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A Large-Scale Validation Study of Measurement Errors in Longitudinal Survey Data

Author

Listed:
  • Kristensen, Nicolai

    (VIVE - The Danish Centre for Applied Social Science)

  • Westergård-Nielsen, Niels C.

    (Copenhagen Business School)

Abstract

In this paper, we analyze measurement and classification errors in several key variables, including earnings and educational attainment, in a matched sample of survey and administrative longitudinal data. The data, spanning 1994-2001 and covering all sectors in the Danish economy, are much more comprehensive than usually seen in validation studies. Measurement errors in earnings are found to be much larger than reported in previous studies limited to one single firm. Individuals who attrite from the panel report their earnings significantly less accurate than individuals who are observed throughout the entire sampling period. Furthermore, females are found to report their earnings significantly more precise than males, part-time workers report significantly less accurate than full-time workers and low-income workers report significantly less accurate than workers with relatively higher income. Classification errors in categorical variables are found to be of about the same magnitude as previously found in the literature. We analyze whether response error in one variable makes it more likely that the same respondent will report other variables with error but do not find support for this hypothesis.

Suggested Citation

  • Kristensen, Nicolai & Westergård-Nielsen, Niels C., 2006. "A Large-Scale Validation Study of Measurement Errors in Longitudinal Survey Data," IZA Discussion Papers 2329, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2329
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    References listed on IDEAS

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    1. Aigner, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," Journal of Econometrics, Elsevier, vol. 1(1), pages 49-59, March.
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    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.
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    7. 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.
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    9. AIGNER, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," LIDAM Reprints CORE 130, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Erich Battistin & Barbara Sianesi, 2006. "Misreported schooling and returns to education: evidence from the UK," CeMMAP working papers CWP07/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. 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. Jungmin Lee & Sokbae Lee, 2012. "Does it Matter WHO Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," ILR Review, Cornell University, ILR School, vol. 65(1), pages 148-160, January.

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    More about this item

    Keywords

    validation; measurement error; classification error;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • I2 - Health, Education, and Welfare - - Education
    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy

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