Using Machine Learning to Understand Veterans' Receipt of Loans in the Paycheck Protection Program

Abstract

This paper provides the first quantitative investigation of the receipt of funds from the Paycheck Protection Program (PPP) among Veterans between April and June. We find that Veterans received 3.5% more loans and 6.8% larger loans than their counterparts (p<0.01), controlling for a wide array of zipcode characteristics and exploits within-zipcode variation in further robustness. We subsequently use machine learning to predict PPP loan receipt among Veterans, finding that characteristics about quality of the local Department of Veterans Affairs medical centers are predictive. We develop models to predict the number of PPP loans awarded to Veteran-owned, finding that the inclusion of local VA medical center characteristics adds almost as much explanatory power as the industry and occupational composition in an area and even more than the education, race, and age distribution combined. Our results suggest that VA medical centers can play an important role in helping Veterans thrive even beyond addressing their direct medical needs.