Labour cost statistics constitute a hierarchical system of multi-annual, yearly and quarterly statistics, designed to provide a comprehensive and detailed picture of the level, structure and short-term development of labour costs in the different sectors of economic activity in the European Union and certain other countries. All statistics are based on a harmonised definition of labour costs.

The quarterly Labour Cost Index (LCI) is one of the Principal European economic indicators. It shows the short-term development of the labour cost, the total cost on an hourly basis of employing labour. In other words, the LCI measures the cost pressure arising from the production factor “labour”.

The data covered by the LCI collection relate to the total average hourly labour costs and its components "wages and salaries" and "employers' social security contributions plus taxes paid minus subsidies received by the employer" (known as the non-wage component of the LCI). The data on vocational training costs and other expenditures such as recruitment costs and working clothes expenditure is not included in the calculation of the labour cost index.

The data is broken down by economic activity (NACE Rev 1.1 Sections C to O (1996Q1-2008Q4) and NACE Rev. 2 Sections B to S (2009Q1 onwards). The data is available for the EU aggregates and the EU Member States, EFTA countries (Iceland and Norway) as well as candidate and potential candidate countries (Serbia and Turkey). The data is available as 'unadjusted data (neither seasonally adjusted nor calendar adjusted data)', 'calendar-adjusted data' as well as 'seasonally and calendar adjusted' data.

The data on the Labour Cost Index is given in the form of index numbers (current base year: 2020) and as annual and quarterly growth rates (comparison with the previous quarter, or the same quarter of the previous year). Since June 2023 (publication of 2023Q1 data) base year of the indices changed from 2016 to 2020. Indices with the base year 2016 will no longer be published.

The National Statistical Institutes compile the indicators based on the available structural and short-term information collected directly from the sampled enterprises or taken from administrative data sources. All enterprises, irrespective of the size (measured by the number of employees) are covered in the LCI.

In addition, Eurostat estimates of the annual labour cost per hour in euros are provided for the EU Member States as well as the whole EU; they were obtained by combining the four-yearly Labour cost survey (LCS) with the quarterly labour cost index. Methodological information related to the annual estimates of hourly labour costs is available in separate metadata accessible here.

Early estimates of the Labour Cost Index (‘flash estimates’ or ‘FEs’) (quarterly)

Since May 2024, Eurostat has started publishing early estimates for the Labour Cost Index (‘flash estimates’ or ‘FEs’). The flash estimates (are published around t+50 days, as specified in the general release calendar of Eurostat, based on the data transmitted at t+45 days. EU countries that participate in the FE data collection are those whose annual number of employees (over the age of 15) represents more than 3% of EU totals or 3% of euro area totals, based on LFS data assessed over a period of three consecutive years. These 9 selected countries (i.e. ‘FE countries ’) are: Germany, France, Italy, Spain, the Netherlands, Belgium, Portugal, Poland and Romania (see table 1).

Table 1: Share of EA/EU employees in each of the participating countries.

(Source: EU-LFS, reference period 2023)

Wages in Italy increased to 33148 EUR/Year in 2024 from 32450 EUR/Year in 2023. This dataset provides the latest reported value for - Italy Average Nominal Monthly Wages - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Nominal Broad U.S. Dollar Index (DTWEXBGS) from 2006-01-02 to 2025-09-19 about trade-weighted, broad, exchange rate, currency, goods, services, rate, indexes, and USA.

Labour cost statistics constitute a hierarchical system of multi-annual, yearly and quarterly statistics, designed to provide a comprehensive and detailed picture of the level, structure and short-term development of labour costs in the different sectors of economic activity in the European Union and certain other countries. All statistics are based on a harmonised definition of labour costs.

The quarterly Labour Cost Index (LCI) is one of the Principal European economic indicators. It shows the short-term development of the labour cost, the total cost on an hourly basis of employing labour. In other words, the LCI measures the cost pressure arising from the production factor “labour”.

The data covered by the LCI collection relate to the total average hourly labour costs and its components "wages and salaries" and "employers' social security contributions plus taxes paid minus subsidies received by the employer" (known as the non-wage component of the LCI). The data on vocational training costs and other expenditures such as recruitment costs and working clothes expenditure is not included in the calculation of the labour cost index.

The data is broken down by economic activity (NACE Rev 1.1 Sections C to O (1996Q1-2008Q4) and NACE Rev. 2 Sections B to S (2009Q1 onwards). The data is available for the EU aggregates and the EU Member States, EFTA countries (Iceland and Norway) as well as candidate and potential candidate countries (Serbia and Turkey). The data is available as 'unadjusted data (neither seasonally adjusted nor calendar adjusted data)', 'calendar-adjusted data' as well as 'seasonally and calendar adjusted' data.

The data on the Labour Cost Index is given in the form of index numbers (current base year: 2020) and as annual and quarterly growth rates (comparison with the previous quarter, or the same quarter of the previous year). Since June 2023 (publication of 2023Q1 data) base year of the indices changed from 2016 to 2020. Indices with the base year 2016 will no longer be published.

The National Statistical Institutes compile the indicators based on the available structural and short-term information collected directly from the sampled enterprises or taken from administrative data sources. All enterprises, irrespective of the size (measured by the number of employees) are covered in the LCI.

In addition, Eurostat estimates of the annual labour cost per hour in euros are provided for the EU Member States as well as the whole EU; they were obtained by combining the four-yearly Labour cost survey (LCS) with the quarterly labour cost index. Methodological information related to the annual estimates of hourly labour costs is available in separate metadata accessible here.

Early estimates of the Labour Cost Index (‘flash estimates’ or ‘FEs’) (quarterly)

Since May 2024, Eurostat has started publishing early estimates for the Labour Cost Index (‘flash estimates’ or ‘FEs’). The flash estimates (are published around t+50 days, as specified in the general release calendar of Eurostat, based on the data transmitted at t+45 days. EU countries that participate in the FE data collection are those whose annual number of employees (over the age of 15) represents more than 3% of EU totals or 3% of euro area totals, based on LFS data assessed over a period of three consecutive years. These 9 selected countries (i.e. ‘FE countries ’) are: Germany, France, Italy, Spain, the Netherlands, Belgium, Portugal, Poland and Romania (see table 1).

Table 1: Share of EA/EU employees in each of the participating countries.

(Source: EU-LFS, reference period 2023)

This dataset contains multiple variables with spatio-temporal information relating to sea-ice and the southern ocean. This collection of data is utilised by the nilas.org platform for dynamically visualising these variables in the web browser. Together they provide a valuable resource for understanding the interactions between physical, climate and biogeochemical parameters. These include variables to understand sea-ice in three dimensions, chlorophyll and sea surface temperature. The time range of these data covers from 1980 until the present and the spatial coverage is Antarctic circumpolar.

Name: Daily Sea Ice Concentration
Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September.
Product: ARTIST (ASI 5) (Spreen et al. 2008)
Source: Universität Bremen
Resolution: 6.125 km nominal
Timeframe: 2012 to present
Notes: Concentrations of less than 15% have been removed.

Name: Monthly Sea Ice Concentration
Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September.
Product: Sea Ice Index (Windnagel et al. 2017)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25 km nominal
Timeframe: 1980 to present
Notes: Concentrations of less than 15% have been removed.

Name: Monthly Anomalies in Sea Ice Concentration
Desc: Anomalies in sea ice concentration show the monthly variation from the long term mean.
Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25km nominal
Timeframe: 1980 to present
Notes: Anomalies are calculated as the difference between the sea ice concentration and the 1981-2010 mean sea ice concentration for that month. Anomalies less than 7.5% are not shown.

Name: Long term monthly mean sea ice extent
Desc: Sea ice extent is calculated as contour lines at 15% and 80% sea ice concentration.
Product: Sea Ice Index (Windnagel et al. 2017)
Source: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Resolution: -
Timeframe: 1980 to present
Notes: Contours with less than 15 vertices are discarded.

Name: Long Term Monthly Mean Sea Ice Extent
Desc: Mean monthly sea ice extent over the 1981-2010 time interval. This is calculated as contour lines at 15% and 80% long term mean (1981-2010) sea ice concentration.
Product: Sea Ice Index (Windnagel et al. 2017)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: -
Timeframe: Long term monthly mean (1981-2010)
Notes: Contours with less than 15 vertices are discarded.

Name: Gridded Freeboard (ATL20) IceSat2
Desc: Sea ice freeboard is the distance between the waterline and the surface height of sea ice in open leads. This dataset contains monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10,V3).
Product: ATL20 (Petty et al. 2020)
Source: NSIDC
Resolution: 25 km nominal
Timeframe: Oct 2018 to July 2022
Notes: Data greater than 1 metre is shown as 1 metre height.

Name: Annual Sea Ice Duration
Desc: Sea ice duration (contour lines) is the number of days sea ice concentrations above 15% occur between consecutive sea ice minima (assumed to occur on Feb 16 each year).
Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25km nominal
Timeframe: 1980 to 2021
Notes:

Name: Sea Ice Duration Anomalies
Desc: Anomalies in sea ice duration show difference in duration of sea ice from the long term mean, where sea ice duration is the number of days sea ice concentrations above 15% occur between consecutive sea ice minima.
Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25km nominal
Timeframe: 1980 to 2021
Notes: Anomalies in sea ice duration are calculated relative to the 1981 to 2010 mean.

Name: Annual Sea Ice Advance
Desc: Sea ice advance is the date when sea ice concentrations persist above 15% after the sea ice minimum.
Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25km nominal
Timeframe: 1980 to 2022
Notes:

Name: Sea Ice Advance Anomalies
Desc: Anomalies in sea ice advance show number of days (early/late) from the long term mean, where sea ice advance is the date when sea ice concentrations persist above 15% after the sea ice minimum.
Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25km nominal
Timeframe: 1980 to 2022
Notes: Anomalies in sea ice advance are calculated relative to the 1981 to 2010 mean.

Name: Annual Sea Ice Retreat
Desc: Sea ice retreat is the date when sea ice concentrations persist below 15% occur after the sea ice maximum.
Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25km nominal
Timeframe: 1980 to 2021
Notes:

Name: Annual Sea Ice Retreat
Desc: Anomalies in sea ice retreat show number of days (early/late) from the long term mean, where sea ice retreat is the date when sea ice concentrations persist below 15% occur after the sea ice maximum.
Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021)
Source: NSIDC (National Snow and Ice Data Center)
Resolution: 25km nominal
Timeframe: 1980 to 2021
Notes: Anomalies in sea ice retreat are calculated relative to the 1981 to 2010 mean.

Name: Monthly Chlorophyll Concentration
Desc: Chlorophyll-a is a proxy for phytoplankton activity in the ocean and is estimated through ocean colour remote sensing.
Product: Ocean Colour Climate Change Initiative (Sathyendranath et al. 2019)
Source: Ocean Colour
Resolution: 4km nominal, displayed at 10km
Timeframe: 1998-Jun 2022
Notes: This product relies on reflectance in the visible spectrum and does not perform well at Southern Hemisphere high latitudes during periods of low light (April to August) or where there is dense sea ice cover.

Name: Monthly Chlorophyll Concentration Anomalies
Desc: Anomalies in the monthly chlorophyll concentration are calculated relative to the 1998-2020 average for that month.
Product: Ocean Colour Climate Change Initiative (Sathyendranath et al. 2019)
Source: Ocean Colour
Resolution: 4km nominal, displayed at 10km
Timeframe: 1998-Jun 2022
Notes: This product relies on reflectance in the visible spectrum and does not perform well at Southern Hemisphere high latitudes during periods of low light (April to August) or where there is dense sea ice cover.

Name: Monthly Sea Surface Temperature
Desc: Sea Surface Temperature (SST) is an estimate of the water temperature at the ocean surface calculated from satellite and surface observations.
Product: Met Office OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) Near Real Time (Good et al. 2020)
Source: Copernicus
Resolution: 0.05° × 0.05° Nominal
Timeframe: Oct 1981 to present
Notes: There is limited validation of SST remote sensing datasets in the marginal ice zone and close to Antarctica. SST within polynyas and within areas of low concentration sea ice are included but not well validated. Where SST under high concentration sea ice cannot be calculated from observations, it defaults to -1.8°C.

Name: Monthly Sea Surface Temperature Anomalies
Desc: Anomalies in the monthly Sea Surface Temperature (SST) are calculated relative to the 1981 to 2010 average for that month.
Product: Met Office OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) Near Real Time (Good et al. 2020)
Source: Copernicus
Resolution: 0.05° × 0.05° Nominal
Timeframe: Oct 1981 to present
Notes: There is limited validation of SST remote sensing datasets in the marginal ice zone and close to Antarctica. SST within polynyas and within areas of low concentration sea ice are included but not well validated. Where SST under high concentration sea ice cannot be calculated from observations, it defaults to -1.8°C.

Name: International Bathymetric Chart of the Southern Ocean Version 2 (IBCSO v2) (Dorschel et al. 2022)
Desc: Bathymetry is a measure of the depth of the ocean, provided for the areas south of 50° S. The product chosen is a digital bathymetric model for the area south of 50° S with special emphasis on the bathymetry of the Southern Ocean. The total data coverage of the seafloor is 23.79% with a multibeam-only data coverage of 22.32%. The remaining 1.47% include singlebeam and other data.
Product: International Bathymetric Chart of the Southern Ocean Version 2 (IBCSO v2) (Dorschel et al. 2022)
Source: Pangaea
Resolution: 5km nominal
Timeframe: Released 2022
Notes: The source product is provided at 500m nominal resolution, which has been regridded to the 5km nominal resolution

PROJECT DESCRIPTION: Replication package for Sabate, Marcela; Fillat, Carmen; Escario, Regina: “Budget deficits and money creation: Exploring their relation before Bretton Woods”, to be published in Explorations in Economic History (accepted December 2018). Panel of seventeen countries from 1870 to 1938. Ten countries are sometimes-floaters before the WWI: Argentina, Bulgaria, Brazil, Chile, Greece, Italy, Japan, Portugal, Romania and Spain. Seven countries are never-floaters before the WWI: Canada, Finland, the Netherlands, Norway, Sweden, the UK and the USA. Equation8.dta (Stata format): Data of public budget, monetary base and nominal GDP. Replication program Equation 8 offers a dynamic heterogeneous estimation of variations in the monetary base on the budget balance. Equation9.dta (Stata format): Data of variations in the monetary base, real GDP per capita( in 1990 Geary-Khamis dollars), average of public spending level , standard deviation of public spending levels, ratio of debt to nominal GDP and number of cabinet changes per year. Replication program Equation 9 offers a dynamic panel estimation of variations in the monetary base on the rest of variables. Abstract of the paper: The sovereign debt crisis in the Eurozone has rekindled the use of the North-South (core-periphery) terminology to refer to the heterogeneity of countries belonging to the Economic and Monetary Union (EMU). In the gold standard literature, this geographical partition had already been employed to oppose the fiscal profligacy and subsequent problems of convertibility of southern countries against the fiscal probity and long convertibility records of their northern counterparts. We provide statistical evidence that the group of countries that, with available data for 1870-1938, exhibited convertibility problems during the classical gold standard, for this reason called the pre-WWI sometimes-floaters, shared a pattern of fiscal dominance. This finding for the sometimes-floaters (southern European and South American countries plus Japan) differs from the non-fiscal dominance pattern that we obtain for the pre-WWI never-floaters (northern Europe and North America countries) when the Great War and its aftermath years are omitted. We also show that the presence of fiscal dominance was partly due to the lower levels of tax efficiency and political stability in the South.