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How Fast Do Wages Adjust to Human-Capital Productivity? Dynamic Panel-Data Evidence from Belgium, Denmark and Finland

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Listed author(s):
  • Andini, Corrado

    ()

    (University of Madeira)

Abstract

The standard human-capital model is based on the assumption that the observed wage of an individual is equal to the monetary value of the individual net human-capital productivity, the so-called net potential wage. We argue that this assumption is rejected by the ECHP data for Belgium, Denmark and Finland. The empirical evidence supports a dynamic approach to the Mincer equation where no equality is imposed but an adjustment between observed and potential earnings is allowed to take place over time. Controlling for regressors' endogeneity, individual heterogeneity and time effects, we estimate a dynamic panel-data wage equation and provide measures of the speed of adjustment in Belgium, Denmark and Finland. Further, we elaborate on the implications of a dynamic approach to the Mincer equation for the computation of the return to schooling, including the implication that this return is not independent of labor-market experience, as suggested by Heckman et al. (2005) and Belzil (2007). Finally, we show that a dynamic wage equation can be seen as the solution of a simple wage-bargaining model and argue that a micro-founded model can fit the data better than a simple adjustment model but requires more theoretical assumptions.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4583.

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Length: 21 pages
Date of creation: Nov 2009
Handle: RePEc:iza:izadps:dp4583
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Keywords: human capital; Mincer equation; wages;

Find related papers by JEL classification:

  • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
  • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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  1. Mikael Lindahl & Alan B. Krueger, 2001. "Education for Growth: Why and for Whom?," Journal of Economic Literature, American Economic Association, vol. 39(4), pages 1101-1136, December.
  2. 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.
  3. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters,in: Schooling, Experience, and Earnings, pages 1-4 National Bureau of Economic Research, Inc.
  4. Martins, Pedro S. & Pereira, Pedro T., 2004. "Does education reduce wage inequality? Quantile regression evidence from 16 countries," Labour Economics, Elsevier, vol. 11(3), pages 355-371, June.
  5. Flannery, Mark J. & Rangan, Kasturi P., 2006. "Partial adjustment toward target capital structures," Journal of Financial Economics, Elsevier, vol. 79(3), pages 469-506, March.
  6. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
  7. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, Enero-Jun.
  8. Corrado Andini, 2007. "Returns to education and wage equations: a dynamic approach," Applied Economics Letters, Taylor & Francis Journals, vol. 14(8), pages 577-579.
  9. Helen Connolly & Peter Gottschalk, 2000. "Differences in Wage Growth by Education Level: Do Less Educated Workers Gain Less from Work Experience?," Boston College Working Papers in Economics 473, Boston College Department of Economics, revised 26 Aug 2006.
  10. Corrado Andini, 2009. "Wage Bargaining and the (Dynamic) Mincer Equation," Economics Bulletin, AccessEcon, vol. 29(3), pages 1842-1849.
  11. Corrado Andini, 2010. "A dynamic Mincer equation with an application to Portuguese data," Applied Economics, Taylor & Francis Journals, vol. 42(16), pages 2091-2098.
  12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  13. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
  14. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
  15. Philip A. Trostel, 2005. "Nonlinearity in the return to education," Journal of Applied Economics, Universidad del CEMA, vol. 8, pages 191-202, May.
  16. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
  17. Corrado Andini, 2007. "The total impact of schooling on within-groups wage inequality in Portugal," Applied Economics Letters, Taylor & Francis Journals, vol. 15(2), pages 85-90.
  18. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
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Cited by:

  1. Kripfganz, Sebastian & Schwarz, Claudia, 2013. "Estimation of Linear Dynamic Panel Data Models with Time-Invariant Regressors," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79756, Verein für Socialpolitik / German Economic Association.
  2. Corrado Andini, 2013. "How well does a dynamic Mincer equation fit NLSY data? Evidence based on a simple wage-bargaining model," Empirical Economics, Springer, vol. 44(3), pages 1519-1543, June.

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