP ($PPP), health services (proxied by population per physician), extent of urbanization, and the female literacy rate. All are found to be important determinants of stunting. A South Asian dummy variable is significant and quite large, indicating (as does ACC/SCN 1993) that additional factors explain South Asia's extreme rates of child stunting. Under the hypothesis that the presence of relatively high rates of low birth weight are at the root of the South Asian puzzle, this variable is added into a second estimating equation, causing the South Asian dummy variable to lose its significance. In a third estimating equation the dummy variable is dropped and replaced with the low birth weight variable. The latter is statistically insignificant in this equation. The author concludes that low birth weight and factors influencing it - particularly the low status of women in South Asia - are important determinants of stunting. However, since low birth weight is endogenous (it is partially determined itself by both per capita income and female literacy), the OLS coefficient estimates are likely to be biased, weakening the study's conclusions.
Frongillo, de Onis, and Hanson (1997) examine the determinants of child stunting using data from 70 developing countries in the 1980s and 1990s. They find national income per capita,11 DES, government health expenditures, access to safe water, and female literacy rates all to be statistically significant factors. In addition to these variables, the study tests for the significance of four others representing countries' socio-economic and demographic structure: proportions of population that are urban, proportions of population in the military, population density, and female share of the labor force. It finds none of these variables to be significant determinants of stunting. As for previous studies, regional effects are found to be strong and significant. They are particularly strong for the "Asia" region, which is represented by 17 countries from South Asia, East Asia, and the Near East.
11 The paper does not specify whether GDP is measured using PPP-adjusted exchange rates.
In conclusion, while suffering from some methodological limitations, the studies reviewed above point to the importance of four key variables as determinants of child malnutrition. These are per capita national incomes, women's education, variables related to health services and the healthiness of the environment, and national food availability. They present conflicting results, however, with respect to women's education, health environments, and food availability. Anand and Ravallion (1993) and Osmani (1997) suggest that, in addition, poverty and variables affecting birth weight, such as women's status, may be key. The studies also point to the importance of accounting for potential differences across regions, most particularly, that the determinants for South Asia may be different than those for the other regions.
Methodological Limitations of Past Cross-Country Studies
From a conceptual standpoint, most studies have not taken into account the differing pathways through which the various determinants of child malnutrition influence it. The danger of not doing so is illustrated in the study by Anand and Ravallion (1993). The analysis shows that income affects health mainly through its influence on government expenditures on social services and poverty. When both income and other variables that income determines are included in the health regression equation, the parameter estimate for income drops substantially in magnitude. This downward bias results not because income is not important, but because its effect is already picked up by the variables it determines. Past studies that have mixed basic, underlying, and immediate determinants in the same regression equation for child malnutrition12 have probably underestimated the strength of impact and statistical significance of determinants lying at broader levels of causality.
12 See Behrman and Deolalikar 1988 for further discussion of the use of "quasi reduced-form estimating equations in analysis of the determinants of health and nutrition."
The studies reviewed here (with the exception of Pritchett and Summers 1996) also have not addressed the important issue of endogeneity, in particular, correlation between the error term and included explanatory variables. Endogeneity can arise from a number of different sources. The first, mentioned earlier, is the presence of reverse causality between child malnutrition and one of the explanatory variables. For example, programs to improve health infrastructure may be targeted to countries with high child malnutrition (the problem of endogenous program placement). The second is the omission of important determinants of child malnutrition (whose effects are relegated to the error term) that may be correlated with the included explanatory variables. Cultural factors influencing caring behaviors, for example, are difficult to measure and are typically unobserved, but are important to nutritional outcomes. Their exclusion can cause widespread omitted variables bias because they may be correlated with included variables like female education (Engle, Menon, and Haddad 1999). The third is the simultaneous determination of child malnutrition and one of the explanatory variables by some third unobserved variable. For example, restrictions on female labor force participation (unobserved) might reinforce women's low status (a potential determinant of child malnutrition) and simultaneously affect child malnutrition through lack of income earned by women. A final source of endogeneity is measurement error in the explanatory variables. If any of these four problems exists, OLS parameter estimates will be biased, leading to inaccuracy in the estimates and error in inferences based on them.