Graduation Year

2020

Date of Submission

12-2020

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Economics

Reader 1

Darren Filson

Reader 2

Manfred Keil

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Terms of Use for work posted in Scholarship@Claremont.

Abstract

The Paycheck Protection Program (PPP) was launched by the Small Business Administration (SBA) on April 3rd 2020 to help small businesses keep their employees on the payroll and to cover other costs impacted by COVID-19. In addition to traditional banks and lenders, the SBA approved FinTech companies to be lenders for PPP loans. FinTech companies often assert that they are making financial services more inclusive. This thesis implements regression models to assess whether FinTech lenders help underserved communities with PPP more than big banks and other lenders.

Using SBA’s loan-level data for the PPP and a Social Deprivation Index (SDI), I categorize lenders into FinTech lenders, big banks, and other lenders. SDI ranges from 1 to 100. Through regression analyses, I examine how the percentage of loans accounted for by FinTech lenders changes over time and how the mean SDI of the loans facilitated by each type of lender changes over time. The unit of time is a week. T-tests and F-tests are used to test hypotheses.

Five hypotheses are tested, and four are supported. The first hypothesis is that FinTech lenders account for a higher percentage of PPP loans over time. I find that each week, the percentage of PPP loans made by FinTech lenders increases by 3.60 percentage points. This effect is statistically significant at the 1% level. The second hypothesis is that FinTech lenders account for a higher percentage of PPP loan value over time. I find that each week, the percentage of PPP loan value made by FinTech lenders increases by 3.46 percentage points. This effect is statistically significant at the 1% level. The third hypothesis is that at the start of the time series, the mean SDI is higher for FinTech lenders, compared to big banks and other lenders. If I compute the mean SDI by weighting by the number of loans made by each lender each week, I find that at the beginning of the PPP, the mean SDI for FinTech lenders is 0.68 higher than other lenders and 1.77 higher than big banks. However, both differences are not statistically significant. If I compute the mean SDI by weighting the loan value made by each lender each week, I find that at the beginning of the PPP, the mean SDI for FinTech lenders is 0.73 lower than other lenders. This difference is not statistically significant. I find that the mean SDI for FinTech lenders is 5.04 higher than big banks. This difference is statistically significant at the 1% level. My fourth hypothesis is that for all types of lenders, the mean SDI rises over time. Using the mean SDI weighted by the number of loans, I find that each week, the mean weekly SDI rises by 0.24 for other lenders, by 1.03 for FinTech lenders, and 0.28 for big banks. These effects are statistically significant at the 1% level for other lenders and FinTech lenders and at the 5% level for big banks. I conclude that for all types of lenders, the mean SDI rises over time. My fifth hypothesis is that the rate of increase in Hypothesis 4 (H4) is higher for FinTech lenders, compared to big banks and other lenders. Using the mean SDI weighted by the number of loans, I find that the rate of increase for FinTech lenders in H4 is 0.79 higher than the other lenders and is 0.75 higher than the big banks. Both effects are statistically significant at the 1% level. Using the mean SDI weighted by the loan value, Hypothesis 4 and Hypothesis 5 are also supported with statistical significance.

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This thesis is restricted to the Claremont Colleges current faculty, students, and staff.

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