Misinformation
During a Pandemic
Leonardo Bursztyn, Aakaash
Rao, Christopher Roth, David Yanagizawa-Drott
BECKER FRIEDMAN INSTITUTE for
Economics, University of Chicago
We study the effects of news coverage of the novel
coronavirus by the two most widely-viewed cable news shows in the United States
– Hannity and Tucker Carlson Tonight, both on Fox
News – on viewers’ behavior and downstream health outcomes.
Carlson warned viewers about the threat posed by the
coronavirus from early February, while Hannity originally dismissed the risks
associated with the virus before gradually adjusting his position starting late
February.
We first validate these differences in content with
independent coding of show transcripts. In line with the differences in
content, we present novel survey evidence that Hannity’s viewers changed
behavior in response to the virus later than other Fox News viewers, while
Carlson’s viewers changed behavior earlier.
We then turn to the effects on the pandemic itself,
examining health outcomes across counties.
First, we document that greater viewership of Hannity relative
to Tucker Carlson Tonight is strongly associated with a
greater number of COVID-19 cases and deaths in the early stages of the
pandemic.
The relationship is stable across an expansive set of robustness tests.
To better identify the effect of differential viewership of the two shows, we employ a novel instrumental variable strategy exploiting variation in when shows are broadcast in relation to local sunset times.
These estimates also show that greater exposure to Hannity relative
to Tucker Carlson Tonight is associated with a greater number
of county-level cases and deaths.
Furthermore, the results suggest that in mid-March,
after Hannity’s shift in tone, the diverging trajectories on COVID-19 cases
begin to revert.
We provide additional evidence consistent with
misinformation being an important mechanism driving the effects in the data.
While our findings cannot yet speak to long-term
effects, they indicate that provision of misinformation in the early stages of
a pandemic can have important consequences for how a disease ultimately affects
the population.
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