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The microeconometric estimation of treatment effects—An overview

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  • Marco Caliendo

    ()

  • Reinhard Hujer

    ()

Abstract

The need to evaluate the performance of active labour market policies is not questioned any longer. Even though OECD countries spend significant shares of national resources on these measures, unemployment rates remain high or even increase. We focus on microeconometric evaluation which has to solve the fundamental evaluation problem and overcome the possible occurrence of selection bias. When using non-experimental data, different evaluation approaches can be thought of. The aim of this paper is to review the most relevant estimators, discuss their identifying assumptions and their (dis-)advantages. Thereby we will present estimators based on some form of exogeneity (selection on observables) as well as estimators where selection might also occur on unobservable characteristics. Since the possible occurrence of effect heterogeneity has become a major topic in evaluation research in recent years, we will also assess the ability of each estimator to deal with it. Additionally, we will also discuss some recent extensions of the static evaluation framework to allow for dynamic treatment evaluation.

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Bibliographic Info

Article provided by Springer in its journal Allgemeines Statistisches Archiv.

Volume (Year): 90 (2006)
Issue (Month): 1 (March)
Pages: 199-215

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Handle: RePEc:spr:alstar:v:90:y:2006:i:1:p:199-215

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Related research

Keywords: Evaluation; effect heterogeneity; matching; dynamic treatments; JEL C40; H43; J68;

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  1. Richard Blundell & Monica Costa Dias, 2008. "Alternative approaches to evaluation in empirical microeconomics," CeMMAP working papers CWP26/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
  3. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
  4. Caliendo, Marco & Kopeinig, Sabine, 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching," IZA Discussion Papers 1588, Institute for the Study of Labor (IZA).
  5. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
  6. Michael Lechner, 2004. "Sequential Matching Estimation of Dynamic Causal Models," University of St. Gallen Department of Economics working paper series 2004 2004-06, Department of Economics, University of St. Gallen.
  7. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  8. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.
  9. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The Nonparametric Identification of Treatment Effects in Duration Models," Econometrica, Econometric Society, vol. 71(5), pages 1491-1517, 09.
  10. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
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