Vietnam, a lower-middle-income economy, faces severe climate risks from heat waves, sea-level rise, and tropical cyclones, which are expected to intensify under ongoing global warming. Using a dynamic general equilibrium model, we analyze economic transition dynamics from 2015 to 2100, incorporating heat-induced labor productivity losses, agricultural land loss, and cyclone-related property damage. We compare a Paris-compatible scenario limiting warming to below 2 °C with a high-emission scenario reaching 4–5 °C. While output and investment impacts remain highly uncertain and statistically indistinguishable across scenarios until 2100, consumption losses are significantly larger under high emissions, mainly driven by heat-related productivity declines, with cyclones contributing most to uncertainty. These findings underscore the importance of considering multiple impact channels beyond output damages in climate-development research.

We exploit an information shock related to the German Supply Chain Due Diligence Act and use detailed customs data to analyze how smaller, non-listed firms respond when expecting accountability for externalities beyond their organizational boundaries. Product-level regressions reveal a substantial reduction in imports from high ESG-risk production sectors. Adjustments occur mainly at the extensive margin, indicating that firms cut ties with high-risk suppliers. The product-level results translate into meaningful changes in overall international procurement for firms with Big Four auditors. Our findings suggest potential limits to mandates requiring firms to integrate broad sustainability considerations into operational decisions.

This paper examines the impact of complex yet precise language, particularly financial jargon, on information dissemination and ultimately market efficiency. As a natural laboratory, we analyze the information exchanged during earnings conference calls, where we instrument jargon with the Plain Writing Act of 2010. Our findings suggest that the Act‘s promotion of plain language usage results in a reduction in complex financial jargon for US firms. However, in contrast to the presumed benefits of accessible language, this reduction in jargon is associated with a decrease in market efficiency, implying that the Act may inadvertently hinder information flow. This finding is particularly important at the juncture where human-generated information is received by machines, which are known to be vunerable to ambiguous inputs.

This paper analyses whether Christian moralities and rules formed differently by Catholics and Protestants impact the likelihood of households to become overindebted. We find that over-indebtedness is lower in regions in which Catholics outweigh Protestants, indicating that Catholics‘ forgiveness culture and a stricter enforcement of rules by Protestants serve as explanations for our results. Our results provide evidence that religion affects the financial situations of individuals and show that even 500 years after the split between Catholics and Protestants, the differences in the mind-sets of both denominations play an important role for situations of severe financial conditions.

Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as a valid instrument to address endogeneity. Using double/debiased machine learning (DML) techniques and partial linear instrumental variable (PLIV) models, we analyze GDP growth forecasts for Germany, conditioning on key exogenous variables such as oil price, exchange rate, and world trade. We find that traditional ordinary least squares (OLS) techniques systematically underestimate the influence of assumption errors, particularly with respect to world trade, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities in the data. However, the effect of oil price assumption errors on GDP forecast errors remains ambiguous. These results underscore the importance of advanced econometric tools to improve the evaluation of macroeconomic forecasts.