We examine real outcomes for biotech startups going public around the Jumpstart Our Business Startups (JOBS) Act. Reduced compliance costs associate with greater innovation capital formation as biotech IPO volume and proceeds increase after the JOBS Act. Biotechs, which conduct over 30% of IPOs since 2012, go public with products earlier in the FDA approval process and more frequently target rare diseases and cancer. Consistent with our survey evidence that managers use compliance savings to invest in R&D, we link the JOBS Act to post-IPO increases in project-level development, such as new patents, clinical trials, and staffing of laboratories. Post-JOBS Act product candidates are more likely to reach key milestones in the FDA approval process and these startups fail at lower rates. Benefits accrue to shareholders without sacrificing financial reporting quality. Our results demonstrate how tailoring regulations for startups can provide economic and societal benefits.

Deposit insurance premiums impose costs on banks’ balance sheets, narrowing profit margins and inducing banks to “search for yield.” This paper estimates the effects of deposit insurance premiums on bank portfolio rebalancing using supervisory data and a kink in the insurance premium schedule. We show that deposit insurance premiums weaken banks’ demand for reserves (a liquid asset with no credit risk) and strengthen the supply of short-term interbank loans (a less liquid asset with credit risk). We discuss the implications of these findings for optimal deposit insurance pricing.

We review the literature on financial intermediation in the process by which new medical therapeutics are financed, developed, and delivered. We discuss the contributing factors that lead to a key finding in the literature—underinvestment in biomedical R&D—and focus on the role that banks and other intermediaries can play in financing biomedical R&D and potentially closing this funding gap. We conclude with a discussion of the role of financial intermediation in the delivery of healthcare to patients.

Global Systemically Important Banks (GSIBs) face additional capital requirements and closer supervision. We study how closer supervision affects corporate credit supply and investigate consequences for firms. GSIB designations reduce lending on average by 5.9% but to risky firms by 7.2%. The consequences are lower asset, sales, and investment growth, especially among high-risk borrowers, and reduced R&D expenditures among all GSIB-dependent firms. Closer supervision therefore reduces banks' risk-taking but has potentially unintended implications for firms' ability to finance innovation, which seems to crucially depend on bank credit. The supervision-induced effects are larger than those attributed to GSIB-specific capital surcharges.

We examine whether natural disaster experiences affect households’ portfolio choice decisions. Using data from the National Longitudinal Survey of Youth 1979, we find that adversely affected households are less likely to participate in risky asset markets. After a disaster shock, households become more risk-averse and lower their expectations on future stock market returns. Such conservative portfolio choices persist even after households relocate to less disaster-prone areas, consistent with risk preferences being altered by disaster experiences. Overall, our evidence suggests that transient but salient experiences can be an important factor in explaining the limited participation puzzle.

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically endogenous market structure. We propose the use of Generalized Instrument Variables methods to identify those dynamic policy functions that are consistent with instrumental variable (IV) restrictions. Extending popular “two-step” methods, these policy functions then identify a set of structural parameters that are consistent with the dynamic model, the IV restrictions and the data. We provide computed illustrations to both single-agent and oligopoly examples. We also present a simple empirical analysis that, among other things, supports the counterfactual study of an environmental policy entailing an increase in sunk costs.

We study learning via shared news. Each period agents receive the same quantity and quality of firsthand information and can share it with friends. Some friends (possibly few) share selectively, generating heterogeneous news diets across agents. Agents are aware of selective sharing and update beliefs by Bayes’s rule. Contrary to standard learning results, we show that beliefs can diverge in this environment, leading to polarization. This requires that (i) agents hold misperceptions (even minor) about friends’ sharing and (ii) information quality is sufficiently low. Polarization can worsen when agents’ friend networks expand. When the quantity of firsthand information becomes large, agents can hold opposite extreme beliefs, resulting in severe polarization. We find that news aggregators can curb polarization caused by news sharing. Our results hold without media bias or fake news, so eliminating these is not sufficient to reduce polarization. When fake news is included, it can lead to polarization but only through misperceived selective sharing. We apply our theory to shed light on the polarization of public opinion about climate change in the United States.

This paper develops a theory that generates an equilibrium relationship between product complexity, transparency, and trust in firms. Complexity, transparency, and the evolution of trust are all endogenous, and equilibrium transparency is nonmonotonic. The least-trusted firms choose the lowest product complexity, remain opaque, and substitute ex ante third-party verification for information disclosure and trust. Firms with an intermediate level of trust choose an intermediate level of complexity and transparency through disclosure, with more trusted firms choosing greater complexity and lower transparency. The most-trusted firms choose maximum complexity while remaining opaque, eschewing both verification and disclosure.

We examine the predictability of stock returns using implied volatility spreads (VS) from individual (nonindex) options. VS can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be an artifact of firms’ sensitivities to aggregate volatility. Examining this empirically, we find that the predictability changes systematically with aggregate volatility and is positively related to the firms’ sensitivities to volatility risk. The alpha generated by VS hedge portfolios can be explained by aggregate volatility risk factors. Our results cannot be explained by firm-specific informed trading, transaction costs, or liquidity.