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Program Evaluation and Research Designs

  • DiNardo, John
  • Lee, David S.

This chapter provides a selective review of some contemporary approaches to program evaluation. One motivation for our review is the recent emergence and increasing use of a particular kind of "program" in applied microeconomic research, the so-called Regression Discontinuity (RD) Design of Thistlethwaite and Campbell (1960). We organize our discussion of these various research designs by how they secure internal validity: in this view, the RD design can been seen as a close "cousin" of the randomized experiment. An important distinction which emerges from our discussion of "heterogeneous treatment effects" is between ex post (descriptive) and ex ante (predictive) evaluations; these two types of evaluations have distinct, but complementary goals. A second important distinction we make is between statistical statements that are descriptions of our knowledge of the program assignment process and statistical statements that are structural assumptions about individual behavior. Using these distinctions,we examine some commonly employed evaluation strategies, and assess them with a common set of criteria for "internal validity", the foremost goal of an ex post evaluation. In some cases, we also provide some concrete illustrations of how internally valid causal estimates can be supplemented with specific structural assumptions to address "external validity": the estimate from an internally valid "experimental" estimate can be viewed as a "leading term" in an extrapolation for a parameter of interest in an ex ante evaluation.

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This chapter was published in:
  • O. Ashenfelter & D. Card (ed.), 2011. "Handbook of Labor Economics," Handbook of Labor Economics, Elsevier, edition 1, volume 4, number 4.
  • This item is provided by Elsevier in its series Handbook of Labor Economics with number 4-05.
    Handle: RePEc:eee:labchp:4-05
    Contact details of provider: Web page: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description

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    1. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6b, January.
    2. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
    3. Joshua Angrist & Eric Bettinger & Michael Kremer, 2006. "Long-Term Educational Consequences of Secondary School Vouchers: Evidence from Administrative Records in Colombia," American Economic Review, American Economic Association, vol. 96(3), pages 847-862, June.
    4. Heckman, J J & Tobias, Justin & Vytlacil, Ed, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Staff General Research Papers 12022, Iowa State University, Department of Economics.
    5. Cook, Thomas D., 2008. ""Waiting for Life to Arrive": A history of the regression-discontinuity design in Psychology, Statistics and Economics," Journal of Econometrics, Elsevier, vol. 142(2), pages 636-654, February.
    6. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6a, January.
    7. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521586115, November.
    8. Philip Oreopoulos, 2006. "Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter," American Economic Review, American Economic Association, vol. 96(1), pages 152-175, March.
    9. Erica Field, 2007. "Entitled to Work: Urban Property Rights and Labor Supply in Peru," The Quarterly Journal of Economics, Oxford University Press, vol. 122(4), pages 1561-1602.
    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.
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