We revisit the employer size wage effect (ESWE) – arguably the most basic and influential departure from the law of one price for labor. Our main result is that this canonical fact disappears completely across establishments within the same firm, even though they operate in different local labor markets. We uncover and dissect this fact by including a firm fixed effect in otherwise standard cross-sectional regressions of wages on establishment size. We implement this demanding specification in population-wide triple-linked firm-establishment-employee data in Germany. This result is new in the ESWE literature (for which our paper also provides the first systematic meta-analysis). This wage-size decoupling is hard to square with the view that employment is determined along a finitely elastic employerspecific labor supply curve – i.e., employers pay exactly the minimum needed for the quantity of labor, but no more – the foundation of the monopsony view. By contrast, large multi-establishment firms (MEF) appear to hire off their labor supply curves (or those curves are very elastic), pay wage premia above the monopsonistic minimum, and leave excess labor supply. We find some evidence for a reemergence of the ESWE within low-premium MEFs. Overall, at least for the 25% of German employment in large firms for which the ESWE disappears, wage setting and employment determination may be better accounted for by alternative models, namely accommodating above-market-clearing wage premia and rationing of labor supply, such as efficiency wage theories.

Firm training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether firm training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without firm training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that training reduces workers’ automation risk by 3.8 percentage points, equivalent to 8% of the average automation risk. The training-induced reduction in automation risk accounts for 15% of the wage returns to firm training. Firm training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Training is similarly effective across gender, age, and education groups, suggesting widely shared benefits rather than gains concentrated in specific demographic segments.

Syndicated loan data provided by DealScan is an essential input in banking research to answer urging questions on bank lending, e.g., in the presence of financial or geopolitical shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice, and we provide guidelines for applied research.

Climate policies do not operate in isolation but propagate through global production networks, affecting industries beyond national borders. This paper combines international input-output data with a granular instrumental variable approach to capture how foreign regulations transmit through upstream and downstream linkages. Distinguishing between market-based policies, non-market regulations, and technology support, the analysis shows that foreign climate policies can enhance domestic productivity, with effects shaped by industry characteristics and operating through technological adjustment along supply chains. The results underscore the importance of accounting for international spillovers when evaluating the economic impact of environmental regulation.

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.

We study the effects of place-based policies on aggregate productivity using administrative data on projects co-financed by the EU in Italy linked to balance sheet data. We exploit quasi-experimental variation in funding for a large place-based policy stemming from measurement error in regional GDP estimates. Results show that the policy likely decreases productivity. Decompositions reveal that aggregate declines are driven by reallocation of labor to low-productivity firms. Mechanism analysis using firm-level event studies reveals that negative reallocation effects are caused by high-productivity firms taking up the funds and subsequently becoming more liquidity constrained, leading to slowdowns in employment growth.

The European Commission’s growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts’ quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.