Development of GDP

Situation in the euro area and the EU

In the second quarter of 2026, seasonally adjusted GDP increased 0.4% quarter on quarter in the euro area and 0.5% in the EU. For comparison, no change had been observed in the first quarter of 2026 in the euro area and a marginal increase (up 0.1%) in the EU.

Situation in the EU countries

Based on the latest quarter-on-quarter rates of change, GDP increased in the second quarter of 2026 in 18 countries and was unchanged in 3 (quarter 2 data are not available for 6 countries at the time of writing). Ireland (up 3.9%), Slovenia (up 1.8%), Lithuania (up 1.7%) and Sweden (up 1.4%) recorded the largest increases of GDP, while no change was observed in Belgium, Austria and Romania.

Situation in non-EU countries

Compared with the previous quarter, GDP expanded in the second quarter of 2026 in Switzerland (up 1.9%), China (up 0.9%), the United States (up 0.4%) and Japan (up 0.3%). In the first quarter of 2026, GDP increased in Iceland (3.7%) and Norway (0.4%).

(*) See latest estimates for more data.

Industrial production

In June 2026, seasonally adjusted industrial production was unchanged month on month in the euro area (0.0% rate of change) and increased slightly in the EU (up 0.2%). In the previous month, there had been increases of 0.3% in both the euro area and the EU.

Production in construction

In June 2026, seasonally adjusted production in construction decreased 1.3% in the euro area and 1.0% in the EU compared with the previous month. In the previous month, it had increased 0.2% in the euro area and 0.1% in the EU.

Retail trade

In June 2026, the seasonally adjusted volume of sales in retail trade decreased compared with the previous month in the euro area (down 0.3%) and the EU (down 0.1%), following on from increases in the previous month (up 0.4% 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 2.9% in July 2026, up 0.1 percentage points from the previous month. In the EU, the annual inflation rate was 3.0% in July 2026, also up 0.1 percentage points from the previous month.

Situation in the EU countries

The highest annual inflation rates (based on the HICP) in July 2026 were recorded in Romania (8.2%) and Lithuania (5.4%), while the lowest rate was recorded in Sweden (0.3%).

Situation in non-EU countries

In July 2026, the annual inflation rate was 5.6% in Iceland, 3.4% in the United States, 2.9% in Norway and 0.7% in Switzerland. In June 2026, the annual inflation rate was 1.7% in Japan and 1.0% in China.

Total unemployment

Situation in the euro area and the EU

In June 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 June 2026, the lowest unemployment rates were recorded in Bulgaria, Cyprus (both 3.0%) and Poland (3.1%). The highest rates were in Finland (10.3%) and Spain (10.1%).

Situation in non-EU countries

The unemployment rate in June 2026 was 5.5% in Iceland, 4.5% in Norway, 4.2% in the United States and 2.5% in Japan. The unemployment rate was 5.5% in Iceland in May 2026 and 5.0% in Switzerland in March 2026.

Youth unemployment

In June 2026, the youth unemployment rate (for people aged 15 to 24 years) was 14.8% in the euro area, down marginally from the previous month (14.9%). By contrast, the rate in the EU was 15.5%, marginally higher than in the previous month (15.4%).

Economic sentiment indicator

Situation in the euro area and the EU

In July 2026, the economic sentiment indicator increased, up 1.5 percentage points in both the euro area and the EU: the index level was 96.9 percentage points in the euro area and 97.2 percentage points in the EU. The increase in the euro area resulted from increases among consumers as well as industry, retail trade and services managers outweighing a decrease in confidence among construction managers.

Situation in the EU countries

In July 2026, the economic sentiment indicator showed mainly positive developments among the EU countries, increasing in 20 and decreasing in 5 (data for Estonia and Malta are not available). The strongest decrease was in Belgium (down 2.2 percentage points), with smaller decreases in Cyprus, Greece, Bulgaria and Poland. The strongest increases were in Luxembourg (up 7.1 percentage points) and Austria (up 5.3 percentage points); elsewhere, increases were 3.8 percentage points or smaller. The index level of the economic sentiment indicator (with a long-term average = 100) varied from 88.8 percentage points in Belgium to 107.6 percentage points in Greece.

Employment expectations indicator

In July 2026, the employment expectations indicator increased strongly (up 3.5 percentage points compared with the previous month) to 97.2 in the euro area; expectations were up 3.3 percentage points in the EU to 97.7. The increase in the euro area in July 2026 was due to less pessimistic plans among industrial, retail trade and service managers combining with more optimistic plans among construction managers.

€-coin

The €-coin increased to 0.54 in July 2026, up from 0.39 in the previous month. The performance of the indicator benefited from the improvement in business and household confidence, especially in forward-looking assessments (according to the latest release of the National Bank of Italy on 4 August 2026).

Business climate indicator

The business climate indicator for the euro area increased to -0.19 points in July 2026 (up from -0.36 points in the previous month).

Coincident indicators

The growth cycle coincident indicator (GCCI) was 0.97 in July 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 increased 0.49% month on month to 98.35 points in July 2026, more than reversing a decrease (down 0.19%) in the previous month. When compared with the same month in the previous year, the Euro-Indikator was 0.46% higher in July 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.
• 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).

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).

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).

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).

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).

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.