Neighborhood Diversity

Many neighborhoods provide access to services and amenities like schools and parks. Not all neighborhoods, however, are invested in equality. Racial segregation may be one reason why minority neighborhoods often have less benefits than White neighborhoods at the same income levels.[1] Although most people living in the United States are satisfied with the racial mix of their community according to a Pew Research Center survey,[2] living in more integrated neighborhoods could decrease racial prejudice, improve education, and benefit health.[3],[4],[5]
Discriminatory housing practices have contributed to racial segregation in American communities, including San Diego.[6] Redlining and other discriminatory housing policies allowed lenders to offer people of color predatory loans and realtors to steer people to minority or mixed neighborhoods, while steering White people to predominantly White neighborhoods.[7],[8] Property values lagged in minority neighborhoods because they were seen as less desirable. Housing discrimination unfairly restricted wealth building and the quality of public services for people living there. This led to the geographic concentration of poverty in majority-Black neighborhoods.[9],[10],[11] Although redlining was banned by the Fair Housing Act of 1968, its legacy lives on.[12]
The American Community Survey 2021 5-year estimates were used to measure racial diversity within neighborhoods in San Diego County.[13] A diversity score was calculated for each ZIP Code Tabulation Area (ZCTA) with a theoretical range from 0 (for no diversity, as occurs when everyone in the ZCTA is of the same race/ethnicity) to 0.875 (as would occur if the population in the ZCTA were exactly equally distributed between the eight racial/ethnic categories used in the table cited[14]). In San Diego County ZCTAs, the scores ranged from 0 to 0.732. The 0 score was registered in one ZCTA where 100% of the population was White. The 0.732 score was in a ZCTA that was 0.04% Some Other Race, 0.1% American Indian or Alaska Native, 1.5% Native Hawaiian or Pacific Islander, 6.3% Multiracial, 11.6% White, 12.7% Black or African American, 29.5% Asian, and 38.3% Hispanic or Latino.
For the map below, the score was divided into five equal groups to display racial/ethnic diversity in San Diego County and were categorized from least to most diverse. In general, the east and coastal parts of the county were less diverse.
Policy actions recommended by the Urban Institute include testing real estate agents, rental housing providers, lending institutions, and mortgage brokers to detect discrimination in the house buying and rental market. Other actions include educating the public through a local fair housing organization or a housing counseling center to address fears and stereotypes that may be associated with diverse neighborhoods.  Affordable housing, Housing Choice Vouchers, and investing in historically marginalized neighborhoods to improve schools, parks, and other amenities may be, incentives to encourage people to move to neighborhoods where they are not the majority race or ethnicity. Finally, enhanced down payment assistance, low-interest loans, and other types of financial assistance may help families move to neighborhoods they may not otherwise afford.[15]
Data Information
Data Source: 2021 American Community Survey 5-Year Estimates, Table B03002.
  • Unavailable data include ZCTAs that are not defined by the U.S. Census Bureau and ZCTAs with missing or censored data.
References
  1. Roithmayr, D. (2004). Locked in Segregation. Virginia Journal of Social Policy & the Law, 12, 197-294.
  2. Horowitz, J. M. (2019). Americans See Advantages and Challenges in Country’s Growing Racial and Ethnic Diversity. https://www.pewresearch.org/social-trends/2019/05/08/americans-see-advantages-and-challenges-in-countrys-growing-racial-and-ethnic-diversity/
  3. Ihlanfeldt, K. R., & Scafidi, B. P. (2002). The Neighbourhood Contact Hypothesis: Evidence from the Multicity Study of Urban Inequality. Urban Studies, 39(4), 619–641. https://doi.org/10.1080/00420980220119499
  4. Acs, G., Pendall, R., Treskon, M., & Khare, A. (2017). The Cost of Segregation: National Trends and the Case of Chicago, 1990–2010. Urban Institute. Retrieved from https://www.urban.org/research/publication/cost-segregation
  5. Wells, A. S., Fox, L., & Cordova-Cobo, D. (2016). How Racially Diverse Schools and Classrooms Can Benefit All Students. The Century Foundation. Retrieved from https://tcf.org/content/report/how-racially-diverse-schools-and-classrooms-can-benefit-all-students/
  6. Ford, L., & Griffin, E. (1979). The ghettoization of paradise. Geographical Review, 69(2), 140–158.
  7. Massey, D. S., & Denton, N. A. (1993). American Apartheid: Segregation and the Making of the Underclass. Harvard University Press, pp. 51-57, 98-100.
  8. Coates, T.-N. (2014). The Case for Reparations. The Atlantic, June 2014. https://www.theatlantic.com/magazine/archive/2014/06/the-case-for-reparations/361631/
  9. Nelson, R. K., Winling, L., Marciano, R., & Nathan, C. (n.d.). Mapping Inequality. American Panorama. Retrieved August 8, 2023, from https://dsl.richmond.edu/panorama/redlining/#loc=12/32.739/-117.174&city=san-diego-ca
  10. Rothstein, R. (2017). The Color of Law, A Forgotten History of How Our Government Segregated America. Liveright.
  11. Gruenstein Bocian, D., & Zhai, R. (2005). Borrowers in Higher Minority Areas More Likely to Receive Prepayment Penalties on Subprime Loans. Center for Responsible Lending. Retrieved from https://www.responsiblelending.org/research-publication/borrowers-higher-minority-areas-more-likely-receive-prepayment-penalties
  12. Mitchell, B., & Franco, J. (2018, March 20). HOLC “redlining” maps: The persistent structure of segregation and economic inequality » NCRC. https://ncrc.org/holc/
  13. U.S. Census Bureau. (n.d.). B03002 – Hispanic or Latino Origin by Race. Retrieved from https://data.census.gov/table?q=B03002&g=310XX00US41740$8600000  
  14. Formally, Ethnic heterogeneity=1-( pAIAN2+ pAsian2+ pBlack2+ pHispanic2+ pNHPI2+ pWhite2+ pMulti2+ pOther2) where p is the proportion of the population in each racial/ethnic category. The number of categories used to calculate the measure impacts the theoretical distribution of the measure, so caution should be used in comparing this measure year-to-year if the measure is modified. Blau, P. (1977). Inequality and Heterogeneity: A Primitive Theory of Social Structure. Free Press.
  15. Turner, M. A., & Rawlings, L. (2009). Promoting Neighborhood Diversity: Benefits, Barriers, and Strategies. The Urban Institute. Retrieved from https://www.urban.org/research/publication/promoting-neighborhood-diversity-benefits-barriers-and-strategies
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Updated February 7, 2024