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.

Firms increasingly rely on markets for technology to acquire innovations developed outside their boundaries, yet acquiring intellectual property rights alone often does not guarantee successful implementation. Many technologies depend on tacit know-how that must be supplied by the provider after the transaction is completed. This paper examines whether common ownership between a technology provider and a potential adopter mitigates this implementation problem. I develop a model in which overlapping institutional investors cause the provider to partially internalize the adopter’s gains from successful implementation, strengthening incentives to transfer tacit know-how. This mechanism operates only when know-how is unverifiable – absent this friction, common ownership leaves matching and outcomes unchanged. Under moral hazard, the model predicts that common ownership increases the likelihood of technology transfer to a given adopter, that this effect is stronger when tacit know-how is more important, and that common ownership improves post-transfer outcomes conditional on adoption. I test these predictions using U.S. patent reassignments between publicly traded firms. Using within-deal variation across competing potential adopters and plausibly exogenous variation from passive index-fund holdings, I show that common ownership increases the likelihood that a firm acquires a technology, particularly when the transferred bundle is more tacit. Common ownership predicts stronger subsequent innovation and higher future firm value, especially when ownership overlap is concentrated among investors with stronger incentives to monitor the provider. These findings show how ownership structure shapes interfirm technology transfer by affecting not only who acquires a technology, but also how much value is created.

Using a market-indicator-based approach, this paper empirically examines whether the stability of the US and EU financial systems is affected by the digital finance revolution driven by BigTechs, FinTechs, and crypto-assets. These three sectors display different downside volatility profiles, with financial intermediaries being particularly sensitive to shocks from the crypto ecosystem only under extremely severe downturns, which are prevented in regulated equity markets. In that vein, we provide evidence that the Markets in Crypto Assets Regulation reduced financial systemic risk in EU. Overall, our empirical analysis shows that markets perceive the performance and riskiness of tech-driven companies and assets in differentiated ways, and that the transmission of shocks from digital finance ecosystems operates uniquely under varying conditions of systemic stress. Finally, we also document asymmetric spillover effects between advanced and emerging economies, with shock transmission from the US and EU to emerging markets being systematically stronger than in the reverse direction.

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.

Based on a sufficient statistics approach, 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. These findings offer policy insights relevant to the EU’s external competitiveness debate, echoing several recommendations from the Draghi report. Achieving export specialization in key sectors requires more than just technological superiority.

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.