Abstract
This paper applies loan-level information from Paycheck Protection Program loans to analyze the coverage of this extraordinary lending program. Most loans were intended to be forgiven making them effectively grants. We show that loans went to a large share of small businesses across most industries in the US, especially to industries that were most negatively impacted by COVID-19 stay-at-home orders. We geocode the loans and then identify that 2021 loans were more concentrated in low- and moderate-income communities, along with census tracts where minority residents are a majority of the population. The growth of nonemployer loans and fintech lending in the program were key components of the broadened reach of the program.
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Notes
For a summary of the legislative history and implementation of the lending program, see the September 2021 GAO report at https://www.gao.gov/products/gao-21-601.
See https://www.sba.gov/sites/sbagov/files/2022-10/2022.10.24_Weekly%20Forgiveness%20Report_Public.pdf for more information.
The SUSB data are derived from the Census Bureau Business Register, which uses tax and other administrative records to track business entities with employees for the Census Bureau’s economic surveys. SUSB data exclude nonemployer firms, along with information on private households, railroads, agricultural production, and most government entities. The 2019 SUSB data files (https://www.census.gov/programs-surveys/susb/data/datasets.html) were released on April 1, 2022.
Nonemployer data provide statistics obtained from business income tax records. The NES data (https://www.census.gov/programs-surveys/nonemployer-statistics/data/tables.html) we use were collected from 2018 and released on June 30, 2022.
For example, the SBA (2021b) provided guidance for loan amounts for “self-employed individuals” who have employees (https://www.govinfo.gov/app/details/FR-2021-03-08/2021-04795).
The SBA data use a variable named “processing method,” which refers to first- and second-draw loans in 2021.
This follows the approach used in Schweitzer and Borawski (2021). The SUSB covers only part of the agricultural sector; so this section excludes SBA loans that were listed as going to agricultural firms.
The specific amount in the PPP is unknowable, but the Department of Justice is pursuing cases (Office of Public Affairs 2021). (https://www.justice.gov/opa/pr/justice-department-takes-action-against-covid-19-fraud).
We use the small business size regulation standards from January 14, 2021, which can be accessed at https://www.ecfr.gov/on/2021-01-14/title-13/chapter-I/part-121.
These figures exclude the Management of Companies and Public Administration sectors, along with loans with no industry code. We also exclude agricultural nonemployers in this part of the analysis. Loans listed as going to this industry are hard to reconcile with the underlying census data.
See more at https://www.govinfo.gov/app/details/FR-2020-04-20/2020-08257 (SBA, 2020).
See the appendix for more information on the SBA’s and our geocoding results.
We obtained the FFIEC data from https://www.ffiec.gov/hmcrpr/cra20tables1-5.pdf (Table 4.1).
These results are from Borawski and Schweitzer (2021).
Chernenko and Scharfstein (2022) use restaurant data from Yelp to identify minority-owned businesses that received PPP loans in Florida. Lester and Wilson (2023) use geocoded PPP loan data to access whether minority census tracts and places that received fewer mortgages were less likely to receive PPP loans. Similar to our results they find lower PPP loan counts and lending amounts for Black and Latinx communities in 2020 data, although conditions improve for Black-majority communities in 2021.
The PPPLF data can be accessed at https://www.federalreserve.gov/monetarypolicy/ppplf.htm.
“How Two Start-Ups Reaped Billions in Fees on Small Business Relief Loans,” October 2021. https://www.nytimes.com/2021/06/27/business/ppp-relief-loans-blueacorn-womply.html?smid=url-share
Capped loan amounts for 2020 and 2021 PPP loans can be found on https://home.treasury.gov/system/files/136/How-to-Calculate-Loan-Amounts.pdf and https://home.treasury.gov/system/files/136/PPP--How-to-Calculate-Maximum-Loan-Amounts-for-First-Draw-PPP-Loans-and-What-Documentation-to-Provide-By-Business-Type.pdf, respectively.
We manually reverse geocode each of these randomly selected observations by entering the address into the FFIEC’s geocoder tool to obtain the census tract code, which can be accessed at https://geomap.ffiec.gov/ffiecgeomap/.
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Schweitzer, M., Guo, A. Basic facts on the coverage of the paycheck protection program. Bus Econ 59, 10–30 (2024). https://doi.org/10.1057/s11369-023-00345-z
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DOI: https://doi.org/10.1057/s11369-023-00345-z