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Articles

Linking Racial Composition, Black–White Inequality, and Regional Difference: The Role of Migration

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Pages 128-144 | Published online: 27 Oct 2017
 

ABSTRACT

The percent black–inequality relationship and the unique position of the South have been cornerstones of research on U.S. racial inequality. Using an innovative methodological approach, I address how migration contributes to our understanding of the percent black–inequality relationship. I find that the educationally selective migration of blacks and whites significantly contributes to the percent black–inequality relationship via compositional changes. However, any explanatory power is limited to the non-South. Migration plays a role in understanding this relationship, yet processes related to black population concentration still generate black disadvantage anew, particularly within the South.

Acknowledgments

I would like to thank my colleagues for providing feedback on earlier drafts of this manuscript, especially Katherine Curtis, Jenifer Bratter, Junia Howell, and Jim Elliott. I also thank the Population Association of America for the opportunity to present this work at the 2014 annual meeting in Boston.

Funding

This work was supported by the core (#R24 HD047873) and training (#T32 HD07014) grants awarded to the Center for Demography and Ecology at the University of Wisconsin, Madison, by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Notes

1. I use “inequality,” “disparities,” and “black disadvantage” interchangeably despite their nuanced connotations. Although I aim to speak to issues of inequality, my data more accurately reflect disparities because my outcome shows aggregate differences and cannot demonstrate the source of that difference. Furthermore, disparities could refer to either a white or black disadvantage, but given that disparities are more often to the disadvantage of blacks I use “disparities” and “inequality” to refer to black disadvantage.

2. Frey’s (Citation1979, Citation1980) work has also addressed the local, compositional consequences of migration by examining the effect of white migration on metropolitan areas. The primary distinction between his work and this study is my focus on local levels of black–white educational inequality rather than on local white educational composition (Frey Citation1979) and changes in the economic tax base of metropolitan areas (Frey Citation1980).

3. Flows with fewer than three migrants were not included in the published data file as a unique observation, and were excluded from my analysis because they could not be attached to specific county pairs.

4. Ideally, the available data would also be cross-tabulated by age group since educational opportunities and expectations have changed over time. Unfortunately, such detailed data (i.e., education data for specific race-age categories) are unavailable for both the migration flow data and the 2000 census baseline estimates.

5. Comparing the standardized coefficients from models with different dependent variables is justified by the similarity of the variables’ standard deviations (19.5 and 20.3, baseline and no-migration, respectively).

6. This is a dichotomous variable adapted from 2003 Rural–Urban Continuum codes provided by the Economic Research Service (http://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx), where the Rural–Urban continuum codes 1–7 represent metropolitan counties (coded 1) and codes 8–9 represent nonmetropolitan counties (coded 0).

Additional information

Funding

This work was supported by the core (#R24 HD047873) and training (#T32 HD07014) grants awarded to the Center for Demography and Ecology at the University of Wisconsin, Madison, by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Notes on contributors

Heather A. O’Connell

Heather A. O’Connell is an Assistant Professor in the Sociology Department at Louisiana State University. Her research centers on understanding the persistence of racial inequality in the United States.

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