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Measurement error and its impact on estimates of income dynamics

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  • Nayoung Lee

    (University of Cincinnati)

Abstract

This paper examines whether reported income generates biases for studies on economic mobility and poverty dynamics. Using a linear measurement error model capturing mean-reverting measurement error, this study finds that substantial classical measurement error exists in reported data, leading to a bias toward zero in the estimate of income dynamics. Time-invariant non-classical measurement error and unobserved heterogeneity offset the effect of classical measurement error. This study also identifies the standard deviation of the measurement error, which is estimated to be about 70% of that of the equation error in the income model, suggesting that random measurement error is substantial.

Suggested Citation

  • Nayoung Lee, 2022. "Measurement error and its impact on estimates of income dynamics," Empirical Economics, Springer, vol. 63(5), pages 2539-2550, November.
    • Handle: RePEc:spr:empeco:v:63:y:2022:i:5:d:10.1007_s00181-022-02218-4
    DOI: 10.1007/s00181-022-02218-4
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    1. Lillard, Lee A & Willis, Robert J, 1978. "Dynamic Aspects of Earning Mobility," Econometrica, Econometric Society, vol. 46(5), pages 985-1012, September.
    2. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    3. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    4. Lisa Daniels, 2001. "Testing alternative measures of microenterprise profits and net worth," Journal of International Development, John Wiley & Sons, Ltd., vol. 13(5), pages 599-614.
    5. Christopher R. Bollinger & Amitabh Chandra, 2005. "Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data," Journal of Labor Economics, University of Chicago Press, vol. 23(2), pages 235-258, April.
    6. Meghir, Costas & Pistaferri, Luigi, 2011. "Earnings, Consumption and Life Cycle Choices," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 9, pages 773-854, Elsevier.
    7. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Costas Meghir & Luigi Pistaferri, 2004. "Income Variance Dynamics and Heterogeneity," Econometrica, Econometric Society, vol. 72(1), pages 1-32, January.
    9. Tullio Jappelli & Luigi Pistaferri, 2010. "The Consumption Response to Income Changes," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 479-506, September.
    10. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    11. Bonggeun Kim & Gary Solon, 2005. "Implications of Mean-Reverting Measurement Error for Longitudinal Studies of Wages and Employment," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 193-196, February.
    12. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
    13. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    14. Stephen R. Bond, 2002. "Dynamic panel data models: a guide to micro data methods and practice," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 141-162, August.
    15. Francisca Antman & David J. McKenzie, 2007. "Earnings Mobility and Measurement Error: A Pseudo-Panel Approach," Economic Development and Cultural Change, University of Chicago Press, vol. 56(1), pages 125-161, October.
    16. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
    17. Neil McCulloch & Bob Baulch, 2000. "Simulating the impact of policy upon chronic and transitory poverty in rural Pakistan," Journal of Development Studies, Taylor & Francis Journals, vol. 36(6), pages 100-130.
    18. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    19. Paul Glewwe & Hai-Anh Hoang Dang, 2011. "Was Vietnam's Economic Growth in the 1990s Pro-Poor? An Analysis of Panel Data from Vietnam," Economic Development and Cultural Change, University of Chicago Press, vol. 59(3), pages 583-608.
    20. Lokshin Michael & Ravallion Martin, 2004. "Household Income Dynamics in Two Transition Economies," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(3), pages 1-33, September.
    21. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    22. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
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    More about this item

    Keywords

    Measurement error; Non-classical measurement error; Economic mobility; Income dynamics;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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