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Title: Replication Data for: Essays on Political Corruption      
dateReleased:
01-14-2016
downloadURL: http://dx.doi.org/10.7910/DVN/TXT8RO
ID:
doi:10.7910/DVN/TXT8RO
description:
This dissertation presents three essays offering explanations for the persistence of corruption despite electoral competition. This file contains replication data for the third essay. The third essay argues that to understand when voters hold politicians accountable for corruption, it is necessary to understand who they perceive to be corrupt. It presents evidence from a survey experiment showing that American voters perceive copartisan politicians to be less corrupt than those from the other political party or without a party label. This pattern is consistent with motivated reasoning in which voters expend extra cognitive resources to process information that contradicts their partisan leanings rather than from the use of party labels as heuristics to avoid cognitive burdens. Furthermore, I show that the ideological orientation of the media source reporting allegations of corruption affects whether they are viewed as credible. Counterstereotypical allegations – i.e., those that come from a media source that is ideologically similar to the politician – are taken more seriously by respondents. In fact, when partisans view counterstereotypical allegations, they exhibit less bias toward copartisans. In sum, this research demonstrates that in-group favoritism poses a challenge to democratic accountability, but that motivated reasoning is bounded by the evidence voters view, and thus that media sources with well-known ideological orientations may serve a particularly important role in encouraging democratic accountability among their bases.
description:
Faller, Julie, 2016, "Replication Data for: Essays on Political Corruption", http://dx.doi.org/10.7910/DVN/TXT8RO, Harvard Dataverse, V1
name:
Faller, Julie
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997