Evidence map›Paper›PMID 36311381›Full record

ArticlePolitical behavior2022

Nail in the Coffin or Lifeline? Evaluating the Electoral Impact of COVID-19 on President Trump in the 2020 Election.

Carlos Algara, Sharif Amlani, Samuel Collitt, Isaac Hale, Sara Kazemian

Erratum issuedAbstract read
In one paragraph

Article in Political behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Carlos AlgaraDepartment of Politics & Government, Claremont Graduate University, Claremont, USA.ORCID 0000-0002-7820-9109
Sharif AmlaniDepartment of Political Science, University of California, Davis, USA.
Samuel CollittDepartment of Political Science, University of California, Davis, USA.
Isaac HaleDepartment of Politics, Occidental College, Los Angeles, USA.
Sara KazemianDepartment of Political Science, University of California, Davis, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

From the onset of the first confirmed case of COVID-19 in January 2020 to Election Day in November, the United States experienced over 9,400,000 cases and 232,000 deaths. This crisis largely defined the campaign between former Vice President Joe Biden and President Donald Trump, centering on the Trump administration's efforts in mitigating the number of cases and deaths. While conventional wisdom suggested that Trump and his party would lose support due to the severity of COVID-19 across the country, such an effect is hotly debated empirically and theoretically. In this research, we evaluate the extent to which the severity of the COVID-19 pandemic influenced support for President Trump in the 2020 election. Across differing modeling strategies and a variety of data sources, we find evidence that President Trump gained support in counties with higher COVID-19 deaths. We provide an explanation for this finding by showing that voters concerned about the economic impacts of pandemic-related restrictions on activity were more likely to support Trump and that local COVID-19 severity was predictive of these economic concerns. While COVID-19 likely contributed to Trump's loss in 2020, our analysis demonstrates that he gained support among voters in localities worst affected by the pandemic. Supplementary Information: The online version contains supplementary material available at 10.1007/s11109-022-09826-x.

Indexed as

2020 US presidential electionCOVID-19 pandemicDonald TrumpRally-’round-the-flagRetrospective voting

Identifiers

PMID36311381
PMCPMC9589550

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.