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Evaluation of sequences of treatments with application to active labor market policies

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  • Vikström, Johan

    (IFAU - Institute for Evaluation of Labour Market and Education Policy)

Abstract

This paper proposes a new framework for analyzing the effects of sequences of treatments with duration outcomes. Applications include sequences of active labor market policies assigned at specific unemployment durations and sequences of medical treatments. We consider evaluation under unconfoundedness and propose conditions under which the survival time under a specific treatment regime can be identified. We introduce inverse probability weighting estimators for various average effects. The finite sample properties of the estimators are investigated in a simulation study. The new estimator is applied to Swedish data on participants in training, in a work practice program and in subsidized employment. One result is that enrolling an unemployed person twice in the same program or in two different programs one after the other leads to longer unemployment spells compared to only participating in a single program once.

Suggested Citation

  • Vikström, Johan, 2015. "Evaluation of sequences of treatments with application to active labor market policies," Working Paper Series 2015:5, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    • Handle: RePEc:hhs:ifauwp:2015_005
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    References listed on IDEAS

    as
    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Martin Biewen & Bernd Fitzenberger & Aderonke Osikominu & Marie Paul, 2014. "The Effectiveness of Public-Sponsored Training Revisited: The Importance of Data and Methodological Choices," Journal of Labor Economics, University of Chicago Press, vol. 32(4), pages 837-897.
    3. Carling, Kenneth & Richardson, Katarina, 2001. "The relative efficiency of labor market programs: Swedish experience from the 1990's," Working Paper Series 2001:2, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    4. 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.
    5. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    6. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460, Elsevier.
    7. Lechner, Michael, 2009. "Sequential Causal Models for the Evaluation of Labor Market Programs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 71-83.
    8. James J. Heckman & Jeffrey A. Smith, 1999. "The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies," NBER Working Papers 6983, National Bureau of Economic Research, Inc.
    9. Heckman, James J & Smith, Jeffrey A, 1999. "The Pre-programme Earnings Dip and the Determinants of Participation in a Social Programme. Implications for Simple Programme Evaluation Strategies," Economic Journal, Royal Economic Society, vol. 109(457), pages 313-348, July.
    10. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1.
    11. Hernan M. A & Brumback B. & Robins J. M, 2001. "Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 440-448, June.
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    More about this item

    Keywords

    Treatment effects; dynamic treatment assignment; dynamic selection; program evaluation; work practice; training; subsidized employment;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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