This paper studies whether social media sentiment predicts merger withdrawals. We find that a one‐standard‐deviation increase in social media sentiment after a merger announcement is associated with a 0.64 percentage point lower probability of withdrawal (16.6% of the average). This effect is unexplained by abnormal price reactions, traditional news, and analyst recommendations. Consistent with manager learning, the informativeness of social media strengthens after firms start corporate Twitter accounts. The informativeness is driven by longer acquisition‐related tweets by fundamental investors, rather than memes and price trend tweets. These findings suggest that social media signals can be important for corporate decisions.

Homeowners insurance is central to managing the rising losses from climate‐related disasters. We show that insurance premiums are subject to starkly different regulations across states, creating persistent cross‐subsidies and price distortions. We employ states' regulatory rules in an instrumental variable estimation and a border discontinuity design to show insurers do not adjust rates in highly regulated states and compensate by raising rates in less regulated states. Rates and risks diverge in the long run, distorting cross‐state risk‐sharing and increasing insurer exits from highly regulated states. We argue these patterns stem from the interactions between rate regulation and insurers' financing constraints.

The impact of uncertainty shocks on firm‐level economic activity depends on their origin in supply chains. Upstream (downstream) uncertainty from suppliers (customers) is associated with variability over future input (output) prices. Consequently, a real‐option production model with time‐to‐build suggests that only upstream uncertainty suppresses investment, since upstream (downstream) uncertainty affects the shorter (longer) run. Production network data show that upstream uncertainty negatively affects firm‐level outcomes. Conversely, downstream uncertainty affects firm‐level outcomes more weakly but positively. At the macro level, these two uncertainties oppositely predict aggregate growth and asset prices. Overall, downstream uncertainty has an expansionary effect, in contrast to other facets of uncertainty.

Using supervisory data on the loan portfolios of large U.S. banks, we document that these banks specialize by concentrating their lending disproportionately in a few industries. This specialization is consistent with banks having industry‐specific knowledge, reflected in reduced risk of loan defaults, lower aggregate charge‐offs, and higher propensity to lend to opaque firms in the preferred industry. Banks attract high‐quality borrowers by offering generous loan terms in their specialized industry, especially to borrowers with alternative options. Banks focus on their preferred industry in times of instability and relatively lower Tier 1 capital as well as after surges in deposits.

We classify the race of over 160,000 U.S. founders and investors and study the venture capital (VC) funding gap for Black entrepreneurs. Only 3.1% of VC‐funded startups are Black‐owned, and they raise half as much VC funding as others. We attribute much of this gap to Black founders having fewer traditional success markers, like patents or entrepreneurial experience. This disparity also affects matching: Black VC partners invest more in Black founders, and these investments have higher successful exit rates. We attribute this outperformance to lower information asymmetries due to network overlap and “screening discrimination,” whereby Black VCs better differentiate among Black founders.

We present results from a field experiment that introduced digital payroll accounts to unbanked factory workers to examine how inexperienced consumers learn to use a new financial technology. We find that exposure to payroll accounts leads to increased account use, accelerated learning, and avoidance of common consumer protection risks. Those receiving electronic wage payments gradually build trust in the technology, learn to use accounts without assistance, and avoid illicit fees. Using experimental variation in assignment to bank versus mobile money accounts, we show that these impacts are concentrated in mobile money accounts, the newer, more complex, and less trusted financial technology.