Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-31T13:49:13.091Z Has data issue: false hasContentIssue false
  • English
  • Français

Bounding Causal Effects in Ecological Inference Problems*

Published online by Cambridge University Press:  21 April 2016

Alejandro Corvalan ,
Emerson Melo ,
Robert Sherman  and
Matt Shum
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Type
Research Notes
Information
Political Science Research and Methods , Volume 5 , Issue 3 , July 2017 , pp. 555 - 565
Copyright
© The European Political Science Association 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

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.

References

Fan, Yanqin, Sherman, Robert, and Shum, Matt. 2014. ‘Identifying Treatment Effects Under Data Combination’. Econometrica 82:811–822.Google Scholar
Fan, Yanqin, Sherman, Robert, and Shum, Matt. 2016. ‘Estimation and Inference in an Ecological Inference Model’. Journal of Econometric Methods 5:17–48.CrossRefGoogle Scholar
King, Gary. 1997. A Solution to the Ecological Inference Problem. Princeton, NJ: Princeton University Press.Google Scholar
King, Gary, Rosen, Ori, and Tanner, Martin. 2004. Ecological Inference: New Methodological Strategies. Cambridge: Cambridge University Press.Google Scholar
Rosenbaum, Paul, and Rubin, Donald. 1983. ‘The Central Role of the Propensity Score in Observational Studies of Causal Effects’. Biometrica 70:41–55.CrossRefGoogle Scholar
Stoye, Jorg. 2009. ‘More on Confidence Intervals for Partially Identified Parameters’. Econometrica 77:1299–1315.Google Scholar