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.

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.

Based on the sufficient statistics approach developed by Huang and Ottaviano (2024), we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation.

Based on the sufficient statistics approach developed by Huang and Ottaviano (2024), we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation.