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

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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.

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Bibliographic Info

Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2010 with number 2010-21.

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Length: 39 pages
Date of creation: Jun 2010
Date of revision:
Handle: RePEc:usg:dp2010:2010-21

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

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References

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  1. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  2. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
  3. Maddala, G S & Jeong, Jinook, 1992. "On the Exact Small Sample Distribution of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 60(1), pages 181-83, January.
  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. Gerfin, Michael & Lechner, Michael & Steiger, Heidi, 2002. "Does Subsidised Temporary Employment Get the Unemployed Back to Work? An Econometric Analysis of Two Different Schemes," IZA Discussion Papers 606, Institute for the Study of Labor (IZA).
  6. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, 05.
  7. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
  8. Kluve, Jochen, 2006. "The Effectiveness of European Active Labor Market Policy," IZA Discussion Papers 2018, Institute for the Study of Labor (IZA).
  9. Markus Frölich, 2008. "Parametric and Nonparametric Regression in the Presence of Endogenous Control Variables," International Statistical Review, International Statistical Institute, vol. 76(2), pages 214-227, 08.
  10. 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.
  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. Markus Frölich, 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.
  13. Jochen Kluve & Christoph M. Schmidt, 2002. "Can training and employment subsidies combat European unemployment?," Economic Policy, CEPR;CES;MSH, vol. 17(35), pages 409-448, October.
  14. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  15. Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
  16. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
  17. 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.
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