Skip to content

Search result

    Showing results 1 - 10 of 34

    Documentation of statistics: Business Demography

    Contact info, Business Dynamics, Business Statistics , Asbjørn Hviid Mikkelsen , +45 29 42 68 36 , AHM@dst.dk , Get documentation of statistics as pdf, Business Demography 2024 , Previous versions , Business Demography 2023, Business Demography 2022, Business Demography 2020, Business Demography 2018, Business Demography 2017, Business Demography 2016, Business Demography 2015, Business Demography 2014, Business Demography 2013, Business Demography 2012, The purpose of the Business Demography is to provide information about the development of enterprise births and deaths as well as the survival of new enterprises over a period of five years. The statistics is comparable from 2019 onwards., Statistical presentation, The statistics cover the annual number of enterprise births and deaths and provides information on turnover and employment. The results are broken down by industry, size class, enterprise form, and geographical location. The statistics are disseminated in Nyt from Statistics Denmark and in our statbank., Read more about statistical presentation, Statistical processing, Data is collected from the Statistical Business Register. Subsequently various processes are undertaken to determine whether or not an enterprise is new (enterprise birth), terminated (enterprise death), or surviving. The controls are done to validate if the new enterprises, which exists in the administrative reality, are in fact real new enterprises, which enters the economy, or if it is an activity that is continued after a takeover or e.g. a split of an existing enterprise., Read more about statistical processing, Relevance, The statistic is used by ministries and governmental agencies, regional and county authorities as well as private sector institutions and enterprises, The statistic is a central indicator for entrepreneurship and for sustainability in the economy, and there is a large demand for using the output in combination with other sources, and following the development of enterprises who survive., Read more about relevance, Accuracy and reliability, The statistics is based on validated register data., Uncertainty is related to relations between units and work places, which are primarily based on digital income reporting by enterprises, and lack of knowledge concerning the transfer of activities from one enterprise to another, including the separation of activities. , Read more about accuracy and reliability, Timeliness and punctuality, The statistics is published annually. The publication is usually available 18 months after the end of the reference year. The statistics is also published with preliminary data 12 months after the end of the reference year. , Read more about timeliness and punctuality, Comparability, The national published Business Demography is a sub-population of the Danish published General enterprise statistics., Covering private enterprises excluding agriculture, the Danish published Business demography can be compared to the Business Statistics published by Eurostat., Read more about comparability, Accessibility and clarity, Results are published in , Nyt fra Danmarks Statistik, . Results are also available in the StatBank under the topic , Business demography, ., Read more about accessibility and clarity

    https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/business-demography

    Documentation of statistics

    How to create a data order

    A data order is a request used to specify which , registers, , , periods, , , populations, , and , external data, you wish to use in the project.,  , Via Projects and Data, Open , Projects and Data, Select , Reorder, Choose the project to which the data order relates, Via the Project Proposal, Go to the Data packages section in the project proposal, Follow the link Create a new order in the text at the top of the page, A new data order is created automatically, Once the data order has been created, a new section called , Order, appears in the navigation menu. Here, you can choose whether to order , register data, , , external data, , or , population creation, ., Please note that for a new project, a data order can be created once a project proposal has been set up, but it can only be submitted after the project proposal has been submitted., How to Order Register Data, Under Register Data, select the registers that should be included in or updated for the project. You can only choose registers from data packages that have either been requested or approved in the project proposal., If the project proposal has already been approved and you wish to use registers from data packages that are not yet approved, you must create a resubmission. Registers from new data packages will only be processed once the resubmission has been approved., Before selecting registers, you may specify the , Preferred order period, at the top of the page if the data should only be delivered for a limited time period., Under , Choose populations, , choose which populations the registers should be extracted for. Under , Distribution of registers on populations, , specify which registers should be extracted for which populations., If a full register extract is required, this must be indicated in the project proposal under the population description. The full register will then automatically appear as a population in the data order. If the project has previously been approved for a full population but this option does not appear, you should contact the project owner at Statistics Denmark., If a population is uploaded to the project, it must be ordered as a population creation, as this is a prerequisite for it to be selected., How to Order External Data, If the project uses data that is not register data, this should be added as , External Data, ., External data is added by:, Creating an order (see above), Selecting the , plus sign, next to , Processing of external data, Entering a title, data source type (dropdown menu), and a brief description, Uploading relevant attachments and variable lists. Read more about the requirements for , linking of external data, External data is currently linked to populations in the same way as register data, and the distribution is specified per population., External data must be ordered when data is uploaded., How to Create a Population, If there is a need to create populations or if external populations are used, these must be created under , Population creation, . Once the population has been created, it can be selected in the data order., To create a new population:, Click , Description, under , Population creation, in the navigation menu, Select , Add Population, Provide a descriptive title, Briefly and precisely describe who the population includes and how it is defined, Upload any relevant attachments, You must also indicate whether the population is uploaded or should be created by Denmark’s Data Portal. If the population is created from a project database, this should be marked with a checkmark., If an existing population needs to be updated, select , New Version, . An overview of the project’s populations will be displayed. Select one, and the system will automatically create a new version that can be further edited., Summary, Once the data order has been completed, a full summary is displayed. It is recommended to review this carefully to ensure that all necessary data has been included. The data order can then be submitted., Note:, For new projects, the data order can only be submitted once the project proposal has been submitted. For already approved projects, a data order can always be submitted within the framework of the most recently approved project proposal., Questions and answers about data orders, Data orders, Is a data order always required to have data transferred to a project? , Yes. A data order must always be created and submitted, and it also serves as documentation of which data are included in the project., Can a data order contain multiple elements, such as register data, external data, and a population? , Yes. When completing the data order, you may submit your entire order at once. However, an expansion requires that the project proposal is updated. If you want to order register data from a new package and already approved external data, you may create two separate orders. The order for external data can be submitted immediately, while the order for register data requires a resubmission of the project proposal before it can be submitted., Can I cancel an order that has been created? , Yes. You can cancel an order before it is sent to Statistics Denmark by going to ‘Projects and data’ and clicking the three dots next to the order. Here you can select ‘Terminate order’., Does the data order replace the previous email correspondence with Statistics Denmark? , It does not fully replace previous email correspondence. Price quotes will still be sent by email, and clarifications and guidance will also continue to take place by email or phone., Data packages, Can data be ordered from data packages that are not approved? , Only if the project proposal is still open. When selecting a register from a non-applied-for data package, the package will be added to the project proposal. If the project proposal is already approved, registers can only be ordered from approved data packages. In this case, a resubmission of the project proposal is required to argue for the necessary data packages., Do data orders for register data need approval from Statistics Denmark when the data package is already approved?, No. A data order does not require additional approval, but a staff member at Denmark’s Data Portal must process and price the data order according to the project’s pricing model., Does it cost anything to add new registers within an approved data package?, It depends on the project’s pricing model. Projects on the new pricing model have all register orders within an approved package covered by the package payment and receive them without additional charges. Projects on the old pricing model pay for register deliveries as usual. All projects will transition to the new pricing model with their first data order after 31st of December 2026., Variables, If only a few variables from a register were previously delivered, can the entire register now be transferred?, As a general rule, all variables in a selected register are delivered. Unless you wish to limit the variables, no further action is needed., If a register is missing a variable in an existing project, what should be done? , If a variable is missing, you may order the register again and receive all variables., Populations, Do all projects need to specify a population?, Yes. All projects must specify at least one population in order to order data. For projects with multiple populations, you must indicate which population the data should be extracted for. For projects with only one population (e.g., full register extract), that population will be preselected. If new population delimitations are needed for register data, the population creation must be ordered. It can then be selected as the basis for delimiting register data., Must a population always be specified in a data order?, Yes. A data order must include an approved population (e.g., full register extract)., Project databases, How is register data handled if it was previously ordered directly from Statistics Denmark but now exists in the project database?, Each data order is independent of previous orders. The data order indicates whether data will be delivered from the project database or from Statistics Denmark. There are no restrictions based on where data was previously delivered from., Can future years still be pre-ordered?, Yes. When selecting register data, you can open each dataset via the shopping cart and complete ‘Pre-order’. At present, you must always create a data order when retrieving data from the project database., Why order population creation via the project database?, If the project requires a delimited population, the project database may choose to create it. If register data is needed that the project database does not have, Denmark’s Data Portal can extract register data for the population created by the project database., Can the data manager still create populations manually?, Yes. The data manager can still create populations independently.

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

    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

    Data Science Lab

    Data Science Lab is a supplement to Statistics Denmark's existing microdata schemes and enables projects that cannot be implemented within the normal framework., Data Science Lab is Statistics Denmark's laboratory for testing new advanced methods and new data sources., We help researchers and analysts to exploit the full potential of Danish register data - combined with modern methods, such as machine learning and AI, or new data sources - in a secure and confidential environment., What is Data Science Lab?, Data Science Lab was established to strengthen the use of:, New advanced methods (e.g. machine learning and AI), New data sources (e.g. text, images, and geolocations), We function as a supplement to Statistics Denmark's existing microdata schemes and enable analyses that cannot be implemented within the normal framework., We collaborate with both Danish and international researchers and analysts., What can we offer?, As collaborators in Data Science Lab, you get access to a unique environment where data, methods, and security go hand in hand., Unique and secure data environment, Secure access to Danish register data, Connection to HPC facilities (e.g. the AI supercomputer Gefion), Possibility of using new data sources (e.g. text, images, geodata), Advanced methods, Possibility to develop and train ML and AI models on microdata, Analysis of large and complex datasets, Professional sparring, Close collaboration with data experts at Statistics Denmark, Statistical consultancy, In short: We make it possible to carry out projects that would otherwise not be possible., Examples of projects, Since its establishment, Data Science Lab has contributed to a number of research projects that demonstrate the potential of combining register data with new methods., Nation-Scale Social Networks, The project investigates how information about an individual's social network can contribute to understanding differences in important life outcomes, such as educational and employment history, wealth, and crime. The project has built a register-based network of relationships between all individuals in Denmark from 1980 onwards. The network includes family, household, neighbour, school, and work relations. It opens up completely new analyses of social dynamics and life courses and forms the basis for advanced models of human life trajectories., Results have been published in, among others:, Nature Computational Science (2024), Scientific Reports (2025), Big life-course data analytics for understanding disease initiation and progression in diabetes and its complications, The project investigated how diseases arise and develop throughout life – with a particular focus on diabetes. By analysing all diseases together, the project provides new insight into the transition from health to disease and the different pathways that can lead to chronic diseases., Results have been published in, among others:, Nature Communications (2024), The Lancet Public Health (2024), Harnessing the power of big data to address the societal challenge of aging, The project contributed to a better understanding of the ageing process and how to promote healthy ageing, using Danish registry data and new advanced methods., Results have been published in, among others:, Nature Aging (2022), Scientific Reports (2023), Do you have an idea for a project?, Do you have an idea for a project, would you like to hear more about the possibilities for collaboration, or do you need help clarifying whether your project best fits with us or under one of our microdata schemes?, Please contact us for a non-committal talk.

    https://www.dst.dk/en/TilSalg/data-til-forskning/data-science-lab

    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: Cultural Business Structure and Labour Market

    Contact info, Science, Technology and Culture, Business Statistics , Anne-Sofie Dam Bjørkman , +45 20 37 54 60 , asd@dst.dk , Get documentation of statistics as pdf, Cultural Business Structure and Labour Market 2023 , Previous versions , Cultural Business Structure and Labour Market 2022, Cultural Business Structure and Labour Market 2021, Cultural Business Structure and Labour Market 2020, Cultural Business Structure and Labour Market 2019, Cultural Business Structure and Labour Market 2018, Cultural Business Structure and Labour Market 2017, Cultural Business Structure and Labour Market 2016, Cultural Business Structure and Labour Market 2015, Cultural Business Structure and Labour Market 2014, Cultural Business Structure and Labour Market 2013, Cultural Business Structure and Labour Market 2012, The purpose of the statistics is to analyze workplaces as well as persons employed within the cultural sector. The statistics is compiled from 2008 and is published annually., Statistical presentation, The statistics on Cultural Business Structure and Labour Market are compiled annually and provide data on workplaces, entrepreneurs, jobs and persons employed in the cultural sector. Detailed results are disseminated through a number of tables in StatBank Denmark and main results and links to tables can be accessed at the subject page for economic conditions for culture as well as in the annual publication Culture (from 2015 and onwards)., Read more about statistical presentation, Statistical processing, The statistics is based on edited register data from the Establishment-related Business Statistics, the Register-based Labour Force Statistics, Educational Attainment, and Business Demography. The number of jobs are calculated for workplaces (establishments) in relevant activity sectors and are linked to personal data on e.g. age and educational status for employees., Read more about statistical processing, Relevance, The statistics is accessible for everybody and can be used for summaries of the entrepreneurship, educational status and employment within the cultural sector. The statistics may be used for analyses, planning and debates on the topic of cultural employment, etc., Read more about relevance, Accuracy and reliability, The statistics is based on validated data from Statistics Denmark's central registers that form the basis of official statistics, refer to the documentations of statistics for Establishment-related Business Statistics, the Register-based Labour Force Statistics, Educational Attainment, and Business Demography. No actual measurement of the quality and no calculations on measures of accuracy has been performed., Read more about accuracy and reliability, Timeliness and punctuality, The statistics is published 20 months after the end of the reference period awaiting the compilation of source register data. It is a fairly new statistics that has been published punctually., Read more about timeliness and punctuality, Comparability, The current time series starts in 2008 and is without any data breaks. The statistics can be compared to selected results from the Establishment-related Business Statistics, the Register-based Labour Force Statistics, Educational Attainment, and Business Demography. Eurostat and Unesco regularly publish reports on the same subject matters as this statistics., Read more about comparability, Accessibility and clarity, These statistics are published annually in the StatBank under the subject , Education and employment in the cultural field, . For further information, go to the , subject page, . , Read more about accessibility and clarity

    https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/cultural-business-structure-and-labour-market

    Documentation of statistics

    Documentation of statistics: Hospitalization

    Contact info, Personal Finances and Welfare, Social Statistics , Jonas Kirchheiner-Rasmussen , +45 61 50 23 80 , RAS@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