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Political Competitiveness, Regression Discontinuity and the Incumbency Effect

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  • Jerome Archambault
  • Stanley L. Winer

Abstract

In an RDD study of the incumbency effect, observations somewhat away from the threshold separating winners and losers in an election are necessarily employed. We consider how incorporating the vote volatility of elections into a preferred index of electoral competitiveness or closeness, in contrast to the often used unadjusted vote share margin, affects the estimated incumbency effect through this route for Liberal party candidates in Canadian general elections, with emphasis on the post-1950 period. Estimation is by local linear nonparametric regression with a data driven bandwidth. We also consider how allowance for the competitiveness and outcomes of prior electoral contests alters the estimated incumbency effect. Comparisons of our results with previous work on incumbency in Canadian elections by Kendall and Rekkas (2012) in this journal are presented, along with a reproduction of their model, for the cases we consider, based on combining our refined and extended electoral data with their (volatility unadjusted) index of electoral closeness and different estimation methodology.

Suggested Citation

  • Jerome Archambault & Stanley L. Winer, 2023. "Political Competitiveness, Regression Discontinuity and the Incumbency Effect," CESifo Working Paper Series 10356, CESifo.
    • Handle: RePEc:ces:ceswps:_10356
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    References listed on IDEAS

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    1. Matias D. Cattaneo & Rocío Titiunik, 2022. "Regression Discontinuity Designs," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 821-851, August.
    2. Daniel, Kermit & Lott, John R, Jr, 1997. "Term Limits and Electoral Competitiveness: Evidence from California's State Legislative Races," Public Choice, Springer, vol. 90(1-4), pages 165-184, March.
    3. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
    4. Andrew C. Eggers & Anthony Fowler & Jens Hainmueller & Andrew B. Hall & James M. Snyder, 2015. "On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from Over 40,000 Close Races," American Journal of Political Science, John Wiley & Sons, vol. 59(1), pages 259-274, January.
    5. Garand, James C. & Gross, Donald A., 1984. "Changes in the Vote Margins for Congressional Candidates: A Specification of Historical Trends," American Political Science Review, Cambridge University Press, vol. 78(1), pages 17-30, March.
    6. Powell, Eleanor Neff & Tucker, Joshua A., 2014. "Revisiting Electoral Volatility in Post-Communist Countries: New Data, New Results and New Approaches," British Journal of Political Science, Cambridge University Press, vol. 44(1), pages 123-147, January.
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    JEL classification:

    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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