The Polls and the U.S. Presidential Election in 2020 …. and 2024
Name
The Polls and the U.S. Presidential Election in 2020 . and 2024.pdf
Description
Published version
Size
1.38 MB
Format
Adobe PDF
Checksum (MD5)
6f17b2a152d6681c22c13e9dce44d865
Author(s)
Barnett, Arnold
Sarfati, Arnaud
Date Issued
May 30, 2023
Journal
Statistics and Public Policy
Publisher
Taylor & Francis
Citation
Barnett, A., & Sarfati, A. (2023). The Polls and the U.S. Presidential Election in 2020 …. and 2024. Statistics and Public Policy, 10(1).
Version
Final published version
Abstract
Arguably, the single greatest determinant of U.S. public policy is the identity of the president. And if trusted, polls not only provide forecasts about presidential-election outcomes but can act to shape those outcomes. Looking ahead to the 2024 U.S. presidential election and recognizing that polls before the 2020 presidential election were sharply criticized, we consider whether such harsh assessments are warranted. Initially, we explore whether such polls as processed by the sophisticated aggregator FiveThirtyEight successfully forecast actual 2020 state-by-state outcomes. We evaluate FiveThirtyEight’s forecasts using customized statistical methods not used previously, methods that take account of likely correlations among election outcomes in similar states. We find that, taken together, the pollsters and FiveThirtyEight did an excellent job in predicting who would win in individual states, even those “tipping point” states where forecasting is more difficult. However, we also find that FiveThirtyEight underestimated Donald Trump’s vote shares by state to a modest but statistically significant extent. We further consider how the polls performed when the more primitive aggregator Real Clear Politics combined their results, and then how well single statewide polls performed without aggregation. It emerges that both Real Clear Politics and the individual polls fared surprisingly well.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/2330443X.2023.2199809
