Attrition in Longitudinal Household Survey Data : Some Tests for Three Developing-Country Samples

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 would support the collection of longitudinal data.

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Bibliographic Details
Main Authors: Alderman, Harold, Behrman, Jere R., Kohler, Hans-Peter, Maluccio, John A., Cotts Watkins, Susan
Language:en_US
Published: World Bank, Washington, DC 2000-09
Subjects:aged, biases, Child Development, data set, data sets, dependent variable, developed countries, developing countries, Development Research, employment, equations, family planning, fathers, Food Policy Research, household characteristics, household data, household income, household level, household survey, household surveys, individual level, informal networks, intervention, interviewer, interviews, labor force, labor force participation, labor market, Living Standards, Living Standards Measurement, migration, Mortality, multivariate analyses, multivariate analysis, multivariate regression, Nutrition, nutritional status, outcome variables, parents, policy research, population size, Population Studies, precision, preschool children, probabilities, probability, radio, random sample, Research Institute, Research Working Papers, researchers, rural areas, siblings, significance level, social networks, social scientists, Specification tests, survey data, urban areas, urban communities, weighting, Household surveys, Longitudinal method, Demographic indicators, Longitudinal data, Sample surveys, Household size, Reduction in force, Migrations, Case studies, Family background, Variables (mathematics), Multivariate analysis, Behavioral responses,
Online Access:http://hdl.handle.net/10986/21336
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