|Overcoming Child Malnutrition in Developing Countries - Past Achievements and Future Choices. 2020 vision for Food, Agriculture, and the Environment. Discussion paper 30 (IFPRI, 2000, 73 p.)|
Cross-country studies are a useful complement to single-country case studies mainly because they exploit the fad that some variables that might be important determinants of child nutrition, such as democracy and womens status, may exhibit greater variation 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 improved data quality. Finally, cross-country analyses can provide a basis for establishing policy priorities on a regional and global basis.
Several concerns regarding cross-country studies have been raised. 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 in Egypt than it is in Ghana. A second concern is that data availability problems are more pronounced at the national level than they are at the household level. Studies must often employ available data as proxies for variables for which one would like to use a more direct measure.
A third concern regarding cross-country studies relates to their applicability below the national level. 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.
Finally, some variables that are influenced by factors outside of the household (exogenous) at the household level must be treated as endogenous at the national level since they reflect choices of national policymakers. For example, while putting health infrastructure into place may reduce child malnutrition, governments may also purposefully target infrastructure expansion programs to areas with high malnutrition. In this case, any positive association between the availability of health services and child nutritional status may not reflect the causal effect of the former on the latter. Thus addressing endogeneity concerns is particularly crucial in cross-national studies (Behrman and Deolalikar 1988).22 Data scarcities, however, make it particularly difficult to do so, and it is often not done.
22 In addition to reverse causality, endogeneity problems may arise from (1) the omission of important determinants of child malnutrition that are correlated with the variables included and (2) the simultaneous determination of child malnutrition and one of the explanatory variables by some third unobserved variable.
The quality of the data used in this study is discussed in Chapter 3. Only the best data available have been used to construct variables that as far as possible measure the key variables in the conceptual framework. In Chapter 3 the steps taken here to address endogeneity issues are discussed. Regarding the concern about subnational applicability, one can only say 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, variation within countries may be wide; policies and programs targeted at a subnational 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 cross-country regression studies relevant to this paper include those that address the determinants of health (an immediate determinant of child nutritional status) and the determinants of child malnutrition, specifically underweight and stunting rates. 23 The main factors examined in the literature on health determinants are per capita national incomes, poverty, female education or literacy rates, and the state of a countrys health environment, including the availability of health services. The outcome variables of interest are measures of life expectancy and premature mortality (see, for example, Anand and Ravallion 1993; Subbarao and Raney 1995; and Pritchett and Summers 1996). Most of the explanatory variables considered in cross-country studies of child malnutrition are the same as for health outcome studies. Almost all also include the amount of food available for human consumption, measured as daily per capita dietary energy supply (DES) derived from food balance sheets (such as ACC/SCN 1993,1994; Gillespie, Mason, and Martorell 1996; Rosegrant, Agcaoili-Sombilla, and Perez 1995; Osmani 1997; and Frongillo, de Onis, and Hanson 1997).
23 See Smith and Haddad 2000 for an in-depth review.
The studies point to the potential importance of four key variables as determinants of child malnutrition. These are per capita national incomes, womens education, variables related to health services and the healthiness of the environment, and per capita national food availability. Anand and Ravallion (1993) and Osmani (1997) suggest that, in addition, poverty and variables affecting birthweight, such as womens status, may be key. Many studies also point to the importance of accounting for potential differences across regions; the determinants for South Asia, in particular, may be different from those for the other regions.
The studies present some conflicting results, however. For example, in each of the following studies, at least one factor is not significantly associated with child malnutrition: per capita food availability (Gillespie, Mason, and Martorell 1996), health environment (Osmani 1997; Rosegrant, Agcaoili-Sombilla, and Perez 1995), and womens education (Rosegrant, Agcaoili-Sombilla, and Perez 1995). The studies also differ widely in the implied strength of the impact of the various factors. Their differing conclusions stem from (1) the use of different proxy variables to represent concepts; (2) the use of different data sources; (3) the use of different estimation methods; and (4) the inclusion of different sets of variables in regression equations.
With respect to the latter, most past studies have not taken into account the differing levels of causality 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), who examine the determinants of life expectancy. Their study shows that national income affects health mainly through its influence on government expenditures, social services, and poverty. When both income and these other variables that income determines are included in the regression equation, the implied strength of incomes impact drops substantially. This downward bias results not because national income is not important, but because its effect is already picked up by the variables it determines. Studies that mix basic, underlying, and immediate determinants in the same regression equation for child malnutrition are likely to underestimate the effects (and statistical significance) of determinants lying at broader levels of causality.