The Netherlands had the highest employment rate among European Union countries in 2025, at 82.5 percent, while Iceland had the highest employment rate among all European countries. The second highest employment rate in the EU was that of Malta, which had an employment rate of 79.9 percent. Italy reported the lowest employment rate in the EU at 62.7 percent.

The seasonally adjusted unemployment rate in member states of the European Union in July 2025. The seasonally adjusted unemployment rate in Spain in July 2025 was 10.4 percent. The unemployment rate represents the share of the unemployed in all potential employees available to the job market. Unemployment rates in the EU The unemployment rate is an important measure of a country or region’s economic health, and despite unemployment levels in the European Union falling slightly from a peak in early 2013 , they remain high, especially in comparison to what the rates were before the worldwide recession started in 2008. This confirms the continuing stagnation in European markets, which hits young jobseekers particularly hard as they struggle to compete against older, more experienced workers for a job, suffering under jobless rates twice as high as general unemployment. Some companies, such as Microsoft and Fujitsu, have created thousands of jobs in some of the countries which have particularly dire unemployment rates, creating a beacon of hope. However, some industries such as information technology, face the conundrum of a deficit of qualified workers in the local unemployed work force, and have to hire workers from abroad instead of helping decrease the local unemployment rates. This skills mismatch has no quick solution, as workers require time for retraining to fill the openings in the growing science-, technology-, or engineering-based jobs, and too few students choose degrees that would help them obtain these positions. Worldwide unemployment also remains high, with the rates being worst in the Middle East and North Africa. Estimates by the International Labour Organization predict that the problem will stabilize in coming years, but not improve until at least 2017.

This dataset provides values for LABOR MARKET CONDITIONS INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Revenue in the Temporary Employment Placement Agencies industry is anticipated to grow at a compound annual rate of 4.1% over the five years through 2025 to €270.9 billion. The COVID-19 outbreak meant key employers of temporary workers in the hospitality and tourist sector shut their doors and companies froze hiring due to economic uncertainty, dealing a sizeable blow to revenue at the beginning of the five-year period. As the economy reopened in 2021, companies quickly resumed hiring, leading to record vacancies, especially within the service sector, driving up revenue for recruitment agencies. The widespread adoption of remote and flexible work arrangements has altered demand patterns, with clients seeking specialised talent for hybrid or short-term digital projects. Labour shortages in healthcare, logistics and IT industries have further fuelled demand for temporary staffing solutions. At the same time, agencies have faced heightened competition from online staffing platforms and digital marketplaces, driving investment in technology and automation to enhance candidate matching and streamline operations. Despite this, recruitment agencies have seen their profit fall over the past five years due to economic uncertainty, inflation and rising business expenses increasing operating costs. An increasingly tight labour market encourages employers to rely on temporary employment placement agencies to compete. Several European countries rank highly regarding temporary workers and have a large short-term job market. For example, in 2023, the Netherlands and Portugal had more than 15% of employed people under temporary contracts, according to Eurostat. Revenue is expected to swell by 4% in 2025 as a tight labour market across Europe encourages employers to rely on temporary-employment placement agencies. Revenue is slated to climb at a compound annual rate of 8.7% over the five years through 2030 to €410.3 billion. While the labour market is likely to remain tight in many countries due to skill mismatches, employers will keep turning to placement agencies for their databases to track and identify the right candidates. Companies will lean on temporary hires as the economic outlook remains unclear. However, threats to demand loom. The automation of more routine jobs will be a threat to some long-standing temporary jobs. Across Europe, countries that traditionally rely on a strong network of short-term workers are implementing policies that may disrupt or expand services – Spain already introduced reforms in late 2021 to increase permanent positions and remove temporary contracts. Temporary employment placement agencies will increasingly deploy AI procedures to increase efficiency, including AI chatbots and CV screening; companies that don’t follow suit risk being left behind in the age of AI evolution.

The data collection 'LFS - specific topics, household statistics' covers a range of statistics on number, characteristics and typologies of households, based on the European Union Labour Force Survey (EU-LFS). The data collection also encompasses some labour market indicators broken down by household composition. Only annual data are available.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

According to a labor force survey in the European Union, indicators for 2024 showed that EU nationals and citizens from other EU countries maintained higher labor force participation and employment rates compared to non-EU27 citizens. Nationals and EU27 citizens had labor force participation rates above ** percent, while non-EU27 citizens were situated at ** percent, and their employment rate was ** percent. Unemployment and youth unemployment rates are highest among non-EU27 citizens, at **** and **** percent respectively.

Employment placement agencies in Europe’s revenue is anticipated to contract at a compound annual rate of 9% over the five years through 2025 to €65.4 billion. The COVID-19 outbreak tanked business confidence and expansion plans because of economic uncertainty after months of global lockdowns, forcing hiring freezes in a tricky time for employment agencies. 2022 marked a resurgence for agencies, with companies entering a hiring frenzy post-pandemic. The labour market is cooling in 2025 amid greater global uncertainty with US tariffs impacting business confidence. Still, employment across Europe remains high. According to Eurostat data, employment in the EU reached a record peak of 75.8% in 2024. Companies enjoyed a post-COVID-19 boom in hiring, as the economy reopened and companies began to look to expand thanks to improved business confidence, which kept employment agencies busy. The labour market has proved resilient against the economic background of high interest rates and high inflation in recent years, but remains tight with several unfilled vacancies. Vacancies have dipped from the sharp rise post-COVID-19 when companies unfroze hiring decisions. Available vacancies are proving difficult to fill in 2025, indicating a skills mismatch between job seekers and roles that agencies are struggling to negotiate. Several countries attempt to address long-standing labour shortages to ameliorate professional mobility and offer training courses for in-demand skills through agencies. France, for example, is addressing youth unemployment through upskilling training programmes. Public sector hiring in Germany and Spain in health and education also pushes revenue growth for agencies compared to stunted private sector demand. Revenue is expected to rise by 8.7% in 2025 amid job cuts in the technology sector. Revenue is projected to swell at a compound annual rate of 13.2% over the five years through 2030 to reach €121.6 billion. Agencies will continue to target revenue growth by elevating their online presence, specialising their services towards more niche sectors and targeting executives and upper management positions. Technological developments remain a threat to recruiters, with HR AI systems like Paradox able to scan networking platforms such as LinkedIn for candidates. Companies’ in-house HR teams are expanding too. The sustainability sector looks to be a hot property job market to target, but potential shortages in both high and low-skilled occupations driven by employment growth in STEM professions and healthcare will create hurdles in the hiring process in other sectors.

EURAXESS Data Scientist Jobs

Explore Job Opportunities Across the European Union

By [source]

About this dataset

This dataset contains a collection of data science job offers from the European EURAXESS database. It includes detailed information on the job title, salary, position type, location, sector and company name - allowing you to see the kind of opportunities available if you pursue a career in data science. With this comprehensive set of data points at your disposal, it's easy to explore highly diverse roles and compare different employers to find the right fit for you; all while gaining valuable insight into recent hiring trends in the European Union's labor market! Whether you are thinking about taking your first steps into Data Science or are already experienced in this field, this dataset provides an up-to-date referential source helping better align your professional aspirations with actual opportunities

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How to use the dataset

This dataset contains job offers for data scientists in the EURAXESS database. It includes relevant information such as company name, job title, salary, location and job description.

The dataset can be used to get an understanding of current trends in data science jobs and salaries in Europe. This can help individuals or companies determine where to focus their resources or look for new data science opportunities.

To start using this dataset, we recommend taking a look at the columns first. There are five main columns - company name, job title, salary (where available), location and job description - which provide detailed information about each individual offer for a data scientist position. By examining these attributes of each position you’ll be able to understand the different requirements for each role across various European countries and begin formulating your search strategy from there.

When considering specific offers within this dataset it's important to consider not just the physical location but other aspects such as potential growth opportunities within organizations or desired levels of seniority regarding developing/applying models with complex datasets as well as fluctuating demands of managing fast-paced projects with tight deadlines etc…so it's advised to read through all of the details provided when evaluating opportunities specifically tailored to your needs accordingly.

If you’re looking beyond just salary numbers though then keep an open mind when examining all available positions since while money is always important; things like more vacation days or flexible working hours may fit well into personal priorities too! Ultimately it's up to you to decide on what parameters work best for you when locating a suitable role via this dataset according to your criteria; financials aside being sure that any prospective employer meets certain standards in terms of coding/database frameworks & principles expected from prospective employees also provides great peace of mind towards landing successful & long-term endeavors so never forget that small detail whilst narrowing down selections!

Research Ideas

• Analyzing the language preferences specified in data science job offers in EURAXESS to gain insight into the language requirements of the data science market across different European countries.
• Comparing salary averages between job postings within EURAXESS to identify potential discrepancies between wages paid for similar positions across countries or differences in job requirements at a given pay grade.
• Identifying trends in other special qualifications (e.g., degree, certification) required for data scientist roles within EURAXESS compared to other similar datasets from other regions such as North America, Asia, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Data Scientist.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

• The dataset is part of Eurostat's collection of population and employment statistics. The code "NAMQ_10_PE" specifically refers to data related to employment and population trends in European countries and likely spans a range of years from 1980 to 2024.

Eurostat provides statistical data on various aspects of the labor market across Europe, including:

• Total Population – The total number of people residing in a particular country or region.
• Labor Force – The portion of the population that is either employed or actively looking for work.
• Employment Rate – The percentage of the working-age population that is employed.
• Unemployment Rate – The percentage of the labor force that is unemployed.
• Youth Employment Rate – The employment rate among young people (typically aged 15-24).

• Sectoral Employment – Employment distribution across various sectors like agriculture, industry, and services.

• **Details of the Dataset **

This dataset would typically cover European Union countries and potentially other European countries (depending on the specific version). The data likely spans multiple years (1980-2024) and provides insights into the demographic and economic changes in these countries over time.

-**Some example insights you might explore:**

Trends in Employment: Analyzing the employment and unemployment rates over time to see how they correlate with major economic events, such as the global financial crisis. Sectoral Shifts: Investigating how the structure of employment has shifted from agriculture and industry to services over the decades. Impact of Population Growth: Exploring how changes in population size relate to changes in employment, labor force participation, and unemployment.

• Link to Eurostat’s Dataset

You can access the Eurostat dataset directly using the following link:

• Eurostat – NAMQ_10_PE Dataset

This link takes you to Eurostat's Labor Force Survey (LFS) data, which includes datasets related to employment, unemployment, and other labor force indicators across EU countries. You can navigate and search for NAMQ_10_PE by using Eurostat’s filtering and search tools. Here, you can download data in various formats such as CSV, Excel, or TSV.

This statistic shows the share of automation probability of median jobs in selected European countries as of 2018, according to calculations by the OECD. In Slovakia, the median job has a ** percent chance of being automated, making the country the most vulnerable to automation in Europe. By comparison, the median worker in England had a ** probability of being automated.

Techsalerator’s Job Openings Data in Europe is a robust and meticulously curated dataset designed to provide businesses, recruiters, labor market analysts, and job seekers with a comprehensive view of employment opportunities across the continent. This dataset aggregates job postings from a wide range of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available in Europe.

Key Features of the Dataset: Comprehensive Coverage:

The dataset captures job postings from numerous sources, including company career pages, job boards, recruitment agencies, and professional networking sites. This broad coverage ensures that the dataset includes job opportunities across various platforms and channels. Daily Aggregation:

Data is updated daily, providing users with real-time insights into the job market. This frequent updating ensures that the information is current and reflects the latest trends and changes in job availability. Sector-Specific Insights:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This categorization helps users analyze job market trends specific to different sectors and industries. Regional Breakdown:

The dataset includes detailed information on job openings across different regions and cities in Europe. This regional breakdown allows users to understand job market dynamics and opportunities in various geographic locations. Role and Skill Analysis:

Data includes information on job roles, required skills, qualifications, and experience levels. This helps job seekers identify opportunities that match their expertise and assists recruiters in finding candidates with the right skill sets. Company Insights:

Users can access data on the companies posting job openings, including company names, industries, and locations. This information is useful for understanding which companies are hiring and where the demand for talent is concentrated. Historical Data:

The dataset may include historical job posting data, enabling trend analysis and comparative studies over time. This feature allows users to track changes in job market demand and employment trends. EU Countries Covered: Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden Benefits of the Dataset: Enhanced Recruitment Strategies: Recruiters and HR professionals can use the dataset to identify trends in job postings, understand competitive hiring practices, and optimize their recruitment strategies based on current market conditions. Labor Market Analysis: Analysts and policymakers can leverage the data to study labor market trends, identify skill gaps, and evaluate employment opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive list of job openings, tailored to their skills and preferred locations, facilitating a more efficient and targeted job search process. Strategic Workforce Planning: Companies can gain insights into the availability of talent in various regions, helping them make informed decisions about expanding operations or setting up new offices. Techsalerator’s Job Openings Data in Europe provides a crucial resource for understanding the dynamic European job market. By offering detailed, up-to-date information on job postings across multiple sectors and regions, it supports effective decision-making for businesses, job seekers, and market analysts alike.

As of the second quarter of 2025, there were approximately 201 million people in employment in the European Union. This figure marks a significant improvement on the previous years, when unemployment in the EU had risen due to the effects of the inflation caused by the rise of the energy prices. Employment in the EU reached a low point during this period of around 188 million people employed in the second quarter of 2020, since which it has risen rapidly, only declining marginally between quarter four of 2020 and quarter one of 2021. The recent history of EU employment growth Total employment in the EU has risen by almost 20 million people since the low point following the great recession and Eurozone crisis in quarter one of 2013. The early 2010s were a particularly difficiult time for the European Union, as the global financial crisis had caused the collapse of property and asset markets, particularly in Greece, Italy, Ireland, Portugal, and Spain. These countries were in many cases forced to provide extraordinary financial assistance to financial institutions, which ballooned their national debt and finally led to sovereign debt crises, with the ECB and IMF stepping in to provide bailouts. These successive crises, as well as the constrained fiscal approach to solving the crises, led to a prolonged period of falling total employment. As the waves of crises receded, the EU went through a prolonged period of job growth, driven in particular by Germany's period of export-led growth from 2015 onwards, in which total employment grew in the EU consistently from quarter two of 2013 to quarter four of 2019.

According to a labor force survey, Germany’s employment rate for nationals was high at **** percent, while for non-EU citizens it dropped significantly to **** percent. France reported its national employment rate at **** percent, while non-EU citizens recorded **** percent. Italy had the lowest national employment rate at **** percent among the four, with non-EU citizens even lower at **** percent.

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Description: These are research indicators of doctorate holders in Europe that were compiled from the criteria and factors of the Eurostat. This dataset consists of data in five categories (i.e. Career Development of Doctorate Holders; Labour Market - Job Vacancy Statistics; Skill-related Statistics; European and International Co-patenting in EPO Applications and Ownership of Inventors in EPO Applications). The Eurostat Research Indicators consist of (1) Doctorate holders who have studied, worked or carried out research in another EU country (%); (2) Doctorate holders by activity status (%); (3) Doctorate holders by sex and age group; (4) Employed doctorate holders working as researchers by length of stay with the same employer (%); (5) Employed doctorate holders working as researchers by job mobility and sectors of performance over the last 10 years (%); (6) Employed doctorate holders by length of stay with the same employer and sectors of performance (%); (7) Employed doctorate holders by occupation (ISCO_88, %); (8) Employed doctorate holders by occupation (ISCO_08, %); (9) Employed doctorate holders in non-managerial and non-professional occupations by fields of science (%); (10) Level of dissatisfaction of employed doctorate holders by reason and sex (%); (11) National doctorate holders having lived or stayed abroad in the past 10 years by previous region of stay (%); (12) National doctorate holders having lived or stayed abroad in the past 10 years by reason for returning into the country (%); (13) Non-EU doctorate holders in total doctorate holders (%); (14) Unemployment rate of doctorate holders by fields of science; (15) Employment in Foreign Affiliates of Domestic Enterprises; (16) Employment in Foreign Controlled Enterprises; (17) Employment rate of non-EU nationals, age group 20-64; (18) Intra-mural Business Enterprise R&D Expenditures in Foreign Controlled Enterprises; (19) Job vacancy rate by NACE Rev. 2 activity - annual data (from 2001 onwards); (20) Job vacancy statistics by NACE Rev. 2 activity, occupation and NUTS 2 regions - quarterly data; (21) Job vacancy statistics by NACE Rev. 2 activity - quarterly data (from 2001 onwards); (22) Value Added in Foreign Controlled Enterprises; (23) Graduates at doctoral level by sex and age groups - per 1000 of population aged 25-34; (24) Graduates at doctoral level, in science, math., computing, engineering, manufacturing, construction, by sex - per 1000 of population aged 25-34; (25) Level of the best-known foreign language (self-reported) by degree of urbanisation; (26) Level of the best-known foreign language (self-reported) by educational attainment level; (27) Level of the best-known foreign language (self-reported) by labour status; (28) Level of the best-known foreign language (self-reported) by occupation; (29) Number of foreign languages known (self-reported) by educational attainment level; (30) Number of foreign languages known (self-reported) by degree of urbanisation; (31) Number of foreign languages known (self-reported) by labour status; (32) Number of foreign languages known (self-reported) by occupation; (33) Population by educational attainment level, sex, age and country of birth (%); (34) Co-patenting at the EPO according to applicants’/inventors’ country of residence - % in the total of each EU Member State patents; (35) Co-patenting at the EPO: crossing inventors and applicants; (36) Co-patenting at the EPO according to applicants’/inventors’ country of residence - number; (37) EU co-patenting at the EPO according to applicants’/ inventors’ country of residence by international patent classification (IPC) sections - number; (38) EU co-patenting at the EPO according to applicants’/inventors’ country of residence by international patent classification (IPC) sections - % in the total of all EU patents; (39) Domestic ownership of foreign inventions in patent applications to the EPO by priority year; (40) Foreign ownership of domestic inventions in patent applications to the EPO by priority year; and (41) Patent applications to the EPO with foreign co-inventors, by priority year.

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

In 2023, the European Union's solar market had employed a total of ******* full-time equivalents (FTEs), including both direct and indirect jobs. The deployment segment had the largest workforce that year, at over ******* FTEs. This was followed by the operations and maintenance segment, but by a wide margin, with employment figures amounting to some ****** FTEs in 2023.

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The European Union Labour Force Survey (EU-LFS) provides population estimates for the main labour market characteristics, such as employment, unemployment, being outside the labour force, hours of work, occupation and much else, as well as important socio-demographic characteristics, such as sex, age, education, households and regions of residence. Since 1999, an inherent part of EU-LFS has been the modules. These were called 'ad hoc modules' until 2020. From 2021 onwards, they are called either '8-yearly modules' when the variables have an 8-yearly periodicity or 'modules on an ad hoc subject' for variables not included in the 8-yearly datasets. Commission delegated regulation (EU) 2020/256 specifies a multiannula rolling plan for the period 2021-2028.

Data and replication files for "Unpacking Migration Costs: Heterogeneous Effects in EU Labor Markets" in Economic Modelling. I employ a tractable two-country search model of unemployment with endogenous migration decisions for workers and apply the model to the context of the European Union. I find that migration costs for workers are important factors in determining migration, unemployment and wages. Increasing migration costs increase unemployment and decrease migration, wages and welfare. This headline result is disaggregated into heterogeneous effects across workers with different countries of origin and migration histories. Workers who move more times, or for longer spells, are more affected by costs than workers who move less or not at all, though non-migrating workers experience changes to their labor market outcomes due to the externalities imposed by migrant workers. Using EU data, I find that costly migration lowers welfare by 11-60% relative to free mobility.