Search result

Documentation of statistics: Insurance companies and pension funds

Contact info, Government Finances , Get documentation of statistics as pdf, Insurance companies and pension funds 2022 , Previous versions, Insurance companies and pension funds 2021, Insurance companies and pension funds 2020, Insurance companies and pension funds 2019, Insurance companies and pension funds 2018, Insurance companies and pension funds 2017, Insurance companies and pension funds 2014, The purpose of these statistics is to quantify insurance companies and pension funds profit and loss accounts and assets and liabilities, in DKK millions on an aggregate level. These statistics are comparable from 2001 and onwards., Statistical presentation, These statistics include an annual statement of the number of insurance and pension companies as well as their profit and loss accounts as well as their income statement in DKK millions. These statistics are conducted for life insurance companies, non-life insurance companies and pension funds., Read more about statistical presentation, Statistical processing, Compared to the source data the wording of certain items can be changed or aggregated., Read more about statistical processing, Relevance, The primary users are public authorities, private business sector and interested citizens., Read more about relevance, Accuracy and reliability, The overall accuracy is considered very high. All data comes from the Danish FSA. In general the sector is subject to a great degree of awareness. For further information please refer to the Danish FSA., Read more about accuracy and reliability, Timeliness and punctuality, These statistics are published 11 months after the end of the reference year. Publications are released on time, as stated in the release calendar. , Read more about timeliness and punctuality, Comparability, Similar statistics are available in other countries. These statistics are comparable since the 2001., Read more about comparability, Accessibility and clarity, These statistics are published in the StatBank under , Insurance companies and pension funds, . For more information go to the , subject page, . , Read more about accessibility and clarity

https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/insurance-companies-and-pension-funds

Documentation of statistics

Documentation of statistics: Folk high school courses

Contact info, Population and Education, Social Statistics , Mattias Nørgaard Matsushita , +45 20 21 87 90 , MMT@dst.dk , Get documentation of statistics as pdf, Folk high school courses 2025 , Previous versions, Folk high school courses 2024, Folk high school courses 2023, Folk high school courses 2021, Courses and Adult Education - Folk High Schools 2020, Courses and Adult Education - Folk High Schools 2019, Courses and Adult Education - Folk High Schools 2018, Courses and Adult Education - Folk High Schools 2017, Courses and Adult Education - Folk High Schools 2016, Courses and Adult Education - Folk High Schools 2015, Courses and Adult Education - Folk High Schools 2014, The purpose is to describe the participation of the population in adult education in the sense of folk high schools and independent prevocational schools. Courses fulfilling the requirements outlined in the Danish Folk high school law are included, but also non-financed courses of a general folk high school nature. The data is a part of Statistic Denmark's register of adult education and continuing training., Statistical presentation, The Statistics gives a complete picture of the populations participation in folk high school courses as well as courses from the independent prevocational schools. Data are collected on number of participants as well as full-time equivalents. In addition data is also collected on the length of the courses. For the latest year, data based on calendar year only contains data for half a year. This is because data is published on school years so that the second half will become available once the next school year is published , Read more about statistical presentation, Statistical processing, Data are collected annually from the schools administrative systems and by manual reporting. Collected data are validated for institution, type of education, course length and personal ID number. In addition it is determined if pupils has been reported several times. After data has been validated it is divided into: type of folk high school, education area and courses length. annual equivalents are calculated based on the course length and number of participants., Read more about statistical processing, Relevance, The statistics are widely used by municipalities, counties, government departments, non-government organizations, the news media and private enterprises. No user satisfaction data has been collected., Read more about relevance, Accuracy and reliability, The most important source of inaccuracy is insufficient registrations by the administrations of the folk high schools. Various control procedures catches up with this as far as possible. In addition there are small differences in the material delivered by the folk high schools. We are currently working on solving this issue. , Read more about accuracy and reliability, Timeliness and punctuality, The statistic is issued once a year, usually in the first quarter of the year including data from approx. 6 months after the end of the collection period., In general the statistic is published in accordance with the announced time. , Read more about timeliness and punctuality, Comparability, There are statistics on folk high schools in Denmark dating back to 1901, but the statistics are in their present form comparable from 2005 to the present. However, the short courses, under 12 weeks, are only included from 2012. There is no common international standard for statistics on folk high schools, but similar statistics can be found for Norway and Sweden. There are other statistics on folk high schools in Denmark, but there may be differences between definitions (e.g. the school year) and calculation methods (e.g. calculation on the basis of grants or actual activity) which may mean that there is no direct comparability., Read more about comparability, Accessibility and clarity, The statistics are published in the StatBank under the subject , Folk high schools courses, ., Researchers can get access to the detailed data of the register of adult education and continuing training by agreement with Statistics Denmark. , Special analyses can be conducted by the Service Department of Statistics Denmark., Read more about accessibility and clarity

https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/folk-high-school-courses

Documentation of statistics

Documentation of statistics: National Accounts: Input-Output and Supply-Use

Contact info, National Accounts, Climate and Environment, Economic Statistics , Peter Rørmose Jensen , +45 40 13 51 26 , PRJ@dst.dk , Get documentation of statistics as pdf, National Accounts Input-Output and Supply-Use 2022 , Previous versions, Input-Output Tables 2020, Input-Output Tables 2019, Input-Output Tables 2018, Input-Output Tables 2017, Input-Output Tables 2016, Input-Output Tables 2015, Input-Output Tables 2011, Supply and use tables are the cornerstone of the Danish national accounts. Here, data for the circulation of goods and services, between Denmark and abroad, enterprises and final consumption are organized in a way that enables full balancing. A number of national accounts variables, including GDP, are published from here. The tables are used to compile input-output tables, which detail the relationships between production, imports and uses in the economy. Conversion to an input-output model enables calculations of multiplier effects, which are indirect relationships in the economy., Statistical presentation, Supply‑use and input‑output tables describe how goods and services are produced, imported and used in the economy. They balance supply and demand and form the basis for GDP calculations. The system covers about 2,350 products and 117 industries and provides detailed breakdowns of consumption, investment and exports. Input‑output tables enable analysis of direct and indirect economic effects and support modelling and environmental‑economic studies., Read more about statistical presentation, Statistical processing, The supply and use tables are compiled from a wide range of sources that have been collected by Statistics Denmark, including accounting statistics and foreign trade statistics. When source data are inserted into the framework, extensive validation, error correction and adaptation to the national accounts definitions are carried out. Data are reconciled to full consistency using both automatic and manual methods. Input-output tables are compiled on the basis of the supply and use tables based on international guidelines. Upon receipt, Eurostat thoroughly checks the data again., Read more about statistical processing, Relevance, Supply‑use tables are mainly used by Statistics Denmark to calculate GDP and other key indicators and to construct input‑output tables. A few external users access them via Research Services, but the most detailed tables are not published due to confidentiality. Input‑output tables support detailed analyses of economic structures, policy impacts and environmental effects and are central to models such as ADAM, MAKRO and Green Reform. Data comply with ENS2010., Read more about relevance, Accuracy and reliability, Supply, use and input-output tables are based on extensive primary data that are checked for errors and reconciled to ensure high precision and consistency, especially in the GDP calculation. Provisional tables are less reliable due to incomplete sources. The necessary central model assumption in compiling input-output tables may lead to some minor over- and under-estimations. Quality is ensured through ongoing checks, audits and compliance with international standards., Read more about accuracy and reliability, Timeliness and punctuality, The input-output tables are released once every year at the same time as the final national accounts. The time of release is 2.5 years after the end of the reference year., Read more about timeliness and punctuality, Comparability, In an international perspective the comparability between Danish and foreign input-output tables is generally good, but not quite as good as in the case of national accounts itself. This is due to the fact that there is an important assumption to be made and this assumption may vary between countries. However, within the framework of the ESA2010 manual it is tried to secure comparability between EU-countries., Read more about comparability, Accessibility and clarity, National accounts and input-output data is disseminated in the , Statbank, and the , input-output subject page, where data can be downloaded in various file formats. Data that are transmitted to Eurostat can be found , here, Read more about accessibility and clarity

https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/national-accounts--input-output-and-supply-use

Documentation of statistics

Documentation of statistics: International Trade in Services by Enterprise Characteristics

Contact info, External Economy , Søren Burman , +45 30 51 45 62 , SBU@dst.dk , Get documentation of statistics as pdf, International Trade in Services by Enterprise Characteristics 2022 , Previous versions, International Trade in Services by Enterprise Characteristics 2021, International Trade in Services by Enterprise Characteristics 2020, International Trade in Services by Enterprise Characteristics 2019, International Trade in Services by Enterprise Characteristics 2018, International Trade in Services by Enterprise Characteristics 2017, The purpose of Trade in Services by Enterprise Characteristics (STEC) is to describe enterprises engaging in international trade in services, how large they are, which economic sector they belong to, how many countries they trade with etc. These statistics have been compiled since 2014 and are comparable until 2018 for legal units. From 2019 and onwards the statistics have been compiled on the basis of the enterprise unit., Statistical presentation, Trade in Services by Enterprise Characteristics is an annual measurement of enterprises involved in international trade in services and their characteristics, stated in value. The statistics are grouped by economic activity, enterprise size, ownership and type of services., Read more about statistical presentation, Statistical processing, These statistics are compiled by combining data for International Trade in services (ITSS) with Business statistics. Data is validated by comparing data with the sources used for compiling the statistics and by comparing the different tables compiled in this statistic., Read more about statistical processing, Relevance, STEC is relevant for analysts and enterprises, for analyses of e.g. globalization and enterprises which contribute to the international trade in services in Denmark. , Read more about relevance, Accuracy and reliability, The accuracy for International Trade in Service by Enterprise Characteristics is closely related to the accuracy of International Trade in Service Statistics which is high on an aggregated level. The revisions follow the revision structure of International Trade in Service Statistics., Read more about accuracy and reliability, Timeliness and punctuality, These statistics are published 10 months after the reference period. They are published without any delays., Read more about timeliness and punctuality, Comparability, STEC have been compiled since 2017 but is comparable from 2014 to 2018 for legal units. From 2019 the statistics have been compiled using the enterprise unit. The data is fully comparable with compiled services in the ITSS and Balance of payments statistics. Comparability with other statistics, such as the business statistics can be limited due to different coverages., Read more about comparability, Accessibility and clarity, These statistics are published annually 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 , International Trade in Services, .The statistics can also be found in various publications and analysis., Read more about accessibility and clarity

https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/international-trade-in-services-by-enterprise-characteristics

Documentation of statistics

Currency codes (ISO 4217), v1:2016

Please note, a more current version of this classification is now available. See the current version , here., Name: , VALUTA_ISO_V1_2016 , Description: , ISO 4217, is the International Standard for currency codes. The purpose of ISO 4217 is to define internationally recognised codes of letters and/or numbers that can be used to identify currencies, e.g. for international money transfers or exchange of currencies. This standard was first published in 1978, but many currency codes have been in use before that., The first two letters of a currency code are consistent with country codes that comply with the ISO 3166 standard. The third letter corresponds, where possible, to the initial letter of a country or territory's currency. For example, the currency code for official currency in Denmark is indicated with "DKK", where "DK" is Denmark's country code in ISO 3166 and "K" is the first letter in "Kroner"., Valid from: , January 1, 2016 , Valid to: , December 31, 2016 , Office: , Metode og Data Science , Contact: , Rohan James Draper, , rjd@dst.dk, , ph. +45 21 33 89 16 , Codes and categories, Open hierarchy, Download , CSV, DDI, AED: UAE dirham, AFN: Afghan afghani, ALL: Albanian lek, AMD: Armenian Dram, ANG: Netherlands Antillian guilder, AOA: Angolan kwanza, ARS: Argentine peso convertible, AWG: Aruban florin, AUD: Australian Dollar, AZN: Azerbaijani manat, BAM: Bosnia and Herzegovina convertible mark, BBD: Barbados Dollar, BDT: Bangladeshi taka, BGN: Bulgarian lev, BHD: Bahraini dinar, BIF: Burundi franc, BMD: Bermudian dollar, BND: Brunei dollar, BOB: Bolivian boliviano, BRL: Brazilian real, BSD: Bahamian dollar, BTN: Bhutanese ngultrum, BWP: Botswana pula, BYR: Belarussian Ruble, BZD: Belize dollar, CAD: Canadian Dollar, CDF: Congolese franc, CHF: Swiss franc, CLP: Chilean peso, CNY: Chinese yuan renminbi, COP: Colombian peso, CRC: Costa Rican colon, CUC: Cuban convertible peso, CUP: Cuban peso national, CVE: Cape Verde escudo, CZK: Czech koruna, DJF: Djibouti franc, DKK: Danish krone, DOP: Dominican peso, DZD: Algerian dinar, EGP: Egyptian pound, ERN: Eritrean nakfa, ETB: Ethiopian birr, EUR: Euro, FJD: Fiji dollar, FKP: Falkland Islands pound, GBP: Pound sterling, GEL: Georgian lari, GHS: Ghanaian cedi, GIP: Gibraltar pound, GMD: Gambian dalasi, GNF: Guinean franc, GTQ: Guatemalan quetzal, GYD: Guyana dollar, HKD: Hong Kong dollar, HNL: Honduran lempira, HRK: Croatian kuna, HTG: Haitian gourde, HUF: Hungarian forint, IDR: Indonesian rupiah, ILS: Israeli new shekel, INR: Indian rupee, IQD: Iraqi dinar, IRR: Iranian rial, ISK: Iceland krona, JMD: Jamaican dollar, JOD: Jordanian dinar, JPY: Japanese yen, KES: Kenyan shilling, KGS: Kyrgyzstani som, KHR: Cambodian riel, KMF: Comorian franc, KPW: North Korean won, KRW: South Korean won, KWD: Kuwaiti dinar, KYD: Cayman Islands dollar, KZT: Kazakhstani tenge, LAK: Lao kip, LBP: Lebanese pound, LKR: Sri Lankan rupee, LRD: Liberian dollar, LSL: Lesotho loti, LYD: Libyan dinar, MAD: Moroccan dirham, MDL: Moldovan leu, MGA: Malagasy ariary, MKD: Macedonian denar, MMK: Myanmar kyat, MNT: Mongolian tugrik, MOP: Macanese pataca, MRO: Mauritanian ouguiya, MUR: Mauritius rupee, MVR: Maldivian rufiyaa, MWK: Malawian kwacha, MXN: Mexican Peso, MYR: Malaysian ringgit, MZN: Mozambican metical, NAD: Namibian dollar, NGN: Nigerian naira, NIO: Nicaraguan cordoba oro, NOK: Norwegian krone, NPR: Nepalese rupee, NZD: New Zealand dollar, OMR: Omani rial, PAB: Panamanian balboa, PEN: Peruvian nuevo sol, PGK: Papua New Guinean kina, PHP: Philippine peso, PKR: Pakistani rupee, PLN: Polish zloty, PYG: Paraguayan guarani, QAR: Qatari rial, RON: Romanian leu, RSD: Serbian dinar, RUB: Russian ruble, RWF: Rwandan franc, SAR: Saudi riyal, SBD: Solomon Islands dollar, SCR: Seychellois rupee, SDG: Sudanese pound, SEK: Swedish krona, SGD: Singapore dollar, SHP: Saint Helena pound, SLL: Sierra Leonean leone, SOS: Somali shilling, SRD: Surinamese dollar, SSP: South Sudanese pound, STD: Sao Tome and Principe dobra, SVC: Salvadoran colón, SYP: Syrian pound, SZL: Swazi lilangeni, THB: Thai baht, TJS: Tajikistani somoni, TMT: Turkmenistani manat, TND: Tunisian dinar, TOP: Tongan pa'anga, TRY: Turkish lira, TTD: Trinidad and Tobago dollar, TWD: New Taiwan dollar, TZS: Tanzanian shilling, UAH: Ukrainian hryvnia, UGX: Ugandan shilling, USD: United States dollar, UYU: Uruguayan peso, UZS: Uzbekistan Sum, VEF: Venezuelan bolivar fuerte, VND: Vietnamese dong, VUV: Vanuatu vatu, WST: Samoan tala, XAF: Central African CFA franc BEAC, XAG: Silver, XAU: Gold, XCD: Eastern Caribbean dollar, XOF: West African CFA franc BCEAO, XPD: Palladium, XPF: CFP franc, XPT: Platinum, XTS: Currency code reserved for testing, XXX: No currency, YER: Yemeni rial, ZAR: South African rand, ZMW: Zambian kwacha, ZWL: Zimbabwe dollar, All versions, Name, Valid from, Valid to, Currency codes (ISO 4217), v1:2019, January 1, 2019, Still valid, Currency codes (ISO 4217), v1:2018, January 1, 2018, December 31, 2018, Currency codes (ISO 4217), v1:2017, January 1, 2017, December 31, 2017, Currency codes (ISO 4217), v1:2016, January 1, 2016, December 31, 2016, Currency codes (ISO 4217), v1:2015, January 1, 2015, December 31, 2015, Currency codes (ISO 4217), v1:2014, January 1, 2014, December 31, 2014

https://www.dst.dk/en/Statistik/dokumentation/nomenklaturer/valuta-iso?id=4b6d82ad-7c37-482f-80d7-7bd02f640d10

Currency codes (ISO 4217), v1:2018

Please note, a more current version of this classification is now available. See the current version , here., Name: , VALUTA_ISO_V1_2018 , Description: , ISO 4217, is the International Standard for currency codes. The purpose of ISO 4217 is to define internationally recognised codes of letters and/or numbers that can be used to identify currencies, e.g. for international money transfers or exchange of currencies. This standard was first published in 1978, but many currency codes have been in use before that., The first two letters of a currency code are consistent with country codes that comply with the ISO 3166 standard. The third letter corresponds, where possible, to the initial letter of a country or territory's currency. For example, the currency code for official currency in Denmark is indicated with "DKK", where "DK" is Denmark's country code in ISO 3166 and "K" is the first letter in "Kroner"., Valid from: , January 1, 2018 , Valid to: , December 31, 2018 , Office: , Metode og Data Science , Contact: , Rohan James Draper, , rjd@dst.dk, , ph. +45 21 33 89 16 , Codes and categories, Open hierarchy, Download , CSV, DDI, AED: UAE dirham, AFN: Afghan afghani, ALL: Albanian lek, AMD: Armenian Dram, ANG: Netherlands Antillian guilder, AOA: Angolan kwanza, ARS: Argentine peso convertible, AWG: Aruban florin, AUD: Australian Dollar, AZN: Azerbaijani manat, BAM: Bosnia and Herzegovina convertible mark, BBD: Barbados Dollar, BDT: Bangladeshi taka, BGN: Bulgarian lev, BHD: Bahraini dinar, BIF: Burundi franc, BMD: Bermudian dollar, BND: Brunei dollar, BOB: Bolivian boliviano, BRL: Brazilian real, BSD: Bahamian dollar, BTN: Bhutanese ngultrum, BWP: Botswana pula, BYN: Belarussian Ruble, BZD: Belize dollar, CAD: Canadian Dollar, CDF: Congolese franc, CHF: Swiss franc, CLP: Chilean peso, CNY: Chinese yuan renminbi, COP: Colombian peso, CRC: Costa Rican colon, CUC: Cuban convertible peso, CUP: Cuban peso national, CVE: Cape Verde escudo, CZK: Czech koruna, DJF: Djibouti franc, DKK: Danish krone, DOP: Dominican peso, DZD: Algerian dinar, EGP: Egyptian pound, ERN: Eritrean nakfa, ETB: Ethiopian birr, EUR: Euro, FJD: Fiji dollar, FKP: Falkland Islands pound, GBP: Pound sterling, GEL: Georgian lari, GHS: Ghanaian cedi, GIP: Gibraltar pound, GMD: Gambian dalasi, GNF: Guinean franc, GTQ: Guatemalan quetzal, GYD: Guyana dollar, HKD: Hong Kong dollar, HNL: Honduran lempira, HRK: Croatian kuna, HTG: Haitian gourde, HUF: Hungarian forint, IDR: Indonesian rupiah, ILS: Israeli new shekel, INR: Indian rupee, IQD: Iraqi dinar, IRR: Iranian rial, ISK: Iceland krona, JMD: Jamaican dollar, JOD: Jordanian dinar, JPY: Japanese yen, KES: Kenyan shilling, KGS: Kyrgyzstani som, KHR: Cambodian riel, KMF: Comorian franc, KPW: North Korean won, KRW: South Korean won, KWD: Kuwaiti dinar, KYD: Cayman Islands dollar, KZT: Kazakhstani tenge, LAK: Lao kip, LBP: Lebanese pound, LKR: Sri Lankan rupee, LRD: Liberian dollar, LSL: Lesotho loti, LYD: Libyan dinar, MAD: Moroccan dirham, MDL: Moldovan leu, MGA: Malagasy ariary, MKD: Macedonian denar, MMK: Myanmar kyat, MNT: Mongolian tugrik, MOP: Macanese pataca, MRU: Mauritanian ouguiya, MUR: Mauritius rupee, MVR: Maldivian rufiyaa, MWK: Malawian kwacha, MXN: Mexican Peso, MYR: Malaysian ringgit, MZN: Mozambican metical, NAD: Namibian dollar, NGN: Nigerian naira, NIO: Nicaraguan cordoba oro, NOK: Norwegian krone, NPR: Nepalese rupee, NZD: New Zealand dollar, OMR: Omani rial, PAB: Panamanian balboa, PEN: Peruvian nuevo sol, PGK: Papua New Guinean kina, PHP: Philippine peso, PKR: Pakistani rupee, PLN: Polish zloty, PYG: Paraguayan guarani, QAR: Qatari rial, RON: Romanian leu, RSD: Serbian dinar, RUB: Russian ruble, RWF: Rwandan franc, SAR: Saudi riyal, SBD: Solomon Islands dollar, SCR: Seychellois rupee, SDG: Sudanese pound, SEK: Swedish krona, SGD: Singapore dollar, SHP: Saint Helena pound, SLL: Sierra Leonean leone, SOS: Somali shilling, SRD: Surinamese dollar, SSP: South Sudanese pound, STN: Sao Tome and Principe dobra, SVC: Salvadoran colón, SYP: Syrian pound, SZL: Swazi lilangeni, THB: Thai baht, TJS: Tajikistani somoni, TMT: Turkmenistani manat, TND: Tunisian dinar, TOP: Tongan pa'anga, TRY: Turkish lira, TTD: Trinidad and Tobago dollar, TWD: New Taiwan dollar, TZS: Tanzanian shilling, UAH: Ukrainian hryvnia, UGX: Ugandan shilling, USD: United States dollar, UYU: Uruguayan peso, UZS: Uzbekistan Sum, VEF: Venezuelan bolivar fuerte, VES: Venezuelan bolivar soberano, VND: Vietnamese dong, VUV: Vanuatu vatu, WST: Samoan tala, XAF: Central African CFA franc BEAC, XAG: Silver, XAU: Gold, XCD: Eastern Caribbean dollar, XOF: West African CFA franc BCEAO, XPD: Palladium, XPF: CFP franc, XPT: Platinum, XTS: Currency code reserved for testing, XXX: No currency, YER: Yemeni rial, ZAR: South African rand, ZMW: Zambian kwacha, ZWL: Zimbabwe dollar, All versions, Name, Valid from, Valid to, Currency codes (ISO 4217), v1:2019, January 1, 2019, Still valid, Currency codes (ISO 4217), v1:2018, January 1, 2018, December 31, 2018, Currency codes (ISO 4217), v1:2017, January 1, 2017, December 31, 2017, Currency codes (ISO 4217), v1:2016, January 1, 2016, December 31, 2016, Currency codes (ISO 4217), v1:2015, January 1, 2015, December 31, 2015, Currency codes (ISO 4217), v1:2014, January 1, 2014, December 31, 2014

https://www.dst.dk/en/Statistik/dokumentation/nomenklaturer/valuta-iso?id=bda171a9-bca0-4065-bec8-18b4608fcbfd

Classification of education (DDU), completed educations, v1:2023

Please note, a more current version of this classification is now available. See the current version , here., Name: , DDU_AUDD_V1_2023 , Description: , DDU stands for Den Danske Uddannelsesklassifikation and is the Danish classification system for all educations in Denmark. Statistics Denmark operates the classification in cooperation with The Ministry of Higher Education and Science (UFM) as well as the Ministry of Children and Education (BUVM). , The classification covers both regulated and private educations and serves as a national standard for organising, describing and comparing the different possibilities for educations within Denmark’s educational system., All educations in DDU have a unique four-digit completed educations code, called AUDD-code, e.g. 4443: Miller. The education is placed in four groups in a hierarchical structure, which categorises the educations based level, differences in content and kinship:, Main area, A one-digit classification which describes the level of education at an aggregated level, Main group, A two-digit classification that represents the element of a legal or functional difference between the main areas in the education types within the same overall educational level. For example, distinctions can be made between vocational bachelors, academic bachelors and other medium cycle further educations within the main area=6,’’Medium cycle further education’’., Middle group, A three-digit classification and the first level in the classification of education where a division is made based on the content differences of the educational programs. Here, the education- and admission regulations are included., Sub group, A four-digit classification, which gives a more detailed content based division of educations within the same middle group., So far, three new statbank tables based on DDU have been published:, UDDALL10: , Educational activity by region, education (DDU), age, sex and status (2005-2022), EUD34: , Educational activity at upper secondary vocational educations by education (DDU), age, ancestry, national origin, sex, status and education part (2005-2022), KVEU20: , Participation in supplementary courses by field of education (DDU), region, age, sex, points in time and unit (2005-2022), Valid from: , December 1, 2023 , Valid to: , January 31, 2026 , Office: , Befolkning og Uddannelse , Contact: , Martin Herskind, , hrs@dst.dk, , ph. +45 21 34 03 31 , Codes and categories, Codes and categories are only available in Danish , All versions, Name, Valid from, Valid to, Classification of education (DDU), completed educations, v1:2026, February 1, 2026, Still valid, Classification of education (DDU), completed educations, v1:2023, December 1, 2023, January 31, 2026

https://www.dst.dk/en/Statistik/dokumentation/nomenklaturer/ddu-audd?id=2a22d134-f097-4d6e-91a6-0cdd6cafca7b

Documentation of statistics: Names

Contact info, Population and Education, Social Statistics , Dorthe Larsen , +45 23 49 83 26 , dla@dst.dk , Get documentation of statistics as pdf, Names 2026 , Previous versions, Names 2021, Names 2020, Names 2017, Names 2016, Names 2014, Statistics on names covers first names and surnames in the Danish population and was established in 1995, initially as a project for the Department of Name Research at Copenhagen University., Statistical presentation, The statistics on names are split up into two different statistics. One concerns the whole population in Denmark at 1st January by first name and surname. The other concerns names given to newborn children during a given year., Read more about statistical presentation, Statistical processing, Statistics on names is based on the Central Person Register (CPR) based on the total population and newborns as in the population statistics. The number of people with different first names and surnames using only the first and the last name for a person., Read more about statistical processing, Relevance, The Department of Nordic Studies and Linguistics (NorS) at Copenhagen University, the media, private people and private businesses are using the statistics for public and private purposes., Read more about relevance, Accuracy and reliability, The statistics are based on the population registered in the Central Person Register (CPR) as calculated in the population statistics, where the main source of uncertainty in the population figure is delayed registration of emigrants., Read more about accuracy and reliability, Timeliness and punctuality, Names of newborn children are published in the middle of July, in the year after end of the reference year. Names of the total population are calculated per 1st January and are published in the middle of February. The statistics are usually published without delay in relation to the scheduled date., Read more about timeliness and punctuality, Comparability, Statistics on names of the total population is comparable since 2002 and statistics on names of newborn children can be compared since 1985. From 1996 the statistics concerning names includes all people living in Denmark. Before 1996 the statistics concerning names only included Danish citizens living in Denmark. The statistics concerning names are internationally comparable., Read more about comparability, Accessibility and clarity, Statistics on names are published in two separate Danish press releases. Lists of names are available on the subject pages concerning , Names of the total population, and , Names of newborn children, ., In the name database , How many Danes have the name..., it is possible to enter a first name and/or surname and find out how many have a given name., In Statistics Denmark's , Barometer of names, it is possible to enter a first name and see the development in how many newborns have been given a given first name over the years., Read more about accessibility and clarity

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

Documentation of statistics

Documentation of statistics: Census of Buildings

Contact info, Short Term Statistics, Business Statistics , Kasper Emil Dueholm Freiman , +45 23 45 47 32 , KFR@dst.dk , Get documentation of statistics as pdf, Census of Buildings 2022 , Previous versions, Census of Buildings 2019, Census of Buildings, 1 January 2018, Census of Buildings, 1 January 2017, Census of Buildings, 1 January 2016, Census of Buildings, 1 January 2015, Census of Buildings, 1 January 2014, The statistics yearly describe the stock of buildings in Denmark. The Census of buildings - together with the Census of Housing - is since 1981 and based on administrative registers a continuing of the questionnaire based Census on Population and Housing., Statistical presentation, The statistics are compiled from a full-scale census, 1st January. Before 2011 are small buildings (e.g. garages, carports, outhouses) excluded from the stock. The statistics describe the stock of buildings analyzed by type of use, size, ownership, heating installation, roof covering and external cladding materials, water and effluent installations, and year of construction. The statistic is used by municipals, ministries, the press, companies and private people., Read more about statistical presentation, Statistical processing, The statistic covers all buildings in the Registry of buildings and Dwellings. Data is validated at the building level. The aggregated numbers are checked against earlier years., Read more about statistical processing, Relevance, The statistic is used by municipalities, counties, government departments, private and semi- private organizations and firms, the news media and private persons. It is used for public and private planning, education and public debate., Read more about relevance, Accuracy and reliability, The quality of the statistic is believed to be high. A survey of the overall accuracy of the Central Register of Buildings and Dwellings has never been conducted. But the degree of unknown variables is small., Read more about accuracy and reliability, Timeliness and punctuality, The statistic is published medio July, normally on time., Read more about timeliness and punctuality, Comparability, With some exceptions the data are consistent back to January 1, 1986(see comparability over time). A complete comparability with the statistic of constructions is, for several reasons, not possible., Read more about comparability, Accessibility and clarity, The newest numbers are published at [STATBANK] https://www.dst.dk/en/Statistik/emner/byggeri-og-anlaeg/bygninger.aspx). The numbers are also used in The Statistical Yearbook and in the Statistical Ten-Year Review., Read more about accessibility and clarity

https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/census-of-buildings

Documentation of statistics

Documentation of statistics: Account Statistics for Fishery

Contact info, Food Industries, Business Statistics , Charlotte Spliid Hansen , +45 29 41 97 76 , chh@dst.dk , Get documentation of statistics as pdf, Account Statistics for Fishery 2024 , Previous versions, Account Statistics for Fishery 2023, Account Statistics for Fishery 2022, Account Statistics for Fishery 2021, Account Statistics for Fishery 2020, Account Statistics for Fishery 2019, Account Statistics for Fishery 2018, Account Statistics for Fishery 2017, Account Statistics for Fishery 2016, Account Statistics for Fishery 2015, Account Statistics for Fishery 2014, Account Statistics for Fishery 2013, Account Statistics for Fishery 2012, The purpose of the Account Statistics for Fishery is to review the economy of the fishery sector. The statistics is used in economic models and as a basis for yearly economic statistical reports for the fishery to EU (DG Mare). The statistics has been produced by Department of Food and Resource Economics at University of Copenhagen since 1996 and was transferred to Statistics Denmark from January 2009., Statistical presentation, The Account Statistics for Fishery covers the commercially fishery by fishing vessels registered in Denmark. The statistics is based on vessel units and is calculated for groups of fishing vessels (fleet segments) based on vessel size and main gear use., Read more about statistical presentation, Statistical processing, The authorized accountants report yearly the account for their fishery client. The collected accounts are thoroughly tested. When all accounts has been approved for statistical use, the sample of approved accounts are used together with register data for the entire population to simulate individual accounts for all units not in the sample. The complete dataset with individual balanced accounts for all units in the population is merged with register data on vessel characteristics, gear use etc. in order to calculate parameters for statistical groups (vessel segments)., Read more about statistical processing, Relevance, The statistics is relevant for government administration, researchers and stakeholders within the fisheries. Furthermore the data is used in the Fleet Economic Report to EU. , Read more about relevance, Accuracy and reliability, The statistic is based on a sample and the results are uncertain. The precision rely on the covering of the sample. Therefore the sample rate is bigger for vessels with high revenue. The aim is each year to include the 100 biggest vessels in the sample, and that approximately 80 per cent of the total value of landings in Danish fishery come from the vessels in the sample. Investments have the most uncertainty, because exchange of a vessel could result in closure of the fishing firm, and set up a new firm to run the new vessel., Read more about accuracy and reliability, Timeliness and punctuality, The statistics is normally made public before one year after the conclusion of the refence year., Read more about timeliness and punctuality, Comparability, The Account Statistics for Fishery is prepared using the same overall principles as the account statistics for agriculture, horticulture and aquaculture. The statistics has been prepared yearly since 1996. Break in series occurs in 2022 due to changes in methods for calculation population cutoff, as well as a new and improved basis for classifying which fishing types the vessels are grouped into. Break in series also occurs in 2001 due to inclusion of unpaid salary to active (working) partners, and in 2009 and again 2012 due to improved calculation of the capital value of fishing rights., Read more about comparability, Accessibility and clarity, The statistics are published yearly in a Danish press release, at the same time as the tables are updated in the StatBank. In the StatBank, the statistics can be found under the subject , Fishery, . For further information, go to the , subject page, . , Read more about accessibility and clarity

https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/account-statistics-for-fishery

Documentation of statistics