Unequal treatment toward copartisans versus non-copartisans is reduced when partisanship can be falsified.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2021
Historique:
received: 04 06 2020
accepted: 14 12 2020
entrez: 27 1 2021
pubmed: 28 1 2021
medline: 8 5 2021
Statut: epublish

Résumé

Studies show that Democrats and Republicans treat copartisans better than they do non-copartisans. However, party affiliation is different from other identities associated with unequal treatment. Compared to race or gender, people can more easily falsify, i.e., lie about, their party affiliation. We use a behavioral experiment to study how people allocate resources to copartisan and non-copartisan partners when partners are allowed to falsify their affiliation and may have incentives to do so. When affiliation can be falsified, the gap between contributions to signaled copartisans and signaled non-copartisans is eliminated. This happens in part because some participants-especially strong partisans-suspect that partners who signal a copartisan affiliation are, in fact, non-copartisans. Suspected non-copartisans earn less than both partners who signal that they are non-copartisans and partners who withhold their affiliation. The findings reveal an unexpected upside to the availability of falsification: at the aggregate level, it reduces unequal treatment across groups. At the individual-level, however, falsification is risky.

Identifiants

pubmed: 33503020
doi: 10.1371/journal.pone.0244651
pii: PONE-D-20-16991
pmc: PMC7840019
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0244651

Commentaires et corrections

Type : ErratumIn

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

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Auteurs

Maria Abascal (M)

Department of Sociology, New York University, New York, New York, United States of America.

Kinga Makovi (K)

Social Science Division, New York University Abu Dhabi, Abu Dhabi, UAE.

Anahit Sargsyan (A)

Social Science Division, New York University Abu Dhabi, Abu Dhabi, UAE.

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Classifications MeSH