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Active Labor Market Policy Effects in a Dynamic Setting

Author

Listed:
  • Bruno Crépon

    (Crest)

  • Marc Ferracci

    (Crest)

  • Grégory Jolivet

    (Crest)

  • Gerard J. van den Berg

    (Crest)

Abstract

This paper develops and implements a method to identify and estimate treatmenteffects in a dynamic setting where treatments may occur at any point in time. Bycombining the standard matching approach to the timing-of-events approach, itdemonstrates that the effect of the treatment on the treated at a given date can beidentified although non-treated may be treated later in time. The approach builds on a"no anticipation" assumption and the assumption of conditional independence betweenthe duration before treatment and the duration before exit. To illustrate the approach,the paper studies the effect of training for unemployed workers in France, using a richregister data set. Training has little impact on unemployment duration. Thecontamination of the standard matching estimator due to later entries into treatment islarge if the treatment probability is high.

Suggested Citation

  • Bruno Crépon & Marc Ferracci & Grégory Jolivet & Gerard J. van den Berg, 2008. "Active Labor Market Policy Effects in a Dynamic Setting," Working Papers 2008-25, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2008-25
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    References listed on IDEAS

    as
    1. Fredriksson, Peter & Johansson, Per, 2004. "Dynamic Treatment Assignment – The Consequences for Evaluations Using Observational Data," IZA Discussion Papers 1062, Institute of Labor Economics (IZA).
    2. Stéphane Carcillo & David Grubb, 2006. "From Inactivity to Work: The Role of Active Labour Market Policies," OECD Social, Employment and Migration Working Papers 36, OECD Publishing.
    3. Bruno Crépon & Rozenn Desplatz, 2003. "The Effets of Payroll Tax Subsidies for Low Wage Workers on Firms Level Decisions," Working Papers 2003-06, Center for Research in Economics and Statistics.
    4. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
    5. Jaap H. Abbring & Gerard J. van den Berg, 2004. "Analyzing the effect of dynamically assigned treatments using duration models, binary treatment models, and panel data models," Empirical Economics, Springer, vol. 29(1), pages 5-20, January.
    6. Fredriksson, Peter & Johansson, Per, 2008. "Dynamic Treatment Assignment," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 435-445.
    7. de Luna, Xavier & Johansson, Per, 2007. "Matching estimators for the effect of a treatment on survival times," Working Paper Series 2007:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    8. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
    9. Richardson, Katarina & van den Berg, Gerard J, 2008. "Duration dependence versus unobserved heterogeneity in treatment effects: Swedish labor market training and the transition rate to employment," Working Paper Series 2008:7, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    10. Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
    11. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The Nonparametric Identification of Treatment Effects in Duration Models," Econometrica, Econometric Society, vol. 71(5), pages 1491-1517, September.
    12. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • H43 - Public Economics - - Publicly Provided Goods - - - Project Evaluation; Social Discount Rate
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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