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Combining Matching and Nonparametric IV Estimation: Theory and an Application to the Evaluation of Active Labour Market Policies

Author

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  • Michael Lechner
  • Markus Froelich
Abstract
In this paper, we show how instrumental variable and matching estimators can be combined in order to identify a broader array of treatment effects. Instrumental variable estimators are known to estimate effects only for the compliers, which often represent only a small subset of the entire population. By combining IV with matching, we can estimate also the treatment effects for the always- and never-takers. In our application to the active labour market programmes in Switzerland, we find large positive employment effects for at least 8 years after treatment for the compliers. On the other hand, the effects for the always- and never-participants are small. In addition, when examining the potential outcomes separately, we find that the compliers have the worst employment outcomes without treatment. Hence, the assignment policy of the caseworkers was inefficient in that the always-participants were neither those with the highest treatment effect nor those with the largest need for assistance.

Suggested Citation

  • Michael Lechner & Markus Froelich, 2010. "Combining Matching and Nonparametric IV Estimation: Theory and an Application to the Evaluation of Active Labour Market Policies," University of St. Gallen Department of Economics working paper series 2010 2010-21, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2010:2010-21
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    References listed on IDEAS

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    1. Gerfin, Michael & Lechner, Michael & Steiger, Heidi, 2005. "Does subsidised temporary employment get the unemployed back to work? Aneconometric analysis of two different schemes," Labour Economics, Elsevier, vol. 12(6), pages 807-835, December.
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    3. Frölich, Markus & Lechner, Michael, 2010. "Exploiting Regional Treatment Intensity for the Evaluation of Labor Market Policies," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1014-1029.
    4. Martin, John P. & Grubb, David, 2001. "What works and for whom: a review of OECD countries' experiences with active labour market policies," Working Paper Series 2001:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    5. 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.
    6. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
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    9. Markus Frlich, 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 77-90, February.
    10. Rafael Lalive & Jan C. van Ours & Josef Zweimueller, "undated". "The Impact of Active Labor Market Programs on the Duration of Unemployment," IEW - Working Papers 041, Institute for Empirical Research in Economics - University of Zurich.
    11. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    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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    14. Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 74-90, January.
    15. Fredriksson, Peter & Johansson, Per, 2002. "Program evaluation and random program starts," Working Paper Series 2003:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    16. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    17. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    18. 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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    21. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
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    Cited by:

    1. John Engberg & Dennis Epple & Jason Imbrogno & Holger Sieg & Ron Zimmer, 2014. "Evaluating Education Programs That Have Lotteried Admission and Selective Attrition," Journal of Labor Economics, University of Chicago Press, vol. 32(1), pages 27-63.
    2. Martin Huber & Giovanni Mellace, 2010. "Sharp IV bounds on average treatment effects under endogeneity and noncompliance," University of St. Gallen Department of Economics working paper series 2010 2010-31, Department of Economics, University of St. Gallen.

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    More about this item

    Keywords

    Local average treatment effect; conditional local IV; matching estimation; heterogeneous treatment effects; active labour market policy; state borders; geographic variation.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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