Syndicated loan data provided by DealScan is an essential input in banking research to answer urging questions on bank lending, e.g., in the presence of financial or geopolitical shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice, and we provide guidelines for applied research.

We study the aggregate, distributional, and welfare effects of fiscal policy responses to Germany’s energy crisis arising in 2022 using a novel ten-agent new Keynesian (TENK) model. The crisis, compounded by the COVID-19 pandemic, led to sharp price increases and significant consumption disparities. Our model, calibrated to Germany’s income and consumption distribution, evaluates key policy interventions. We find that untargeted transfers had the largest short-term aggregate impact, while targeted transfers for lower-income households were most cost-effective. Other instruments yielded comparably limited welfare gains. The results highlight how targeted fiscal measures can address distributional effects and stabilize consumption during crises.

We measure desired labour supply at the extensive (employment) margin in two representative surveys of the U.S. and German populations. We elicit reservation raises: the percent wage change that renders a given individual indifferent between employment and nonemployment. It is equal to her reservation wage divided by her actual, or potential, wage. The reservation raise distribution is the nonparametric aggregate labour supply curve. Locally, the curve exhibits large short-run elasticities above 3, consistent with business cycle evidence. For larger upward shifts, arc elasticities shrink towards 0.5, consistent with quasi-experimental evidence from tax holidays. Existing models fail to match this nonconstant, asymmetric curve.

This paper develops a quantitative theory of how people weigh the risks of infections against the benefits of engaging in social interactions that contribute to the spread of infectious diseases. Our framework takes into account the effects of public policies and private behavior on the spread of the disease. We evaluate the model using a novel micro panel dataset on consumption expenditures of young and older people across the first three waves of COVID-19 in Portugal. Our model highlights the critical role of expectations in shaping how human behavior influences the dynamics of epidemics.