12 February 2007
This week, the Applied Statistics Workshop will present a talk by Jens Hainmueller, a Ph.D. candidate at in the Government Department at Harvard. Prior to joining the department, he received degrees from the London School of Economics and the Kennedy School of Government. His work has appeared in International Organization and the Journal of Legislative Studies. He will present a talk entitled "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program." This talk is based on joint work with Alberto Abadie and Alexis Diamond; their paper and supporting software are available from the workshop website. The presentation will be at noon on Wednesday, February 14 in Room N354, CGIS North, 1737 Cambridge St. As always, lunch will be provided. An abstract of the paper follows on the jump:
Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program
Alberto Abadie – Harvard University and NBER
Alexis Diamond – Harvard University
Jens Hainmueller – Harvard University
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