Reforms could increase investment in high-tech startups that power innovation

Blog post by Nathaniel Arnold, Guillaume Claveres, Jan Frie | The European Union has a productivity problem. Its people produce nearly 30 percent less per hour worked than they would have, had real output per hour worked increased in line with that in the United States since 2000.

A failure to sufficiently develop innovative startups into “superstar” firms is one of the reasons for the bloc’s poor productivity growth.
Europe’s fragmented economy and financial system partly underly this problem. Without a more frictionless single market for goods, services, labor, and capital, it’s more expensive and difficult for successful startups to scale up.
On top of that, Europe’s bank-based financial system is not well-suited to finance risky startups. High-tech startups often develop new technologies and business models, which are risky and may be hard for banks to assess. And the value of startups often lies in their people, ideas, and other intangible capital, which is difficult to pledge as collateral for a bank loan. Banks are also constrained by rules that (rightly) limit lending to risky firms without collateral—even fast-growing ones that are likely to make large profits later.
European pools of private capital are also smaller and more fragmented than in the US. Europeans park more of their savings in bank accounts rather than capital markets. Americans invested $4.60 in equity, investment funds, and pension or insurance funds for every dollar invested in such assets by Europeans in 2022. In part, this is because Europeans rely more on pay-as-you-go pensions than Americans. But regardless of the reason, the end result is less availability of equity financing for companies.
The fragmentation of markets stems in part from national laws, regulations, and taxes that hamper cross-border consolidation, capital raising, and risk-sharing. Many institutional investors prefer to allocate capital to companies based in their own countries. This often applies to investments in venture capital as well, especially in smaller funds.
Greater venture capital investments could spur productivity and strengthen the EU’s innovation ecosystem. But Europe’s shallow pools of venture capital are starving innovative startups of investment and making it harder to boost economic growth and living standards.
As our new paper argues, measures to strengthen the EU’s venture capital markets and remove cross-border financial frictions to pension funds and insurers investing in venture capital could increase the flow of funding to promising startups and fuel productivity gains.
The EU lost its largest venture capital center, London, following the United Kingdom’s vote to leave the Union in 2016 and its remaining centers lack the scale of those in the United States.
Over the past decade, the EU’s venture capital investments averaged just 0.3 percent of gross domestic product, less than one-third the average in the US. American venture capital funds raised $800 billion more than their European counterparts over this period.

Venture capitalists invest heavily in high-risk research and development activities that are pivotal to spreading innovative ideas and raising overall growth. They are skilled at picking promising startups and channeling resources to the best-performing companies.
Compared with competitors across the Atlantic, Europe’s more established startups also have less attractive options to grow through initial public offerings in the EU. This reduces the incentives to invest in them in the first place. And, when fast-growing startups start to scale up quickly, they often need to seek financing abroad because the availability in Europe is limited—the so-called scale up financing gap. Many startups then move operations overseas when they get that scale up financing from abroad. Europe then loses out on many of the benefits of having startups succeed at home—both the direct growth impact and positive spillovers such as technology diffusion.
National authorities could take several steps to support their domestic venture capital markets:

The venture capital sector is characterized by high risk and information asymmetries, but also positive externalities not internalized by individual investors. Well-designed preferential tax treatments for equity investments in startups and venture capital funds could help jumpstart the sector where it is underdeveloped or non-existent due to these market failures.
Reduce regulatory and tax frictions to investing in venture capital. Developing private pension funds would have multiple benefits, including expanding domestic capital pools to invest in capital markets and venture capital.
Enable national public financial institutions—which have played an important role in supporting the development of the venture capital sector in some countries—to expand capital availability and other support to venture capital funds and innovative startups. They should invest on commercial terms and help attract more private capital, especially from institutional investors such as pension funds and insurers. This can be done quickly before other efforts bear fruit.

Measures at the European level would help too. The single most important step the EU could take is to complete the single market for goods, services, labor, and capital. This will take time.
More immediately, the authorities could:

Fine-tune rules for insurers and other investors in larger venture capital funds to reduce impediments to investing in venture capital, especially to support growth financing.
Expand the capacity and instruments of the European Investment Fund (EIF) and the European Investment Bank to channel more resources to venture capital funds and innovative startups.
Encourage the EIF to develop a fund-of-funds aimed at attracting capital from institutional investors across the EU that would finance large venture capital funds with a pan-EU focus. This would help reduce fragmentation of capital pools, increase the familiarity of institutional investors with venture capital as an asset class, and help close the scale up financing gap.

Over the medium term:

Reduce stock market fragmentation to boost their depth, liquidity, and valuations, which will make listing in the EU more attractive. This is a key part of the capital market union agenda, but will be more politically challenging.

While government interventions are often a less-than-perfect solution, they may be needed in the near term to accelerate the development of the venture capital sector and financing for innovative startups. This would not only spur EU productivity, but also bolster competitiveness. More venture capital financing for “clean tech” sectors would also support the EU’s green ambitions and reduce the need to rely on costly subsidies that could distort the single market.

Full post can be found here

Compliments of the IMF

The post IMF | Europe Can Better Support Venture Capital to Boost Growth and Productivity first appeared on European American Chamber of Commerce New York [EACCNY] | Your Partner for Transatlantic Business Resources.

Blog post by Pierre-Olivier Gourinchas | Our global growth projections are unchanged at 3.2 percent this year and slightly higher at 3.3 percent for next year, but there have been notable developments beneath the surface since the April World Economic Outlook.

Growth in major advanced economies is becoming more aligned as output gaps are closing. The United States shows increasing signs of cooling, especially in the labor market, after a strong 2023. The euro area, meanwhile, is poised to pick up after a nearly flat performance last year.
Asia’s emerging market economies remain the main engine for the global economy. Growth in India and China is revised upwards and accounts for almost half of global growth. Yet prospects for the next five years remain weak, largely because of waning momentum in emerging Asia. By 2029, growth in China is projected to moderate to 3.3 percent, well below its current pace.
As in April, we project global inflation will slow to 5.9 percent this year from 6.7 percent last year, broadly on track for a soft landing. But in some advanced economies, especially the United States, progress on disinflation has slowed, and risks are to the upside.

In our latest WEO update, we find that risks remain broadly balanced, but two downside near-term risks have become more prominent.
First, further challenges to disinflation in advanced economies could force central banks, including the Federal Reserve, to keep borrowing costs higher for even longer. That would put overall growth at risk, with increased upward pressure on the dollar and harmful spillovers to emerging and developing economies.
Mounting empirical evidence, including some of our own, points to the importance of global ‘headline’ inflation shocks—mostly energy and food prices—in driving the inflation surge and subsequent decline across a broad range of countries.
The good news is that, as headline shocks receded, inflation came down without a recession. The bad news is that energy and food price inflation are now almost back to prepandemic levels in many countries, while overall inflation is not.
One reason, as I emphasized previously, is that goods prices remain high relative to services, a legacy of the pandemic initially boosting goods demand while restricting their supply. This makes services comparatively cheaper, increasing their relative demand—and, by extension, that of the labor needed to produce them. This is putting upward pressure on services prices and wages.
Indeed, services prices and wage inflation are the two main areas of concern when it comes to the disinflation path, and real wages are now close to prepandemic levels in many countries. Unless goods inflation declines further, rising services prices and wages may keep overall inflation higher than desired. Even absent further shocks, this is a significant risk to the soft-landing scenario.

Second, fiscal challenges need to be tackled more directly. The deterioration in public finances has left many countries more vulnerable than foreseen before the pandemic. Gradually and credibly rebuilding buffers, while still protecting the most vulnerable, is a critical priority. Doing so will free resources to address emerging spending needs such as the climate transition or national and energy security.
More importantly, stronger buffers provide the fiscal resources needed to address unexpected shocks. However, too little is being done, magnifying economic policy uncertainty. Projected fiscal consolidations are largely insufficient in too many countries. It is concerning that a country like the United States, at full employment, maintains a fiscal stance that pushes its debt-to-GDP ratio steadily higher, with risks to both the domestic and global economy. The increasing US reliance on short-term funding is also worrisome.
With higher debt, slower growth, and larger deficits, it would not take much for debt trajectories to become much less comfortable in many places, especially if markets send government bond spreads higher, with risks for financial stability.

Unfortunately, economic policy uncertainty extends beyond fiscal considerations. The gradual dismantling of our multilateral trading system is another key concern. More countries are now going their own way, imposing unilateral tariffs or industrial policy measures whose compliance with World Trade Organization rules is questionable at best. Our imperfect trading system could be improved, but this surge in unilateral measures isn’t likely to deliver lasting and shared global prosperity. If anything, it will distort trade and resource allocation, spur retaliation, weaken growth, diminish living standards, and make it harder to coordinate policies that address global challenges, such as the climate transition.
Instead, we should focus on sustainably improving medium-term growth prospects through more efficient allocation of resources within and across countries, better education opportunities and equality of chances, faster and greener innovation and stronger policy frameworks.
Macroeconomic forces—desired national savings and domestic investment together with global rates of return on capital—are the primary determinants of external balances. Should these imbalances be excessive, trade restrictions would be both costly and ineffective at addressing the underlying macroeconomic causes. Trade instruments have their place in the policy arsenal, but because international trade is not a zero-sum game, they should always be used sparingly, within a multilateral framework, and to correct well-identified distortions. Unfortunately, we find ourselves increasingly at a remove from these basic principles.
As the eight decades since Bretton Woods have shown, constructive multilateral cooperation remains the only way to ensure a safe and prosperous economy for all.
 

 
Full post can be found here

Compliments of the IMF

The post IMF | Global Growth Steady Amid Slowing Disinflation and Rising Policy Uncertainty first appeared on European American Chamber of Commerce New York [EACCNY] | Your Partner for Transatlantic Business Resources.

Blog Post by Jakob Feveile Adolfsen, Danielle Kedan and Marie-Sophie Lappe | The green transition will significantly increase demand for key minerals over the coming decades. The impact on energy prices will ultimately depend on how supply adjusts. The ECB Blog looks at the geopolitical risks involved.
The green transition relies on certain key minerals, in particular lithium, copper, nickel, cobalt, manganese, and graphite. Assuming that the transition takes place in accordance with the Paris Agreement, demand for these key inputs will almost quadruple by 2040.[1] The impact on energy prices will depend on how supply adjusts. Ensuring the necessary supply of these “green minerals” is therefore vital.
Russia’s attack on Ukraine illustrated how geopolitical developments can significantly affect commodity markets and inflation.[2] IMF research confirms this and underlines that geopolitical fragmentation might disrupt the green transition by impairing access to green minerals.[3] The questions are therefore: what are the geopolitical risks to green minerals supply? What are the political relationships between main consumers and suppliers? And what can be done to contain demand for and secure supplies of green minerals?
What could cause supply disruptions?
The mining of raw minerals is mainly concentrated in developing and emerging market economies in South America and Africa (Chart 1). Supplies of green minerals are currently more concentrated than for other commodities such as oil, even compared to when OPEC was formed (Chart 2). The concentration of mine production makes green minerals particularly susceptible to supply chain disruptions and trade restrictions. In fact, all minerals explored in this blog are currently subject to export restrictions.[4]

Chart 1
Share of three largest mine producers (left) and reserve holders (right)

(percent)

Sources: United States Geological Survey, British Geological Survey, and ECB staff calculations.
Latest observation: 2021 (annual).

Chart 2
Concentration of mine production and reserves

(Herfindahl–Hirschman Index)

Sources: United States Geological Survey, British Geological Survey, BP Statistical Review of World Energy and ECB staff calculations.
Notes: The Herfindahl-Hirschman Index (HHI) is a measure of market concentration. The HHI can range from 0 to 10,000. The higher the number, the more concentrated the market. An HHI of less than 1,500 is generally considered to be a competitive market, an HHI of 1,500 to 2,500 is considered moderately concentrated, and an HHI of 2,500 or greater is highly concentrated. 1960 denotes the year when OPEC was established.
Latest observation: 2021 (annual).

Certain mineral-rich countries are reportedly looking to form mineral cartels, although thus far without success.[5] There are multiple factors that determine success in forming a cartel, such as barriers to entry, political stability and inelasticity of demand.[6] Among these factors, concentration of supply can facilitate a cartel’s ability to control market shares, and thereby prices.
A successful cartel ultimately requires agreement amongst members and a high degree of trust to maintain collusion. Based on voting patterns in the United Nations (UN), the top three miners of copper, nickel and graphite generally show high political alignment, with a disagreement score that is below the UN average and comparable with or even below that of the oil cartel, OPEC(+) (Chart 3). This would suggest some political basis for cartel formation. Although political disagreement scores among miners of lithium, cobalt and manganese are above the UN average, this is largely skewed by the presence of Australia, which tends to disagree with other top raw producers of these three minerals. This suggests that potential cartels might form that exclude Australia, as was the case in the oil market where the United States has never been a part of OPEC.

Chart 3
Political disagreement among top-three mine producers and reserve holders

(index – higher value indicates higher disagreement among key producers)

Sources: United States Geological Survey, United Nations General Assembly Voting Data (see Bailey, M. A., A. Strezhnev and E. Voeten (2017), “Estimating Dynamic State Preferences from United Nations Voting Data”, Journal of Conflict Resolution, 61(2)) and ECB staff calculations.
Notes: The disagreement score for each mineral is calculated as the average political disagreement among the top-three producers or reserve holders. For OPEC(+), the average political disagreement between all member countries is used.
Latest observation: 2020.

What’s the political connection between consumers and suppliers?
Geopolitically, China currently appears to be better positioned than the EU and the United States in terms of securing supplies of green minerals. China exhibits the lowest level of political disagreement with the main mine producers of green minerals (Chart 4). This is a trend that began at the end of the Cold War. The comparable political disagreement levels for the EU are generally above the UN averages, but still well below levels for the United States.

Chart 4
Political disagreement of the EU, the United States and China with mine producers over time

(index – higher value indicates higher disagreement with key mine producers)

Sources: United States Geological Survey, United Nations General Assembly Voting Data (Bailey, Strezhnev and Voeten, 2017) and ECB staff calculations.
Notes: Political disagreement between two countries is calculated as the distance between preference scores based on records of UN voting for every year. Each region’s disagreement score with minerals producers is calculated as the average of the region’s disagreement with the five largest producers in every year, weighted by the market share of that producer for every mineral. Solid lines are the averages across the six minerals covered in this blog post. Dashed lines indicate the maximum and minimum disagreement scores for the respective years.
Latest observation: 2020.

China has also strategically positioned itself in the green mineral supply chain by becoming the single largest processer of nickel, copper, lithium and cobalt, accounting for between 35-70% of processing activity. Moreover, its upstream control of raw commodities has been increasing due to investments in mining abroad. Although both the United States and EU have taken steps to build their own supply chains of critical materials, considerable investment will be needed to catch up with China’s investments, suggesting that China will retain its dominant position for the foreseeable future.
So what can be done?
Several actions can be taken to enhance the security of green mineral supplies. First, encouraging private investment in mineral extraction and refining can help to diversify supply risks. By facilitating extraction of reserves and the entry of new producers, this could weaken the power of current market leaders. Second, research into substitute materials for green technologies is producing promising first results, and could reduce future demand for green minerals. Third, these minerals are recyclable. This means that there is a secondary source of supply that is likely to grow in the future as both the stock of recyclable products and investment into recycling technologies increase.
The EU recently took an important step in adopting the Critical Raw Materials Act. By 2030, at least 10% of the EU’s annual consumption of critical raw materials should be extracted within the EU, and 40% should be processed domestically. Moreover, approvals procedures for raw materials projects will be streamlined and strategic projects will benefit from access to finance and shorter approvals timeframes. Recyclability of minerals is also promoted, as at least 15% of annual consumption should be covered by domestic recycling in 2030. The share of imports from a single third-party country should also not exceed 65% of annual consumption. This benchmark aims to diversify supply risks, which will be further supported by the creation of a raw materials club for countries interested in strengthening global supply chains and the establishment of an EU export credit facility to reduce the risks of investing abroad.
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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International Energy Agency (2021), “The Role of Critical Minerals in Clean Energy Transitions”.
From an historical perspective, the geopolitical risks for Europe related to green minerals are similar to those it has faced in sourcing other inputs for non-green use, such as oil from the Middle East and gas from Russia.
See Alvarez, J. A., Andaloussi, M. B., Maggi, C., Sollaci, A., Stuermer, M., & Topalova, P. (2023). “Geoeconomic Fragmentation and Commodity Markets” (No. 2023/201). International Monetary Fund.
See OECD database on Trade in Raw Materials (2022).
Indonesia announced in October 2022 that it was studying the possibility of forming an OPEC-style cartel for nickel, cobalt and manganese. Argentina, Chile and Bolivia, the so-called “lithium triangle” which together account for 30% of mining and around 50% of reserves, are reportedly in discussions to form a lithium cartel. See “Indonesia considers OPEC-style cartel for battery metals”, Financial Times, 31 October 2022.
For a more detailed discussion, see Charlton (1977), Politics and Commodity Cartels, Peach Research, Vol. 9 and Levenstein and Suslow (2006), “What Determines Cartel Success?”, Journal of Economic Literature, 44(1).

 
 
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The Transatlantic Business & Investment Council (TBIC) is the official European representative for selected counties, cities, and corporations from over 30 U.S. States. It is our mission to promote transatlantic trade and investment. To that end, TBIC bridges the gap between U.S. Economic Development Organizations (EDOs) and European investors looking to enter or expand in the U.S. market.
This latest issue of our quarterly features an analysis of the newly published data for Q4 2023 and revised data for the previous quarters, as recently released by the U.S. Bureau of Economic Analysis (BEA). With $66.2 billion worth of investment, fourth quarter FDI inflows decreased from inflows in Q3 2023, which were revised to the slightly higher amount of $73.1 billion (up from $67.8 billion). Of this $66.2 billion in Q4 2023 FDI inflows, approximately $21.1 billion were in the manufacturing sector, or roughly, 31.9% – very consistent with the proportion from previous quarters.
Within manufacturing, noticeable increases in FDI inflows were observed from Q3 2023 to Q4 2023 in the food, machinery, and electronics sectors. The machinery sector experienced an increase in investment from $1.6 billion in Q3 2023 to $3.8 billion in Q4 2023 (or a 137.5% increase). Meanwhile, investments in the food and electronics sectors increased by approximately $1.3 billion and $500 million, respectively.
This issue includes a time series focusing on Spanish foreign direct investment into the United States. Spain’s FDI stock in the United States amounted to a total of $75.71 billion at the end of 2022, with the inflow being $3.96 billion, $571 million, $2.86 billion, and -$481 million in Q1, Q2, Q3, and Q4 of 2023 respectively (the numbers for Q3 and Q4 may however still be subject to revision). The Spanish economy is particularly reliant on international conditions – both export and tourism are hugely important for the health of the economy. Recovering from a slump of GDP growth induced by the COVID-19 pandemic in 2020 at -11.2%, the economy grew by 6.4% in 2020 and 5.8% in 2021. Household consumption, stronger-than-expected manufacturing, and the upswing of tourism and exports (especially the latter two) were attributed as the chief causes of this recovery. The most important sectors for the Spanish economy are automotive, chemicals, and construction. Agriculture and food sectors also figure highly in the list, along with tourism. Exports comprise over 40% of the GDP. Spain ranks as the fourth largest economy in the EU. The GDP per capita is at 89% of the EU average, placing it at the 20th place within the EU. A range of brand leaders come from Spain – for example Zara, whose revenue exceeds that of H&M, the Gap, and Uniqlo. Spain is a dynamic country, shares a long history of diplomatic and economic relations with the U.S., and is also a destination for future FDI and trade show trips for TBIC, such as the Advanced Manufacturing Show in Madrid this November
In addition to the focus on the Spanish economy, this issue also highlights how global geopolitical trends have impacted FDI trends, and how transatlantic ties have become increasingly important with time. Foreign threats and looming supply chain issues bring closer the transatlantic ties, and understanding this can benefit EDOs in attracting European investments. There continue to be many reasons to maintain confidence about U.S.-European economic conditions.
Find a PDF version of this document here.
 
 
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Blog post by Jim Tebrake, Francien Berry, Darja Milic | As economies worldwide strive to reduce emissions and achieve sustainable growth, reliable data is crucial. It forms the foundation for informed decision-making, guiding policy development, implementation, and monitoring.

Recognizing this, the Group of Twenty is stepping up efforts to enhance the scope and quality of climate-related data. Through the third phase of the Data Gaps Initiative, it seeks to better integrate climate data with macroeconomic statistics. Doing so allows us to better understand the environmental impact of economic activities and the effectiveness of climate policies.
The Chart of the Week highlights one such useful metric: greenhouse gas emission intensities, which measures emission levels relative to industry output. This shows which industries are producing more pollutants per dollar of output and can guide efforts to reduce emissions in the most impactful areas.

The chart reveals some promising trends. Specifically, the data show notable reductions in emission intensities within agricultural and industrial sectors, the latter encompassing electricity, mining, and water industries. This is particularly encouraging as these sectors together account for over 75 percent of all G20 emissions and are among the most emission intensive. This decline in emission intensities suggests that lower carbon sources of energy, cleaner technologies, and energy efficiency improvements are bearing fruit. To better understand how these dynamics are playing out across industries and economies more granular data is required.  Supplying more granular data to better understand the transition towards a lower carbon economy is an important objective of the G20 Data Gaps Initiative.
While emission intensities in some key sectors are falling, the overall pace is still insufficient to decouple economic growth from emissions in time to meet climate goals. The 2023 Intergovernmental Panel on Climate Change’s report indicates that global greenhouse gas emissions would need to decline by at least 43 percent by 2030 compared to 2019 levels to keep global warming from exceeding 1.5 degrees Celsius. Achieving this reduction in emissions while maintaining economic growth would drive emission intensities towards zero—where we ultimately need them to rest.
A deeper dive into the data also reveals significant and persistent gaps. While more data are becoming available through the data initiative, most G20 economies still do not regularly produce these statistics or do so without the necessary sectoral detail. This lack of robust data coverage hampers effective management and decision-making, reinforcing the adage, “You can’t manage what you don’t measure.”
Greenhouse gas emissions from productive activities are only one part of a country’s carbon footprint. For a full understanding of global progress, particularly from the perspective of implementing border adjustments, it is necessary to account for emission reductions achieved by shifting pollution-intensive activities to economies outside of the G20. However, addressing this issue requires a global perspective. One goal of the initiative is to expand carbon footprints for the G20 that reflect each country’s emissions, irrespective of where the emissions physically occur.
The ongoing initiatives under the third phase to standardize and expand air emission accounts go beyond mere procedural adjustments; they play a critical role in better aligning economic policies with sustainable goals. As the work progresses, it will continue to improve the ways in which countries measure and ultimately manage their environmental impact.
—The full progress report on the Data Gaps Initiative’s third phase can be accessed here. Data produced in this area can be accessed from the countries’ own dissemination platforms and from the IMF’s Climate Change Indicators Dashboard.

Full post can be found here

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OECD labour markets remain tight, with total employment higher than before the COVID-19 pandemic and the OECD unemployment rate close to its lowest level since at least 2001. Jobs growth has slowed, however, and real wages have recovered to pre-2020 levels in only 19 of the 35 OECD countries with available data, despite some catching up in the past quarters. Gender employment participation gaps are narrowing, with female employment up by about 5% in May this year from December 2019, compared with 3% for men.
The OECD Employment Outlook 2024 estimates that OECD-wide employment, which reached 662 million in May 2024 – up by about 25% since 2000 – is expected to grow at around 0.7% per annum over 2024-25. The OECD-wide unemployment rate stood at 4.9% in May 2024, and is projected to inch up slightly. It was 0.2 percentage points higher for women than for men.
Real wages have been catching up on the lost ground in 2022 and the first part of 2023. By the first quarter of 2024, annual real wage growth was positive in 29 of the 35 OECD countries for which data are available, with an average increase across all countries of 3.5%. Analysis in this Employment Outlook indicates a reversal of recent trends that saw profits growing faster than wages. Wages are now recovering some of the lost ground, while there is room for profits to provide additional buffering for wage growth given the significant growth in profits over the past two to three years.
Minimum wages are above 2019 levels in real terms in virtually all OECD countries. In May 2024, the real minimum wage was 8.3% higher than five years earlier at the median across the 30 OECD countries with a national statutory minimum wage, thanks to significant nominal increases of statutory minimum wages to support the lowest paid during the high inflation period over the past two to three years. Evidence suggests that wages have been performing better in the lower part of the wage distribution, with nominal wages growing more in lower-pay industries and occupations and among workers with low education.
This year’s edition also analyses the impact that ambitious climate change mitigation packages aimed at achieving net-zero greenhouse gas emissions by 2050 will have on labour markets and on the jobs of millions of workers worldwide.
“Strong labour markets, with strong jobs growth, have been central to the economic resilience of OECD countries over the past several years. In the period since the pandemic, OECD employment has increased to a record high, despite the challenges posed by inflation and slow productivity growth,” OECD Secretary-General Mathias Cormann said. “The climate transition will lead to significant shifts in labour markets, from high-emissions industries towards new opportunities in green-driven jobs. Policy priorities should be to enable the necessary jobs mobility, including through effective training programmes in impacted sectors, to support workers who have lost their job or whose jobs are at risk through the transition and to promote green-focused innovation, entrepreneurship and jobs.”
While aggregate employment effects of the climate transition are estimated to be limited in the short run, the Employment Outlook notes that the climate transition is expected to lead to significant shifts and disruptions. Jobs will be lost in the shrinking greenhouse gas-intensive industries, while many others will be created in expanding low-emissions activities. About 20% of the OECD workforce is employed in green-driven occupations that will likely be positively impacted by the climate transition. This includes jobs that directly contribute to emissions reductions and also those that produce intermediate goods and services for environmentally sustainable activities. High-skill green-driven jobs usually pay higher-than-average wages, but low-skill green-driven jobs tend to have worse job quality than other low-skill jobs, suggesting that these sectors may be a relatively unattractive option for low-skilled workers.
Workers in shrinking high-emissions industries – which account for 80% of all greenhouse gas emissions but only 7% of employment – face 24% larger earnings losses on average during the six years following a mass layoff than in other industries. Women are less likely to work in green-driven occupations but, at the same time, men are more likely to work in declining high-emissions sectors. The current underrepresentation of females in science, technology, engineering and mathematics (STEM) educational fields and enduring gender stereotypes raise concerns about women’s capacity to benefit from high-skill green-driven jobs.
Policies should help facilitate job transitions and accompany workers towards new opportunities in green-driven jobs while mitigating earnings losses of displaced workers. These include early intervention measures, effective training programmes and targeted in-work support approaches such as time-limited wage subsidy schemes.
 
View full press release here
 
 
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Blog post by By Ursel Baumann, Annalisa Ferrando, Dimitris Georgarakos, Yuriy Gorodnichenko, Judit Rariga and Timo Reinelt | Firms’ inflation expectations are key for monetary policy makers. The ECB Blog presents new survey data on these expectations, evidence on what influences them, how they change when new information becomes available, and if they matter for the plans and choices firms make.
Setting prices, negotiating wages, deciding how much to invest or how many workers to employ – these are all choices companies make that have profound implications. When taking these decisions, firms typically consider their own business needs, the behaviour of their competitors, the state of the economy and how they think it will evolve. How firms form their expectations about inflation, and how these expectations influence their decisions, is therefore important for central banks.
The inflation expectations of euro area firms are not very well understood. One reason is that they are not measured in a consistent way across countries. The 2020/21 ECB monetary policy strategy review highlighted this information gap, and so the ECB recently expanded its euro area wide firm survey, the Survey on the Access to Finance of Enterprises (SAFE), to include regular questions on firms’ inflation expectations. This new source of data now allows us to explore several important questions: What influences firms’ inflation expectations? Do firms’ inflation expectations react to news about the inflation outlook? And do these expectations matter for firms’ plans and decisions?[1]
 
What influences firms’ inflation expectations?
Chart 1 provides a first glimpse of euro area firms’ inflation expectations as of June 2023, with three points standing out. First, firms tended to expect inflation one year ahead at 5.8 percent on average, to be somewhat higher than actual inflation (of 5.5 percent) at the time. Second, they expected that inflation would come down, with expectations of inflation 3-years-ahead and 5-years-ahead at 5.0 and 4.8 percent on average, respectively. Third, firms have very different views on where they expect inflation to be in the future, especially the more distant future, as shown by the dispersion in their responses. While on average firms expect inflation to moderate over time and come closer to the ECB’s 2% inflation target, they have more widely differing views on where inflation is going in the longer run.
We also find that inflation expectations of euro area firms are distinct from those of households or professional forecasters. Firms on average expect higher inflation than professional forecasters, but in this they are similar to households.[2] At the same time, firms’ expectations one year ahead are more widely dispersed than those of professional forecasters and less than those of households.

 
Chart 1
The distribution of inflation expectations across euro area firms.

x-axis: level of inflation expectations in percentages per annum, y-axis: share of firms

Source: Survey on the Access to Finance of Enterprises and authors’ calculations.
Note: The chart shows kernel densities of firms’ inflation expectations at various horizons.

The new SAFE data also provides insights on the factors that influence firms’ inflation expectations. For example, firms’ inflation expectations correlate strongly with the characteristics of companies. Concretely, smaller and younger firms have, on average, higher inflation expectations than larger and older firms (Chart 2, panels (a) and (b)). Also, firms’ investment plans, leverage and access to credit seem to play a role. Firms with higher debt ratios, for instance, tend to have higher inflation expectations (Chart 2, panel (c)). Finally, it matters who within the surveyed firms is responding: male managers tend to have lower inflation expectations than female ones, and chief financial officers have lower expectations compared with respondents that have other positions within the firm.

 
Chart 2
The relationship between firms’ characteristics and inflation expectations

x-axis: number of employees and firm age (in logarithms relative to the average), leverage (in percentage points relative to the average), y-axis: firms’ 1-year inflation expectations (in percentage points relative to the average)

Source: Survey on the Access to Finance of Enterprises, Orbis and authors’ calculations.
Notes: The charts show binned scatter plots of various firm characteristics and firms’ inflation expectations. The shown data is shown relative to the average within countries, sectors and survey waves.

 
Do firms’ inflation expectations react to inflation news?
So, are firms’ inflation expectations affected by information about the euro area inflation outlook? In our study, we use a randomised controlled trial to tackle this question. Specifically, we first asked firms where they expect inflation to be in one year, in three years, and in five years. We then randomly split the firms into two groups. The so-called “treatment group” was given the latest available expert forecast for inflation, which we took from the ECB’s Survey of Professional Forecasters (SPF).[3] This “information treatment” revealed to firms that professional forecasters expected inflation to be 2.8% in one year. The control group received no information. The firms in the treatment group were then asked again about their inflation expectations at the three time horizons. These firms then had the opportunity to revise their inflation expectations in the light of the information received – while the control group was not asked again.
Overall, firms tended to respond to new information about inflation by adjusting their expectations.
Once the firms in the treatment group had learnt about the inflation forecast, they significantly changed their inflation expectations. For the 1-year-ahead inflation expectations, this change can be seen in the yellow line in Chart 3. Firms reduced their expectations around halfway towards the inflation forecast of 2.8% that had been provided to them. This means that firms that expected inflation to stand at 11% in a year’s time, for example, revised their expectations downwards to around 7%. In contrast, firms that already expected inflation to be close to the 2.8% expert forecast saw little need to change their expectations.
The revised expectations of firms in the treatment group is shown by the yellow line which is less steep than the blue line (45°), which represents the control group of firms that were not given the possibility to adjust their expectations. Overall, firms tended to respond to new information about inflation by adjusting their expectations. This happened despite the survey having been run during a high inflation period in June 2023, during which firms may already have been more aware of publicly available information such as current inflation, forecasts, and the inflation target of the central bank.

 
Chart 3
Firms’ adjustment of 1-year-ahead inflation expectations upon learning about the inflation forecast

x-axis: firms’ expectations before the information treatment, y-axis: firms’ expectations after the information treatment (both in %)

Notes: The chart is a binscatter plot of firms’ inflation expectations before the randomised information treatment (“prior expectations”) vs after the information treatment (“posterior expectations”). Huber weights are applied to minimise the influence of outliers.

 
Firms’ inflation expectations matter for their business decisions
The final step was to analyse whether firms’ inflation expectations matter for their economic plans. We looked in particular at how new information about the inflation outlook affected firms’ plans and decisions about setting wages and prices, about employment and other costs.
We observe that firms with higher inflation expectations plan to increase their prices by more, while also expecting larger cost and wage increases. Relative to the control group, firms that received the information treatment, and thus lowered their inflation expectations, generally planned smaller price increases and were more likely to keep prices unchanged. These firms also expected wage and cost increases to be generally more moderate. This suggests that firms do respond to new information about the inflation outlook by adjusting not only their beliefs about inflation, but also their decisions.
In conclusion, euro area firms’ inflation expectations in the SAFE represent a rich and novel source of data. It complements the wealth of information stemming from surveys of households (CES) and professional forecasters (SPF) in the euro area. These data are key to improving our understanding of how firms form expectations and how that affects their decisions. Our results suggest that successful policy communication matters to guide inflation expectations, which in turn affect firms’ economic plans.
 

The analysis underlying this blog is published in Baumann, U., Ferrando, A., Georgarakos, D., Gorodnichenko, Y. and Reinelt, T. (2024), “SAFE to Update Inflation Expectations? New Survey Evidence on Euro Area Firms”, ECB Working Paper Series, No 2949.
The ECB Consumer Expectations Survey’s (CES) corresponding 1-year-ahead average inflation expectations stood at 5.1% in June 2023, while the ECB Survey of Professional Forecasters (SPF) average 1-year-ahead forecast stood at 2.8% in the second quarter of 2023.
The randomised controlled trial was conducted in June 2023. The exact wording of the information provision was: “We would now like to provide you with some information about the expected inflation rate in the euro area going forward. The Survey of Professional Forecasters (SPF) is a survey of professional economists. According to the latest data, they expect, on average, inflation in 12 months to be 2.8%.”

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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Canada is joining the growing group of non-EU countries who have associated to the EU’s research and innovation programme, Horizon Europe, and will work jointly on large-scale projects tackling our biggest challenges.
Today, Iliana Ivanova, Commissioner for Innovation, Research, Culture, Education and Youth, and Francois-Philippe Champagne, Canadian Minister of Innovation, Science and Industry, are signing the agreement that gives Canadian researchers and organisations the opportunity to participate in the programme on equal terms with their EU counterparts.
Commission President Ursula von der Leyen and Canadian Prime Minister Justin Trudeau announced the conclusion of negotiations for Canada’s association to Pillar II of Horizon Europe at the EU-Canada Summit on 24 November 2023. Today, they welcomed the announcement through a joint statement.
Canada associates to the Pillar II of Horizon Europe, which funds collaborative research projects across a wide range of domains. Canadian entities can now join and lead research consortia with some of the world’s best research organisations to tackle global challenges together. They will get the opportunity to be funded directly from the programme, while Canada will contribute to its budget.
While awaiting the signature, a transitional arrangement had been in place for Canadian entities. This means that they were able to apply and be evaluated as prospective beneficiaries in Horizon Europe proposals for all calls implementing Pillar II already in the budget 2024 onwards.
 
Background
Horizon Europe is the EU’s key funding programme for research and innovation with a budget of €93.5 billion for 2021-27. It tackles climate change, helps to achieve the UN’s Sustainable Development Goals and boosts the EU’s competitiveness and growth. Pillar II is the largest collaborative part of the programme with a budget of €52.4 billion that is focused on shared global challenges: climate, energy, digital economy and health.
The other Horizon Europe pillars, including Excellent Science and Innovative Europe, remain open to Canadian organisations and researchers. This is also the case of the Marie Skłodowska-Curie Actions (MSCA), the EU’s reference programme for doctoral education, postdoctoral training and collaborative research, which accounts for half of the current projects between the EU and Canada under Horizon Europe.
Canadian entities currently participate in 155 Projects under Horizon Europe. Canadian institutions have received over €6 million from Horizon Europe so far. Of this, €2.3 million consists of European Research Council grants. Furthermore, €1.9 million has been granted to Canadian partners in projects under Pillar II and €1.8 million has been granted under Research Infrastructures.
As of today, 19 countries are associated, either based on their membership of the European Economic Area (EEA); or as acceding countries, candidate countries and potential candidates; as European Neighbourhood Policy (ENP) countries; or as other third countries and territories that fulfil a set of criteria related to their economic, political and research and innovation systems. Formal negotiations to associate to Horizon Europe were recently concluded with the Republic of Korea. Negotiations are ongoing with Switzerland, while preparatory talks are taking place with Japan and Singapore.
For more information, please contact:

Thomas Regnier, Spokesperson

Roberta Verbanac, Press Officer

 
 
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Keynote speech by Piero Cipollone, Member of the Executive Board of the European Central Bank, at the National Conference of Statistics on official statistics at the time of artificial intelligence

It is a pleasure to be here today to discuss the implications of artificial intelligence (AI) from a central bank’s perspective.[1]
The world is witnessing extraordinary advances in the field of AI.[2] We are moving from analytical AI models designed to perform specific tasks[3] to generative AI models capable of creating human-like content.
The burgeoning interest in generative AI has boosted AI adoption.[4] A recent international survey revealed that almost three-quarters of organisations had adopted AI for one or more business functions, and around two-thirds of them are using generative AI. Nevertheless, just 8% reported using AI for five or more business functions – suggesting that we are still in the initial stages of AI integration.[5]
AI can be applied to a wide spectrum of activities, from routine and repetitive tasks to knowledge-based and creative work. It has been argued that AI is a general-purpose technology – akin to the steam engine, electricity or the computer – with the potential to transform our economies in the long run.[6]
But, like the computer before it, AI may involve a paradox similar to the one made famous by the economist Robert Solow: “You can see the computer age everywhere but in the productivity statistics.”[7]
The dawn of the computer era saw information and communication technology (ICT) profoundly alter our personal lives and the economy. Today, our workplaces, homes and social lives are interwoven with digitalisation.
At the European Central Bank (ECB), our Information Systems department has become the largest business area within the institution. ICT has become key to our core tasks, from the economic models that underpin our forecasts to monetary policy implementation and the operation of market infrastructures.
Yet technology has not fundamentally changed the way we think about monetary policy. Moreover, just as Solow observed, the macroeconomic impact of ICT on productivity has not been as large as might have been expected – at least outside of the tech sector.
Indeed, the transformative potential of AI may not always be productivity-enhancing. Consider, for example, a recent AI-generated deepfake video of the actor Tom Cruise dressed in a bathrobe and singing Elton John’s “Tiny Dancer” to Paris Hilton that went viral on TikTok.[8]
Might we see another Solow paradox emerge in the context of AI? In other words, what is the potential of AI to boost the productive capacity of the economy, as well as that of central banks?
Today I will take stock of what we know about the impact of AI on the economy and discuss its possible implications for our monetary policy. I will then share the ECB’s perspective on how we can best use AI in our central banking tasks, while putting in place the necessary safeguards for its responsible use.
 
The macroeconomic impact of AI and its implications for monetary policy
 
The macroeconomic impact of AI
AI could affect the economy in several areas that are particularly relevant for the conduct of monetary policy. Today I will highlight just three of them.

AI and productivity
The first area relates to productivity.
The potential of AI to raise productivity is undeniable – from acting as a powerful coding assistant to running autonomous “smart factories”. AI could increase productivity through various channels – for example, via direct productivity effects that boost total factor productivity or through individual production factors.
Indeed, several studies already point to sizeable AI-induced productivity gains at firm level.[9] But estimates of aggregate effects over the coming decade differ markedly across studies – from an increase in annual total factor productivity growth of less than 0.1 percentage points to annual labour productivity growth of up to 1.5 percentage points.[10]
The eventual outcome will depend on whether we see a rapid and broad-based adoption and diffusion of AI across all sectors of the economy. Up until now, the sheer speed of diffusion across sectors and firms has little historical precedent.[11] And survey evidence suggests that adoption by European firms nearly matches that of North America.[12]
But a key risk stems from the possibility that most of the value created by AI is extracted by a few companies that end up dominating the AI ecosystem.[13] This is a key reason why productivity gains from AI at firm level may not translate into sustained value-added gains at the aggregate level, as market power increases costs. We saw this happen already with the rise of IT[14], which resulted in productivity gains being concentrated in the IT sector and primarily benefiting countries with large, successful tech firms. This is also reflected in the unprecedented concentration of market value in the “Magnificent Seven” firms in the United States.[15] These are currently benefiting from the AI boom and making higher yearly profits than all the listed companies of France, Germany and Italy combined.
This has important implications for Europe. As Mario Draghi recently observed, EU productivity growth over the past twenty years would have been on a par with that of the United States if it were not for the tech sector.[16] Current data point to the euro area trailing behind the United States in terms of private investment in AI[17], as well as patent applications and journal publications in the field.[18] It is therefore critical to devise a European AI strategy with a threefold aim: to preserve competition in the AI space[19]; to create an ecosystem that supports’ European AI firms’ competitiveness, generating sectoral productivity gains over time[20]; and to support the diffusion of AI across the economy, facilitating the development of AI-supported products and services.[21]

AI and the labour market
The labour market is the second area of the economy that is likely to be affected by AI.[22]
New technologies can substitute or complement labour. On the one hand, automation implies capital taking over a task previously performed by a worker. On the other, productivity tends to increase with the automation of tasks, which may contribute to increased labour demand for non-automated tasks if price reduction brought about by productivity-improving technology spurs strong demand growth.[23] And new technology can lead to the creation of new kinds of jobs.[24] Whether AI represents an opportunity or risk for employment depends on the net effect.
ECB staff analysis suggests that around 25% of jobs in European countries are in occupations that are highly exposed to AI-enabled automation, while another 30% have a medium degree of exposure.[25] Other research finds that knowledge-intensive services in particular – including finance and insurance, advertising, consultancy and IT – are most likely to be affected by AI.[26]
Initial evidence for Europe suggests that, on average, occupations more exposed to AI have seen an increase in their share in total employment – although mostly for highly skilled occupations and younger workers, and with significant heterogeneity across countries.[27] But the ultimate impact on employment remains uncertain and is likely to hinge on equipping the workforce with skills that complement AI.[28]
 
AI and financial stability
The third area of the economy that may be affected by AI is financial stability.
Certainly, AI can bring benefits to the table. The application of AI could allow banks to conduct more efficient risk assessments and capital and liquidity planning.[29] But there are also risks. If new AI tools are used widely in the financial system and AI suppliers are concentrated, operational risk, market concentration and too-big-to-fail externalities may increase. Furthermore, widespread AI adoption could heighten the potential for herd behaviour, market correlation, deception, manipulation and conflicts of interest.[30]
 
Implications for monetary policy
Central banks, including the ECB, are monitoring these developments closely.[31] Not only does AI influence the environment in which we operate, it also affects how that environment interacts with our monetary policy.
 
Inflation
First, AI could affect cost pressures in the economy in both directions.
We may see AI exerting downward pressure on prices in various ways. For instance, if the net effect of AI is that it substitutes labour and increases productivity, we could see a reduced risk of labour shortages and downward pressure on unit labour cost growth. This is especially relevant in the euro area, where unemployment is at a record low and the working age population is projected to decline by 19% by the end of the century as a result of population ageing.[32]
AI could also lead to a decline in energy prices through its impact on the supply side, for instance through enhanced grid management and more efficient energy consumption. And it could provide consumers with better tools for price comparison.
But AI could also create upward price pressures.
For instance, the uptake of AI will also have an impact on global energy demand, with the computational power required for sustaining AI’s rise doubling every 100 days.[33] This could push up energy costs. Moreover, AI may encourage discriminatory pricing by facilitating the real-time analysis of consumer demand and price elasticities. And algorithms consistently learn to charge collusive prices that are higher than competitive ones, even without communicating with one another – in part by exploiting well-known biases that deviate from rational consumer behaviour.[34]
 
Monetary policy transmission
Second, we may see AI affect monetary policy transmission.
AI is likely to create new winners and losers in the labour and capital market, with consequences for income and wealth distribution.[35] This matters for monetary policy because it can influence people’s marginal propensity to consume and their access to credit, which in turn affect how demand responds to changes in monetary policy.
Moreover, if AI leads to a change in financial structures, such as an increase in non-bank intermediation,[36] it may have further implications for monetary policy transmission. There is evidence to suggest that compared with banks, non-banks are more responsive to monetary policy measures that influence longer-term interest rates, such as asset purchases. Non-banks also exhibit higher levels of credit, liquidity and duration risk compared with the banking sector.[37]
 
The natural rate of interest
Third, AI may go on to influence the natural rate of interest.[38]
If AI boosts productivity growth and potential output, we may see upward pressure being exerted on the natural rate of interest, as demand increases for capital to invest in new technologies and expand production capabilities.
But if AI leads to higher rates of labour displacement and causes rising income inequality, we may see some downward pressure on the natural rate, owing to an increase in precautionary savings and a subsequent boost to the supply of loanable funds.
 
Using AI in central banking: AI at the ECB
These developments will play out over time outside the walls of the ECB, and we will be monitoring them closely. But within the ECB’s walls, AI also has the potential to help with multiple tasks.[39]
Let me give you a few examples.
 
Statistics
Given that we are here at the National Conference of Statistics, it is fitting to begin with statistics. The ECB needs trustworthy and high-quality statistical products, services and a wide range of data to inform its monetary policy decisions.
A key lesson from the global financial crisis was that aggregate statistics alone are insufficient to grasp the complexity of financial markets. We need more granular data.
As you can imagine, the resulting datasets are so vast in terms of the number of observations they contain that collecting and disseminating them requires the use of statistical processes and analytical methods that surpass traditional statistical approaches.
Around six years ago the ECB began exploring the application of AI to improve the efficiency and effectiveness of its statistical processes. And these efforts are reaping dividends.
We use AI to improve the quality of our datasets, from identifying and matching observations across datasets to using modern machine learning techniques for quality assurance.[40]
Moreover, large language models (LLMs) can support statistical processes in ways that were once simply not feasible. These include unlocking new and non-traditional data sources – for instance, unstructured data like text, image, video or audio. These sources can complement and enhance our existing data collections.
 
Economic analysis
AI is also increasingly being incorporated into the economic analyses we carry out to help us prepare our monetary policy decisions.
AI can identify patterns in data more effectively than traditional methods. This is particularly true for non-linearities, which have been playing a bigger role in an environment that is becoming more shock prone. AI also enables the real-time analysis of economic indicators, helping central banks to make more timely policy decisions – a particularly valuable capability in times of crisis.
What do these applications look like in practice?
ECB staff use AI to nowcast inflation. This includes web-scraping price data and using LLMs for data classification. We are currently exploring the use of Big Data and new generative AI models in close cooperation with the BIS Innovation Hub.[41]
Staff are also applying machine learning models to euro area inflation forecasting, accounting for possible non-linearities.[42] These models are already performing well compared with our conventional forecast and survey-based measures of inflation expectations. Another project is employing machine learning techniques to nowcast global trade.[43]
Staff are also exploring the possibilities opened up by innovative datasets. For instance, projects include using a combination of text data and machine learning techniques to quantify risks and tensions in the global economy and exploring the use of satellite data to track economic activity.[44]
 
Communication
Central bank communication is another area in which AI can contribute.
AI could help in areas where central bank communication is key, such as ensuring that policy decisions are well understood and keeping inflation expectations anchored. With AI, we can rapidly analyse vast volumes of media reporting and market commentary.
Moreover, AI can help us communicate with the public in all parts of the euro area. As a European institution, the ECB communicates in all 24 official languages of the EU. Even today, AI and machine translation are helping us meet a demand for translation that exceeds 6 million pages per year. Without those tools, the ECB’s language services would be limited to covering around 150,000 pages per year.
AI can also help broaden our reach by simplifying key messages and communication products for targeted audiences[45] that have less awareness or knowledge of the ECB.[46] And it could help us answer any questions the public may have.
All these innovations could ultimately make the ECB better understood, facilitate the effectiveness of our monetary policy and boost our accountability.
 
Market infrastructures and payments
AI might also bring profound changes to the field of market infrastructures and payments.
The technology could help design and develop innovative payments services customised to consumers’ needs and preferences. And it could help foster financial inclusion, for example, by facilitating voice activated payments. These potential developments are clearly relevant when it comes to the ECB’s role in promoting efficient, integrated and inclusive payments.
Moreover, AI could also help us oversee payment systems. There is an opportunity to use AI as part of early warning models that aim to identify financial stability risks related to financial market infrastructures before these risks materialise. And it could play a supporting role in the scrutiny of information provided by overseen entities, helping us ensure that their practices align with the applicable regulatory frameworks.
The ECB has developed an AI action plan to facilitate the adoption of AI wherever it is relevant to our tasks. It aims to develop and deploy the necessary AI tools and infrastructure, while fostering AI skills and ensuring the technology is used safely and responsibly.
 
The limits of AI: putting the necessary safeguards in place
Let me now turn to the limits of AI.
A key strength of human intelligence is the ability to reflect on its limits. As the philosopher Immanuel Kant once wrote, “we can cognize of things a priori only what we ourselves have put into them”.[47] But AI does not have this capacity for self-reflection. Nor does it have the ability to produce its own safeguards independently of human critical thinking. We therefore need to be aware of the limits of AI and their implications for the ECB, so that we can put the necessary guardrails in place.
First, we need to ensure confidentiality and privacy.
Given the sensitivity of central banks’ decisions, guaranteeing confidentiality is a key condition for the in-house use of AI. Likewise, when it comes to data use, AI will increase concerns about privacy, underlining the importance of applying technological and governance safeguards and complying with regulations such as the EU AI Act.
Take, for instance, the AI solutions we use for our statistics. These tools need to provide comprehensive documentation. This is a prerequisite for clarifying how AI solutions have been used to assess, improve or integrate data. Trust in these solutions comes from first understanding them.
The second risk stemming from AI is the degree to which it can be used to spread false information and data, facilitate fraud or launch cyberattacks.
Since late 2022 there has been a 53-fold increase in generative AI-related incidents and hazards reported in the media.[48] It is one thing for an AI-generated deepfake video of Tom Cruise to go viral. But it is quite another when a deepfake of a policymaker goes viral – particularly at moments of crisis, when attention levels are high and volatility and uncertainty are already pronounced.
At the same time, AI can be used to detect and address such risks. It can help prevent and detect cyberattacks by identifying anomalies in user, system and network behaviours in real time.[49]
The third risk emerges from what we might describe as an over-arching dependence on AI. And this can manifest itself in several ways.
For instance, a greater dependence on AI may inadvertently increase the risk of falling into an “echo chamber” trap.
Given that LLMs are trained using available data and information – which, over time, will increasingly be produced by AI – there is a risk of AI becoming self-referential or repeating existing biases.
To the extent that this dynamic increases the impact of central bank communication on markets, while central banks look to the markets for information, it could increase the risk of central bank echo chambers emerging.[50] This could, for instance, increase the risks of using forward guidance.[51]
An excessive reliance on AI could also reduce our own operational resilience.
As AI becomes a bigger part of our way of working, we may find ourselves growing more dependent on it for core tasks. That is why it is so important to understand the properties of the AI algorithms and models we use to reduce the risks of a potential “black box” effect.
Similarly, if it is not used responsibly, AI could also suppress the diversity and originality of thought, thereby increasing the risk of groupthink and confirmation bias. The mathematician Alan Turing once famously asked, “Can machines think?”[52] The last thing we want is for the same question to be asked about central bankers who end up being too reliant on AI.
A key feature of human cognition is the ability to question existing theories, produce new ones and identify data to test them.[53] This ability needs to be preserved. ECB Governing Council meetings are best understood as a process of comparing views on the economy, considering alternative interpretations of economic developments and assessing risks from multiple perspectives. The ongoing uncertainty in the economy shows that we need to do this more, rather than less.
The overall lesson is that humans need to remain firmly in control, not only to ensure a trustworthy use of AI systems, but also to address questions of accountability and maintain the public’s trust in the central bank.
 
Conclusion
To conclude, as we enter the AI age, we face the challenge of realising its potential while managing its risks.
Whether AI will show up in productivity statistics or create a new paradox remains uncertain. To an extent, whether we face an AI productivity paradox will partly depend on our ability to accurately measure its contribution – and statisticians have an important role to play given the complexity of measuring intangible capital.[54]
But as with other technologies, for AI to be able to produce its full effects, the right ecosystem must be in place – one that facilitates competition in the AI sphere, ensures a fair distribution of possible productivity gains, establishes robust regulatory and ethical safeguards and fosters the corresponding skills in the labour market.
For central banks, AI offers opportunities for innovation and efficiency gains, from economic analysis to communication. But there are also risks that must be considered, and we are duly building appropriate safeguards.
As we integrate AI into our processes, we must ensure that human judgement and critical thinking remain at the forefront. This balance will be essential to maintaining trust in our data, our decisions and the broader financial system.
Thank you.

I would like to thank Jean-Francois Jamet and Simon Mee for their help in preparing this speech, and Siria Angino, Katrin Arnold, Maciej Brzezinski, António Dias Da Silva, Ferdinand Dreher, Maximilian Freier, Gabriel Glöckler, Guzmán González-Torres, Alexander Hodbod, Daniel Kapp, Baptiste Meunier, Roberto Motto, Chiara Osbat, Tom Sanders, Jürgen Schaff, Hanni Schölermann, David Sondermann, Anton Van der Kraaij and Balázs Zsámboki for their input and comments.
Artificial intelligence is a collective term for machine-enabled cognitive processing. The Organisation for Economic Co-operation and Development (OECD) defines artificial intelligence as “a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” See OECD (2024), “OECD AI Principles”, May.
For instance, shopping recommendations or text analysis.
Within two months of its launch in late 2022, ChatGPT had already attracted 100 million users. See The Economist (2023), “ChatGPT mania may be cooling, but a serious new industry is taking shape”, 21 September.
See McKinsey (2024), “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value”, 30 May.
See Crafts, N. (2021), “Artificial intelligence as a general-purpose technology: an historical perspective”, Oxford Review of Economic Policy, Vol. 37, Issue 3, Autumn 2021, pp. 521-536 and Agrawal, A. et al. (2019), “Economic Policy for Artificial Intelligence”, Innovation Policy and the Economy, Vol. 19.
Solow, R.M. (1987), “We’d Better Watch Out”, New York Times Book Review.
See TikTok, @ParisHilton and Forbes (2022), “The Story Behind Paris Hilton’s Viral TikTok With DeepTomCruise”, 22 November.
See, for example, Dell’Acqua, F. et al. (2023), “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality”, Harvard Business School Technology & Operations Management Unit Working Paper, No 24-013.
For instance, see Acemoglu, D. (2024), “The Simple Macroeconomics of AI”, MIT, 5 April; Briggs, J. and Kodnani, D. (2023), “The Potentially Large Effects of Artificial Intelligence on Economic Growth”, Goldman Sachs, 26 March. For an overview, see Filippucci, F. et al. (2024), “Should AI stay or should AI go: The promises and perils of AI for productivity and growth”, VoxEU, 2 May.
One international survey finds that three out of every five white-collar workers are already using generative AI on a weekly basis. The survey encompasses 16 countries spanning the Americas, Europe, Asia and Oceania. See Oliver Wyman Forum (2024), “How generative AI is transforming business and society: the good, the bad, and everything in between”, p. 23.
In 2023 the proportion of European firms reporting the use of AI technologies stood at 57%, compared with 61% in North America, 58% in Asia Pacific and 48% in China. See Maslej, N. et al. (2024) “The AI Index 2024 Annual Report”, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April.
Acemoglu, D. and Johnson, S. (2023), “Big Tech Is Bad. Big A.I. Will Be Worse”, The New York Times, 9 June.
De Ridder, M. (2024), “Market Power and Innovation in the Intangible Economy”, American Economic Review, Vol. 114, Issue 1, pp. 199-251. See also Philippon, T. (2019), The great reversal: How America gave up on free markets, Harvard University Press.
The “Magnificent Seven” comprise Microsoft, Apple, Nvidia, Alphabet, Amazon, Meta and Tesla. These companies now make up close to one-third of the market value of the S&P index.
See Draghi, M. (2024), “An Industrial Strategy For Europe”, acceptance speech at the Monastery of San Jeronimo de Yuste for the Carlos V European Award, 14 June: “If we were to exclude the tech sector, EU productivity growth over the past twenty years would be on par with that of the United States.” Empirical evidence also indicates that the ICT-intensive sector in the United States has experienced a much higher increase in labour productivity than in Europe: euro area countries have been less efficient than the United States in both adopting IT technologies and leveraging them to achieve labour productivity gains. See Bergeaud (2024), “The past, present and future of European productivity”, paper presented at the ECB Forum on Central Banking 2024.
In 2023 private investment in AI reached USD 67 billion in the United States compared with USD 11 billion in the EU and the United Kingdom combined. See Maslej, N. et al. (2024), Ibid.
Euro area firms filed on average 475 AI-related patents per year from 2002 to 2022, three times less than the United States and twice less than China. In terms of citation-adjusted AI journal publications, the United States also took the lead over the euro area and China. See Bergeaud (2024), Ibid.
See Coeuré, B. (2024), “Comments on ‘The simple macroeconomics of transformative AI’ by Daron Acemoglu”, Economic Policy Panel, Brussels, 4 April and Coeuré, B. (2024), “Artificial intelligence: making sure it’s not a walled garden”, keynote address at the Bank for International Settlements – Financial Stability Institute policy implementation meeting on big tech in insurance, 19 March.
Measures in that direction include investing in AI education, encouraging venture capital investment and an environment that supports AI startups, increasing the mobility of financial capital across EU countries, and strengthening the link between European universities and European AI firms to convert AI research into marketable innovations. See Bergeaud (2024), Ibid.
See Meyers, Z. and Springford, J. (2023), “How Europe can make the most of AI”, Centre for European Reform Policy Brief, 14 September.
See Albanesi, S. et al. (2023), “Reports of AI ending human labour may be greatly exaggerated”, Research Bulletin, No 113, ECB, 28 November.
An elastic demand may support employment even in the face of automation, as productivity growth is reflected in prices and product demand increases. See, for example, Bessen, J. (2020). “Automation and jobs: when technology boosts employment”, Economic Policy, Volume 34, Issue 100, pp. 589-626.
About 60% of employment in 2018 can be classified under job titles that did not exist back in 1940. See Autor, D. et al. (2021), “New frontiers: the origin and content of new work, 1940-2018”, MIT Working Paper, July.
Albanesi, S. et al. (2023) “New technologies and jobs in Europe” Working Paper Series, No 2831, ECB.
See Figure 8 in Organisation for Economic Co-operation and Development (2024), “The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges”, OECD Artificial Intelligence Papers, No 15, 16 April.
Albanesi, S. et al. (2023) “New technologies and jobs in Europe”, Working Paper Series, No 2831, ECB.
See Green, A. (2024), “Artificial intelligence and the changing demand for skills in the labour market”, OECD Artificial Intelligence Papers, No 14, OECD Publishing, Paris.
See Figure B.2 in Leitner, G. et al. (2024), “The rise of artificial intelligence: benefits and risks for financial stability”, Financial Stability Review, ECB, May.
See Leitner, G. et al. (2024), “The rise of artificial intelligence: benefits and risks for financial stability”, Financial Stability Review, ECB, May; Gensler, G. (2023), “Isaac Newton to AI”, Remarks before the National Press Club, 17 July; Gensler, G. and Bailey, L. (2020), “Deep Learning and Financial Stability”, 1 November; and Gensler, G. (2024), “AI, Finance, Movies, and the Law”, Prepared Remarks before the Yale Law School.
Bank for International Settlements (2024), “Artificial intelligence and the economy: implications for central banks”, BIS Annual Economic Report 2024, Chapter III, 25 June.
As noted in Freier, M. et al. (2023), “EUROPOP2023 demographic trends and their euro area economic implications”, Economic Bulletin, Issue 3, ECB.
See Ammanath, B. (2024), “How to manage AI’s energy demand — today, tomorrow and in the future”, World Economic Forum, 25 April.
See Calvano, E. et al. (2020), “Artificial Intelligence, Algorithmic Pricing, and Collusion”, American Economic Review, Vol. 110, Issue 10, pp. 3267-97; for biases, see OECD (2024), “The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges”, OECD Artificial Intelligence Papers, No 15, 16 April, pp. 33-34.
See Cazzaniga, M. et al. (2024), “Gen-AI: Artificial Intelligence and the Future of Work”, IMF Staff Discussion Notes, No 2024/001.
The use of artificial intelligence for credit scoring could allow big tech with access to large consumer data to rapidly expand in the area of financial services and to challenge banks’ traditional role in financing the economy and serving as the first point of contact for financial services. See Boot, A., Hoffmann, P., Laeven, L. and Ratnovski, L. (2021), “Fintech: what’s old, what’s new?”, Journal of Financial Stability, Vol. 53.
See Work stream on non-bank financial intermediation (2021), “Non-bank financial intermediation in the euro area: implications for monetary policy transmission and key vulnerabilities”, Occasional Paper Series, No 270.
The natural rate of interest is the real rate of interest that is neither expansionary nor contractionary.
See also Moufakkir, M. (2023), “Careful embrace: AI and the ECB”, The ECB Blog.
This allows us to identify and prioritise anomalous observations and outliers that require further attention, assessment and potential treatment.
Osbat, C. (2022), “What micro price data teach us about the inflation process: web-scraping in PRISMA”, SUERF Policy Brief, No 470, 17 November.
See, for instance, Lenza, M. et al. (2023), “Forecasting euro area inflation with machine learning models”, Research Bulletin, No 112, ECB, 17 October.
For example, see Menzie, C. et al. (2023), “Nowcasting world trade with machine learning: a three-step approach”, Working Paper Series, No 2836, ECB.
See, for instance, d’Aspremont, A. (2024), “Satellites turn “concrete”: tracking cement with satellite data and neural networks”, Working Paper Series, No 2900, ECB.
On layered communication, see Work stream on monetary policy communications (2021), “Clear, consistent and engaging: ECB monetary policy communication in a changing world”, Occasional Paper Series, No 274, ECB; see also Bholat, D. et al. (2018), “Enhancing central bank communications with behavioural insights”, Staff Working Paper Series, Bank of England, No 750, August.
Survey evidence suggests that there is a lack of understanding of the ECB’s tasks. Two-thirds of euro area citizens believe that it is the ECB’s task to stabilise the foreign exchange rate, while over one-third think that the ECB’s role is to finance governments. See Chart 7 in Gardt, M. et al. (2021), “ECB communication with the wider public”, Economic Bulletin, Issue 8, ECB.
Kant, I. (1781), Critique of pure reason.
OCED (2024), “OECD Digital Economy Outlook 2024 (Volume 1): Embracing The Technology Frontier”, 14 May, p. 38.
See Cipollone, P. (2024), “One step ahead: protecting the cyber resilience of financial infrastructures”, introductory remarks at the ninth meeting of the Euro Cyber Resilience Board for pan-European Financial Infrastructures, 17 January; and Bank for International Settlements, “Project Raven: using AI to assess financial system’s cyber security and resilience”.
The central bank may no longer observe independent signals about the state of the economy from financial markets, instead mainly seeing the mirror image of its own communications.
Echo chamber dynamics can create a circularity between market prices and forward guidance. See Morris, S. and Shin, H. S. (2018), “Central Bank Forward Guidance and the Signal Value of Market Prices”, AEA Papers and Proceedings, Vol. 108, May, pp. 572-577.
Turing, A. (1950), “Computing Machinery and Intelligence”, Mind, Vol. LIX, Issue 236, October, pp. 433-460.
For instance, see the role of paradigm shifts in scientific development in Kuhn, T. (1962), The structure of scientific revolutions. See also Felin, T. and Holweg, M. (2024), “Theory Is All You Need: AI, Human Cognition, and Decision Making”, 24 February.
See Brynjolfsson, E. et al. (2017), “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics”, NBER Working Paper Series, No 24001, November.

 
 
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