Published online by Cambridge University Press: 21 April 2016
This note illustrates a new method for making causal inferences with ecological data. We show how to combine aggregate outcomes with individual demographics from separate data sources to make causal inferences about individual behavior. In addressing such problems, even under the selection on observables assumption often made in the treatment effects literature, it is not possible to identify causal effects of interest. However, recent results from the partial identification literature provide sharp bounds on these causal effects. We apply these bounds to data from Chilean mayoral elections that straddle a 2012 change in Chilean electoral law from compulsory to voluntary voting. Aggregate voting outcomes are combined with individual demographic information from separate data sources to determine the causal effect of the change in the law on voter turnout. The bounds analysis reveals that voluntary voting decreased expected voter turnout, and that other causal effects are overstated if the bounds analysis is ignored.
Alejandro Corvalan, Professor of Economics, Department of Economics, Universidad Diego Portales, Av. Santa Clara 797 Huechuraba, Santiago 8580000, Chile ([email protected]). Emerson Melo, Assistant Professor of Economics, Department of Economics, Indiana University, 307 Wyllie Hall, 100 S Woodlawn, Bloomington, IN 47408, USA ([email protected]). Robert Sherman, Professor of Economics and Statistics, Division of Humanities and Social Sciences, California Institute of Technology, M/S 228-77, Pasadena, CA 91125, USA ([email protected]). Matt Shum, Professor of Economics, Division of Humanities and Social Sciences, California Institute of Technology, M/S 228-77, Pasadena, CA 91125, USA ([email protected]). Corvalan acknowledges financial support from the Institute for Research in Market Imperfections and Public Polity, ICM IS130002.