Period Specialized and non-specialized food shops Non-food shops Total retail sales except automotive fuela Automotive fuel sales Total retail salesa
in stores not in stores totala
predominantly food and beverages food, beverages and tobacco total manufactured goods in non-specialized shops textiles, clothing, footwear furniture and electrical goods books, computer equipment and others, total books, newspapers computer equipment and others pharmaceutical and medical goods, cosmetics articles, total pharmaceutical and medical goods cosmetics articles second-hand goods mail order and internet market and other non-storeb
2022 January 98.3 100.8 98.8 96.8 93.3 100.6 101.1 92.1 101.5 105.8 106.0 99.6 107.9 100.0 .. 99.3 98.6 103.9 99.2
February 102.0 101.8 102.1 116.1 112.5 102.0 106.7 109.5 106.3 100.4 96.5 102.2 100.6 103.3 .. 104.4 103.2 106.4 103.3
March 100.6 99.0 100.4 98.0 96.9 100.7 98.5 91.0 99.7 100.6 102.0 100.6 94.5 97.1 .. 99.9 99.5 121.9 106.3
April 98.9 99.0 99.1 98.0 102.4 97.9 103.9 106.6 102.9 100.7 101.0 99.8 103.7 101.2 .. 98.7 99.9 91.6 97.8
May 99.2 101.4 99.4 99.1 113.3 98.3 98.3 97.8 98.2 97.6 99.7 99.7 111.5 100.4 .. 99.3 99.0 104.5 98.6
June 98.6 99.9 98.8 93.4 86.6 96.5 96.4 103.3 96.1 100.9 100.1 99.0 95.6 96.9 .. 95.9 97.4 96.3 96.6
July 99.1 98.7 98.9 100.7 104.0 101.3 100.7 97.4 101.2 99.5 101.0 100.0 97.9 96.9 .. 100.8 99.7 98.8 99.3
August 99.4 99.4 99.5 99.4 98.8 99.2 98.9 98.2 99.2 99.7 100.0 98.6 97.8 100.3 .. 99.8 99.6 96.4 98.9
September 99.9 99.7 99.7 102.1 110.1 100.2 101.0 99.1 101.4 99.4 99.7 99.8 110.9 99.7 .. 101.7 100.5 102.0 100.2
October 95.4 100.1 96.8 98.4 90.4 98.0 97.5 99.5 97.5 98.6 99.9 101.3 94.3 102.7 .. 98.3 98.6 101.5 98.9
November 99.4 99.5 99.3 97.2 103.7 99.5 101.6 101.4 101.6 103.2 99.5 100.5 98.0 98.6 .. 100.3 99.8 101.9 100.0
December 98.4 100.0 98.4 96.9 103.1 97.0 100.1 98.0 100.7 101.6 104.2 101.0 118.9 97.0 .. 99.1 98.9 80.3 96.7
2023 January 102.5 101.8 102.6 101.0 93.4 98.3 100.3 102.8 99.4 95.7 95.9 99.8 83.0 100.2 .. 99.3 100.2 93.3 99.7
February 99.1 96.2 98.6 95.5 97.4 95.1 99.0 94.6 99.2 105.5 103.3 101.4 105.4 98.8 .. 96.8 98.2 100.2 98.2
March 97.5 99.7 97.9 100.9 96.0 97.8 97.0 104.9 96.7 100.3 100.2 99.1 97.9 99.1 .. 100.4 98.7 101.3 100.2
April 102.0 100.2 101.9 96.9 97.2 100.1 95.5 97.4 95.1 101.6 100.2 102.3 93.4 100.7 .. 98.1 99.9 99.8 99.4
May 99.9 99.9 99.7 99.4 104.9 98.0 105.8 99.7 105.9 99.8 100.8 99.1 96.1 102.5 .. 100.9 100.1 101.0 99.6
June 101.4 101.2 101.3 102.7 102.7 101.6 99.5 99.0 99.8 100.8 100.2 101.5 108.3 96.8 .. 101.0 101.3 99.1 100.9
July 101.0 99.3 100.7 98.8 102.1 99.0 98.6 99.0 98.8 97.9 99.6 100.0 96.1 104.3 .. 100.2 100.2 101.9 100.1
August 97.8 98.2 97.8 100.7 97.7 99.0 98.3 98.0 98.8 102.0 100.2 102.6 103.0 99.2 .. 100.2 99.2 98.9 99.3
September 102.0 103.2 102.2 98.1 92.4 100.6 101.6 102.4 101.4 101.0 101.2 100.5 94.8 99.0 .. 98.6 100.7 100.2 100.3
October 99.7 98.2 99.4 99.7 103.0 99.4 99.4 97.7 99.7 99.2 100.4 100.2 101.4 102.9 .. 101.3 100.0 99.8 99.8
November 99.7 100.1 99.7 100.2 101.3 96.8 99.7 98.6 99.8 99.6 100.2 99.1 102.0 98.2 .. 99.0 99.7 100.1 99.7
December 101.1 100.9 100.8 101.7 105.5 103.2 103.2 99.7 104.3 103.7 101.5 102.8 101.8 103.8 .. 102.6 102.1 106.1 103.0
2024 January 99.6 100.0 99.8 98.3 94.8 102.0 98.0 98.7 96.8 97.6 98.9 101.3 92.5 97.8 .. 98.9 98.9 92.5 98.6
February 100.7 100.9 100.6 104.3 106.0 102.4 104.1 100.9 104.2 101.3 100.1 99.7 104.7 101.7 .. 102.6 100.7 100.7 100.5
March 101.5 102.4 102.0 99.1 94.1 99.4 97.7 97.0 97.5 101.1 101.2 100.6 99.2 100.4 .. 98.8 101.6 99.9 101.6
April 100.0 98.0 99.6 98.5 105.0 99.3 95.3 99.4 95.7 100.1 99.7 102.0 103.5 99.7 .. 100.4 99.0 101.6 99.0
May 101.4 102.2 101.7 98.8 97.8 99.7 101.7 101.2 100.8 102.8 101.7 100.2 98.2 100.5 .. 99.7 101.0 98.8 100.9
June 98.9 96.8 98.4 100.2 104.8 101.7 98.9 100.0 99.1 101.0 99.7 101.1 98.0 102.5 .. 101.0 99.9 99.1 99.7
July 100.6 99.4 100.5 102.4 96.6 100.1 100.5 97.9 101.0 98.9 101.0 100.0 101.2 100.6 .. 100.5 100.2 101.8 100.5
August 101.4 101.4 101.4 98.2 97.3 100.3 100.9 101.9 101.2 100.7 99.9 100.5 96.7 99.7 .. 99.4 101.0 98.2 100.6
September 99.3 98.7 99.1 101.7 105.0 99.7 99.0 100.0 98.9 100.0 100.3 100.5 102.4 100.9 .. 101.1 99.2 100.5 99.4
October 100.5 102.8 100.8 99.7 93.0 100.9 101.1 98.6 101.3 101.2 101.1 101.3 101.2 97.9 .. 99.5 100.6 101.4 100.6
November 99.9 100.0 99.8 101.5 109.2 102.1 103.1 99.4 103.4 98.5 99.4 100.9 100.2 99.7 .. 101.0 100.8 100.4 100.6
December 100.3 100.8 100.2 97.8 93.0 97.9 96.1 100.6 96.5 101.1 99.2 100.3 98.5 103.9 .. 99.4 99.0 97.9 99.0
2025 January 100.5 100.0 100.6 104.1 102.9 101.7 101.4 101.9 100.4 102.8 102.6 101.7 99.1 98.4 .. 101.4 101.5 102.2 101.5
February 99.9 99.0 99.7 97.8 99.2 98.5 98.3 97.5 98.2 101.7 99.6 100.8 98.7 101.1 .. 98.7 99.8 100.0 99.9
March 100.3 98.1 99.8 100.8 103.9 100.6 99.9 102.0 100.1 96.5 99.3 101.1 104.7 101.2 .. 101.4 99.5 99.0 99.4
April 100.9 102.4 101.4 103.0 97.7 100.7 102.8 98.6 102.8 102.1 100.3 99.4 100.1 101.9 .. 101.0 101.7 102.8 101.7
May 99.7 96.7 99.0 100.4 99.8 101.6 98.4 98.8 98.0 99.4 99.0 102.3 97.0 98.3 .. 99.7 99.4 95.8 99.2
June 101.1 100.9 101.2 100.0 102.1 98.8 97.3 98.9 97.6 102.1 100.1 101.4 96.5 104.0 .. 100.4 100.4 102.6 100.6
July 99.6 98.3 99.3 98.8 97.0 99.1 100.9 102.3 101.0 99.4 99.8 100.6 104.0 99.4 .. 99.9 100.1 97.1 99.8
August 101.1 100.4 100.9 101.4 106.8 101.1 102.0 98.7 102.6 101.9 99.9 101.2 101.7 100.1 .. 101.1 100.4 102.7 100.6
September 100.1 100.6 100.1 99.2 92.7 99.2 97.9 97.7 98.1 99.1 100.4 100.4 97.8 102.5 .. 99.9 100.5 99.1 100.4
October 99.6 98.6 99.3 101.4 106.1 99.9 101.7 102.4 101.4 99.9 99.8 100.1 100.8 100.0 .. 101.0 100.4 101.4 100.4
November 101.0 101.0 100.9 99.9 98.9 101.1 100.8 100.3 100.7 99.4 98.8 101.9 103.1 100.8 .. 100.2 100.0 100.6 100.1
December 99.7 100.8 99.8 102.4 95.6 100.5 101.8 99.0 102.8 102.3 102.2 99.0 108.4 97.3 .. 100.1 100.4 99.0 100.4
2026 January 100.6 96.9 99.9 97.5 106.4 97.2 100.8 96.3 100.3 97.8 97.3 101.8 97.3 106.0 .. 100.7 100.2 105.8 100.5
February
March
April
May
June
July
August
September
October
November
December

Footnotes

*Retail trade data are seasonally adjusted in line with Eurostat recommendations using the JDemetra+ 2.2.0 software and the TRAMO-SEATS method, applying the direct approach. Under this approach, both the aggregate series and the component series are adjusted directly and independently of each other. This method ensures that each time series is adjusted with the best possible model specification, thereby removing all identifiable seasonal effects from every series. Further information on the seasonal adjustment process is available at the following link: About seasonal adjustment. Consistent with the direct approach, the best-fitting model is selected separately for each retail trade category. As a natural consequence, calendar effects (working-day effect, Easter effect, leap-year effect), the functional form describing seasonal patterns, and the type and magnitude of outliers may differ across categories. Therefore, neither the working-day adjusted volume indices nor the seasonally and calendar-adjusted volume indices are not consistent with the aggregate, meaning that the “total retail trade” volume index cannot be obtained as the weighted average of the component indices.
aFrom January 2020 data also include market and other non-store retail sales.
bA time series of sufficient length is not available for the data adjustment.

Explanation of symbols

..
Datum not available.
blue number
Preliminary datum.
.