Using a linked database of Paycheck Protection Program (PPP) loans and Yelp-listed restaurants, Fei and Yang document that businesses owned by minority racial groups are more likely to use fintech lenders than traditional lenders. They develop a simple two-sided matching model to show that this phenomenon can be potentially attributed to differences in performance among borrowers, racial disparities in lending relationships, and race-dependent values of borrower-lender matches. Empirically, the researchers do not find consistent evidence that operational performance is an explanation. Fei and Yang find that minority-owned restaurants are less likely to have lending relationships and that restaurants without lending relationships are more likely to use fintech lenders. They also find a more negative minority-non-minority gap in operational performance for fintech lenders, suggesting minority-owned businesses have higher matching values with fintech lenders. Fei and Yang do not find a similar pattern for first-time bank participants, community development financial institutions, credit unions, or other non-federally insured lenders. Overall, their results suggest that there are racial barriers in traditional loan distribution channels and this can be at least partially addressed by fintech lenders.
Using a large sample of Florida restaurants, Chernenko and Scharfstein document significant racial disparities in the utilization of the Paycheck Protection Program (PPP). Only a small fraction of these disparities can be explained by distance to bank branches. Within the same ZIP code, Black- and Hispanic-owned firms are 20.5% and 7.0% less likely than white-owned firms to receive PPP loans. These disparities in PPP utilization are driven by disparities in bank lending, which are greater in counties in which white people exhibit more racial bias against Black people. In these more racially biased counties, Black-owned businesses substitute to nonbank PPP loans and loans provided directly by the Small Business Administration through its Economic Injury Disaster Loan program.
This paper studies how entrepreneurs form new beliefs after making forecast errors. Hebert uses survey-based micro data that are representative of the population of French entrepreneurs, and Hebert finds that 21% of entrepreneurs make optimistic errors, while 36% make pessimistic errors, suggesting that a minority of entrepreneurs are initially well-calibrated. Although optimism and pessimism are persistent types over time, Hebert shows that the likelihood of making errors declines within individuals over time. After overestimating their development and hiring prospects, optimistic entrepreneurs revise their beliefs downward, whereas pessimistic entrepreneurs, who underestimate their prospects, revise upward. The evidence is consistent with entrepreneurs who learn from their past errors. In addition, the ability to correctly forecast sales and employment and revising beliefs are correlated with better performance and growth, and even more so for entrepreneurs who started with pessimistic beliefs.
Jeffers, Lyu, and Posenau provide the first analysis of the risk exposure and consequent risk-adjusted performance of impact investing funds, private market funds with dual financial and social goals. They introduce a new dataset of impact fund cash flows constructed from financial statements. When accounting for market risk exposure, impact funds underperform the market, though not more so that comparable private market strategies. The researchers exploit known distortions in measures of VC performance to characterize the risk profile of impact funds. Impact funds have substantially lower market beta than VC funds, contradicting the idea of sustainability as a “luxury good.” Jeffers, Lyu, and Posenau find that impact fund cash flows do not exhibit positive correlation with a public market sustainability factor, consistent with the idea that private and public market sustainability strategies capture distinct exposures.
Bernstein, Mehta, Townsend, and Xu analyze a field experiment conducted on AngelList Talent, a large online search platform for startup jobs. In the experiment, whether a startup was funded by a toptier VC and/or whether it was funded recently is randomly highlighted in job search results. Bernstein, Mehta, Townsend, and Xu find that the same startup receives significantly more interest from job seekers when the fact that it was funded by a top-tier VC is highlighted. In contrast, highlighting the fact that a startup was funded recently has no effect. The effect of highlighting top-tier VCs is not driven by low-quality candidates and is stronger for earlier-stage startups. The results provide the first direct evidence that VCs can add value to startups passively, simply by attaching their names to their portfolio companies.
Chioda, Contreras-Loya, Gertler, and Carney study the medium-term impacts of the Skills for Effective Entrepreneurship Development (SEED) program, an innovative in-residence 3-week mini-MBA program for high school students modeled after western business school curricula and adapted to the Ugandan context. The program featured two separate treatments: the hard-skills MBA features a mix of approximately 75% hard skills and 25% soft skills; the soft skills curriculum has the reverse mix. Using dataon 4,400 youth from a nationally representative sample in a 3-arm feld experiment in Uganda, the 3.5-year follow-up demonstrated that training was effective in improving both hard and soft skills, but only soft skills were directly linked to improvements in self-efficacy, persuasion, and negotiation. Youth in both groups were more likely to start enterprises and more successful in ensuring their businesses' survival. The program led to significantly larger profits (27.8% and34.8% for hard- and soft- treatment arms respectively) and larger business capital investments (72.5% and 58.8% for SEED hard and SEED soft, respectively). Relative to the control group, SEED entrepreneurs created 550 new businesses and 985 additional jobs. The individual's skill upgrade was rewarded by substantially higher earnings; 38.7% and 21.2% increases in earnings for those who attended hard- and soft-training, respectively, largely generated through self-employment. Both SEED curricula were very cost-effective; one (two) months’ worth of extra earnings as a direct consequence of having attended the SEED hard (soft) program would exceed its total cost.
This paper was distributed as Working Paper 28845, where an updated version may be available.
Decision-making processes in the context of innovation and entrepreneurship are characterized by high uncertainty and prone to decision-making biases. In this paper Coali, Gambardella, and Novelli explore the implications of adopting what they call a scientific approach to decision making, based on probabilistic reasoning. The researchers develop a structural model to disentangle and identify two separate but complementary effects of this approach. The estimation of their structural model, based on data from two randomized control trials (RCTs) involving early stage start-ups, shows that scientific entrepreneurs tend to be more conservative in assessing the value over their ideas, an effect that Coali, Gambardella, and Novelli call debiasing effect. It also shows that, conditional on their decision to remain operational, scientific entrepreneurs tend to perform better, an effect that they call learning effect. Coali, Gambardella, and Novelli discuss the implications for future research and entrepreneurial practice.