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When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID‐19 pandemic
Author(s) -
Porumbescu Gregory,
Moynihan Donald,
Anastasopoulos Jason,
Olsen Asmus Leth
Publication year - 2023
Publication title -
governance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.46
H-Index - 76
eISSN - 1468-0491
pISSN - 0952-1895
DOI - 10.1111/gove.12701
Subject(s) - scapegoating , blame , framing (construction) , social psychology , politics , political science , psychology , public relations , law , structural engineering , engineering
Public officials use blame avoidance strategies when communicating performance information. While such strategies typically involve shifting blame to political opponents or other governments, we examine how they might direct blame to ethnic groups. We focus on the COVID‐19 pandemic, where the Trump administration sought to shift blame by scapegoating (using the term “Chinese virus”) and mitigate blame by positively framing performance information on COVID‐19 testing. Using a novel experimental design that leverages machine learning techniques, we find scapegoating outgroups backfired, leading to greater blame of political leadership for the poor administrative response, especially among conservatives. Backlash was strongest for negatively framed performance data, demonstrating that performance framing shapes blame avoidance outcomes. We discuss how divisive blame avoidance strategies may alienate even supporters.