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Dealing with negative marginal utilities in the discrete choice modelling of labour supply

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  • Islam, Nizamul
  • Liégeois, Philippe

Abstract

In discrete choice labour supply analysis, it is often reasonably expected that utility is increasing with income. Yet, analyses based on discrete choice models sometimes mention that, when no restriction is imposed a priori in the statistical optimization program, the monotonicity condition is not fully satisfied ex post. Obviously, the standard statistical optimization program might be completed with conditions (one per individual) imposing positive marginal utilities. Unfortunately, such a high-dimensional program most often appears to be rather time-consuming in order to be solved, if not practically unsolvable. In order to overcome this drawback, some authors impose general parametric restrictions a priori (hence reducing de facto the dimension of the parameter set), which is sufficient to lead to positive marginal utilities ex post. However, those restrictions might sometimes appear to be unnecessarily too severe and then generate a sub-optimal set of estimated values for the parameters of the utility function. Alternatively, we show that it may be easy to avoid unnecessary restrictions. The high-dimensional program including conditions for positive marginal utilities for all can sometimes be equivalently replaced by a one-dimensional one. At the end, no observation is hopefully showing negative marginal utility anymore at optimum.

Suggested Citation

  • Islam, Nizamul & Liégeois, Philippe, 2010. "Dealing with negative marginal utilities in the discrete choice modelling of labour supply," EUROMOD Working Papers EM6/10, EUROMOD at the Institute for Social and Economic Research.
  • Handle: RePEc:ese:emodwp:em6-10
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    References listed on IDEAS

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    1. 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.
    2. Richard Blundell & Alan Duncan & Julian McCrae & Costas Meghir, 2000. "The labour market impact of the working families’ tax credit," Fiscal Studies, Institute for Fiscal Studies, vol. 21(1), pages 75-103, March.
    3. Keane, Michael & Moffitt, Robert, 1998. "A Structural Model of Multiple Welfare Program Participation and Labor Supply," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 553-589, August.
    4. José Labeaga & Xisco Oliver & Amedeo Spadaro, 2008. "Discrete choice models of labour supply, behavioural microsimulation and the Spanish tax reforms," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(3), pages 247-273, September.
    5. Islam, Nizamul & Liégeois, Philippe & Berger, Frederic, 2010. "Discrete choice modelling of labour supply in Luxembourg through EUROMOD microsimulation," EUROMOD Working Papers EM5/10, EUROMOD at the Institute for Social and Economic Research.
    6. Frédéric Berger & Nizamul Islam & Philippe Liégeois, 2011. "Behavioural Microsimulation and Female Labour Supply in Luxembourg," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 54(4), pages 389-420.
    7. Arthur van Soest, 1995. "Structural Models of Family Labor Supply: A Discrete Choice Approach," Journal of Human Resources, University of Wisconsin Press, vol. 30(1), pages 63-88.
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    Cited by:

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    2. Islam, Nizamul & Flood, Lennart, 2015. "A Tax Benefit Model for Policy Evaluation in Luxembourg: LuxTaxBen," IZA Discussion Papers 9152, Institute of Labor Economics (IZA).

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    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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