Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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


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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

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 The 2015 American Community Survey 5-year data files were collected from Census Bureau file transfer protocol (FTP) server ( Full details on the variables used are included in Supplementary Discussion, Section 4. The street grid and associated variables were obtained from OpenStreetMap data ( The public transport schedules and associated data were obtained from each city’s General Transit Feed Specification, via the Transitland platform ( The minimum dataset needed for replicating our full set of results is available at 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 The replication code for the empirical analysis is available at Source data are provided with this paper.


  1. Wilson, W. J. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy (Univ. Chicago Press, 1987).

  2. Sampson, R. J. Great American City: Chicago and the Enduring Neighborhood Effect (Univ. Chicago Press, 2012).

  3. Chetty, R. & Hendren, N. The impacts of neighborhoods on intergenerational mobility II: county-level estimates. Q. J. Econ. 133, 1163–1228 (2018).

    Article  Google Scholar 

  4. Ludwig, J. et al. Neighborhood effects on the long-term well-being of low-income adults. Science 337, 1505–1510 (2012).

    Article  CAS  Google Scholar 

  5. Goering, J. & Feins, J. D. Choosing a Better Life? Evaluating the Moving to Opportunity Social Experiment (Urban Institute Press, 2003).

  6. Small, M. L. & Newman, K. Urban poverty after The Truly Disadvantaged: the rediscovery of the family, the neighborhood, and culture. Annu. Rev. Sociol. 27, 23–45 (2001).

    Article  Google Scholar 

  7. Sharkey, P. & Faber, J. W. Where, when, why, and for whom do residential contexts matter? Moving away from the dichotomous understanding of neighborhood effects. Annu. Rev. Sociol. 40, 559–579 (2014).

    Article  Google Scholar 

  8. Small, M. L. & McDermott, M. The presence of organizational resources in poor urban neighborhoods: an analysis of average and contextual effects. Soc. Forces 84, 1697–1724 (2006).

    Article  Google Scholar 

  9. Faber, J. W. Segregation and the cost of money: race, poverty, and the prevalence of alternative financial institutions. Soc. Forces 98, 819–848 (2019).

    Article  Google Scholar 

  10. Hegerty, S. W. Commercial bank locations and “banking deserts”: a statistical analysis of Milwaukee and Buffalo. Ann. Reg. Sci. 56, 253–271 (2016).

    Article  Google Scholar 

  11. Walker, R. E., Keane, C. R. & Burke, J. G. Disparities and access to healthy food in the United States: a review of food deserts literature. Health Place 16, 876–884 (2010).

    Article  Google Scholar 

  12. Moore, L. & Roux, A. V. D. Association of neighborhood characteristics with the location and type of food stores. Am. J. Public Health 96, 325–331 (2006).

    Article  Google Scholar 

  13. Goodstein, R. M. & Rhine, S. L. W. The effects of bank and nonbank provider locations on household use of financial transaction services. J. Bank Financ. 78, 91–107 (2017).

    Article  Google Scholar 

  14. Hogarth, J. M., Anguelov, C. E. & Lee, J. Who has a bank account? Exploring changes over time, 1989-2001. J. Fam. Econ. Issues 26, 7–30 (2005).

    Article  Google Scholar 

  15. FDIC 2019 Summary of deposits highlights. FDIC Q. 14, 31–43 (2020).

    Google Scholar 

  16. Results from survey of consumer finance. Federal Reserve Board (2013).

  17. Consumers and mobile financial services. Federal Reserve Board (2016).

  18. FDIC. Brick-and-mortar banking remains prevalent in an increasingly virtual world. FDIC Q. 9, 37–51 (2015).

    Google Scholar 

  19. Burhouse, S., et al. 2013 FDIC national survey of unbanked and underbanked households. Federal Deposit Insurance Corporation (2014).

  20. Wilson, W. J. When Work Disappears: The World of the New Urban Poor (Knopf, 1996).

  21. Anderson, E. Code of the Street: Decency, Violence, and the Moral Life of the Inner City (WW Norton, 1999).

  22. Venkatesh, S. A. Gang Leader for a Day: A Rogue Sociologist Takes to the Streets (Penguin, 2008).

  23. Caskey, J. P. Bank representation in low-income and minority urban communities. Urban Aff. Q. 29, 617–638 (1994).

    Article  Google Scholar 

  24. Simpson, W. & Buckland, J. Dynamics of the location of financial institutions: who is serving the inner city? Econ. Dev. Q. 30, 358–370 (2016).

    Article  Google Scholar 

  25. Faber, J. W. Cashing in on distress: the expansion of fringe financial institutions during the Great Recession. Urban Aff. Rev. 54, 663–696 (2018).

    Article  Google Scholar 

  26. Friedline, T. & Kepple, N. Does community access to alternative financial services relate to individuals’ use of these services? Beyond individual explanations. J. Consum. Policy 40, 51–79 (2017).

    Article  Google Scholar 

  27. Caskey, J. Fringe Banking: Check-Cashing Outlets, Pawnshops, and the Poor (Russell Sage Foundation, 1994).

  28. Carter, S. P., Skiba, P. M. & Tobacman, J. in Financial Literacy: Implications for Retirement Security and the Financial Marketplace (eds Mitchell, O. S. & Lusardi, A) 145–157 (Oxford Univ. Press, 2010).

  29. Agarwal, S., Skiba, P. M. & Tobacman, J. Payday loans and credit cards: new liquidity and credit scoring puzzles? Am. Econ. Rev. Pap. Proc. 99, 412–417 (2009).

    Article  Google Scholar 

  30. Baradaran, M. How the poor got cut out of banking. Emory Law J. 62, 483–548 (2013).

    Google Scholar 

  31. Melzer, B. T. The real costs of credit access: evidence from the payday lending market. Q. J. Econ. 126, 517–555 (2011).

    Article  Google Scholar 

  32. Laraia, B. A., Siega-Riz, A. M., Kaufman, J. S. & Jones, S. J. Proximity of supermarkets is positively associated with diet quality index for pregnancy. Prev. Med. 39, 869–875 (2004).

    Article  Google Scholar 

  33. Langford, M., Higgs, G. & Dallimore, D. J. Investigating spatial variations in access to childcare provision using network-based geographic information system models. Soc. Policy Admin. 53, 661–677 (2018).

    Article  Google Scholar 

  34. Macdonald, L. Associations between spatial access to physical activity facilities and frequency of physical activity; how do home and workplace neighbourhoods in West Central Scotland compare? Int J. Health Geogr. 18, 2 (2019).

    Article  Google Scholar 

  35. Gross, M. B., Hogarth, J. M., Manohar, A. & Gallegos, S. Who uses alternative financial services, and why? Consum. Interests Annu. 58, 1–13 (2012).

    Google Scholar 

  36. Stegman, M. A. & Faris, R. Payday lending: a business model that encourages chronic borrowing. Econ. Dev. Q. 17, 8–32 (2003).

    Article  Google Scholar 

  37. Payday lending zoning laws and legislation, Appendix 1: list of payday lender ordinances. Consumer Federation of America (2020);

  38. Small, M. L. & Adler, L. The role of space in the formation of social ties. Annu. Rev. Sociol. 45, 111–132 (2019).

    Article  Google Scholar 

  39. Smith, T. E., Smith, M. M. & Wackes, J. Alternative financial service providers and the spatial void hypothesis. Reg. Sci. Urban Econ. 38, 205–227 (2008).

    Article  Google Scholar 

  40. Baradaran, M. How the Other Half Banks: Exclusion, Exploitation, and the Threat to Democracy (Harvard Univ. Press, 2015).

  41. Taylor, K.-Y. Race for Profit: How Banks and the Real Estate Industry Undermined Black Homeownership (Univ. of North Carolina Press, 2019).

  42. Friedline, T. & Chen, Z. Digital redlining and the fintech marketplace: evidence from U.S. zip codes. J. Consum. Aff. (2020).

  43. Goodstein, R., Lloro, A., Rhine, S. L. W. & Weinstein, J. What accounts for racial and ethnic differences in credit use? FDIC Division of Depositor and Consumer Protection working paper no. 2018-01 (Federal Deposit Insurance Corporation, 2018).

  44. Aliprantis, D., Carroll, D. R. & Young, E. R. What explains neighborhood sorting by income and race? Working paper no. 18-08 R. Federal Reserve Bank of Cleveland (2019).

  45. Pattillo, M. Black middle-class neighborhoods. Annu. Rev. Sociol. 31, 305–329 (2005).

    Article  Google Scholar 

  46. Pattillo, M. Black on the Block: The Politics of Race and Class in the City (Univ. Chicago Press, 2007).

  47. Massey, D., & Denton, N. American Apartheid: Segregation and the Making of the Underclass (Harvard Univ. Press, 1993).

  48. Pattillo-McCoy, M. Black Picket Fences: Privilege and Peril among the Black Middle Class (Univ. Chicago Press, 1999).

  49. Charles, C. Z. The dynamics of racial residential segregation. Annu. Rev. Sociol. 29, 167–207 (2003).

    Article  Google Scholar 

  50. Tienda, M. & Fuentes, N. Hispanics in metropolitan America: new realities and old debates. Annu. Rev. Sociol. 40, 499–520 (2014).

    Article  Google Scholar 

  51. Galster, G. C. & Santiago, A. Neighborhood ethnic composition and outcomes for low-income Latino and African American children. Urban Stud. 54, 482–500 (2017).

    Article  Google Scholar 

  52. Small, M. L. & Pager, D. Sociological perspectives on racial discrimination. J. Econ. Perspect. 34, 49–67 (2020).

    Article  Google Scholar 

Download references


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



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.

Corresponding author

Correspondence to Mario L. Small.

Ethics declarations

Competing interests

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing