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Abstract
Longitudinal household data can have considerable advantages over much more
widely used cross-sectional data. The collection of longitudinal data, however, may be
difficult and expensive. One problem that has concerned many analysts is that sample
attrition may make the interpretation of estimates problematic. Such attrition may be
particularly severe in areas where there is considerable mobility because of migration
between rural and urban areas. Many analysts share the intuition that attrition is likely to
be selective on characteristics such as schooling and that high attrition is likely to bias
estimates made from longitudinal data. This paper considers the extent of and
implications of attrition for three longitudinal household surveys from Bolivia, Kenya,
and South Africa that report very high per-year attrition rates between survey rounds. Our
estimates indicate that (1) the means for a number of critical outcome and family
background variables differ significantly between attritors and nonattritors; (2) a number
of family background variables are significant predictors of attrition; but (3) nevertheless,
the coefficient estimates for “standard” family background variables in regressions and
probit equations for the majority of the outcome variables considered in all three data sets
are not affected significantly by attrition. Therefore, attrition apparently is 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 results for developed economies,
suggest that for these outcome variables—despite suggestions of systematic attrition from univariate comparisons between attritors and nonattritors, multivariate estimates of
behavioral relations of interest may not be biased due to attrition.