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Houses in ‘religiously mixed’ areas of NI cost moreHuyen NguyenBBC, August 6, 2025
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.
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.
We study the aggregate, distributional, and welfare effects of fiscal policy responses to Germany’s energy crisis arising in 2022 using a novel ten-agent new Keynesian (TENK) model. The crisis, compounded by the COVID-19 pandemic, led to sharp price increases and significant consumption disparities. Our model, calibrated to Germany’s income and consumption distribution, evaluates key policy interventions. We find that untargeted transfers had the largest short-term aggregate impact, while targeted transfers for lower-income households were most cost-effective. Other instruments yielded comparably limited welfare gains. The results highlight how targeted fiscal measures can address distributional effects and stabilize consumption during crises.
Im Koalitionsvertrag von CDU, CSU und SPD vom 7. Februar 2018 formuliert die neue Bundesregierung ihre rentenpolitischen Ziele. Diese sind vor dem Hintergrund der Bevölkerungsdynamik in Deutschland zu sehen. Ab dem Jahr 2020 wird sich die Altersstruktur der deutschen Bevölkerung deutlich verändern. In diesem Beitrag werden Simulationsrechnungen zu den Konsequenzen der rentenpolitischen Maßnahmen aus dem Koalitionsvertrag für die Finanzierung der gesetzlichen Rentenversicherung mit Hilfe eines Simulationsmodells dargestellt. Die im Koalitionsvertrag vorgesehenen Leistungsausweitungen verursachen langfristig Kosten in Höhe von etwa 2½ Prozentpunkten beim Beitragssatz zur gesetzlichen Rentenversicherung. Es werden ferner Maßnahmen – auch im Vergleich zu den Rentensystemen anderer Länder – diskutiert, mit denen der Anstieg des Beitragssatzes begrenzt werden könnte.
