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Election polling errors across time and space

Author

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  • Will Jennings

    (University of Southampton)

  • Christopher Wlezien

    (University of Texas at Austin)

Abstract

Are election polling misses becoming more prevalent? Are they more likely in some contexts than others? Here we undertake an over-time and cross-national assessment of prediction errors in pre-election polls. Our analysis draws on more than 30,000 national polls from 351 general elections in 45 countries between 1942 and 2017. We proceed in the following way. First, building on previous studies, we show how errors in national polls evolve in a structured way over the election timeline. Second, we examine errors in polls in the final week of the election campaign to assess performance across election years. Third, we undertake a pooled analysis of polling errors—controlling for a number of institutional and party features—that enables us to test whether poll errors have increased or decreased over time. We find that, contrary to conventional wisdom, the recent performance of polls has not been outside the ordinary. However, the performance of polls does vary across political contexts and in understandable ways.

Suggested Citation

  • Will Jennings & Christopher Wlezien, 2018. "Election polling errors across time and space," Nature Human Behaviour, Nature, vol. 2(4), pages 276-283, April.
  • Handle: RePEc:nat:nathum:v:2:y:2018:i:4:d:10.1038_s41562-018-0315-6
    DOI: 10.1038/s41562-018-0315-6
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    Cited by:

    1. Adrien Fabre & Bénédicte Apouey & Thomas Douenne & Jean-Michel Fourniau & Louis-Gaëtan Giraudet & Jean-François Laslier & Solène Tournus, 2021. "Who Are the Citizens of the French Convention for Climate?," CIRED Working Papers halshs-03265053, HAL.
    2. Fronzetti Colladon, Andrea, 2020. "Forecasting election results by studying brand importance in online news," International Journal of Forecasting, Elsevier, vol. 36(2), pages 414-427.
    3. Fetzer, Thiemo & Yotzov, Ivan, 2023. "(How) Do electoral surprises drive business cycles? Evidence from a new dataset," The Warwick Economics Research Paper Series (TWERPS) 1468, University of Warwick, Department of Economics.
    4. Scott R. Baker & Aniket Baksy & Nicholas Bloom & Steven J. Davis & Jonathan A. Rodden, 2020. "Elections, Political Polarization, and Economic Uncertainty," NBER Working Papers 27961, National Bureau of Economic Research, Inc.
    5. Jennings, Will & Lewis-Beck, Michael & Wlezien, Christopher, 2020. "Election forecasting: Too far out?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 949-962.
    6. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    7. Olsson, Henrik, 2021. "Election polling is not dead: A Bayesian bootstrap method yields accurate forecasts," OSF Preprints nqcgs, Center for Open Science.
    8. Nguyen, Phuc Lam Thy & Alsakka, Rasha & Mantovan, Noemi, 2023. "Political preferences and stock markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    9. Nguyen, Phuc Lam Thy & Alsakka, Rasha & Mantovan, Noemi, 2023. "The impact of sovereign credit ratings on voters’ preferences," Journal of Banking & Finance, Elsevier, vol. 154(C).
    10. Hamish Greenop‐Roberts, 2022. "Forecasting Federal Elections: New Data From 2010–2019 and a Discussion of Alternative and Emerging Methods," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 55(1), pages 25-39, March.
    11. Mongrain, Philippe & Nadeau, Richard & Jérôme, Bruno, 2021. "Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 289-301.
    12. Lauderdale, Benjamin E. & Bailey, Delia & Blumenau, Jack & Rivers, Douglas, 2020. "Model-based pre-election polling for national and sub-national outcomes in the US and UK," International Journal of Forecasting, Elsevier, vol. 36(2), pages 399-413.
    13. Dieter Stiers & Anna Kern, 2021. "Cyclical accountability," Public Choice, Springer, vol. 189(1), pages 31-49, October.
    14. Isakov, Michael & Kuriwaki, Shiro, 2020. "Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016," OSF Preprints 29pvm, Center for Open Science.
    15. Rami Zeedan, 2019. "The 2016 US Presidential Elections: What Went Wrong in Pre-Election Polls? Demographics Help to Explain," J, MDPI, vol. 2(1), pages 1-18, March.

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