Electoral Polls and Economic Uncertainty: an Analysis of the Last Two U.S. Presidential Elections

2026-01-29

AI Summary

This study investigates the dynamic relationship between polling support and economic-financial uncertainty during the 2020 and 2024 U.S. presidential elections. Employing a time-varying dynamic conditional correlation (DCC) model, the analysis integrates multidimensional indicators—including the Aruoba-Diebold-Scotti business conditions index, the VIX fear gauge, inflation expectations, and trade policy uncertainty—marking the first application of DCC methodology to political election data. The findings reveal a strong correlation between polling trends and economic uncertainty in 2020, significantly influenced by exogenous shocks such as the pandemic. In contrast, this correlation nearly vanishes in 2024, suggesting that under conditions of heightened political polarization, the influence of economic fundamentals on electoral outcomes has markedly diminished.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Humans and AI: Voting
• Reasoning under Uncertainty: Decision/Utility Theory

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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 events (e.g., assassination attempt, candidate changes) muted the economic channel. The study highlights how the interplay between voter sentiment, financial markets, and uncertainty varies across electoral contexts, offering a methodological contribution through the application of Dynamic Conditional Correlation models to political data and policy-relevant insights on the conditions under which economic fundamentals influence electoral dynamics.

Problem

• electoral polls
• economic uncertainty
• financial markets
• voter sentiment
• presidential elections

Innovation

• Dynamic Conditional Correlation
• electoral polls
• economic uncertainty
• political polarization
• financial markets