Gentrification, Mobility, and Exposure to Contextual Determinants of Health

Abstract

This study examines exposure to four contextual Determinants of Health (cDOH): healthcare access (Medically Underserved Areas), socioeconomic condition (Area Deprivation Index), air pollution (Nitrogen Dioxide (NO₂), Particulate Matter 2.5 (PM 2.5) and Particulate Matter 10 (PM 10)), and walkability (National Walkability Index) among residents of gentrifying and not gentrifying lower income neighborhoods in central cities for the 100 largest metropolitan regions in the US using their location in 2006 and 2019 based on individual level consumer trace data. Individuals who lived in gentrifying neighborhoods as of 2006 had more favorable cDOH in terms of MUA, ADI and Walkability Index and similar levels of pollution. Between 2006 and 2019, they experienced worse changes in MUAs, ADI, and Walkability Index but a greater improvement in exposure to air pollutants. The negative changes are driven by movers, while stayers actually experience a relative improvement in MUAs and ADI and larger improvements in exposure to air pollutants. The findings indicate that gentrification may contribute to health disparities through changes in exposure to cDOH through mobility to communities with worse cDOH among residents of gentrifying neighborhoods although results in terms of exposure to health pollutants are mixed.

Keywords:

• Gentrification
• contextual social determinants of health
• mobility

Acknowledgements

The authors thank Housing Policy Debate Editors, Dr. Vincent Reina and Dr. George Galster, the Editors of the Special Issue on “Gentrification, Housing, and Health Outcomes”, Dr. Joseph Gibbons and Dr. Derek Hydra and three anonymous reviewers for their useful comments and suggestions during the review process. The authors are grateful to the Center for Studies in Demography & Ecology at the University of Washington, the University Washington and the Runstad Department of Real Estate for purchasing the consumer data. The support of Phil Hurvitz and the University of Washington Data Collaborative in providing the data infrastructure to access the data is also much appreciated. Partial support for this research came from a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant, P2C HD042828, to the Center for Studies in Demography & Ecology at the University of Washington. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Figure 1. cDOH measures by gentrifying and not gentrifying status for selected regions.

Table

Table

Notes

Notes

1 We acknowledge that gentrification can also take place in suburban (Markley, 2018) and rural (Sherman, 2021) communities but focus here on central cities due to shared historical patterns of disinvestment and reinvestment in public and private goods in their communities that are expected to be directly related to cDOH.

2 Distance to the center is estimated as the Euclidian distance in km between the individual address and City Hall for a central-city MSA, as determined and made available by the U.S. Census Bureau (https://www.census.gov/library/publications/2012/dec/c2010sr-01.html). This variable aims to capture the spatial location of the units within the urban system, with more central locations expected to be more desirable. See Holian (2019) for a discussion of the available measures of centrality.

3 Following the rule of thumb values developed by Oster (2019) and used by Brummet and Reed, we use Rmax = 1.3 times the R² for the model with full controls and little delta = 1, assuming the influence of remaining unobservables is proportional to the influence of full controls.