We study whether early stage investors have gender biases using a proprietary data set from AngelList that allows us to observe private interactions between investors and fundraising startups. We find that male investors express less interest in female entrepreneurs compared to observably similar male entrepreneurs. In contrast, female investors express more interest in female entrepreneurs. These findings do not appear to be driven by within-gender screening/monitoring advantages or gender differences in risk preferences. Moreover, the male-led startups that male investors express interest in do not outperform the female-led startups they express interest in—they underperform. Overall, the evidence is consistent with gender biases.

We estimate the impact of venture capital (VC) contract terms on startup outcomes and the split of value between the entrepreneur and investor, accounting for endogenous selection via a novel dynamic search-and-matching model. The estimation uses a new, large data set of first financing rounds of startup companies. Consistent with efficient contracting theories, there is an optimal equity split between agents, which maximizes the probability of success. However, venture capitalists (VCs) use their bargaining power to receive more investor-friendly terms compared to the contract that maximizes startup values. Better VCs still benefit the startup and the entrepreneur due to their positive value creation. Counterfactuals show that reducing search frictions shifts the bargaining power to VCs and benefits them at the expense of entrepreneurs. The results show that the selection of agents into deals is a first-order factor to take into account in studies of contracting.

We study how technological shocks to the cost of starting new businesses have led the venture capital model to adapt in fundamental ways over the prior decade. We both document and provide a framework to understand the changes in the investment strategy of venture capitalists (VCs) in recent years – an increased prevalence of a “spray and pray” investment approach – where investors provide a little funding and limited governance to an increased number of startups that they are more likely to abandon, but where initial experiments significantly inform beliefs about the future potential of the venture. This adaptation and related entry by new financial intermediaries has led to a disproportionate rise in innovations where information on future prospects is revealed quickly and cheaply, and reduced the relative share of innovation in complex technologies where initial experiments cost more and reveal less.

We quantify the costs of major disclosure and governance regulations by exploiting a regulatory quirk: many rules trigger when a firm's public float exceeds a threshold. Consistent with firms avoiding costly regulation, we document significant bunching around three major regulatory thresholds. Estimations reveal that the three examined rules' compliance costs range from 1.2% to 1.8% of market capitalization for firms near thresholds. For a median U.S. public company, total costs amount to 4.3% of market capitalization, and at least 2.3% absent regulatory avoidance frictions. These cost estimates are robust across various extrapolation assumptions, ranging from 2.1% to 6.3% of market capitalization. Regulatory costs have a greater impact on private firms' IPO decisions than on public firms' going private decisions, but such costs only explain a small part of the decline in the number of public firms.