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Dynamic treatment assignment and evaluation of active labor market policies

Author

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  • Vikström, Johan

Abstract

This paper considers treatment evaluation in a discrete time setting in which treatment can start at any point in time. We consider evaluation under unconfoundedness and propose a dynamic inverse probability weighting estimator. A typical application is an active labor market program that can start after any elapsed unemployment duration. The identification and estimation results concern both cases with one single treatment as well as sequences of programs. The new estimator is applied to Swedish data on participants in a training program and a work practice program. The work practice program increases re-employment rates. Most sequences of the two programs are inefficient when compared to one single program episode.

Suggested Citation

  • Vikström, Johan, 2017. "Dynamic treatment assignment and evaluation of active labor market policies," Labour Economics, Elsevier, vol. 49(C), pages 42-54.
    • Handle: RePEc:eee:labeco:v:49:y:2017:i:c:p:42-54
    DOI: 10.1016/j.labeco.2017.09.003
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    Citations

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    Cited by:

    1. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    2. Marco Caliendo & Robert Mahlstedt & Gerard J. van den Berg & Johan Vikström, 2023. "Side effects of labor market policies," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(2), pages 339-375, April.
    3. Jonas Wood & Karel Neels, 2019. "Does Mothers’ Parental Leave Uptake Stimulate Continued Employment and Family Formation? Evidence for Belgium," Social Sciences, MDPI, vol. 8(10), pages 1-24, October.
    4. Lombardi, Stefano & van den Berg, Gerard J. & Vikström, Johan, 2020. "Empirical Monte Carlo evidence on estimation of Timing-of-Events models," Working Paper Series 2020:26, IFAU - Institute for Evaluation of Labour Market and Education Policy, revised 05 Jan 2021.
    5. Andrea Albanese & Bart Cockx & Yannick Thuy, 2020. "Working time reductions at the end of the career: Do they prolong the time spent in employment?," Empirical Economics, Springer, vol. 59(1), pages 99-141, July.
    6. Herbert Brücker & Albrecht Glitz & Adrian Lerche & Agnese Romiti, 2021. "Occupational Recognition and Immigrant Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 39(2), pages 497-525.
    7. Miroslav Stefanik, 2021. "Shifting the Training Choice Decision to the Jobseeker—The Slovak Experience," LABOUR, CEIS, vol. 35(2), pages 192-213, June.
    8. Gerard J. van den Berg & Petyo Bonev & Enno Mammen, 2020. "Nonparametric Instrumental Variable Methods for Dynamic Treatment Evaluation," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 355-367, May.
    9. Paul Muller & Bas van der Klaauw & Arjan Heyma, 2020. "Comparing econometric methods to empirically evaluate activation programs for job seekers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 526-547, August.
    10. Stephen Kastoryano & Bas van der Klaauw, 2022. "Dynamic evaluation of job search assistance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 227-241, March.
    11. Balavac-Orlic, Merima & Posadas, Josefina, 2023. "One (program) for all or all (programs) for one: Evaluation of the employment program opportunity for all of the federation of Bosnia and Herzegovina," Economic Systems, Elsevier, vol. 47(1).

    More about this item

    Keywords

    Program evaluation; Treatment effects; Work practice; Training; Survival time;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy

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