Political Sectarianism

Partisans Despise the Other Party More Than They Love Theirs

Supporters of political parties now operate like warring sects

Political polarization between U.S. parties has only escalated since Newt Gingrich’s partisan attacks against President Bill Clinton in the 1990s. But for the first time, contempt for the other political party is greater than affection for own’s own, according to a new study published in the journal Science by social psychologist and IPR associate Eli Finkel, IPR political scientists James Druckman and Mary McGrath, and others. The authors coin the term “political sectarianism” to describe the bitter partisanship. Like religious sectarianism, the political version is marked by powerful emotions about sin, public shaming, and those who abandon or renounce the political faith.

“The current state of political sectarianism produces prejudice, discrimination, and cognitive distortion, undermining the ability of government to serve its core functions of representing the people and solving the nation’s problems,” Finkel said.

Using nationally representative survey data since the 1970s, the interdisciplinary researchers measured the difference over time between Americans’ affection for adherents of their own party and dislike of the supporters of the other. Although affection remains steady for one’s own, loathing for the other now exceeds it.

“Things have gotten much more severe in the past decade, and there is no sign we’ve hit bottom,” Druckman said. “Partisans perceive even greater differences, believing, for example, that the other party is ideologically extreme, engaged, and hostile.”

They pinpoint “othering” the opposing party, aversion to the other party, and moralization—or attaching immorality to the other party—as the key elements of political sectarianism.

Eli Finkel is a professor of social psychology and management and organizations and IPR associate. James Druckman is the Payson S. Wild Professor of Political Science and IPR associate director and fellow. Mary McGrath is an assistant professor political science and an IPR fellow.

Model Uses Cell Phone Data to Predict COVID-19’s Spread

Study identifies ‘super-spreader’ sites, shows how to protect those most at risk

Using anonymous cell phone data to map the hourly movements of 98 million people, a team of Stanford and Northwestern researchers created a computer model that accurately predicted the spread of COVID-19 in 10 of the largest U.S. cities in spring 2020.

Perhaps just as importantly, their model also shows that mobility policy has a critical role to play in reducing disparities in coronavirus infections and death rates.

“We show that mobility policy—which policymakers fully control—likely has large effects in generating disparities,” said IPR sociologist Beth Redbird, a co-author of the study published in Nature. Their study merges demographic data and epidemiological estimates with the cell phone location data. The researchers then analyzed three factors that drive infection risk—where people go in the course of a day, how long they linger, and how many other people are visiting the same place at the same time.

The model appears to confirm that most COVID-19 transmissions occur at “superspreader” sites, such as restaurants and fitness centers, where people remain in close quarters for extended periods. home more often because their jobs require it. They shop at smaller, more crowded establishments than those with higher incomes, who can work from home, use online grocery shopping and delivery, and frequent more spacious businesses.