Development of GDP

Situation in the euro area and the EU

In the first quarter of 2026, seasonally adjusted GDP was unchanged (0.0% rate of change) quarter on quarter in the euro area and increased 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 1.5%), Estonia and Malta (both up 1.1%) recorded the largest increases of GDP, while the decreases were observed in Ireland (down 7.0%), Sweden (down 0.2%), France and Lithuania (both 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, the United States (both up 0.5%) and Norway (up 0.4%).

(*) See latest estimates for more data.

Industrial production

In May 2026, seasonally adjusted industrial production decreased month on month by 0.2% in the euro area and by 0.1% in the EU. In the previous month, there had been increases of 0.3% in the euro area and 0.2% in the EU.

Production in construction

In May 2026, seasonally adjusted production in construction increased 0.4% in the euro area and 0.3% in the EU compared with the previous month. In the previous month, it had increased 0.1% in the euro area and 0.5% in the EU.

Retail trade

In May 2026, the seasonally adjusted volume of sales in retail trade increased compared with the previous month in the euro area (up 0.2%) and the EU (up 0.5%), following on from decreases in the previous month of 0.3% in the euro area and 0.6% 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 2.8% in June 2026, down 0.4 percentage points from the previous month. In the EU, the annual inflation rate was 2.9% in June 2026, down 0.4 percentage points from the previous month.

Situation in the EU countries

The highest annual inflation rates (based on the HICP) in June 2026 were recorded in Romania (9.2%), Lithuania (5.4%) and Bulgaria (5.2%), while the lowest rates were recorded in Sweden (1.0%) and Czechia (1.1%).

Situation in non-EU countries

In June 2026, the annual inflation rate was 5.1% in Iceland, 3.5% in the United States, 2.6% in Norway, 1.0% in China and 0.7% in Switzerland. In May 2026, the annual inflation rate was 1.5% in Japan.

Total unemployment

Situation in the euro area and the EU

In May 2026, the seasonally adjusted unemployment rate was 6.2% in the euro area, unchanged from the previous month. In the EU, the rate was 5.9%, also unchanged from the previous month.

Situation in the EU countries

In May 2026, the lowest unemployment rates were recorded in Bulgaria, Czechia (both 2.9%), Cyprus and Poland (both 3.1%). The highest rates were in Finland (10.8%) and Spain (10.3%).

Situation in non-EU countries

The unemployment rate in May 2026 was 4.4% in Norway, 4.3% in the United States and 2.5% in Japan. The unemployment rate was 5.8% in Iceland in April 2026 and 5.0% in Switzerland in March 2026.

Youth unemployment

In May 2026, the youth unemployment rate (for people aged 15 to 24 years) was 14.7% in the euro area, unchanged from the previous month. In the EU, the rate was 15.2%, marginally higher than in the previous month (15.1%).

Economic sentiment indicator

Situation in the euro area and the EU

In June 2026, the economic sentiment indicator increased, up 1.3 percentage points in both the euro area and the EU: the index level was 95.0 percentage points in the euro area and 95.1 percentage points in the EU. The increase in the euro area resulted from a decrease in confidence among construction managers being outweighed by increases among consumers as well as industry, retail trade and services managers.

Situation in the EU countries

In June 2026, the economic sentiment indicator showed mainly positive developments among the EU countries, increasing in 15 and decreasing in 10 (data for Estonia and Malta are not available). The strongest increases were in the Netherlands (up 4.1 percentage points), Ireland (up 3.1 percentage points), Sweden (up 2.6 percentage points), Finland (up 2.3 percentage points) and Slovenia (up 2.0 percentage points). By far, the strongest decrease was in Romania (down 7.3 percentage points), followed by Austria (down 2.9 percentage points). Apart from these 7 countries, the latest month-on-month changes ranged between 1.7 and -1.4 percentage points. The index level of the economic sentiment indicator (with a long-term average = 100) varied from 83.5 percentage points in Romania to 108.3 percentage points in Greece.

Employment expectations indicator

In June 2026, the employment expectations indicator decreased (down 2.2 percentage points compared with the previous month) to 92.2 in the euro area; expectations were down 2.3 percentage points in the EU to 92.9. The decrease in the euro area in June 2026 was due to more pessimistic plans among retail trade and service managers and less optimistic plans among construction managers outweighing the less pessimistic employment plans among industrial managers.

€-coin

The €-coin decreased to 0.39 in June 2026, down from 0.45 in the previous month. The performance of the indicator continues to be affected by weak business and consumer confidence (according to the latest release of the National Bank of Italy on 2 July 2026).

Business climate indicator

The business climate indicator for the euro area decreased to -0.38 points in June 2026 (down from -0.27 points in the previous month).

Coincident indicators

The growth cycle coincident indicator (GCCI) was 0.96 in June 2026, the business cycle coincident indicator (BCCI) was 0.02 and the acceleration cycle coincident indicator (ACCI) was 0.99, 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 decreased 0.20% month on month to 97.86 points in June 2026, more than reversing a slightly smaller increase (up 0.14%) in the previous month. When compared with the same month in the previous year, the Euro-Indikator was 0.08% higher in June 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). Note that no new observations are available to Eurostat for the trend-cycle decomposition and the extension of the time series has been paused.

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.
• The Beveridge-Nelson filter.

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). Note that no new observations are available to Eurostat for the trend-cycle decomposition and the extension of the time series has been paused.

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.
• The Beveridge-Nelson filter.

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). Note that no new observations are available to Eurostat for the trend-cycle decomposition and the extension of the time series has been paused.

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.
• The Beveridge-Nelson filter.

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). Note that no new observations are available to Eurostat for the trend-cycle decomposition and the extension of the time series has been paused.

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.
• The Beveridge-Nelson filter.

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). Note that no new observations are available to Eurostat for the trend-cycle decomposition and the extension of the time series has been paused.

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.
• The Beveridge-Nelson filter.

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). Note that no new observations are available to Eurostat for the trend-cycle decomposition and the extension of the time series has been paused.

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.
• The Beveridge-Nelson filter.

Read more in the methodological note.