Development of GDP

Situation in the euro area and the EU

In the first quarter of 2026, seasonally adjusted GDP decreased moderately, down 0.2% quarter on quarter in the euro area and 0.1% in the EU. For comparison, increases of 0.2% had been observed in Q4 2025 in both the euro area and the EU.

Situation in the EU countries

Based on the latest quarter-on-quarter rates of change, GDP increased in Q1 2026 in 19 countries, was unchanged in 4 and decreased in 4. Denmark (up 1.9%), Estonia and Malta (both up 1.1%) recorded the largest increases of GDP, while the decreases were observed in Ireland (down 12.1%), Lithuania (down 0.3%), Sweden (down 0.2%) and France (down 0.1%).

Situation in non-EU countries

Compared with the previous quarter, GDP expanded in the first quarter of 2026 in Iceland (up 3.7%), China (up 1.3%), Switzerland (up 0.7%), Japan (up 0.5%), Norway and the United States (both up 0.4%).

(*) See latest estimates for more data.

Industrial production

In April 2026, seasonally adjusted industrial production increased month on month by 0.1% in both the euro area and the EU. In the previous month, there had been an increase of 0.4% in the euro area and 0.8% in the EU.

Production in construction

In April 2026, seasonally adjusted production in construction increased 0.6% in the euro area and 0.8% in the EU compared with the previous month. In the previous month, it had increased 1.7% in the euro area and 2.1% in the EU.

Retail trade

In April 2026, the seasonally adjusted volume of sales in retail trade decreased compared with the previous month in the euro area (down 0.4%) and the EU (down 0.5%), following on from increases in the previous month of 0.8% in the euro area and 1.1% in the EU.

Inflation

Situation in the euro area and the EU

According to the latest data from Eurostat, the annual inflation rate (based on the HICP) in the euro area was 3.2% in May 2026, up 0.2 percentage points from the previous month. In the EU, the annual inflation rate was 3.3% in May 2026, up 0.1 percentage points from the previous month.

Situation in the EU countries

The highest annual inflation rates (based on the HICP) in May 2026 were recorded in Romania (9.7%), Bulgaria (6.3%), Lithuania (5.1%), Greece and Croatia (both 4.9%), while the lowest rate was recorded in Sweden (1.1%).

Situation in non-EU countries

In May 2026, the annual inflation rate was 4.8% in Iceland, 4.2% in the United States, 3.1% in Norway, 1.2% in China and 0.9% in Switzerland. In April 2026, the annual inflation rate was 1.4% in Japan.

Total unemployment

Situation in the euro area and the EU

In April 2026, the seasonally adjusted unemployment rate was 6.3% in the euro area, unchanged from the previous month. In the EU, the rate was 6.0%, also unchanged from the previous month.

Situation in the EU countries

In April 2026, the lowest unemployment rates were recorded in Bulgaria (2.8%), Poland (3.0%), Czechia (3.1%) and Cyprus (3.2%). The highest rates were in Finland (10.7%) and Spain (10.3%).

Situation in non-EU countries

The unemployment rate in April 2026 was 5.8% in Iceland, 4.6% in Norway, 4.3% in the United States and 2.5% in Japan. The unemployment rate was 5.0% in Switzerland in March 2026.

Youth unemployment

In April 2026, the youth unemployment rate (for people aged 15 to 24 years) was 14.7% in the euro area, down strongly from 15.1% in the previous month. In the EU, the rate was 15.1%, down strongly from 15.6% in the previous month.

Economic sentiment indicator

Situation in the euro area and the EU

In May 2026, the economic sentiment indicator increased, up 0.3 percentage points in both the euro area and the EU: the index level was 93.5 percentage points in the euro area and 93.7 percentage points in the EU. The increase in the euro area resulted from decreases in confidence among industry, construction and retail trade managers being outweighed by increases among services managers and consumers.

Situation in the EU countries

In May 2026, the economic sentiment indicator showed mainly positive developments among the EU countries, increasing in 17 and decreasing in 8 (data for Estonia and Malta are not yet available). The strongest increases were in Cyprus (up 3.9 percentage points), Finland (up 3.6 percentage points) and Slovenia (up 3.1 percentage points). The strongest decrease was in Ireland (down 1.4 percentage points). Apart from these 4 countries, the latest month-on-month changes ranged between 2.0 and -0.9 percentage points. The index level of the economic sentiment indicator (with a long-term average = 100) varied from 89.5 percentage points in Belgium and Denmark to 107.5 percentage points in Greece.

Employment expectations indicator

In May 2026, the employment expectations indicator increased (up 2.8 percentage points compared with the previous month) to 94.7 in the euro area; expectations were up 2.1 percentage points in the EU to 95.4. The increase in the euro area in May 2026 was due to less pessimistic plans among service, retail trade and industrial managers outweighing the moderately less optimistic employment plans among construction managers.

€-coin

The €-coin decreased to 0.45 in May 2026, down from 0.52 in the previous month. The performance of the indicator was affected by the low levels of consumer and business confidence, partly due to weakening demand and to pressures on supply chains (according to the latest release of the National Bank of Italy on 4 June 2026).

Business climate indicator

The business climate indicator for the euro area increased marginally to -0.26 points in May 2026 (up from -0.27 points in the previous month).

Coincident indicators

The growth cycle coincident indicator (GCCI) was 0.98 in May 2026, the business cycle coincident indicator (BCCI) was 0.02 and the acceleration cycle coincident indicator (ACCI) was 0.98, indicating a phase of slowdown for the euro area.

The GCCI, BCCI and ACCI are synthetic indicators which are experimental in nature and calculated for Eurostat’s business cycle clock. The GCCI shows the probability of a slowdown in the economy and signals the peaks and troughs of the growth cycle, the BCCI shows the probability of a recession and signals the peaks and troughs of the business cycle and the ACCI shows the probability of a deceleration in the growth rate and signals the peaks and troughs of the growth rate cycle. The probability is given on a scale from 0 to 1.

Read more about the methodology in the Statistics Explained article about the business cycle clock.

DZ BANK’s Euro-Indikator

The DZ BANK’s Euro-Indikator increased 0.16% month on month to 98.07 points in May 2026, following on from a similar size decrease (down 0.18%) in the previous month. When compared with the same month in the previous year, the Euro-Indikator was 0.36% higher in May 2026.

Trend-cycle estimates

This graph shows the quarterly GDP in current prices for the euro area, together with trend-cycle decompositions.

A trend-cycle decomposition method aims at further decomposing seasonally adjusted data into a trend component and a cyclical component (expressed as the deviation from the trend).

The trend provides information on longer-term movements in the seasonally adjusted series over several years; the cycle is a sequence of smoother fluctuations around the longer-term trend, in part characterised by alternating periods of expansion and contraction.

You can choose to show the estimates using four different filters.

• Hodrick-Prescott (HP): a filter widely used in macroeconomics to fit a smooth curve through a set of points.
• Christiano-Fitzgerald (CF): a well-known approximation to an ideal band pass filter, estimated non-parametrically.
• The Harvey and Trimbur decomposition, which is probably the most well-known model within the Unobserved Components (UC) framework.
• Beveridge-Nelson (BN): a model-based filter that decomposes a time series into a permanent trend component and a transitory cyclical component using long-horizon forecasts

Read more in the methodological note.

Trend-cycle estimates

This graph shows the monthly industrial production index for the euro area, together with trend-cycle decompositions.

A trend-cycle decomposition method aims at further decomposing seasonally adjusted data into a trend component and a cyclical component (expressed as the deviation from the trend).

The trend provides information on longer-term movements in the seasonally adjusted series over several years; the cycle is a sequence of smoother fluctuations around the longer-term trend, in part characterised by alternating periods of expansion and contraction.

You can choose to show the estimates using four different filters.

• Hodrick-Prescott (HP): a filter widely used in macroeconomics to fit a smooth curve through a set of points.
• Christiano-Fitzgerald (CF): a well-known approximation to an ideal band pass filter, estimated non-parametrically.
• The Harvey and Trimbur decomposition, which is probably the most well-known model within the Unobserved Components (UC) framework.
• Beveridge-Nelson (BN): a model-based filter that decomposes a time series into a permanent trend component and a transitory cyclical component using long-horizon forecasts

Read more in the methodological note.

Trend-cycle estimates

This graph shows the quarterly employment in thousand people for the euro area, together with trend-cycle decompositions.

A trend-cycle decomposition method aims at further decomposing seasonally adjusted data into a trend component and a cyclical component (expressed as the deviation from the trend).

The trend provides information on longer-term movements in the seasonally adjusted series over several years; the cycle is a sequence of smoother fluctuations around the longer-term trend, in part characterised by alternating periods of expansion and contraction.

You can choose to show the estimates using four different filters.

• Hodrick-Prescott (HP): a filter widely used in macroeconomics to fit a smooth curve through a set of points.
• Christiano-Fitzgerald (CF): a well-known approximation to an ideal band pass filter, estimated non-parametrically.
• The Harvey and Trimbur decomposition, which is probably the most well-known model within the Unobserved Components (UC) framework.
• Beveridge-Nelson (BN): a model-based filter that decomposes a time series into a permanent trend component and a transitory cyclical component using long-horizon forecasts

Read more in the methodological note.

Trend-cycle estimates

This graph shows the quarterly GDP in current prices for the EU, together with trend-cycle decompositions.

A trend-cycle decomposition method aims at further decomposing seasonally adjusted data into a trend component and a cyclical component (expressed as the deviation from the trend).

The trend provides information on longer-term movements in the seasonally adjusted series over several years; the cycle is a sequence of smoother fluctuations around the longer-term trend, in part characterised by alternating periods of expansion and contraction.

You can choose to show the estimates using four different filters.

• Hodrick-Prescott (HP): a filter widely used in macroeconomics to fit a smooth curve through a set of points.
• Christiano-Fitzgerald (CF): a well-known approximation to an ideal band pass filter, estimated non-parametrically.
• The Harvey and Trimbur decomposition, which is probably the most well-known model within the Unobserved Components (UC) framework.
• Beveridge-Nelson (BN): a model-based filter that decomposes a time series into a permanent trend component and a transitory cyclical component using long-horizon forecasts

Read more in the methodological note.

Trend-cycle estimates

This graph shows the monthly industrial production index for the EU, together with trend-cycle decompositions.

A trend-cycle decomposition method aims at further decomposing seasonally adjusted data into a trend component and a cyclical component (expressed as the deviation from the trend).

The trend provides information on longer-term movements in the seasonally adjusted series over several years; the cycle is a sequence of smoother fluctuations around the longer-term trend, in part characterised by alternating periods of expansion and contraction.

You can choose to show the estimates using four different filters:

• Hodrick-Prescott (HP): a filter widely used in macroeconomics to fit a smooth curve through a set of points.
• Christiano-Fitzgerald (CF): a well-known approximation to an ideal band pass filter, estimated non-parametrically.
• The Harvey and Trimbur decomposition, which is probably the most well-known model within the Unobserved Components (UC) framework.
• Beveridge-Nelson (BN): a model-based filter that decomposes a time series into a permanent trend component and a transitory cyclical component using long-horizon forecasts

Read more in the methodological note.

Trend-cycle estimates

This graph shows the quarterly employment in thousand people for the EU, together with trend-cycle decompositions.

A trend-cycle decomposition method aims at further decomposing seasonally adjusted data into a trend component and a cyclical component (expressed as the deviation from the trend).

The trend provides information on longer-term movements in the seasonally adjusted series over several years; the cycle is a sequence of smoother fluctuations around the longer-term trend, in part characterised by alternating periods of expansion and contraction.

You can choose to show the estimates using four different filters.

• Hodrick-Prescott (HP): a filter widely used in macroeconomics to fit a smooth curve through a set of points.
• Christiano-Fitzgerald (CF): a well-known approximation to an ideal band pass filter, estimated non-parametrically.
• The Harvey and Trimbur decomposition, which is probably the most well-known model within the Unobserved Components (UC) framework.
• Beveridge-Nelson (BN): a model-based filter that decomposes a time series into a permanent trend component and a transitory cyclical component using long-horizon forecasts

Read more in the methodological note.