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Using Matching Estimators to Evaluate Alternative Youth Employment Programs: Evidence from France, 1986-1988

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  • Fougère, Denis
  • Crépon, Bruno
  • Brodaty, Thomas

Abstract

In this paper we apply the statistical framework recently proposed by Imbens (1999) and Lechner (1999) to identify the causal effects of multiple treatments under the conditional independence assumption. We show that under this assumption, matching with respect to the ratio of the scores allows to estimate nonparametrically the average conditional treatment effect for any pair of treatments. Consequently it is possible to estimate this effect by implementing nonparametric matching estimators, which were recently studied by Heckman, Ichimura, Smith and Todd (1998) and Heckman, Ichimura and Todd (1998). The application concerns the youth employment programs that were set up in France during the eighties to improve the labour market prospects of the most disadvantaged and unskilled young workers. The empirical analysis makes use of nonexperimental longitudinal micro data collected by INSEE (Institut National de la Statistique et des Etudes Economiques, Paris) from 1986 to 1988.

Suggested Citation

  • Fougère, Denis & Crépon, Bruno & Brodaty, Thomas, 2000. "Using Matching Estimators to Evaluate Alternative Youth Employment Programs: Evidence from France, 1986-1988," CEPR Discussion Papers 2604, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:2604
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    References listed on IDEAS

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    1. Lechner, Michael, 1999. "Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption," IZA Discussion Papers 91, Institute of Labor Economics (IZA).
    2. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    3. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
    4. Fougere, Denis & Kramarz, Francis & Magnac, Thierry, 2000. "Youth employment policies in France," European Economic Review, Elsevier, vol. 44(4-6), pages 928-942, May.
    5. Thierry Magnac, 1997. "State Dependence and Heterogeity in youth Employment Histories," Working Papers 97-47, Center for Research in Economics and Statistics.
    6. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    7. Liliane Bonnal & Denis Fougère & Anne Sérandon, 1997. "Evaluating the Impact of French Employment Policies on Individual Labour Market Histories," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 683-713.
    8. Guido W. Imbens, 1999. "The Role of the Propensity Score in Estimating Dose-Response Functions," NBER Technical Working Papers 0237, National Bureau of Economic Research, Inc.
    9. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    10. 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.
    11. James J. Heckman & Jeffrey A. Smith, 1999. "The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies," NBER Working Papers 6983, National Bureau of Economic Research, Inc.
    12. 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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    More about this item

    Keywords

    Econometric evaluation methods; Matching estimators; Multiple treatments; Competing-risks duration models; Youth employment policies;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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