Addressing Non-ignorable Panel Attrition Using External Population Data: Analysis of Demographic Events From Survey Data

Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event probabilities in the absence of additional assumptions. The paper introduces an adjustment procedure based on Bayes Theorem that directly addresses the problem of nonignorable dropout. It uses population information external to the survey sample to convert estimates of event probabilities and marginal effects of covariates on them that are conditional on retention in the longitudinal data to unconditional estimates of these quantities. In many plausible and verifiable circumstances, it produces estimates of the marginal effect of covariates closer to the true unconditional quantities than the conditional estimates obtained from estimation using the survey data alone.

Automated summary

Longitudinal survey data provides rich covariate measures for empirical analysis of demographic events, but wave-on-wave dropout poses a challenge. This paper introduces an adjustment procedure based on Bayes Theorem to address the nonignorable dropout problem. It utilizes external population information to convert conditional estimates to unconditional estimates, resulting in more accurate estimates of the marginal effects of covariates on event probabilities.