We study how idiosyncratic income risk shapes the aggregate and distributional effects of labor and capital income taxation in dynamic general equilibrium models. To this end, we compare a heterogeneous-agent (HA) model with uninsurable idiosyncratic labor productivity risk and a ten-representative-agent (TE) model in which households correspond to fixed wealth deciles without such risk. At the aggregate level, both models generate qualitatively similar responses; however, the HA model exhibits a smaller recessionary impact driven by precautionary savings behavior, which stabilizes investment. At the distributional level, the models differ sharply. In the HA framework, tax shocks trigger endogenous mobility across wealth deciles. These inter-decile transition dynamics tend to benefit lower deciles. In contrast, the TA model features fixed household positions. Our findings highlight that while simpler multi-representative-agent models can approximate aggregate dynamics well, they may miss important distributional adjustment channels. The relevance of these mechanisms ultimately depends on the empirical importance of mobility across the wealth distribution, pointing to a key trade-off between model simplicity and accuracy.

Climate change and inequality are critical and interrelated issues. Despite growing empirical evidence on the distributional implications of climate policies and climate risks, mainstream model-based assessments are often silent on the interplay between climate change and economic inequality. Here we fill this gap through an ensemble of eight large-scale integrated assessment models that belong to different economic paradigms and feature income heterogeneity. We quantify the distributional implications of climate impacts and of the varying compensation schemes of climate policies compatible with the goals of the Paris Agreement. By 2100, climate impacts will increase inequality by 1.4 points of the Gini index on average. Maintaining global mean temperature below 1.5 °C reduces long-term inequality increase by two-thirds but increases it slightly in the short term. However, equal per-capita redistribution can offset the short-term effect, lowering the Gini index by almost two points. We quantify model uncertainty and find robust evidence that well-designed policies can help stabilize climate and promote economic inclusion.

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.