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Evaluating Programmes: Experiments, Non-Experiments and Propensity Scores

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  • Denis Conniffe

    (Economic and Social Research Institute (ESRI))

  • Vanessa Gash

    (Economic and Social Research Institute (ESRI))

  • Philip J.

    (Economic and Social Research Institute (ESRI))

Abstract

Evaluations of programmes ?for example, labour market interventions such as employment schemes and training courses- usually involve comparison of the performance of a treatment group (recipients of the programme) with a control group (non-recipients) as regards some response (gaining employment for example). But the idea of randomisation of individuals to groups is rarely possible in the social sciences and there may be substantial differences between groups in the distribution of individual characteristics than can affect response. Past practice in economics has been to try to use multiple regression models to adjust away the differences in observed characteristics, while also testing for sample selection bias. The Propensity Score approach, which is widely applied in epidemiology and related fields, focuses on the idea that ?matching? individuals in the groups should be compared. The appropriate matching measure is usually taken to be the prior probability of programme participation. This paper describes the key ideas of the Propensity Score method, compares it with the common approach in economics, reviews the arguments in the literature and illustrates application by reanalysis of some Irish data on training courses.

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File URL: http://www.esri.ie/UserFiles/publications/20071003121527/WP126.pdf
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Bibliographic Info

Paper provided by Economic and Social Research Institute (ESRI) in its series Papers with number WP126.

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Length: 31 pages
Date of creation: Mar 2000
Date of revision:
Handle: RePEc:esr:wpaper:wp126

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  1. 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.
  2. Joel L. Horowitz & Charles F. Manski, 1996. "Censoring of Outcomes and Regressors Due To Survey Nonresponse: Identification and Estimation Using Weights and Imputations," Econometrics 9602007, EconWPA, revised 06 Mar 1996.
  3. Joshua D. Angrist & Alan B. Krueger, 1990. "Does Compulsory School Attendance Affect Schooling and Earnings?," NBER Working Papers 3572, National Bureau of Economic Research, Inc.
  4. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
  5. 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.
  6. Little, Roderick J A, 1985. "A Note about Models for Selectivity Bias," Econometrica, Econometric Society, vol. 53(6), pages 1469-74, November.
  7. repec:att:wimass:9001 is not listed on IDEAS
  8. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-28, May.
  9. 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.
  10. Imbens, Guido W & Rubin, Donald B, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 555-74, October.
  11. Joshua D. Angrist, 1995. "Conditioning on the Probability of Selection to Control Selection Bias," NBER Technical Working Papers 0181, National Bureau of Economic Research, Inc.
  12. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  13. 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.
  14. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
  15. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  16. 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.
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