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    Linking of geographic data

    Register data from Statistics Denmark can be supplemented with geographic data. This may include information about geographic areas (such as clusters and polygons) or distance information between a person’s residence and various institutions, such as hospitals, general practitioners, workplaces, or educational institutions., Denmark’s Data Portal can provide guidance and solve tasks within, among others, the following areas:, Grid cells, Clusters, Polygons/preparation of maps, Distance calculations, including road distances, Catchment area analyses/population formations, Questions regarding statistical disclosure control, Validation of users’ geographic classifications, If you have a task that falls outside these categories, you are still welcome to contact us. We are happy to discuss possibilities regarding whether and how the task can be solved., When such geographic data are linked to register data, they must be documented in DDV App and comply with Statistics Denmark’s specific requirements for statistical disclosure control of geographic data., Examples of Tasks, Case 1: Catchment Area Analysis of Coastal Development, Some municipalities along the west coast of Jutland wish to examine how legislation from 2017, which enables coastal development, affects the area across a number of parameters, including:, Impact on property price development, Impact on the municipality’s population composition, Impact on businesses/jobs/institutional capacity, Risks of storm and water damage in line with coastal construction, including development/revision of risk zone classifications for buildings and infrastructure, Formation of clusters, The user was advised by Denmark’s Data Portal to use clusters defined based on distance parameters from the coast. The clusters were formed as constant distances from the coastline and subdivided into 0–500 m, 500–1000 m, and 1000–1500 m from the coast. A limited number of clusters were defined, as they were also delimited by municipal boundaries., Possibilities for expanding the analysis, With information on which addresses belong to each cluster, the user was able to examine how the selected parameters developed within each cluster., At the same time, this classification ensured that clusters had sufficient size to meet statistical disclosure control requirements, i.e. at least 50 households in each cluster., Delivery time, The task was delivered as a tailored solution using existing data on municipalities and coastline positions in Denmark. In addition, data from the Danish Address Register (DAR) and Statistics Denmark’s population register (BEF) were used., A task of this kind can typically be delivered within two weeks after clarification between Denmark’s Data Portal and the user., Case 2: Geographic Clusters, Background, A user wishes to perform analyses based on populations in school districts across the country., Process, The user submits a GIS file (in a geographic data format) with geographic clusters in the form of school districts (polygons/areas), which can be downloaded free of charge from a joint municipal data register. The user wants these converted into addresses that can be linked to register data via address IDs., An address ID or address code is a key that uniquely identifies an address, either as a house number (access or entrance address) or a unit address (floor/apartment address). In practice, the address variable OPGIKOM is used, which together with KOM (municipality code) uniquely identifies access addresses in Denmark. OPGIKOM consists of road code and house number, including perhaps a letter., OPGIKOM is available in other Statistics Denmark registers, making it possible to link, for example, the population register (BEF) to these school districts (via the key register BEFADR)., Analytical possibilities, Register data can be linked across registers using the CPR number and address ID, and geographic distribution of analysis parameters can be examined using cluster information., Consultancy, Clarification of input data quality takes place before the task is carried out by Denmark’s Data Portal, and a time frame is agreed., In the delivered school districts, small gaps were identified where no active school district was registered. There were also many overlapping districts, as some districts cover up to 6th grade, while others cover all primary school levels or only 10th grade, meaning they fully or partially overlap geographically., As part of Denmark’s Data portals processing of geographic clusters, cluster size is also checked to ensure that no district contains fewer than 50 households (= occupied addresses). If districts are too small, they do not comply with Statistics Denmark’s data confidentiality policy., Delivery time, A task of this type can normally be completed within a framework of 10 hours and delivered within two weeks., Alternative Solutions, Many users apply geographic clusters based on grid cells, where 100x100 m cells are the smallest geographic units that can be linked to register data. Grid cells are aggregated in sparsely populated areas to meet the requirement of at least 50 households., Statistics Denmark can provide a set of standard clusters based on grid cells., In principle, there are no restrictions on which districts a user can apply for analysis, as long as they meet the requirement of at least 50 households. If business data are included in combination with geographic clusters, other minimum size requirements apply. These are determined in the specific project and depend on the scope and level of detail in the business data used., Case 3: Distance Calculation Between Residences and Upper Secondary Education, Background, A user wants to measure the distance between young people’s residence and the upper secondary education institution they attend or have attended within a defined period., Process, The user uploads a file containing the variables: city, address, and region for both residences and educational institutions. Input data quality is checked, and it is agreed whether distance calculations should be straight-line or road distance., Depending on address format, address cleaning may be required, including adding coordinates necessary for distance calculations., When completed, a SAS file is delivered with anonymised addresses for residences and institutions, along with distance in the desired unit (meters, kilometres)., Delivery time, Such tasks typically require between 15 and 30 hours, depending on scope and type of distance. Straight-line calculations require fewer hours than road distance calculations. Calculating distances between all residences and all institutions takes longer than limiting the calculation to, for example, the same region., Tasks with Grid Cell Data, Grid cell data divides Denmark into fixed cells based on coordinates, meaning the structure remains constant over time, unlike administrative divisions such as municipalities or parishes., Denmark’s Data Portal provides grid cell data at 100 m, 1 km, and 10 km resolution, linked with KOM and OPGIKOM (address11)., As a rule of thumb, grid data is most reliable from the municipal reform in 2007 onwards. It can also be used for earlier years, but coverage decreases the further back in time one goes., Denmark’s Data Portal offers two options:, Delivery of counts of households and individuals per grid cell from 1980 to the latest available year. These can be aggregated into clusters, after which a dataset is produced with cluster code, municipality, and anonymised OPGIKOM., Delivery of standard clusters. Based on the 100x100 m grid cells, Denmark’s Data Portal has developed standard clusters that can be linked to Statistics Denmark’s register data via KOM and OPGIKOM. These clusters are constructed according to the principle that they should be small, while still meeting statistical disclosure control requirements for all years in the time series for which the clusters can be applied. In addition, the clusters are composed of grid cells based on principles of proximity and population size within each cluster. As far as possible, the clusters are formed from grid cells that are adjacent to one another. However, exceptions may occur where clusters consist of grid cells that are not contiguous., Special Requirements for Statistical Disclosure Control of Geographic Data, When working with geographic data on the research server, specific requirements apply regarding how data may be used and displayed. For all geographic divisions, including clusters and grid cells, each unit must contain at least 50 households in the nighttime population per register year., If a project uses clusters across multiple years, this requirement must be met for every year, including newly added years. This may mean that previously delivered data can no longer remain in the project if they fail to meet requirements after updates., Pricing for Geodata Tasks, Pricing for geodata tasks varies. When we receive an inquiry, we prepare a framework agreement with an estimated maximum time consumption based on factors such as task complexity and data quality. After delivery, billing is based on actual time spent. See more under , Prices and price agreements., Getting Started, If you would like assistance with a geodata task, please contact your Project owner at Statistics Denmark and describe the task and data.

    https://www.dst.dk/en/TilSalg/data-til-forskning/databestilling/tilknytning-af-geografiske-data

    Documentation of statistics: Job Vacancies

    Contact info, Labour Market, Social Statistics , Monica Wiese Christensen , +45 21 73 34 69 , MWC@dst.dk , Get documentation of statistics as pdf, Job Vacancies 2026 Quarter 2 , Previous versions , Job Vacancies 2026 Quarter 1, Job Vacancies 2025 Quarter 4, Job Vacancies 2025 Quarter 3, Job Vacancies 2025 Quarter 2, Job Vacancies 2025 Quarter 1, Job Vacancies 2024 Quarter 4, Job Vacancies 2024 Quarter 3, Job Vacancies 2024 Quarter 2, Job Vacancies 2024 Quarter 1, Job Vacancies 2023 Quarter 4, Job Vacancies 2023 Quarter 3, Job Vacancies 2023 Quarter 2, Job Vacancies 2023 Quarter 1, Job Vacancies 2022 Quarter 4, Job Vacancies 2022 Quarter 3, Job Vacancies 2022 Quarter 2, Job Vacancies 2022 Quarter 1, Job Vacancies 2021 Quarter 4, Job Vacancies 2021 Quarter 3, Job Vacancies 2021 Quarter 2, Job Vacancies 2021 Quarter 1, Job Vacancies 2020 Quarter 4, Job Vacancies 2020 Quarter 3, Job Vacancies 2020 Quarter 2, Job Vacancies 2020 Quarter 1, Job Vacancies 2019 Quarter 4, Job Vacancies 2019 Quarter 3, Job Vacancies 2019 Quarter 2, Job Vacancies 2019 Quarter 1, Job Vacancies 2018 Quarter 4, Job Vacancies 2018 Quarter 3, Job Vacancies 2018 Quarter 2, Job Vacancies 2018 Quarter 1, Job Vacancies 2017 Quarter 4, Job Vacancies 2017 Quarter 3, Job Vacancies 2017 Quarter 2, Job Vacancies 2017 Quarter 1, Job Vacancies 2016 Quarter 4, Job Vacancies 2016 Quarter 3, Job Vacancies 2016 Quarter 2, Job Vacancies 2016 Quarter 1, Job Vacancies 2015 Quarter 4, Job Vacancies 2015 Quarter 3, Job Vacancies 2015 Quarter 2, Job Vacancies 2015 Quarter 1, Job Vacancies 2014 Quarter 4, The statistic illustrate the quarterly development in number of job vacancies and the job vacancy rate. The statistics are based on both survey and register data. Survey data are used for workplaces in the private sector, whereas register data are used for workplaces in the public sector., The statistics can be used as a labour market indicator together with other indicators. The Job Vacancy Statistics are subject to EU regulation and are compiled according to the same guidelines in all EU Member States., Statistical presentation, The statistics illustrate the quarterly development in the real number of job vacancies and the job vacancy rate. The job vacancy rate is calculated as the number of job vacancies in relation to the sum of job vacancies and occupied posts., The statistics are broken down by industry (economic activity), size, region and sector., Read more about statistical presentation, Statistical processing, For the private sector: Data are collected via electronic questionnaires on https://virk.dk/ as a sample of approximately 9,000 workplaces. Before 2026, when only industry groups B-N were covered, the sample consisted of approximately 7,000 workplaces. Data are checked for errors and missing values are imputed before grossing-up to a population total., For the public sector: Register data are used primarily from https://www.jobnet.dk. Based on a comprehensive survey, partly financed by EUROSTAT, models have been established that make register data from https://www.Jobnet.dk compatible with the statistical requirements., Read more about statistical processing, Relevance, The users of the statistics are primary the press, private companies, private persons and Eurostat. The statistic is used in analysis about the demand for labour and in the public debate. Data on job vacancies are collected in accordance with similar guidelines by all EU Member States, which implies that the statistics are suitable for comparing the development in the number of job vacancies across the EU Member States., Read more about relevance, Accuracy and reliability, For the private sector: As with all other sample-based statistics, there is some uncertainty associated with the estimates. As in other EU Member States, the coefficient of variation (CV), which is the standard deviation in relation to the estimate, is used in calculating the uncertainty. For the total number of occupied posts, the coefficient of variation (CV) is normally below 1 percent, while for the total number of job vacancies it is 2-5 percent. For industry groups and size groups, the CV is relatively high. This is primarily due to the large variations between the reported number of job vacancies and the many reports with zero job vacancies., For the public sector: Since public workplaces are legally obliged to post job vacancies on https://www.jobnet.dk, the administrative data source is assumed to be close to full coverage for the public labour market. However, there will be workplaces that do not post vacancies on https://www.jobnet.dk, even though this is legally required. Methodological decisions have been made based on assumptions in the models for handling intended job vacancies and handling presumed misreporting, and calibrated on the basis of the test survey in May 2024 financed by EUROSTAT. The number of occupied posts is measured as the number of occupied posts at the end of the quarter, obtained from SBR data, and therefore not on the counting date., Read more about accuracy and reliability, Timeliness and punctuality, Data are released around 75 days after the reference quarter., Read more about timeliness and punctuality, Comparability, As of the 1st quarter of 2026, the statistics changed from Dansk Branchekode 2007 (DB07, Danish subdivision of the EU classification NACE Rev 2) to Dansk Branchekode 2025 (DB25, defined on the new EU classification NACE Rev. 2.1)., From 2026 onwards, the population sample is drawn based on DB25. In 2026, the sample population was also expanded from covering industries B-N (DB07), corresponding to B-O (DB25), to covering industries B-TUV (DB25). This implies a transition from partial to full industry coverage, with the exception of industry group A (Agriculture, Forestry and Fishing). The number of job vacancies by industry in the period 2010-2025 has been converted from DB07 to DB25 using a conversion matrix that takes quarter, enterprise size and industry into account., In connection with the conversion, industry G has been split into three industries: G, J and TUV. The former partial industry coverage did not include industry TUV. Units belonging to industry TUV have therefore been omitted from the conversion. In 2025, this led to a reduction of the population by 3,497 units, corresponding to 8.5 percent of the old industry G. As a result, the archived pre-2026 tables and the updated post-2026 tables in StatBank Denmark will not have the same totals., Read more about comparability, Accessibility and clarity, These statistics are published quarterly in a Danish press release, at the same time as the tables are updated in the StatBank. In the StatBank, these statistics can be found under the subject , Job vacancies, . For further information, go to the , subject page, ., Read more about accessibility and clarity

    https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/job-vacancies

    Documentation of statistics

    Documentation of statistics: Hospitalization

    Contact info, Personal Finances and Welfare, Social Statistics , Line Neerup Handlos , +45 26 64 03 00 , LHA@dst.dk , Get documentation of statistics as pdf, Hospitalization 2025 , Previous versions , Hospitalization 2024, Hospitalization 2023, Hospitalization 2022, Hospitalization 2021, Hospitalization 2019, Hospitalization 2018, Hospitalisation 2017, Hospitalization 2016, Hospitalization 2015, Hospitalization 2014, Hospitalization 2013, The purpose of the Hospital Utilisation Statistics is to shed light on the connection between hospitals stays and social and demographic conditions. The statistics have been compiled since 1990, but are comparable in their current form only from 2017 onwards., Statistical presentation, The statistics are an annual inventory of stays at public and private somatic and psychiatric hospital wards within one calendar year. The statistics show how hospital stays vary with demographic and social factors, such as residence, sex, age, educational level, labour market affiliation and relatives. The statistics are published in News from Statistics Denmark and in the StatBank., Read more about statistical presentation, Statistical processing, The statistics are based on data retrieved from the National Patient Registry, which is shared with Statistics Denmark by the Danish Health Data Authority. Background data from Statistics Denmark are linked to the registry, and summaries and counts are produced — for example, the number of hospital stays and patients in public and private somatic and psychiatric hospital departments during the calendar year., Read more about statistical processing, Relevance, Public and private stakeholders, as well as the general population, can use the statistics to extract data on the population’s hospital utilisation for various analyses, research, public debate, etc. The statistics make it possible to produce figures for specific diagnosis groups and to link information on hospital utilisation with sociodemographic factors such as place of residence, education, labour market attachment, and origin. This is made possible by linking data from the National Patient Register with population register data from Statistics Denmark., Read more about relevance, Accuracy and reliability, The National Patient Register is validated by the Danish Health Data Agency and the accuracy of the register data must be considered to be high because the registration has a long tradition and a high priority for administrative purposes. Accordingly, the overall accuracy of the Hospital Utilisation Statistics is high. , Read more about accuracy and reliability, Timeliness and punctuality, The statistics are published within approximately seven months after the end of the reference period., Read more about timeliness and punctuality, Comparability, The statistics have been developed since 1990, but are only comparable in their current form from 2017 onwards., Eurostat and the OECD make comparable statistics in this field. There are a number of organizational and institutional conditions that we must keep in mind when comparing countries. , Read more about comparability, Accessibility and clarity, The statistics are released in the newsletter Nyt from Statistics Denmark (in Danish only) and the Statbank, Statbank tables on , hospitalisation utilisation, . For further information, go to the , subject page, ., Read more about accessibility and clarity

    https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/hospitalization

    Documentation of statistics

    Databank of basic data

    Here you can read a general description of the databank of basic data in Denmark’s Data Portal (DDP)., Basic data (also called ‘DDP basic data’ or ‘data in the databank of basic data in Denmark’s Data Portal’) refers to the microdata that DDP offers to external users for research and statistical purposes. Statistics Denmark (DST) has collected a wide range of register data with historical information in a bank of basic data within the DDP App. The various registers come from both external sources and internal statistical offices. Thematically, the data cover a broad spectrum, and the statistical unit may be individuals, addresses, enterprises, library loans, motor vehicles, and more. All basic data must comply with a set of standards for formats and naming etc., The data undergo extensive processing before being placed in the bank of basic data. There are several reasons for this:, Standardization saves users time-consuming preparation: by ensuring uniform data, external users can avoid a significant amount of manual data processing., Key variables must be standardized to enable data linkage: combining data across years and registers requires a common standard for key variables., Key variables must be standardized to enable pseudonymization: data can only be pseudonymized correctly if variables follow fixed standards and naming., Purpose of the databank of basic data, The purpose of the databank of basic data is to collect microdata for research and analysis in a way that makes it easy and straightforward to make microdata available to researchers., Content and use of the databank of basic data, DDP aims to ensure that all DST data from the official statistical program are available as basic data. This primarily includes microdata related to individuals, enterprises, or addresses., DST also holds data that are not part of the official statistical program but which DDP has received or collected for various reasons. This type of data is also made available as basic data to support reuse, rather than requiring the statistical offices to design customized extracts for the users., To qualify as basic data, a number of conditions must be met. For certain data, special considerations regarding data confidentiality, funding arrangements or data quality may influence how the data can be used. DST enters into agreements with other authorities (and data owners) for regular deliveries of register data that can be made available to researchers. These data are also placed in the databank of basic data. Read more under , Data from other data providers for the databank of basic data, ., Before data may be used on the research server, variables that can directly identify individuals undergo pseudonymization. This means that all variables containing identification information such as CPR numbers, CVR numbers, addresses, and property numbers, are recoded in a pseudonymization process before being transferred to the user’s project. Each register includes a marking of which variables must be pseudonymized. When new variables are added to a register, DDP assesses, together with the data owner and based on the data confidentiality policy, whether the variables must be pseudonymized before the data can be released for research and analysis., Applying the procedures and guidelines described, ensures that data stored in the databank of basic data and presented within DST follow a standardized format. This makes it easy for researchers to access the data and navigate the available datasets. Read more about where to find documentation for basic data on the page , Documentation of data, .

    https://www.dst.dk/en/TilSalg/data-til-forskning/generelt-om-data/grunddatabanken

    Surveys

    Contact Information, Senior Advisor, Bo Lønberg Bilde, , mobile +45 91 37 64 26, Head of Division, Marie Fuglsang, , mobile +45 20 35 39 25, Statistics Denmark conducts surveys for both private and public customers. We carry out nationwide surveys with a random sample of the Danish population and businesses. We can also use our extensive registers as a basis to form a sample. This means that we can carry out interviews among almost any sample in Denmark, such as: , Parents of children in kindergarten , Nurses who completed their training 3-5 years ago , Car owners in the metropolitan area , Export Companies in the capital area , Companies of a certain size in Jutland   , There are many more options than the ones mentioned above. Contact us to learn more about the possibilities., The surveys are carried out either via a web form, using telephone interviews or a postal questionnaire or a combination of these three methods., Delivery, Results from the survey can be delivered as anonymised data on an individual and company level and/or coupled with register data in tables. If you want to analyze data from Statistics Denmark registers coupled with data from the study on an individual or company level this can be done through our Division of Research. Read more about the possibilities here , www.dst.dk/research, ., Price, The price depends on the content and scope of the survey. When the design of a particular survey is settled, we will send you a final price quote. It is also possible to get supplementary questions included on the monthly omnibus survey – an inexpensive way to conduct small studies with high quality.

    https://www.dst.dk/en/TilSalg/interview

    Not in Employment, Education or Training (NEET)

    Register-based indicator for the share of young persons from 16-29 years who are not in education, employment, or training (NEET). , Introduction , NEET is an acronym for "Not in Employment, Education or Training", and this set of statistics accounts for the number of young persons from 16-29 years who are not in employment or education. The statistical data period is the last week of November, and a person is categorised as not active (NEET), if he or she is not in employment in that week and is not in education either in that same week as well as the preceding three weeks., The statistics are based on the Labour Market Accounts (LMA) and facilitate the calculation of a NEET indicator based on register data. The statistics have taken inspiration from the NEET indicator, which is defined by Eurostat as well as the OECD, and which is calculated from the interview-based Labour Force Survey (LFS)., Documentation , Documentation of statistics, Get an overview of the purpose, content and quality of the statistics. Here you can find information on the sources that the statistics are derived from, what the statistics contains and how often it is published., Labour Market Account, Key figures , In Statbank Denmark, you can find more data on Population (16-29 years) (NEET3), Population (16-29 years) by NEET status, age, sex and time, Unit: , Number, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, Active, Age, total, Men, 456,881, 465,724, 471,014, 472,558, 469,944, 469,774, 480,662, 479,900, 479,777, 486,052, Women, 434,930, 442,651, 449,283, 452,928, 451,233, 451,979, 460,859, 462,820, 462,684, 466,940, Not active (NEET), Age, total, Men, 68,397, 68,158, 68,275, 68,594, 69,540, 66,924, 58,135, 61,620, 62,925, 58,669, Women, 67,627, 68,284, 67,428, 66,534, 66,661, 62,689, 55,789, 59,073, 60,303, 57,473, In Statbank Denmark, you can find more data on Population (16-29 years) (NEET4), Related content in Not in Employment, Education or Training (NEET), Tables in Statbank , Scheduled releases , Contact, Pernille Stender, Phone: +45 24 92 12 33, Mail: , psd@dst.dk

    https://www.dst.dk/en/Statistik/emner/arbejde-og-indkomst/befolkningens-arbejdsmarkedsstatus/unge-uden-for-beskaeftigelse-og-uddannelse-neet

    Subject page

    Non-standard forms of employment

    How many people work on a temporary contract, for only a few hours per week, through digital platforms, or have more than one job? Non-standard forms of employment refer to work arrangements that differ from the traditional permanent full-time position, as well as from self-employed individuals with employees. Here, you can gain insight into how widespread selected atypical forms of employment are among employed people in Denmark and follow developments over time., Non-standard forms of employment , includes forms of work that differ from the traditional permanent full-time position and from traditional self-employment with employees. Examples include temporary employment, digital platform work, temp work, and holding multiple jobs simultaneously. A defining characteristic is that the connection to the labor market is typically less stable than in standard full-time employment., Temporary Employment , Is a paid job with a fixed or limited duration. This means that the job either ends at a predetermined date or upon completion of a specific task or period, such as a project or the temporary replacement of an absent employee. Temporary employment therefore differs from permanent jobs, which do not have a predetermined end date., Economically active indicator , An enterprise is considered economically active if it is assessed to carry out economic activity of a certain scale. This may, for example, be the case if the labour input corresponds to at least 0.5 full-time equivalent (FTE) employment, or if the enterprise's turnover, purchases of goods and services, imports, exports, value added, or total assets exceed specified thresholds., Prevalence and development in non-standard forms of employment, The figure shows the prevalence and development of selected forms of non-standard forms of employment. Five indicators are included, each describing a different type of employment and presented as a share of either all employed persons or employees only. , The indicators are not mutually exclusive, meaning that the same individual may be included in more than one indicator., Read about the individual indicators in the methodology documentation (pdf) in danish,  , In Statbank Denmark, you can find more data on Non-standard forms of employment (per cent) (ATYP001), More about the figure , Last update , 9.6.2026, Next update , 26.11.2026, Source data , The Labour Force Survey is quarterly based on a stratified sample. The sample was reduced in the 1st quarter of 2016. The reduction will be implemented successively and the sample size will be reduced from 40,532 individuals to 34.320 persons aged 15 to 74 years in the 1st quarter of 2017 when the reduction is fully implemented. Until the year 2020 the LFS has been collected at the individual level for 15-74-year-olds. From 2021 the population has changed to also include the age group from 75-89 years. On a quarterly basis the sample has thus increased from 34,320 people to 36,020 people. , In 2022q1, a new stratification was introduced in the LFS. Register data on employment and register unemployment are utilized to a greater extent, in order to obtain a greater number of responses in some of the groups that suffer from low response rates. For starters, the population is divided into four groups: 1) in stable employment, 2) in registered unemployment, 3) neither in stable employment nor registered unemployment and 4) persons aged 75 to 89. In addition, groups 1 and 3 are divided into the age groups 15-29, 30-64 and 65-74. This results in eight different strata, which are used in the sample selection from 2022q1 onwards. As the LFS consists of four panels, each appearing for six quarters, this stratification was fully implemented in Q2 2023., The sample is weighted to represent the population as it was at the end of the previous quarter. Different administrative resources are used to select the sample. Administrative sources are also used to obtain various background information on the people interviewed, for example on educational level or workplace., These registers (among others) are being used for the Labour Force Survey: , Central Population Register (CPR) , Population Register , The Register of Labour Market Statistics (RAM) , Register based-labour force statistics (RAS) , Education classification (DISCED) , Employees, Register of income, Business statistics register, Read more about sources, method and quality in the documentation of statistics on Labour Force Survey (LFS) , Employed persons with multiple jobs and fee-based income, Here you can see the share of employed persons with multiple concurrent jobs or fee-based income (multiple job-holders). This includes, among others, employees with a secondary job or fee-based work, as well as self-employed persons who have additional work alongside their business. , In Statbank Denmark, you can find more data on Employed persons (per cent of employed persons) (ATYP013), More about the figure , Last update , 9.6.2026, Next update , 26.11.2026, Source data , Starting from the publication on April 28, 2015, RAS is based on the Labor Market Account (AMR_UN), which is a longitudinal register. In this context, RAS has been revised back to November 2008. At the same time, the dating of the statistics was changed, so that it is now dated according to the reference point at the end of November. This means that the most recent assessment is labeled as the end of November 2024, whereas previously it would have been labeled 2025. , Data in AMR_UN come from a number of other sources:, The eIncome Register, The Business Statistics Register, The Statistics on People Receiving Public Benefits, The Education Statistics, The Income Statistics, The Population Statistics, The Maternity and Sickness Benefits Statistics, The Occupational Classification Module, Before 2008, the basic data for employees came from the central information form register at SKAT, and these data were not longitudinal., Read more about sources, method and quality in the documentation of statistics on Register-Based Labour Force Statistics , Self-employed without employees, The figure shows the number and income distribution of self-employed persons without employees, broken down by level of economic activity. Income is measured as total pre-tax income in Danish kroner., In Statbank Denmark, you can find more data on Self-employed (primary work) without employees (ATYP021), More about the figure , Last update , 9.6.2026, Next update , 26.11.2026, Source data , Starting from the publication on April 28, 2015, RAS is based on the Labor Market Account (AMR_UN), which is a longitudinal register. In this context, RAS has been revised back to November 2008. At the same time, the dating of the statistics was changed, so that it is now dated according to the reference point at the end of November. This means that the most recent assessment is labeled as the end of November 2024, whereas previously it would have been labeled 2025. , Data in AMR_UN come from a number of other sources:, The eIncome Register, The Business Statistics Register, The Statistics on People Receiving Public Benefits, The Education Statistics, The Income Statistics, The Population Statistics, The Maternity and Sickness Benefits Statistics, The Occupational Classification Module, Before 2008, the basic data for employees came from the central information form register at SKAT, and these data were not longitudinal., Read more about sources, method and quality in the documentation of statistics on Register-Based Labour Force Statistics , Involuntary part-time employment, The figure shows the share of employed persons who report working part-time because they have been unable to find a full-time job. , In Statbank Denmark, you can find more data on Involuntary part-time work (15-74-year-olds) (per cent and number) (ATYP041), More about the figure , Last update , 9.6.2026, Next update , 9.3.2027, Source data , The Labour Force Survey is quarterly based on a stratified sample. The sample was reduced in the 1st quarter of 2016. The reduction will be implemented successively and the sample size will be reduced from 40,532 individuals to 34.320 persons aged 15 to 74 years in the 1st quarter of 2017 when the reduction is fully implemented. Until the year 2020 the LFS has been collected at the individual level for 15-74-year-olds. From 2021 the population has changed to also include the age group from 75-89 years. On a quarterly basis the sample has thus increased from 34,320 people to 36,020 people. , In 2022q1, a new stratification was introduced in the LFS. Register data on employment and register unemployment are utilized to a greater extent, in order to obtain a greater number of responses in some of the groups that suffer from low response rates. For starters, the population is divided into four groups: 1) in stable employment, 2) in registered unemployment, 3) neither in stable employment nor registered unemployment and 4) persons aged 75 to 89. In addition, groups 1 and 3 are divided into the age groups 15-29, 30-64 and 65-74. This results in eight different strata, which are used in the sample selection from 2022q1 onwards. As the LFS consists of four panels, each appearing for six quarters, this stratification was fully implemented in Q2 2023., The sample is weighted to represent the population as it was at the end of the previous quarter. Different administrative resources are used to select the sample. Administrative sources are also used to obtain various background information on the people interviewed, for example on educational level or workplace., These registers (among others) are being used for the Labour Force Survey: , Central Population Register (CPR) , Population Register , The Register of Labour Market Statistics (RAM) , Register based-labour force statistics (RAS) , Education classification (DISCED) , Employees, Register of income, Business statistics register, Read more about sources, method and quality in the documentation of statistics on Labour Force Survey (LFS) , Part-time work of less than 15 hours per week, Here you can see the development in the share of employees with a weekly working time of less than 15 hour. The measure includes working hours from all employee jobs. , In Statbank Denmark, you can find more data on Employees (per cent) (ATYP083), More about the figure , Last update , 9.6.2026, Next update , 26.11.2026, Source data , Starting from the publication on April 28, 2015, RAS is based on the Labor Market Account (AMR_UN), which is a longitudinal register. In this context, RAS has been revised back to November 2008. At the same time, the dating of the statistics was changed, so that it is now dated according to the reference point at the end of November. This means that the most recent assessment is labeled as the end of November 2024, whereas previously it would have been labeled 2025. , Data in AMR_UN come from a number of other sources:, The eIncome Register, The Business Statistics Register, The Statistics on People Receiving Public Benefits, The Education Statistics, The Income Statistics, The Population Statistics, The Maternity and Sickness Benefits Statistics, The Occupational Classification Module, Before 2008, the basic data for employees came from the central information form register at SKAT, and these data were not longitudinal., Read more about sources, method and quality in the documentation of statistics on Register-Based Labour Force Statistics , Temporary employment, The figure shows the share of employees in temporary employment, broken down by age groups. , In Statbank Denmark, you can find more data on Temporary employment (15-74-year-olds) (per cent and number) (ATYP051), More about the figure , Last update , 9.6.2026, Next update , 9.3.2027, Source data , The Labour Force Survey is quarterly based on a stratified sample. The sample was reduced in the 1st quarter of 2016. The reduction will be implemented successively and the sample size will be reduced from 40,532 individuals to 34.320 persons aged 15 to 74 years in the 1st quarter of 2017 when the reduction is fully implemented. Until the year 2020 the LFS has been collected at the individual level for 15-74-year-olds. From 2021 the population has changed to also include the age group from 75-89 years. On a quarterly basis the sample has thus increased from 34,320 people to 36,020 people. , In 2022q1, a new stratification was introduced in the LFS. Register data on employment and register unemployment are utilized to a greater extent, in order to obtain a greater number of responses in some of the groups that suffer from low response rates. For starters, the population is divided into four groups: 1) in stable employment, 2) in registered unemployment, 3) neither in stable employment nor registered unemployment and 4) persons aged 75 to 89. In addition, groups 1 and 3 are divided into the age groups 15-29, 30-64 and 65-74. This results in eight different strata, which are used in the sample selection from 2022q1 onwards. As the LFS consists of four panels, each appearing for six quarters, this stratification was fully implemented in Q2 2023., The sample is weighted to represent the population as it was at the end of the previous quarter. Different administrative resources are used to select the sample. Administrative sources are also used to obtain various background information on the people interviewed, for example on educational level or workplace., These registers (among others) are being used for the Labour Force Survey: , Central Population Register (CPR) , Population Register , The Register of Labour Market Statistics (RAM) , Register based-labour force statistics (RAS) , Education classification (DISCED) , Employees, Register of income, Business statistics register, Read more about sources, method and quality in the documentation of statistics on Labour Force Survey (LFS) , Reasons for temporary employment, Here you can see the reasons for temporary employment among employees, broken down by age groups. Reasons may include structurally time-limited positions such as internships and project-based contracts, the individual’s own preference for temporary work, or the inability to obtain permanent employment. , In Statbank Denmark, you can find more data on Temporary employment (15-74-year-olds) (per cent) (ATYP053), More about the figure , Last update , 9.6.2026, Next update , 9.3.2027, Source data , The Labour Force Survey is quarterly based on a stratified sample. The sample was reduced in the 1st quarter of 2016. The reduction will be implemented successively and the sample size will be reduced from 40,532 individuals to 34.320 persons aged 15 to 74 years in the 1st quarter of 2017 when the reduction is fully implemented. Until the year 2020 the LFS has been collected at the individual level for 15-74-year-olds. From 2021 the population has changed to also include the age group from 75-89 years. On a quarterly basis the sample has thus increased from 34,320 people to 36,020 people. , In 2022q1, a new stratification was introduced in the LFS. Register data on employment and register unemployment are utilized to a greater extent, in order to obtain a greater number of responses in some of the groups that suffer from low response rates. For starters, the population is divided into four groups: 1) in stable employment, 2) in registered unemployment, 3) neither in stable employment nor registered unemployment and 4) persons aged 75 to 89. In addition, groups 1 and 3 are divided into the age groups 15-29, 30-64 and 65-74. This results in eight different strata, which are used in the sample selection from 2022q1 onwards. As the LFS consists of four panels, each appearing for six quarters, this stratification was fully implemented in Q2 2023., The sample is weighted to represent the population as it was at the end of the previous quarter. Different administrative resources are used to select the sample. Administrative sources are also used to obtain various background information on the people interviewed, for example on educational level or workplace., These registers (among others) are being used for the Labour Force Survey: , Central Population Register (CPR) , Population Register , The Register of Labour Market Statistics (RAM) , Register based-labour force statistics (RAS) , Education classification (DISCED) , Employees, Register of income, Business statistics register, Read more about sources, method and quality in the documentation of statistics on Labour Force Survey (LFS) , Digital platform work, The table shows the share of employed persons who have performed digital platform work within the past month. Digital platform work refers to work tasks mediated through digital platforms or apps. , Share of employed persons who have performed digital platform work within the past month, Unit: , Per cent, Has worked on digital platforms, Has not worked on digital platforms, 2024Q3, 0.3, 99.7, In Statbank Denmark, you can find more data on Employees (15-74-year-olds) (per cent) (ATYP091), More about the figure , Last update , 9.6.2026, Source data , The Labour Force Survey is quarterly based on a stratified sample. The sample was reduced in the 1st quarter of 2016. The reduction will be implemented successively and the sample size will be reduced from 40,532 individuals to 34.320 persons aged 15 to 74 years in the 1st quarter of 2017 when the reduction is fully implemented. Until the year 2020 the LFS has been collected at the individual level for 15-74-year-olds. From 2021 the population has changed to also include the age group from 75-89 years. On a quarterly basis the sample has thus increased from 34,320 people to 36,020 people. , In 2022q1, a new stratification was introduced in the LFS. Register data on employment and register unemployment are utilized to a greater extent, in order to obtain a greater number of responses in some of the groups that suffer from low response rates. For starters, the population is divided into four groups: 1) in stable employment, 2) in registered unemployment, 3) neither in stable employment nor registered unemployment and 4) persons aged 75 to 89. In addition, groups 1 and 3 are divided into the age groups 15-29, 30-64 and 65-74. This results in eight different strata, which are used in the sample selection from 2022q1 onwards. As the LFS consists of four panels, each appearing for six quarters, this stratification was fully implemented in Q2 2023., The sample is weighted to represent the population as it was at the end of the previous quarter. Different administrative resources are used to select the sample. Administrative sources are also used to obtain various background information on the people interviewed, for example on educational level or workplace., These registers (among others) are being used for the Labour Force Survey: , Central Population Register (CPR) , Population Register , The Register of Labour Market Statistics (RAM) , Register based-labour force statistics (RAS) , Education classification (DISCED) , Employees, Register of income, Business statistics register, Read more about sources, method and quality in the documentation of statistics on Labour Force Survey (LFS) , Reasons for temp work, The figure shows the reasons for temp work, broken down by age groups. Individuals are classified according to whether they work as temp workers because they have been unable to obtain a permanent job or because they have chosen to do so themselves. , In Statbank Denmark, you can find more data on Temp workers (15-74-year-olds) (per cent) (ATYP063), More about the figure , Last update , 9.6.2026, Next update , 9.3.2027, Source data , The Labour Force Survey is quarterly based on a stratified sample. The sample was reduced in the 1st quarter of 2016. The reduction will be implemented successively and the sample size will be reduced from 40,532 individuals to 34.320 persons aged 15 to 74 years in the 1st quarter of 2017 when the reduction is fully implemented. Until the year 2020 the LFS has been collected at the individual level for 15-74-year-olds. From 2021 the population has changed to also include the age group from 75-89 years. On a quarterly basis the sample has thus increased from 34,320 people to 36,020 people. , In 2022q1, a new stratification was introduced in the LFS. Register data on employment and register unemployment are utilized to a greater extent, in order to obtain a greater number of responses in some of the groups that suffer from low response rates. For starters, the population is divided into four groups: 1) in stable employment, 2) in registered unemployment, 3) neither in stable employment nor registered unemployment and 4) persons aged 75 to 89. In addition, groups 1 and 3 are divided into the age groups 15-29, 30-64 and 65-74. This results in eight different strata, which are used in the sample selection from 2022q1 onwards. As the LFS consists of four panels, each appearing for six quarters, this stratification was fully implemented in Q2 2023., The sample is weighted to represent the population as it was at the end of the previous quarter. Different administrative resources are used to select the sample. Administrative sources are also used to obtain various background information on the people interviewed, for example on educational level or workplace., These registers (among others) are being used for the Labour Force Survey: , Central Population Register (CPR) , Population Register , The Register of Labour Market Statistics (RAM) , Register based-labour force statistics (RAS) , Education classification (DISCED) , Employees, Register of income, Business statistics register, Read more about sources, method and quality in the documentation of statistics on Labour Force Survey (LFS) , On the statistics – documentation, sources and method, Gain an overview of the purpose, contents and quality of the statistics. Learn about the data sources of the statistics, the contents of the statistics and how often they are published., See the documentation of statistics to learn more:, Labour Force Survey (LFS) , The purpose of the Labour Force Survey (LFS) is giving a description of the labour market status of the population. The LFS gives insight into how many people are employed, unemployed or outside the labour force (economically inactive). The LFS also manages to measure information like how many people are working part time; how many hours men in their 30s or 40s usually work; or how many elderly people outside the labour market would like to have a job. The LFS has been conducted yearly since 1984, and from 1994 the survey has been conducted continuously throughout the year., Read more about sources, method and quality in the documentation of statistics on Labour Force Survey (LFS), Labour Market Account , New Labour Market Account concerning the population´s labour market status have been developed by Statistics Denmark. , The primary purpose of the Labour Market Accounts (LMA) is to provide a complete overview of the population´s labour market status compiled in terms of full-time persons, covering a given period of time or a given point-in-time., Read more about sources, method and quality in the documentation of statistics on Labour Market Account, Register-Based Labour Force Statistics , The purpose of the Register-Based Labour Force Statistics (RAS) is to measure the population’s primary attachment to the labour market. This attachment is recorded at the end of November and compiled once a year. The first RAS compilation was made at the end of November 1980., Read more about sources, method and quality in the documentation of statistics on Register-Based Labour Force Statistics, Need more data on Non-standard forms of employment?, More detailed figures are available, for example for multiple job holders by sex, age, educational attainment, or municipality of residence, as well as temp work by industry. There are also statistics linking employee jobs with immigrant background., Go to the StatBank, Contact, Ida Frederikke Mathiesen, Phone: +45 21 49 48 53, Mail: , ifm@dst.dk

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