We evaluate how a positive, technology-driven shock to bank liquidity affects small business dynamics across different size distributions. We first show that banks receiving positive liquidity shocks increase lending to relatively larger SMEs, not to the smallest firms. This finding is consistent with the view that a positive liquidity shock enhances bank charter values, thereby reducing risk-taking incentives. Moreover, such disproportionate credit allocation leads to a crowding-out effect on micro firms. When larger SMEs grow faster and exit less because of better access to credit, their expansion stifles the development of micro firms, whose access to credit remains unchanged.

We examine how investor demand for leverage shapes asset management fees. We show that in the sample of U.S. equity mutual funds: (1) fees increase in fund market beta precisely for beta larger than one; (2) this relation becomes stronger and high-beta funds experience larger inflows when leverage constraints tighten; and (3) low net alphas are especially common among high-beta funds. These results are consistent with a model in which asset managers compete for leverage-constrained investors with heterogeneous risk aversion. The asymmetric relation between betas and fees also extends to the HML and SMB factors.

Banks’ information technology (IT) capabilities affect their ability to serve customers during the COVID-19 pandemic, which generates an unexpected and unprecedented shock that shifts banking services from in-person to digital. Amid mobility restrictions, banks with better IT experience larger reductions in physical branch visits and larger increases in website traffic, implying a larger shift to digital banking. Stronger IT banks are able to originate more Paycheck Protection Program loans to small business borrowers, especially in areas with more severe COVID-19 outbreaks, higher internet use, and higher bank competition. Those banks also attract more deposit flows and receive better mobile customer reviews during the pandemic.

Why did banks experience massive deposit inflows during the pandemic? We discover that deposit interest rates at bank branches in counties with higher COVID-19 infection rates fell by more than rates at branches—even branches of the same bank—in counties with lower infection rates. Credit drawdowns, national policies, such as the Payment Protection Program, and a flight-to-safety do not account for these cross-branch changes in deposit rates. Evidence suggests that higher local COVID-19 infection rates are associated with households’ greater anxiety about future job and income losses, anxiety that induces households to reduce spending and increase deposits.

Adverse market events can affect credit supply not only by hurting financial fundamentals but also by changing the risk-taking behaviors of individual decision-makers. We provide micro-level evidence of this individual decision-making channel in the U.S. mortgage market. We find that mortgage application rejection rates are more sensitive to foreclosure intensity when loan officers are more exposed to foreclosure news, despite the same housing market and bank fundamentals. Loans originated from the affected branches have lower ex post default rates, consistent with higher lending standards being applied. In the aggregate, this effect results in tighter credit supply during housing market downturns.

We show that attention constraints on decision-makers create barriers to financial inclusion. Using administrative data on retail loan-screening processes, we find that attention-constrained loan officers exert less effort reviewing applicants of lower socioeconomic status (SES) and reject them more frequently. More importantly, when externally imposed increases in loan officers’ workloads tighten attention constraints, loan officers are even more prone to quickly reject low-SES applicants but quickly accept very high-SES applicants without careful review. Such selective attention allocation further widens the approval rate gap between high- and low-SES applicants—a unique prediction of this attention-based mechanism.