The three European countries with the greatest share of their business communities stating that they expect their number of employees to decrease over 2025 are Serbia, Germany and Austria. Almost one quarter of Serbian businesses believed they would have to lay off workers in 2025. By contrast, almost all the businesses in Turkey believe that they would either increase their number of employees or that their employment figures would remain unchanged during the year.

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.

The Netherlands had the highest employment rate among European Union countries in 2024, 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 78.4 percent. Italy reported the lowest employment rate in the EU at 62.3 percent.

The statistic reflects the seasonally adjusted unemployment rate in member states of the European Union in November 2024. The seasonally adjusted unemployment rate in Spain in November 2024 was 11.2 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 job seekers 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.

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.

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.

As of the third quarter of 2023, the number of persons who had transitioned from unemployment to employment over the previous three months was equivalent to 23.2 percent of all unemployed persons. This marked a decrease from the high point of transitions to employment, which occurred in quarter two of 2022, when the rate was at 26.3 percent. The rate of labor market transitions has been increasing consistently since its low point in quarter one of 2013, albeit with seasonal variations.

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.

Labour market policy (LMP) measures refer to public labour market interventions where the main activity of participants is other than job-search related and where participation usually results in a change in labour market status. LMP measures cover primarily interventions that provide temporary support for groups that are disadvantaged in the labour market (unemployed, employed at risk, and inactive persons). LMP measures are classified by type of action and cover the following categories: training, job rotation and job sharing, employment incentives, supported employment and rehabilitation, direct job creation, and start-up incentives. Participants in LMP measures are presented here as annual average stock, i.e. the average number of persons participating in an intervention at any point during the year. The annual average stock can also be interpreted as the number of person-years of participation in an intervention.

The datasets consist of a corpus of legal sources in the EU legal system mentioning the expression “labour market” (1968 – March 2023). Data were collected from the Eur-Lex website and then coded by hand according to pre-established definitions of “labour market”. The database contains information on the year, subject matter, and institutional author of the legal text, as well as hyperlinks to the original documents.

Agencies in this industry list employment vacancies, refer applicants to jobs, or directly place them in permanent or temorary employment. For permanent positions, emloyees placed aren't employed by the agency but for temporary employment they are. It includes the activities of online placement agencies and human resources provision.

Revenue in the Temporary Employment Agency industry is anticipated to drop at a compound annual rate of 4% in the five years through 2024 to €236.5 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 - a sizeable blow to revenue in the three years through 2022. Workers on temporary contracts represented a significant chuck of employment losses in all quarters of 2020. According to Eurostat data, temporary employment declined across Europe in the four years from 2017 to 2020, dipping from 13.8% to 11.9%. Since COVID-19 has slowed, companies have resumed hiring as confidence levels have been restored and vacancy levels have soared. An increasingly tight labour market encourages employers to rely on temporary employment placement agencies to fight in an increasingly competitive market. Several countries rank highly in terms of temporary workers with a large short-term job market. In 2022, the Netherlands and Spain have more than 15% of employed people under temporary contracts, according to Eurostat. Industry revenue is expected to shrink by 1.6% in 2024. Revenue is expected to grow at an annual rate of 4.5% in the five years through 2029 to €295.4 billion. With 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 and inflation keeps squeezing budgets. 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 implanting policies that may disrupt or expand services. Spain has already introduced reforms that are taking effect to increase permanent positions and remove temporary contracts, while Italy is expanding its voucher scheme to encourage temporary hires.

Cedefop skill supply and demand forecasts provide comprehensive information on the future labour market trends in Europe.

The forecasts act as an early warning mechanism to help to alleviate potential labour market imbalances and support different labour market actors in making informed decisions.

The labour force represents people in the population 15 + who are economically active, i.e. the labour force includes employed and disposable unemployed persons (actively seeking for jobs).

People from the population 15+ who are not considered as labour force are those voluntary unemployed (not seeking for job and even if offered they are likely to refuse it), disabled, retired or on parental leave etc.

The calculations are based on the demographic forecasts and assumptions on the future development participation (activity) rates by different age groups, genders and countries. From the labour market point of view labour force represents labour/skill supply.

Europe Pre Employment Assessment Tools accounted for a share of over 30% of the global market size of USD 555.36 million in 2023.

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.

LMP statistics are one of the data sources for monitoring the Employment Guidelines (part II of the Europe 2020 Integrated Guidelines). The guidelines specifically refer to the provision of active labour market policies, which cover LMP measures and LMP services, and adequate social security systems, which include LMP support. The scope of LMP statistics is limited to public interventions which are explicitly targeted at groups of persons with difficulties in the labour market: the unemployed, persons employed but at risk of involuntary job loss and inactive persons who would like to enter the labour market. Data on public expenditure and participants (stock and flows) are collected annually from administrative sources. According to the LMP methodology, labour market interventions are classified by type of action.

This is a large dataset which contains the labour market statistics data series published in the monthly Labour Market Statistics Statistical Bulletin. The dataset is overwritten every month and it therefore always contains the latest published data. The Time Series dataset facility is primarily designed for users who wish to customise their own datasets. For example, users can create a single spreadsheet including series for unemployment, claimant count, employment and workforce jobs, rather than extracting the required data from several separate spreadsheets published on the website.

Job mismatches and their labour market effects among school-leavers in Europe

LMP statistics are one of the data sources for monitoring the Employment Guidelines (part II of the Europe 2020 Integrated Guidelines). The guidelines specifically refer to the provision of active labour market policies, which cover LMP measures and LMP services, and adequate social security systems, which include LMP support.

The scope of LMP statistics is limited to public interventions which are explicitly targeted at groups of persons with difficulties in the labour market: the unemployed, persons employed but at risk of involuntary job loss and inactive persons who would like to enter the labour market.

Data on public expenditure and participants (stock and flows) are collected annually from administrative sources. According to the LMP methodology, labour market interventions are classified by type of action.

The European Union Labour Force Survey is a rotating random sample survey covering the population in private households in currently 34 European countries. The main aim of the LFS is to provide comparable information on employed, unemployed and inactive persons of working age (15 years and above) in European countries. The definitions of employment and unemployment used in the LFS closely follow the International Labour Organisations guidelines. Common classifications used are: NACE , ISCO, ISCED, NUTS. Core topics of the LFS are demographic background; labour status; employment characteristics of the main job; hours worked; second job; previous work experience of person not in employment; search for employment; methods used during previous four weeks to find work; main labour status; education and training; situation one year before survey; income; atypical work. Since 1999 the LFS also includes so called 'ad hoc modules' on a yearly but rotating basis.