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Active labor market policy effects in a dynamic setting

Author

Listed:
  • Crépon, Bruno

    (CREST-INSEE)

  • Ferracci, Marc

    (University of Marne-la-Vallée)

  • Jolivet, Grégory

    (University of Bristol)

  • van den Berg, Gerard J.

    () (IFAU - Institute for Labour Market Policy Evaluation)

Abstract

This paper implements a method to identify and estimate treatment effects in a dynamic setting where treatments may occur at any point in time. By relating the standard matching approach to the timing-of-events approach, it demonstrates that effects of the treatment on the treated at a given date can be identified even though non-treated may be treated later in time. The approach builds on a "no anticipation" assumption and the assumption of conditional independence between the duration until treatment and the counterfactual durations until exit. To illustrate the approach, the paper studies the effect of training for unemployed workers in France, using a rich register data set. Training has little impact on unemployment duration. The contamination of the standard matching estimator due to later entries into treatment is large if the treatment probability is high.

Suggested Citation

  • Crépon, Bruno & Ferracci, Marc & Jolivet, Grégory & van den Berg, Gerard J., 2009. "Active labor market policy effects in a dynamic setting," Working Paper Series 2009:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  • Handle: RePEc:hhs:ifauwp:2009_001
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    References listed on IDEAS

    as
    1. 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.
    2. Fredriksson, Peter & Johansson, Per, 2008. "Dynamic Treatment Assignment," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 435-445.
    3. 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.
    4. Fredriksson, Peter & Johansson, Per, 2004. "Dynamic Treatment Assignment – The Consequences for Evaluations Using Observational Data," IZA Discussion Papers 1062, Institute of Labor Economics (IZA).
    5. 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.
    6. 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.
    7. 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.
    8. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Treatment; program participation; unemployment duration; training; propensity score; matching; contamination bias;

    JEL classification:

    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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