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Racial Disparities in Access to Small Business Credit: Evidence from the Paycheck Protection Program
Sabrina T. Howell, New York University and NBER
Theresa Kuchler, New York University and NBER
David Snitkof, Ocrolus
Johannes Stroebel, New York University and NBER
Jun Wong, University of Chicago

We explore the sources of racial disparities in small business lending by studying the $806 billion Paycheck Protection Program (PPP), which was designed to support small business jobs during the COVID-19 pandemic. PPP loans were administered by private lenders but federally guaranteed, largely eliminating unobservable credit risk as a factor in explaining differential lending by race. We document that even after controlling for a firm’s zip code, industry, loan size, PPP approval date, and other characteristics, Black-owned businesses were 12.1 percentage points (70% of the mean) more likely to obtain their PPP loan from a fintech lender than a traditional bank. Among conventional lenders, smaller banks were much less likely to lend to Black-owned firms, while the Top-4 banks exhibited little to no disparity after including controls. We use novel data to show that the disparity is not primarily explained by differences in pre-existing bank or credit relationships, firm financial positions, fintech affinity, borrower application behavior, or racial differences in rates of fraudulent PPP applications. In contrast, we document that Black-owned businesses’ higher rate of borrowing from fintechs compared to smaller banks is particularly large in places with high anti-Black racial animus, pointing to a potential role for discrimination in explaining some of the racial disparities in small business lending. We find evidence that when small banks automate their lending processes, and thus reduce human involvement in the loan origination process, their rate of PPP lending to Black-owned businesses increases, with larger effects in places with more racial animus.


This paper was distributed as Working Paper 29364, where an updated version may be available.

Household Financial Decision-Making after Natural Disasters: Evidence from Hurricane Harvey
Alejandro del Valle, Georgia State University
Therese C. Scharlemann, Federal Reserve Board
Stephen Shore, Georgia State University

Hurricane Harvey brought more than four feet of rainfall to the Houston area in August 2017, leading to substantial flooding in many areas. del Valle, Scharlemann, and Shore merge data on credit card accounts, mortgages, and flooding depth at the ZIP+4 location level to compare the household credit response to Hurricane Harvey in parts of Houston that were more and less affected by flooding. The researchers find that hurricane-affected households use marginal credit in a price-sensitive manner, and that credit substitutes for physical hardening. del Valle, Scharlemann, and Shore show that flood-induced borrowing occurred at interest rates well below standard credit card rates, and that new balances were paid off quickly. Revolving balances on standard credit cards were approximately unaffected, but flooding caused a dramatic increase in originations of cards with promotional interest rates. While borrowers in all areas took advantage of special mortgage forbearance offers, take-up increased with flooding intensity. Conditional on flooding, households in floodplains - and particularly in houses subject by code to physical hardening - drew far less new credit.

Fertility and Savings: The Effect of China’s Two-Child Policy on Household Savings
Scott R. Baker, Northwestern University and NBER
Efraim Benmelech, Northwestern University and NBER
Zhishu Yang, Tsinghua University
Jacky Zhang, Durham University

China's high household savings rate has attracted great academic interest but remains a puzzle. Potential explanations include demographic, policy, and financial causes. Yet a lack of reliable microlevel data on household finances makes it difficult to assess the relative importance of each factor. This paper uses individual income and spending transactions linked to demographic characteristics and financial information on loan applications and credit availability from a large Chinese bank in Inner Mongolia. Baker, Benmelech, Yang, and Zhang match a large subset of bank customers to administrative records covering marriage and births and obtain a unique view into consumption and saving patterns around important life events. Their results point toward identifying income growth, financial instability, and credit access, rather than such directives as the one-child policy, as the primary causes of high levels of savings among Chinese households.

What Triggers Mortgage Default? New Evidence from Linked Administrative and Survey Data
David C. Low, Consumer Financial Protection Bureau

Despite decades of research, it is still unclear why mortgage borrowers default because existing datasets are quite limited. This paper studies why borrowers default using a survey specifically designed for the purpose, with a sample drawn from (and matched to) a very rich administrative mortgage dataset. Low finds that other datasets with less information on adverse life events systematically understate their frequency. Adverse life events trigger nearly all defaults; many of these shocks are not unemployment or even a drop in income, and so will be missed by most existing studies. In contrast, the uniquely rich data used in this paper confirms previous empirical results that many defaults are not triggered by negative home equity, contrary to the predictions of the most popular models in the literature.

Efficiency in Household Decision Making: Evidence from the Retirement Savings of US Couples
Taha Choukhmane, Massachusetts Institute of Technology
Lucas Goodman, Department of the Treasury
Cormac O'Dea, Yale University and NBER

Pareto efficiency is a core assumption of most models of the household. Choukhmane, Goodman, and O'Dea test this assumption using a new dataset covering the retirement saving contributions of 1.3 million U.S. couples. While a vast literature has failed to reject household efficiency in developed countries, the researchers find evidence of widespread inefficiency in their setting: retirement contributions are not allocated to the account of the spouse with the highest employer match rate. This lack of coordination cannot be explained by inertia, auto-enrollment, or simple heuristics. Instead, Choukhmane, Goodman, and O'Dea find that indicators of weaker marital commitment correlate with the incidence of inefficient allocations.

Financial Inclusion Across the United States
Motohiro Yogo, Princeton University and NBER
Andrew Whitten, Department of the Treasury
Natalie Cox, Princeton University

Yogo, Whitten, and Cox document new facts about participation in bank and retirement accounts, based on the universe of U.S. households with a member aged 50 to 59 in administrative tax data. Financial participation is much higher than that reported in survey data, especially for low-income households. However, financial participation declines among low-income households from 2008 to 2018. Geographic variation in financial participation relates to income inequality rather than racial segregation or access to financial services. Based on instrumental variables, Yogo, Whitten, and Cox estimate the long-run impact of access to employer retirement plans on retirement account participation, which is especially large for low- and middle-income households.

Friends and Family Money: P2P Transfers and Financially Fragile Consumers
Tetyana Balyuk, Emory University
Emily Williams, Harvard University

Balyuk and Williams assess the impact that real time money transfer technology has on consumer outcomes, particularly during periods of financial fragility. They do this by developing a new data set that documents use of Zelle - the most widely used P2P money transfer technology in the U.S. today - constructed from transaction level data from a large data aggregator for millions of U.S. consumers. The researcherse combine these data with a hand-collected data set on Zelle partnerships for 1,113 financial institutions. Finally, Balyuk and Williams introduce a novel instrument by identifying the location of consumers' close social circle using transactions data. They make use of the fact that money transfer technology is a network good and hence your own use is a function of the use by others, and particularly that of your close friends and family. The researchers then use variation in Zelle bank partnerships at the location of consumers' residence and consumers' close social circle to instrument for Zelle use. Balyuk and Williams compare users residing in the same city with similar incomes, but with different exposure to Zelle, and observe consumer outcomes during periods of financial fragility. Balyuk and Williams find that Zelle use results in fewer overdrafts and higher consumption by financially fragile consumers. Consumers substitute away from traditional methods of transferring cash towards Zelle, an effect that is more pronounced for smaller transfer sizes and low-income consumers who are more price-sensitive.

Racial Disparities in Housing Returns
Amir Kermani, University of California, Berkeley and NBER
Francis Wong, NBER Postdoctoral Fellow

Kermani and Wong document the existence of a racial gap in realized housing returns that is an order of magnitude larger than disparities arising from housing costs alone, and is driven almost entirely by differences in distressed home sales (i.e. foreclosures and short sales). Black and Hispanic homeowners are both more likely to experience a distressed sale and to live in neighborhoods where distressed sales erase more house value. Importantly, absent financial distress, houses owned by minorities do not appreciate at slower rates than houses owned by non-minorities. Racial differences in income stability and liquid wealth explain a large share of the differences in distress. Kermani and Wong use quasi-experimental variation in loan modifications to show that policies that restructure mortgages for distressed minorities can increase housing returns and reduce the racial wealth gap.


This paper was distributed as Working Paper 29306, where an updated version may be available.


Francis Annan, Georgia State University
Cynthia Balloch, London School of Economics
Giorgia Barboni, Warwick University
Taylor Begley, Washington University in St. Louis
Daniel Ben-David, Intuit
Nathan E. Blascak, Federal Reserve Bank of Philadelphia
Vicki Bogan, Cornell University
Claire Celerier, University of Toronto
Alejandro del Valle, Georgia State University
Ha Diep-Nguyen, Purdue University
Jane Dokko, Federal Reserve Bank of Chicago
Ross Epstein, SafeGraph
Pengjie Gao, University of Notre Dame
Marco Giacoletti, University of Southern California
Talia Gillis, Columbia University
Radhakrishnan Gopalan, Washington University, St. Louis
Makada Henry-Nickie, Brookings Institution
Andrew Hertzberg, Federal Reserve Bank of Philadelphia
Xing Huang, Washington University in St. Louis
Robert Hunt, Federal Reserve Bank of Philadelphia
Benjamin Kay, Federal Reserve Board
Peter Kelly, University of Notre Dame
Erik J. Mayer, Southern Methodist University
Alexander Michaelides, Imperial College London
Scott T. Nelson, University of Chicago
Jordan Nickerson, University of Washington
Alberto G. Rossi, Georgetown University
Therese C. Scharlemann, Federal Reserve Board
David Sovich, University of Kentucky
Christopher Trepel, Fenway Summer Ventures
Nathanaël Vellekoop, University of Toronto
Evan White, University of California at Berkeley
Zhishu Yang, Tsinghua University
Jacky Zhang, Durham University
Bilal Zia, The World Bank

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