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Regional Heterogeneity and U.S. Presidential Elections

Author

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  • Ahmed, R.
  • Pesaran, M. H.

Abstract

This paper develops a recursive model of voter turnout and voting outcomes at U.S. county level to investigate the socioeconomic determinants of recent U.S. presidential elections. It is shown that the relationship between many socioeconomic variables and voting outcomes is not uniform across U.S. regions. By allowing for regional heterogeneity and using high-dimensional variable selection algorithms, we can explain and correctly predict the unexpected 2016 Republican victory. Key factors explaining voting outcomes include incumbency effects, voter turnout, local economic performance, un-employment, poverty, educational attainment, house price changes, urban-rural scores, and international competitiveness. Our results corroborate evidence of 'short-memory' among voters: economic fluctuations realized a few months prior to the election are indeed powerful predictors of voting outcomes as compared to their longer- term analogues. The paper then presents real time forecasts for the 2020 U.S. Presidential Election based on data available at the end of July 2020 which are then updated based on data available as of mid-October.

Suggested Citation

  • Ahmed, R. & Pesaran, M. H., 2020. "Regional Heterogeneity and U.S. Presidential Elections," Cambridge Working Papers in Economics 2092, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2092
    Note: mhp1
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    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2092.pdf
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    References listed on IDEAS

    as
    1. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018. "A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models," Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.
    2. Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020. "Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks," CESifo Working Paper Series 8475, CESifo.
    3. Pesaran, M. Hashem, 2015. "Time Series and Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780198759980.
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    Cited by:

    1. Boumans, Dorine & Gründler, Klaus & Potrafke, Niklas & Ruthardt, Fabian, 2024. "Political leaders and macroeconomic expectations: Evidence from a global survey experiment," Journal of Public Economics, Elsevier, vol. 235(C).

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    More about this item

    Keywords

    Voter Turnout; Popular and Electoral College Votes; Simultaneity and Recursive Identification; High Dimensional Forecasting Models; Lasso; OCMT;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

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