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Bayesian Cross-Sectional Analysis of the Conditional Distribution of Earnings of Men in the United States, 1967-1996

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  • Geweke, John
  • Keane, Michael

Abstract

This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full conditional distribution. It applies these methods to the relationship of earnings of men in the United States to their age and education over the period 1967 through 1996. Principal findings include increasing returns to both education and experience over this period, rising variance of earnings conditional on age and education, a negatively skewed and leptokurtic conditional distribution of log earnings, and steadily increasing inequality with asymmetric and changing impacts on high- and low-wage earners. These results are insensitive to several alternative nonparametric specifications of the distribution of earnings conditional on age and education.

Suggested Citation

  • Geweke, John & Keane, Michael, 2005. "Bayesian Cross-Sectional Analysis of the Conditional Distribution of Earnings of Men in the United States, 1967-1996," MPRA Paper 54281, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:54281
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    References listed on IDEAS

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    1. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
    2. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    3. Geweke, John & Keane, Michael, 2000. "An empirical analysis of earnings dynamics among men in the PSID: 1968-1989," Journal of Econometrics, Elsevier, vol. 96(2), pages 293-356, June.
    4. 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.
    5. Gallant, A. Ronald, 1981. "On the bias in flexible functional forms and an essentially unbiased form : The fourier flexible form," Journal of Econometrics, Elsevier, vol. 15(2), pages 211-245, February.
    6. Barnett, William A. & Jonas, Andrew B., 1983. "The Muntz-Szatz demand system : An application of a globally well behaved series expansion," Economics Letters, Elsevier, vol. 11(4), pages 337-342.
    7. Jacob Mincer, 1958. "Investment in Human Capital and Personal Income Distribution," Journal of Political Economy, University of Chicago Press, vol. 66(4), pages 281-281.
    8. Wong, Chi-ming & Kohn, Robert, 1996. "A Bayesian approach to additive semiparametric regression," Journal of Econometrics, Elsevier, vol. 74(2), pages 209-235, October.
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    Cited by:

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    2. Huffman Wallace E. & Huffman Sonya K & Rickertsen Kyrre & Tegene Abebayehu, 2010. "Over-Nutrition and Changing Health Status in High Income Countries," Forum for Health Economics & Policy, De Gruyter, vol. 13(1), pages 1-44, June.
    3. Huffman, Wallace, 2009. "Technology and Innovation in World Agriculture: Prospects for 2010-2019," Staff General Research Papers Archive 13060, Iowa State University, Department of Economics.

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    More about this item

    Keywords

    Bayesian nonparametric inference; smoothness priors; Wiener process; mixture of normals; smoothly mixing regressions;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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