Electoral Polls and Economic Uncertainty: an Analysis of the Last Two U.S. Presidential Elections
Giampiero M. Gallo, Demetrio Lacava, Edoardo Otranto

TL;DR
This study analyzes how the relationship between electoral polls and economic uncertainty indicators varies across the 2020 and 2024 U.S. presidential elections, revealing dynamic correlations influenced by shocks and political events.
Contribution
It introduces the application of Dynamic Conditional Correlation models to political polling and uncertainty data, highlighting how economic fundamentals impact electoral dynamics under different contexts.
Findings
2020 correlations were highly dynamic and event-driven
2024 correlations remained stable and near zero
Economic shocks influenced poll-uncertainty relationships in 2020
Abstract
This paper examines the dynamic relationship between electoral polls and indicators of economic and financial uncertainty during the last two U.S. presidential elections (2020 and 2024). Using daily polling data on Donald Trump and measures such as the Aruoba-Diebold-Scotti Business Conditions Index, the 5-year Breakeven Inflation Rate, the Trade Policy Uncertainty index, and the VIX, we estimate conditional correlation models to capture time-varying interactions. The analysis reveals that in 2020, correlations between polls and uncertainty measures were highly dynamic and event-driven, reflecting the influence of exogenous shocks (COVID-19, oil price collapse) and political milestones (primaries, debates). In contrast, during the 2024 campaign, correlations remained close to zero, stable, and largely unresponsive to shocks, suggesting that entrenched polarization and non-economic…
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Taxonomy
TopicsFiscal Policies and Political Economy · Market Dynamics and Volatility · Electoral Systems and Political Participation
