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Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data

Author

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  • Dean Hyslop

    (Motu Economic and Public Policy Research)

  • Wilbur Townsend

    (Motu Economic and Public Policy Research)

Abstract

This paper analyses the measurement error and earnings dynamics of two sources of individuals' annual earnings from Statistics New Zealand's Survey of Family, Income and Employment (SoFIE) and administrative linked employer-employee data (LEED) earnings reported in the Integrated Database Infrastructure (IDI). First, SoFIE reported earnings are 2-4% lower than LEED earnings on average, and slightly more variable; while the difference between the two reported earnings accounts for 25-30% of the variance in either report. Second, we reject the joint hypothesis that SoFIE earnings are reported with classical measurement error and LEED earnings are recorded without error. We estimate that the statistical reliability of LEED measured earnings (0.87{0.91) is higher than that of SoFIE earnings (0.83{0.85). Third, the differences between SoFIE and LEED earnings are negatively correlated with both individuals' average (LEED) earnings over the sample period and their annual transitory deviations. These differences can be characterised longitudinally by both persistent and serially correlated transitory factors. Fourth, we formulate and estimate a model for SoFIE and LEED earnings, which includes dynamics for true earnings and for measurement errors in both SoFIE and LEED. Female earnings are more variable than males', due both to permanent and transitory effects, and transitory shocks are relatively stronger for women. Allowing for measurement error in LEED, we find no evidence of mean-reverting error in SoFIE. Fifth, the models imply measurement errors dominate the observed changes in male earnings, and account for large fractions of the changes in female earnings.

Suggested Citation

  • Dean Hyslop & Wilbur Townsend, 2016. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data," Working Papers 16_18, Motu Economic and Public Policy Research.
    • Handle: RePEc:mtu:wpaper:16_18
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    1. Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-366, July.
    2. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318, Elsevier.
    3. 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.
    4. Dean R. Hyslop, 2001. "Rising U.S. Earnings Inequality and Family Labor Supply: The Covariance Structure of Intrafamily Earnings," American Economic Review, American Economic Association, vol. 91(4), pages 755-777, September.
    5. 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.
    6. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    7. Costas Meghir & Luigi Pistaferri, 2004. "Income Variance Dynamics and Heterogeneity," Econometrica, Econometric Society, vol. 72(1), pages 1-32, January.
    8. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    9. 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.
    10. Abowd, John M & Card, David, 1989. "On the Covariance Structure of Earnings and Hours Changes," Econometrica, Econometric Society, vol. 57(2), pages 411-445, March.
    11. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    12. 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.
    13. Lillard, Lee A & Weiss, Yoram, 1979. "Components of Variation in Panel Earnings Data: American Scientists, 1960-70," Econometrica, Econometric Society, vol. 47(2), pages 437-454, March.
    14. 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.
    15. Hause, John C, 1980. "The Fine Structure of Earnings and the On-the-Job Training Hypothesis," Econometrica, Econometric Society, vol. 48(4), pages 1013-1029, May.
    16. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
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    Cited by:

    1. Stephen P. Jenkins & Fernando Rios-Avila, 2023. "Finite mixture models for linked survey and administrative data: Estimation and postestimation," Stata Journal, StataCorp LP, vol. 23(1), pages 53-85, March.
    2. Hyslop, Dean R. & Townsend, Wilbur, 2017. "Employment misclassification in survey and administrative reports," Economics Letters, Elsevier, vol. 155(C), pages 19-23.
    3. Jenkins, Stephen P. & Rios-Avila, Fernando, 2020. "Modelling errors in survey and administrative data on employment earnings: Sensitivity to the fraction assumed to have error-free earnings," Economics Letters, Elsevier, vol. 192(C).
    4. Seonyoung Park & Donggyun Shin, 2019. "Inflation And Wage Rigidity/Flexibility In The Short Run," Economic Inquiry, Western Economic Association International, vol. 57(3), pages 1675-1697, July.
    5. Emmanuel Flachaire & Nora Lustig & Andrea Vigorito, 2023. "Underreporting of Top Incomes and Inequality: A Comparison of Correction Methods using Simulations and Linked Survey and Tax Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(4), pages 1033-1059, December.
    6. Madeira, Carlos & Margaretic, Paula, 2022. "The impact of financial literacy on the quality of self-reported financial information," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    7. Jenkins, Stephen P. & Rios-Avila, Fernando, 2021. "Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data," IZA Discussion Papers 14405, Institute of Labor Economics (IZA).
    8. Okamura, Kazuaki & Islam, Nizamul, 2021. "Multinomial employment dynamics with state dependence and heterogeneity: Evidence from Japan," Economic Modelling, Elsevier, vol. 101(C).
    9. Dean Hyslop & Wilbur Townsend, 2017. "The longer term impacts of job displacement on labour market outcomes," Working Papers 17_12, Motu Economic and Public Policy Research.
    10. Seonyoung Park & Donggyun Shin, 2019. "Inflation And Wage Rigidity/Flexibility In The Short Run," Economic Inquiry, Western Economic Association International, vol. 57(3), pages 1675-1697, July.

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    More about this item

    Keywords

    Panel data; earnings dynamics; measurement error; validation study;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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