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Replication Data for: Partisanship in a Pandemic: Biased Voter Assessments of Past and Present Government Performance

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Partisanship in a Pandemic: Biased Voter Assessments of Past and Present Government Performance
 
Identifier https://doi.org/10.7910/DVN/7QO4RV
 
Creator Snow, Dan
 
Publisher Harvard Dataverse
 
Description Accountability relies on voters accurately evaluating government performance in addressing the important issues of the day. This requirement arguably applies to an even greater extent when addressing fundamental societal crises. However, partisanship can bias evaluations, with government partisans perceiving outcomes more favorably, or attributing less responsibility for bad outcomes. We examine partisan motivated reasoning in the context of the Covid-19 pandemic crisis, using panel data and a survey experiment of over 6000 respondents in which vignettes prime respondents about the UK government’s successes and failures in tackling the pandemic. We also propose a novel extension of the partisan bias thesis: partisans arrive at biased judgements of government competence by recalling the past performance of the government differently, according to whether or not their favored party held power at that time. We find that even in the relatively consensual partisan context of the UK’s response to Covid-19, where both major parties endorsed both lockdown and vaccination programs, there is evidence of both current and recall partisan biases: Opposition partisans are more likely to blame the government for negative outcomes and less likely to recall positive aspects of the government’s recent and past performance unless prompted to do so. Our findings have implications for understanding the limits of democratic accountability under crisis conditions.
 
Subject Social Sciences
Partisan Biases, Recall Biases; Covid-19, Survey Experiment, Panel Data
 
Date 2024-03-09
 
Contributor Snow, Dan