Abstract: Crises and disasters give voters an opportunity to observe the incumbent’s response and reward or punish them for successes and failures. Yet even when voters perceive events similarly, they tend to attribute responsibility selectively, disproportionately crediting their party for positive developments and blaming opponents for negative developments. Using original time series data, we show that partisan disagreement over U.S. President Donald Trump’s responsibility for the COVID-19 pandemic quickly emerged alongside the pandemic’s onset in March 2020. Three original survey experiments show that the valence of information about the country’s performance against the virus contributes causally to such gaps. A Bayesian model of information processing anticipates our findings more closely than do theories of partisan-motivated reasoning. These findings shed new light on selective attribution’s implications for democratic accountability. This work is coauthored with Shikhar Singh (Yale).
Matt Graham is a postdoctoral researcher at the Institute for Data, Democracy, and Politics, George Washington University. He specializes in American political behavior and quantitative methods. He earned his Ph.D. from Yale University in December 2020. His research examines political ignorance and misperceptions, support for undemocratic politicians, and other threats to democratic accountability.
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