What quotas have been put in place on steel imports into the EU and whom do they concern?
The EU’s steel measure, which enters into application on 1 July 2026, reduces duty-free imports of 26 categories of steel products into the EU by an average of 47% as compared with the quotas under steel safeguard. As of 1 July 2026, a total of 18.3 million tonnes of steel will be allowed to enter the EU duty-free each year. Today’s implementing regulation sets out the fair and objective methodology under which that quota will be distributed among the EU’s trading partners. Out-of-quota imports will be subject to a tariff of 50%.
What kind of methodology did the Commission use to determine the distribution of the steel quota?
The EU’s steel quota has been divided amongst the EU’s trading partners using a fair and objective methodology in line with criteria laid down in the Steel Regulation. This methodology is in line with WTO rules and acknowledges any agreements in principle reached between the EU and a trading partner at the WTO (under Article XXVIII GATT negotiations). It also distinguishes between FTA and non-FTA partners, ensuring that FTA partners get better treatment in the form of higher quota volume. Finally, the methodology is mindful of diversification and security of supply issues.
How specifically was the quota divided between individual trading partners?
Half of the EU’s annual import quota of 18.3 million tonnes – so 9.15 million tonnes – has been distributed exclusively to partners with free trade agreements (FTAs) with the EU, with the remaining half available to all trading partners (including FTA partners) without discrimination.
Of the part available exclusively to FTAs, a larger share are country-specific allocations. These are granted to countries which have historically held at least 5% share of import volumes (based on an average share of imports in the years 2022-2024). The remaining volumes are available for exports from all FTA partners on a competitive basis.
What special considerations have been given to the EU’s FTA partners?
The tariff quota distribution aims to minimise the impact of the EU’s steel measure on its FTA partners within the limits of the Steel Regulation and without compromising the measure’s effectiveness.
Half of the EU’s annual import quota has been reserved exclusively for preferential trading (FTA) partners, with the remaining half available to all trading partners – including FTA partners – without discrimination. Moreover, many FTA partners will receive secure country-specific quotas proportionate to their historic import volumes.
Most of the EU’s FTA partners will therefore see a market access reduction significantly lower than the average reduction of 47% foreseen by the Steel Regulation.
How are the EU’s new steel measure and the specific tariff quota distribution WTO-compatible?
Article 28 of the General Agreement on Tariffs and Trade (GATT) allows a WTO member to modify or withdraw its bound tariff rates. To do so legally, the country must enter negotiations with affected trading partners to offer compensatory adjustments to maintain the overall balance of trade concessions.
Since proposing its steel measure in October 2025, the EU has engaged in constructive Article 28 negotiations with more than twenty trading partners (mostly FTA partners) at the WTO. Based on these discussions, the Commission has sought to cater for FTA partners’ main concerns to minimise the impact of the steel measure on them without undermining the measure’s effectiveness. A significant number of partners provisionally agreed to their allocated quotas as a result.
Why have the EU’s EEA trading partners been excluded from the steel measure?
EEA countries are not subject to tariff quotas or duties under the steel measure. Such a differentiated approach is justified due to their very close and unique level of integration in the EU’s internal market.
EEA countries were already excluded from the steel safeguard on the same grounds. Their levels of exports remained stable (and rather limited in volumes) over 8 years. Therefore, the combination of their unique status vis-a-vis the EU, which no other FTA has, and their limited volumes of exports warrant such treatment.
However, to avoid that third country producers attempt to use this exclusion as a loophole in the future, importers of covered steel products from the EEA will need to fulfil melt and pour evidence requirements just as all others concerned.
How does the measure relate to the Windsor Framework solution as regards Northern Ireland?
The Windsor Framework includes a solution for steel of UK origin moving from Great Britain to Northern Ireland, consisting of specific TRQs, for the benefit of the economy of Northern Ireland.
The Implementing Regulation provides the same TRQs volumes for these movements, in full respect of the commitments under the Windsor Framework.
Won’t third countries retaliate against the EU’s measure or specific quota distributions?
The EU’s steel measure has been prepared in line with WTO rules, and the EU has actively engaged in Article XXVIII GATT discussions with its trading partners in Geneva to ensure that preferential partners could maintain their overall level of concessions. As a result, a significant number of the EU’s FTA partners have agreed in principle to their tariff quota allocation.
Ultimately, the only way to avoid this proliferation of unilateral measures is to address the root of overcapacity collectively. The EU remains fully committed to advancing on that objective together with like-minded partners, both bilaterally and collectively in the context of the Global Forum on Steel Excess Capacity (GFSEC).
How will the new quotas interact with existing/future trade defence (TDI) measures on various steel products?
TDI measures such as anti-dumping and countervailing duties apply from the first ton imported. There are several product categories under the scope of the proposal subject to TDI measures. This means that such imports will pay the duties in place under TDI measures and, if they exhaust the tariff quota under the measure, an additional 50% duty would apply.
Why has the EU introduced a measure on steel? 
Global overcapacity, often driven by non-market policies and practices, keeps growing relentlessly, and is already at unsustainable levels – currently over 620 million tonnes, estimated to reach 721 million tonnes – more than 5 times the EU’s annual steel consumption.
In parallel, a growing number of third countries are closing their markets to imports, often in the form of tariffs. This creates a very big risk of trade diversion into the EU market, in a situation where import penetration in terms of imports’ market share remains at historically high levels.
The steel measure entered into force on 1 July 2026 and thus ensures a continued and highly effective level of protection for the EU steel sector following the expiry of the EU’s steel safeguard on 30 June 2026.
It is very important to preserve a strong steel industry, securing investments for its successful decarbonisation, and to avoid undermining our strategic autonomy in this key sector.
The Commission remains fully committed to finding a collective solution to the problem of global overcapacity. However, until that materialises, the EU needs to take firm action and with this proposal, it is doing so.
 
 
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Place-based policies offer regions left behind by globalization a path beyond economic populism.

One of the most firmly held beliefs in economics is that free trade is good for humanity. Yet that confidence in the economic virtue of open markets can blind the profession to the complications of deep global economic ties. When in the 1990s the world leapt into frenzied globalization, policymakers touted the potential efficiency gains but gave short shrift to possible painful distributional consequences. Those consequences have now come back to bite.
The United States’ embrace of higher import tariffs, China’s pursuit of aggressive industrial policies, and the United Kingdom’s exit from the European Union—all of which occurred around 2016—heralded a more fractious global economic order, which, according to Canadian Prime Minister Mark Carney, is steering the world toward geoeconomic rupture.
It took decades to build the international institutions, negotiate the multilateral agreements, and enact the domestic economic reforms that wrought the modern era of globalization. It could be torn down quickly and in unpredictable ways that upend economies. To avoid such a rupture calls for a clear-eyed look at why globalization has become unpopular worldwide and a credible alternative to economic nationalism that addresses the dislocations integration has caused.
The accession of China, India, and other newly liberalizing economies to the World Trade Organization (WTO) in the 1990s and 2000s lifted hundreds of millions of people out of poverty in one of the greatest improvements in human well-being. This much standard economic analysis foretold: We expected efficiency gains and we got them. What economists failed to foresee was the profoundly disruptive impacts of rapid globalization on labor markets in high-income countries (and some middle-income ones, too).
To be sure, emerging technologies and the rise of services were also disruptive. In concert, they probably determined more recent changes in high-income-country living standards than international trade did. But it was signature changes in trade policy—the formation of the EU in 1992, the enactment of the North American Free Trade Agreement in 1994, and China’s accession to the WTO in 2001—that got the public’s attention. Many voters in Europe and the US came to blame these and related policy changes for their economic discontent, which later morphed into support for nationalist and populist political movements that champion economic isolation.
Disruptive globalization
Most economists missed the disruptive impacts of globalization because they misunderstood three things about how labor markets adjust to large shocks. First, they failed to recognize how long-standing industrial specialization patterns exposed manufacturing regions to rising import competition from China and other developing economies.
Since the Industrial Revolution, manufacturing has been highly concentrated geographically, which led Alfred Marshall, the 19th century political economist, to develop his insights on productivity gains from the spatial agglomeration of economic activity. Concentrated first in major urban centers, manufacturing over the course of the 20th century relocated to smaller and medium-sized industry towns and cities.
When China began its spectacular manufacturing export growth in the 1990s, those industry towns were ground zero for the ensuing trade shock. Import-competing regions of France, Germany, the UK, the US, and other high-income nations bore the brunt of manufacturing job loss caused by deepening import penetration. At the same time, superstar cities in those countries, which specialized in business services, finance, high tech, and other knowledge-intensive industries, saw their exports and incomes boom.
Economic models of the time were tuned to capture national impacts of freer trade. Consequently (and consequentially), they missed the highly uneven regional impacts of globalization. We had not expected winners and losers to be so starkly differentiated by place.

Public opinion has soured on globalization in high-income countries because many middle- and low-wage workers ended up on the losing end of economic openness.

Lasting scars
Second, economists did not anticipate the magnitude of the scarring effects from manufacturing job loss. The fetishization of manufacturing by those who espouse economic nationalism is not without reason: The sector has long offered a pay premium relative to what people could earn in other lines of work, especially for those without a college degree. When factories closed or laid off large numbers of employees, as occurred in many high-income countries during the China trade shock, industrial workers lost their pay premium. Most, faced with the alternatives of lower-wage jobs in services or exit from the labor force, never replaced their lost earnings.
Although economists first documented the scarring effects of job loss in the early 1990s, they did not appreciate until later that when job loss is regionally concentrated, individual scarring aggregates into large negative local income shocks. Once displaced, former factory workers spent less on nontraded goods and services, paid less for housing, and contributed less in taxes to support local public services—all of which depressed incomes where manufacturing was declining.
Again, because economic models were tuned to account for national-level adjustment to international trade, they projected that workers displaced in import-competing sectors (manufacturing) would simply shift into sectors in which exports were expanding (knowledge-intensive services). Import displacement and export absorption did occur, but among largely disjointed sets of people.
Lack of mobility
Third, economists overlooked the lack of geographic labor mobility among less-educated and older workers in response to changing economic conditions. Standard economic models posit a spatial equilibrium condition: If real earnings rise or fall in one region, the migration of labor between regions will smoothly arbitrage away spatial differences in pay. In theory, labor mobility transmits localized economic shocks to other regions, dissipating shock impacts and ensuring that regional spikes in joblessness are temporary. In practice, interregional migration works slowly: Spatial equilibration to shocks can take decades.
The slowness of regional migration has been one of the hardest lessons for economists to internalize. Surely, in economies as large as the US and the EU, where millions of jobs are created and millions destroyed each year, job losses in a subset of industrial regions should be easily offset. Such logic is mistaken—first, because it projects frequent job transitions among younger workers to older workers, who are markedly less agile, and second, because it assumes that upward-sloping job ladders available to more educated workers are equally accessible to the less educated. Modern labor markets are undeniably dynamic. But that dynamism has been least evident among workers most exposed to deindustrialization.
Regional disparities
It has been painful for countries to learn of globalization’s dark side, which entails widening regional economic disparities and a tendency for former industry towns to get stuck with high joblessness and few well-paying jobs for less-educated workers. In the moment globalization was causing manufacturing job losses, countries had viable policy options to cope with the disruption, including generous and immediate assistance for displaced workers and safeguard tariffs that would have spread import surges over longer periods of time.
Two decades later, such policies are neither available nor relevant. Countries are left to decide whether and how to address regional economic distress caused by globalization, long after the distress materialized. Selecting the right policies calls for a clear understanding of the economic problems countries are trying to solve.
One option to help left-behind regions is simply to let market forces do their thing. The outmigration of labor and the retirement of displaced workers would ultimately help forsaken regions shrink to a smaller, more efficient size. Businesses would close, downtowns would board up buildings, and young labor force entrants would launch their careers elsewhere. If we believe that there are no economic distortions that impede labor market adjustment to adverse shocks, or if we believe that governments cannot implement effective remedies for such distortions, then laissez-faire may make sense. It is important to recognize, however, that although market forces may alleviate spatial differences in economic well-being, they are likely to do so ever so slowly.
Pittsburgh experience
Consider the experience of Pittsburgh, which many cite as a successful example of adjustment to deindustrialization. Through the first half of the 20th century, the city was a global center for steel manufacturing. After 1970, import competition, technological change, and other forces contributed to a prolonged industrial decline, during which joblessness and economic hardship were endemic. Although Pittsburgh today is home to health care, life sciences, and robotics, its transformation took more than a full generation. During this period the economic opportunity of local people was circumscribed by distress. For every Pittsburgh, there are several other former industry towns that did not find a path back to prosperity. Long-term adjustment meant diminished incomes, housing prices, and urban amenities.
A second option to help left-behind regions is to target affected individuals through means-tested entitlement programs. Unemployment insurance, income support for low-income households, housing and energy subsidies, and subsidized health care are common ways to help people in hard times. If we don’t have confidence that insurance and credit markets can insulate people against adverse shocks, then expansive social insurance programs may make sense.
Yet such programs condition assistance on individual or household well-being—not on the state of the local labor market. Social insurance may help people avoid sharp declines in consumption during episodes of hardship, but they do not address the causes of regional economic distress. Such programs may make economic adjustment less painful, but they are unlikely to speed adjustment along.
Trade tariffs
A third option to help left-behind regions is to target the industries whose decline was responsible for economic distress. Recent US import tariffs, for instance, have been justified in part by the claim that they will help bring manufacturing jobs back to communities hollowed out by globalization. On the surface, addressing the negative labor-market consequences of trade by blocking imports may appear sensible. However, the origin of regional distress is not import competition per se but the scarring effects of job loss and regions’ inability to adjust to decline in a major industry.
Import tariffs would not prevent job loss from technological change, artificial intelligence, or other shocks that may be disruptive in the future. Trade protection targets economic distress indirectly and therefore poorly. It is not surprising that US tariffs have done little to restore manufacturing employment or earnings growth in regions harmed by the China trade shock.
A final option is to target left-behind regions through policies that promote local economic development. Place-based policies subsidize investment in human and physical capital with the aim of upgrading labor productivity, earnings, and economic structures in distressed regions. In theory, such policies are justified if the social return on investment is relatively high in communities with elevated joblessness and low wages. In practice, place-based policy has long been controversial among economists, given concerns over rent capture by special interests and informational challenges in program design.
Successful policy
Recent empirical research clarifies where place-based policy works well and where it does not. Less-effective policies—also among the most visible—include the use of tax subsidies to compete for major investment by large firms. Subsidy competitions tend to transfer most of the economic surplus from new investment to the investors themselves, leaving “winning” regions with a winner’s curse of high tax expenditure per job created.
More effective place-based policies condition subsidies on local distress, monitor compliance with program objectives, and design programs for specific contexts via ongoing experimentation. Examples include tax incentives for investing in low-income communities (such as enterprise zone programs with rigorous selection and auditing of participating firms) and sectoral worker training initiatives (active labor market programs, which have been applied in many high- and middle-income countries).
Of these four options, only place-based policy directly targets outcomes that alleviate regional distress: moving out-of-work adults into employment, bringing more well-paying jobs where they are scarce, and making regions more attractive for future investment.
Public opinion has soured on globalization in high-income countries because many middle- and low-wage workers ended up on the losing end of economic openness. The prosperous future they were promised did not arrive. To restore faith in global economic integration we must correct the mistakes of the past and offer a credible alternative to economic nationalism.

 
 
 
Compliments of the International Monetary Fund The post IMF | Righting Globalizations’ Wrongs first appeared on European American Chamber of Commerce New York [EACCNY] | Your Partner for Transatlantic Business Resources.

From 1 July 2026, the EU will introduce a temporary €3 customs duty on low-value parcels imported from outside the EU, mainly through e-commerce. This includes a wide range of products commonly bought online, such as clothing, toys, electronics, and other consumer goods worth up to €150.
Every day, millions of low-value parcels enter the EU. Many contain products that do not meet EU safety standards or are undervalued or falsely declared to avoid customs duties. At the same time, the current customs duty exemption gives non-EU sellers an unfair advantage over businesses that manufacture or sell products in the EU.
The new duty will apply per item, based on tariff classification and not quantity. In practice, this means

if you buy 5 T-shirts, a €3 customs duty will be applied (as all T-shirts fall under the same tariff classification)
if you buy 3 T-shirts and a watch, a €6 customs duty will be applied (as the T-shirts and the watch fall under two different tariff classifications)

The seller or importer will be responsible for declaring and paying the duty as part of the customs process.
The new measure will help create fairer competition for EU businesses, better protect consumers from unsafe products, tackle customs fraud, and address environmental concerns over mass shipping.
The EU is working to modernise customs procedures to strengthen the single market and ensure that all businesses selling into the EU compete on equal terms while meeting the EU’s safety and compliance standards.
 
 
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Today’s AI policies will shape tomorrow’s job market.
Artificial intelligence has reignited an old fear—that technology will eliminate work faster than economies can adapt. Variations of this concern appear every time powerful new technologies emerge. What feels different today is the speed, scope, and visibility of AI’s advance, particularly in cognitive tasks long assumed to be uniquely human.
Yet history shows that whenever new technology emerges, economies ultimately undergo deep structural transformation. This allows labor markets to adapt to the potential of the new technology.
Inevitably, many jobs will be changed by AI adoption. Some will be enhanced. Others may be rendered obsolete. But the decline of particular types of jobs is not the same as a sustained reduction in overall employment. If history is any guide, it is not the technology itself that could cause mass unemployment, but the policies deployed in response.
The aggregate impact of AI will depend on how the economy adjusts to it, including whether productivity gains reduce costs, expand demand, and support the creation of new tasks and firms and thereby drive workers and capital toward new uses—enough to offset or outweigh the inevitable loss of some positions or categories of jobs.
The outcomes of the transformation will depend not only on the technological shock itself but more importantly on the policies and institutions that govern the adjustment. If policymakers respond to AI with the wrong set of policies—the kind that delay rather than facilitate adjustment—they risk causing worse rather than better labor market outcomes. Misguided policies could end up slowing growth rather than raising it and increasing inequality rather than containing or reducing it.
Past technological shocks
This time could always be different. But the overwhelming weight of historical evidence supports the claim that innovative technologies do not cause mass unemployment.
The emergence of new general‑purpose technologies is not new. Over the past two centuries, economies have repeatedly absorbed technologies that transformed production, reorganized firms, and displaced entire categories of work—from the steam engine and electrification to computing and the internet.
Each of these technologies disrupted existing jobs and skills. Each provoked anxiety about the future of jobs and work. And each ultimately raised productivity, lowered prices, increased real incomes, and supported higher employment.
Handloom weavers were displaced by mechanized looms. Typists declined as word processing spread. Travel agents were displaced by online booking platforms. In each case, the disappearance of a task was visible and politically salient.
But new jobs emerged—often in sectors that barely existed before. Railways created demand for engineers, mechanics, logistics managers, and entirely new financial services. Electrification enabled industries ranging from household appliances to mass retail and refrigeration. Computing gave rise to software development, data analysis, digital design, and a wide range of professional services. Aggregate employment did not collapse. It ultimately increased.
It is important to recognize that these gains were neither automatic nor immediate. They were staggered and gradual and required diffusion, complementary investment, and institutional adaptation. Nonetheless, a systematic review of the economic literature published in 2023, covering more than 100 studies, showed that the labor displacing effect of technology is more than offset by labor creation.
What changed was not simply the number of jobs, but the content of work. As recent IMF research shows, jobs evolve not only because workers move across occupations, but because of shifts in the skills required within the same occupation. Roughly 1 in 10 job vacancies in advanced economies now lists a new skill. This is how technological change usually unfolds: not through wholesale job destruction but by reshaping what workers do and the skills they need.
Markets’ absorption of new technologies reflects a well‑known economic dynamic. As processes become more efficient, costs fall. Lower costs translate into lower prices. Lower prices stimulate demand. And higher demand supports higher output and employment. Economists often refer to this as the Jevons paradox, and it has been observed repeatedly across sectors, from energy to transport to information processing.
The key point is that new technology expands economic activity—by opening new markets, increasing scale, and supporting entirely new forms of production, consumption, and employment.
What this means for AI
This is likely to be the net impact of AI. By reducing the cost of analysis, prediction, communication, coordination, and—increasingly in its agentic form—action, AI makes a wide range of services cheaper and more scalable. This will eventually increase demand, while also enabling new products, services, and firms.
A recurring mistake in today’s debate is a modern version of what economists have long called the “lump‑of‑labor fallacy”—the belief that there is a fixed amount of work to be done, so that if machines do more, there will be less for people to do. History tells a different story: Technological progress has created more jobs than it has destroyed. And as IMF research shows, when AI complements human labor and productivity gains are sufficiently large, AI adoption can lead to higher growth and incomes for most workers.
None of this implies that the adjustment will be smooth or costless. Even when technological change ultimately supports net job creation, the transition can be disruptive. Three issues in particular deserve close attention.
First, we do not yet know what the new work will look like, who will do it, or where it will be located. And the social and political disruption created by that uncertainty, at both the individual and societal levels, can be significant. IMF staff estimates find that approximately 40 percent of jobs globally could be affected by AI in some way—not necessarily eliminated, but changed. That includes changes in task composition, skill requirements, and organizational structure. Many jobs will be enhanced by AI. Some may be rendered obsolete. The structure of labor demand could also change. Emerging evidence suggests near term gains may be strongest for high- and low-skill workers, while demand for middle-skill and entry-level positions could weaken. However, predicting the long-term trajectory of trends in labor demand is exceptionally uncertain. Whatever shifts do materialize will have important political economy consequences that policymakers will need to manage.
Second, AI could accelerate churn in labor markets that may present challenges for some of those markets. The scale of preexisting labor market churn, moreover, is often underappreciated. In the United States, total nonfarm employment is roughly 160 million, and there are approximately 60 million hires and 60 million separations every year. This extraordinary scale of job creation and destruction is happening every day throughout every corner of society. Countries with less flexible labor markets may struggle to reallocate resources in the world of AI more than those with a higher degree of churn.
Third, the labor market adjustment caused by AI could be slowed or distorted by policy choices, institutional frictions, or market failures. We have seen this before. Early uses of electricity focused on powering existing factory layouts rather than reorganizing production lines to seize the full potential of the new technology. Early uses of computing automated clerical tasks before enabling entirely new businesses and organizational forms. In both cases, the largest productivity gains came later, once complementary investments and institutional changes caught up.
Economic historians sometimes describe this dynamic as an “Engels’ pause,” after Friedrich Engels’ analysis of the combination of rapid economic growth and stagnant wage growth Britain experienced in the first half of the 19th century. The term has come to denote a period in which new technologies diffuse through the economy, disrupting existing structures, before new business models and activities fully emerge. During that period, gains can appear uneven, and labor market adjustment can be painful. Distributional changes may be material and cause social and political disruption.
Policies to facilitate adjustment
The task for policymakers will be to maximize the potential benefits from AI while insuring against the potential negative consequences. This won’t be easy: Realizing the benefits from AI will require large shifts in labor and capital across the economy, which can be disruptive if the process of job reallocation is prolonged or poorly managed. What should policy aim to do and not do?
The overarching response to a structural shock like AI should be structural policies—designed to facilitate adjustment rather than prevent it. These include labor market policies that support mobility and reemployment, product market policies that promote competition, and financial and legal frameworks that allow capital and assets to be reallocated efficiently and productively.
Labor market policies are particularly important. Many existing labor market institutions are designed to deal with cyclical, not structural, shocks. Furlough programs, job retention subsidies, and temporary layoff protections can be highly effective when aggregate demand falls temporarily and then recovers. Such measures are much less effective when entire sectors need to shrink, and new ones need to expand. What works for a recession does not necessarily work for a technological transition.
Policies that help workers navigate transitions without locking economies into outdated structures can also play a useful role, especially retraining and upskilling programs. These programs should be widely accessible and designed to maintain private sector incentives for both businesses and employees so that new employment relationships can take hold without government support.
But policymakers should not rely too much on these policies, since the track record of many active labor market policies is mixed and may not sufficiently address the scale of the challenge posed by AI. Governments often lack the necessary information, incentives, and institutional capacity, particularly in fast‑moving technological environments. AI itself will provide a significant opportunity both for upgrading active labor market policies and for turbocharging the employment services industry, given its capacity for personalized education and reducing information frictions.
On the other hand, policies that slow adjustment, by protecting specific jobs, firms, or sectors, would delay reallocation, reduce productivity growth, and ultimately lead to worse labor market outcomes. There is an understandable tendency to respond to disruption with protection. But this can end up harming the very people it is intended to help. If policymakers make it more expensive for companies to employ or fire workers, companies will pay less or not create jobs at all.
AI regulation
A similar nuanced approach should be applied to AI regulation.
Guardrails are clearly necessary in some areas, including when it comes to cybersecurity and the protection of children. In these cases, risks are concrete and externalities are clear. But there should be a presumption against protective action unless there is strong evidence of harm or the presence of clear risks. A rush toward regulation without a clear rationale—for example, generalized fear about job losses—could leave society worse off because of prolonged misallocation of resources and drifting away from the technological frontier.
AI regulation that focuses on permission structures rather than restrictions on AI use can be productive. For example, heavily regulated sectors like health care and finance may need additional regulatory certainty on the suitability of using AI systems in order to realize the productivity benefits of AI adoption.
More broadly, regulation should promote business dynamism. This entails reducing barriers to entry to avoid regulatory capture and excessive market concentration and maintain the significant competitive dynamics currently evident in the AI ecosystem. Policymakers also need to ensure that bankruptcy and restructuring frameworks are working efficiently to support the speedy reallocation of resources.
Developing economies
The stakes are particularly high for emerging markets and low‑income countries.
For these economies, AI presents a genuine leapfrogging opportunity. Digital delivery of services can overcome physical infrastructure constraints. AI‑enabled diagnostics can expand health care access, and automated compliance tools can lower the cost of formality for small firms. Governments can also use AI to improve tax administration, customs, and social protection delivery.
But AI-related risks specific to emerging markets and low‑income countries are substantial. If AI leads to sustained productivity gains in advanced economies first, income gaps could grow. And these gaps would widen further if economic rents from AI became geographically concentrated. Capital could flow uphill, diverting much‑needed financing away from lower‑income countries.
These risks are amplified by existing structural constraints in some developing economies: slow labor reallocation, barriers to firms’ entry and exit, limited access to financing, weak legal systems, and ill-defined property rights.
Policies should therefore be tailored to countries’ level of preparedness. More developed and better-prepared economies should focus on innovation and diffusion—through R&D investment, improved access to financing, and a business environment that fosters innovative firms—along with regulatory frameworks that enable safe and widespread AI use. Lower-income and less-prepared economies should focus initially on building digital infrastructure—especially reliable and affordable power generation—and on education, with a greater focus on earlier attachment to the labor market, lifelong learning, and skills that complement rather than compete with technology. These investments can support AI adoption while advancing broader development goals; they should be embedded in a strategy that safeguards fiscal sustainability and is aligned with absorptive capacity.
Role of the IMF
History offers many examples of policies designed to slow structural change that ended up entrenching inefficiency, delaying recovery, and worsening outcomes. If protection becomes the dominant response, the economic and social disruptions associated with AI adoption could overwhelm the potential benefits. A prolonged and politically difficult period of weak growth and stalled adjustment could ensue—another version of Engels’ pause.
In the worst case, a protective response could cause a political backlash against the technological progress and creative destruction that underpins long-term improvements in living standards. Engels’ pause in the United Kingdom coincided with the rise of the Luddite movement. And while Engels did not coin the term Engels’ pause, his experiences during this period informed his subsequent collaboration with Karl Marx on the development of the political philosophy of Marxism. Though neither the Luddites nor Marxists succeeded in 19th century Britain as a response to Engels’ pause, variants elsewhere have damaged standards of living in the subsequent two centuries.
The goal of policy should not be to protect specific jobs, companies, or industries. It should be to incentivize employees and businesses to adapt and to unlock the productivity gains from AI. This requires flexibility, dynamism and sound structural policies—not stasis.
Institutions like the IMF can help realize this crucial policy goal. We are strengthening our surveillance of AI‑related structural changes. We are supporting members in designing reform strategies and thinking through trade-offs. And we are facilitating international cooperation and knowledge sharing on AI‑related best practices to help avoid some of the pitfalls of earlier periods of economic transformation.
We also see AI as a major opportunity to enhance our own operations. Used effectively, AI can help us do more with the same resources, improve the quality and speed of our analysis, and further increase the value we deliver to our membership.
We do not know what the future of work will look like. But it will be heavily shaped by the policies and institutions that govern how economies adapt to technological change. Our choices today will determine whether this transformation lifts growth and prosperity—or leaves our societies, politics, and institutions struggling to catch up.
 
 
Compliments of the International Monetary Fund The post IMF | Artificial Intelligence and the Economics of Adjustment first appeared on European American Chamber of Commerce New York [EACCNY] | Your Partner for Transatlantic Business Resources.

Today, the Council formally adopted two regulations implementing the tariff-related commitments set out in the EU-US Joint Statement of 21 August 2025. The adoption completes the legislative process and confirms the EU’s commitment to a stable, predictable and mutually beneficial transatlantic trade relationship, while preserving the necessary guardrails to protect European economic interests.
“We are committed to a strong and open transatlantic partnership with our historic ally, but openness must go hand in hand with safeguarding our interests. These measures achieve both, supporting stable and predictable trade flows with the US while ensuring the EU can respond swiftly and proportionately when the deal is not respected or its interests are at stake. We are sending a strong signal that Europe is open to the world, but also clear about protecting its businesses and workers.”- Michael Damianos, Minister of energy, commerce and industry of the Republic of Cyprus
The two regulations remove the remaining EU customs duties on US industrial goods, introduce preferential access for certain US seafood and non-sensitive agricultural products through tariff rate quotas and reduced tariffs, and extend the suspension of duties on lobster imports, including processed lobster (from all countries on a most favoured nation basis).
The regulations also contain reinforced safeguard and suspension mechanisms. In particular, the regulations provide for a dedicated safeguard mechanism enabling the Commission to act swiftly in cases of significant import surges causing or threatening to cause serious injury to EU operators, and strengthen the EU’s ability to suspend tariff preferences where the US does not respect its commitments, undermines the objectives of the Joint Statement, or otherwise disrupts balanced trade relations, including through discriminatory measures.
Next steps
The two regulations will now be signed and published in the Official Journal, entering into force on the day following their publication.
The main regulation will cease to apply at the end of 2029. By 30 June 2029, the Commission will present a comprehensive assessment of their impact on EU-US trade flows, tariff revenue and economic effects, including on SMEs, and will accompany it with a legislative proposal to extend the application of the regulations, where appropriate.
The regulation concerning lobster imports will apply retroactively from 1 August 2025 and will expire on 31 July 2030 unless further action is taken.
Background
The European Union and the United States have the largest bilateral trade and investment relationship and the most integrated economic relationship in the world, representing almost 30% of the global trade in goods and services and 43% of global GDP. EU-US trade in goods and services has doubled over the last decade, surpassing €1.7 trillion in 2025. This deep and comprehensive partnership is underpinned by mutual investment: in 2024 EU and US firms held over €4.8 trillion in investments in each other’s markets.
Proposed by the European Commission on 28 August 2025, the two regulations will enact the EU’s tariff reductions set forth in paragraph 1 of the EU-US Joint Statement of 21 August 2025. The first (main) regulation eliminates the remaining customs duties on US industrial goods and grants preferential market access, including via tariff rate quotas (TRQs) and reduced tariffs for certain US seafood and non-sensitive agricultural products. The second regulation focuses on extending the duty suspension for imports of lobster, including processed lobster, from all countries on a most favoured nation basis.
 
 
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Today, the European Commission adopted an ambitious tax simplification package designed to simplify EU tax rules and reduce compliance burdens for businesses. The package comprises of two proposals, the Taxation Omnibus and the Recast of the Directive on Administrative Cooperation (DAC) and will modernise the EU’s direct tax framework and strengthen the competitiveness of the Single Market while maintaining the existing strong level of protection against tax fraud, evasion and avoidance. The package is expected to save EU businesses around €8 billion annually, of which €3.3 billion in administrative costs.
Tax Simplification Package
Over the past decade, the EU has significantly developed its direct taxation framework. Most notably, developments have addressed the challenges arising from globalisation, digitalisation, the rise of aggressive tax planning practices and the need to strengthen the functioning of the internal market.  This framework has delivered important results. However, the cumulative effect of successive legislative initiatives has also increased complexity and compliance costs for businesses operating cross-border.
The proposal addresses these issues and ensures that the Union’s direct tax framework remains coherent, proportionate and effective. Its goal is to simplify the acquis in direct taxation, reduce unnecessary compliance burdens, enhance legal certainty, and facilitate cross-border activity in the internal market.
The Omnibus on Direct Taxation, it introduces key measures, such as:

Simplifying cumbersome rules to improve the internal market: The Omnibus introduces an exemption from withholding tax on all cross-border payments of dividends, interest, and royalties between companies in the EU. By removing upfront procedural requirements and simplifying refund processes, the measure will facilitate financing, encourage investment, and enhance competitiveness. This measure alone should bring EU taxpayers savings and benefits of around €5.3 billion annually.
Facilitating Financing: The Omnibus removes unnecessary restrictions on genuine third-party and market financing, making it easier for businesses to invest in the internal market. The Omnibus also simplifies the interest limitation rule in the Anti-Tax Avoidance Directive (ATAD) by eliminating implementation options and making the de minimis threshold mandatory. These changes will bring about compliance and administrative reductions amounting to over €500 million per year.
Eliminating Duplication: The Omnibus removes overlapping provisions between the Controlled Foreign Company (CFC) rules and the global minimum tax (Pillar Two), reducing unnecessary complexity and overlaps. This measure should save businesses approximately €160 million in compliance costs annually.

The main objectives of the DAC recast proposal are to simplify, clarify and enhance the EU legal framework for administrative cooperation in the field of direct taxation. By bringing together the DAC and its eight amendments into one single legal text, the legislation is more user-friendly and coherent, thereby improving legal certainty.
The recast introduces some key measures, such as:

Removing reporting obligations for certain cross-border arrangements: The recast removes reporting obligations for Multinational Enterprise (MNE) groups subject to the minimum 15% tax rate under Pillar 2 rules, generating compliance cost savings of around €300 million. It also eliminates reporting requirements for all other EU businesses for certain cross-border tax arrangements that provide limited added value for tax administrations, reducing reporting volumes by 35% and saving €40 million annually.
 Supporting the Circular Economy: The recast increases the reporting threshold for the online sales of goods, removing reporting obligations on over 10 million private sellers, particularly those selling second-hand goods. This measure delivers compliance cost savings of €678 million for digital platforms.
Improving Taxpayer Identification: The recast introduces a new verification tool for taxpayer identification numbers, ensuring that tax administrations can efficiently and effectively identify all reported taxpayers.

Next steps
The package will now be submitted to the European Parliament for consultation and the Council for adoption.
Background
Since the start of this mandate, simplification has been a core priority of the Commission’s work, with clear targets of at least 25% reduction in administrative burdens (35% for SMEs) and EUR 37.5 billion in annual savings by 2029. With the packages proposed today, the Commission has already put forward twelve omnibus packages and a broad set of targeted measures last year, cutting over €18 billion in recurring annual administrative costs.
But this is not just about reducing paperwork – simplification is a core part of the Commission’s competitiveness agenda. It is about changing Europe’s regulatory culture: designing rules that are clearer from the start, more proportionate, and easier for businesses, especially SMEs, to understand and comply with. The aim is to keep Europe’s high standards, while making it easier to invest, innovate and grow across the Single Market.
 
 
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Blog | The adoption rate of AI is rising rapidly, but the intensive use that drives transformation and generates macroeconomic gains remains rare. This blog explores what sets intensive AI users apart and what firms need to deeply integrate AI into their production processes.
The advent of AI has been widely hailed as a driver of productivity growth. Yet simply adopting AI does not guarantee measurable improvements in firms’ efficiency. What does matter is what they use the new technology for. Firms that apply AI to core processes tend to generate more value than those that restrict its use to peripheral or routine tasks. Intensive use, meaning use that goes beyond infrequent or moderate levels, particularly when linked to innovation and the expansion of products and services, is more likely to boost productivity and support economic growth.
So far, however, very few firms in the euro area actually use AI intensively. Understanding what differentiates intensive users from other firms and what enables them to fully leverage AI is therefore essential. To that end, we took a closer look at the ECB’s Survey on the access to finance of enterprises (SAFE).
AI diffusion continues to rise, but deepening takes time
How large is AI’s footprint so far? As SAFE – our survey of over 5,000 firms across euro area countries – and other recent studies show, diffusion is increasing quickly. In the last quarter of 2025, more than 70% of firms reported using AI (Chart 1, panel a), a trend that looks set to continue. Nearly half of the firms that were not using AI in 2025 plan to invest in it in 2026.
At the same time, most firms report using AI only infrequently or moderately, with only 7% of euro area firms reporting intensive use. There are, however, considerable differences across countries, sectors and firms. While the likelihood of adopting AI increases with firm size – this has been well established in the literature[1] – intensive use is relatively more common among small firms (Chart 1, panel b). Younger firms – those active for less than ten years – also report intensive use more frequently than older firms. Overall, however, it is size rather than age that appears to be more closely associated with intensive AI use.
The line of business also makes a difference. Intensive use is particularly prevalent in services, especially – and unsurprisingly – in high-tech, knowledge-intensive services like the information and communication (ICT) sector. These sectors include developers and providers of AI tools, which tend to be highly digitalised. They also have access to abundant data and computing infrastructure and employ workers with strong technical skills.
Chart 1
AI use and intensity

a) Use and intensive use of AI

b) Use and intensive use of AI by firm size and sector

(percentage of firms)

(percentage of firms)

Source: ECB Survey on the access to finance of enterprises, round 37 (October-December 2025).
Notes: Panel a) shows the weighted share of firms using AI and using AI intensively. Red dots represent the smallest and the highest weighted average for countries. Panel b) shows the weighted share of firms using AI intensively out of all firms using AI for employment-based size classes, manufacturing, information and communication services and other services. Detailed sector information is from Orbis. Intensive use refers to responses indicating “significant use” when assessing the adoption of AI technologies by the firm.

Why do firms use AI? The reasons differ among intensive and moderate users. Firms at an early stage of adoption often cite cost reductions and improvements in operational efficiency as their main reasons for using it. By contrast, intensive users are more frequently motivated by growth and innovation. Responses to the SAFE survey show that intensive users are more likely to mention employment growth[2], as well as support for research and development. They also mention the expansion of products and services as key reasons for adopting AI.
Intensive use driven by peer pressure
Beyond firm and sector factors, survey results indicate that peer pressure is a key driver of intensive AI use. When firms see their peers investing in AI, they fear a potential competitive disadvantage and therefore also feel the need to use the technology more intensively (Chart 2).
A similar pattern emerges when looking at expectations about the diffusion of future AI investment. Firms that expect a higher future share of AI users among similar-sized firms in their sector are likely to intensify their own use of the technology (Chart 2, panel a). The peer pressure effect is primarily driven by incumbent, well-established firms rather than young firms. This means that incumbent firms adopt AI more intensively as they feel threatened by young firms that are technologically advanced, as well as by their high-performing peers (Chart 2, panel b).
Chart 2
Impact of peer pressure on AI use: intensive versus moderate users

a) Current versus future peer pressure

b) Peer pressure of young and incumbent firms

(Average marginal effects)

(Average marginal effects)

Source: ECB Survey on the access to finance of enterprises, round 37 (October-December 2025).
Notes: Perceived AI use and expected future AI use refer to the current and future share of firms investing in AI in the same sector and size class as perceived by respondents. The charts report average marginal effects from firm-level regressions where the dependent variable is a binary dummy taking the value 1 for intensive AI use and 0 for moderate or infrequent AI use. A 10 percentage point increase in the current (future) AI investment rate increases the probability that a firm is AI-intensive by about 1.9 (1.4) percentage points. Young firms are firms that are less than five years old. Survey-weighted regressions with industry, country, firm size and age fixed effects. Whiskers represent 90% confidence intervals.

The peer pressure effect is most pronounced in ICT and professional services. These sectors have a high share of young firms, a large presence of high-growth firms and exposure to technologically advanced foreign competitors. Taken together, these features indicate highly dynamic and competitive business environments. In these settings, incumbent firms are compelled to intensively adopt advanced technologies, such as AI, to remain competitive.
Click here to access table.
Intensive AI use requires broader financing
The SAFE results also show that more than 84% of firms reporting intensive AI use have invested in the technology. Only 33% of moderate users, however, have done so. Looking ahead, 99% of intensive users plan to invest in AI in 2026, allocating around 20% of their total investment to AI-related activities.
This shows that investments that go beyond purchasing licences for general AI tools typically require more substantial funding. Indeed, integrating AI into core processes, such as developing customised solutions or upgrading digital infrastructure, often entails larger and longer-term restructuring. And these investments cannot easily be financed through short-term instruments such as trade credit or own funds.
Our analysis comparing firms in the same industry and country, and of similar size and age, shows that firms using AI intensively are more likely to combine several sources of financing. In the euro area, companies have fewer ways to raise money directly from investors or financial markets, so they depend more on bank loans to finance AI investments (Chart 3). This contrasts with findings that compare AI users with non-users, where firms using AI are more likely to rely on own funds compared with firms not using AI.
Interestingly, it also matters who the company owners are. As our statistics show, firms with public shareholders are more likely to adopt AI. This could reflect a greater willingness of professional management to adopt new technologies. However, being publicly owned does not mean a company is more likely to go from moderate to intensive AI use.
Chart 3
Impact of financing and ownership on intensive use of AI

a) Impact of various financing sources

b) Impact of market ownership

Source: ECB Survey on the access to finance of enterprises, round 37 (October-December 2025).
Notes: Panel a) indicates whether firms view each financing source as relevant. A financing source is considered relevant if the firm used it in the past or is considering using it in the future. Panel b) indicates whether firms are owned by public shareholders (i.e. listed firms) or report other types of ownership. The charts report average marginal effects from firm-level regressions, where the dependent variable is a binary dummy taking the value 1 for intensive AI use and 0 for moderate or infrequent AI use. Survey-weighted regression with industry, country, firm size and age fixed effects. Whiskers represent 90% confidence intervals.

What can help firms intensify their AI use?
As shown above, only a fraction of firms use AI intensively. The macroeconomic impact of AI will depend on whether firms move beyond initial experimentation and begin using the technology intensively in their core activities. So, what do firms need to expand their use?
Here, it is important to look beyond the broader structural constraints such as competitive pressure, market dynamism and access to financing. Unsurprisingly, the survey results suggest that technological factors matter. Firms most often cite shortages of AI-related skills (40%), limited usefulness of current AI technologies for their business needs (28%) and incompatibility with existing systems (26%).
Targeted policy support could help address some of these issues. In particular, it could help small and medium-sized enterprises scale up their efforts. Promoting the sharing of successful use cases could, for instance, help raise awareness of AI’s potential. Furthermore, applied training programmes for managers, employees and IT specialists, together with subsidised advisory services, could also help firms strengthen the skills needed to implement AI effectively.
The views expressed in each blog entry are those of the author(s) and do not necessarily represent the views of the European Central Bank and the Eurosystem.
 
 
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Secure, private and free-to-use means of payment, both online and offline

Privacy safeguards built in

Limits to individual holding, pilot testing and coordinated public awareness campaigns

The digital euro would offer citizens and businesses a private, secure and innovative way to pay, while reducing reliance on non-EU providers.

On Tuesday, the Economic and Monetary Affairs Committee adopted its position on the single currency package, consisting of three files. The one on the establishment of the digital euro was adopted by 43 votes to 14, with 1 abstention.
The digital euro would be a new, electronic form of money issued by the European Central Bank (ECB) and would work online and offline. Online payments would be processed through an account-based system, while offline payments would work directly via local storage devices. The offline functionality would be equivalent to using physical cash, as losing the device would mean losing the offline money with no refund possible.
Privacy
Privacy-by-design and privacy-by-default principles would be built into the digital euro. Cutting-edge technologies, such as “zero-knowledge proofs”, would allow transactions to be verified without exposing personal data, which would be processed only to the extent strictly necessary for the system to function. The ECB would not have access to personal identification data.
Distribution model
All payment service providers (PSPs), including banks, e-money providers, post offices, and regulated crypto-asset providers, could distribute the digital euro across the EU. Most businesses would be required to accept it. Exceptions would apply to the self-employed, and small and micro enterprises that do not accept other digital payments.
Temporary refusals, such as during a power outage, would also be allowed under specific conditions. Visitors, tourists and, in some cases, people living outside the euro area would also be able to use it.
Fees and charges
Basic services, such as opening an account, holding and managing funds, and getting at least one payment instrument, would be free of charge. PSPs could charge for extra services, with the exception of account maintenance inactivity penalties or service bundling. Fees for merchant and inter-provider would be capped, while offline payments would be entirely fee-free.
Financial stability and holding limits
To protect the financial system, there would be a cap on how many digital euros any individual could hold. MEPs proposed the EU ceiling should be set by the Commission, based on ECB recommendations, and reviewed at least every two years. MEPs want the Parliament to have full decision-making powers in this process.
Businesses would not be allowed to hold digital euros, except to accumulate incoming payments for up to 24 hours. Crucially, the digital euro would not earn or cost any interest.
Seamless launch and the ECB’s role
MEPs want to ensure that the ECB’s role would be kept separate from its monetary policy functions. Before the launch, the ECB should finalise a rulebook, build the infrastructure, run real-life pilot tests, and iron out liability rules with particular attention to offline risks, like double-spending. Once authorised, a roll-out period of at least 24 months would follow, giving banks, providers, and users time to prepare. Governments and providers would also run awareness campaigns.
The single currency package
A second file on the provision of digital euro services by payment services providers incorporated in member states whose currency is not the euro, adopted by 43 votes to 9, with 6 abstentions, would allow banks and PSPs from non-euro EU countries to distribute the digital euro, subject to the same rules, while the ECB would retain the power to restrict access and use. Non-euro EU member states would also need to appoint a national authority to monitor any impact on their own currency.
A third file, on legal tender of euro banknotes and coins, adopted by 46 votes to 4, with 8 abstentions, would oblige euro area countries to keep cash accessible and plan for digital payment disruptions. Businesses would not be allowed to ban cash through “no cash” signs or standard contract terms. Member states would also need to check cash availability regularly, with special attention to vulnerable groups, such as the elderly, low-income individuals, and the unbanked.
Quote
Rapporteur Fernando Navarrete Rojas (EPP, ES) said: “With the single currency package, we are protecting citizens’ freedom to choose how they pay. We are strengthening access to and acceptance of cash, while making central bank money available in digital form. The digital euro will complement cash, never replace it. No one should be forced away from cash, and no one should be left without a secure, resilient and genuinely European digital payment option.
“Europe does not have to choose between the digital euro and successful private payment solutions. We need both to work together. The agreement rightly recognises the dual approach: existing standards and infrastructure should be reused wherever possible. This will allow European payment solutions to connect to a common acceptance infrastructure and become interoperable across borders.
“The agreement also ensures that privacy will be built into the digital euro from the outset. Europeans will gain a secure digital payment option while remaining in control of both their money and their personal data.”
Next steps
The negotiating mandates for the three texts will be announced at the start of the July plenary session. The final legislation will have to be negotiated with the Council before coming into force.

 
 
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As firms around the world adopt AI tools, the impact of AI on labour markets is being widely discussed.[1] While AI’s potential to disrupt job markets could be significant, its effects on aggregate employment appear to be muted so far. Still, there is growing evidence that AI is negatively affecting employment for specific occupational sub-groups, particularly junior workers in highly exposed occupations.[2] This box analyses the effects of AI on employment growth in recent years, focusing on the United States, where such effects are likely to have become visible earlier than in other major economies, given that it is home to some of the most advanced early-adopting firms and has a relatively flexible labour market.

The impact of AI on job growth can be both positive and negative, as highlighted in recent literature on the subject. A well-known framework developed by Acemoglu and Restrepo (2018) distinguishes between the positive effect new technologies have on employment growth by enabling higher productivity, and the negative effect they create owing to job displacement, with the net impact on a country’s employment depending on the relative importance of those effects. Empirically assessing the impact of AI on employment at this early stage is difficult (Lane, 2026). Hampole et al. (2025) show that while in the United States firm-wide adoption of AI generates positive employment effects, these effects mask substantial heterogeneity across occupational groups. Initial evidence for the European Union suggests that firms that adopt AI technologies experience higher productivity gains, without the technology replacing labour in the short term (Aldasoro et al., 2026). This aligns with recent ECB survey findings that firms with high levels of AI adoption or AI-related investment are more likely to employ additional staff (Lebastard and Sondermann, 2026).
In the United States, the number of jobs in occupations with a high AI substitution risk has fallen in recent years. Applying an index developed by Pizzinelli et al. (2023) to measure AI substitution risk, each occupation is categorised into one of three categories, corresponding to a low, medium and high risk of AI substitution.[3] A calculation of average employment growth for each of those categories in the United States suggests that employment in jobs with a high risk of AI substitution (e.g. economists, graphic designers) declined on average by more than 4% between 2019 and 2025 (Chart A).[4] By contrast, employment in jobs with a low risk of AI substitution (e.g. electricians, high school teachers) increased by 13% over the same period. As a consequence, the composition of US employment has changed. The share of low-risk jobs in total US employment has increased from 23% to 25%, while the share of high-risk jobs has dropped from 35% to 33%.

Chart A
Employment growth and share in total employment of occupations grouped by AI substitution risk – United States

(percentages)

Sources: Bureau of Labor Statistics, Pizzinelli et al. (2023) and ECB staff calculations.

An empirical analysis confirms that AI has already led to a reallocation of jobs within the US labour market. The impact of AI substitution risk on employment growth is estimated using the same classification of occupations by level of AI substitution risk as before. The analysis uses a difference-in-difference approach and separately estimates the impact of an occupation’s risk of AI substitution on its employment growth for each year (2020-2025) compared with the base year (2019). It also includes a constant and sector-specific fixed effects corresponding to three-digit North American Industry Classification System (NAICS) subsectors, controlling for shocks (e.g. COVID-19), sector-specific developments and unobserved heterogeneities.[5] The results indicate a growing wedge between job growth in occupations with a high AI substitution risk compared with occupations with a low AI substitution risk (Chart B).[6] All else being equal, between 2019 and 2025 jobs with a high substitution risk grew by around 15 percentage points less than jobs with a low substitution risk. This is in line with studies showing that AI is affecting job growth for specific occupational sub-groups. Overall, while the consequences of AI for aggregate employment to date remain inconclusive, the analysis finds that it has had a relative impact on US employment growth since 2019.[7] This impact has accelerated since the launch of ChatGPT in late 2022.

Chart B
Impact of AI on US employment growth – difference between high and low risk of substitution

(percentage points)

Sources: Bureau of Labor Statistics, Pizzinelli et al. (2023) and ECB staff calculations.
Notes: The line shows the estimated relative impact of AI exposure on employment growth for each year compared with 2019. The model uses a difference-in-difference approach and separately estimates the impact of an occupation’s risk of AI substitution on its employment growth for each year (2020-2025) compared with the base year (2019). The top and bottom 1% of employment growth have been winsorised to control for outliers. The model also includes a constant and sector-specific fixed effects corresponding to three-digit NAICS subsectors. Results have been rescaled to indicate the difference between high and low AI substitution risk. The shaded area corresponds to the 95% confidence interval.

The relative impact of AI on job growth has not yet translated into significant differences in wage growth. As is the case for employment effects, although the impact of AI on wages and inequality is fiercely debated in the literature, empirical evidence of it is scarce. Using the same methodology as before, an analysis of median hourly wage growth by occupation reveals that AI substitution risk has had no significant impact on wage growth since 2019 (Chart C).[8] Over time, as the labour market continues to adjust and AI tools become more generative, income effects may be more pronounced.[9]

Chart C
Impact of AI on US wage growth – difference between high and low risk of substitution

(percentage points)

Sources: Bureau of Labor Statistics, Pizzinelli et al. (2023) and ECB staff calculations.
Notes: The line shows the estimated relative impact of AI exposure on median hourly wage growth for each year compared with 2019. The model uses a difference-in-difference approach and separately estimates the impact of an occupation’s risk of AI substitution on its wage growth for each year (2020-2025) compared with the base year (2019). The model also includes a constant and sector-specific fixed effects corresponding to three-digit NAICS subsectors. Results have been rescaled to indicate the difference between high and low AI substitution risk. The shaded area corresponds to the 95% confidence interval.

References
Acemoglu, D. and Restrepo, P. (2018), “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment”, American Economic Review, American Economic Association, Vol. 108(6), pp. 1488-1542.
Aldasoro, I., Gambacorta, L., Pal, R., Revoltella, D., Weiss, C. and Wolski, M. (2026), “AI Adoption, Productivity and Employment: Evidence from European Firms”, BIS Working Papers, No 1325, Bank for International Settlements.
Brynjolfsson, E., Chandar, B. and Chen, R. (2025), “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence”, Stanford Digital Economy Lab.
Felten, E., Raj, M. and Seamans, R. (2021), “Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses”, Strategic Management Journal, 42(12), pp. 2195-2217.
Hampole, M., Papanikolaou, D., Schmidt, L.D.W. and Seegmiller, B. (2025), “Artificial Intelligence and the Labor Market”, NBER Working Papers, No 33509, National Bureau of Economic Research.
Hui, X., Reshef, O. and Zhou, L. (2023), “The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market”, CESifo Working Paper Series, No 10601, CESifo.
Lane, P.R. (2026), “AI and the euro area economy”, Keynote Speech at the ECB-SAFE-RCEA International Conference on the Climate-Macro-Finance Interface (3CMFI), European Central Bank, Frankfurt, 23 March.
Lambert, P. and Schindler, Y. (2026), “The Broken Ladder: AI, Remote Work, and Early-Career Hiring”, May, SSRN.
Lebastard, L. and Sondermann, D. (2026), “Artificial Intelligence: Friend or Foe for Hiring in Europe Today?”, The ECB Blog, European Central Bank, 4 March.
Massenkoff, M. and McCrory, P. (2026), “Labor Market Impacts of AI: A New Measure and Early Evidence”, Anthropic Economic Research.
Pizzinelli, C., Panton, A.J., Mendes Tavares, M., Cazzaniga, M. and Longji, L. (2023), “Labor Market Exposure to AI: Cross-country Differences and Distributional Implications”, IMF Working Papers, No 2023/216, International Monetary Fund.

The box focuses on the labour market effects of AI adoption on the demand side and does not explicitly capture potential employment gains arising from the supply side, such as job creation linked to investment in AI development and deployment.

See, for example, Brynjolfsson et al. (2025) for an analysis of US payroll data. Note that Lambert and Schindler (2026) question the finding that generative AI is replacing junior workers. They find that exposure to generative AI is strongly correlated with another post-pandemic shock: working from home.

Pizzinelli et al. (2023) adapt the widely used index created by Felten et al. (2021) by factoring in the complementarity of occupations to AI, assuming that a lower complementarity to AI coupled with a high exposure to AI bears a higher risk of AI substitution and therefore job loss. For example, according to this extended index, a computer programmer and a computer science teacher have the same exposure to AI. However, as AI is more complementary to the teacher’s tasks, the teacher has a lower risk of job substitution than the computer programmer. Pizzinelli et al. call their index “complementarity-adjusted AI occupational exposure”. For ease of reading, it is referred to as “AI substitution risk” in this box. It should also be noted that AI does not only include large language models, but also other – earlier available – applications such as image recognition and automated translation.

As the focus lies on recent developments, the last pre-pandemic year (2019) is taken as the base year for the analysis. However, AI is likely to have already impacted the US labour market prior to 2019.

For example, a decline in manufacturing jobs might be unrelated to AI and instead be driven by other structural developments such as offshoring. As some manufacturing jobs run a high risk of AI substitution (e.g. inventory management or order picking), such a decline could mistakenly be attributed to AI.

It should be noted that the framework does not explicitly control for AI adoption.

Massenkoff and McCrory (2026) undertake a similar analysis for US unemployment rates and find no significant rise in the unemployment of workers in the most exposed occupations. This could also point to a reallocation of jobs within the US labour market.

The impact of AI on wages also depends on labour supply and demand dynamics, which cannot be distinguished in the framework used here.

In one of the few available empirical studies in this area, Hui et al. (2024) assess the impact generative AI models have had on freelancers registered on a large online hiring platform since 2022 and find reductions in both the employment and earnings of highly affected occupations.

 
 
 
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Blog | New policy tracker shows many governments pursuing costly responses to fuel and food price hikes, leaving less room to address future challenges.
As governments move quickly to shield people and businesses from the energy shock caused by the war in the Middle East, early evidence suggests many countries are resorting to untargeted and potentially expensive policies amid tight budgets. If the recent peace talks lead to a quick normalization of trade and oil flows, and prices go back to their historic trends, the challenge for many governments will be how unwind this support.
A new IMF Global Policy Tracker has recorded nearly 900 policy measures introduced across about 170 countries since the beginning of the war, both in advanced and emerging and developing economies. Fiscal measures dominate the response with governments cushioning the impact of higher energy prices by limiting pass-through to consumers and firms.
The tracker illustrates an important pattern. The composition and sequencing of today’s policies broadly resemble those deployed during the 2022 energy shock. But for many countries, circumstances are not the same: debt service burdens are rising for many countries and fiscal space remains limited, amid an environment of heightened uncertainty and recurrent shocks. Also, the exposures and disruptions of the current energy shock differ from the previous shocks. Both make policy design more consequential.

One shock, different responses
Our new tracker shows that, in advanced economies, almost half of the measures are subsidies to energy producers and distributors. Another third are cuts to fuel excise taxes aimed at containing retail price increases. European countries, for example, have leaned heavily on fiscal and pricing measures to cushion households.
Meanwhile, emerging economies have deployed a more varied policy mix. In addition to fiscal measures, which account for around half of recorded policies, many have used price controls—such as fuel price caps or adjustments to pricing formulas—and other administrative interventions. In the Middle East and Central Asia, monetary and financial tools play a larger role, alongside fiscal expansion in oil-exporting economies. African countries rely more on pricing and supply-side measures, while parts of Asia have turned to demand management, including conservation and rationing. The Western Hemisphere region shows a more mixed approach.

Policy space also matters. Countries with higher levels of debt and heightened fiscal risks, including emerging market economies, have relied more on pricing measures and demand suppression, including through fuel rationing, mandated remote work, and travel restrictions.
A group of countries has taken a more fiscally sustainable yet politically difficult path: allowing administered prices to rise, scaling back subsidies, or suspending price-smoothing mechanisms. These choices preserve price signals and contain fiscal costs, but they also require strong safety nets (or new interventions, such as containing public transportation tariffs) to protect vulnerable households.
Noble, but potentially costly and risky intentions
The dominance of price containment policies reflects a common objective: to cushion households and firms from a sharp increase in energy costs. Yet a large share of measures described as temporary lack clear expiration dates or fiscal cost estimates. This is how interim support can become permanent: extended incrementally, difficult to unwind, and increasingly costly if prices remain elevated. It is just one of several risks:

Fiscal costs can escalate quickly. Broad-based subsidies and tax cuts are expensive, particularly when extended beyond the initial phase of a shock. Price caps by oil importing countries risk becoming impossible to finance if global fuel prices escalate further.
Costs do not disappear when they are not visible in standard government fiscal accounts. Pricing measures that compress margins—especially in state-owned energy companies—can generate losses that later surface as contingent liabilities on the public balance sheet.
More subtly, widespread suppression of price pass-through can weaken adjustment at the global level. When many countries simultaneously shield consumers, demand responds less, contributing to tighter markets and potentially higher global prices. Individually rational policies can collectively amplify the shock.
Finally, by spending more freely now, governments will limit their scope to take further action if, for example, we see an escalation of the conflict, more energy disruptions, or other shocks. The more fiscal space is used today on broad price support, the less remains available tomorrow to respond to new challenges.

Protect people, not prices
Energy shocks force policymakers to choose if adjustment happens via prices or is absorbed by budgets. The early responses so far show a clear preference for containing prices. That is understandable. But if sustained, it risks higher fiscal costs and distorted incentives, especially if energy prices eventually normalize.
The alternative is less politically palatable but more fiscally responsible and sustainable: allow prices to adjust and ensure fiscal interventions are temporary and targeted. Some countries are already moving in this direction. Others would be well advised to follow suit.
In an uncertain, shock-prone world, keeping powder dry matters as much as acting quickly. The principle remains simple: protect people, not prices.
 
 
Compliments of the International Monetary Fund The post IMF | The Energy Shock Is Testing Government Budgets first appeared on European American Chamber of Commerce New York [EACCNY] | Your Partner for Transatlantic Business Resources.