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IPW estimation and related estimators for evaluation of active labor market policies in a dynamic setting

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  • Vikman, Johan

    (Uppsala Center for Labor Studies)

Abstract

This paper considers treatment evaluation in a discrete time setting in which treatment could start at any point in time. A typical application is an active labor market policy program which could start after any elapsed unemployment duration. It is shown that various average effects on survival time are identified under unconfoundedness and no-anticipation and inverse probability weighting (IPW) estimators are provided for these effects. The estimators are applied to a Swedish work practice program. The IPW estimator is compared with related estimators. One conclusion is that the matching estimator proposed by Fredriksson and Johansson (2008)1 overlooks a selective censoring problem.

Suggested Citation

  • Vikman, Johan, 2014. "IPW estimation and related estimators for evaluation of active labor market policies in a dynamic setting," Working Paper Series, Center for Labor Studies 2014:8, Uppsala University, Department of Economics.
    • Handle: RePEc:hhs:uulswp:2014_008
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    File URL: http://www.ucls.nek.uu.se/digitalAssets/297/297202_320148.pdf
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    References listed on IDEAS

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    1. 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.
    2. de Luna, Xavier & Johansson, Per, 2009. "Non-Parametric Inference for the Effect of a Treatment on Survival Times with Application in the Health and Social Sciences," IZA Discussion Papers 3966, Institute of Labor Economics (IZA).
    3. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    5. Bruno Crépon & Marc Ferracci & Grégory Jolivet & Gerard J. van den Berg, 2009. "Active Labor Market Policy Effects in a Dynamic Setting," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 595-605, 04-05.
    6. Forslund, Anders & Liljeberg, Linus & von Trott zu Solz, Leah, 2013. "Job practice: an evaluation and a comparison with vocational labour market training programmes," Working Paper Series 2013:6, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    7. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    8. Fredriksson, Peter & Johansson, Per, 2008. "Dynamic Treatment Assignment," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 435-445.
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    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.
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    12. Michael Lechner, 1999. "Nonparametric bounds on employment and income effects of continuous vocational training in East Germany," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 1-28.
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    More about this item

    Keywords

    Treatment effects; dynamic treatment assignment; program evaluation; work practice;
    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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