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Imputing Top‐Coded Income Data in Longitudinal Surveys

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  • Li Tan

Abstract

The incomes of top earners are typically top‐coded in survey data. I show that the accuracy of imputed income values for top earners in longitudinal surveys can be improved significantly by incorporating information from multiple time periods into the imputation process in a simple way. Moreover, I introduce an innovative, nonparametric empirical Bayes imputation method that further improves imputation quality. I show that the empirical Bayes imputation method reduces the RMSE of imputed income values by 19–51% relative to standard approaches in the literature. I also illustrate the benefits of the empirical Bayes method for investigating multi‐year income inequality.

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  • Li Tan, 2021. "Imputing Top‐Coded Income Data in Longitudinal Surveys," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(1), pages 66-87, February.
    • Handle: RePEc:bla:obuest:v:83:y:2021:i:1:p:66-87
    DOI: 10.1111/obes.12400
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    References listed on IDEAS

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