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Fundamentals Trump Polls in 2020 Democratic Primaries
Insights from the Field
Democratic primaries
2020 election
forecasting models
Joe Biden
Iowa method
American Politics
PS
1 other files
Dataverse
Fundamentals Matter: Forecasting the 2020 Democratic Presidential Nomination was authored by Andrew Dowdle, Randall Adkins, Karen Sebold and Wayne Steger. It was published by Cambridge in PS in 2021.

Headline:

How flawed are forecasts of U.S. Democratic presidential nominations? Our study reveals that fundamental factors, not early contest momentum, best predicted Joe Biden's eventual nomination win.

Introduction:

Standard forecasting methods focusing on campaign fundraising and early primary results often miss the mark when there’s no clear frontrunner.

Methods & Findings:

We replicated models from 1980-2016, which typically emphasize pre-primary variables like endorsements or Iowa/New Hampshire performance.

When applied to the chaotic 2020 primaries, these same 'fundamental' factors still proved most accurate for predicting Biden’s rise.

Key Insight:

Our analysis shows that core political fundamentals—such as candidate characteristics and broad voter support—are more predictive than campaign dynamics in early contests.

Takeaway:

This suggests 2024 forecasters should re-prioritize fundamental factors over short-term polling.

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PS: Political Science & Politics
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