Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program
Building on an idea in Abadie and Gardeazabal (2003), this article investigates the application of synthetic control methods to comparative case studies. We discuss the advantages of these methods and apply them to study the effects of Proposition 99, a large-scale tobacco control program that California implemented in 1988. We demonstrate that following Proposition 99 tobacco consumption fell markedly in California relative to a comparable synthetic control region. We estimate that by the year 2000 annual per-capita cigarette sales in California were about 26 packs lower than what they would have been in the absence of Proposition 99. Given that many policy interventions and events of interest in social sciences take place at an aggregate level (countries, regions, cities, etc.) and affect a small number of aggregate units, the potential applicability of synthetic control methods to comparative case studies is very large, especially in situations where traditional regression methods are not appropriate. The methods proposed in this article produce informative inference regardless of the number of available comparison units, the number of available time periods, and whether the data are individual (micro) or aggregate (macro). Software to compute the estimators proposed in this article is available at the authors' web-pages.
All authors are affiliated with Harvard's Institute for Quantitative Social Science (IQSS). We thank Jake Bowers, Dan Hopkins, and seminar participants at the 2006 APSA Meetings in Philadelphia for helpful comments. Funding for this research was generously provided by NSF grant SES-0350645 (Abadie). The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.
Abadie, A., A. Diamond, and J. Hainmueller. “Synthetic Control Methods for Comparative Case Studies:Estimating the Effect of California’s Tobacco Control Program." Journal of the American Statistical Association 105 (2010): 493-505.