Programme Heterogeneity and Propensity Score Matching: An Application to the Evaluation of Active Labour Market Policies

This paper investigates the question whether it really matters for microeconometric evaluation studies to take account of the fact that the programmes under consideration are heterogeneous. Assuming that selection into the different sub-programmes and the potential outcomes are independent given observable characteristics, estimators based on different propensity scores are compared and applied to the analysis of the active labour market policy in a Swiss region. Furthermore, the issues of heterogeneous effects and aggregation are addressed. The econometric considerations as well as the results of the application suggest that an approach that incorporates the possibility of having multiple programmes could be an important tool in applied work.

[1]  Joshua D. Angrist,et al.  Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants , 1998 .

[2]  V. Hajivassiliou,et al.  Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models , 1993 .

[3]  J. Geweke,et al.  Alternative computational approaches to inference in the multinomial probit model , 1994 .

[4]  M. Lechner.  Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption , 1999, SSRN Electronic Journal.

[5]  J. Heckman,et al.  The Economics and Econometrics of Active Labor Market Programs , 1999 .

[6]  D. Rubin.  ASSIGNMENT TO TREATMENT GROUP ON THE BASIS OF A COVARIATE , 1976 .

[7]  M. Lechner.  Earnings and Employment Effects of Continuous Gff-the-Job Training in East Germany After Unification , 1995 .

[8]  M. Lechner,et al.  A Microeconometric Evaluation of Active Labor Market Policy in Switzerland , 2001 .

[9]  Bruno Crépon,et al.  Using matching estimators to evaluate alternative youth employment programs: Evidence from France, 1986–1988 , 2000 .

[10]  D. McFadden.  Econometric analysis of qualitative response models , 1984 .

[11]  Paul A. Ruud,et al.  Handbook of Econometrics: Classical Estimation Methods for LDV Models Using Simulation , 1993 .

[12]  P. Holland.  Statistics and Causal Inference , 1985 .

[13]  J. Angrist,et al.  Empirical Strategies in Labor Economics , 1998 .

[14]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[15]  G. Imbens.  The Role of the Propensity Score in Estimating Dose-Response Functions , 1999 .

[16]  D B Rubin,et al.  Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism. , 1991, Biometrics.

[17]  A. Roy.  Some thoughts on the distribution of earnings , 1951 .

[18]  D. McFadden.  Econometric Models of Probabilistic Choice , 1981 .

[19]  Michael Keane,et al.  A Note on Identification in the Multinomial Probit Model , 1992 .

[20]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[21]  Petra E. Todd,et al.  Matching As An Econometric Evaluation Estimator , 1998 .

[22]  D. Rubin,et al.  Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .