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Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

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  • Roman Horvath

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic)

  • Ali Elminejad

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic)

  • Tomas Havranek

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic & Czech National Bank, Prague, Czech Republic)

Abstract

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models and models of taxation. We show that, for the extensive margin elasticity, two biases conspire to systematically produce large positive estimates when the elasticity is in fact zero. Among 723 estimates in 36 studies, the mean reported elasticity is 0.5. One half of that number is due to publication bias: larger estimates are reported preferentially. The other half is due to identification bias: studies with less exogenous time variation in wages report larger elasticities. Net of the biases, the literature implies a zero mean elasticity and, with 95% confidence, is inconsistent with calibrations above 0.25. To derive these results we collect 23 variables that reflect the context in which the elasticity was obtained, use nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to address model uncertainty.

Suggested Citation

  • Roman Horvath & Ali Elminejad & Tomas Havranek, 2020. "Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply," Working Papers IES 2020/32, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2020.
  • Handle: RePEc:fau:wpaper:wp2020_32
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    More about this item

    Keywords

    Frisch elasticity; labor supply; extensive margin; meta-analysis; publication bias; Bayesian model averaging;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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