Yan Wang

This study explores the compound crisis that struck China in the 1930s, fueled by the overlapping impacts of the Great Depression and frequent rare and severe natural disasters. By utilizing a newly constructed annual climate index based on the self-calibrated Palmer Drought Severity Index (scPDSI) and financial crises data from Reinhart and Rogoff (2008, 2009), we identify key periods of compounded disruptions during the Great Depression. Employing a Dynamic Stochastic General Equilibrium (DSGE) model specifically designed for China’s multi-sector economy under the silver standard, we assess the contributions of external financial shocks and internal climate shocks to macroeconomic fluctuations. Our findings demonstrate that climate shocks had a dominant and lasting impact on agricultural output and GDP, far outweighing the effects of the Great Depression, which primarily affected trade through declining exports and imports. The analysis further uncovers how disruptions in agriculture propagated through other sectors, intensifying economic instability. Counterfactual simulations reveal that modern monetary policies, such as flexible interest rate adjustments, could have mitigated short-term fluctuations but would have required effective fiscal coordination to avoid resource misallocation and maintain agricultural investment. The study highlights the critical role of agriculture as a transmission channel for compound shocks and provides policy insights for modern economies, emphasizing the need for coordinated monetary and fiscal responses to manage overlapping crises.

Investing according to ESG concerns has become popular and controversial. Companies that score well on ESG concerns may attract socially conscious investors. But do ESG scores collectively and individually impact stock market performance? This paper weighs in on this issue by analyzing the impact that changes in ESG scores have on both the excess stock market returns (alpha) and risk-adjusted returns (Sharpe ratio). We also analyze the differential impact of ESG on financial performance among U.S. manufacturing and banking firms. Using quantile regression analysis, our results show a nonlinear relationship, characterized by a U-shaped and an inverted-U shaped relationship between ESG ratings and alpha and Sharpe ratios respectively. Moreover, we find significant differences regarding the impact of governance, environmental, and social on excess stock market returns in both industries.

The interplay between financial risks and climate instability presents profound challenges to global economic stability. This study defines compound crises as events where financial crises and rare natural disasters coincide within the same year, leading to economic losses exceeding 1% of GDP. It examines the interactions between climate crises and six types of financial crises—banking, currency, inflation, systemic, domestic debt, and sovereign external debt crises. By analyzing key compound crises from 1900 to 2023, the study investigates their global characteristics and transmission mechanisms. Using statistical tests, including the t-test and Mann-Whitney U-test, the research provides empirical evidence on the amplifying role of rare natural disasters in financial instability. The results demonstrate that rare natural disasters significantly heighten the probability of currency and sovereign external debt crises. Multi-crisis combinations, particularly those involving banking crises coupled with currency and systemic crises, exhibit the strongest amplification effects. These findings highlight that natural disasters primarily amplify external financial risks and multi-crisis combinations, particularly through mechanisms such as exchange rate fluctuations and debt sustainability. As such, the study underscores policy responses to mitigate systemic vulnerabilities and prevent cascading risks. The study provides practical insights for policymakers, particularly in developing countries, on enhancing resilience to climate-driven financial instability.

This study examines the price revolution in Qing China (1644–1911), focusing on silver flows, climate shocks, and socio-economic factors in the North China Plain (1736–1911). Using ARDL and NARDL models, the findings reveal strong long-term co-integration among these variables. Unlike early modern Europe, Qing China's price revolution was not solely driven by monetary expansion but resulted from the interaction between silver inflows and climate variability. Silver supply had a strong long-term positive effect on prices, yet the silver-to-copper ratio exhibited opposing short- and long-run effects, suggesting that liquidity constraints tempered inflation over time. Climate shocks had asymmetric effects, with droughts exerting a persistent upward pressure on prices, while floods and temperature variations primarily affected short-term volatility. Population growth had no significant impact, challenging conventional Malthusian narratives. These findings underscore the necessity of nonlinear models in historical economic analysis and provide a broader framework for understanding how climate and financial shocks shape price dynamics, both in historical contexts and contemporary economies vulnerable to similar risks.