Mon, Oct 24 at noon:
Academic innovation & the global public research university, James Hilton
Smith, Jeffrey A., and P.E. Todd. 2005. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?" Journal of Econometrics, 125:305-353.
This paper applies cross-sectional and longitudinal propensity score matching estimators to data from the National Supported Work (NSW) Demonstration that have been previously analyzed by LaLonde (1986) and Dehejia and Wahba (1999, 2002). We find that estimates of the impact of NSW based on propensity score matching are highly sensitive to both the set of variables included in the scores and the particular analysis sample used in the estimation. Among the estimators we study, the difference-in-differences matching estimator performs the best. We attribute its performance to the fact that it eliminates potential sources of temporally invariant bias present in the NSW data, such as geographic mismatch between participants and nonparticipants and the use of a dependent variable measured in different ways for the two groups. Our analysis demonstrates that while propensity score matching is a potentially useful econometric tool, it does not represent a general solution to the evaluation problem. (C) 2004 Elsevier B.V. All rights reserved.
Country of focus: United States of America.