We use logit and Markov switching models to assess, in (pseudo-)real-time, the ability of 27 indicators to predict systemic financial crises in the European Union. Before the global financial crisis (GFC), some models provided early warning signals, but it is unclear whether a specific model would have been favored over other candidate models providing contradictory evidence. Only after the GFC do debt service ratios, credit-to-GDP gaps as well as house price-to-income and house price-to-rent ratios appear as robust early warning indicators. Our results highlight that the predictive ability of indicators may change due to new risk factors or policy actions.

This paper introduces a new methodology to date systemic financial stress events in a transparent, objective and reproducible way. The financial cycle is captured by a monthly Country-Level Index of Financial Stress (CLIFS). Based on two Markov-switching and one threshold vector autoregressive model, information from the CLIFS and industrial production are combined to identify those episodes of financial market stress that are associated with a substantial negative impact on the real economy. By applying this framework to 27 European Union countries, the paper is a first attempt to provide a chronology of systemic financial stress episodes as a complement to the expert-detected events that are currently available.

Mayshar et al. (2022) apply an instrumental variables identification strategy to data from nearly 1,000 societies included in the Ethnographic Atlas to claim that cultivation of cereals (appropriable by elites), rather than increased land productivity following the adoption of agriculture, led to the development of the state. We show two things. (1) Evidence for the appropriability theory holds when moving from a tribe-chiefdom to a state and not more broadly. (2) Conclusions are driven by a handful of outliers with statistical significance at the 10% level lost with winsorization at 2.1% (or trimming at 1.2%) of locations by cereal advantage.