This study estimates the firm-level employment effects of investment grants in Germany. In addition to the average treatment effect on the treated, we examine discrimination in the funding rules as a potential source of effect heterogeneity. We combine a staggered difference-in-differences approach with a matching procedure at the cohort level. The findings reveal a positive effect of investment grants on employment development. The subsample analyses yield strong evidence for heterogeneous effects based on firm characteristics and the economic environment. They highlight the responsibility of the local funding authorities to clarify ex ante which goals of a funding programme are most important in their regions.

This study provides new evidence on the impact of the EU Cohesion Policy on income growth in less developed regions. Our panel includes data from all European regions for the years 1989-2020. Using a fuzzy Regression Discontinuity Design, we model treatment dynamics by applying a random effects estimator. Based on digitized historical data, we precisely replicate the policy rule and correctly classify the regions’ eligibility status. Results show that the policy has a moderate positive effect on GDP per capita growth in the targeted regions.

This paper examines the macroeconomic and distributional effects of the European Union’s transition to Net Zero emissions through a gradually increasing carbon tax. I develop a New Keynesian Environmental DSGE model with two household types and distinct energy and non-energy sectors. Five alternative uses of carbon tax revenues are considered: equal transfers to households, targeted transfers to Hand-to-Mouth households, subsidies to green energy firms, and reductions in labor and capital income taxes. In the absence of technological progress, the carbon tax policy induces a persistent increase in energy prices and a reduction in GDP, investment, and consumption. Headline inflation falls below zero in the medium run, reflecting weaker aggregate demand. Distributional outcomes vary significantly depending on the implemented revenue recycling scheme: targeted transfers are the most progressive but entail larger macroeconomic costs, while subsidies and tax cuts mitigate output and investment losses but are less effective in narrowing the consumption gap. A limited foresight scenario, in which agents learn about policy targets sequentially, generates more volatile adjustment paths and temporary inflationary spikes around announcements, but long-run outcomes remain close to the baseline.

Climate policy design must balance emissions mitigation with concerns for fairness, particularly as climate change disproportionately affects the poorest households within and across countries. Integrated Assessment Models used for global climate policy evaluation have so far typically not considered inequality effects within countries. To fill this gap, we develop a global Integrated Assessment Model representing national economies and subnational income, mitigation cost, and climate damage distribution and assess a range of climate policy schemes with varying levels of effort sharing across countries and households. The schemes are consistent with limiting temperature increases to 2 °C and account for the possibility to use carbon tax revenues to address distributional effects within and between countries. We find that carbon taxation with redistribution improves global welfare and reduces inequality, with the most substantial gains achieved under uniform taxation paired with global per capita transfers. A Loss and Damage mechanism offers significant welfare improvements in vulnerable countries while requiring only a modest share of global carbon revenues in the medium term. The poorest households within all countries may benefit from the transfer scheme, in particular when some redistribution is made at the country level. Our findings underscore the potential for climate policy to advance both environmental and social goals, provided revenue recycling mechanisms are effectively implemented. In particular, they demonstrate the feasibility of a welfare improving global climate policy involving limited international redistribution.

In this study, establishment-level employment effects of investment grants in Germany are estimated. In addition to the quantitative effects, I provide empirical evidence of funding effects on different aspects of employment quality (earnings, qualifications, and job security) for the period 2004 to 2020. The database combines project-level treatment data, establishment-level information on firm characteristics and employee structure, and regional information at the district-level. For the estimations, I combine the difference-in-differences approach of Callaway and Sant’Anna (2021) with ties matching at the cohort level. The estimations yield positive effects on the number of employees, but point to contradicting effects of investment grants on different aspects of employment quality.

Why are children with lower socioeconomic status (SES) substantially less likely to be enrolled in child care? We study whether barriers in the application process work against lower-SES children — the group known to benefit strongest from child care enrollment. In an RCT in Germany with highly subsidized child care (N = 607), we offer treated families information and personal assistance for applications. We find substantial, equity-enhancing effects of the treatment, closing half of the large SES gap in child care enrollment. Increased enrollment for lower-SES families is likely driven by altered application knowledge and behavior. We discuss scalability of our intervention and derive policy implications for the design of universal child care programs.

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 corporate-loan pricing decisions of a major, systemic bank during the Greek financial crisis. A unique aspect of our data set is that we observe both the actual interest rate and the “break-even rate” (BE rate) of each loan, as computed by the bank’s own loan-pricing department (in effect, the loan’s marginal cost). We document that low-BE-rate (safer) borrowers are charged significant markups, whereas high-BE-rate (riskier) borrowers are charged smaller and even negative markups. We rationalize this de facto cross-subsidization through the lens of a dynamic model featuring depressed collateral values, impaired capital-market access, and limit pricing.

We show that the response of banks’ net interest margin (NIM) to monetary policy shocks is state dependent. Following a period of low (high) Federal Funds rates, a contractionary monetary policy shock leads to an increase (decrease) in NIM. Aggregate economic activity exhibits a similar state-dependent pattern. To explain these dynamics, we develop a banking model in which social interactions influence households’ attentiveness to deposit interest rates. We embed that framework within a nonlinear heterogeneous-agent NK model. The estimated model accounts well quantitatively for our key empirical findings.

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.