Vachuska Karl
University of Wisconsin-Madison, 1180 Observatory Drive, Madison, WI 53706, USA.
Socius. 2024 Jan-Dec;10. doi: 10.1177/23780231241286366. Epub 2024 Nov 3.
Contemporary sociological research emphasizes the need to analyze inequality beyond nominal categories. While research has grown in this regard at the individual level, little research has pursued this approach with neighborhoods. This paper explores how names can serve as a measure of the perceived typicality associated with race, and how names are associated with neighborhood characteristics. Analyses on data with the names of over 300 million Americans demonstrate that name-based racial composition more fully explains socioeconomic disparities among neighborhoods than conventional survey-based racial composition metrics. Neighborhoods with the most Black-sounding names demonstrate greater socioeconomic disadvantage than neighborhoods with the most individuals self-identifying as Black. Additionally, naming patterns explain variation in socioeconomic inequality within both predominately-nominally Black and predominately-nominally white neighborhoods-where little nominal racial variation exists. This research suggests that infracategorical measures of race can provide additional predictive power to nominal measures of racial composition when analyzing neighborhood inequalities.
当代社会学研究强调超越名义类别来分析不平等的必要性。虽然在个体层面上这方面的研究有所增加，但很少有研究在邻里层面采用这种方法。本文探讨了名字如何能够作为与种族相关的感知典型性的一种衡量标准，以及名字如何与邻里特征相关联。对超过3亿美国人名字数据的分析表明，基于名字的种族构成比传统的基于调查的种族构成指标更能充分解释邻里之间的社会经济差异。名字听起来最像黑人的邻里比那些自我认同为黑人的个体最多的邻里表现出更大的社会经济劣势。此外，命名模式解释了在名义上主要为黑人或主要为白人的邻里内部社会经济不平等的差异，而这些邻里在名义上的种族差异很小。这项研究表明，在分析邻里不平等时，种族的亚分类衡量标准可以为种族构成的名义衡量标准提供额外的预测力。
