This paper analyzes the distribution and composition of pre-tax national income in Germany since 1992, combining personal income tax returns, household survey data, and national accounts. Inequality rose from the 1990s to the late 2000s due to falling labor incomes among the bottom 50% and rising incomes in the top 10%. This trend reversed after 2007 as labor incomes across the bottom 90% increased. The top 1% income share, dominated by business income, remained relatively stable between 1992 and 2019. A large share of Germany’s top 1% earners are non-corporate business owners in labor-intensive professions. At least half of the business owners in P99-99.9 and a quarter in the top 0.1% operate firms in professional services – a pattern mirroring the United States. From 1992 to 2019, Germany’s top 0.1% income concentration exceeded France’s and matched U.S. levels until the late 2000s.

This paper investigates the critical role of church membership in the process of economic recovery after high-impact natural disasters. We document a significant adverse treatment effect of the 2005 hurricane season in the Southeastern United States on establishment-level productivity. However, we find that establishments in counties with higher rates of church membership saw a significantly stronger recovery in terms of productivity for 2005–10. We also show that church membership is correlated with post-disaster entrepreneurship activities and population growth.

This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.