I analyze the effects of bank competition on gender and racial gaps in entrepreneurship. Exploiting interstate bank deregulation from 1994 to 2021, I find that stronger bank competition increases the quantity and quality of banking services provided to minority borrowers. Developing a novel measure of discrimination using narrative information in the complaints filed with the Consumer Financial Protection Bureau, I show that bank competition reduces discrimination, loosening the financial constraints of female and minority entrepreneurs. Stronger bank competition also reduces the gender and racial gap in firm performance and business equity accumulation, fostering wealth equality and generating equitable economic growth.

We analyze, benchmark, and run randomized controlled trials on a panel of 7,463 U.S. entrepreneurs making incentivized sales forecasts. We assess accuracy using a novel administrative dataset obtained in collaboration with a leading US payment processing firm. At baseline, only 13% of entrepreneurs can forecast their firm’s sales in the next three months within 10% of the realized value, with 7.3% of the mean squared error attributable to bias and the remaining 92.7% attributable to noise. Our first intervention rewards entrepreneurs up to $400 for accurate forecasts, our second requires respondents to review historical sales data, and our third provides forecasting training. Increased reward payments significantly reduce bias but have no effect on noise, despite inducing entrepreneurs to spend more time answering. The historical sales data intervention has no effect on bias but significantly reduces noise. Since bias is only a minor part of forecasting errors, reward payments have small effects on mean squared error, while the historical data intervention reduces it by 12.4%. The training intervention has negligible effects on bias, noise, and ultimately mean squared error. Our results suggest that while offering financial incentives make forecasts more realistic, firms may not fully realize the benefits of having easy access to past performance data

We employ a dynamic search-and-matching model with bargaining between venture capitalists (VCs) and startups, utilizing two symmetric incentivized resume rating (IRR) experiments involving real US VCs and startups, to explain the matching outcome in the US entrepreneurial finance industry. Participants evaluate randomized profiles of potential collaborators, incentivized by the real opportunities for preferred cooperative partnerships. Using these experimental behaviors and real-world portfolio data as inputs to our structural estimation, we identify a significant impact of various human and non-human traits on equilibrium continuation values, matching likelihoods, and payoffs from matching for both startups and VCs. These traits include startups’ human assets (i.e., educational background, entrepreneurial experiences) and non-human assets (i.e., traction, business model), as well as investors’ human capital (i.e., entrepreneurial experiences) and organizational capital (i.e., previous financial performance, fund size). Results show that, while the total value of matching increases, the share of a startup/VC's payoff in the total value of matching diminishes substantially (in the range of .65 to .35) when the counterparty type becomes more attractive. Ultimately, we find that variations in the matching likelihood play a dominant role in explaining how the expected payoff from collaboration varies for startups and VCs when dealing with attractive and unattractive counterparty types.