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Attrition in longitudinal household survey data - some tests for three developing-country samples

Harold Alderman (), Jere Behrman, Hans-Peter Kohler, John Maluccio () and Susan Cotts Watkins

No 2447, Policy Research Working Paper Series from The World Bank

Abstract: For capturing dynamic demographic relationships, longitudinal household data can have considerable advantages over more widely used cross-sectional data. But because the collection of longitudinal data may be difficult and expensive, analysts must assess the magnitudes of the problems, specific to longitudinal, but not to cross-sectional data. One problem that concerns many analysts is that sample attrition may make the interpretation of estimates problematic. Such attrition may be especially severe where there is considerable migration between rural, and urban areas. And attrition is likely to be selective on such characteristics as schooling, so high attrition is likely to bias estimates. The authors consider the extent, and implications of attrition for three longitudinal household surveys from Bolivia, Kenya, and South Africa that report very high annual attrition rates between survey rounds. Their estimates indicate that: 1) the means for a number of critical outcome, and family background variables differ significantly between those who are lost to follow-up, and those who are re-interviewed. 2) A number of family background variables are significant predictors of attrition. 3) Nevertheless, the coefficient estimates for standard family background variables in regressions, and probit equations for the majority of outcome variables in all three data sets, are not significantly affected by attrition. So attrition is apparently not a general problem for obtaining consistent estimates of the coefficients of interest for most of these outcomes. These results, which are very similar to those for industrial countries, suggest that multivariate estimates of behavioral relations may not be biased because of attrition. This wold support the collection of longitudinal data.

Keywords: Statistical&Mathematical Sciences; Public Health Promotion; Health Monitoring&Evaluation; Scientific Research&Science Parks; Educational Sciences; Science Education; Scientific Research&Science Parks; Health Monitoring&Evaluation; Poverty Assessment; Statistical&Mathematical Sciences (search for similar items in EconPapers)
Date: 2000-09-30
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