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Discrete Choice Labor Supply: Conditional Logit vs. Random Coefficient Models

  • Peter Haan

Estimating labor supply functions using a discrete rather than a continuous specification has become increasingly popular in recent years. On basis of the German Socioeconomic Panel (GSOEP) I test which specification of discrete choice is the appropriate model for estimating labor supply: the standard conditional logit model or the random coefficient model. To the extent that effect heterogeneity is present in empirical models of labor supply functions, the application of a random coefficient model is necessary to avoid biased estimates. However, because of the complex structure, random coefficient models defy calculating confidence intervals of marginal effects or elasticities. Therefore, if heterogeneity is nonexistent or does not lead to a significant bias in the derived labor supply elasticities, standard discrete choice models provide the more favorable choice. Due to their simple structure, conditional logit models are far less computational intensive providing standard tools to calculate confidence intervals of elasticities. My findings suggest that effect heterogeneity is present when estimating a discrete choice model of labor supply drawing on data of the GSOEP. However, the labor supply elastisities derived form the specifications with and without random effects do not differ significantly. That leads to the conclusion that the standard discrete choice model, attractive for its simple structure, provides an adequate model choice for the analysis of labor supply functions based on the GSOEP.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.41156.de/dp394.pdf
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Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 394.

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Length: 19 p.
Date of creation: 2004
Date of revision:
Handle: RePEc:diw:diwwpp:dp394
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  1. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
  2. Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2.
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  8. Gerfin, Michael & Leu, Robert E., 2003. "The Impact of In-Work Benefits on Poverty and Household Labour Supply: A Simulation Study for Switzerland," IZA Discussion Papers 762, Institute for the Study of Labor (IZA).
  9. Laisney, François & Beninger, Denis & Beblo, Miriam, 2003. "Family Tax Splitting: A Microsimulation of its Potential Labour Supply and Intra-household Welfare Effects in Germany," ZEW Discussion Papers 03-32, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  10. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
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