2012
DOI: 10.1111/j.1540-5907.2012.00584.x
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Irreplaceable Legislators? Rethinking Minority Representatives in the New Century

Abstract: We use state legislator ideology estimates (standardized W‐nominate values) to examine whether Latino and African American legislator ideological differences can be explained away by traditional constituency characteristics like partisanship and demographics. We find instead that both Black and Latino legislators are unique “types.” Our evidence supports the theoretical presumption that there is a minority dimension to legislative voting and that it is uniquely personified by minority officeholders. White, Bla… Show more

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Cited by 55 publications
(49 citation statements)
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“…50 Similarly, Solomos and Back and Garbaye 39 Cutler 2002. 40 Juenke and Preuhs 2012;McDermott 1998;Sigelman et al 1995. 41 Buttice and Milazzo 2011.…”
Section: Group-specific Issuesmentioning
confidence: 98%
“…50 Similarly, Solomos and Back and Garbaye 39 Cutler 2002. 40 Juenke and Preuhs 2012;McDermott 1998;Sigelman et al 1995. 41 Buttice and Milazzo 2011.…”
Section: Group-specific Issuesmentioning
confidence: 98%
“…Existing research on legislator responsiveness has mostly examined the connection between legislators and black constituents, although emerging research has found that increasing Latino populations has had an influence on the legislative process, at both the state and federal level (Casellas, 2007;Juenke and Pruehs, 2012;Rouse, 2013). There has been scant research on the legislative representation of Asian populations, and we are aware of only one study of legislative responsiveness that includes Asians and Latinos (Wong, Lajevardi and Nicholson, 2017).…”
Section: Race Political Representation and Legislator Responsivenessmentioning
confidence: 99%
“…[16] is one of the first texts of using data driven methods in social sciences, and takes a very critical stance. 2 The most recent articles containing W-NOMINATE as content of regression models are [17][18][19]. model to the data, RDTA goes the same way backwards: its starting point is located within the dataset.…”
Section: Regression and Decision Treesmentioning
confidence: 99%