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.

This paper proposes to explain the productivity growth slowdown with the poaching of disruptive inventors by firms these inventors threaten with their research. I build an endogenous growth model with incremental and disruptive innovation and an inventor labor market where this defensive poaching takes place. Incremental firms poach more as they grow, which lowers the probability of disruption and makes large incremental firms even more prevalent. I perform an event study around disruptive innovations to confirm the main features of the model: Disruptions increase future research productivity, hurt incumbent inventors and raise the probability of future disruption. Without disruption, technology classes slowly trend even further towards incrementalism. I calibrate the model to the global patent landscape in 1990 and show that the model predicts 52% of the decline of disruptive innovation until 2010.

We provide the first systematic evidence on the effectiveness of a contested policy in Germany to help displaced workers. So-called “transfer companies” (Transfergesellschaften) employ displaced workers for a fixed period, during which time workers are provided with job-search assistance and are paid a wage which is a substantial fraction of their pre-displacement wage. Using rich and accurate data on workers’ employment patterns before and after displacement, we compare the earnings and employment outcomes of displaced workers who entered transfer companies with those that did not. Workers can choose whether or not to accept a position in a transfer company, and therefore we use the availability of a transfer company at the establishment level as an IV in a model of one-sided compliance. Using an event study, we find that workers who enter a transfer company have significantly worse post-displacement outcomes, but we show that this is likely to be the result of negative selection: workers who lack good outside opportunities are more likely to choose to enter the transfer company. In contrast, ITT and IV estimates indicate that the use of a transfer company has a positive and significant effect on employment rates five years after job loss, but no significant effect on earnings. In addition, the transfer company provides significant additional compensation to displaced workers in the first 12 months after job loss.