The Impact of Corona Populism: Empirical Evidence from Austria and Theory

TL;DR

This paper examines how a major Austrian party's shift to corona populism during COVID-19 influenced voter behavior, beliefs, and pandemic outcomes, revealing complex effects of political messaging on public health perceptions and reporting.

Contribution

It provides empirical evidence on the co-evolution of public opinion and party policy during a crisis, highlighting the effects of corona populism on voter preferences and pandemic data interpretation.

Findings

Corona populism increased support among previously skeptical voters.

Beliefs about COVID-19 diverged among supporters after the party's policy shift.

Testing bias explains the paradox of rising deaths without more reported cases.

Abstract

I study the co-evolution between public opinion and party policy in situations of crises by investigating a policy U-turn of a major Austrian right-wing party (FPOE) during the Covid-19 pandemic. My analysis suggests the existence of both i) a "Downsian" effect, which causes voters to adapt their party preferences based on policy congruence and ii) a "party identification" effect, which causes partisans to realign their policy preferences based on "their" party's platform. Specifically, I use individual-level panel data to show that i) "corona skeptical" voters who did not vote for the FPOE in the pre-Covid-19 elections of 2019 were more likely to vote for the party after it embraced "corona populism", and ii) beliefs of respondents who declared that they voted for the FPOE in 2019 diverged from the rest of the population in three out of four health-dimensions only after the turn, causing them to underestimate the threat posed by Covid-19 compared to the rest of the population. Using aggregate-level panel data, I study whether the turn has produced significant behavioral differences which could be observed in terms of reported cases and deaths per capita. Paradoxically, after the turn the FPOE vote share is significantly positively correlated with deaths per capita, but not with the reported number of infections. I hypothesize that this due to a self-selection bias in testing, which causes a correlation between the number of "corona skeptics" and the share of unreported cases after the turn. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study that involves information about participants' true vs. reported infection status. I finally study a simple heterogeneous mixing epidemiological model and show that a testing bias can indeed explain the apparent paradox of an increase in deaths without an increase in reported cases.