#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Migration and political polarization in the U.S.: An analysis of the county-level migration network


Autoři: Xi Liu aff001;  Clio Andris aff001;  Bruce A. Desmarais aff002
Působiště autorů: Department of Geography, Pennsylvania State University, University Park, PA, United States of America aff001;  Department of Political Science, The Institute for CyberScience, Pennsylvania State University, University Park, PA, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225405

Souhrn

Research question

From gridlock in lawmaking to shortened holiday family dinners, partisan polarization pervades social and political life in the United States. We study the degree to which the dynamics of partisan polarization can be observed in patterns of county-to-county migration in the U.S. Specifically, we ask whether migration follows patterns that would lead individuals to homogeneous or heterogeneous partisan exposure, using annual county-to-county migration networks from 2002 to 2015. Adjusting for a host of factors, including geographic distance, population, and economic variables, we test the degree to which migration flows connect counties with similar political preferences.

Findings

Our central finding is that over the period studied, county-to-county migration flows connect counties with similar partisan voting profiles. Moreover, partisan sorting is most pronounced among the most politically extreme counties. The implication of this finding in the context of partisanship is that U.S. migration patterns reinforce partisan sorting, limiting the degree to which individuals will experience cross-the-aisle local social contacts through spatial interaction. This finding builds on existing research that has documented (1) that individuals prefer to move to and live in locations inhabited by co-partisans, and (2) that local geographic areas have become more polarized in recent decades. Our results indicate that large scale patterns of polarized migration flows serve as a potential mechanism that contributes to geographic partisan polarization.

Klíčová slova:

Animal migration – Census – Economic geography – Economics of migration – Elections – Network analysis – Unemployment rates – United States


Zdroje

1. Andris C, Lee D, Hamilton MJ, Martino M, Gunning CE, Selden JA. The rise of partisanship and super-cooperators in the US House of Representatives. PloS One. 2015;10(4):e0123507. doi: 10.1371/journal.pone.0123507 25897956

2. Sulkin T, Schmitt C. Partisan polarization and legislators’ agendas. Polity. 2014;46(3):430–448. doi: 10.1057/pol.2014.9

3. Aldrich JH, Montgomery JM, Sparks DB. Polarization and ideology: Partisan sources of low dimensionality in scaled roll call analyses. Political Analysis. 2014;22(4):435–456. doi: 10.1093/pan/mpt048

4. Endres K, Panagopoulos C. Boycotts, buycotts, and political consumerism in America. Research & Politics. 2017;4(4):1–9. https://doi.org/10.1177/2053168017738632.

5. Chen MK, Rohla R. The effect of partisanship and political advertising on close family ties. Science. 2018;360(6392):1020–1024. doi: 10.1126/science.aaq1433 29853686

6. Hopkins DJ. The increasingly United States: How and why American political behavior nationalized. Chicago: University of Chicago Press; 2018.

7. Tolbert CJ, Smith DA, Green JC. Strategic voting and legislative redistricting reform: district and statewide representational winners and losers. Political Research Quarterly. 2009;62(1):92–109. doi: 10.1177/1065912908314201

8. Hendricks JS. Popular election of the president: Using or abusing the Electoral College? Election Law Journal. 2008;7(3):218–226.

9. McDonald I. Migration and sorting in the American electorate: Evidence from the 2006 Cooperative Congressional Election Study. American Politics Research. 2011;39(3):512–533. doi: 10.1177/1532673X10396303

10. Tam Cho WK, Gimpel JG, Hui IS. Voter migration and the geographic sorting of the American electorate. Annals of the Association of American Geographers. 2013;103(4):856–870. doi: 10.1080/00045608.2012.720229

11. Gimpel JG, Hui IS. Seeking politically compatible neighbors? The role of neighborhood partisan composition in residential sorting. Political Geography. 2015;48:130–142. doi: 10.1016/j.polgeo.2014.11.003

12. Gimpel JG, Hui IS. Political fit as a component of neighborhood preference and satisfaction. City & Community. 2018;17(3):883–905. doi: 10.1111/cico.12320

13. Carlson C, Gimpel JG. Political implications of residential mobility and stasis on the partisan balance of locales. Political Geography. 2019;71:103–114. doi: 10.1016/j.polgeo.2019.02.011

14. Charyyev B, Gunes MH. Complex network of United States migration. Computational Social Networks. 2019;6(1):1. doi: 10.1186/s40649-019-0061-6

15. Lang C, Pearson-Merkowitz S. Partisan sorting in the United States, 1972–2012: new evidence from a dynamic analysis. Political Geography. 2015;48:119–129. doi: 10.1016/j.polgeo.2014.09.015

16. Johnston R, Jones K, Manley D. The growing spatial polarization of presidential voting in the United States, 1992–2012: Myth or reality? PS: Political Science & Politics. 2016;49(4):766–770.

17. Windzio M. The network of global migration 1990–2013: Using ERGMs to test theories of migration between countries. Social Networks. 2018;53:20–29. doi: 10.1016/j.socnet.2017.08.006

18. Liu X, Hollister R, Andris C. Wealthy Hubs and Poor Chains: Constellations in the US Urban Migration System. In: Agent-Based Models and Complexity Science in the Age of Geospatial Big Data. Springer; 2018. p. 73–86.

19. Breunig C, Cao X, Luedtke A. Global migration and political regime type: A democratic disadvantage. British Journal of Political Science. 2012;42(4):825–854. doi: 10.1017/S0007123412000051

20. McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: Homophily in social networks. Annual Review of Sociology. 2001;27(1):415–444. doi: 10.1146/annurev.soc.27.1.415

21. Mollica KA, Gray B, Treviño LK. Racial homophily and its persistence in newcomers’ social networks. Organization Science. 2003;14(2):123–136. doi: 10.1287/orsc.14.2.123.14994

22. Robins G, Pattison P, Kalish Y, Lusher D. An introduction to exponential random graph (p*) models for social networks. Social Networks. 2007;29(2):173–191.

23. Rivellini G, Terzera L, Amati V. Individual, dyadic and network effects in friendship relationships among Italian and foreign schoolmates. Genus. 2012;67(3):1–27.

24. Data ME, Lab S. County Presidential Election Returns 2000-2016; 2018. Available from: https://doi.org/10.7910/DVN/VOQCHQ.

25. Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press; 2006.

26. Krackardt D. QAP partialling as a test of spuriousness. Social Networks. 1987;9(2):171–186. doi: 10.1016/0378-8733(87)90012-8

27. Dekker D, Krackhardt D, Snijders TA. Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika. 2007;72(4):563–581. doi: 10.1007/s11336-007-9016-1 20084106

28. Rayer S, Brown DL. Geographic diversity of inter-county migration in the United States, 1980–1995. Population Research and Policy Review. 2001;20(3):229–252. doi: 10.1023/A:1010654618859

29. Denslow D, Pakhotina N. The effect of local taxes and spending on the migration of the elderly to high-amenity destinations. In: Proceedings of the Annual Conference on Taxation and Minutes of the Annual Meeting of the National Tax Association. vol. 98; 2005. p. 407–414.

30. Saks RE, Wozniak A. Labor reallocation over the business cycle: New evidence from internal migration. Journal of Labor Economics. 2011;29(4):697–739. doi: 10.1086/660772

31. Pacheco GA, Rossouw S, Lewer J. Do non-economic quality of life factors drive immigration? Social Indicators Research. 2013;110(1):1–15. doi: 10.1007/s11205-011-9924-4

32. Ravuri E. The effect of the housing crisis on interstate migration in the counties of Florida, 2008–2009 and 2013–2014. Southeastern Geographer. 2016;56(3):346–367. doi: 10.1353/sgo.2016.0037

33. Ravuri E. Determinants of Net Migration in Montana. Great Plains Research. 2010;20:179–192.

34. Fournier GM, Rasmussen DW, Serow WJ. Elderly migration as a response to economic incentives. Social Science Quarterly. 1988;69(2):245.

35. Reuveny R, Moore WH. Does environmental degradation influence migration? Emigration to developed countries in the late 1980s and 1990s. Social Science Quarterly. 2009;90(3):461–479. doi: 10.1111/j.1540-6237.2009.00569.x

36. Dekker D, Krackhardt D, Snijders T. Multicollinearity robust QAP for multiple regression. In: 1st Annual Conference of the North American Association for Computational Social and Organizational Science; 2003. p. 22–25.

37. Lazer D, Pentland AS, Adamic L, Aral S, Barabasi AL, Brewer D, et al. Life in the network: the coming age of computational social science. Science. 2009;323(5915):721.


Článek vyšel v časopise

PLOS One


2019 Číslo 11
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#