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On hourly wages and weekly earnings in the current population survey

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  • Liu, Long

Abstract

This paper examines hourly wages and weekly earnings in the Outgoing Rotation Group (ORG) and the March Supplement of Current Population Survey (CPS) from 1998 to 2004. The findings suggest that the ORG contains less errors than the March CPS, and that weekly earnings contain less errors than hourly wages. The paper further finds that earnings differ systematically in the ORG and in the March CPS by gender and education levels.

Suggested Citation

  • Liu, Long, 2009. "On hourly wages and weekly earnings in the current population survey," Economics Letters, Elsevier, vol. 105(1), pages 113-116, October.
    • Handle: RePEc:eee:ecolet:v:105:y:2009:i:1:p:113-116
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    References listed on IDEAS

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    1. 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.
    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. 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.
    4. Dan A. Black & Amelia M. Haviland & Seth G. Sanders & Lowell J. Taylor, 2008. "Gender Wage Disparities among the Highly Educated," Journal of Human Resources, University of Wisconsin Press, vol. 43(3), pages 630-659.
    5. 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.
    6. 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.
    7. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    8. 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.
    9. 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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