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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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    1. Richard Burkhauser & Shuaizhang Feng & Stephen Jenkins & Jeff Larrimore, 2009. "Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data," Working Papers 09-26, Center for Economic Studies, U.S. Census Bureau.
    2. John S. Heywood & Patrick L. O’Halloran, 2005. "Racial Earnings Differentials and Performance Pay," Journal of Human Resources, University of Wisconsin Press, vol. 40(2).
    3. Steven Haider & Gary Solon, 2006. "Life-Cycle Variation in the Association between Current and Lifetime Earnings," American Economic Review, American Economic Association, vol. 96(4), pages 1308-1320, September.
    4. Bhuller, Manudeep & Mogstad, Magne & Salvanes, Kjell G., 2011. "Life-Cycle Bias and the Returns to Schooling in Current and Lifetime Earnings," IZA Discussion Papers 5788, Institute of Labor Economics (IZA).
    5. Orazio Attanasio & Luigi Pistaferri, 2014. "Consumption Inequality over the Last Half Century: Some Evidence Using the New PSID Consumption Measure," American Economic Review, American Economic Association, vol. 104(5), pages 122-126, May.
    6. Shin, Donggyun & Solon, Gary, 2011. "Trends in men's earnings volatility: What does the Panel Study of Income Dynamics show?," Journal of Public Economics, Elsevier, vol. 95(7-8), pages 973-982, August.
    7. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
    8. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    9. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2011. "Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 63-81, January.
    10. Philip Armour & Richard V. Burkhauser & Jeff Larrimore, 2016. "Using The Pareto Distribution To Improve Estimates Of Topcoded Earnings," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 1263-1273, April.
    11. Matteo Richiardi & Ambra Poggi, 2014. "Imputing Individual Effects in Dynamic Microsimulation Models. An application to household formation and labour market participation in Italy," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 3-39.
    12. Peter H. Friesen & Danny Miller, 1983. "Annual Inequality and Lifetime Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 98(1), pages 139-155.
    13. Thomas Lemieux, 2006. "Increasing Residual Wage Inequality: Composition Effects, Noisy Data, or Rising Demand for Skill?," American Economic Review, American Economic Association, vol. 96(3), pages 461-498, June.
    14. Feng, Shuaizhang & Burkhauser, Richard V. & Butler, J.S., 2006. "Levels and Long-Term Trends in Earnings Inequality: Overcoming Current Population Survey Censoring Problems Using the GB2 Distribution," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 57-62, January.
    15. Shane T. Jensen & Stephen H. Shore, 2015. "Changes in the Distribution of Earnings Volatility," Journal of Human Resources, University of Wisconsin Press, vol. 50(3), pages 811-836.
    16. NICHOLS Austin, 2008. "Trends in Income Inequality, Volatility and Mobility Risk," IRISS Working Paper Series 2008-10, IRISS at CEPS/INSTEAD.
    17. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2008. "Trends in U.S. Wage Inequality: Revising the Revisionists," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 300-323, May.
    18. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    19. Mary C. Daly & Robert G. Valletta, 2006. "Inequality and Poverty in United States: The Effects of Rising Dispersion of Men's Earnings and Changing Family Behaviour," Economica, London School of Economics and Political Science, vol. 73(289), pages 75-98, February.
    20. Richard V. Burkhauser & Shuaizhang Feng & Stephen P. Jenkins & Jeff Larrimore, 2012. "Recent Trends in Top Income Shares in the United States: Reconciling Estimates from March CPS and IRS Tax Return Data," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 371-388, May.
    21. Jiaying Gu & Roger Koenker, 2017. "Unobserved Heterogeneity in Income Dynamics: An Empirical Bayes Perspective," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 1-16, January.
    22. Robert F. Bordley & James B. McDonald & Anand Mantrala, 1997. "Something New, Something Old: Parametric Models for the Size of Distribution of Income," Journal of Income Distribution, Ad libros publications inc., vol. 6(1), pages 5-5, June.
    23. Molly Dahl & Thomas DeLeire & Jonathan A. Schwabish, 2011. "Estimates of Year-to-Year Volatility in Earnings and in Household Incomes from Administrative, Survey, and Matched Data," Journal of Human Resources, University of Wisconsin Press, vol. 46(4), pages 750-774.
    24. Stephen P. Jenkins, 2009. "Distributionally‐Sensitive Inequality Indices And The Gb2 Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(2), pages 392-398, June.
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