NEW YORK—The Federal Reserve Bank of New York’s Center for Microeconomic Data today released the January 2025 Survey of Consumer Expectations, which shows that inflation expectations were unchanged at the short- and medium-term horizons, and increased at the longer-term horizon. Commodity price expectations rose across the board, with the expected price change for gas, food, medical care, education, and rent all increasing. Labor market expectations were mixed, with job loss and job finding expectations both rising and unemployment expectations falling to the lowest level since July 2021. Household spending growth expectations also declined in January, hitting the lowest level seen in the last four years.
The main findings from the January 2025 Survey are:
Inflation

Median inflation expectations were unchanged at 3.0% at both the one- and three-year-ahead horizons. Median five-year-ahead inflation expectations rose by 0.3 percentage point to 3.0% in January. This increase was driven primarily by respondents with a high-school education or less. The survey’s measure of disagreement across respondents (the difference between the 75th and 25th percentile of inflation expectations) increased at the one- and five-year horizons and was unchanged at the three-year horizon.
Median inflation uncertainty—or the uncertainty expressed regarding future inflation outcomes—was unchanged at the one-year horizon, declined at the three-year horizon, and increased at the five-year horizon.
Median home price growth expectations rose by 0.1 percentage point to 3.2%. This increase was driven by respondents in the West census region. This series has been moving in a narrow range between 3.0% and 3.3% since August 2023.
Year-ahead commodity price expectations rose across the board, increasing by 0.6 percentage point for the price of gas to 2.6%, 0.6 percentage point for the price of food to 4.6%, 1.0 percentage point for the cost of medical care to 6.8%, 0.2 percentage point for the cost of college to 5.9%, and 0.5 percentage point for rent to 6.0%.

Labor Market

Median one-year-ahead earnings growth expectations increased by 0.2 percentage point to 3.0% in January. This series has been moving within a narrow range between 2.7% and 3.0% since January 2024.
Mean unemployment expectations—or the mean probability that the U.S. unemployment rate will be higher one year from now—decreased by 0.6 percentage point to 34.0%, the measure’s lowest reading since July 2021. The decline was driven by respondents with no college degree, those with an annual income below $100,000, and those above age 40.
The mean perceived probability of losing one’s job in the next 12 months increased by 2.3 percentage points to 14.2%. This increase was broad based across demographic groups, but most pronounced for those over the age of 60. The mean probability of leaving one’s job voluntarily in the next 12 months also increased by 1.7 percentage points to 19.9%. This increase was most pronounced for those with an annual household income below $50,000.
The mean perceived probability of finding a job in the next three months if one’s current job was lost increased by 1.3 percentage points to 51.5%. This increase was driven by those with an annual household income below $100,000.

Household Finance

The median expected growth in household income increased by 0.2 percentage point to 3.0% in January. The series has been moving in a narrow range between 2.8% and 3.1% since August 2023.
Median household spending growth expectations declined by 0.4 percentage point to 4.4%, its lowest reading since January 2021, but remains above pre-pandemic levels. The decline was broad-based across age, income, and education groups.
Perceptions of credit access compared to a year ago improved in January, with the net share of households reporting it is easier versus harder to obtain credit than one year ago increasing. Expectations for future credit availability also improved.
The average perceived probability of missing a minimum debt payment over the next three months decreased by 0.9 percentage point to 13.3%. This series remains above its 12-month trailing average of 13.0%.
The median expected year-ahead change in taxes at current income level increased by 0.2 percentage point to 3.2%, but remains well below its 12-month trailing average of 3.9%.
Median year-ahead expected growth in government debt increased by 0.1 percentage point to 6.0%. This reading is well below the series 12-month trailing average of 8.6%.
The mean perceived probability that the average interest rate on saving accounts will be higher in 12 months decreased by 0.2 percentage point to 25.0%.
Perceptions about households’ current financial situations compared to a year ago deteriorated in January, with the net share of households reporting a worse versus better situation compared to a year ago rising. Similarly, year-ahead expectations about households’ financial situations also deteriorated in January.
The mean perceived probability that U.S. stock prices will be higher 12 months from now increased by 0.5 percentage point to 40.3%.

About the Survey of Consumer Expectations (SCE)

The SCE contains information about how consumers expect overall inflation and prices for food, gas, housing, and education to behave. It also provides insight into Americans’ views about job prospects and earnings growth and their expectations about future spending and access to credit. The SCE also provides measures of uncertainty regarding consumers’ outlooks. Expectations are also available by age, geography, income, education, and numeracy.
The SCE is a nationally representative, internet-based survey of a rotating panel of approximately 1,300 household heads. Respondents participate in the panel for up to 12 months, with a roughly equal number rotating in and out of the panel each month. Unlike comparable surveys based on repeated cross-sections with a different set of respondents in each wave, this panel allows us to observe the changes in expectations and behavior of the same individuals over time. For further information on the SCE, please refer to an overview of the survey methodology here, the interactive chart guide, and the survey questionnaire.
 
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By Marinela-Daniela Filip, Daphne Momferatou and Susana Parraga-Rodriguez | At the heart of the euro area’s competitiveness challenges lies weak productivity growth. The ECB Blog looks at how this makes it more difficult to carry out monetary policy.

While companies in the euro area are getting more productive, they are doing so at a much slower pace than their competitors. Weak productivity growth is putting monetary policy in a difficult situation. When the economy struggles to grow and loses competitiveness this can increase inflationary pressures and reduce the space for monetary policy to manoeuvre. In this post we take a closer look how the loss of competitiveness affects monetary policy, where the issue has come from, and what can be done to reverse the trend.
How exactly does monetary policy interact with productivity?
In general, productivity growth – i.e. how many goods and services can be produced per hour worked – is a key determinant of the overall potential of an economy to grow. That, in turn, affects its natural rate of interest, which is an important factor for effective monetary policy. This is because a central bank raising its policy rate above the natural rate can cool down the economy. Lowering the policy rate below the natural rate can stimulate the economy. At the natural rate of interest, savings and investment are balanced and the economy can grow at its full potential without overheating and pushing inflation up or down too much. At this rate the economy can operate with price stability and full employment – an ideal state that sometimes is referred to as a “goldilocks economy”.
Higher productivity leads to higher potential output, which eventually leads to a higher natural interest rate. This gives a central bank greater scope to stimulate the economy in difficult times via lower rates while still keeping prices stable. It also makes the transmission of monetary policy to the real economy more effective, i.e. policy interest rates translate more directly into how restrictive or loose the financing conditions for people and businesses are.
However, when resources are not allocated efficiently and productivity is low, firms struggle to increase output. This low economic growth makes firms more sensitive to interest rate changes. Then, even small rate hikes by central banks to ensure price stability can further dampen growth, leading to a vicious circle. This is because low growth makes businesses more cautious about investment. Business sentiment may further deteriorate when high uncertainty and geopolitical tensions make energy and other raw materials more expensive. In such a situation, the room for monetary policy to manoeuvre shrinks.
And what about price stability in particular?
When input costs are growing fast, strong productivity growth can help the central bank to contain inflation. How? A sudden increase in input costs such as wages and energy can lead to cost-push inflation if not accompanied by corresponding productivity gains. And when higher prices lead to higher wage demands, this can create a wage-price spiral.[1] That is why the ECB looks at unit labour costs (ULC) when it assesses wage and price developments. ULC are essentially the labour compensation needed to produce one unit of output and are a widely used indicator of competitiveness. Overall, lower ULC are associated with greater competitiveness. ULC developments can be decomposed into the contributions of compensation per employee and productivity. Robust productivity growth can go hand-in-hand with higher wages without compromising competitiveness. Chart 1 shows how the interplay of labour compensation and productivity significantly affects ULC. But production costs can of course also increase due to other factors, such as energy. Likewise, firms may push prices when seeking to increase their profits.
During the post-pandemic recovery, for example, high commodity prices and supply bottlenecks led to strong price increases in the euro area, initially alongside higher profits, requiring the ECB to increase interest rates.[2] Over time, the price increases fed into higher wage demands so that workers could make up for their lost purchasing power. This led to strong ULC increases and weak productivity growth, but they were buffered by higher profits and a wage-price spiral was avoided.

Chart 1
Euro area ULC and components

Sources: Eurostat and ECB calculations.
Note: Data for 2024 refers to the first three quarters of the year. The evolution of unit labour costs (ULC) in 2020 and 2021 was affected by the job retention schemes.

When assessing euro area ULC developments, it is important to also look at what is happening in individual member countries. Differences in competitiveness are normal in a currency union when they reflect temporary adjustments to shocks or catching-up processes. But longer-term divergences can result in the euro area’s single monetary policy becoming less than optimal for individual countries.[3] Countries with their own national currency have the option to regain competitiveness by lowering policy interest rates and devaluing their currency, but this option is no longer available in a currency union. The ECB sets its monetary policy by looking at the euro area as a whole and thus cannot address the specific needs of each country. That’s why it’s important to close competitiveness and inflation differentials between euro area countries. It doesn’t just strengthen the European economies; it also helps the ECB to implement its monetary policy more effectively.
Why is euro area productivity falling behind anyway?
Productivity in the euro area has been slowing consistently over the past three decades, falling behind that of the United States (Chart 2). This is due in part to inefficient work and ineffective technologies and the slow increase in machinery and equipment used per worker. Lacklustre productivity compared to other economies can also be traced to the weaker performance of European “frontier firms”. These are the most technologically advanced and productive companies within a particular industry, especially in the information and communication technology sectors. This relative underperformance is closely linked to lower firm dynamism (generally the rate at which firms enter, grow and exit the market), less investment and breakthrough innovation, and slower adoption of digital technologies.[4]

Chart 2
Labour productivity gap between the euro area and the United States

(USD 2010, purchasing power parity per hours worked)

Sources: Bergeaud, A., Cette, G. and Lecat, R. (2016), “Productivity Trends in Advanced Countries between 1890 and 2012”, The Review of Income and Wealth, September; also long-term productivity database.
Notes: The euro area represents the aggregation of Germany, Spain, France, Italy, the Netherlands, and Finland. As explained in the paper, in 2012 these six countries together represented 84% of euro area GDP. Last observation available is 2022.

So, what can we do?
Low productivity growth in the euro area and the loss of competitiveness is amplified by additional challenges already on the horizon. The recent energy crisis demonstrated why Europe needs to be competitive, resilient and less dependent on other regions. Mario Draghi’s report on the future of European competitiveness and Enrico Letta’s report on empowering the Single Market rightly stress the urgent need for policies to boost competitiveness and resilience. Coordinated and combined efforts are needed to develop and swiftly adopt concrete policy proposals such as the ones included in the recently published Competitiveness Compass for the EU. As detailed above, a more competitive economy also matters for the ECB, as it can support monetary policy in keeping prices stable, stabilising the economy, and thereby increasing the living standards of all euro area citizens.
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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For topics relating to banking supervision, why not have a look at The Supervision Blog?

See I. Schnabel (2023). The risks of stubborn inflation. Speech at the Euro50 Group conference on “New challenges for the Economic and Monetary Union in the post-crisis environment”.
E. Hahn (2023). “How have unit profits contributed to the recent strengthening of euro area domestic price pressures?” ECB Economic Bulletin Issue 4/2023.
M.D. Filip, D. Momferatou and R. Setzer (2023). “Inflation and competitiveness divergences in the euro area countries” ECB Economic Bulletin Issue 4/2023
See M.D. Filip, D. Momferatou and S. Parraga-Rodriguez (2025). “European competitiveness: the role of institutions and the case for structural reforms” ECB Economic Bulletin Issue 1/2025

 
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Prepared by Claus Brand, Noëmie Lisack and Falk Mazelis
Estimates of the natural rate of interest, or r*, show trends that are of fundamental significance for monetary policy, but are subject to important caveats. r* is defined as the real rate of interest that is neither expansionary nor contractionary. Measures of r* are typically constructed as an equilibrium value towards which interest rates tend to gravitate in the medium to long term, as aggregate saving and investment imbalances abate and the inflationary or disinflationary pressures that may have developed as a consequence of those imbalances dissipate. These measures are also informative regarding the risk of short-term interest rates becoming constrained by their effective lower bound. However, available measures of r* are fraught with measurement and model specification challenges and are highly uncertain, reflecting, to different degrees, model, parameter, filter and real-time data uncertainty. While estimates of r* provide complementary information for monetary policy decisions and aid communication on the stance of monetary policy, these cannot be seen as a mechanical gauge of appropriate monetary policy at any point in time. In conducting monetary policy, there is no alternative to taking decisions on the basis of a comprehensive analysis of the data and their macroeconomic implications. In the euro area, in particular, the focus of such an assessment is threefold: the inflation outlook in light of the incoming economic and financial data, the dynamics of underlying inflation and the strength of monetary policy transmission.
Ranges of point estimates from different r* models indicate a very high degree of model uncertainty. Model uncertainty is the variability in estimates of r* that arises from using different models. Since r* is unobservable, economists rely on a range of models to estimate it. These models may incorporate different definitions of the benchmark rate, for example the instrument used by the central bank to conduct monetary policy. Different models may also rely on alternative determinants, such as measures of economic slack or the time horizons over which inflation eventually stabilises. Models can be clustered by type of measure – such as slow-moving equilibrium measures and cyclical inflation-stabilising measures. Slow-moving r* measures are anchored to long-run economic trends but may not capture short-term fluctuations. Cyclical r* measures reflect short-term dynamics and exhibit inflation-stabilising properties but can be sensitive to temporary shocks and are less stable. Balancing these trade-offs is challenging.

Chart A
Real natural rates of interest in the euro area

(percentages per annum)

Sources: ECB calculations, Eurosystem estimates, Federal Reserve Bank of New York and Consensus Economics.
Notes: Estimates displayed for survey-based, term structure-based and semi-structural measures are based on the same measures referred to in the box entitled “Estimates of the natural interest rate for the euro area: an update”, Economic Bulletin, Issue 1, ECB, 2024. The DSGE-based estimate is not included here. HLW-based measures, which do not ensure a stationary real rate gap, are displayed separately from other semi-structural measures. The latest observations are for the third quarter of 2024 for Holston, Laubach and Williams (2023), Grosse-Steffen, Lhuissier, Marx and Penalver (mimeo), and Carvalho (2023); and for the fourth quarter of 2024 for all other estimates.

Chart A shows a wide range of point estimates for the real natural rate. Following a modest post-pandemic increase, the updated range of point estimates of the real natural rate of interest for the euro area has remained broadly unchanged since the end of 2023 and is consistent with the estimates documented in Issue 1, 2024 of the Economic Bulletin.[1] We distinguish between four categories of measures. The median from survey-based measures is indicated by the red line. Measures shown by the dark blue area are derived from models of the term structure of interest rates. Those derived from semi-structural models are shown by the dark yellow area. Finally, three estimates derived from the Holston-Laubach-Williams (HLW) model are shown separately, by the light yellow area. The latter measures are not available for the fourth quarter of 2024.[2] Taking only the measures shown in the dark blue and dark yellow areas that were possible to update to the very end of 2024, the most recent estimates of real r* span a range between -½% and +½% (see the dark blue and dark yellow intervals corresponding to the fourth quarter of 2024 in Chart A).[3] The way to translate those measures into their nominal counterparts is measure-specific. Some of the models produce both real and nominal versions of r*, while others estimate only one version. In the latter case, the missing value must be derived by adding or subtracting the ECB’s 2% medium-term inflation target or the model-consistent medium-term inflation expectations from the model estimate. When the three estimates derived from versions of the HLW model are factored in, the range of real r* is -½% to 1% and the corresponding nominal range is 1¾% to 3%.[4] Referring only to those measures included in the dark blue and dark yellow areas for which an update to the end of 2024 is available, the estimates of the nominal r* from the most recent interval range between 1¾% and 2¼%. Given the estimation uncertainties highlighted in this box, such ranges should be viewed as merely indicative.
Natural rate estimates are further surrounded by uncertainties in model parameters. Point estimates typically display an outcome that is conditional on a single estimate of the model parameters – commonly the “most likely” value. However, the econometric methods used to estimate the model parameters generate a whole set of plausible alternative estimates. Bayesian estimation techniques, for example, concentrate on the probability distributions of parameters rather than on their fixed-point estimates. Embracing this approach allows the use of a distribution of values for each model’s r* estimate, which reflects the statistical uncertainty affecting the estimation of that model’s parameters. Taking one semi-structural model whose point estimate is included in the range shown in Chart A (the model by Brand and Mazelis, 2019), it can be seen that the parameter uncertainty surrounding each of the point estimates for r* can be quite large (Chart B, dark blue range).

Chart B
Parameter and filter uncertainty around the real natural rate estimates in the model by Brand and Mazelis

(percentages per annum)

Source: ECB calculations.
Notes: Estimates are based on Brand, C. and Mazelis, F., “Taylor-rule consistent estimates of the natural rate of interest”, Working Paper Series, No 2257, ECB, Frankfurt am Main, March 2019 (extended to include stochastic volatility in the output gap, a long-term interest rate, asset purchase effects and the effective lower bound). We employ the RISE toolbox for parameter estimation and regime-switching Kalman filtering, which enables the extraction of covariance matrices of unobserved states (see Maih, J. “Efficient perturbation methods for solving regime-switching DSGE models,” Working Paper, 01/2015, Norges Bank, 16 January 2015). Parameter and filter uncertainties are displayed as 95% uncertainty bands, calculated following the methods for statistical inference with the Kalman filter described in Chapter 13.7 of Hamilton, J.D., “Time Series Analysis”, Princeton University Press, 1994. Since computing the maximum likelihood estimate directly is impractical in our setting, we use the mode of the posterior distribution as an approximation. The filter uncertainty is based on the regime-specific covariance matrix of unobserved states from the predominant regime in the model, which features low volatility in the output gap and a policy rate that follows the Taylor rule. Considering regime-specific covariances or joint covariance matrices across different regimes would further enlarge the uncertainty ranges.

An additional source of uncertainty comes from the fact that r* is an unobservable variable that must be inferred from observable data – a challenge known as filtering. Since r* cannot be observed directly, we must deduce it from the economic data that we can measure. Accordingly, additional filter uncertainty is associated with obtaining an informative signal from the data. Like parameter uncertainty, the filter uncertainty range is time-varying. Cumulatively, parameter and filter uncertainty together can span up to several percentage points (Chart B, light blue range) even for a single model.[5]
Different data samples and revisions in backdata amplify the impact of filter uncertainty, leading to large ex post variations in in-sample point estimates of r* and adding yet another layer of uncertainty. Model-specific estimates of r* can vary significantly when observations are added or backdata are revised.[6] Chart C illustrates the considerable extent of this sensitivity using the widely referenced approach of Holston, Laubach and Williams (2023). Over time, as updates become available, revisions in previously obtained estimates can be as large as 1 percentage point. Most recently, end-of-sample point estimates have varied by similar magnitudes from one quarter to the next.

Chart C
Vintages of point estimates of the real natural rate of interest for the euro area from the model by Holston, Laubach and Williams

(percentages per annum)

Source: Federal Reserve Bank of New York.
Note: See Holston, K., Laubach, T. and Williams, J.C., “Measuring the Natural Rate of Interest after COVID-19”, Federal Reserve Bank of New York Staff Reports, No 1063, June 2023. The latest estimate, published for the third quarter of 2024, is also displayed at the lower end of the light yellow range in Chart A.

Despite the uncertainties involved, tracking broad movements in the natural rate over time provides qualitative insights into underlying economic trends. Notwithstanding the uncertainties associated with estimating r*, its trends contain information about developments in saving-investment imbalances that may create inflationary or disinflationary pressures, as well as about the extent to which the short-term interest rate might become constrained by the lower bound. The persistently low estimates of r* over the period 2015-22 displayed in Chart A, for example, reflect the persistent weakness in aggregate demand at the time and the low inflationary pressures that it generated. While in the post-pandemic environment estimates suggest some increase in r*, current estimates continue to be measurably below those prevailing before the global financial crisis, pointing to still lingering lower-bound risks in the event of sufficiently large disinflationary shocks.
The inherent uncertainties as well as conceptual shortcomings limit the usefulness of available natural rate estimates for conducting monetary policy in real time. Because of the multiple types of uncertainty and the focus on the short-term interest rate instrument – as opposed to broader measures of financing conditions, which can have a stronger impact on spending – the usefulness of r* as an indicator to support the calibration of the monetary policy stance is greatly limited, making it difficult to use as a rate-setting norm at policy meetings. Many models used do not construe r* as stabilising inflation in line with target but as merely indicating levels towards which interest rates gravitate over the longer term. As a function of historical shocks, such “equilibrium” interest rate measures are also largely backward-looking. By the time that equilibrium level is expected to be reached, the economy may well have already been exposed to further shocks, possibly causing the equilibrium rate of interest to drift and requiring monetary policy to offset these shocks. Furthermore, the connection between an r* defined in terms of the short-term interest rate instrument of monetary policy and the broader economy may itself change, as monetary policy transmission depends on a broader set of financing conditions – including the cost and availability of bank credit, and prices in a range of asset markets. The link between the short-term interest rate instrument and broader indicators for monetary policy is state-contingent and typically not stable. Accounting for these conceptual shortcomings and uncertainties is crucial for interpreting r* estimates.

The range reported in Chart A is also broadly consistent with the set of estimates published recently by the Bank for International Settlements. See Benigno, G., Hofmann, B., Nuño, G., Sandri, D, “Quo vadis, r*? The natural rate of interest after the pandemic”, BIS Quarterly Review, March 2024, pp. 17-30.
The HLW estimates are also shown separately, by the light yellow area, because of their methodological differences with respect to other semi-structural measures, shown by the dark yellow area. In particular, the family of HLW models (see Holston et al., 2017) posits a backward-looking relationship between the real interest rate gap, economic slack and inflation. Because of the inclusion of an accelerationist Phillips curve, the resulting r* estimate stabilises inflation around a random drift, i.e. an inflation level not necessarily close to the central bank’s inflation target. HLW-based approaches do not typically include an interest rate equation and thus have no mechanism to support a stationary real rate gap. Resulting estimates of persistently negative real rate gaps in the euro area are, however, challenging to reconcile with the inflation shortfalls observed over the period between the global financial crisis and the pandemic. Moreover, the marked flatness of the estimated Phillips and investment-savings curves amplifies filtering uncertainty, thus generating an “imprecision of the estimate” where, as acknowledged by Holston et al. (2017), “the average standard error for r* is very large, … and hence r* is barely identified”. On the theoretical foundations and econometrics of HLW, see Laubach, T. and Williams, J.C., “Measuring the Natural Rate of Interest”, The Review of Economics and Statistics, Vol. 85, No 4, November 2003, pp. 1063-1070, and Holston, K., Laubach, T. and Williams, J.C., “Measuring the natural rate of interest: International trends and determinants”, Journal of International Economics, Elsevier, Vol. 108, Supplement 1, May 2017, pages S59-S75.
Values reported in this box for both real and nominal r* are rounded to the nearest 25 basis point increment.
As a cross-reference, the HLW estimate for the euro area in the third quarter of 2024 published by the Federal Reserve Bank of New York was at 1.84% in nominal terms. For other HLW-type specifications tracked by the Eurosystem, see the approach explained by Carvalho, A., “The euro area natural interest rate – Estimation and importance for monetary policy”, Banco de Portugal Economic Studies, Vol. IX, No. 3, July 2023.
The wide dispersion is due in part to the relatively flat aggregate demand and Phillips curves embedded in semi-structural models used for estimating r* and is not specific to the Brand and Mazelis model. In comparison, the HLW estimates from the third quarter of 2024 mentioned earlier display an additional observability challenge, leading to a cumulative parameter and filter uncertainty range that is greater by an order of magnitude. Given uncertainty bands as large as +/- 10 percentage points, it is not clear whether the HLW estimate is ever different from 0% or any other interest rate level observed throughout the sample period. Fiorentini et al. (2018) demonstrate that flat aggregate demand and Phillips curves significantly increase filter uncertainty. See Fiorentini, G., Galesi, A., Pérez-Quirós, G. and Sentana, E., “The rise and fall of the natural interest rate”, Working Papers, No 1822, Banco de España, 2018.
All unobserved variable estimates suffer from the issue of data revisions and differences in data vintages. As highlighted by Orphanides and van Norden (2002), real-time estimates of the output gap are particularly unreliable as a result of substantial data revisions. See Orphanides, A. and van Norden, S., “The Unreliability of Output-Gap Estimates in Real Time”, The Review of Economics and Statistics, Vol. 84, No 4, November 2002, pp. 569-583.

 
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The gap between development financing needs and available resources could swell to USD 6.4 trillion by 2030 without a major overhaul of the financing system, according to a new OECD report.
The Global Outlook on Financing Sustainable Development 2025: Towards a more resilient and inclusive architecture shows that although total external finance to developing countries reached USD 5.24 trillion in 2022, it remained significantly below the USD 9.24 trillion estimated to be required annually to achieve the 2030 Agenda.
Financing needs have risen by 36% between 2015 and 2022, due in large part to climate change and geopolitical uncertainty, but resources provided have only increased by 22% over the same period – a 60% gap. Without an agreement this year on major reform of the international financing architecture, the report estimates that the financing gap will balloon to USD 6.4 trillion by 2030.

“The development financing gap is not insurmountable. The challenge lies in mobilising resources at scale, channelling financial assets into transformative investments such as clean energy transitions and sustainable infrastructure,” OECD Secretary-General Mathias Cormann said.
The report, published ahead of the upcoming Conference on Financing for Development in Seville, calls for updating the financing framework for post-2025 to redirect the capital available globally, starting with balancing ambition and practicality in addressing sustainable development priorities. Inclusive governance and policy coherence are critical to overcoming the hurdles, as disparities in decision-making structures and resource allocation undermine global trust and co-operation.
Despite a rebound from the COVID-19 pandemic, financing for sustainable development is still insufficient to meet growing needs, the report shows. While official development assistance (ODA) hit a record of USD 223.3 billion in 2023 among members of the OECD Development Assistance Committee (DAC), further commitments are required to ensure effective support to fill the needs of partner countries, particularly for investments in clean energy transition.
Remittances have been the dominant source of external financial flows to developing countries, a more than 30% increase since 2015 to reach USD 476 billion in 2023. However, transfer fees remain twice the level of the Sustainable Development Goals (SDGs) target of 3%, resulting in USD 16 billion in annual losses to households sending and receiving the money.
Strengthening domestic resource mobilisation is key for effective state functioning, but the tax-to-GDP ratio in low-income countries remains an average 11.44% in 2022, below the recommended 15% threshold. Meanwhile, debt levels in developing countries continue to rise. Between 2015 and 2024, the number of countries in debt difficulty and at high risk of debt distress increased from 16 to 24 and from three to 11, respectively.
As the economic gap between rich and poor nations has diverged rather than converged, the report calls for actions to renew the development financing system to better align money for sustainable development. For instance, there are already USD 461 trillion worldwide in financial assets — sufficient to cover the gap 115 times over — but the misalignment of these resources, including the USD 1.53 trillion spent on subsidising fossil fuels in 2022, needs to be redirected to achieve the 2030 Agenda.
To enhance accountability and transparency in resource allocation, the report also urges strengthening the global financing for development monitoring system to restore trust among all countries.
The Global Outlook provides a basis for the upcoming discussions at the Fourth International Conference on Financing for Development in Seville, where from 30 June countries will negotiate how to reform the global financing architecture for the implementation of the SDGs.
Download the report.

 
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The Federal Reserve Board on Wednesday released the hypothetical scenarios for its annual stress test, which helps ensure that large banks can lend to households and businesses even in a severe recession. Additionally, the Board released two hypothetical elements designed to probe different risks through its “exploratory analysis” of the banking system. The exploratory analysis will not affect bank capital requirements.
The Board’s annual stress test evaluates the resilience of large banks by estimating losses, net revenue, and capital levels—which provide a cushion against losses—under hypothetical recession scenarios that extend two years into the future. This year, 22 banks will be tested against a severe global recession with heightened stress in both commercial and residential real estate markets, as well as in corporate debt markets. The scenarios are not forecasts and should not be interpreted as predictions of future economic conditions.
In the 2025 stress test scenario, the U.S. unemployment rate rises nearly 5.9 percentage points, to a peak of 10 percent. The unemployment rate increase is accompanied by severe market volatility, a widening of corporate bond spreads, and a collapse in asset prices, including about a 33 percent decline in house prices and a 30 percent decline in commercial real estate prices.
Large banks with substantial trading or custodial operations are also required to incorporate a counterparty default scenario component to estimate potential losses from the unexpected default of the firm’s largest counterparty amid an acute market shock. In addition, banks with large trading operations will be tested against a global market shock component that primarily stresses their trading and related positions.
The table below shows the components of the annual stress test that apply to each bank, based on data as of the third quarter of 2024.
This year’s exploratory analysis includes two separate hypothetical elements that will assess the resilience of the banking system to a wider range of risks. One of the hypothetical elements examines how banks would react to credit and liquidity shocks in the non-bank financial institution sector during a severe global recession.
The second element of the exploratory analysis includes a market shock that will be applied only to the largest and most complex banks. This shock hypothesizes the failure of five large hedge funds with reduced global economic activity and higher inflation.
The exploratory analysis is distinct from the stress test and will explore additional hypothetical risks to the broader banking system, rather than focusing on firm-specific results. The Board will publish aggregate results for the exploratory analysis alongside the annual stress test results in June 2025.
As the Board previously announced, it plans to take steps soon to reduce the volatility of stress test results and begin to improve model transparency in the 2025 stress test. Additionally, it intends to begin the public comment process on its comprehensive changes to the stress test this year.

Make Full Screen

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Subject to global market shock
Subject to counterparty default

American Express Company

Bank of America Corporation
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The Bank of New York Mellon Corporation

X

Barclays US LLC
X
X

BMO Financial Corp.

Capital One Financial Corporation

The Charles Schwab Corporation

Citigroup Inc.
X
X

DB USA Corporation
X
X

The Goldman Sachs Group, Inc.
X
X

JPMorgan Chase & Co.
X
X

M&T Bank Corporation2

Morgan Stanley
X
X

Northern Trust Corporation

The PNC Financial Services Group, Inc.

RBC US Group Holdings LLC2

State Street Corporation

X

TD Group US Holdings LLC

Truist Financial Corporation

UBS Americas Holding LLC

U.S. Bancorp

Wells Fargo & Company
X
X

1. The information listed in this table is based on third quarter 2024 data. Return to text
2. M&T Bank Corporation and RBC US Group Holdings LLC elected to opt into the 2025 stress test. Return to text

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Blog post by By Stefan Gebauer, Georgios Georgiadis, Fédéric Holm-Hadulla and Thomas Kostka | The monetary policies of the ECB and the US Federal Reserve are not always in sync. But how does the Fed’s policy affect the euro area economy? This ECB Blog looks at how monetary policy in the United States travels across the Atlantic and what this means for the ECB.
The global economy is interconnected. Central banks’ monetary policies therefore often affect other economies. These “monetary policy spillovers” are particularly relevant for economies as closely linked as the United States and the euro area. Spillovers from US monetary policy initially work in the opposite direction to ECB monetary policy, but then later in the same direction. For instance, a surprise tightening of US policy leads to an initial increase in euro area inflation as the euro weakens. However, over time, tighter US monetary policy drags down euro area inflation much like tighter ECB policy would.
In more detail, when the Fed unexpectedly raises interest rates to curb inflation and economic activity, euro area inflation increases on average over the three months following the announcement. This is because, following a surprise tightening in the United States, the euro immediately weakens against the dollar. And that makes imported goods more expensive. This effect is particularly relevant for euro area imports priced in US dollars, such as oil and other commodities. The weaker euro also cheapens euro area exports to the United States. This, in turn, supports economic activity and prices in the euro area slightly. Hence, over the short run, the spillovers from a surprise US monetary policy tightening on the euro area economy work in the opposite direction to the effects of a monetary policy tightening by the ECB. This is visible in the left-hand parts of each subplot of Chart 1.
Over time, however, the initial inflationary impact of higher policy rates in the US is compensated by broader disinflationary pressures. Tighter US financing conditions spill over to the euro area through global financial markets, leading to a slowdown in euro area economic activity. At the same time, the Fed’s tightening also weighs on economic activity in the US over time, with lower demand by US households and firms also implying lower imports from the euro area. Overall, the medium-term effects of US monetary policy on the euro area therefore resemble the dampening impact of similar actions by the ECB. This means that – while the immediate impacts might differ – in the medium term US monetary policy pushes the euro area economy in the same direction as euro area policy changes would. These effects can be seen for the same variables on the right-hand parts of each subplot in Chart 1.

Chart 1
Impact of euro area (EA) and US monetary policy shocks on the euro area economy

Source: ECB staff calculations.
Notes: Impulse responses (IR) for selected horizons, scaled to 25 bps reaction in the 3-months OIS rate and the 3-months US Treasury bill rate, respectively, derived by local projections (Jorda (2005)). ECB and Fed monetary policy shocks are identified via high-frequency movements in short-term market rates over a narrow window (ca. 135 minutes) around ECB and Fed policy meetings applying “poor man’s” sign restrictions as in Jarocinski and Karadi (2020). Euro area shocks are extracted from the policy-event database of Altavilla et al. (2019). The euro area outcome variables are: (i) the Harmonised Index of Consumer Prices (left panel), (ii) industrial production (right panel). All variables are in log-levels. The local projections control for the lagged dependent variables, the Euro-Dollar exchange rate, euro area exports to and euro area imports from the US, the euro area unemployment rate, log-levels of US industrial production and the US Consumer Price Index, the euro area Composite Indicator of Systemic Stress (CISS), the IMF Commodities Price Index, the GDP-weighted 10-year yields on euro area sovereign bonds, the euro area 3-months OIS rate, and the other jurisdiction’s monetary policy shocks. Moreover, forward dummies for the COVID-19 pandemic (from March 2020 to April 2023) and the war in Ukraine (from March 2022 onwards) enter the model at the same horizon as the dependent variables. All data are at monthly frequency for the 2002m1-2024m5 period. Standard errors are derived from the Newey and West (1987) estimator to account for autocorrelation and heteroskedasticity. Results are comparable to estimates presented in Ca’ Zorzi et al. (2023). Additional details are available in Gebauer et al. (2025).

How did we come up with these findings?
We used an empirical method called local projections to analyse the impact of US monetary policy surprises, or “shocks”, on the euro area. Shocks correspond to the change in monetary policy that goes beyond the regular response of a central bank to economic developments. Studying such shocks provides clearer insights into the cause-and-effect relationships of policy with subsequent economic developments. The monetary policy shocks are derived by measuring financial market movements in a tight time window around monetary policy decisions on either side of the Atlantic. Having isolated the shocks, we integrated them into a local projections model that – based on historical regularities – tells us how economic variables, such as euro area inflation and industrial production, respond to US and euro area monetary policy shocks over time. This approach helps distinguish between short-term and medium-term effects and thus provides a nuanced understanding of how policy impacts unfold.
And why is it important?
Our findings have important implications for the conduct of the ECB’s monetary policy in the presence US monetary policy spillovers. Simply mimicking a surprise move in US policy rates might avert short-term spillovers, such as higher (or lower) inflation. Over the medium term, however, this would add to the tightening (or easing) impulse from abroad which, all else equal, would create risks that the ECB in turn undershoots (or overshoots) its domestic inflation target. Accounting for such medium-term spillovers is important because of the explicit medium-term orientation embedded in the ECB’s inflation target.
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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For topics relating to banking supervision, why not have a look at The Supervision Blog?
References
Altavilla, C., Brugnolini, L., Gürkaynak, R. S., Motto, R., and Ragusa, G. (2019). Measuring euro area monetary policy. Journal of Monetary Economics, 108:162–179.
Ca’ Zorzi, M., Dedola, L., Georgiadis, G., Jarocinski, M., Stracca, L., and Strasser, G. (2023). Making waves: Monetary policy and its asymmetric spillovers in a
globalised world. International Journal of Central Banking, 19(2):95–144.
Gebauer, S., Georgiadis, G., Holm-Hadulla, F., and Kostka, T. (2025). Macroeconomic effects of (de-) synchronized monetary policy shocks. mimeo.
Georgiadis, G. and Jarocinski, M. (2023). Global spillovers from multi-dimensional us monetary policy. ECB Working Paper Series, 2881.
Jarocinski, M. and Karadi, P. (2020). Deconstructing monetary policy surprises—the role of information shocks. American Economic Journal: Macroeconomics, 12(2):1–43.
Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1):161–182.
Newey, W. K. and West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55:703– 708.
 
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Blog post by Christine Lagarde, President of the European Central Bank, and Ursula von der Leyen, President of the European Commission | Remaining competitive is fundamental for Europe’s future. We need faster economic growth and higher productivity to protect the quality of life for Europeans – from their jobs and incomes to their security and welfare.
Europe must act. Our competitiveness is at risk. While a global revolution in artificial intelligence unfolds, the EU could find itself on the sidelines. Our traditional manufacturing champions are losing global market share. Geopolitical shifts are turning dependencies into vulnerabilities and burdening our companies with high energy prices.
Europe must and will find its place in this new world. The prospects for our continent are better than they might seem. The EU has strengths on which it can build – and it has a plan to fix its weaknesses.
Europe has strong economic fundamentals. We have institutions governed by the rule of law, and an independent central bank committed to price stability. Inflation is returning to the ECB’s 2% target, allowing borrowing costs to fall. Public debts and deficits are lower than in other major economies.
Europe also has the necessary ingredients to catch up in the technological race. The EU turns out almost as many STEM graduates per million inhabitants as the United States. That talent produces a lot of ideas: Europe’s share in global patent grants is close to that of the United States. And we have the money to finance them, with households saving around €1.3 trillion every year.
We have an opportunity to bring down energy prices in a lasting way. The shift to secure, low-cost clean energy sources is on track: by 2030, over 40% of our energy consumption will come from renewables. And we are well placed to become a global hub for clean tech innovation, especially as some countries strike out in a different direction.
While others must cut their dependencies by building up domestic capacity, the EU can choose from a broader set of options owing to its unique position in global trade. We are the top trading partner for over 70 nations and we continue to strike new agreements, most recently with 400 million Latin Americans. And in a deal with the EU, what you see is what you get.
But these strengths are meaningless if Europe is hamstrung by its weaknesses. We need profound change on three fronts.
First, we need to make the EU an easier place for innovative companies to grow. Only one-third of university patents in Europe are commercialised, while companies that try to scale up in our Single Market face many internal barriers. Despite our savings, entrepreneurs lack access to risk capital, because capital markets are still too fragmented.
Second, we need to make Europe a better place to invest. Two out of three EU companies say that regulation is a key obstacle to investment, while just 14% of them are using AI. Firms still face long permitting procedures, onerous reporting requirements and diverging enforcement of digital rules.
Third, we need to make doing business in Europe cheaper, especially in terms of energy costs. While the shift to renewables creates good jobs and strengthens energy security and independence, it also comes with greater intermittency and greater energy losses through curtailment. For the benefits of decarbonisation to show up in companies’ bills, we need massive investment in grids and storage and smarter market design.
Europe has got the message. This week, the European Commission presented its Competitiveness Compass which sets out ambitious proposals to address these shortcomings. From now on, the EU will strive not only to lower the barriers facing companies, but also to ensure that they have the resources they need to thrive here – be it finance, compute, energy or skills.
For example, the Commission will propose a so-called “28th regime” for innovative companies, allowing them to benefit from a single legal framework across the EU for aspects of corporate law, insolvency, labour law and taxation. It will launch a plan for a Savings and Investments Union, which will ensure that innovative companies can find the financial backing they need.
The EU will also give companies access to our world-leading network of supercomputers. This will help develop new advanced technologies and spread AI faster among established champions. The ECB will play its part too by keeping Europe at the forefront of digital payment technologies, including through the digital euro project.
In parallel, the regulatory burden will be lightened by an unprecedented simplification effort, starting next month. This will include a far-reaching simplification of legislation on sustainable finance reporting and due diligence. And energy prices will be brought down through a range of measures to integrate markets, increase contracted energy and reduce taxes.
This is only a snapshot of what lies ahead. Companies and households want to see action – and a wave of actions are coming. We can no longer squander our strengths with self-imposed handicaps. There is too much at stake. We are ready to do whatever is necessary to bring Europe back on track.
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As of Sunday, 2 February, the first rules under the Artificial Intelligence Act (AI Act) started to apply. This includes the AI system definition, AI literacy, as well as a very limited number of prohibited AI use cases outlined in the AI Act that pose unacceptable risks in the EU.
To facilitate innovation in AI, the Commission will publish guidelines on AI system definition. This aims to assist the industry in determining whether a software system constitutes an AI system.
The Commission will also release a living repository of AI literacy practices gathered from AI systems’ providers and deployers. This will encourage learning and exchange among them while ensuring that users develop the necessary skills and understanding to effectively use AI technologies.
To help ensure compliance with the AI Act, the Commission will publish guidelines on the prohibited AI practices that are posing unacceptable risks to citizens’ safety and fundamental rights.
These guidelines will explain the legal concepts and provide practical use cases, based on stakeholder input. They are not binding and will be updated as necessary. The Commission has launched several initiatives to promote innovation in AI, from the AI innovation package supporting startups and SMEs to the upcoming AI Factories which will provide access to the massive computing power that start-ups, industry and researchers need to develop their AI models and systems.
More information and the guidelines will be available here.
(For more information: Thomas Regnier — Tel.: + 32 2 299 10 99; Nika Blazevic — Tel. + 32 2 299 27 17) 
 
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Today, the Commission presents the Competitiveness Compass, the first major initiative of this mandate providing a strategic and clear framework to steer the Commission’s work.
The Compass sets a path for Europe to become the place where future technologies, services, and clean products are invented, manufactured, and put on the market, while being the first continent to become climate neutral.
Over the last two decades, Europe has not kept pace with other major economies due to a persistent gap in productivity growth. The EU has what is needed to reverse this trend with its talented and educated workforce, capital, savings, Single Market, unique social infrastructure, provided it acts urgently to tackle longstanding barriers and structural weaknesses that hold it back.
President of the European Commission, Ursula von der Leyen, said: “Europe has everything it needs to succeed in the race to the top. But, at the same time, we must fix our weaknesses to regain competitiveness. The Competitiveness Compass transforms the excellent recommendations of the Draghi report into a roadmap. So now we have a plan. We have the political will. What matters is speed and unity. The world is not waiting for us. All Member States agree on this. So, let’s turn this consensus into action.”
Three core areas for action: innovation, decarbonisation and security
The Draghi Report identified three transformational imperatives to boost competitiveness, and the Compass sets out an approach and a selection of flagship measures to translate each of these imperatives into reality:

Closing the innovation gap: The EU must reignite its innovation engine. We want to create a habitat for young innovative start-ups, promote industrial leadership in high growth sectors based on deep technologies and promote the diffusion of technologies across established companies and SMEs. In this respect, the Commission will propose ‘AI Gigafactories’ and ‘Apply AI‘ initiatives to drive development and industrial adoption of AI in key sectors. It will table action plans for advanced materials, quantum, biotech, robotics and space technologies. A dedicated EU Start-up and Scale-up Strategy will address the obstacles that are preventing new companies from emerging and scaling up. A proposal for a 28th legal regime will simplify applicable rules, including relevant aspects of corporate law, insolvency, labour and tax law, and reduce the costs of failure. This will make it possible for innovative companies to benefit from one single set of rules wherever they invest and operate in the Single Market.

A joint roadmap for decarbonisation and competitiveness: The Compass identifies high and volatile energy prices as a key challenge and sets out areas for intervention to facilitate access to clean, affordable energy. The upcoming Clean Industrial Deal will set out a competitiveness-driven approach to decarbonisation, aimed at securing the EU as an attractive location for manufacturing, including for energy intensive industries, and promoting clean tech and new circular business models. An Affordable Energy Action Plan will help bring down energy prices and costs, while an Industrial Decarbonisation Accelerator Act will extend accelerated permitting to sectors in transition. In addition, the Compass foresees tailor-made action plans for energy intensive sectors, such as steel, metals, and chemicals, sectors which are the backbone of the European manufacturing system, but are the most vulnerable in this phase of the transition.

Reducing excessive dependencies and increasing security. The EU’s ability to diversify and reduce dependencies will hinge on effective partnerships. The EU already has the largest and fastest growing network of trade agreements in the world covering 76 countries that account for almost half of the EU’s trade. To keep diversifying and strengthening our supply chains, the Compass refers to a new range of Clean Trade and Investment Partnerships to help secure supply of raw materials, clean energy, sustainable transport fuels, and clean tech from across the world. Within the internal market, the review of the Public Procurement rules will allow for the introduction of a European preference in public procurement for critical sectors and technologies.

Five horizontal enablers for competitiveness
The three pillars are complemented by five horizontal enablers, which are essential to underpin competitiveness across all sectors:

Simplification: This enabler aims at reducing drastically the regulatory and administrative burden. It also involves a systematic effort to make procedures for accessing EU funds and getting EU administrative decisions simpler, faster, and lighter. The upcoming Omnibus proposal will simplify sustainability reporting, due diligence, and taxonomy. Furthermore, the Commission will facilitate doing business for thousands of small mid-cap companies. The Compass sets a target of cutting by at least 25% the administrative burden for firms and by at least 35% for SMEs.

Lowering barriers to the Single Market: For 30 years, the Single Market has been Europe’s tried and tested engine for competitiveness. To improve its functioning across all industries, a Horizontal Single Market Strategy will modernise the governance framework, removing intra-EU barriers and preventing the creation of new ones. In addition, the Commission will take the opportunity to make standard-setting processes faster and more accessible, in particular for SMEs and start-ups.

Financing competitiveness. The EU lacks an efficient capital market that turns savings into investments. The Commission will present a European Savings and Investments Union to create new savings and investment products, provide incentives for risk capital, and ensure investments flow seamlessly across the EU. A refocused EU budget will streamline access to EU funds in line with EU priorities.

Promoting skills and quality jobs. The foundation of Europe’s competitiveness is its people. To ensure a good match between skills and labour market demands, the Commission will present an initiative to build a Union of Skills focusing on investment, adult and lifelong learning, future-proof skills creation, skill retention, fair mobility, attracting and integrating qualified talent from abroad and the recognition of different types of training to enable people to work across our Union.

Better coordination of policies at EU and national level. The Commission will introduce a Competitiveness Coordination Tool, which will work with Member States to ensure implementation at EU and national level of shared EU policy objectives, identify cross-border projects of European interest, and pursue related reforms and investments. In the next Multiannual Financial Framework, a Competitiveness Fund will replace multiple existing EU financial instruments with similar objectives, providing financial support to the implementation of actions under the Competitiveness Coordination Tool.

Background
On 27 November 2024, President von der Leyen announced a Competitiveness Compass as the first major initiative of the Commission in this mandate, building on the Draghi report and providing the framework for the Commission’s work on competitiveness in this mandate.
In her State of the European Union Speech of 2023, President von der Leyen announced to have asked former Italian Prime Minister Mario Draghi to prepare a report on the future of European Competitiveness. The Report warns that Europe will no longer be able to rely on many of the factors that have supported growth in the past. It lays out a clear diagnosis and provides concrete recommendations to put Europe onto a different trajectory.  Many of the recommendations are already reflected in the Political Guidelines and mission letters by the President to the members of the College.
 
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