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Ignorance Is Bias

The Effect of Latino Losers on Models of Latino Representation

Bibliographic Data

ID6229397
AuthorsEric Gonzalez Juenke (0000-0001-5551-2204, Michigan State University, corresponding author)
Year2014
Volume58
Issue3
Pages593-603
Publication date2014-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Political Science (JOURNAL)
Journal identifiersISSN: 0092-5853 • E-ISSN: 1540-5907
PublisherWiley (PUBLISHER • GB)
DOI10.1111/ajps.12092
OpenAlexW1756172943
LanguageEN
Citations received33
References cited42

Nearly every aggregate study of minority legislative representation has observed outcomes of elections (officeholders), rather than the supply of minority candidates. Because of this, scholars have left a large amount of important data, the election losers, out of their models of minority representation. The evidence presented in this article demonstrates that voters in the United States cannot choose minority officeholders because there are rarely minority candidates on the ballot. I use state legislative candidate data from Carsey et al. () and Klarner et al. () to test models of Latino representation that correct for first‐stage selection bias. Once candidate self‐selection is taken into account, the probability of electing a Latino increases enormously. I then use data from 2010 to make out‐of‐sample predictions, which clearly favor the conditional model. Thus, our current understanding of Latino representation is significantly biased by ignoring the first stage of an election, a candidate's decision to run

Ballot · Econometrics · Economics · Ignorance · Legislature · Majority rule · Political science · Politics · Representation (politics · Selection (genetic algorithm · Selection bias · Statistics · Voting · Artificial Intelligence · Computer Science · Electoral Systems and Political Participation · Gender Politics and Representation · Judicial and Constitutional Studies · Law · Mathematics · Psychology · Social Psychology

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Unique citing works33
Citations per year3
Citation span2015 - 2026 (12)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 33

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