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Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components: A Re-Analysis of the California GAIN Program

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  • V. Joseph Hotz
  • Guido W. Imbens
  • Jacob A. Klerman

Abstract

In this paper, we explore ways of combining experimental data and non-experimental methods to estimate the differential effects of components of training programs. We show how data from a multi-site experimental evaluation in which subjects are randomly assigned to any treatment versus a control group who receives no treatment can be combined with non-experimental regression-adjustment methods to estimate the differential effects of particular types of treatments. We also devise tests of the validity of using the latter methods. We use these methods and tests to re-analyze data from the MDRC Evaluation of California%u2019s Greater Avenues to Independence (GAIN) program. While not designed to estimate the differential effects of the Labor Force Attachment (LFA) training and Human Capital Development (HCD) training components used in this program, we show how data from this experimental evaluation can be used in conjunction with non-experimental methods to estimate such effects. We present estimates of both the short- and long-term differential effects of these two training components on employment and earnings. We find that while there are short-term positive differential effects of LFA versus HCD, the latter training component is relatively more beneficial in the longer-term.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11939.

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Date of creation: Jan 2006
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Publication status: published as Hotz, V. Joseph, Guido W. Imbens and Jacob A. Klerman. "Evaluating The Differential Effects Of Alternative Welfare-to-Work Training Components: A Reanalysis Of The California GAIN Program," Journal of Labor Economics, 2006, v24(3,Jul), 521-566.
Handle: RePEc:nbr:nberwo:11939

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  1. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
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  4. Mitnik, Oscar A., 2009. "How Do Training Programs Assign Participants to Training? Characterizing the Assignment Rules of Government Agencies for Welfare-to-Work Programs in California," IZA Discussion Papers 4024, Institute for the Study of Labor (IZA).
  5. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2000. "The Long-Term Gains from GAIN: A Re-Analysis of the Impacts of the California GAIN Program," NBER Working Papers 8007, National Bureau of Economic Research, Inc.
  6. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, 01.
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  8. V. Joseph Hotz & Guido W. Imbens & Julie H. Mortimer, 1999. "Predicting the Efficacy of Future Training Programs Using Past Experiences," NBER Technical Working Papers 0238, National Bureau of Economic Research, Inc.
  9. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
  10. 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.
  11. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
  12. 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.
  13. 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.
  14. Lechner, Michael, 1999. "Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption," IZA Discussion Papers 91, Institute for the Study of Labor (IZA).
  15. James J. Heckman, 1989. "Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training," NBER Working Papers 2861, National Bureau of Economic Research, Inc.
  16. Hilary Williamson Hoynes, 1996. "Local Labor Markets and Welfare Spells: Do Demand Conditions Matter?," NBER Working Papers 5643, National Bureau of Economic Research, Inc.
  17. 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.
  18. Friedlander, Daniel & Robins, Philip K, 1995. "Evaluating Program Evaluations: New Evidence on Commonly Used Nonexperimental Methods," American Economic Review, American Economic Association, vol. 85(4), pages 923-37, September.
  19. 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.
  20. 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.
  21. Couch, Kenneth A, 1992. "New Evidence on the Long-Term Effects of Employment Training Programs," Journal of Labor Economics, University of Chicago Press, vol. 10(4), pages 380-88, October.
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