The 2007–9 financial crisis began with increased uncertainty over funding conditions in money markets. We show that funding uncertainty can explain diverse elements of commercial banks’ behavior during the crisis, including: (i) reductions in lending volumes, balance sheets, and profitability; (ii) more intense competition for retail deposits (including deposits turning into a “loss leader”); (iii) stronger lending cuts by more highly extended banks with a smaller deposit base; (iv) weaker pass-through from changes in the central bank’s policy rate to market interest rates; and (v) a binding “zero lower bound” as well as a rationale for unconventional monetary policy.

This paper studies leverage regulation when equity investors and/or creditors have distorted beliefs relative to a planner. We characterize how the optimal regulation responds to arbitrary changes in investors’/creditors’ beliefs, relating our results to practical scenarios. We show that the optimal regulation depends on the type and magnitude of such changes. Optimism by investors calls for looser leverage regulation, while optimism by creditors, or jointly by both investors/creditors, calls for tighter leverage regulation. Our results apply to environments with (i) planners with imperfect knowledge of investors’/creditors’ beliefs, (ii) monetary policy, (iii) bailouts and pecuniary externalities, and (iv) endogenous beliefs.

We study an economy with financial frictions in which a regulator designs a test that reveals outside information about a firm's quality to investors. The firm can also disclose verifiable inside information about its quality. We show that the regulator optimally aims for “public speech and private silence,” which is achieved with tests that give insiders an incentive to stay quiet. We fully characterize optimal tests by developing tools for Bayesian persuasion with incentive constraints, and use these results to derive novel guidance for the design of bank stress tests, as well as benchmarks for socially optimal corporate credit ratings.

We document that the expectations of households, firms, and professional forecasters in standard surveys simultaneously extrapolate from recent events and underreact to new information. Existing models of expectation formation, whether behavioral or rational, cannot account for these observations. We develop a rational theory of extrapolation based on limited attention, which is consistent with this evidence. In particular, we show that limited, asymmetric attention to procyclical variables can explain the coexistence of extrapolation and underreactions. We illustrate these mechanisms in a microfounded macroeconomic model, which generates expectations consistent with the survey data, and show that asymmetric attention increases business cycle fluctuations.

Using a comprehensive dataset of privacy policies, firm characteristics, consumer tracking, and cybersecurity incidents, we document several stylized facts about the heterogeneity of firms’ data extraction practices and the influence of privacy regulations. Rather than adopting standardized boilerplate privacy policies, we find substantial within-industry differences correlated with firms’ technical sophistication; firms engaging in data extraction have lengthier policies, seeking to hedge legal risks. Firms with intermediate technical sophistication appear to follow a “collect and share” model, collecting large amounts of consumer data and sharing it with third parties for processing, thus creating cybersecurity risks. Conversely, high sophistication firms appear to implement a “receive and process” model, consistent with a two-tier data market in which data flow from intermediate to high sophistication firms.

Innovations in statistical technology in functions including credit‐screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.