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Labor econometrics

In: Handbook of Econometrics

Author

Listed:
  • Heckman, James J.
  • Macurdy, Thomas E.

Abstract

This paper presents and extends the index function model of Karl Pearson (1901) that underlies all recent models in labor econometrics. In this framework, censored, truncated and discrete random variables are interpreted as the manifestation of various sampling schemes for underlying index function models. A unified derivation of the densities and regression representations for index function models is presented. Methods of estimation are discussed with an emphasis on regression and instrumental variable procedures.We demonstrate how a variety of substantive models in labor economics can be given an econometric representation within the index function framework. Models for the analysis of unemployment, labor force participation, job turnover, the impact of interventions on earnings (and other outcomes) and hours of work are formulated as special cases of the general index function model. By casting these diverse models in a common mold we demonstrate the essential commonalities in the econometric approach required for their formulation and estimation.

Suggested Citation

  • Heckman, James J. & Macurdy, Thomas E., 1986. "Labor econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 32, pages 1917-1977, Elsevier.
    • Handle: RePEc:eee:ecochp:3-32
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    Citations

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    Cited by:

    1. Johannessen, Karl-Arne & Hagen, Terje P., 2012. "Variations in labor supply between female and male hospital physicians: Results from a modern welfare state," Health Policy, Elsevier, vol. 107(1), pages 74-82.
    2. Mroz, Thomas A., 1999. "Discrete factor approximations in simultaneous equation models: Estimating the impact of a dummy endogenous variable on a continuous outcome," Journal of Econometrics, Elsevier, vol. 92(2), pages 233-274, October.
    3. Natale, Fabrizio & Borrello, Alessandra & Motova, Arina, 2015. "Analysis of the determinants of international seafood trade using a gravity model," Marine Policy, Elsevier, vol. 60(C), pages 98-106.
    4. Zaal, Fred & Oostendorp, Remco H., 2002. "Explaining a Miracle: Intensification and the Transition Towards Sustainable Small-scale Agriculture in Dryland Machakos and Kitui Districts, Kenya," World Development, Elsevier, vol. 30(7), pages 1271-1287, July.
    5. Toomet, Ott & Henningsen, Arne, 2008. "Sample Selection Models in R: Package sampleSelection," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i07).
    6. Chen, Songnian, 1999. "Distribution-free estimation of the random coefficient dummy endogenous variable model," Journal of Econometrics, Elsevier, vol. 91(1), pages 171-199, July.
    7. Wang, Liqun, 1998. "Estimation of censored linear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 84(2), pages 383-400, June.
    8. Keane, Michael, 2010. "The Tax-Transfer System and Labour Supply," MPRA Paper 55167, University Library of Munich, Germany.
    9. Andrew Cohen & Michael Mazzeo, 2010. "Investment Strategies and Market Structure: An Empirical Analysis of Bank Branching Decisions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 38(1), pages 1-21, August.
    10. Fernando Coloma & Bernardita Vial, 2003. "Desempleo e Inactividad Juvenil en Chile," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 40(119), pages 149-171.

    More about this item

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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