Program Evaluation and Research Designs

84 Pages Posted: 4 Jun 2010 Last revised: 9 Jun 2010

See all articles by John E. DiNardo

John E. DiNardo

University of Michigan at Ann Arbor - Gerald R. Ford School of Public Policy; National Bureau of Economic Research (NBER)

David Lee

Princeton University

Date Written: May 2010

Abstract

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.

Suggested Citation

DiNardo, John and Lee, David, Program Evaluation and Research Designs (May 2010). NBER Working Paper No. w16016. Available at SSRN: https://ssrn.com/abstract=1612610

John DiNardo (Contact Author)

University of Michigan at Ann Arbor - Gerald R. Ford School of Public Policy ( email )

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David Lee

Princeton University ( email )

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