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Work Experience as a Source of Specification Error in Earnings Models: Implications for Gender Wage Decompositions

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  • Regan, Tracy L.

    (Boston College)

  • Oaxaca, Ronald L.

    (University of Arizona)

Abstract

We address the bias from using potential vs. actual experience in earnings models. Statistical tests reject the classical errors-in-variable framework. The nature of the measurement error is best viewed as a model misspecification problem. We correct for this by modeling actual experience as a stochastic regressor and predicting experience using the NLSY79 and the PSID. Predicted experience measures are applied to the IPUMS. Our results suggest that potential experience biases the effects of schooling and the rates of return to labor market experience. Using such a measure in earnings models underestimates the explained portion of the male-female wage gap.

Suggested Citation

  • Regan, Tracy L. & Oaxaca, Ronald L., 2006. "Work Experience as a Source of Specification Error in Earnings Models: Implications for Gender Wage Decompositions," IZA Discussion Papers 1920, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp1920
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    1. Heather Antecol & Kelly Bedard, 2002. "The Relative Earnings of Young Mexican, Black, and White Women," ILR Review, Cornell University, ILR School, vol. 56(1), pages 122-135, October.
    2. Mincer, Jacob & Polachek, Solomon, 1974. "Family Investment in Human Capital: Earnings of Women," Journal of Political Economy, University of Chicago Press, vol. 82(2), pages 76-108, Part II, .
    3. Heather Antecol & Kelly Bedard, 2004. "The Racial Wage Gap: The Importance of Labor Force Attachment Differences across Black, Mexican, and White Men," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
    4. Bound, John & Solon, Gary, 1999. "Double trouble: on the value of twins-based estimation of the return to schooling," Economics of Education Review, Elsevier, vol. 18(2), pages 169-182, April.
    5. Ronald Oaxaca & Michael Ransom, 2003. "Using Econometric Models for Intrafirm Equity Salary Adjustments," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 1(3), pages 221-249, December.
    6. Murphy, Kevin M & Welch, Finis, 1990. "Empirical Age-Earnings Profiles," Journal of Labor Economics, University of Chicago Press, vol. 8(2), pages 202-229, April.
    7. Blau, Francine D & Kahn, Lawrence M, 1996. "Wage Structure and Gender Earnings Differentials: An International Comparison," Economica, London School of Economics and Political Science, vol. 63(250), pages 29-62, Suppl..
    8. Ronald L. Oaxaca & Michael R. Ransom, 1999. "Identification in Detailed Wage Decompositions," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 154-157, February.
    9. Tracy L. Regan & Ronald L. Oaxaca & Galen Burghardt, 2007. "A Human Capital Model Of The Effects Of Ability And Family Background On Optimal Schooling Levels," Economic Inquiry, Western Economic Association International, vol. 45(4), pages 721-738, October.
    10. Behrman, Jere R. & Rosenzweig, Mark R., 1999. ""Ability" biases in schooling returns and twins: a test and new estimates," Economics of Education Review, Elsevier, vol. 18(2), pages 159-167, April.
    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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    More about this item

    Keywords

    decomposition; specification error; experience; gender;
    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
    • 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

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