The reduction of infant and child death is one of the eight Millennium Development Goals (MDGs). In addition, one of the Goal 1 indicators is child malnutrition (Table 1). A central question for the development community is thus to understand the factors underlying child health and nutritional status. What are the determinants of these indicators, which of these determinants are amenable to policy intervention and which are the most effective channels for influencing health and nutrition outcomes?
Many regression-based studies have been carried out to analyze these determinants. The earliest such studies were carried out using cross-country data (e.g. Rodgers, 1979). However, as described in section 2, models of child health and nutrition ascribe a central role to child and household characteristics, which are lost in the aggregation to national level. Potentially more insightful is analysis using data collected from household surveys which can include such variables. Many such studies have been published with both the increased availability of household data, in particular from the Demographic Health Survey (DHS)1 and the Living Standards Measurement Survey (LSMS), and the computer power to analyze large data sets.
* Contact: hwhite@worldbank.org. The authors are grateful to Osvaldo Feinstein for comments on an earlier draft. However, the views expressed here are those of the authors alone, and cannot be taken as the opinion of the World Bank.
1 DHS was preceded by the World Fertility Survey in the 1970s and Contraceptive Prevalence Survey in the early 1980s. These data did not produce the volume of papers generated by DHS data, presumably on account of the relative lack of computer power. These days a household survey can be downloaded from the internet or obtained on CD, usually in a form ready for analysis with one of the most common statistical packages (SPSS or Stata).
2 This paper summarizes the conclusions from these statistical studies of the determinants of child health (infant and child mortality) and nutritional status. We restrict our attention to papers utilizing household survey data given the ability of such studies to comprehensively model these determinants. The results from the various studies are combined using meta-analysis, which calculates the statistical significance of a variable included in more than one study by combining the results of those studies.
We begin in Part 2 with a brief review of theory to introduce the relevant variables and their classification. Part 3 discusses data and variable definition and econometric issues, including the use of meta-analysis. The results are presented in Part 4 and part 5 concludes. Annexes provide more details of the studies reviewed in this paper.