Correcting for Sampling Bias in the Measurement of Welfare and Poverty in the Côte d’Ivoire Living Standards Survey

The sampling aspects of a household data set are important to analysts. The early years of the C6te d'lvoire Living Standards Survey (CILss) had a sampling bias, which seri-ously affected estimates of population statistics such as household size. The bias arose from sampling procedures that overrepresented larger dwellings. Assuming that sam-ples drawn in later years were unbiased, a correction procedure is applied that uses weights based on household size. Results from the weighted data are then compared with the unweighted findings to assess the seriousness of the bias. Estimates of house-hold expenditure per capita in the early years of the survey are found to be significantly underestimated, resulting in an overestimation of poverty. The sampling bias also resulted in an underestimation of the upward trend in poverty during 1985-88. The CILSS has been a popular and fruitful data set for policy analysis. These findings, however, cast doubt on the robustness of earlier work. Thus, the effort to trace sam-pling information is particularly worthwhile for policy-oriented applied research. Over the years household surveys have proved to be a popular and valuable data source for empirical research in microeconomics. In developing countries house-hold survey data have become more available in the past decade, partly as a result of international programs such as the UN National Household Surve