Using administrative data on credit profiles matched with unemployment insurance (UI) for individuals in the U.S., we show that laid-off workers with access to Uber rely less on household debt, experience fewer delinquencies, and are less likely to apply for UI benefits. Our empirical strategy exploits both the staggered market entry of Uber across cities and the differential benefit of its entry across car owners based on car age, a key eligibility requirement of the platform. We conclude that the introduction of Uber reduced reliance on these alternative means of smoothing extreme income shocks.

Following the onset of COVID-19, research production in economics and finance (measured by the posting of working papers) increased by 29%. Production increases were widespread across geographies, job titles, departments, and ages with larger increases in top departments and for people under the age of 35. Men and women both experienced production increases with the exception of women between the age of 35 and 49, who experienced no production gains despite large increases for men in the same age group. COVID-19 increased reliance on past coauthorship networks, with larger production gains for authors that are more central to the network.

We examine whether “rating shopping” or “rating catering” is a more accurate characterization of rating agency interactions regarding collateralized debt obligations (CDOs). Although investors paid a premium for dual ratings, AAA CDO tranches rated by both Moody's and S&P defaulted more frequently than tranches rated by only one of them, which is inconsistent with pure rating shopping. Rating agencies made upward adjustments beyond their model when their competitor had more lenient assumptions. Finally, consistent with rating catering, S&P's and Moody's adjustments and disagreements at security issuance were reflected in subsequent rating downgrades, suggesting that adjustments were harmful.

We estimate Moody’s preference for accurate versus biased ratings using hand-collected data on the internal labor market outcomes of its analysts. We find that accurate analysts are more likely to be promoted and less likely to depart. The opposite is true for analysts who downgrade more frequently, who assign ratings below those predicted by a ratings model, and whose downgrades are associated with large negative market reactions. Downgraded firms are also more likely to be assigned a new analyst. These patterns are consistent with Moody’s balancing its desire for accuracy against its corporate clients’ desire for higher ratings.