2018
DOI: 10.1002/psp.2218
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Migration as an opportunity to register changing partisan loyalty in the United States

Abstract: We explore the connection between residential migration choices and political party identification by movers who change their political party registration. We find that an impressive number of migrants choose neighbourhoods that favor their new party of registration. The association between the party change of the migrant and the balance of local partisanship in their new neighborhood cannot be accounted for by a process of neighbourhood socialisation because the move is too recent for socialisation pressures … Show more

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Cited by 6 publications
(4 citation statements)
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References 49 publications
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“…What results from this motivation to belong to a social group in the American political context is “the tendency [for] people identifying as Republicans or Democrats to view opposing partisans negatively and co-partisans positively” (Iyengar & Westwood, 2015, p. 691). Additionally, there seems to be a concurrent dynamic among partisans to cluster with co-partisans and away from out-partisans at multiple geographic levels (Brown & Enos, 2021; Cho et al, 2019), which is in line with work showing increasingly homogeneous districts for members of Congress (Abramowitz et al, 2006; Levendusky et al, 2008; McCarty et al, 2009). Brown and Enos (2021) show the extent of such partisan segregation by creating individual-level spatially weighted measures of partisan segregation using registered voters in the United States.…”
Section: Introductionsupporting
confidence: 78%
“…What results from this motivation to belong to a social group in the American political context is “the tendency [for] people identifying as Republicans or Democrats to view opposing partisans negatively and co-partisans positively” (Iyengar & Westwood, 2015, p. 691). Additionally, there seems to be a concurrent dynamic among partisans to cluster with co-partisans and away from out-partisans at multiple geographic levels (Brown & Enos, 2021; Cho et al, 2019), which is in line with work showing increasingly homogeneous districts for members of Congress (Abramowitz et al, 2006; Levendusky et al, 2008; McCarty et al, 2009). Brown and Enos (2021) show the extent of such partisan segregation by creating individual-level spatially weighted measures of partisan segregation using registered voters in the United States.…”
Section: Introductionsupporting
confidence: 78%
“…Bishop (2008) argues that people are choosing with their feet through both in‐ and out‐migration to create communities with like‐minded partisans. While there is evidence to support Bishop's findings (Cho, Gimpel, and Hui 2013, 2018; Martin and Webster 2018; Brown and Enos 2021; Brown and Enos 2021), there are several detractors led mainly by Morris Fiorina and his Stanford students and colleagues. The Stanford school is skeptical of the claim that partisans are geographically sorting into different communities (Abrams and Fiorina 2012; Mummolo and Nall 2017; Klinkner and Hapanowicz 2005), have found little evidence that “John Q. Public” was more ideologically polarized in the 1980s, 1990s, or early 2000s (Levendusky 2009, p. 72), and show evidence that partisan preference is not as strong an influence on peoples’ choices of which town to move to compared to all the other factors (schools, housing costs, proximity to work, etc.)…”
Section: Why 1996?mentioning
confidence: 97%
“…When people live in a region that is not in line with their political values and beliefs, this has negative consequences on their satisfaction with their neighborhood (Gimpel and Hui 2018;Hui 2013;Lütjen and Matschoß 2015), their sense of belonging (Motyl et al 2014), and social relationships (Chopik and Motyl 2016). Finally, when people move, they choose regions that match their worldviews (Cho et al 2013;Gallego et al 2016;Maxwell 2019;McDonald 2011), but it has been observed that people also move to politically incongruent regions (Cho et al 2019;Mummolo and Nall 2017).…”
Section: Internal Migration and Being Left Behind Sociallymentioning
confidence: 99%