Utilizing near real-time QuickBooks data from over 1.6 million small businesses and a targeted survey, this paper highlights the critical role credit card financing plays for small business activity. We examine a two year period beginning in January of 2021. A turbulent period during which, credit card usage by small U.S. businesses nearly doubled, interest payments rose by 60%, and delinquencies reached 2.8%. We find, first, monthly credit card payments were up to three times higher than loan payments during this time. Second, we use targeted surveys of these small businesses to establish credit cards as a key financing source in response to firm-level shocks, such as uncertain cash flows and overdue invoices. Third, we establish the importance of credit cards as an important financial transmission mechanism. Following the Federal Reserve’s rate hikes in early 2022, banks cut credit card supply, leading to a 15.75% drop in balances and a 10% decline in revenue growth, as well as a 1.5% decrease in employment growth among U.S. small businesses. These higher rates also rendered interest payments unsustainable for many, contributing to half of the observed increase in delinquencies. Lastly, a simple heterogeneous firm model with a cash-in-hand constraint illustrates the significant macroeconomic impact of credit card financing on small business activity.

We study whether and how EU banks comply with tighter macroprudential policy (MPP). Observing contractual details for more than one million securitized loans, we document an elusive risk-shifting response by EU banks in reaction to tighter loan-to-value (LTV) restrictions between 2009 and 2022. Our staggered difference-in-differences reveals that banks respond to these MPP measures at the portfolio level by issuing new loans after LTV shocks that are smaller, have shorter maturities, and show a higher collateral valuation while holding constant interest rates. Instead of contracting aggregate lending as intended by tighter MPP, banks increase the number and total volume of newly issued loans. Importantly, new loans finance especially properties in less liquid markets identified by a new European Real Estate Index (EREI), which we interpret as a novel, elusive form of risk-shifting.

This paper examines how firms’ exposure to supply chain disruptions (SCD) affects firm outcomes in the European Union (EU). Exploiting heterogeneous responses to workplace closures imposed by sourcing countries during the pandemic as a shock to SCD, we provide empirical evidence that firms in industries relying more heavily on foreign inputs experience a significant decline in sales compared to other firms. We document that external finance, particularly bank financing, plays a critical role in mitigating the effects of SCD. Furthermore, we highlight the unique importance of bank loans for small and solvent firms. Our findings also indicate that highly diversified firms and those sourcing inputs from less distant partners are less vulnerable to SCD.

Wir untersuchen die kurz- und langfristigen Auswirkungen eines starken Anstiegs chinesischer Importe auf Wahlergebnisse in Europa. Populistische sowie links- und rechtsextreme Parteien gewannen erst viele Jahre nach dem Höhepunkt des China-Schocks bedeutenden Zuwachs an Wählerstimmen. Wir zeigen, dass die Auswirkungen von Importschocks überwiegend zugunsten populistischer Parteien ausfallen. In geringerem Maße profitieren in der kurzen Frist zudem linksextreme Parteien, langfristig hingegen rechtsextreme Parteien. Die Effekte auf das Wahlverhalten sind jedoch moderat und wir schlussfolgern, dass Importschocks den Aufstieg der politischen Ränder nur in begrenztem Maße erklären können.

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.