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Evaluating continuous training programs using the generalized propensity score1


  • Kluve, Jochen
  • Schneider, Hilmar
  • Uhlendorff, Arne
  • Zhao, Zhong


This paper assesses the dynamics of treatment effects arising from variation in the duration of training. We use German administrative data that have the extraordinary feature that the amount of treatment varies continuously from 10 days to 395 days (i.e. 13 months). This feature allows us to estimate a continuous dose-response function that relates each value of the dose, i.e. days of training, to the individual post-treatment employment probability (the response). The dose-response function is estimated after adjusting for covariate imbalance using the generalized propensity score, a recently developed method for covariate adjustment under continuous treatment regimes. Our data have the advantage that we can consider both the actual and planned training durations as treatment variables: If only actual durations are observed, treatment effect estimates may be biased because of endogenous exits. Our results indicate an increasing dose-response function for treatments of up to 100 days, which then flattens out. That is, longer training programs do not seem to add an additional treatment effect.

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  • Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating continuous training programs using the generalized propensity score1," Technical Reports 2007,39, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  • Handle: RePEc:zbw:sfb475:200739

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    References listed on IDEAS

    1. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    2. Jere R. Behrman & Yingmei Cheng & Petra E. Todd, 2004. "Evaluating Preschool Programs When Length of Exposure to the Program Varies: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 108-132, February.
    3. Michael Lechner & Ruth Miquel & Conny Wunsch, 2011. "Long‐Run Effects Of Public Sector Sponsored Training In West Germany," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 742-784, August.
    4. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    5. Schneider, Hilmar & Uhlendorff, Arne, 2006. "Die Wirkung der Hartz-Reform im Bereich der beruflichen Weiterbildung," IZA Discussion Papers 2255, Institute for the Study of Labor (IZA).
    6. Flores-Lagunes, Alfonso & Gonzalez, Arturo & Neumann, Todd C., 2007. "Estimating the Effects of Length of Exposure to a Training Program: The Case of Job Corps," IZA Discussion Papers 2846, Institute for the Study of Labor (IZA).
    7. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
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    Cited by:

    1. Heyer, Gerd & Koch, Susanne & Stephan, Gesine & Wolff, Joachim, 2011. "Evaluation der aktiven Arbeitsmarktpolitik: Ein Sachstandsbericht für die Instrumentenreform 2011 (Evaluation of active labor market programs : a summary of recent results for the German program refor," IAB Discussion Paper 201117, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Clausen, Jens & Heinesen, Eskil & Hummelgaard, Hans & Husted, Leif & Rosholm, Michael, 2009. "The effect of integration policies on the time until regular employment of newly arrived immigrants: Evidence from Denmark," Labour Economics, Elsevier, vol. 16(4), pages 409-417, August.
    3. repec:eee:jfpoli:v:69:y:2017:i:c:p:97-109 is not listed on IDEAS
    4. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    5. Katrin Hohmeyer, 2011. "Effectiveness of One-Euro-Jobs: Do programme characteristics matter?," Post-Print hal-00719485, HAL.
    6. Heinrich, Carolyn J. & Brill, Robert, 2015. "Stopped in the Name of the Law: Administrative Burden and its Implications for Cash Transfer Program Effectiveness," World Development, Elsevier, vol. 72(C), pages 277-295.
    7. Kluve, Jochen & Lehmann, Hartmut & Schmidt, Christoph M., 2008. "Disentangling Treatment Effects of Active Labor Market Policies: The Role of Labor Force Status Sequences," Labour Economics, Elsevier, vol. 15(6), pages 1270-1295, December.
    8. Yong-Seok Choi & Siwook Lee, 2013. "Productivity, Markups and Export Intensity: Evidence from Korean Manufacturing," Korean Economic Review, Korean Economic Association, vol. 29, pages 329-350.
    9. BIA Michela & FLORES Carlos A. & MATTEI Alessandra, 2011. "Nonparametric Estimators of Dose-Response Functions," LISER Working Paper Series 2011-40, LISER.
    10. Crombrugghe Denis de & Espinoza Henry & Heijke Hans, 2010. "Determinants of dropout behaviour in a job training programme for disadvantaged youths," ROA Research Memorandum 008, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    11. Seamus McGuinness & Philip J. O'Connell & Elish Kelly, 2014. "The Impact of Training Programme Type and Duration on the Employment Chances of the Unemployed in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 45(3), pages 425-450.
    12. Mohl, Philipp & Hagen, Tobias, 2008. "Which is the Right Dose of EU Cohesion Policy for Economic Growth?," ZEW Discussion Papers 08-104, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    13. Takeshima, Hiroyuki & Adhikari, Rajendra Prasad & Kaphle, Basu Dev & Shivakoti, Sabnam & Kumar, Anjani, 2016. "Determinants of chemical fertilizer use in Nepal: Insights based on price responsiveness and income effects:," IFPRI discussion papers 1507, International Food Policy Research Institute (IFPRI).
    14. Michela Bia & Alessandra Mattei, 2012. "Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(4), pages 485-516, November.
    15. Takeshima, Hiroyuki, 2015. "Identifying the effects of market imperfections for a scale biased agricultural technology: Tractors in Nigeria," 2015 Conference, August 9-14, 2015, Milan, Italy 211937, International Association of Agricultural Economists.

    More about this item


    Training; program evaluation; continuous treatment; generalized propensity score;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy


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