Risk perceptions and politics: Evidence from the COVID-19 pandemic

Abstract

Politics may color interpretations of facts, and thus perceptions of risk. We find that a higher share of Trump voters in a county is associated with lower perceptions of risk during the COVID-19 pandemic. Controlling for COVID-19 case counts and deaths, as Trump's vote share rises in the local area, individuals search less for information on the virus and its potential economic impacts, and engage in fewer visits to non-essential businesses. Our results suggest that politics and the media may play an important role in determining the formation of risk perceptions, and may therefore affect both economic and health-related reactions to unanticipated health crises.

Keywords: COVID-19; Expectations; Pandemics; Polarization; Political partisanship; Risk perceptions; Social distancing and compliance.

Fig. 1

Trends in search shares, behaviors and COVID-19 cases. We plot the national average trends for each of our outcomes of interest over the first few months of 2020 against the cumulative number of confirmed COVID-19 cases in the US. In Panel A, we plot the average search share for COVID-19 on Google (left panel), as well as search share for unemployment benefits related terms (right panel). In Panel B, we plot the average daily level of our two behavior change variables. In the left panel, we plot the daily average of the percentage change in distance traveled in the county (relative to the pre-COVID period), while in the right panel, we plot the daily average of the percentage change in visits to non-essential businesses in the county (relative to the pre-COVID-period). A vertical line marks March 16, the day that the federal guidelines for social distancing where announced.

Fig. 2

Search share and political polarization – Trump vote share. We plot our two measures of search share on the Trump VS in the 2016 election in each of the Nielsen DMAs. In the left panel we use COVID-19 search shares while we use the search share for unemployment in the right. Inn each of the panels we control for the log number of confirmed cases, population density, income per capita, population, the day of the week, and the number of days since the first COVID-19 case in the DMA.

Fig. 3

Event studies for changes in search shares around confirmed cases and deaths for high and low Trump vote share areas. We plot abnormal search share for COVID-19 relative to five days before the first confirmed case of COVID-19 in a DMA (left panel) and the first COVID-19 death (right panel). These estimates are done for high (dotted) and low (solid) Trump vote share DMAs. The estimates are obtained by estimating an OLS where the daily log search share is regressed on event time dummies. In each specification, we include controls for DMA time-invariant characteristics like population, percapita income, and population density. We also control for calendar time trends via day fixed effects. Moreover, in the first death event study, we also control for time since the first confirmed case of COVID-19.

Fig. 4

Behavior change and political polarization. We plot our two county social distancing measures on the Trump VS in the 2016 presidential election in each of the counties. The left panel uses the percentage change in the average distance traveled in the county while on the right panel, we examine the percentage change in visits to non-essential businesses in the county. In each of the plots, we control for the log number of confirmed COVID-19 cases, population density, income per capita, population the day of the week, and the number of days since the first case of COVID-19 in the county.

Fig. 5

Trump vote share and behavior change – robustness. We plot the differential changes in the percentage change in travel distancing (right panel) and visits to non-essential businesses for high Trump VS counties to low Trump VS counties by calendar time. The estimates are obtained by regressing the behavior change measures on the interaction between high Trump VS county and the day indicator. In each figure, we plot three specifications, including county and day fixed effects, adding state by day fixed effects, and finally adding controls for COVID-19 cases and deaths. The higher the coefficient, the lower the behavior change in high Trump share counties as compared to low trump share counties. Each of the estimates includes 95% confidence intervals. The standard errors to estimate these intervals are clustered at the county level.

Fig. 6

Change in distance traveled for high and low Trump vote share counties. We plot the cumulative reaction concerning changes in distance traveled for high and low Trump share counties around each of the orders along with 0.95% confidence intervals. These are estimated from the specification for column 1 of Table 3 but using only the high and low Trump share counties for variation.

Fig. 7

COVID-19 scare at CPAC and changes in social distancing behavior. We plot the cumulative change in the percentage change in distance traveled (left panel) and the percentage change in non-essential visits given a 20% increase in the number of confirmed COVID_19 cases after the CPAC announcement in high and low Trump vote share counties. We obtain these estimates by estimating models like those for columns (1) and (2) in Table 2. Specifically, we augment the models by using a post-CPAC indicator and interacting them with the base variables used in the models for Table 2. Each plotted estimate includes 95% confident intervals, and standard errors are clustered at the county level.

Fig. 8

The role of the media - Trump vote share and behavior changes. We provide two sets of analyses to examine the media's role in affecting risk perceptions using the CPAC scare. Panel A provides binned scatterplots relating the search share for COVID-19 on the average ratio of Fox News searches to MSNBC News searches on Google in the DMAs during 2019. We control for the log number of confirmed cases, population density, income per capita, population, the day of the week, the number of days since the first case of COVID-19 in the DMA in each plot. We focus on the impact of CPAC on the relation between our measures and the Fox News ratio by partitioning pre and post-CPAC event searches. In Panel B, we examine the differential change between high and low Trump share counties in social distancing behavior and risk perceptions based on news viewership. Specifically, we plot the cumulative change in the percentage change in distance traveled (left panel) and the percentage change in non-essential visits given a 20% increase in the number of confirmed after the CPAC announcement in high and low trump vote share counties as in Fig. 7 with the addition of the cumulative change in High trump areas that have high Fox News viewership. We obtain these estimates by estimating models like those in columns (1) and (2) in Table 2. Specifically, we augment the models by using a Post-CPAC indicator and interacting them with the base variables used in the models for Table 2 as well as indicator variables for high Fox News viewership in the county (defined as counties in the top quintile of Fox News viewership in 2019 based on Nielsen data). Each plotted estimate includes 95% confident intervals, and standard errors are clustered at the county level.

Fig. 9

Risk perceptions and share of the population over age 60. We examine the relation between the share of the population over age 60 and search share (Panel A) and changes in the daily distance traveled (Panel B). For each measure, we examine both the fundamental relation (left column) and the differential effect based on high Trump VS. The search share panels are measured at the Nielsen DMA level, while the daily travel distance change is measured at the county level. In each of the plots, we control for the log number of confirmed cases, population density, income per capita, population, the day of the week, and the number of days since the first COVID-19 case in the DMA or county.

Fig. 10

Social distancing behavior and teleworking. We examine the relation between social distancing behavior and the share of the workforce that is easily done at home (Telework). The Telework measure is obtained from Dingel and Neiman (2020). They classify the feasibility of working at home for all occupations and merge this classification with occupational employment counts for the US. In the left column, we examine the fundamental relation while in the right column, we examine the differential effect based on high Trump vote share counties. In each of the figures, we control for the log number of confirmed cases, population density, income per capita, population, the day of the week, and the number of days since the first COVID-19 case in the DMA or county.