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A dynamic Mincer equation with an application to Portuguese data

Author

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  • Corrado Andini

Abstract

This article argues in favour of a dynamic specification of the Mincer equation, where the past observed earnings play the role of additional explanatory variable for current observed earnings. A dynamic approach offers an explanation why the return to schooling in terms of observed earnings is not independent of labour-market experience, as suggested by some recent empirical evidence for the United States.

Suggested Citation

  • Corrado Andini, 2010. "A dynamic Mincer equation with an application to Portuguese data," Applied Economics, Taylor & Francis Journals, vol. 42(16), pages 2091-2098.
  • Handle: RePEc:taf:applec:v:42:y:2010:i:16:p:2091-2098
    DOI: 10.1080/00036840701765429
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    References listed on IDEAS

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    1. Telhado Pereira, Pedro & Silva Martins, Pedro, 2002. "Is there a return-risk link in education?," Economics Letters, Elsevier, vol. 75(1), pages 31-37, March.
    2. Taylor, John B., 1999. "Staggered price and wage setting in macroeconomics," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 15, pages 1009-1050 Elsevier.
    3. 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.
    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. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
    6. Heckman, James J. & Lochner, Lance J. & Todd, Petra E., 2006. "Earnings Functions, Rates of Return and Treatment Effects: The Mincer Equation and Beyond," Handbook of the Economics of Education, Elsevier.
    7. Corrado Andini, 2007. "Returns to education and wage equations: a dynamic approach," Applied Economics Letters, Taylor & Francis Journals, vol. 14(8), pages 577-579.
    8. Santiago Budría & Pedro Telhado-Pereira, 2011. "Educational Qualifications And Wage Inequality: Evidence For Europe," Revista de Economia Aplicada, Universidad de Zaragoza, Departamento de Estructura Economica y Economia Publica, vol. 19(2), pages 5-34, Autumn.
    9. Card, David, 1999. "The causal effect of education on earnings," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 30, pages 1801-1863 Elsevier.
    10. James J. Heckman & Lance J. Lochner & Petra E. Todd, 2003. "Fifty Years of Mincer Earnings Regressions," NBER Working Papers 9732, National Bureau of Economic Research, Inc.
    11. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    12. Pedro Telhado Pereira & Pedro Silva Martins, 2004. "Returns to education and wage equations," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 525-531.
    13. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    14. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, vol. 45(1), pages 1-22, January.
    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. 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.
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    Citations

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    Cited by:

    1. Andini, Corrado, 2017. "Tertiary Education for All and Wage Inequality: Policy Insights from Quantile Regression," IZA Policy Papers 132, Institute for the Study of Labor (IZA).
    2. 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.
    3. Andini, Corrado, 2009. "How Fast Do Wages Adjust to Human-Capital Productivity? Dynamic Panel-Data Evidence from Belgium, Denmark and Finland," IZA Discussion Papers 4583, Institute for the Study of Labor (IZA).
    4. Andersson, Roland & Nabavi Larijani, Pardis & Wilhelmsson, Mats, 2013. "The impact of vocational education and training on income in Sweden," Working Paper Series 13/4, Royal Institute of Technology, Department of Real Estate and Construction Management & Centre for Banking and Finance (cefin).
    5. Andini, Corrado, 2014. "Persistence Bias and Schooling Returns," IZA Discussion Papers 8143, Institute for the Study of Labor (IZA).
    6. 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.
    7. Andini, Corrado, 2013. "Earnings persistence and schooling returns," Economics Letters, Elsevier, vol. 118(3), pages 482-484.
    8. repec:prg:jnlcfu:v:2017:y:2017:i:3:id:501:p:57-68 is not listed on IDEAS

    More about this item

    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
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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