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Measurement error in longitudinal earnings data: evidence from Germany

Author

Listed:
  • Schmillen, Achim

    (Weltbank, Washington)

  • Umkehrer, Matthias

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Wachter, Till von

    (University of California, Los Angeles; National Bureau of Economic Research, Cambridge, USA; Centre for Economic Policy Research, London; IZA - Institute of Labor Economics, Bonn)

Abstract

"We present evidence on the extent of measurement error in German longitudinal earnings data. Qualitatively, we confirm the main result of the international literature: longitudinal earnings data are relatively reliable in a cross section but much less so in first differences. Quantitatively, in the cross section our fndings are very similar to those of Bound and Krueger (J Labor Econ 9:1–24, 1991) and Pischke (J Bus Econ Stat 13:305–314, 1995) for the United States while we find even stronger evidence that frst-differencing exacerbates measurement error problems. We also show that measurement error in our survey data is not“classical” as it is negatively correlated with administrative earnings and positively autocorrelated over an extended period of time. Additionally, we estimate a model of measurement error stemming from underreporting of transitory earnings shocks in combination with a white-noise component and make a number of methodological contributions. Our results are robust to the use of two different linked Survey administrative data sets and various other sensitivity checks." (Author's abstract, IAB-Doku, © Springer) ((en))

Suggested Citation

  • Schmillen, Achim & Umkehrer, Matthias & Wachter, Till von, 2024. "Measurement error in longitudinal earnings data: evidence from Germany," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 58, pages 1-8.
    • Handle: RePEc:iab:iabjlr:v:58:p:art.08
    DOI: 10.1186/s12651-024-00366-x
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    References listed on IDEAS

    as
    1. David Card & Jörg Heining & Patrick Kline, 2013. "Workplace Heterogeneity and the Rise of West German Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(3), pages 967-1015.
    2. 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).
    3. repec:iab:iabfme:200502(en is not listed on IDEAS
    4. 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.
    5. Martha Stinson, 2002. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Survey and SSA Administrative Data," Longitudinal Employer-Household Dynamics Technical Papers 2002-24, Center for Economic Studies, U.S. Census Bureau.
    6. Stüber, Heiko & Grabka, Markus M. & Schnitzlein, Daniel D., 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-8.
    7. 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.
    8. Dean R. Hyslop & Wilbur Townsend, 2020. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 457-469, April.
    9. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    10. Marco Caliendo & Alexandra Fedorets & Malte Preuss & Carsten Schröder & Linda Wittbrodt, 2023. "The short- and medium-term distributional effects of the German minimum wage reform," Empirical Economics, Springer, vol. 64(3), pages 1149-1175, March.
    11. Peter Valet & Jule Adriaans & Stefan Liebig, 2019. "Comparing survey data and administrative records on gross earnings: nonreporting, misreporting, interviewer presence and earnings inequality," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 471-491, January.
    12. 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.
    13. 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.
    14. Mellow, Wesley & Sider, Hal, 1983. "Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 331-344, October.
    15. Henrike Junge, 2017. "From Gross to Net Wages in German Administrative Data Sets," Data Documentation 89, DIW Berlin, German Institute for Economic Research.
    16. Mark Trappmann & Jonas Beste & Arne Bethmann & Gerrit Müller, 2013. "The PASS panel survey after six waves [Die PASS-Panelbefragung nach sechs Wellen]," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 46(4), pages 275-281, December.
    17. 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.
    18. Christian Dustmann & Attila Lindner & Uta Schönberg & Matthias Umkehrer & Philipp vom Berge, 2022. "Reallocation Effects of the Minimum Wage," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 267-328.
    19. D. L. Oberski & A. Kirchner & S. Eckman & F. Kreuter, 2017. "Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1477-1489, October.
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    Cited by:

    1. Marco Caliendo & Katrin Huber & Ingo E. Isphording & Jakob Wegmann, 2024. "On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages," Papers 2411.04751, arXiv.org.
    2. Nico Thurow, 2025. "Characterizing Measurement Error in the German Socio-Economic Panel Using Linked Survey and Administrative Data," Papers 2501.03015, arXiv.org.

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    More about this item

    Keywords

    Bundesrepublik Deutschland ; IAB-Open-Access-Publikation ; Befragung ; IAB-Weiterbildungspanel ; Datenqualität ; Messfehler ; Einkommensentwicklung ; Haushaltseinkommen ; Längsschnittuntersuchung ; Panel ; Antwortverhalten ; private Haushalte ; Reliabilität ; PASS-ADIAB ; 2006-2010;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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