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Evaluating State Programmes - “Natural Experiments” and Propensity Scores

  • Denis Conniffe

    (The Economic and Social Research Institute, Dublin)

  • Vanessa Gash

    (The Economic and Social Research Institute, Dublin)

  • Philip J. O'Connell

    (The Economic and Social Research Institute, Dublin)

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 ideal of randomisation of individuals to groups is rarely possible in the social sciences and there may be substantial differences between groups in the distributions of individual characteristics that 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 and illustrates its application by reanalysis of some Irish data on training courses.

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Article provided by Economic and Social Studies in its journal Economic and Social Review.

Volume (Year): 31 (2000)
Issue (Month): 4 ()
Pages: 283-308

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Handle: RePEc:eso:journl:v:31:y:2000:i:4:p:283-308
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  1. Callan, Tim & Barry Reilly, 1992. "Unions and the wage distribution in Ireland," Discussion Papers in Economics 18/92, Department of Economics, University of Sussex.
  2. 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.
  3. Joshua Angrist & Alan Krueger, 1990. "Does Compulsory School Attendance Affect Schooling and Earnings?," Working Papers 653, Princeton University, Department of Economics, Industrial Relations Section..
  4. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
  5. 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.
  6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  7. 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.
  8. Little, Roderick J A, 1985. "A Note about Models for Selectivity Bias," Econometrica, Econometric Society, vol. 53(6), pages 1469-74, November.
  9. 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.
  10. 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.
  11. 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.
  12. 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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