Band 21, Heft 2

The euro area, like other advanced economies, faced high inflation over the past years. We analyse empirically the impact of inflation on public finances in the euro area, focusing on the question of whether at such high levels, inflation could have a different impact on the primary budget balance. To this end, we estimate a fiscal reaction function for euro area countries and find evidence of non-linear short-term effects of HICP inflation on the primary balance. Over the period 1999–2022, we unveil an inverse U-turn relationship and an inflation turning point – beyond which its short-term (contemporaneous) impact on the primary balance starts being negative. These results reflect primarily the most recent high inflation episode and indicate that in such conditions inflation can be costly for public finance flows even in the shorter run.

Zombie firms have been a prominent yet controversial subject of academic and policy debates in recent years. In this article, we first revisit the economic consequences and driving factors of zombie companies and then, based on this assessment, discuss the implications of artificial intelligence (AI) for zombie firms. We document that the share of zombie firms in advanced economies has risen considerably over the past three decades, and that this increase has been a significant drag on productivity growth. We further find that persistently low interest rates are a significant causal factor underlying the rise of zombie companies. Turning to AI, we argue that an AI-induced productivity boom may counteract the drag from zombie firms and improve firm performance, mitigating corporate zombification. Moreover, by leading to higher interest rates that force zombie firms to exit markets, AI may boost productivity further in the longer run – a so far overlooked channel in the debate on the economic implications of AI.

The energy transition will have profound and varying effects across the globe. We provide an evidence-based qualitative analysis and assess how clean technologies are evolving – mainly wind, solar and electric vehicles – and the challenges and opportunities the transition poses for fossil fuel and metals and minerals producers in the short and long term. We describe the likely macroeconomic consequences of the energy transition and identify the countries that are most positively and negatively exposed. A small number of fossil fuel-producing countries are likely to be severely hit. Meanwhile, a concentrated group of minerals producers should experience large net benefits. Fuel importers – that is, most of the world – should benefit to varying degrees.

Carbon Border Adjustment Mechanism (CBAM) revenues are increasingly being discussed as a potential new EU own resource. The aim of this paper is to quantify the revenue potential of the CBAM for the EU budget until 2035. Furthermore, this paper examines the distributional implications of CBAM revenues for Member States. It proposes a methodology to quantify the revenue potential of CBAM and calculates a number of revenue scenarios. The distribution of CBAM revenue among all Member States is compared with the distribution of the GNI-based resource in order to illustrate the distributional effects. According to these results, the annual revenue potential of the CBAM is of the order of €1 billion in the first years after its introduction, rising to around €14 billion by 2035. The distributional effects compared to GNI proportionality are small. The scenario analysis shows the sensitivity of the CBAM revenue forecasts, as they depend crucially on various parameters.

The war in Europe has not only re-drawn strategic lines and trade flows, but with complex sanctions in place, may also have a lasting impact on currency regimes. Beyond the Russia-Ukraine conflict, a different, slower but still more strategic shift is taking place in relations between the United States and China. The US is not seeking a full decoupling of trade between China and the US and its allies – there are many deep dependencies on both sides and, overall, trade continues to grow. China, on the other hand has been casting its political and economic capital into strategic alliances that include notions of a new currency regime – in parts because of the sanctions imposed by the G7 alliance. The question for this paper therefore is whether it is still a question of if de-dollarisation is happening or rather how fast.

This paper surveys the empirical literature on causes and consequences of academic freedom that has grown up since the release of new data in 2020. The literature suggests that democracy and multi-party politics contribute to more academic freedom. However, declines in academic freedom may predict autocratisation. In addition, the literature shows that academic freedom contributes to better research, innovation and productivity growth.

Some observers expect that the current wave of new tools based on artificial intelligence (AI) models, such as the large language models, will have strong effects on labor productivity. I present definitions and classifications that help understanding AI as an economic input. I then review theoretical and empirical arguments about macroeconomic productivity effects of AI and conclude that research has so far found no indication that productivity effects of the diffusion of AI are likely to be higher than those associated with the internet boom around the year 2000. While considerable uncertainty around future effects remains, a recent review and calibration exercise by Acemoglu, D. (2024. The Simple Macroeconomics of AI.  Cambridge, MA: National Bureau of Economic Research, Working Paper 32487) suggests that the effects might be a lot lower.

This paper examines the impact of new technologies, particularly automation and artificial intelligence (AI), on labor markets. The existing literature documents ambiguous and only limited overall employment effects, while new technologies induce significant shifts in workforce composition. The implied firm-level productivity gains primarily benefit larger, skilled-labor-intensive firms. AI adoption remains limited but continues to reshape skill demands. The implied worker reallocation is costly, exacerbating inequality. This calls for policies such as targeted support for displaced workers, investment in education and skill development, promoting technology diffusion, and encouraging complementary human capital investments.

Artificial intelligence (AI) constitutes a new technological revolution that has far-reaching, somehow paradigmatic implications for the economy and as well as for the society – for good and for evil. Given the specific properties of artificial intelligence this article analyses the economic consequences of AI on productivity and growth, the distribution of income as well as on firms, markets, and competition. It argues that artificial intelligence is creating a new economic order that requires new approaches to economic policy and regulation. Issues of how to maintain competition, protect consumer rights, promote AI literacy and preserve ethical standards are at the center of a holistic AI governance.

The integration of artificial intelligence (AI) in the workplace is at a nascent stage, presenting both substantial opportunities and challenges for productivity growth. We argue that AI’s potential will only be truly realized through strategic investments in human skills and comprehensive organizational redesign. Drawing on interdisciplinary insights, we highlight the critical role of AI-human collaboration, continuous workforce skill development, and adaptive organizational practices. We conclude with recommendations to create a human-centered environment conducive to AI-driven productivity gains through its assistance, augmentation, and automation capabilities.

This article examines the transformative potential of artificial intelligence (AI) in asset management, highlighting how AI can enhance research, decision-making, communication, and trading processes. AI, particularly through machine learning (ML) and generative models, can significantly reduce analysts’ time on data collection and analysis, offer standardized recommendations, and improve communication efficiency. However, risks include potential biases and a lack of transparency in AI-driven decisions.

Solving the EU’s productivity problem will require greater use of technologies. Artificial intelligence is of particular interest given its potential to boost productivity in the services sector, where productivity growth in Europe has been particularly weak. While touted as a mechanism to boost trust in and the use of AI in Europe, the EU’s AI Act risks dampening investment. Aspects of its design and implementation should mitigate these risks, however, so that the overall impact of the bloc’s landmark law on European productivity will likely prove modest. The bigger problem is that European leaders’ focus on the law has detracted from more important steps to boost productivity – such as developing the EU’s capital markets union, addressing the lack of digital skills, and developing the single market to boost business dynamism.

AI is characterized by the features of a general purpose technology and thus has the potential to spur innovation and productivity across sectors. While large digital U.S. companies are dominant players in the development of AI models, Europe is lagging behind. The European Union has agreed on the AI Act in order to allow for innovation, in particular for SMEs, and to prevent harm to society. However, regulation may have ambiguous effects on the state of the digital economy in Europe and of AI in particular. We point out these potential effects and suggest measures to speed up AI development and usage in Europe. We thereby build on recommendations of the Commission of Experts for Research and Innovation (EFI) as well as the Scientific Advisory Board of the German Ministry of Economic Affairs and Climate Action.