Is "Threat" in the Eye of the Researcher? Theory and Measurement in the Study of State‐Level Immigration Policymaking.

U.S. states politics & government; IMMIGRATION policy; UNITED States immigration policy; FEDERAL-state controversies; RACE relations; RACISM; POWER (Social sciences); METHODOLOGY

This study initiates a methodological critique of the state‐level immigration policy literature through the lens of the racial threat and group power perspectives. First, I highlight the conceptual problems related to the application of such theories to legislature‐level data analysis. Next, I demonstrate the methodological and analytical problems that raise concerns about ad hoc theorizing in this field. Using counts of hostile and welcoming immigration legislation (2005–2011), I demonstrate that outgroup size measures correlate positively with both dependent variables while measures of population growth rate yield null results unless used on their own. These results suggest that the use of legislature‐level models with demographic indicators does not allow us to gain a clear understanding of whether and how population dynamics influence immigration policymaking. Based on these findings, I recommend that when using demographic indicators as key explanatory variables, researchers provide evidence of result consistency across multiple model specifications and also test the models with both hostile and inclusive policy variables. Such protocols would help avoid ad hoc theorizing. [ABSTRACT FROM AUTHOR]

借由种族威胁和群体力量的视角，本研究对有关州级移民政策的文献提出了方法论上的批判。首先，我强调了一些概念性问题，这些问题是由上述文献理论被用于分析立法机构层级的数据而引起的。接下来，我展示了对这一领域进行特殊理论化而带来的方法问题和解析问题。通过使用敌对型和友好型移民立法的数据（2005年至2011年），我的结果显示，外群规模的测量值对两个因变量都具有正相关性，而人口增长率的测量值则产生零结果，除非人口增长率测量值独立作用于因变量。这些结果表明，使用具有人口指标的立法机构层级的模型并不能让我们清楚地了解到人口动态是否以及如何影响移民政策的决策。基于这些研究结果，我建议，当使用人口统计指标作为关键解释变量时，研究人员应该提供多个模型指标之间结果一致性的证据，并且使用敌对或包容的政策变量来检验这些模型。这样的建议将有助于避免特殊理论。 [ABSTRACT FROM AUTHOR]

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