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

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Author Info

  • Regan, Tracy L.

    ()
    (University of Miami)

  • 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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 1920.

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Length: 43 pages
Date of creation: Jan 2006
Date of revision:
Publication status: published in: Journal of Population Economics, 2009, 22 (2), 463 - 499
Handle: RePEc:iza:izadps:dp1920

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Keywords: decomposition; specification error; experience; gender;

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  1. Hartog,Joop & Maassen van den Brink,Henriƫtte (ed.), 2007. "Human Capital," Cambridge Books, Cambridge University Press, number 9780521873161, Fall.
  2. 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 S29-62, Suppl..
  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. Murphy, Kevin M & Welch, Finis, 1990. "Empirical Age-Earnings Profiles," Journal of Labor Economics, University of Chicago Press, vol. 8(2), pages 202-29, April.
  5. John Bound & Gary Solon, 1998. "Double Trouble: On the Value of Twins-Based Estimation of the Return to Schooling," NBER Working Papers 6721, National Bureau of Economic Research, Inc.
  6. Heather Antecol & Kelly Bedard, 2002. "The Relative earnings of young Mexican, black, and white women," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 56(1), pages 122-135, October.
  7. 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.
  8. Bound, John, et al, 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 345-68, July.
  9. Ronald Oaxaca & Michael Ransom, 2003. "Using Econometric Models for Intrafirm Equity Salary Adjustments," Journal of Economic Inequality, Springer, vol. 1(3), pages 221-249, December.
  10. Jacob Mincer & Solomon Polacheck, 1974. "Family Investments in Human Capital: Earnings of Women," NBER Chapters, in: Economics of the Family: Marriage, Children, and Human Capital, pages 397-431 National Bureau of Economic Research, Inc.
  11. 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 S76-S108, Part II, .
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Cited by:
  1. Drolet, Marie & Mumford, Karen A., 2009. "The Gender Pay Gap for Private Sector Employees in Canada and Britain," IZA Discussion Papers 3957, Institute for the Study of Labor (IZA).
  2. Almeida-Santos, Filipe & Chzhen, Yekaterina & Mumford, Karen A., 2010. "Employee Training and Wage Dispersion: White and Blue Collar Workers in Britain," IZA Discussion Papers 4821, Institute for the Study of Labor (IZA).
  3. Chzhen, Yekaterina & Mumford, Karen, 2011. "Gender gaps across the earnings distribution for full-time employees in Britain: Allowing for sample selection," Labour Economics, Elsevier, vol. 18(6), pages 837-844.

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