DDI_IRL_1979_PHC_v01_M_v7.5_A_IPUMS Census of Population of Ireland, 1979 IPUMS May 20, 2024 MetadataEditor Version 7.5 October 2024. NEW FEATURES. --Historical data from NAPP project now available from IPUMS-International. --Historical census data from Canada, Denmark, the United Kingdom, Germany, Iceland, Norway, Sweden, and the United States for the period 1703 to 1911 are now available from IPUMS-International. The complete count and sample datasets were previously disseminated by the North Atlantic Population Project (NAPP). Where possible, the data have been integrated into existing IPUMS-International variable coding schema. Some new variables have been created that are available only for these pre-1960 datasets. NAPP data users should note that many NAPP variables are available from IPUMS-International by different names. For a complete list of NAPP variables that have been renamed in IPUMS-Interational, refer to the crosswalk. --Individual country shapefiles for the third-level administrative level of geography are now available for a few IPUMS samples. --New spatially harmonized previous-residence variables at the second administrative level of geography are available for several samples in this data release. More information is available here. Users should note that many older migration variables are available by different names. Refer to this table for a crosswalk of old and corresponding new migration variables. --IPUMS now hosts the Census Mosaic data collection. Census Mosaic identifies, gathers, harmonizes, and distributes surviving historical census microdata from regions of Continental Europe where complete centralized records are not available. The Mosaic project was founded by a consortium of historical social scientists in Europe. Data can be downloaded as static files from the Census Mosaic website. Although the data are not yet integrated fully into IPUMS International, variables have been standardized and harmonized to be roughly compatible with IPUMS coding structures. NEW SAMPLES. --Full-count datasets for Great Britain 1851, 1861, 1871 (Scotland only), 1891, and 1901. --Full-count dataset for Sweden 1910. Denmark (1845, 1880, and 1885) --Labor force surveys from Spain and eight new labor force surveys from Italy added to the series. Newly added countries: Benin, Cote d'Ivoire, Finland, Guatemala, Honduras, Laos, Lesotho, Mauritius, Myanmar, Papua New Guinea, Russia, Slovak Republic, Suriname, Togo, and Zimbabwe New samples for: Bolivia, Cambodia, Cambodia, Chile, Cuba, Cote d'Ivoire, Egypt (1848 and 1868, historical samples), Fiji, Guinea, Ireland, Israel, Italy, Lao PDR, Mexico, Morocco, Nepal, Netherlands, Palestine, Peru, Philippines, Puerto Rico, Rwanda, Senegal, Sierra Leone, South Africa, Switzerland, Uganda, United States, United Kingdom, United States, Vietnam, and Zimbabwe SUPPLEMENTAL DATA. Data from censuses from Benin and Lesotho that record individual fertility and/or mortality events were made available in IPUMS-International. These files can be downloaded and linked to data produced by the extract system. NEW VARIABLES. --IPUMS-International now provides harmonized and year-specific geography variables for all countries including 13 new samples from Dominican Republic, Germany, Indonesia, Israel, Malaysia, Mongolia, Nicaragua, Nigeria, Palestine, Paraguay, Thailand, United Kingdom, and Uruguay. First-level and second-level year specific geography variables are also available for all countries. IPUMS provides corresponding, downloadable GIS boundary files for all harmonized and year specific geography variables. More information about IPUMS geography variables is available here. --IPUMS International now provides spatially harmonized previous-residence variables at the first administrative level of geography. The codes for the spatially harmonized previous-residence variables match the spatially harmonized place of current residence. More information is available here. --IPUMS International provides spatially harmonized previous-residence variables at the first administrative level of geography for all samples; previously available country-specific migration variables at the first administrative level that were not fully harmonized spatially have been phased out. Spatially harmonized previous-residence variables at the second administrative level of geography are available for selected samples. More information is available here. Users should note that many older migration variables are available by different names. Refer to this table for a crosswalk of old and corresponding new migration variables. --IPUMS International now provides spatially harmonized previous-residence variables at the first administrative level of geography for all samples. Spatially harmonized previous-residence variables at the second administrative level of geography are available for several samples in this data release. More information is available here. Users should note that many older migration variables are available by different names. Refer to this table for a crosswalk of old and corresponding new migration variables. --Lower (third) level geography codes and GIS files have been added for Bangladesh, China, Ethiopia, Mali, Rwanda, and Zimbabwe. Some geography codes and labels might have changed for these countries to accommodate the newer lower level geography. --Added more detailed 3-digit industry and occupation variables for China 2000. EDITED SAMPLES. --Revised full-count data for Great Britain 1881 --Revised full-count datasets for Sweden 1890 and 1900. The revision includes the following changes that improve comparability across Sweden datasets: --Revisions to certain ethnicity and work variables (and the underlying source data): ORIGIN, LABFORCE, OCCHISCO, OCRELATE, OCSTATUS. --Revisions to unharmonized source variables: SE1890A_HISCOSE, SE1890A_HISCRELSE, SE1890A_HISCSTATSE, SE1890A_OCCMULTISE, SE1900A_HISCOSE, SE1900A_HISCRELSE, SE1900A_HISCSTATSE, SE1900A_OCCMULTISE. --A new United States 1850 full-count dataset now matches the corresponding dataset distributed by the USA IPUMS data project. The source variable US1850A_0502 (HISTID) provides a linking key to match person records to the USA version of the data. The IPUMS International version of the data contains names, which the USA version cannot distribute. EDITED VARIABLES. An error affecting HHWT for South Africa 2007 was corrected. The existing values were adjusted by a factor of 0.01. AGEMARR was edited to add data for Hungary 1980 and 1990. Harmonized and year-specific geography variables for Brazil and Colombia have been edited to accommodate for the availability of refined municipal boundaries. Users should be aware that codes and labels have changed in all harmonized and year specific geography varaibles for these two countries. Errors affecting BPLSE2 (formerly BPLPARSE) for Sweden 1890 and the underlying source variable were corrected. Several thousand cases were incorrectly coded as 258101000. These cases have been updated with the correct code: 258171000. Harmonized geography variables for Italy, Philippines, Rwanda, and United States have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year specific geography varaibles for these countries. More information about IPUMS geography variables is available here. The codes for the source variable RW2002A_0419 were corrected to include 0 and 8 as possible responses, which were previously identified as 'unknown years' within primary education. Errors affecting EDUCFJ for Fiji 2006 were corrected. A problem with PERWT for Tanzania 2012 was corrected. The previous weights were adjusted to properly reflect population totals. MOMLOC, POPLOC, and PARRULE were updated for the United States 2010 and 2015 samples to include additional information on subfamilies. Prior to this correction, persons above age 17 were not receiving links to their co-resident mothers and fathers. An error affecting codes for the URBAN variable in Egypt 1986 for Cairo, Alexandria, Port-Said, and Suez was corrected. An error in INCEARN affecting Venezuela 2001 was corrected. Earned income in the source variable VE2001A_0440 is interpreted as a monthly amount, thus adjustments previously applied to convert data from daily or weekly income were supressed. All the six Brazil samples in IPUMS International were replaced with higher density samples. An edited version of the Chile 2017 sample was introduced to correct an error in household breaks. Errors affecting codes for GEO1_ZA in South Africa 2011 and ENUTS1 in United Kingdom 1991 were corrected. Harmonized geography variables for Cambodia, Fiji, and Nepal have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography varaibles for these countries. More information about IPUMS geography variables is available here. An error in PERWT affecting Nepal 2001 was corrected. Errors affecting a code in GQ for Brazil 2010 and Indonesia 2010 were corrected. Both census samples now identify 1-person units created by splitting a large household. An error in MARRNUM affecting Indonesia 1976 was corrected. Some codes for GEO1_EG2006 and GEO2_EG2006 were edited. Harmonized geography variables for Bolivia, Cuba, Guinea, Ireland, Morocco, Palestine, Senegal, South Africa, and Uganda have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography variables for these countries. More information about IPUMS geography variables is available here. An error in INCEARN affecting Brazil 1980 was corrected. An error in EDATTAIN affecting Ireland 1971 and 1981 was corrected. A small proportion of person records in Mexico 1960 were re-classified in MIGRATEP based on information about their current and previous residence. These were previously coded to 'different major administrative unit', even though their place of residence suggests that their last move was within the same major administrative unit. The second-level technician (higher) degrees for Spain 1991, 2001, and 2011 were re-classified into post-secondary technical education in EDATTAIN. An error affecting codes for SEX for Egypt 1848 and 1868 was corrected. The values for male and female had been reversed. A problem with HHWT and PERWT for Canada 2011 was corrected. The previous weights were adjusted to properly reflect population totals. Harmonized geography variables for Cambodia, Lao PDR, Mexico, Peru, Switzerland, Vietnam, Puerto Rico, United Kingdom, and United States have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography variables for these countries. More information about IPUMS geography variables is available here. Harmonized geography variables for Chile and Sierra Leone have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography variables for these countries. More information about IPUMS geography variables is available here. An error affecting codes for COMPUTER for Senegal 2013 was corrected. An error affecting labels available in IND for Peru 1993 was corrected. An error affecting codes for persons previously residing abroad for MIG1_5_BO in Bolivia 2001 and 2012 was corrected. EDUCAR, EDATTAIN, and YRSCHOOL were adjusted in the Argentina samples to incorporate information on completion of education levels in the data harmonization. HHWT and PERWT were calibrated in Kenya 1979 to properly reflect the population distribution by province. In GQ (group quarters status), persons residing in hospitals of all types were reclassified to 'institutional group quarters' from 'other group quarters,' making their treatment consistent with GQTYPE. Errors affecting codes for BPLBJ2 in Benin 1979, 1992, and 2002 were corrected. Errors affecting codes for GEO2_BR1970 in Brazil 1970 were corrected. Census of Population of Ireland, 1979 - IPUMS Subset PHC Ireland 1979 (IPUMS Harmonized Subset) IRL_1979_PHC_v01_M_v7.5_A_IPUMS Central Statistics Office IPUMS (c) Copyright 1979, Central Statistics Office and Minnesota Population Center MetadataEditor Central Statistics Office Population and Housing Census [hh/popcen] IPUMS International DOI:10.18128/D020.V7.5 Version 7.5. The datasets contain selected variables from the original census microdata plus harmonized variables from the IPUMS-International database. Demographic Variables -- PERSON Geography: Global Variables -- HOUSEHOLD Technical Household Variables -- HOUSEHOLD Constructed Family Interrelationship Variables -- PERSON Geography: F-N Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Constructed Household Variables -- HOUSEHOLD Technical Person Variables -- PERSON Migration: Global Variables -- PERSON Ireland Region Persons, households, and dwellings Age is grouped into categories; very short form UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: No - Households: yes - Individuals: yes - Group quarters: yes UNIT DESCRIPTIONS: - Dwellings: no - Households: A private household is defined as a group of persons living together (usually but not necessarily related), jointly occupying the whole or part of a private dwelling house, flat, or temporary dwelling and sharing a common budget. A person who lives alone or a person who occupies only part of the living accommodation but does not normally share a common budget with the other occupants is regarded as a separate household. - Group quarters: A non-private household is a boarding house, hotel, guest house, barrack, hospital, nursing home, boarding schools, religious institution, welfare institution, prison, or ship, etc. However, proprietors and manager of hotels, principals of boarding schools, persons in charge of various other types of institutions and members of staff who, with their families, occupy flats on the premises are considered as private households. All persons present in Ireland at the time of census, including visitors and those in residence. Usual residents temporarily absent from the State and members of the Defence Forces, who on Census night, were serving abroad with the United Nations were excluded. Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Ireland 1979 Note: Age is grouped into categories; very short form MICRODATA SOURCE: Central Statistics Office SAMPLE SIZE (person records): 337686. SAMPLE DESIGN: A 10% random sample of the recoded household records from each county was selected. The records within each county were sorted randomly before output to the sample file. Face-to-face [f2f] The information is based on Form A - Household Schedule. de facto, CENSUS DAY: April 1, 1979 Self-weighting (expansion factor=10) IPUMS International distributes integrated microdata of individuals and households only by agreement of collaborating national statistical offices and under the strictest of confidence. Before data may be distributed to an individual researcher, an electronic license agreement must be signed and approved. To gain access to the data, a researcher must agree to the following: (1) Implement security measures to prevent unauthorized access to census microdata. Under IPUMS International agreements with collaborating agencies, redistribution of the data to third parties is prohibited. (2) Use the microdata for the exclusive purposes of scholarly research and education. Researchers must explicitly agree to not use microdata acquired for any commercial or income-generating venture. (3) Maintain the confidentiality of persons, households, and other entities. Any attempt to ascertain the identity of persons or households from the microdata is prohibited. Alleging that a person or household has been identified is also prohibited. (4) Report all publications based on these data to IPUMS International, which will in turn pass the information on to the relevant national statistical agencies. Once a project is approved, a password is issued and data may be acquired through the Internet. Penalties for violating the license include: revocation of the license, recall of all microdata acquired, filing of a motion of censure to the appropriate professional organizations, and civil prosecution under the relevant national or international statutes. These safeguards mirror the principles from the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. Employees of the Minnesota Population Center who work with the census microdata to produce the harmonized database also sign agreements to respect the confidentiality of the data. IPUMS International works with each country's statistical office to minimize the risk of disclosure of respondent information. The details of the confidentiality protections vary across countries, but in all cases, names and detailed geographic information are suppressed and top-codes are imposed on variables such as income that might identify specific persons. In addition, IPUMS International uses a variety of technical procedures to enhance confidentiality protection. These include the following: (1) Swapping an undisclosed fraction of records from one administrative district to another to make positive identification of individuals impossible. (2) Randomizing the placement of households within districts to disguise the order in which individuals were enumerated or the data processed. (3) Aggregating codes of sensitive characteristics (e.g., grouping together very small ethnic categories) (4) Top- and bottom-coding continuous variables to prevent identification of extreme cases. The safety record for public-use census microdata is apparently perfect. In almost four decades of use, there has not been a single verified breach of statistical confidentiality. The measures implemented by the IPUMS International are designed to extend this record. Central Statistics Office Steven Ruggles, Lara Cleveland, Rodrigo Lovaton, Sula Sarkar, Matthew Sobek, Derek Burk, Dan Ehrlich, Quinn Heimann, Jane Lee. Integrated Public Use Microdata Series, International: Version 7.5 [dataset]. Minneapolis, MN: IPUMS, 2024. https://doi.org/10.1 [dataset]. Minneapolis, MN: IPUMS, 2024. https://doi.org/10.18128/D020.V7.5 Researchers should also acknowledge the statistical agency that originally produced the data: Ireland, Central Statistics Office. Census of Population of Ireland, 1979 The licensing agreement for use of IPUMS International data requires that users supply IPUMS International with the title and full citation for any publications, research reports, or educational materials making use of the data or documentation. Copies of such materials are also gratefully received at ipums@umn.edu. Printed matter should be sent to: IPUMS International Minnesota Population Center University of Minnesota 50 Willey Hall 225 19th Avenue South Minneapolis, MN 55455 An adapted version of the dataset, harmonized for international comparability, is available from IPUMS International (https://international.ipums.org/international/) under the following conditions: IPUMS International distributes integrated microdata of individuals and households only by agreement of collaborating national statistical offices and under the strictest of confidence. Before data may be distributed to an individual researcher, an electronic license agreement must be signed and approved. To gain access to the data, a researcher must agree to the following: (1) Implement security measures to prevent unauthorized access to census microdata. Under IPUMS International agreements with collaborating agencies, redistribution of the data to third parties is prohibited. (2) Use the microdata for the exclusive purposes of scholarly research and education. Researchers must explicitly agree to not use microdata acquired for any commercial or income-generating venture. (3) Maintain the confidentiality of persons, households, and other entities. Any attempt to ascertain the identity of persons or households from the microdata is prohibited. Alleging that a person or household has been identified is also prohibited. (4) Report all publications based on these data to IPUMS International, which will in turn pass the information on to the relevant national statistical agencies. Once a project is approved, a password is issued and data may be acquired through the Internet. Penalties for violating the license include: revocation of the license, recall of all microdata acquired, filing of a motion of censure to the appropriate professional organizations, and civil prosecution under the relevant national or international statutes. These safeguards mirror the principles from the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. Employees of the Minnesota Population Center who work with the census microdata to produce the harmonized database also sign agreements to respect the confidentiality of the data. The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses. IRL1979_PHC-H-H Household records 98 25 IRL1979_PHC-P-H Person records 337686 34 Record type H Household P Person RECTYPE identifies the type of record for the case: household or person. NOTE: RECTYPE is an alphabetic (character string) variable with a value of 'H' for household records and 'P' for person records. RECTYPE will not appear as a variable in the default rectangular extracts produced by the data extract system. It is only available in hierarchical extracts, to distinguish between the two record types. IPUMS Country 032 Argentina 051 Armenia 040 Austria 050 Bangladesh 112 Belarus 204 Benin 068 Bolivia 072 Botswana 076 Brazil 854 Burkina Faso 116 Cambodia 120 Cameroon 124 Canada 152 Chile 156 China 170 Colombia 188 Costa Rica 192 Cuba 208 Denmark 214 Dominican Republic 218 Ecuador 818 Egypt 222 El Salvador 231 Ethiopia 242 Fiji 246 Finland 250 France 276 Germany 288 Ghana 300 Greece 320 Guatemala 324 Guinea 332 Haiti 340 Honduras 348 Hungary 352 Iceland 356 India 360 Indonesia 364 Iran 368 Iraq 372 Ireland 376 Israel 380 Italy 384 Ivory Coast 388 Jamaica 400 Jordan 404 Kenya 417 Kyrgyz Republic 418 Laos 426 Lesotho 430 Liberia 454 Malawi 458 Malaysia 466 Mali 480 Mauritius 484 Mexico 496 Mongolia 504 Morocco 508 Mozambique 104 Myanmar 524 Nepal 528 Netherlands 558 Nicaragua 566 Nigeria 578 Norway 586 Pakistan 275 Palestine 591 Panama 598 Papua New Guinea 600 Paraguay 604 Peru 608 Philippines 616 Poland 620 Portugal 630 Puerto Rico 642 Romania 643 Russia 646 Rwanda 662 Saint Lucia 686 Senegal 694 Sierra Leone 703 Slovak Republic 705 Slovenia 710 South Africa 728 South Sudan 724 Spain 729 Sudan 740 Suriname 752 Sweden 756 Switzerland 834 Tanzania 764 Thailand 768 Togo 780 Trinidad and Tobago 792 Turkey 800 Uganda 804 Ukraine 826 United Kingdom 840 United States 858 Uruguay 862 Venezuela 704 Vietnam 894 Zambia 716 Zimbabwe COUNTRY gives the country from which the sample was drawn. The codes assigned to each country are those used by the UN Statistics Division and the ISO (International Organization for Standardization). IPUMS Year 1703 1703 1729 1729 1787 1787 1801 1801 1819 1819 1845 1845 1848 1848 1850 1850 1851 1851 1852 1852 1860 1860 1861 1861 1865 1865 1868 1868 1870 1870 1871 1871 1875 1875 1880 1880 1881 1881 1885 1885 1890 1890 1891 1891 1900 1900 1901 1901 1910 1910 1911 1911 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1966 1966 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 YEAR gives the year in which the census or survey was taken. For samples that span years, the midpoint or first year of the interval is reported. IPUMS IPUMS sample identifier 032197001 Argentina 1970 032198001 Argentina 1980 032199101 Argentina 1991 032200101 Argentina 2001 032201001 Argentina 2010 051200101 Armenia 2001 051201101 Armenia 2011 040197101 Austria 1971 040198101 Austria 1981 040199101 Austria 1991 040200101 Austria 2001 040201101 Austria 2011 050199101 Bangladesh 1991 050200101 Bangladesh 2001 050201101 Bangladesh 2011 112199901 Belarus 1999 112200901 Belarus 2009 204197901 Benin 1979 204199201 Benin 1992 204200201 Benin 2002 204201301 Benin 2013 068197601 Bolivia 1976 068199201 Bolivia 1992 068200101 Bolivia 2001 068201201 Bolivia 2012 072198101 Botswana 1981 072199101 Botswana 1991 072200101 Botswana 2001 072201101 Botswana 2011 076196001 Brazil 1960 076197001 Brazil 1970 076198001 Brazil 1980 076199101 Brazil 1991 076200001 Brazil 2000 076201001 Brazil 2010 854198501 Burkina Faso 1985 854199601 Burkina Faso 1996 854200601 Burkina Faso 2006 116199801 Cambodia 1998 116200401 Cambodia 2004 116200801 Cambodia 2008 116201301 Cambodia 2013 116201901 Cambodia 2019 120197601 Cameroon 1976 120198701 Cameroon 1987 120200501 Cameroon 2005 124185201 Canada 1852 124187101 Canada 1871 124188101 Canada 1881 124189101 Canada 1891 124190101 Canada 1901 124191101 Canada 1911 124197101 Canada 1971 124198101 Canada 1981 124199101 Canada 1991 124200101 Canada 2001 124201101 Canada 2011 152196001 Chile 1960 152197001 Chile 1970 152198201 Chile 1982 152199201 Chile 1992 152200201 Chile 2002 152201701 Chile 2017 156198201 China 1982 156199001 China 1990 156200001 China 2000 170196401 Colombia 1964 170197301 Colombia 1973 170198501 Colombia 1985 170199301 Colombia 1993 170200501 Colombia 2005 188196301 Costa Rica 1963 188197301 Costa Rica 1973 188198401 Costa Rica 1984 188200001 Costa Rica 2000 188201101 Costa Rica 2011 192200201 Cuba 2002 192201201 Cuba 2012 208178701 Denmark 1787 208180101 Denmark 1801 208184501 Denmark 1845 208188001 Denmark 1880 208188501 Denmark 1885 214196001 Dominican Republic 1960 214197001 Dominican Republic 1970 214198101 Dominican Republic 1981 214200201 Dominican Republic 2002 214201001 Dominican Republic 2010 218196201 Ecuador 1962 218197401 Ecuador 1974 218198201 Ecuador 1982 218199001 Ecuador 1990 218200101 Ecuador 2001 218201001 Ecuador 2010 818184801 Egypt 1848 818186801 Egypt 1868 818198601 Egypt 1986 818199601 Egypt 1996 818200601 Egypt 2006 222199201 El Salvador 1992 222200701 El Salvador 2007 231198401 Ethiopia 1984 231199401 Ethiopia 1994 231200701 Ethiopia 2007 242196601 Fiji 1966 242197601 Fiji 1976 242198601 Fiji 1986 242199601 Fiji 1996 242200701 Fiji 2007 242201401 Fiji 2014 246201001 Finland 2010 250196201 France 1962 250196801 France 1968 250197501 France 1975 250198201 France 1982 250199001 France 1990 250199901 France 1999 250200601 France 2006 250201101 France 2011 276181901 Germany 1819 (Mecklenburg) 276197001 Germany 1970 (West) 276197101 Germany 1971 (East) 276198101 Germany 1981 (East) 276198701 Germany 1987 (West) 288198401 Ghana 1984 288200001 Ghana 2000 288201001 Ghana 2010 300197101 Greece 1971 300198101 Greece 1981 300199101 Greece 1991 300200101 Greece 2001 300201101 Greece 2011 320196401 Guatemala 1964 320197301 Guatemala 1973 320198101 Guatemala 1981 320199401 Guatemala 1994 320200201 Guatemala 2002 324198301 Guinea 1983 324199601 Guinea 1996 324201401 Guinea 2014 332197101 Haiti 1971 332198201 Haiti 1982 332200301 Haiti 2003 340196101 Honduras 1961 340197401 Honduras 1974 340198801 Honduras 1988 340200101 Honduras 2001 348197001 Hungary 1970 348198001 Hungary 1980 348199001 Hungary 1990 348200101 Hungary 2001 348201101 Hungary 2011 352170301 Iceland 1703 352172901 Iceland 1729 352180101 Iceland 1801 352190101 Iceland 1901 352191001 Iceland 1910 356198341 India 1983 356198741 India 1987 356199341 India 1993 356199941 India 1999 356200441 India 2004 356200941 India 2009 360197101 Indonesia 1971 360197601 Indonesia 1976 360198001 Indonesia 1980 360198501 Indonesia 1985 360199001 Indonesia 1990 360199501 Indonesia 1995 360200001 Indonesia 2000 360200501 Indonesia 2005 360201001 Indonesia 2010 364200601 Iran 2006 364201101 Iran 2011 368199701 Iraq 1997 372190101 Ireland 1901 372191101 Ireland 1911 372197101 Ireland 1971 372197901 Ireland 1979 372198101 Ireland 1981 372198601 Ireland 1986 372199101 Ireland 1991 372199601 Ireland 1996 372200201 Ireland 2002 372200601 Ireland 2006 372201101 Ireland 2011 372201601 Ireland 2016 376197201 Israel 1972 376198301 Israel 1983 376199501 Israel 1995 376200801 Israel 2008 380200101 Italy 2001 380201101 Italy 2011 380201121 Italy 2011 Q1 LFS 380201221 Italy 2012 Q1 LFS 380201321 Italy 2013 Q1 LFS 380201421 Italy 2014 Q1 LFS 380201521 Italy 2015 Q1 LFS 380201621 Italy 2016 Q1 LFS 380201721 Italy 2017 Q1 LFS 380201821 Italy 2018 Q1 LFS 380201921 Italy 2019 Q1 LFS 380202021 Italy 2020 Q1 LFS 384198801 Ivory Coast 1988 384199801 Ivory Coast 1998 388198201 Jamaica 1982 388199101 Jamaica 1991 388200101 Jamaica 2001 400200401 Jordan 2004 404196901 Kenya 1969 404197901 Kenya 1979 404198901 Kenya 1989 404199901 Kenya 1999 404200901 Kenya 2009 417199901 Kyrgyz Republic 1999 417200901 Kyrgyz Republic 2009 418199501 Laos 1995 418200501 Laos 2005 418201501 Laos 2015 426199601 Lesotho 1996 426200601 Lesotho 2006 430197401 Liberia 1974 430200801 Liberia 2008 454198701 Malawi 1987 454199801 Malawi 1998 454200801 Malawi 2008 458197001 Malaysia 1970 458198001 Malaysia 1980 458199101 Malaysia 1991 458200001 Malaysia 2000 466198701 Mali 1987 466199801 Mali 1998 466200901 Mali 2009 480199001 Mauritius 1990 480200001 Mauritius 2000 480201101 Mauritius 2011 484196001 Mexico 1960 484197001 Mexico 1970 484199001 Mexico 1990 484199501 Mexico 1995 484200001 Mexico 2000 484200501 Mexico 2005 484201001 Mexico 2010 484201501 Mexico 2015 484202001 Mexico 2020 484200521 Mexico 2005 Q1 LFS 484200522 Mexico 2005 Q2 LFS 484200523 Mexico 2005 Q3 LFS 484200524 Mexico 2005 Q4 LFS 484200621 Mexico 2006 Q1 LFS 484200622 Mexico 2006 Q2 LFS 484200623 Mexico 2006 Q3 LFS 484200624 Mexico 2006 Q4 LFS 484200721 Mexico 2007 Q1 LFS 484200722 Mexico 2007 Q2 LFS 484200723 Mexico 2007 Q3 LFS 484200724 Mexico 2007 Q4 LFS 484200821 Mexico 2008 Q1 LFS 484200822 Mexico 2008 Q2 LFS 484200823 Mexico 2008 Q3 LFS 484200824 Mexico 2008 Q4 LFS 484200921 Mexico 2009 Q1 LFS 484200922 Mexico 2009 Q2 LFS 484200923 Mexico 2009 Q3 LFS 484200924 Mexico 2009 Q4 LFS 484201021 Mexico 2010 Q1 LFS 484201022 Mexico 2010 Q2 LFS 484201023 Mexico 2010 Q3 LFS 484201024 Mexico 2010 Q4 LFS 484201121 Mexico 2011 Q1 LFS 484201122 Mexico 2011 Q2 LFS 484201123 Mexico 2011 Q3 LFS 484201124 Mexico 2011 Q4 LFS 484201221 Mexico 2012 Q1 LFS 484201222 Mexico 2012 Q2 LFS 484201223 Mexico 2012 Q3 LFS 484201224 Mexico 2012 Q4 LFS 484201321 Mexico 2013 Q1 LFS 484201322 Mexico 2013 Q2 LFS 484201323 Mexico 2013 Q3 LFS 484201324 Mexico 2013 Q4 LFS 484201421 Mexico 2014 Q1 LFS 484201422 Mexico 2014 Q2 LFS 484201423 Mexico 2014 Q3 LFS 484201424 Mexico 2014 Q4 LFS 484201521 Mexico 2015 Q1 LFS 484201522 Mexico 2015 Q2 LFS 484201523 Mexico 2015 Q3 LFS 484201524 Mexico 2015 Q4 LFS 484201621 Mexico 2016 Q1 LFS 484201622 Mexico 2016 Q2 LFS 484201623 Mexico 2016 Q3 LFS 484201624 Mexico 2016 Q4 LFS 484201721 Mexico 2017 Q1 LFS 484201722 Mexico 2017 Q2 LFS 484201723 Mexico 2017 Q3 LFS 484201724 Mexico 2017 Q4 LFS 484201821 Mexico 2018 Q1 LFS 484201822 Mexico 2018 Q2 LFS 484201823 Mexico 2018 Q3 LFS 484201824 Mexico 2018 Q4 LFS 484201921 Mexico 2019 Q1 LFS 484201922 Mexico 2019 Q2 LFS 484201923 Mexico 2019 Q3 LFS 484201924 Mexico 2019 Q4 LFS 484202021 Mexico 2020 Q1 LFS 484202023 Mexico 2020 Q3 LFS 496198901 Mongolia 1989 496200001 Mongolia 2000 504198201 Morocco 1982 504199401 Morocco 1994 504200401 Morocco 2004 504201401 Morocco 2014 508199701 Mozambique 1997 508200701 Mozambique 2007 104201401 Myanmar 2014 524200101 Nepal 2001 524201101 Nepal 2011 528196001 Netherlands 1960 528197101 Netherlands 1971 528200101 Netherlands 2001 528201101 Netherlands 2011 558197101 Nicaragua 1971 558199501 Nicaragua 1995 558200501 Nicaragua 2005 566200621 Nigeria 2006 566200721 Nigeria 2007 566200821 Nigeria 2008 566200921 Nigeria 2009 566201021 Nigeria 2010 578180101 Norway 1801 578186501 Norway 1865 578187501 Norway 1875 578190001 Norway 1900 578191001 Norway 1910 586197301 Pakistan 1973 586198101 Pakistan 1981 586199801 Pakistan 1998 275199701 Palestine 1997 275200701 Palestine 2007 275201701 Palestine 2017 591196001 Panama 1960 591197001 Panama 1970 591198001 Panama 1980 591199001 Panama 1990 591200001 Panama 2000 591201001 Panama 2010 598198001 Papua New Guinea 1980 598199001 Papua New Guinea 1990 598200001 Papua New Guinea 2000 600196201 Paraguay 1962 600197201 Paraguay 1972 600198201 Paraguay 1982 600199201 Paraguay 1992 600200201 Paraguay 2002 604199301 Peru 1993 604200701 Peru 2007 604201701 Peru 2017 608199001 Philippines 1990 608199501 Philippines 1995 608200001 Philippines 2000 608201001 Philippines 2010 616197801 Poland 1978 616198801 Poland 1988 616200201 Poland 2002 616201101 Poland 2011 620198101 Portugal 1981 620199101 Portugal 1991 620200101 Portugal 2001 620201101 Portugal 2011 630197001 Puerto Rico 1970 630198001 Puerto Rico 1980 630199001 Puerto Rico 1990 630200001 Puerto Rico 2000 630200501 Puerto Rico 2005 630201001 Puerto Rico 2010 630201501 Puerto Rico 2015 630202001 Puerto Rico 2020 642197701 Romania 1977 642199201 Romania 1992 642200201 Romania 2002 642201101 Romania 2011 643200201 Russia 2002 643201001 Russia 2010 646199101 Rwanda 1991 646200201 Rwanda 2002 646201201 Rwanda 2012 662198001 Saint Lucia 1980 662199101 Saint Lucia 1991 686198801 Senegal 1988 686200201 Senegal 2002 686201301 Senegal 2013 694200401 Sierra Leone 2004 694201501 Sierra Leone 2015 703199101 Slovak Republic 1991 703200101 Slovak Republic 2001 703201101 Slovak Republic 2011 705200201 Slovenia 2002 710199601 South Africa 1996 710200101 South Africa 2001 710200701 South Africa 2007 710201101 South Africa 2011 710201601 South Africa 2016 728200801 South Sudan 2008 724198101 Spain 1981 724199101 Spain 1991 724200101 Spain 2001 724201101 Spain 2011 724200521 Spain 2005 Q1 LFS 724200522 Spain 2005 Q2 LFS 724200523 Spain 2005 Q3 LFS 724200524 Spain 2005 Q4 LFS 724200621 Spain 2006 Q1 LFS 724200622 Spain 2006 Q2 LFS 724200623 Spain 2006 Q3 LFS 724200624 Spain 2006 Q4 LFS 724200721 Spain 2007 Q1 LFS 724200722 Spain 2007 Q2 LFS 724200723 Spain 2007 Q3 LFS 724200724 Spain 2007 Q4 LFS 724200821 Spain 2008 Q1 LFS 724200822 Spain 2008 Q2 LFS 724200823 Spain 2008 Q3 LFS 724200824 Spain 2008 Q4 LFS 724200921 Spain 2009 Q1 LFS 724200922 Spain 2009 Q2 LFS 724200923 Spain 2009 Q3 LFS 724200924 Spain 2009 Q4 LFS 724201021 Spain 2010 Q1 LFS 724201022 Spain 2010 Q2 LFS 724201023 Spain 2010 Q3 LFS 724201024 Spain 2010 Q4 LFS 724201121 Spain 2011 Q1 LFS 724201122 Spain 2011 Q2 LFS 724201123 Spain 2011 Q3 LFS 724201124 Spain 2011 Q4 LFS 724201221 Spain 2012 Q1 LFS 724201222 Spain 2012 Q2 LFS 724201223 Spain 2012 Q3 LFS 724201224 Spain 2012 Q4 LFS 724201321 Spain 2013 Q1 LFS 724201322 Spain 2013 Q2 LFS 724201323 Spain 2013 Q3 LFS 724201324 Spain 2013 Q4 LFS 724201421 Spain 2014 Q1 LFS 724201422 Spain 2014 Q2 LFS 724201423 Spain 2014 Q3 LFS 724201424 Spain 2014 Q4 LFS 724201521 Spain 2015 Q1 LFS 724201522 Spain 2015 Q2 LFS 724201523 Spain 2015 Q3 LFS 724201524 Spain 2015 Q4 LFS 724201621 Spain 2016 Q1 LFS 724201622 Spain 2016 Q2 LFS 724201623 Spain 2016 Q3 LFS 724201624 Spain 2016 Q4 LFS 724201721 Spain 2017 Q1 LFS 724201722 Spain 2017 Q2 LFS 724201723 Spain 2017 Q3 LFS 724201724 Spain 2017 Q4 LFS 724201821 Spain 2018 Q1 LFS 724201822 Spain 2018 Q2 LFS 724201823 Spain 2018 Q3 LFS 724201824 Spain 2018 Q4 LFS 724201921 Spain 2019 Q1 LFS 724201922 Spain 2019 Q2 LFS 724201923 Spain 2019 Q3 LFS 724201924 Spain 2019 Q4 LFS 724202021 Spain 2020 Q1 LFS 724202022 Spain 2020 Q2 LFS 724202023 Spain 2020 Q3 LFS 724202024 Spain 2020 Q4 LFS 729200801 Sudan 2008 740200401 Suriname 2004 740201201 Suriname 2012 752188001 Sweden 1880 752189001 Sweden 1890 752190001 Sweden 1900 752191001 Sweden 1910 756197001 Switzerland 1970 756198001 Switzerland 1980 756199001 Switzerland 1990 756200001 Switzerland 2000 756201101 Switzerland 2011 834198801 Tanzania 1988 834200201 Tanzania 2002 834201201 Tanzania 2012 764197001 Thailand 1970 764198001 Thailand 1980 764199001 Thailand 1990 764200001 Thailand 2000 768196001 Togo 1960 768197001 Togo 1970 768201001 Togo 2010 780197001 Trinidad and Tobago 1970 780198001 Trinidad and Tobago 1980 780199001 Trinidad and Tobago 1990 780200001 Trinidad and Tobago 2000 780201101 Trinidad and Tobago 2011 792198501 Turkey 1985 792199001 Turkey 1990 792200001 Turkey 2000 800199101 Uganda 1991 800200201 Uganda 2002 800201401 Uganda 2014 804200101 Ukraine 2001 826185101 United Kingdom 1851 (England and Wales) 826185102 United Kingdom 1851 (Scotland) 826185103 United Kingdom 1851 (2% sample) 826186101 United Kingdom 1861 (England and Wales) 826186102 United Kingdom 1861 (Scotland) 826187101 United Kingdom 1871 (Scotland) 826188101 United Kingdom 1881 (England and Wales) 826188102 United Kingdom 1881 (Scotland) 826189101 United Kingdom 1891 (England and Wales) 826189102 United Kingdom 1891 (Scotland) 826190101 United Kingdom 1901 (England and Wales) 826190102 United Kingdom 1901 (Scotland) 826191101 United Kingdom 1911 (England and Wales) 826196101 United Kingdom 1961 826197101 United Kingdom 1971 826199101 United Kingdom 1991 826200101 United Kingdom 2001 840185001 United States 1850 (100%) 840185002 United States 1850 (1%) 840186001 United States 1860 (1%) 840187001 United States 1870 (1%) 840188001 United States 1880 (100%) 840188002 United States 1880 (10%) 840190001 United States 1900 (5%) 840191001 United States 1910 (1%) 840196001 United States 1960 840197001 United States 1970 840198001 United States 1980 840199001 United States 1990 840200001 United States 2000 840200501 United States 2005 840201001 United States 2010 840201501 United States 2015 840202001 United States 2020 858196301 Uruguay 1963 858196302 Uruguay 1963 (full count) 858197501 Uruguay 1975 858197502 Uruguay 1975 (full count) 858198501 Uruguay 1985 858198502 Uruguay 1985 (full count) 858199601 Uruguay 1996 858199602 Uruguay 1996 (full count) 858200621 Uruguay 2006 858201101 Uruguay 2011 858201102 Uruguay 2011 (full count) 862197101 Venezuela 1971 862198101 Venezuela 1981 862199001 Venezuela 1990 862200101 Venezuela 2001 704198901 Vietnam 1989 704199901 Vietnam 1999 704200901 Vietnam 2009 704201901 Vietnam 2019 894199001 Zambia 1990 894200001 Zambia 2000 894201001 Zambia 2010 716201201 Zimbabwe 2012 SAMPLE identifies the IPUMS sample from which the case is drawn. Each sample receives a unique 9-digit code. The code is structured as follows: The first 3 digits are the ISO/UN codes used in COUNTRY The next 4 digits are the year of the census/survey The final 2 digits identify the sample within the year. For the last two digits, censuses or large census-like surveys have a value "0" (e.g, 01) in the second-to-last digit, household surveys have a value of "2" (e.g., 21), and employment surveys have a value of "4" (e.g., 41). IPUMS Household serial number SERIAL is an identifying number unique to each household in a given sample. All person records are assigned the same serial number as the household record that they follow. (Person records also have their own unique identifiers -- see PERNUM.) The combination of SAMPLE and SERIAL provides a unique identifier for every household in the IPUMS-International database; SAMPLE, SERIAL and PERNUM uniquely identify every person in the database. SERIAL can be used to identify dwellings in some samples. In these samples, the first 7 digits of SERIAL provide the dwelling number common to all households that were sampled from the same structure. The last three digits give the sequence of the household within the dwelling. The following is a list of samples in which dwellings can be inferred: Chile 1970, 1992, 2002Colombia 1993, 2005Costa Rica 1984, 2000Cuba 2002Dominican Republic 1981, 2002, 2010Ecuador 1990, 2001Germany 1971Hungary 1980, 1990, 2001Jamaica 1982, 1991, 2001Malaysia 1970, 1991, 2000Mexico 1995, 1990, 2000, 2005Nigeria 2006Panama 2000Peru 1993, 2007Portugal 1981, 1991, 2001Spain 1991Uruguay 2011Venezuela 1990, 2001Vietnam 1989In all other samples, the last 3 digits are always zeroes. SERIAL was constructed for IPUMS-International, and has no relation to the serial number in the original datasets. The U.S. 1900 sample and 1880 10% sample have multi-household dwellings that can be identified using the last 3 digits of SERIAL. SERIAL is a 10-digit numeric variable. The last 3 digits of SERIAL indicate household number within dwelling for selected samples noted in the variable description. In all other samples, the last 3 digits are always zeroes. IPUMS Number of person records in the household PERSONS indicates how many person records are included in the household (i.e., the number of person records associated with the household record in the sample). These person records will all have the same serial number (SERIAL) as the household record. The information contained in the household record will normally apply to all of these persons. PERSONS is a 4-digit numeric variable. IPUMS Household weight HHWT indicates the number of households in the population represented by the household in the sample. For the samples that are truly weighted (see the comparability discussion), HHWT must be used to yield accurate household-level statistics. NOTE: HHWT has 2 implied decimal places. That is, the last two digits of the eight-digit variable are decimal digits, but there is no actual decimal in the data. HHWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description. IPUMS Subsample number 00 1st 1% subsample 01 2nd 1% subsample 02 3rd 1% subsample 03 4th 1% subsample 04 5th 1% subsample 05 6th 1% subsample 06 7th 1% subsample 07 8th 1% subsample 08 9th 1% subsample 09 10th 1% subsample 10 11th 1% subsample 11 12th 1% subsample 12 13th 1% subsample 13 14th 1% subsample 14 15th 1% subsample 15 16th 1% subsample 16 17th 1% subsample 17 18th 1% subsample 18 19th 1% subsample 19 20th 1% subsample 20 21st 1% subsample 21 22nd 1% subsample 22 23rd 1% subsample 23 24th 1% subsample 24 25th 1% subsample 25 26th 1% subsample 26 27th 1% subsample 27 28th 1% subsample 28 29th 1% subsample 29 30th 1% subsample 30 31st 1% subsample 31 32nd 1% subsample 32 33rd 1% subsample 33 34th 1% subsample 34 35th 1% subsample 35 36th 1% subsample 36 37th 1% subsample 37 38th 1% subsample 38 39th 1% subsample 39 40th 1% subsample 40 41st 1% subsample 41 42nd 1% subsample 42 43rd 1% subsample 43 44th 1% subsample 44 45th 1% subsample 45 46th 1% subsample 46 47th 1% subsample 47 48th 1% subsample 48 49th 1% subsample 49 50th 1% subsample 50 51st 1% subsample 51 52nd 1% subsample 52 53rd 1% subsample 53 54th 1% subsample 54 55th 1% subsample 55 56th 1% subsample 56 57th 1% subsample 57 58th 1% subsample 58 59th 1% subsample 59 60th 1% subsample 60 61st 1% subsample 61 62nd 1% subsample 62 63rd 1% subsample 63 64th 1% subsample 64 65th 1% subsample 65 66th 1% subsample 66 67th 1% subsample 67 68th 1% subsample 68 69th 1% subsample 69 70th 1% subsample 70 71st 1% subsample 71 72nd 1% subsample 72 73rd 1% subsample 73 74th 1% subsample 74 75th 1% subsample 75 76th 1% subsample 76 77th 1% subsample 77 78th 1% subsample 78 79th 1% subsample 79 80th 1% subsample 80 81st 1% subsample 81 82nd 1% subsample 82 83rd 1% subsample 83 84th 1% subsample 84 85th 1% subsample 85 86th 1% subsample 86 87th 1% subsample 87 88th 1% subsample 88 89th 1% subsample 89 90th 1% subsample 90 91st 1% subsample 91 92nd 1% subsample 92 93rd 1% subsample 93 94th 1% subsample 94 95th 1% subsample 95 96th 1% subsample 96 97th 1% subsample 97 98th 1% subsample 98 99th 1% subsample 99 100th 1% subsample SUBSAMP allocates each case to one of 100 subsample replicates, randomly numbered from 0 to 99. Each subsample is nationally representative and preserves any stratification of the sample from which it is drawn. Users who need a representative subset of a sample can use SUBSAMP to select their cases. For example, to randomly extract 10% of the cases from a sample, select any 10 of the 100 subsamples. IPUMS Group quarters (collective dwelling) status 00 Vacant 10 Households 20 Group quarters (collective), n.s. 21 Institutions 22 Other group quarters 29 1-person unit created by splitting large household 99 Unknown/group quarters not identified GQ identifies households as vacant dwellings, group quarters, or private households. Group quarters -- collective dwellings -- are generally institutions and other group living arrangements such as rooming houses and boarding schools. Institutions often retain persons under formal supervision or custody, such as correctional institutions, military barracks, asylums, or nursing homes. Educational and religious group dwellings (e.g., boarding schools, convents, monasteries, etc.) are also included in the institutional classification. Group quarter designations are often useful for understanding the universe of households that answered questions about household characteristics. Censuses will often exclude group quarters from such questions. IPUMS Continent and region of country 11 Eastern Africa 12 Middle Africa 13 Northern Africa 14 Southern Africa 15 Western Africa 21 Caribbean 22 Central America 23 North America 24 South America 31 Central Asia 32 Eastern Asia 33 Southern Asia 34 South-Eastern Asia 35 Western Asia 41 Eastern Europe 42 Northern Europe 43 Southern Europe 44 Western Europe 51 Australia and New Zealand 52 Melanesia 53 Micronesia 54 Polynesia REGIONW identifies the continent and region of each country. IPUMS 1st subnational geographic level, world [consistent boundaries over time] GEOLEV1 indicates the major administrative unit in which the household was enumerated. The variable incorporates the geographies for every country, to enable cross-national geographic analysis over time. First administrative units in GEOLEV1 have been spatiotemporally harmonized to provide spatially consistent boundaries across samples in each country. GEOLEV1 is a 6-digit numeric variable. GEOLEV1 codes and labels can be found here. Codes, labels, frequencies, and information about boundary changes for each country can be found in the country specific harmonized variable e.g. GEO1_BR. IPUMS Nomenclature of Territorial Units for Statistics 1, Europe 0101 AT1 / Ostösterreich 0102 AT2 / Südösterreich 0103 AT3 / Westösterreich 0601 DE1 / Baden-Württemberg 0602 DE2 / Bayern 0603 DE3 / Berlin 0604 DE4 / Brandenburg 0605 DE5 / Bremen 0606 DE6 / Hamburg 0607 DE7 / Hessen 0608 DE8 / Mecklenburg-Vorpommern 0609 DE9 / Niedersachsen 0610 DEA / Nordrhein-Westfalen 0611 DEB / Rheinland-Pfalz 0612 DEC / Saarland 0613 DED / Sachsen 0614 DEE / Sachsen-Anhalt 0615 DEF / Schleswig-Holstein 0616 DEG / Thüringen 0901 ES1 / Noroeste 0902 ES2 / Noreste 0903 ES3 / Comunidad de Madrid 0904 ES4 / Centro (E) 0905 ES5 / Este 0906 ES6 / Sur 0907 ES7 / Canarias 0909 ES / Unknown 1101 FR1 / Île de France 1102 FR2 / Bassin Parisien 1103 FR3 / Nord - Pas-de-Calais 1104 FR4 / Est 1105 FR5 / Ouest 1106 FR6 / Sud-Ouest 1107 FR7 / Centre-Est 1108 FR8 / Méditerranée 1109 FR9 / Département d’Outre-Mer 1199 FR99 / Unknown 1201 EL1 / Voreia Ellada 1202 EL2 / Kentriki Ellada 1203 EL3 / Attiki 1204 EL4 / Nisia Aigaiou, Kriti 1400 IE0 / Republic of Ireland 1503 ITC / Nord-Ovest 1506 ITF / Sud 1507 ITG / Isole 1508 ITH / Nord-Est 1509 ITI / Centro 2101 PL1 / Centralny 2102 PL2 / Południowy 2103 PL3 / Wschodni 2104 PL4 / Północno-zachodni 2105 PL5 / Południowo-zachodni 2106 PL6 / Północny 2201 PT1 / Continente 2202 PT2 / Região Autónoma dos Açores 2203 PT3 / Região Autónoma da Madeira 2301 RO1 / Macroregiunea Unu 2302 RO2 / Macroregiunea Doi 2303 RO3 / Macroregiunea Trei 2304 RO4 / Macroregiunea Patru 2501 SI0 / Slovenia 2600 SK0 / Slovensko 2701 UKC / North East (England) 2702 UKD / North West (England) 2703 UKE / Yorkshire and the Humber (England) 2704 UKF / East Midlands (England) 2705 UKG / West Midlands (England) 2706 UKH / East of England (England) 2707 UKI / LONDON (England) 2708 UKJ / South East (England) 2709 UKK / South West (England) 2710 UKL / WALES 2711 UKM / SCOTLAND 2712 UKN / NORTHERN IRELAND 3400 CH0/Switzerland 3901 TR1 / Istanbul 3902 TR2 / Bati Marmara 3903 TR3 / Ege 3904 TR4 / Dogu Marmara 3905 TR5 / Bati Anadolu 3906 TR6 / Akdeniz 3907 TR7 / Orta Anadolu 3908 TR8 / Bati Karadeniz 3909 TR9 / Dogu Karadeniz 3911 TRA / Kuzeydogu Anadolu 3912 TRB / Ortadogu Anadolu 3913 TRC / Güneydogu Anadolu 9999 UNKNOWN ENUTS1 identifies the Nomenclature of Territorial Units for Statistics (NUTS) within Europe in which the household was enumerated. NUTS1 is the first level territorial units within countries. NUTS is a standard administrative division of the European Union, and was developed by the EU. The European Free Trade Association extends the NUTS system to several additional countries outside of the EU, and they are also incorporated into this variable. ENUTS1 corresponds to the 2010 version of NUTS1 released by Eurostat. IPUMS has added ENUTS1_2013 for the more recent samples. ENUTS1_2013 is an amendment to the annexes to the ENUTS1 classification. The last 2-digits of the ENUTS1 variable provide the NUTS1 code. The labels include the standard code for the NUTS1 system and the name of the NUTS1 region, separated by a slash. Smaller sub-national units are available for most countries in ENUTS2 and ENUTS3. The full set of geography variables for the countries can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Nomenclature of Territorial Units for Statistics 2, Europe 0111 AT11 / Burgenland 0112 AT12 / Niederösterreich 0113 AT13 / Wien 0121 AT21 / Kärnten 0122 AT22 / Steiermark 0131 AT31 / Oberösterreich 0132 AT32 / Salzburg 0133 AT33 / Tirol 0134 AT34 / Vorarlberg 0911 ES11 / Galicia 0912 ES12 / Principado de Asturias 0913 ES13 / Cantabria 0921 ES21 / País Vasco 0922 ES22 / Comunidad Foral de Navarra 0923 ES23 / La Rioja 0924 ES24 / Aragón 0930 ES30 / Comunidad de Madrid 0941 ES41 / Castilla y León 0942 ES42 / Castilla-La Mancha 0943 ES43 / Extremadura 0951 ES51 / Cataluña 0952 ES52 / Comunidad Valenciana 0953 ES53 / Illes Balears 0961 ES61 / Andalucía 0962 ES62 / Región de Murcia 0963 ES63 / Ciudad Autónoma de Ceuta 0964 ES64 / Ciudad Autónoma de Melilla 0970 ES70 / Canarias 0999 ES / Unknown 1110 FR10 / Île de France 1121 FR21 / Champagne-Ardenne 1122 FR22 / Picardie 1123 FR23 / Haute-Normandie 1124 FR24 / Centre 1125 FR25 / Basse-Normandie 1126 FR26 / Bourgogne 1130 FR30 / Nord-Pas-de-Calais 1141 FR41 / Lorraine 1142 FR42 / Alsace 1143 FR43 / Franche-Comté 1151 FR51 / Pays de la Loire 1152 FR52 / Bretagne 1153 FR53 / Poitou-Charentes 1161 FR61 / Aquitaine 1162 FR62 / Midi-Pyrénées 1163 FR63 / Limousin 1171 FR71 / Rhône-Alpes 1172 FR72 / Auvergne 1181 FR81 / Lanquedoc-Roussillon 1182 FR82 / Provence-Alpes-Côte d'Azur 1183 FR83 / Corse 1191 FR91 / Guadeloupe 1192 FR92 / Martinique 1193 FR93 / Guyane 1194 FR94 / Réunion 1199 FR99 / Unknown 1211 EL11 / Anatoliki Makedonia, Thraki 1212 EL12 / Kentriki Makedonia 1213 EL13 / Dytiki Makedonia 1214 EL14 / Thessalia 1221 EL21 / Ipeiros 1222 EL22 / Ionia Nisia 1223 EL23 / Dytiki Ellada 1224 EL24 / Sterea Ellada 1225 EL25 / Peloponnisos 1230 EL30 / Attiki 1241 EL41 / Voreio Aigaio 1242 EL42 / Notio Aigaio 1243 EL43 / Kriti 1401 IE01 / Border, Midland and Western 1402 IE02 / Southern and Eastern 1531 ITC1 / Piemonte + ITC2 / Valle d'Aosta 1533 ITC3 / Liguria 1534 ITC4 /Lombardia 1561 ITF1 / Abruzzo 1562 ITF2 / Molise 1563 ITF3 / Campania 1564 ITF4 / Puglia 1565 ITF5 / Basilicata 1566 ITF6 / Calabria 1571 ITG1 / Sicilia 1572 ITG2 / Sardegna 1581 ITH1 / Bolzano-Bozen + ITH2 / Trento 1583 ITH3 / Veneto 1584 ITH4 / Friuli-Venezia Giulia 1585 ITH5 / Emilia-Romagna 1591 ITI1 / Toscana 1592 ITI2 / Umbria 1593 ITI3 / Marche 1594 ITI4 / Lazio 2111 PL11 / Łódzkie 2112 PL12 / Mazowieckie 2121 PL21 / Małopolskie 2122 PL22 / Śląskie 2131 PL31 / Lubelskie 2132 PL32 / Podkarpackie 2133 PL33 / Świętokrzyskie 2134 PL34 / Podlaskie 2141 PL41 / Wielkopolskie 2142 PL42 / Zachodniopomorskie 2143 PL43 / Lubuskie 2151 PL51 / Dolnośląskie 2152 PL52 / Opolskie 2161 PL61 / Kujawsko-Pomorskie 2162 PL62 / Warmińsko-Mazurskie 2163 PL63 / Pomorskie 2211 PT11 / Norte 2215 PT15 / Algarve 2216 PT16 / Centro (P) 2217 PT17 / Lisboa 2218 PT18 / Alentejo 2220 PT20 / Região Autónoma dos Açores 2230 PT30 / Região Autónoma da Madeira 2311 RO11 / Nord-Vest 2312 RO12 / Centru 2321 RO21 / Nord-Est 2322 RO22 / Sud-Est 2331 RO31 / Sud - Muntenia 2332 RO32 / Bucuresti - Ilfov 2341 RO41 / Sud-Vest Oltenia 2342 RO42 / Vest 2501 SI01 / Vzhodna Slovenija 2502 SI02 / Zahodna Slovenija 2599 SI / Unknown 2601 SK01 / Bratislavský kraj 2602 SK02 / Západné Slovensko 2603 SK03 / Stredné Slovensko 2604 SK04 / Východné Slovensko 3401 CH01 / Région Lémanique 3402 CH02 / Espace Mittelland 3403 CH03 / Nordwestschweiz 3404 CH04 / Zurich 3405 CH05 / Ostschweiz 3406 CH06 / Zentralschweiz 3407 CH07 / Ticino 3910 TR10 / Istanbul 3921 TR21 / Tekirdag, Edirne, Kirklareli 3922 TR22 / Balikesir, Çanakkale 3931 TR31 / Izmir 3932 TR32 / Aydin, Denizli, Mugla 3933 TR33 / Manisa, Afyonkarahisar, Kütahya, Usak 3941 TR41 / Bursa, Eskisehir, Bilecik 3942 TR42 / Kocaeli, Sakarya, Düzce, Bolu, Yalova 3951 TR51 / Ankara 3952 TR52 / Konya, Karaman 3961 TR61 / Antalya, Isparta, Burdur 3962 TR62 / Adana, Mersin 3963 TR63 / Hatay, Kahramanmaras, Osmaniye 3971 TR71 / Kirikkale, Aksaray, Nigde, Nevsehir, Kirsehir 3972 TR72 / Kayseri, Sivas, Yozgat 3981 TR81 / Zonguldak, Karabük, Bartin 3982 TR82 / Kastamonu, Çankiri, Sinop 3983 TR83 / Samsun, Tokat, Çorum, Amasya 3990 TR90 / Trabzon 3991 TRA1 / Erzurum, Erzincan, Bayburt 3992 TRA2 / Agri, Kars, Igdir, Ardahan 3993 TRB1 / Malatya, Elazig, Bingöl, Tunceli 3994 TRB2 / Van, Mus, Bitlis, Hakkari 3995 TRC1 / Gaziantep, Adiyaman, Kilis 3996 TRC2 / Sanliurfa, Diyarbakir 3997 TRC3 / Mardin, Batman, Sirnak, Siirt ENUTS2 identifies the Nomenclature of Territorial Units for Statistics (NUTS) within Europe in which the household was enumerated. NUTS2 is the second level territorial units within countries. NUTS is a standard administrative division of the European Union, and was developed by the EU. The European Free Trade Association extends the NUTS system to several additional countries outside of the EU, and they are also incorporated into this variable. ENUTS2 corresponds to the 2010 version of NUTS2 released by Eurostat. The code labels include the standard code for the NUTS2 system and the name of the NUTS2 region, separated by a slash. The full set of geography variables for the countries can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Nomenclature of Territorial Units for Statistics 3, Europe 01111 AT111 / Mittelburgenland + AT113 / Südburgenland 01112 AT112 / Nordburgenland 01121 AT121 / Mostviertel-Eisenwurzen 01122 AT122 / Niederösterreich-Süd 01123 AT123 / Sankt Pölten 01124 AT124 / Waldviertel 01125 AT125 / Weinviertel 01126 AT126 / Wiener Umland/Nordteil 01127 AT127 / Wiener Umland/Südteil 01130 AT130 / Wien 01211 AT211 / Klagenfurt-Villach 01212 AT212 / Oberkärnten 01213 AT213 / Unterkärnten 01221 AT221 / Graz 01222 AT222 / Liezen 01223 AT223 / Östliche Obersteiermark 01224 AT224 / Oststeiermark 01225 AT225 / West- und Südsteiermark 01226 AT226 / Westliche Obersteiermark 01311 AT311 / Innviertel 01312 AT312 / Linz-Wels 01313 AT313 / Mühlviertel 01314 AT314 / Steyr-Kirchdorf 01315 AT315 / Traunviertel 01321 AT321 / Lungau + AT322 / Pinzgau-Pongau 01323 AT323 / Salzburg und Umgebung 01331 AT331 / Außerfern + AT334 / Tiroler Oberland 01332 AT332 / Innsbruck 01333 AT333 / Osttirol + AT335 / Tiroler Unterland 01341 AT341 / Bludenz-Bregenzer Wald 01342 AT342 / Rheintal-Bodenseegebiet 09111 ES111 / A Coruña 09112 ES112 / Lugo 09113 ES113 / Ourense 09114 ES114 / Pontevedra 09120 ES120 / Asturias 09130 ES130 / Cantabria 09211 ES211 / Álava 09212 ES212 / Guipúzcoa 09213 ES213 / Vizcaya 09220 ES220 / Navarra 09230 ES230 / La Rioja 09241 ES241 / Huesca 09242 ES242 / Teruel 09243 ES243 / Zaragoza 09300 ES300 / Madrid 09411 ES411 / Ávila 09412 ES412 / Burgos 09413 ES413 / León 09414 ES414 / Palencia 09415 ES415 / Salamanca 09416 ES416 / Segovia 09417 ES417 / Soria 09418 ES418 / Valladolid 09419 ES419 / Zamora 09421 ES421 / Albacete 09422 ES422 / Ciudad Real 09423 ES423 / Cuenca 09424 ES424 / Guadalajara 09425 ES425 / Toledo 09431 ES431 / Badajoz 09432 ES432 / Cáceres 09511 ES511 / Barcelona 09512 ES512 / Girona 09513 ES513 / Lleida 09514 ES514 / Tarragona 09521 ES521 / Alicante/Alacant 09522 ES522 / Castellón/Castelló 09523 ES523 / Valencia/València 09532 ES532 / Mallorca + ES533 / Menorca + ES531 / Eivissa y Formentera 09611 ES611 / Almería 09612 ES612 / Cádiz 09613 ES613 / Córdoba 09614 ES614 / Granada 09615 ES615 / Huelva 09616 ES616 / Jaén 09617 ES617 / Málaga 09618 ES618 / Sevilla 09620 ES620 / Murcia 09630 ES630 / Ceuta 09640 ES640 / Melilla 09705 ES705 / Gran Canaria + ES704 / Fuerteventura + ES708 / Lanzarote 09709 ES709 / Tenerife + ES706 / La Gomera + ES707 / La Palma + ES703 / El Hierro 09999 ES / Unknown 12111 EL111 / Evros 12112 EL112 / Xanthi 12113 EL113 / Rodopi 12114 EL114 / Drama 12115 EL115 / Kavala 12121 EL121 / Imathia 12122 EL122 / Thessaloniki 12123 EL123 / Kilkis 12124 EL124 / Pella 12125 EL125 / Pieria 12126 EL126 / Serres 12127 EL127 / Chalkidiki and Aghion Oros 12131 EL131 / Grevena 12132 EL132 / Kastoria 12133 EL133 / Kozani 12134 EL134 / Florina 12141 EL141 / Karditsa 12142 EL142 / Larisa 12143 EL143 / Magnisia 12144 EL144 / Trikala 12211 EL211 / Arta 12212 EL212 / Thesprotia 12213 EL213 / Ioannina 12214 EL214 / Preveza 12221 EL221 / Zakynthos 12222 EL222 / Kerkyra 12223 EL223 / Kefallinia 12224 EL224 / Lefkada 12231 EL231 / Aitoloakarnania 12232 EL232 / Achaia 12233 EL233 / Ilia 12241 EL241 / Voiotia 12242 EL242 / Evvoia 12243 EL243 / Evrytania 12244 EL244 / Fthiotida 12245 EL245 / Fokida 12251 EL251 / Argolida 12252 EL252 / Arkadia 12253 EL253 / Korinthia 12254 EL254 / Lakonia 12255 EL255 / Messinia 12300 EL300 / Attiki 12411 EL411 / Lesvos 12412 EL412 / Samos 12413 EL413 / Chios 12421 EL421 / Dodekanissos 12422 EL422 / Kyklades 12431 EL431 / Iraklio 12432 EL432 / Lasithi 12433 EL433 / Rethymni 12434 EL434 / Chania 14011 IE011 / Border 14012 IE012 / Midlands 14013 IE013 / West 14021 IE021 / Dublin 14022 IE022 / Mid-East 14023 IE023 / Mid-West 14024 IE024 / South-East 14025 IE025 / South-West 15311 ITC11 / Torino 15312 ITC12 / Vercelli 15313 ITC13 / Biella 15314 ITC14 / Verbano-Cusio-Ossola 15315 ITC15 / Novara 15316 ITC16 / Cuneo 15317 ITC17 / Asti 15318 ITC18 / Alessandria 15320 ITC20 / Aosta 15331 ITC31 / Imperia 15332 ITC32 / Savona 15333 ITC33 / Genova 15334 ITC34 / La Spezia 15341 ITC41 / Varese 15342 ITC42 / Como 15343 ITC43 / Lecco 15344 ITC44 / Sondrio 15346 ITC46 / Bergamo 15347 ITC47 / Brescia 15348 ITC48 / Pavia 15349 ITC49 / Lodi 15350 ITC4A / Cremona 15351 ITC4B / Mantova 15352 ITC4C / Milano 15353 ITC4D / Monza e della Brianza 15611 ITF11 / L'Aquila 15612 ITF12 / Teramo 15613 ITF13 / Pescara 15614 ITF14 / Chieti 15621 ITF21 / Isernia 15622 ITF22 / Campobasso 15631 ITF31 / Caserta 15632 ITF32 / Benevento 15633 ITF33 / Napoli 15634 ITF34 / Avellino 15635 ITF35 / Salerno 15643 ITF43 / Taranto 15644 ITF44 / Brindisi 15645 ITF45 / Lecce 15646 ITF46 / Foggia 15647 ITF47 / Bari 15648 ITF48 / Barletta-Andria-Trani 15651 ITF51 / Potenza 15652 ITF52 / Matera 15661 ITF61 / Cosenza 15662 ITF62 / Crotone 15663 ITF63 / Catanzaro 15664 ITF64 / Vibo Valentia 15665 ITF65 / Reggio di Calabria 15711 ITG11 / Trapani 15712 ITG12 / Palermo 15713 ITG13 / Messina 15714 ITG14 / Agrigento 15715 ITG15 / Caltanissetta 15716 ITG16 / Enna 15717 ITG17 / Catania 15718 ITG18 / Ragusa 15719 ITG19 / Siracusa 15733 Sud Sardegna 15725 ITG25 / Sassari 15726 ITG26 / Nuoro 15727 ITG27 / Cagliari 15728 ITG28 / Oristano 15729 ITG29 / Olbia-tempio 15730 ITG2A / Ogliastra 15731 ITG2B / Medio-campidano 15732 ITG2C / Carbonia-Iglesias 15810 ITH10 / Bolzano-Bozen 15820 ITH20 / Trento 15831 ITH31 / Verona 15832 ITH32 / Vicenza 15833 ITH33 / Belluno 15834 ITH34 / Treviso 15835 ITH35 / Venezia 15836 ITH36 / Padova 15837 ITH37 / Rovigo 15841 ITH41 / Pordenone 15842 ITH42 / Udine 15843 ITH43 / Gorizia 15844 ITH44 / Trieste 15851 ITH51 / Piacenza 15852 ITH52 / Parma 15853 ITH53 / Reggio nell'Emilia 15854 ITH54 / Modena 15855 ITH55 / Bologna 15856 ITH56 / Ferrara 15857 ITH57 / Ravenna 15858 ITH58 / Forlì-Cesena 15859 ITH59 / Rimini 15911 ITI11 / Massa-Carrara 15912 ITI12 / Lucca 15913 ITI13 / Pistoia 15914 ITI14 / Firenze 15915 ITI15 / Prato 15916 ITI16 / Livorno 15917 ITI17 / Pisa 15918 ITI18 / Arezzo 15919 ITI19 / Siena 15920 ITI1A / ELosseto 15921 ITI21 / Perugia 15922 ITI22 / Terni 15931 ITI31 / Pesaro e Urbino 15932 ITI32 / Ancona 15933 ITI33 / Macerata 15934 ITI34 / Ascoli Piceno 15935 ITI35 / Fermo 15941 ITI41 / Viterbo 15942 ITI42 / Rieti 15943 ITI43 / Roma 15944 ITI44 / Latina 15945 ITI45 / Frosinone 22111 PT111 / Minho-Lima 22112 PT112 / Cávado 22113 PT113 / Ave 22114 PT114 / ELande Porto 22115 PT115 / Tâmega 22116 PT116 / Entre Douro e Vouga 22117 PT117 / Douro 22118 PT118 / Alto Trás-os-Montes 22150 PT150 / Algarve 22161 PT161 / Baixo Vouga 22162 PT162 / Baixo Mondego 22163 PT163 / Pinhal Litoral 22165 PT165 / Dão-Lafões 22166 PT16B / Oeste 22167 PT16C / Médio Tejo 22169 PT164 / Pinhal Interior Norte + PT166 / Pinhal Interior Sul + PT167 / Serra da Estrela + PT168 / Beira Interior Norte + PT169 / Beira Interior Sul + PT16A / Cova da Beira 22171 PT171 / Grande Lisboa 22172 PT172 / Península de Setúbal 22185 PT185 / Lezíria do Tejo 22189 PT181 / Alentejo Litoral + PT182 / Alto Alentejo + PT183 / Alentejo Central + PT184 / Baixo Alentejo 22200 PT200 / Região Autónoma dos Açores 22300 PT300 / Região Autónoma da Madeira 23111 RO111 / Bihor 23112 RO112 / Bistrita Nasaud 23113 RO113 / Cluj 23114 RO114 / Maramures 23115 RO115 / Satu Mare 23116 RO116 / Salaj 23121 RO121 / Alba 23122 RO122 / Brasov 23123 RO123 / Covasna 23124 RO124 / Harghita 23125 RO125 / Mures 23126 RO126 / Sibiu 23211 RO211 / Bacau 23212 RO212 / Botosani 23213 RO213 / Iasi 23214 RO214 / Neamt 23215 RO215 / Suceava 23216 RO216 / Vaslui 23221 RO221 / Braila 23222 RO222 / Buzau 23223 RO223 / Constanta 23224 RO224 / Galati 23225 RO225 / Tulcea 23226 RO226 / Vrancea 23311 RO311 / Arges 23312 RO312 / Calarasi 23313 RO313 / Dimbovita 23314 RO314 / Giurgiu 23315 RO315 / Ialomita 23316 RO316 / Prahova 23317 RO317 / Teleorman 23321 RO321 / Bucuresti 23322 RO322 / Ilfov 23411 RO411 / Dolj 23412 RO412 / Gorj 23413 RO413 / Mehedinti 23414 RO414 / Olt 23415 RO415 / Valcea 23421 RO421 / Arad 23422 RO422 / Caras Severin 23423 RO423 / Hunedoara 23424 RO424 / Timis 25011 SI011 / Pomurska 25012 SI012 / Podravska 25013 SI013 / Koroka 25014 SI014 / Savinjska 25015 SI015 / Zasavska 25016 SI016 / Spodnjeposavska 25017 SI017 / Jugovzhodna Slovenija 25018 SI018 / Notranjsko-kraka 25021 SI021 / Osrednjeslovenska 25022 SI022 / Gorenjska 25023 SI023 / Gorika 25024 SI024 / Obalno-kraka 25999 SI / Unknown 26010 SK010 / Bratislavský kraj 26021 SK021 / Trnavský kraj 26022 SK022 / Trenčiansky kraj 26023 SK023 / Nitriansky kraj 26031 SK031 / Žilinský kraj 26032 SK032 / Banskobystrický kraj 26041 SK041 / Prešovský kraj 26042 SK042 / Košický kraj 34011 CH011 / Vaud 34012 CH012 / Valais 34013 CH013 / Geneva 34021 CH021 / Bern 34022 CH022 / Freiburg 34023 CH023 / Solothurn 34024 CH024 / Neuchatel 34025 CH025 / Jura 34031 CH031 / Basel-Stadt 34032 CH032 / Basel-Landschaft 34033 CH033 / Aargau 34040 CH040 / Zurich 34051 CH051 / Glarus 34052 CH052 / Schaffhausen 34053 CH053 / Appenzell Ausserrhoden + CH054 / Appenzell Innerrhoden 34055 CH055 / St. Gallen 34056 CH056 / Graubundun 34057 CH057 / Thurgau 34061 CH061 / Luzern 34062 CH062 / Uri 34063 CH063 / Schwyz 34064 CH064 / Obwalden 34065 CH065 / Nidwalden 34066 CH066 / Zug 34070 CH070 / Ticino 39100 TR100 / Istanbul 39211 TR211 / Tekirdag 39212 TR212 / Edirne 39213 TR213 / Kirklareli 39221 TR221 / Balikesir 39222 TR222 / Çanakkale 39310 TR310 / Izmir 39321 TR321 / Aydin 39322 TR322 / Denizli 39323 TR323 / Mugla 39331 TR331 / Manisa 39332 TR332 / Afyon 39333 TR333 / Kütahya 39334 TR334 / Usak 39411 TR411 / Bursa 39412 TR412 / Eskisehir 39413 TR413 / Bilecik 39421 TR421 / Kocaeli 39422 TR422 / Sakarya 39423 TR423 / Düzce 39424 TR424 / Bolu 39425 TR425 / Yalova 39510 TR510 / Ankara 39521 TR521 / Konya 39522 TR522 / Karaman 39611 TR611 / Antalya 39612 TR612 / Isparta 39613 TR613 / Burdur 39621 TR621 / Adana 39622 TR622 / Içel (Mersin) 39631 TR631 / Hatay 39632 TR632 / Kahramanmaras 39633 TR633 / Osmaniye 39711 TR711 / Kirikkale 39712 TR712 / Aksaray 39713 TR713 / Nigde 39714 TR714 / Nevsehir 39715 TR715 / Kirsehir 39721 TR721 / Kayseri 39722 TR722 / Sivas 39723 TR723 / Yozgat 39811 TR811 / Zonguldak 39812 TR812 / Karabük 39813 TR813 / Bartin 39821 TR821 / Kastamonu 39822 TR822 / Çankiri 39823 TR823 / Sinop 39831 TR831 / Samsun 39832 TR832 / Tokat 39833 TR833 / Çorum 39834 TR834 / Amasya 39901 TR901 / Trabzon 39902 TR902 / Ordu 39903 TR903 / Giresun 39904 TR904 / Rize 39905 TR905 / Artvin 39906 TR906 / Gümüshane 39910 TRA11 / Erzurum 39911 TRA12 / Erzincan 39912 TRA13 / Bayburt 39921 TRA21 / Agri 39922 TRA22 / Kars 39923 TRA23 / Igdir 39924 TRA24 / Ardahan 39931 TRB11 / Malatya 39932 TRB12 / Elazig 39933 TRB13 / Bingöl 39934 TRB14 / Tunceli 39941 TRB21 / Van 39942 TRB22 / Mus 39943 TRB23 / Bitlis 39944 TRB24 / Hakkari 39951 TRC11 / Gaziantep 39952 TRC12 / Adiyaman 39953 TRC13 / Kilis 39961 TRC21 / Sanliurfa 39962 TRC22 / Diyarbakir 39971 TRC31 / Mardin 39972 TRC32 / Batman 39973 TRC33 / Sirnak 39974 TRC34 / Siirt ENUTS3 identifies the Nomenclature of Territorial Units for Statistics (NUTS) within Europe in which the household was enumerated. NUTS3 is the third level territorial units within countries. NUTS is a standard administrative division of the European Union, and was developed by the EU. The European Free Trade Association extends the NUTS system to several additional countries outside of the EU, and they are also incorporated into this variable. ENUTS3 corresponds to the 2010 version of NUTS3 released by Eurostat. The code labels include the standard code for the NUTS3 system and the name of the NUTS3 region, separated by a slash. The full set of geography variables for the countries can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Population density of GEOLEV1 unit, in persons per square kilometer POPDENSGEO1 indicates the population density in persons per square kilometer of the major administrative unit in which the household was enumerated. The major administrative unit of the household is identified by the GEOLEV1 variable. The area of units in GEOLEV1 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. POPDENSGEO1 is an 8-digit string variable listing the population density in persons per square kilometer. IPUMS Area of GEOLEV1 unit in square kilometers AREAMOLLWGEO1 indicates the area in square kilometers of the major administrative unit in which the household was enumerated. The major administrative unit of the household is identified by the GEOLEV1 variable. The area of units in GEOLEV1 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. AREAMOLLWGEO1 is a 10-digit string variable listing the area in square kilometers. IPUMS Ireland, Region 1971 - 2011 [Level 1; consistent boundaries, GIS] 372001 Dublin 372002 Midlands 372003 Mid-East, Border 372004 Mid-West, South-East 372006 West 372008 South West GEO1_IE identifies the household's regional authority within Ireland in all sample years. Regional authorities are the first level administrative units of the country. GEO1_IE is spatially harmonized to account for political boundary changes across census years. Some detail is lost in harmonization. A GIS map (in shapefile format), corresponding to GEO1_IE can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Ireland can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Ireland, Region 1979 [Level 1; GIS] 001 Border 002 Dublin 003 Mid-East 004 Midlands 005 Mid-West 006 South-East 007 South-West 008 West GEO1_ IE1979 identifies the household's region within Ireland in 1979. Regions are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_ IE1979 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Ireland can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level of any country refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Household classification 00 Vacant household 01 One-person household 02 Married/cohab couple, no children 03 Married/cohab couple with children 04 Single-parent family 05 Polygamous family 06 Extended family, relatives only 07 Composite household, family and non-relatives 08 Non-family household 09 Unclassified subfamily 10 Other relative or non-relative household 11 Group quarters 99 Unclassifiable HHTYPE is a constructed variable that describes the composition of households. HHTYPE is constructed from information in RELATE (relationship to head), from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father), and from information on group quarters status, GQ. IPUMS Number of families in household Vacant household 1 1 family 2 2 families 3 3 families 4 4 families 5 5 families 6 6 families 7 7 families 8 8 families 9 9 or more families NFAMS is a constructed variable that indicates the number of families within each household. Family membership is defined by FAMUNIT. A "family" is any group of persons related by blood, adoption, or marriage. An unrelated individual within the household is considered a separate family. Thus, a household consisting of a widow and a domestic employee contains two families; a household consisting of a large, multi-generation extended family with no persons unrelated to the head counts as a single family. NFAMS is constructed from information in RELATE (relationship to head) and from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). See those variable descriptions for more detail. IPUMS Number of married couples in household No married couples in household 1 1 couple 2 2 couples 3 3 couples 4 4 couples 5 5 couples 6 6 couples 7 7 couples 8 8 couples 9 9 or more couples NCOUPLES is a constructed variable indicating the number of married/in-union couples within a household. NCOUPLES is constructed using the IPUMS-International pointer variable SPLOC (spouse's location in the household). IPUMS Number of mothers in household No mothers in household 1 1 mother 2 2 mothers 3 3 mothers 4 4 mothers 5 5 mothers 6 6 mothers 7 7 mothers 8 8 mothers 9 9 or more mothers in household NMOTHERS is a constructed variable indicating the number of mothers -- of persons of any age -- within a household. NMOTHERS is constructed using the IPUMS-International pointer variable MOMLOC (mother's location in the household). IPUMS Number of fathers in household No fathers in household 1 1 father 2 2 fathers 3 3 fathers 4 4 fathers 5 5 fathers 6 6 fathers 7 7 fathers 8 8 fathers 9 9 or more fathers in household NFATHERS is a constructed variable indicating the number of fathers -- of persons of any age -- within a household. NFATHERS is constructed using the IPUMS-International pointer variable POPLOC (father's location in the household). IPUMS Head's location in household HEADLOC gives the person number (PERNUM) of the head of household in samples in which persons are organized into households. HEADLOC is a 3-digit numeric variable. IPUMS Dwelling number Ireland 1979: All households This variable indicates the dwelling number. This is a 6-digit numeric variable with 0 implied decimal places IPUMS Household number (within dwelling) Ireland 1979: All households 1 1 This variable indicates the household number (within dwelling). IPUMS Number of persons in household Ireland 1979: All households 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 This variable indicates the number of persons in household. IPUMS Dwelling created by splitting apart a large dwelling or household Ireland 1979: All households No problem 1 Yes: households within a large dwelling were split apart into separate dwellings 2 Yes: persons within a large household were split apart into separate dwellings This variable indicates if the dwelling was created by splitting apart a large dwelling or household. IPUMS Household type <svar a="all" v="IE79A021"><span class="em">B.</span><br />[] 1 Private household in house or flat<br />[] 2 Private household in caravan, mobile home etc.<br />[] 3 Non-private household<br /></svar> Ireland 1979: All households 1 Private household in house or flat 2 Private household in caravan/mobile home 3 Non-private household This variable indicates the household type. IPUMS Communal dwelling Ireland 1979: All households 1 Private household 2 Communal establishment This variable indicates communal dwelling. IPUMS Person number PERNUM numbers all persons within each household consecutively (starting with "1" for the first person record of each household). When combined with SAMPLE and SERIAL, PERNUM uniquely identifies each person in the IPUMS-International database. PERNUM is a 4-digit numeric variable. IPUMS Person weight PERWT indicates the number of persons in the actual population represented by the person in the sample. For the samples that are truly weighted (see the comparability discussion), PERWT must be used to yield accurate statistics for the population. NOTE: PERWT has 2 implied decimal places. That is, the last two digits of the eight-digit variable are decimal digits, but there is no actual decimal in the data. PERWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description. IPUMS Mother's location in household MOMLOC is a constructed variable that indicates whether or not the person's mother lived in the same household and, if so, gives the person number of the mother (see PERNUM). MOMLOC makes it easy for researchers to link the characteristics of children and their (probable) mothers. The method by which probable child-mother links are identified is described in PARRULE. The general design of MOMLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly. For more details on the construction of MOMLOC, see the Comparability section of PARRULE and this paper on IPUMSI family linking methodology. Note: MOMLOC identifies social relationships (such as stepmother and adopted mother) as well as biological relationships. The variable STEPMOM is designed to identify some of these social relationships. To restrict MOMLOC to biological mothers, such as for own children fertility estimation, MOMLOC should be reset to zero when STEPMOM is greater than zero. MOMLOC is a 3-digit numeric variable. Codes0 = No mother of this person present in the household. 1 or higher = The person number of this person's mother IPUMS Father's location in household POPLOC is a constructed variable that indicates whether or not the person's father lived in the same household and, if so, gives the person number of the father (see PERNUM). POPLOC makes it easy for researchers to link the characteristics of children and their (probable) fathers. The method by which probable child-father links are identified is described in PARRULE. The general design of POPLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly. For more details on the construction of POPLOC, see the Comparability section of PARRULE and this paper on IPUMSI family linking methodology. Note: POPLOC identifies social relationships (such as stepfather and adopted father) as well as biological relationships. The variable STEPPOP is designed to identify some of these social relationships. To restrict POPLOC to biological mothers, such as for own children fertility estimation, POPLOC should be reset to zero when STEPPOP is greater than zero. POPLOC is a 3-digit numeric variable. Codes0 = No father of this person present in the household. 1 or higher = The person number of this person's father IPUMS Spouse's location in household SPLOC is a constructed variable that indicates whether or not the person's spouse lived in the same household and, if so, gives the person number (PERNUM) of the spouse. SPLOC makes it easy for researchers to link the characteristics of (probable) spouses. The method by which probable spouse-spouse links are identified is described in SPRULE. The general design of SPLOC and other constructed variables is modeled on the methods developed for IPUMS-USA "Family Interrelationships", but the details vary significantly. For more details on the construction of SPLOC, see the Comparability section of SPRULE and this paper on IPUMSI family linking methodology. SPLOC is a 3-digit numeric variable. Codes0 = No spouse of this person present in the household. 1 or higher = The person number of this person's spouse IPUMS Rule for linking parent 00 No parent of person in household 11 Link to head or spouse, unambiguous 12 Link to head or spouse, ambiguous 21 Child-Grandchild, within empirical child cap 22 Child-Grandchild, within constructed child cap 23 Child-Grandchild, exceeds child cap 31 Specified Other Relatives, within empirical child cap 32 Specified Other Relatives, within constructed child cap 33 Specified Other Relatives, exceeds child cap 41 Other Relatives, within empirical child cap 42 Other Relatives, within constructed child cap 51 Non-Relatives, within empirical child cap 52 Non-Relatives, within constructed child cap PARRULE describes the criteria by which the IPUMS International variables MOMLOC and POPLOC linked the person to a probable mother and/or father. IPUMS International establishes child-parent links according to five basic rules, and PARRULE gives the number of the rule that applied to the link in question. A link to any parent automatically generates a second link to that parent's spouse or partner, so only one rule is needed to describe both MOMLOC and POPLOC. The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology. IPUMS Rule for linking spouse 00 No spouse present 01 Rule 1: strong relationship pairing, couple adjacent 02 Rule 2: strong relationship pairing, couple not adjacent 03 Rule 3: weak relationship pairing, couple adjacent 04 Rule 4: weak relationship pairing, couple not adjacent 05 Rule 5: weak consensual union pairings 06 Rule 6: sample-specific rules (usually child-to-child) SPRULE explains the criteria by which the IPUMS-International variable SPLOC linked the person to his/her probable spouse. IPUMS International establishes spouse-spouse links according to five basic rules, and SPRULE gives the number of the rule that applied to the link in question. A sixth rule identifies sample-specific linking procedures only imposed in selected instances. The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology. IPUMS Probable stepmother Biological mother or no mother present 1 Mother has no children born or surviving 2 Child reports mother is deceased 3 Explicitly identified step relationship 4 Mother reports no children in the home 5 Age difference implausible 6 Child exceeds known fertility of mother STEPMOM indicates whether a person's mother, as identified by MOMLOC, was most probably not the person's biological mother. Non-zero values of STEPMOM explain why it is probable that the person's mother was a step- or adopted mother. A value of 0 indicates no likely stepmother because (1) the mother identified in MOMLOC was probably the biological mother or (2) there is no mother of this person present in the household. The codes for STEPMOM are as follows: 0 = Biological mother or no mother of this person present in household. 1 = Mother has no children born or surviving. 2 = Child reports mother is deceased. 3 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner, stepchild/child-in-law). 4 = Mother reports no children in the home. 5 = Age difference between mother and child was less than 12 or greater than 54 years. 6 = Child exceeds known fertility of mother. In cases where more than one criterion for a likely stepmother is met, STEPMOM will take the value of the criterion with the lowest code. See PARRULE for a description of the linking process. Users should note that there are many stepmothers and adopted mothers in the population that cannot be identified with information available in the censuses. Therefore, STEPMOM will always under-represent their actual number in the population. IPUMS Probable stepfather Biological father or no father present 1 Child reports father is deceased 2 Explicitly identified step relationship 3 Age difference implausible 4 Spouse of mother 5 Identified as adopted 6 Surname difference -- male child or never-married female STEPPOP indicates whether a person's father, as identified by POPLOC, was most probably not the person's biological father. Non-zero values of STEPPOP explain why it is probable that the person's father was a step- or adopted father. A value of 0 indicates no likely stepfather because (1) the father identified in POPLOC was probably the biological father or (2) there is no father of this person present in the household. The codes for STEPPOP are as follows: 0 = Biological father or no father of this person present in household. 1 = Child reports father is deceased. 2 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner; stepchild/child-in-law). 3 = Age difference between father and child was less than 12 or greater than 54 years. In cases where more than one criterion for a likely stepfather is met, STEPPOP will take the value of the criterion with the lowest code. See PARRULE for a description of the linking process. Users should note that there are many stepfathers and adopted fathers in the population that cannot be identified with information available in the censuses. Therefore, STEPPOP will always under-represent their actual number in the population. IPUMS Man with more than one wife linked No more than one wife linked via SPLOC 1 More than one wife linked via SPLOC POLYMAL indicates if a man had more than one wife linked to him in the constructed IPUMS variable SPLOC -- Spouse's Location in Household. The point of POLYMAL is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record. IPUMS Woman is second or higher order wife Person is not the 2nd or higher order wife linked via SPLOC 1 Person is the 2nd or higher order wife linked via SPLOC POLY2ND indicates if a woman was the second or higher order wife linked to a husband in the constructed IPUMS variable SPLOC -- Spouse's Location in Household. The variable does not suggest the actual marital order of wives, only their relative positions in the person order of the household as it was enumerated. The point of POLY2ND is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record. IPUMS Family unit membership FAMUNIT is a constructed variable indicating to which family within the household a person belongs. All persons related to the household head receive a 1 (see RELATE). Each secondary family or secondary individual receives a higher code. For purposes of FAMUNIT, secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). FAMUNIT is a 4-digit numeric variable. CodesIf there is only one group of related individuals within the household, all of them will be coded "1;" if there is a second, separate such group listed on the form, all of them will be coded "2," and so on. IPUMS Number of own family members in household 0001 1 family member present 0002 2 family members present 0003 3 family members present 0004 4 0005 5 0006 6 0007 7 0008 8 0009 9 0010 10 0011 11 0012 12 0013 13 0014 14 0015 15 0016 16 0017 17 0018 18 0019 19 0020 20 0021 21 0022 22 0023 23 0024 24 0025 25 0026 26 0027 27 0028 28 0029 29 0030 30 0031 31 0032 32 0033 33 0034 34 0035 35 0036 36 0037 37 0038 38 0039 39 0040 40 0041 41 0042 42 0043 43 0044 44 0045 45 0046 46 0047 47 0048 48 0049 49 0050 50 0051 51 0052 52 0053 53 0054 54 0055 55 0056 56 0057 57 0058 58 0059 59 0060 60 0061 61 0062 62 0063 63 0064 64 0065 65 0066 66 0067 67 0068 68 0069 69 0070 70 0071 71 0072 72 0073 73 0074 74 0075 75 0076 76 0077 77 0078 78 0079 79 0080 80 0081 81 0082 82 0083 83 0084 84 0085 85 0086 86 0087 87 0088 88 0089 89 0090 90 0091 91 0092 92 0093 93 0094 94 0095 95 0096 96 0097 97 0098 98 0099 99 or more persons FAMSIZE counts the number of the person's own family members living in the household with her/him, including the person her/himself. These include all persons related to the person by blood, adoption, or marriage as indicated by the census forms or inferred from them. FAMSIZE is calculated from the units identified in the IPUMS constructed variable FAMUNIT (family unit membership). The primary family is defined as all persons related to the head in the RELATE variable. Secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). IPUMS Number of own children in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 or more children in household NCHILD provides a count of the person's own children living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. IPUMS Number of own children under age 5 in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 or more own children under age 5 in household 98 One or more children have unknown age NCHLT5 provides a count of the person's own children under age five living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. IPUMS Age of eldest own child in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 or older 98 One or more children have unknown age 99 No own child in household ELDCH gives the age of the person's oldest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. ELDCH is top-coded at age 50 or older. IPUMS Age of youngest own child in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 or older 98 One or more children have unknown age 99 No own child in household YNGCH gives the age of the person's youngest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. YNGCH is top-coded at age 50 or older. IPUMS Relationship to household head [general version] 1 Head 2 Spouse/partner 3 Child 4 Other relative 5 Non-relative 6 Other relative or non-relative 9 Unknown RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). IPUMS Relationship to household head [detailed version] 1000 Head 2000 Spouse/partner 2100 Spouse 2200 Unmarried partner 2210 Civil union 2300 Same-sex spouse/partner 3000 Child 3100 Biological child 3200 Adopted child 3300 Stepchild 3400 Child/child-in-law 3500 Child/child-in-law/grandchild 3600 Child of unmarried partner 4000 Other relative 4100 Grandchild 4110 Grandchild or great grandchild 4120 Great grandchild 4130 Great-great grandchild 4200 Parent/parent-in-law 4210 Parent 4211 Stepparent 4220 Parent-in-law 4300 Child-in-law 4301 Daughter-in-law 4302 Spouse/partner of child 4310 Unmarried partner of child 4400 Sibling/sibling-in-law 4410 Sibling 4420 Stepsibling 4430 Sibling-in-law 4431 Sibling of spouse/partner 4432 Spouse/partner of sibling 4500 Grandparent 4510 Great grandparent 4600 Parent/grandparent/ascendant 4700 Aunt/uncle 4800 Other specified relative 4810 Nephew/niece 4820 Cousin 4830 Sibling's sibling-in-law 4900 Other relative, not elsewhere classified 4910 Other relative with same family name 4920 Other relative with different family name 4930 Other relative, not specified (secondary family) 5000 Non-relative 5100 Friend/guest/visitor/partner 5110 Partner/friend 5111 Friend 5112 Partner/roommate 5113 Housemate/roommate 5120 Visitor 5130 Ex-spouse 5140 Godparent 5150 Godchild 5200 Employee 5210 Domestic employee 5220 Relative of employee, n.s. 5221 Spouse of servant 5222 Child of servant 5223 Other relative of servant 5300 Roomer/boarder/lodger/foster child 5310 Boarder 5311 Boarder or guest 5320 Lodger 5330 Foster child 5340 Tutored/foster child 5350 Tutored child 5400 Employee, boarder, or guest 5500 Other specified non-relative 5510 Agregado 5520 Temporary resident, guest 5600 Group quarters 5610 Group quarters, non-inmates 5620 Institutional inmates 5900 Non-relative, n.e.c. 6000 Other relative or non-relative 9999 Unknown RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). IPUMS Relationship to head, Europe 10 Reference person / Head 20 Spouse or partner 21 Husband or wife 22 Partner in consensual union 30 Child/child-in-law of head or of spouse/partner 31 Spouse or partner of child of head 40 Parent of head, of spouse, or of partner 50 Other relative of head, spouse, or partner 60 Non-relative of head 61 Foster child 62 Boarder 63 Domestic servant 64 Other 99 Not stated / unknown ERELATE describes for the European samples the relationship of the individual to the head of household -- sometimes called the householder or reference person. ERELATE has been classified according to the recommendations of the Conference of European Statisticians for the 2010 Population and Housing Censuses. IPUMS Age 000 Less than 1 year 001 1 year 002 2 years 003 3 004 4 005 5 006 6 007 7 008 8 009 9 010 10 011 11 012 12 013 13 014 14 015 15 016 16 017 17 018 18 019 19 020 20 021 21 022 22 023 23 024 24 025 25 026 26 027 27 028 28 029 29 030 30 031 31 032 32 033 33 034 34 035 35 036 36 037 37 038 38 039 39 040 40 041 41 042 42 043 43 044 44 045 45 046 46 047 47 048 48 049 49 050 50 051 51 052 52 053 53 054 54 055 55 056 56 057 57 058 58 059 59 060 60 061 61 062 62 063 63 064 64 065 65 066 66 067 67 068 68 069 69 070 70 071 71 072 72 073 73 074 74 075 75 076 76 077 77 078 78 079 79 080 80 081 81 082 82 083 83 084 84 085 85 086 86 087 87 088 88 089 89 090 90 091 91 092 92 093 93 094 94 095 95 096 96 097 97 098 98 099 99 100 100+ 999 Not reported/missing AGE gives age in years as of the person's last birthday prior to or on the day of enumeration. IPUMS Age, grouped into intervals 01 0 to 4 02 5 to 9 03 10 to 14 04 15 to 19 05 0 to 5 06 6 to 10 07 10 to 15 08 11 to 14 09 15 to 17 10 16 to 19 11 18 to 24 12 20 to 24 13 25 to 29 14 30 to 34 15 35 to 39 16 40 to 44 17 45 to 49 18 50 to 54 19 55 to 59 20 60 to 64 21 65 to 69 22 70 to 74 23 75 to 79 24 80 to 84 25 85+ 98 Unknown AGE2 gives computed years of age grouped into intervals. IPUMS Sex 1 Male 2 Female 9 Unknown SEX reports the sex (gender) of the respondent. IPUMS Marital status [general version] NIU (not in universe) 1 Single/never married 2 Married/in union 3 Separated/divorced/spouse absent 4 Widowed 9 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS Marital status [detailed version] 000 NIU (not in universe) 100 Single/never married 110 Engaged 111 Never married and never cohabited 200 Married or consensual union 210 Married, formally 211 Married, civil 212 Married, religious 213 Married, civil and religious 214 Married, civil or religious 215 Married, traditional/customary 216 Married, monogamous 217 Married, polygamous 219 Married, spouse absent (historical samples) 220 Consensual union 300 Separated/divorced/spouse absent 310 Separated or divorced 320 Separated or annulled 330 Separated 331 Separated legally 332 Separated de facto 333 Separated from marriage 334 Separated from consensual union 335 Separated from consensual union or marriage 340 Annulled 350 Divorced 400 Widowed 410 Widowed or divorced 411 Widowed from consensual union or marriage 412 Widowed from marriage 413 Widowed from consensual union 420 Widowed, divorced, or separated 999 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS Marital status, Europe NIU (not in universe) 1 Never married 2 Married 3 Widowed and not remarried 4 Divorced/separated and not remarried 5 Widowed or divorced 9 Unknown / missing EMARST describes for the European samples the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. European census instructions generally limit marital status to legal unions, but there are exceptions. EMARST has been classified according to the recommendations given by the Conference of European Statisticians for the 2010 Population and Housing Censuses. IPUMS Relationship to head of subfamily 1000 Head 2000 Spouse/partner 2100 Spouse 2200 Unmarried partner 3000 Child 3100 Biological child 3200 Adopted or step child 4000 Other relative 4100 Grandchild 4200 Parent/parent-in-law 4210 Parent 4220 Parent-in-law 4300 Child-in-law 4400 Sibling/sibling-in-law 4410 Sibling 4430 Sibling-in-law 4500 Grandparent 4600 Parent/grandparent 4810 Nephew/niece 4900 Other relative, n.e.c. 5000 Non-relative 5120 Visitor 5210 Domestic employee 5220 Relative of employee, n.s. 5300 Roomer/boarder/lodger/foster child 5310 Boarder 5311 Boarder or guest 5400 Employee, boarder or guest 5510 Agregado 5600 Group quarters 6000 Other relative or non-relative 9998 Unknown 9999 NIU (not in universe) SUBFREL describes the relationship of the individual to the head of the subfamily (in most cases, conjugal unit). It is distinct from RELATE, which identifies a person's relationship to the head of the household. There can be multiple subfamilies within households. The particular subfamily to which a person belongs is recorded in SUBFNUM. Persons living alone without other family are identified as "heads" of family. IPUMS Subfamily membership number 0000 Non-family or sub-family not identified 0001 1st subfamily 0002 2nd subfamily 0003 3rd subfamily 0004 4th subfamily 0005 5th subfamily 0006 6th subfamily 0007 7th subfamily 0008 8th subfamily 0009 9th subfamily 0010 10th subfamily 0011 11th subfamily 0012 12th subfamily 0013 13th subfamily 0099 Unknown SUBFNUM gives the number of the subfamily to which the person belongs within the household (1 = first subfamily, 2 = second subfamily, etc.). SUBFNUM records the identification of subfamilies in the original dataset, which generally correspond to conjugal units and their offspring. IPUMS Person number (within household) Ireland 1979: All persons 00 Household record 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 This variable indicates the person number (within household). IPUMS Relationship to family head <svar a="all" v="IE79A400">3. Relationship to head of household _______<br /><div class="i1">Write "Head", "Wife", "Son", "Daughter", "Visitor", "Patient", "Employee", etc. as appropriate.<br /><br />Anyone in a private household whose usual residence is elsewhere should be described as "Visitor" whether related to the head of the household or not.</div><br /></svar> Ireland 1979: All persons 1 Head 2 Spouse 3 Child 4 Visitor, other relative, or nonrelative 9 Unknown This variable indicates the relationship of the person to the head of household. IPUMS Sex <svar a="all" v="IE79A401">2. Sex<br /><div class="i1">[] 1 Male<br />[] 2 Female</div><br /></svar> Ireland 1979: All persons 1 Males 2 Females This variable indicates the person's sex. IPUMS Age <svar a="all" v="IE79A402">4. Date of birth<br /><div class="i1">Use numbers: e.g., 14/2/1936<br /><br />Day___<br />Month___<br />Year___</div><br /></svar> Ireland 1979: All persons 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20-24 25 25-29 30 30-34 35 35-39 40 40-44 45 45-49 50 50-54 55 55-59 60 60-64 65 65-69 70 70-74 75 75-79 80 80-84 85 85+ This variable indicates the person's age. IPUMS Marital status <svar a="all" v="IE79A403">5. Marital status<br /><div class="i1">The marital status indicated should relate to the person's present legal status.<br />If under 15 years of age (i.e., born after 1 April, 1964), please check box 1.<br /><br />[] 1 Child<br />[] 2 Single<br />[] 3 Married<br />[] 4 Widowed<br />[] 5 Other status</div><br /></svar> Ireland 1979: Persons age 15 years and older [discrepancies: none] NIU (not in universe) 2 Single 3 Married 4 Widowed This variable indicates the person's marital status. IPUMS Changed residence from outside state <svar a="all" v="IE79A404">6. Change of residence from outside the state<br /><div class="i1">Did the person change [his/her] permanent residence to Ireland (Republic) from outside the country during the 12 months before 31 March, 1979?<br /><br />[] 1 Yes<br />[] 2 No</div><br /></svar> Ireland 1979: Persons who are not permanent residents [discepancies: not verifiable] NIU (Not in universe) 1 Yes 2 No This variable indicates if the person changed their residence from abroad to Ireland. IPUMS