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Abstract
Amid the growing affordable housing crisis and widespread gentrification over the last decade, people have been moving less than before and increasingly live in shared and often crowded households across the U.S. Crowded housing has various negative health implications, including stress, sleep disorders, and infectious diseases. Difference-in-difference analysis of a unique, large-scale longitudinal consumer credit database of over 450,000 San Francisco Bay Area residents from 2002 to 2020 shows gentrification affects the probability of residents shifting to crowded households across the socioeconomic spectrum but in different ways than expected. Gentrification is negatively associated with low- socioeconomic status (SES) residents’ probability of entering crowded households, and this is largely explained by increased shifts to crowded households in neighborhoods outside of major cities showing early signs of gentrification. Conversely, gentrification is associated with increases in the probability that middle-SES residents enter crowded households, primarily in Silicon Valley. Lastly, crowding is positively associated with COVID-19 case rates, beyond density and socioeconomic and racial composition in neighborhoods, although the role of gentrification remains unclear. Housing policies that mitigate crowding can serve as early interventions in displacement prevention and reducing health inequities.
Acknowledgments
The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of San Francisco or the Federal Reserve System. We are grateful to Tim Thomas, the reviewers, and the editors for providing feedback on this manuscript and to Ruben Anguiano, Vineet Gupta, Vasudha Kumar, Becky Liang, A. J. Nadel, and Brooke Tran for their excellent research assistance.
Disclosure Statement
No potential conflict of interest was reported by the authors.
Notes
1 Authors’ calculations using the 2000 U.S. Census and the 2019 American Community Survey 1-year estimates adjusted to 2019 real U.S. dollars.
2 Cultural, social, political, and commercial displacement are other critical forms of displacement that are beyond the scope of this study.
3 Calculations by the authors based on the 2000 U.S. Census and the 2019 American Community Survey 1-year estimates.
4 The data that Equifax makes available to researchers do not include actual SSNs or any other identifying information, such as names, full addresses, or demographics (besides age).
5 We ran a supplementary regression analysis with a longitudinal panel of residents who were in the Bay Area in 2010, dropping individuals once they moved from their 2010 neighborhood. The substantive findings are similar to the ones we present for stayers except there are no significant differences for high-SES residents across gentrification categories.
6 Due to data limitations, we are not able to account for the possibility that some individuals age into the data set, increasing the number of adults in the household. The results are similar from analyses measuring shifts to crowded households by an increase of at least two adults, which suggests that this limitation does not drive the findings.
7 In Napa County, there were five areas for which only a range was specified (6–50), although areas with counts above 32 were listed. We assumed that the count in the area with the smallest population was 6 and that in the largest area was 32, and that counts for the three areas in between were proportionate to their population sizes. Results are similar to those presented when we bottom-code all areas with low ranges to 0 instead of 10.
8 We exclude tracts with fewer than 100 residents or 50 housing units at either the beginning or the end of the period.
9 In supplementary analyses, we separately analyze whether high-income tracts are undergoing “super-gentrification,” in which middle-class neighborhoods become even wealthier and more expensive (Lees Citation2003). About 37% of high-income tracts in the Bay Area experience this trajectory, and we did not find differences between super-gentrifying and other high-income tracts in the probability of shifting to crowded housing. We also ran an analysis categorizing tracts as gentrifying based only changes in their housing prices, regardless of whether they were gentrifiable. We do not find overall differences across these categories, but the results are similar to the main results across SES categories, except there are no differences for low-SES residents. Together, these findings suggest that price increases alone are not driving differences.
10 Credit scores are based on a variety of factors, including previous payment history, outstanding debts, length of credit history, new accounts, and the types of credit used (Federal Reserve Board 2007; Fair Isaac Corporation Citation2015). Low credit scores are often a result of delinquency, large increases in one’s debt, or events of public record, such as bankruptcy or foreclosures (Anderson Citation2007).
11 The baseline year for measuring gentrification is based on the 2006–2010 ACS, so it is not surprising that there are departures during this window. In this model, the difference between gentrifying and nongentrifying neighborhoods is weaker (p < .10), but the results by SES are similar to those shown.
12 White-mixed tracts include tracts with shares of Black residents of less than 10% and total share of other non-White groups of less than 50%. They comprise 39.6% of tracts. Black-other tracts are tracts that are over 10% Black but do not contain substantial shares of other non-White groups. They comprise 15.2% of tracts. Mixed/other tracts include tracts that have a total share of other non-White groups of greater than 50% (41.7%) and multiethnic tracts that have over 40% White, 10% Black, and 10% another ethnoracial group (3.1%).
13 Because the Bay Area has a higher cost of living than the rest of the U.S., the distribution of residents categorized as middle-SES in this study reflects the distribution of people below 100% of the metropolitan area median income.
14 Following Hause et al. (Citation2021), we determined the COVID-19 case rate thresholds for the categories mapped by multiplying the 7-day thresholds used by the U.S. Centers for Disease Control and Prevention by 56—the number of weeks between the first reported COVID-19 case in the Bay Area and when we obtained the COVID-19 data.
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Jackelyn Hwang
Jackelyn Hwang is an Assistant Professor of Sociology at Stanford University and Director of the Changing Cities Research Lab. Her primary research interests are in the fields of urban sociology, race and ethnicity, immigration, and inequality. Her recent projects leverage innovative data and methods to examine how changes in U.S. cities affect racial segregation and inequality to inform policy solutions that promote racial equity. Her work has appeared in the American Journal of Sociology, American Sociological Review, City & Community, Demography, and Social Forces, among others and has been supported by the American Sociological Association, the Chan Zuckerberg Initiative, the Joint Center for Housing Studies, and the National Science Foundation, among others.
Bina Patel Shrimali
Bina Patel Shrimali, DrPH is Vice President of Community Development at the Federal Reserve Bank of San Francisco, which conducts research on the structural barriers to economic opportunity for low-income communities and communities of color. In this role, she provides guidance for the department’s research agenda and publications that advance healthy and resilient communities, a thriving labor force, and inclusive financial systems. Recent topics of her own work include the role of economic conditions on racial health disparities at birth and student loan debt. Bina also has extensive experience pioneering cross-sector collaboration to improve child health outcomes at the Alameda County Public Health Department. Bina received her BA in Economics and English, her master’s in Epidemiology and Biostatistics, and her doctorate in Public Health, all from UC Berkeley.