David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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For effective decisionmaking, policymakers and program managers often need detailed information about the welfare of the population, including knowledge about which specific areas are most affected by poverty and undernutrition. Household sample surveys are an important source of information, yet because the typical sample size is only a few thousand observations, the information is only useful for inferences at high levels of aggregation, such as the nation or large regional units. In contrast, data sources with wider coverage, such as national censuses, rarely capture detailed information on welfare levels. Recently small-area estimation techniques have been applied to the study of poverty to produce estimates of poverty, or poverty maps, for small geographic units. This paper uses household survey and unit record census data from Tanzania to explore the possibility of applying small-area estimation methods to the study of children's nutritional status as measured by anthropometry. Overall, undernutrition models have had lower explanatory power than poverty models, which has important implications for the precision of the small-area estimates. The analysis finds that applying small-area estimation techniques to anthropometric data is feasible, although the relatively low explanatory power of the regressions does limit both the degree of disaggregation possible and the power to detect significant differences in undernutrition prevalence between districts and subdistricts. In the case of Tanzania, the nutrition mapping approach reveals considerable heterogeneity in nutritional status within regions and within districts. The most striking finding is the much lower levels of undernutrition in areas classified as urban, including relatively small district centers.
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