Knowledge that Transforms

We model how a cyber attack may be amplified through the U.S. financial system, focusing on the wholesale payments network. We estimate that spillovers of an attack on one of the five most active banks would impair 31% of the network, on average, with some days significantly worse. When other banks respond by liquidity hoarding, forgone payment activity can reach more than 2.5 times daily GDP. A reverse stress test shows attacks on groups of small banks can also impair a significant amount of the network. Interconnectedness through third-party providers and financial market utilities poses additional risks.

We utilize the staggered arrival of Uber and Lyft—large sources of on-demand, platform-enabled gig opportunities—in U.S. cities to examine the effect of the arrival of flexible gig work opportunities on new business formation. The introduction of gig opportunities is associated with an increase of ∼5% in the number of new business registrations in the local area, and a correspondingly-sized increase in small business lending to newly registered businesses. Internet searches for entrepreneurship-related keywords increase ∼7%. These effects are strongest in locations where proxies for ex ante economic uncertainty regarding the viability of new businesses are larger. Our findings suggest that the introduction of the gig economy creates fallback opportunities for would-be entrepreneurs that reduce risk and encourage new business formation.

New technology promises to expand the supply of financial services to small businesses poorly served by banks. Does it succeed? We study the response of FinTech to financial services demand created by the introduction of the Paycheck Protection Program. FinTech is disproportionately used in ZIP codes with fewer bank branches, lower incomes, and more minority households, and in industries with fewer banking relationships. It is also greater in counties where the economic effects of the COVID-19 pandemic were more severe. Substitution between FinTech and banks is economically small, implying that FinTech mostly expands, rather than redistributes, the supply of financial services.

Building parable economies embedding econometricians, we view alternative estimators (Instrumental variables, fuzzy regression discontinuity, natural experiments, OLS, event studies) from the perspective of privately informed decision-makers, e.g., CFOs. Instrumental variable estimates can be misleading since randomization through observable instruments eliminates signal content arising from discretion. If the goal is informing discretionary decisions, rather than predicting outcomes after forced/mistaken actions, instrumentation is problematic, whereas OLS or event studies can be sufficient. The analysis shows that the utility of alternative estimators hinges upon often neglected assumptions about agent/econometrician information sets, as distinct from exclusion restrictions. We recommend parable economy estimation before real-world IV estimation.

Diversified acquirer shareholders can profit from value-destroying acquisitions not only through their target stakes, but also through stakes in non-merging rival firms. Announcement losses are largely mitigated for the average acquirer shareholder when accounting for wealth effects on their rival stakes. Ownership by acquirer shareholders in non-merging rivals is negatively associated with deal quality and positively associated with deal completion. Funds with more rival ownership are more likely to vote in favor of the acquisition. Overall, these results show that many so-called “bad deals” are often in the interest of acquirer-firm shareholders.

We study the benefits and costs of collateral requirements in bank lending markets with asymmetric information. We estimate a structural model of firms’ credit demand for secured and unsecured loans, banks’ contract offering and pricing, and firm default using credit registry data in a setting where asymmetric information problems are pervasive. We provide evidence that collateral mitigates adverse selection and moral hazard. With counterfactual experiments, we quantify how an adverse shock to collateral values propagates to credit supply, credit allocation, interest rates, default, bank profits, and document the relative importance of banks’ pricing and rationing in response to this shock.

We develop a new methodology to estimate the impact of a financial transaction tax (FTT) on financial market outcomes. In our sequential trading model, there are price-elastic noise and informed traders. We estimate the model through maximum likelihood for a sample of 60 NYSE stocks in 2017. We quantify the effect of introducing an FTT given the parameter estimates. An FTT increases the proportion of informed trading, improves information aggregation, but lowers trading volume and welfare. For some less liquid stocks, however, an FTT blocks private information aggregation.

We develop a theory linking “misallocation,” i.e., dispersion in marginal products of capital (MPK), to macroeconomic risk. Dispersion in MPK depends on (i) heterogeneity in firm-level risk and (ii) the magnitude of risk premia. Stock market-based measures imply that risk considerations explain about 25% of MPK dispersion among US firms and rationalize a large persistent component in firm-level MPK, consistent with the micro-level data. Time-varying risk premia lead to countercyclical MPK dispersion alongside procyclical capital reallocation. Risk-based MPK dispersion in part shapes the dynamic behavior of aggregate productivity, namely, its long-run level, volatility and skewness.

Recent work uses option prices to derive lower bounds for the risk premia of the market portfolio and individual stocks. We test the bounds conditionally. We cannot reject that they are valid, but we do reject that they are tight. Using the market bounds as forecasts appears unreasonable in many cases due to their high slackness. Adding past mean slackness is a potential improvement but is hampered by the brevity of the available data series. The correlation of the stock bounds with subsequent returns stems primarily from the time series rather than the cross section.

We study the impact of an epidemic disease on modern financial development by exploiting geographic variations in the precolonial survival conditions of the TseTse fly, which transmits an epidemic disease that is harmful to humans and fatal to livestock in Africa. Using newly georeferenced data, we discover that firms and households in regions historically more exposed to the epidemic disease have less access to external financing today. Exploring the channels, we find that people in historically infested regions are less likely to trust others and financial institutions, to share credit information and to learn and adopt new financial technologies.