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Banks, alternative institutions and the spatial–temporal ecology of racial inequality in US cities

Nature Human Behaviour volume 5, pages 1622–1628 (2021)Cite this article

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Abstract

Research has made clear that neighbourhood conditions affect racial inequality. We examine how living in minority neighbourhoods affects ease of access to conventional banks versus alternative financial institutions (AFIs) such as check cashers and payday lenders, which some have called predatory. Based on more than 6 million queries, we compute the difference in the time required to walk, drive or take public transport to the nearest bank versus AFI from the middle of every block in each of 19 of the largest cities in the United States. The results suggest that race is strikingly more important than class: even after numerous conditions are accounted for, the AFI is more often closer than the bank in low-poverty racial/ethnic minority neighbourhoods than in high-poverty white ones. Results are driven not by the absence of banks but by the prevalence of AFIs in minority areas. Gaps appear too large to reflect simple differences in preferences.

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Fig. 1: AFIs are easier to get to as proportion minority in neighbourhood increases, regardless of whether neighbourhood is high or low poverty.
Fig. 2: Could race differences in demand account for the pattern? Banks are still harder to get to in low-poverty, college-educated, minority homeowner neighbourhoods than high-poverty, low-education, white renter neighbourhoods.

Data availability

The Google Places establishment data were collected using a Google Maps API Premium Plan. The licence precludes publicly sharing the Places location data. Instead, we provide the travel times by foot, car and public transport from the centroid of each block, aggregated to the block group. These travel times are available at https://github.com/urbaninformaticsandresiliencelab/bnk_afi_si/tree/master/_all_data. The 2015 American Community Survey 5-year data files were collected from Census Bureau file transfer protocol (FTP) server (https://www.census.gov/data/developers/data-sets/acs-5year.2015.html). Full details on the variables used are included in Supplementary Discussion, Section 4. The street grid and associated variables were obtained from OpenStreetMap data (https://www.openstreetmap.org). The public transport schedules and associated data were obtained from each city’s General Transit Feed Specification, via the Transitland platform (https://www.transit.land/). The minimum dataset needed for replicating our full set of results is available at https://github.com/urbaninformaticsandresiliencelab/bnk_afi_si/tree/master/modeling_data_cleaned. Source data are provided with this paper.

Code availability

The travel times were calculated with the open-source GraphHopper routing engine and OpenTripPlanner, using OpenStreetMap data. The main results were produced using STATA. The replication code for processing travel times is available at https://github.com/urbaninformaticsandresiliencelab/bnk_afi_si/tree/master/scripts/python. The replication code for the empirical analysis is available at https://github.com/urbaninformaticsandresiliencelab/bnk_afi_si/tree/master/scripts/stata. Source data are provided with this paper.

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Acknowledgements

The authors thank J. Beshears, T. García Mathewson and R. Sampson for comments, and M. Mobius for helpful early conversations. M.L.S. received funding from Harvard University and the Harvard Project on Race, Class and Cumulative Adversity, at the Hutchins Center, to support this project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Sociology, Harvard University, Cambridge, MA, USA

    Mario L. Small & Mo Torres

  2. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA

    Armin Akhavan & Qi Wang

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Contributions

M.L.S. designed research, performed research, analysed data and drafted paper. A.A. created dataset and visualizations, analysed data, produced replication package and edited paper. M.T. performed research and edited paper. Q.W. co-created dataset, performed research and edited paper.

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Correspondence to Mario L. Small.

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The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Megan Doherty Bea, George Galster and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary information

Supplementary Discussion and Supplementary Tables 1–3.

Reporting summary

Source data

Source Data Fig. 1

Data for Fig. 1. AFI easier to get to as proportion minority in neighbourhood increases, regardless of whether neighbourhood is poor or non-poor. Adjusted and unadjusted included probability that AFI establishment is faster to get to.

Source Data Fig. 2

Data for Fig. 2. Could race differences in demand account for the pattern? Banks still harder to get to in low-poverty, college-educated, minority homeowner neighbourhoods than high-poverty, low-education, white renter neighbourhoods.

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Small, M.L., Akhavan, A., Torres, M. et al. Banks, alternative institutions and the spatial–temporal ecology of racial inequality in US cities. Nat Hum Behav 5, 1622–1628 (2021). https://doi.org/10.1038/s41562-021-01153-1

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