Sharif Amlani’s research while affiliated with University of California, Davis and other places

The literature finds that partisanship drives negative emotional evaluations of out-partisans. Yet, scholars base these insights on measures–like thermometers, candidate evaluations, and social-distance measures–that discount the sentiment attached to individuals’ negative attitudes. We introduce a unique measure of affect capturing the motivation underpinning partisans’ attitudes. Our measure asks respondents for one-word to describe voters in their party and the opposing party. Then respondents code the sentiment behind their word choice themselves. Together, our measure produces qualitative and quantitative measures of respondents’ affect. We find that our self-coded open-ended measure has strong face validity and correlates strongly with existing affect measures. This measure advances our understating of partisan affect by allowing scholars a window into respondents’ state of mind. Scholars can easily apply our measure’s procedure beyond partisanship to other groups of interest.

The emergence of COVID-19 spurred the fastest development of a vaccine in history. Yet, a large proportion of Americans remain hesitant to receive it. Our paper investigates how the social networks we inhabit might explain persistent vaccine hesitancy. We argue that the COVID-19 vaccination status of respondents’ closest associates inhibits or motivates their decision to receive a COVID-19 vaccine. To test our argument, we conduct an original survey asking respondents a battery of questions about the people with whom individuals most frequently discuss vaccines and COVID-19. Our survey reports that individuals’ discussion networks are polarized by vaccination status. Concurrently, there is a strong association between the social network’s vaccination status and the respondent’s vaccination status. This association is so robust that partisanship does not moderate the association between discussants’ vaccination status and respondents’ vaccination status. Together, our results imply that unvaccinated individuals remain hesitant because they face reinforcing social pressure from their closest associates. The unique timing of our survey, during an unprecedented vaccination campaign against a novel disease, offers a snapshot of how relationships may affect attitudes.

[This corrects the article DOI: 10.1007/s11109-022-09826-x.].

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.

Scholars argue that contemporary American elections are pronounced in their degree of partisanship and nationalization. While much of this work largely uncovers a heightened degree of nationalization in contemporary elections, little is known about how far back these patterns generalize. Given the limited availability of American electoral data, this work also generally focuses on a single office or during a certain segment of the post-war period since 1946. Moreover, this work largely focuses on states as salient units of analysis, masking potential variation found in U.S. counties, the smallest geographical unit constituting panel observations over time and across elections. In this note, we leverage a novel dataset of county-level election returns for President, U.S. Senate, and Governor, to specify a model assessing whether American elections are more nationalized and partisan than during any other period since the Civil War. We find evidence that presidential and Senate elections are more partisan today than any period since the Civil War, while gubernatorial elections are as partisan today as they were during the late 1800s. Our findings have implications for contemporary-based theories explaining the rise of partisanship in American elections and demonstrates the utility of county-level data in assessing electoral changes in America.