A Note on Close Elections and Regression Analysis of the Party Incumbency Advantage

11 Pages Posted: 5 Mar 2014

See all articles by Peter M. Aronow

Peter M. Aronow

Yale University - Department of Political Science

David R. Mayhew

Yale University - Department of Political Science and Institution for Social and Policy Studies

Winston Lin

Department of Statistics and Data Science

Date Written: March 1, 2014

Abstract

Much research has recently been devoted to understanding the effects of party incumbency following close elections, typically using a regression discontinuity design. Researchers have demonstrated that close elections in the United States may systematically favor certain types of candidates, and that a research design that focuses on close elections may therefore be inappropriate for estimation of the incumbency advantage. We demonstrate that any issues raised with the study of close elections may be equally applicable to the ordinary least squares analysis of electoral data, even when the sample contains all elections. When vote share is included as part of a covariate control strategy, the estimate produced by an ordinary least squares regression that includes all elections either exactly reproduces or approximates the regression discontinuity estimate.

Keywords: regression discontinuity, incumbency advantage, close elections, causal inference

JEL Classification: C13

Suggested Citation

Aronow, Peter Michael and Mayhew, David R. and Lin, Winston, A Note on Close Elections and Regression Analysis of the Party Incumbency Advantage (March 1, 2014). Available at SSRN: https://ssrn.com/abstract=2403455 or http://dx.doi.org/10.2139/ssrn.2403455

Peter Michael Aronow (Contact Author)

Yale University - Department of Political Science ( email )

P.O. Box 208301
New Haven, CT 06520-8269
United States

David R. Mayhew

Yale University - Department of Political Science and Institution for Social and Policy Studies ( email )

Box 208269
New Haven, DC 06520-8269
United States
203-432-5237 (Phone)

Winston Lin

Department of Statistics and Data Science ( email )

Wharton School
Philadelphia, PA 19104
United States

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