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The Significance of Name-Based Racial Composition in Analyzing Neighborhood Disparities.

Karl Vachuska1

  • 1University of Wisconsin-Madison, 1180 Observatory Drive, Madison, WI 53706, USA.

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Summary
This summary is machine-generated.

Analyzing neighborhood inequality using names reveals deeper socioeconomic disparities than traditional race categories. Name-based racial composition offers a more accurate measure of disadvantage.

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Area of Science:

  • Sociology
  • Urban Studies
  • Racial Inequality Research

Background:

  • Contemporary sociology calls for analyzing inequality beyond broad racial categories.
  • Existing research on this topic primarily focuses on the individual level, with limited exploration at the neighborhood level.
  • The utility of perceived racial typicality, as indicated by names, for understanding neighborhood characteristics remains underexplored.

Purpose of the Study:

  • To investigate how names can serve as a proxy for perceived racial typicality.
  • To examine the association between names and neighborhood socioeconomic characteristics.
  • To determine if name-based racial composition offers a more nuanced understanding of neighborhood inequalities compared to traditional metrics.

Main Methods:

  • Analysis of a large dataset comprising names of over 300 million Americans.
  • Comparison of socioeconomic disparities using name-based racial composition versus conventional survey-based racial composition metrics.
  • Examination of naming patterns' predictive power for socioeconomic inequality within neighborhoods defined by nominal racial composition.

Main Results:

  • Name-based racial composition more comprehensively explains neighborhood socioeconomic disparities than conventional metrics.
  • Neighborhoods with a high prevalence of 'Black-sounding' names exhibit greater socioeconomic disadvantage than those with a high self-identification of Black residents.
  • Naming patterns account for socioeconomic inequality variations within both predominantly 'Black-named' and 'White-named' neighborhoods, even where nominal racial variation is minimal.

Conclusions:

  • Infracategorical measures of race, derived from names, provide significant predictive power for analyzing neighborhood inequalities.
  • Perceived racial typicality, as reflected in names, is a crucial factor in understanding socioeconomic disparities at the neighborhood level.
  • This approach enhances traditional racial composition metrics, offering a more granular view of inequality.