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Journal ArticleDOI

Politicized Places: Explaining Where and When Immigrants Provoke Local Opposition

Daniel J. Hopkins
- 01 Feb 2010 - 
- Vol. 104, Iss: 01, pp 40-60
TLDR
This article developed the politicized places hypothesis, an alternative that focuses on how national and local conditions interact to construe immigrants as threatening, and tested the hypothesis using new data on local anti-immigrant policies.
Abstract
In ethnic and racial terms, America is growing rapidly more diverse. Yet attempts to extend racial threat hypotheses to today's immigrants have generated inconsistent results. This article develops the politicized places hypothesis, an alternative that focuses on how national and local conditions interact to construe immigrants as threatening. Hostile political reactions to neighboring immigrants are most likely when communities undergo sudden influxes of immigrants and when salient national rhetoric reinforces the threat. Data from several sources, including twelve geocoded surveys from 1992 to 2009, provide consistent support for this approach. Time-series cross-sectional and panel data allow the analysis to exploit exogenous shifts in salient national issues such as the September 11 attacks, reducing the problem of residential self-selection and other threats to validity. The article also tests the hypothesis using new data on local anti-immigrant policies. By highlighting the interaction of local and national conditions, the politicized places hypothesis can explain both individual attitudes and local political outcomes.

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Posted ContentDOI

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Journal ArticleDOI

Diversity and violence during conflict migration: The Troubles in Northern Ireland

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References
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Book ChapterDOI

Prospect theory: an analysis of decision under risk

TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
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Data Analysis Using Regression and Multilevel/Hierarchical Models

TL;DR: Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.
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TL;DR: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes.
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TL;DR: The Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data and Inference by Data Augmentation Methods for Normal Data provide insights into the construction of categorical and mixed data models.
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