DDI_ISL_1729_PHC_v01_M_v7.5_A_IPUMS 1729 Census of Iceland IPUMS May 21, 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. 1729 Census of Iceland - IPUMS Subset PHC Iceland 1729 (sample) (IPUMS Harmonized Subset) ISL_1729_PHC_v01_M_v7.5_A_IPUMS Kingdom of Denmark IPUMS (c) Copyright 1729, Kingdom of Denmark and Minnesota Population Center MetadataEditor Kingdom of Denmark 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: F-N Variables -- HOUSEHOLD Technical Household Variables -- HOUSEHOLD Constructed Family Interrelationship Variables -- PERSON Household Economic Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Constructed Household Variables -- HOUSEHOLD Work Variables -- PERSON Other Person Variables -- PERSON Technical Person Variables -- PERSON Geography: Global Variables -- HOUSEHOLD Other Household Variables -- HOUSEHOLD Iceland Commune Persons, households, and dwellings Contains data for three counties only: Rangárvallasýsla, Árnessýsla, Hnappadalssýsla UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: No - Households: yes - Individuals: yes - Group quarters: yes UNIT DESCRIPTIONS: - Dwellings: no - Households: Yes - Group quarters: Yes All inhabitants of Iceland Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Iceland 1729 (sample) Note: Contains data for three counties only: Rangárvallasýsla, Árnessýsla, Hnappadalssýsla MICRODATA SOURCE: Kingdom of Denmark SAMPLE SIZE (person records): 8072. SAMPLE DESIGN: Full-count data for three counties: Rangárvallasýsla, Árnessýsla, and Hnappadalssýsla. More information available Face-to-face [f2f] de jure, CENSUS DAY: Not applicable 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. Kingdom of Denmark 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: Iceland, Kingdom of Denmark. 1729 Census of Iceland 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. ISL1729_PHC-H-H Household records 906 30 ISL1729_PHC-P-H Person records 8072 54 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 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 Number of unrelated persons 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9+ UNREL indicates the number of persons in the household who are unrelated to the head as defined in the variable RELATE. 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 Iceland, County 1703 - 1910 352010 Skaftafellssýsla 352011 Austur-Skaftafellssýsla 352021 Vestur-Skaftafellssýsla 352030 Rangárvallasýsla 352040 Vestmannaeyjasýsla 352050 Árnessýsla 352060 Gullbringusýsla 352070 Kjósarsýsla 352080 Borgarfjarðarsýsla 352090 Mýrarsýsla 352100 Hnappadalssýsla 352110 Snæfellsnessýsla 352120 Dalasýsla 352130 Barðastrandasýslur 352140 Ísafjarðarsýsla 352141 Vestur-Ísafjarðarsýsla 352151 Norður-Ísafjarðarsýsla 352160 Strandasýsla 352170 Húnavantssýsla 352180 Skagafjarðarsýsla 352190 Eyjafjarðasýsla 352200 Þingeyjarsýsla 352201 Suður-Þingeyjarsýsla 352211 Norður-Þingeyjarsýsla 352220 Múlasýsla 352221 Norður-Múlasýsla 352231 Suður-Múlasýsla 352240 Reykjavíkurkaupstaður GEO1_ IS1703_1910 identifies the county in Iceland where the household was located in 1703, 1729, 1801, 1901, and 1910. Other geography variables for Iceland include PARISHIS, and COMMUNEIS. GEO1_IS1703_1910 is harmonized by name and does not take into consideration changing province boundaries. It is not mappable. An image of Iceland counties is available here. IPUMS Iceland, County 1729 201 Rangárvallasýsla 221 Árnessýsla 321 Hnappadalssýsla GEO1_IS1729 identifies the county in Iceland where the household was located in 1729. Other geography variables include GEO1_IS1703_1910 and COMMUNEIS. The 1729 census of Iceland was conducted in three counties only: Rangárvallasýsla, Árnessýsla, and Hnappadalssýsla. An image of Iceland counties is available here. IPUMS Iceland, Commune 10110 Skeggjastaðahreppur 10120 Vopnafjarðarhreppur 10123 Jökuldalshreppur 10125 Fellnahreppur 10132 Vallnahreppur 10137 Borgarfjarðarhreppur 10144 Loðmundarfjarðarhreppur 10148 Seyðisfjarðarhreppur 10153 Mjóafjarðarhreppur 10162 Norðfjarðarhreppur 10166 Fljótsdalshreppur 10172 Skriðdalshreppur 10178 Reyðarfjarðarhreppur 10182 Fáskrúðsfjarðarhreppur 10188 Breiðdalshreppur 10192 Berunesshreppur 10196 Álftafjarðarhreppur 11255 Lón Nes og Mýrar 11267 Borgarhafnarhreppur 11290 Hofshreppur 11323 Kleifahreppur 11333 Leiðvallarhreppur 11343 Dyrhólahreppur 20112 Eyjafjallasveit 20124 Austur-Landeyjahreppur 20125 Vestur-Landeyjahreppur 20134 Fljótshlíðarhreppur 20146 Hvolhreppur 20155 Rangárvallahreppur 20167 Landmannahreppur 20179 Holtamannahreppur 22111 Bæjarhreppur 22113 Stokkseyrarhreppur 22116 Sandvíkurhreppur 22122 Hraungerðishreppur 22126 Villingaholtshreppur 22128 Skeiðahreppur 22131 Gnúpverjahreppur 22137 Hrunamannahreppur 22149 Biskupstungnahreppur 22164 Grímsneshreppur 22173 Þingvallahreppur 22188 Ölfushreppur 22191 Selvogshreppur 23121 Grindavíkurhreppur 23130 Hafnahreppur 23140 Rosmhvalaneshreppur 23150 Vatnsleysustrandarhreppur 23182 Álftaneshreppur 23198 Seltjarnarneshreppur 24143 Mosfellshreppur 24160 Kjalarneshreppur 24192 Kjósarhreppur 30120 Strandarhreppur 30123 Skilmannahreppur 30127 Akraneshreppur 30133 Leirár- og Melahreppur 30138 Andakílshreppur 30145 Skorradalshreppur 30153 Syðri Reykjadalshreppur 30162 Reykholtsdalshreppur 30178 Ásasveit 31111 Hvítársíðuhreppur 31116 Þverárhlíðarhreppur 31125 Norðurárdalshreppur 31132 Stafholtstungnahreppur 31144 Borgarhreppur 31152 Álftaneshreppur 31174 Hraunhreppur 32170 Kolbeinsstaðahreppur 32180 Eyjarhreppur 32190 Miklaholtshreppur 33110 Staðarsveit 33115 Breiðuvíkurhreppur 33119 Neshreppur 33129 Helgafellssveit 33136 Eyrarsveit 33142 Skógarstrandarhreppur 34114 Hörðadalshreppur 34118 Miðdalahreppur 34122 Haukadalshreppur 34128 Laxárdalshreppur 34132 Hvammssveit 34144 Fellsstrandarhreppur 34162 Skarðstrandarhreppur 34174 Saurbæjarsveit 35110 Geiradalshreppur 35123 Reykhólahreppur 35127 Gufudalshreppur 35130 Skálmarnesmúlahreppur 35136 Flateyjarhreppur 35147 Barðastrandarhreppur 35150 Rauðasandshreppur 35158 Tálknafjarðarhreppur 35162 Dalahreppur 35176 Suðurfjarðahreppur 36113 Auðkúluhreppur 36117 Þingeyrarhreppur 36121 Mýrahreppur 36124 Mosvallahreppur 36128 Súgandafjarðarhreppur 36131 Hólshreppur 36134 Skutulsfjarðarhreppur 36137 Súðavíkurhreppur 36140 Ögursveit 36143 Vatnsfjarðarsveit 36146 Langadalsströnd 36152 Snæfjallaströnd 36158 Grunnavíkursveit 36161 Aðalvíkurhreppur 37115 Trjekyllisvíkurhreppur 37140 Kaldaðarneshreppur 37170 Staðarhreppur 37177 Tröllatunguhreppur 37185 Bitruhreppur 37193 Hrútafjarðarhreppur 40118 Staðarhreppur 40127 Torfustaðahreppur 40136 Vatnsneshreppur 40145 Þverárhreppur 40154 Þorkelshólshreppur 40163 Ásshreppur 40169 Sveinsstaðahreppur 40172 Torfalækjarhreppur 40178 Svínadalshreppur 40181 Bólstaðarhlíðarhreppur 40189 Engihlíðarhreppur 40197 Vindhælishreppur 41111 Skefilsstaðahreppur 41113 Sauðárhreppur 41116 Reynistaðarhreppur 41127 Seiluhreppur 41135 Lýtingsstaðahreppur 41140 Blönduhlíðarhreppur 41155 Rípurhreppur 41158 Viðvíkurhreppur 41161 Hólahreppur 41167 Höfðastrandarhreppur 41173 Sléttuhlíðarhreppur 41176 Fljótahreppur 42110 Grímsey 42116 Siglunesshreppur 42122 Ólafsfjarðarhreppur 42136 Svarfaðardalshreppur 42142 Hvammshreppur 42154 Skriðuhreppur 42162 Glæsibæjarhreppur 42174 Hrafnagilshreppur 42186 Saurbæjarhreppur 42192 Aungulsstaðahreppur 43110 Svalbarðsstrandarhreppur 43117 Grýtubakkahreppur 43128 Hálshreppur 43137 Ljósavatnshreppur 43146 Skútustaðahreppur 43158 Helgastaðahreppur 43170 Húsavíkurhreppur 43173 Keldunesshreppur 43179 Skinnastaðahreppur 43182 Presthólahreppur 43188 Svalbarðshreppur 43191 Sauðaneshreppur 99999 Unknown COMMUNEIS reports the commune in Iceland where the household was located. COMMUNEIS is harmonized by name and does not take into consideration changing province boundaries. It is not mappable. IPUMS Farm status, IPUMS historical 1 Non-farm 2 Farm FARMIPUM identifies farm households according to the IPUMS-USA definition. Any household containing a person with the occupation "farmer" is coded as a farm, including group quarters. Farmers are identified as individuals with an OCCHISCO code between 61110 and 61400. In the United States, we also check if there is anyone in the household with an US1880A_0406 code of 100. For the two percent sample of United Kingdom 1851, we use the OCCUK code of 100. Any household containing a farmer under these definitions is regarded as a farm. FARMIPUM does not require the household to be in a non-urban area. The variable FARM restricts farm status to non-urban areas only, based on the (URBAN) variable. IPUMS Farm status, historical 1 No one in household with any connection to agriculture 2 At least one household member works as farmer 3 At least one household member works as agricultural laborer 4 At least one household member works as a cottar 9 Unknown FARM indicates the connection of a household to agriculture. To accommodate differences in census enumeration practices and occupational structure, FARM is not constructed in the same way as FARMIPUM. FARM classifies a household's connections to agriculture into four levels: 1: Nobody in the household indicates any connection with agriculture 2: At least one member of the household is a farmer, and the household's location meets the population [density] threshold. Farmers are identified by having an occupation in minor group 6-1 (See the OCCHISCO codes). If the only agricultural occupation(s) in the household were husbandmen or cottars (61115), the household did not receive this code. Husbandmen and cottars are found in Denmark, Norway, Sweden, and Iceland, and their residence was not always on a farm.For all full-count England and Wales datasets and for Scotland 1881, a parish population density threshold of 75 persons per acre was applied. For the two percent United Kingdom 1851 sample, a population density threshold of 64 persons per acre was applied. Households with farmers that were in locations with greater population [densities] were not given this code. Parish density information is not available in Scotland 1861, 1871, or 1891. FARM is defined based on occupation alone in these samples.3: At least one member of the household was an agricultural laborer; or in Norway there was a cottar in the household, who had no other trade. 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 Size of primary family, historical PRMFAMSZ is the size of the primary, or first, family in the household. It is the FAMSIZE of the first family unit (FAMUNIT) recorded in the household. PRMFAMSZ and FAMSIZE are constructed from information in RELATE (relationship to head of household) and from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). Note: Users should be aware that in the United States census of 1880, where relationship to the head was not transcribed for non-relatives, FAMSIZE and PRMFAMSZ may be inaccurate in some cases. PRMFAMSZ is a 4-digit numeric variable. IPUMS Number of servants in household, historical 000 001 1 002 2 003 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 65 067 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 82 084 84 085 85 086 86 087 87 088 88 090 90 091 91 093 93 096 96 097 97 098 98 099 99 SERVANTS is a constructed variable that indicates the number of servants in the household, who are not related to the household head but are likely to be working in the household. SERVANTS was constructed uniformly for all pre-1950 samples, based on information in RELATE and occupation (OCCHISCO or OCC50US). Specifically, in all countries SERVANTS counts the number of people in the household who meet the following conditions: 1) their relationship to head was non-relative and their occupation indicated they were a servant; 2) an occupation response other than servant but a relationship code of servant; 3) an occupation of servant and a surname different from the head of household (for samples that lack relationship-to-head information). The variable SERVANTS is top-coded at 99. IPUMS Dwelling number Iceland 1729: All households This constructed variable indicates dwelling number. This is a 4-digit numeric variable with 0 implied decimal places IPUMS Number of persons in household Iceland 1729: All households 001 1 002 2 003 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 34 036 36 037 37 039 39 040 40 042 42 043 43 046 46 047 47 048 48 050 50 051 51 053 53 054 54 057 57 058 58 059 59 061 61 062 62 066 66 068 68 093 93 101 101 141 141 159 159 This constructed variable indicates the number of persons in a household. IPUMS Dwelling created by splitting apart a large dwelling or household Iceland 1729: 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 constructed variable indicates that a dwelling was created by splitting apart a large dwelling or household. IPUMS County Iceland 1729: All households 201 Rangárvallasýsla 221 Árnessýsla 321 Hnappadalssýsla This variable indicates the county in which the household is located. IPUMS Commune Iceland 1729: All households 20112 Eyjafjallasveit 20124 Austur-Landeyjahreppur 20125 Vestur-Landeyjahreppur 20134 Fljótshlíðarhreppur 20146 Hvolhreppur 20155 Rangárvallahreppur 20167 Landmannahreppur 20179 Holtamannahreppur 22111 Gaulverjarbæjarhreppur 22113 Stokkseyrarhreppur 22116 Sandvíkurhreppur 22122 Hraungerðishreppur 22126 Villingaholtshreppur 22128 Skeiðahreppur 22131 Gnúpverjahreppur 22137 Hrunamannahreppur 22149 Biskupstungnahreppur 22164 Grímsneshreppur 22173 Þingvallahreppur 22188 Ölfushreppur 22191 Selvogshreppur 32170 Kolbeinsstaðahreppur 32180 Eyjahreppur 32190 Miklaholtshreppur This household variable indicates the county and commune in which the household is located. IPUMS Number of households in house or farm Iceland 1729: 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 22 22 23 23 99 Unknown This variable indicates the number of households in a house or on a farm. IPUMS Type of farm used to determine occupation for heads Iceland 1729: All households This variable indicates the type of farm that was used to determine the occupation of the household head. This is a 8-digit numeric variable with 0 implied decimal places IPUMS Name of farm house Iceland 1729: All households This household variable indicates the farm house name. This is a 36-digit numeric variable with 0 implied decimal places IPUMS Commune as transcribed Iceland 1729: All households This variable indicates the commune in which the household is located (string). This is a 22-digit numeric variable with 0 implied decimal places IPUMS County as transcribed Iceland 1729: All households This variable indicates the county in which the household is located (string). This is a 5-digit numeric variable with 0 implied decimal places IPUMS Additional information given in the source Iceland 1729: All households This variable indicates any additional household information given in the source. This is a 20-digit numeric variable with 0 implied decimal places IPUMS Observations by typist Iceland 1729: All households This variable indicates any observations of the household by the enumerator. This is a 49-digit numeric variable with 0 implied decimal places 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 spouse (historical) No spouse link 1 Unambiguous adjacent link 2 Unambiguous non-adjacent links -- consistent relationship to head/age differences 3 Weak relationship pairing -- adjacent links 4 Weak relationship pairing -- non-adjacent links SPRULEH explains the criteria by which the variable SPLOC linked the person to a probable spouse for the pre-1950 datasets. Spouse-spouse links are established according to four basic rules, and SPRULEH gives the number of the rule that applied to the link in question. SPRULEH works the same way for all countries. In all countries both partners are required to be age 12 and older and to either have a marital status of married or be the unmarried partner of the household head (see MARST and RELATEH). In addition, both partners were required to share the same surname, except when linking heads to spouses or in the Scandinavian samples where SURSIM does not capture family relationships (e.g. patronymic surnames in Norway and non-reporting of child surnames in Sweden). In some samples, however, we were compelled to customize the links beyond what could be implemented more generally. The codes for SPRULEH are as follows. A lower-numbered rule (greater than 0) takes precedence over a higher numbered rule. All relationship pairings are not available in every sample. Additional conditions were added to the rules for some samples. 0 = No spouse of this person present in household. 1 = A married/in-union woman and a married/in-union man were linked because she immediately followed or preceded him in the dataset and both persons' relationship to the household head (see RELATEH) justified a link, as follows: Head to spouseUnmarried partner to spouseParent to parentParent-in-law to parent-in-lawChild to child-in-lawSibling to sibling-in-law2 = A married woman and a married man who did not appear adjacently on the form were linked because: they had one of the relationship sets listed in rule 1the woman was at least 13 years oldthe man was at least 15 years oldthe man was not more than 25 years older than the woman, ANDthe woman was not more than 10 years older than the man.3 = A married/in-union woman and a married/in-union man were linked because she immediately followed or preceded him in the dataset and both persons' relationship to the household head (RELATEH) suggested a possible pairing and the female spouse was no more than 10 years older or 25 years younger than the male spouse. The possible relationship pairings are as follows: Other relative to other relative (RELATEH codes 1000, 1001, 1061)Non-relative to non-relative (RELATEH codes 1000-1303)Child to other relativeSibling to other relativeGrandchild to grandchild-in-lawGrandparent to grandparent/grandparent-in-law/grandparent-in-lawGrandchild to other relativeAunt to uncleNephew/niece to nephew/nieceCousin to cousinUnknown to unknown; missing to missingHead to unknown or missingChild to unknown or missing4 = Same as rule 3, but the married/in-union woman was not adjacent to the married/in-union man in the data. IPUMS Probable stepmother (historical) No stepmother present 1 Improbable age difference 2 Spouse of father 3 Identified stepmother 5 Identified as adopted STEPMOMH indicates for the pre-1950 datasets whether a person's mother, as identified by MOMLOC, was most probably not the person's biological mother. Non-zero values of STEPMOMH 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. Only persons under age 19 are linked to their mothers. The codes for STEPMOMH are as follows: 0 = Biological mother or no mother of this person present in household. 1 = Age difference between mother and child was less than 12 or greater than 54 years. 2 = Link was established only because the mother was married to father. 3 = Child explicitly identified relationship as stepchild. 5 = Child explicitly identified as adopted. If more than one value applied to the case, the lowest value was assigned. See MOMRULEH 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, STEPMOMH will always under-represent their actual number in the population. IPUMS Probable stepfather (historical) No stepfather present 1 Improbable age difference 2 Spouse of mother 3 Identified stepfather 5 Identified as adopted STEPPOPH indicates for the pre-1950 datasets whether a person's father, as identified by POPLOC, was most probably not the person's biological father. Non-zero values of STEPPOPH 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. Only persons under age 19 are linked to their fathers. The codes for STEPPOPH are as follows: 0 = Biological father or no father of this person present in household. 1 = Age difference between father and child was less than 14 years. 2 = Link was established only because the father was married to mother. 3 = Explicitly identified relationship (stepchild). 5 = Explicitly identified relationship (adopted). If more than one value applied to the case, the lowest value was assigned. See POPRULEH 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, STEPPOPH will always under-represent their actual number in the population. IPUMS Rule for linking mother (historical) No mother link 1 Unambiguous mother link 2 Daughter/grandchild/greatgrandchild link 3 Ambiguous mother link 7 Spouse of father becomes stepmother MOMRULEH explains the criteria by which the variable MOMLOC linked the person to a probable mother in pre-1950 datasets. Pre-1950 datasets in IPUMS International establish child-mother links according to four basic rules, and MOMRULEH gives the number of the rule that applied to the link in question. MOMRULEH works the same way for all countries. A lower-numbered rule (greater than 0) takes precedence over a higher numbered rule. The codes for MOMRULEH are as follows: 0 = No mother of this person present in the household. 1 = Unambiguous relationship. This rule covers four basic cases, and occurs when the person linked is 10 to 69 years younger than the probable parent. All relationship pairings are not available in every sample. The allowable relationship pairings are as follows: Child linked to head or spouse/unmarried partner of the headHead or sibling linked to parentSpouse or sibling-in-law linked to parent-in-lawChild/child-in-law linked to head or spouse2 = Grandchildren. Potential mothers are females listed as child or child- in-law in RELATE who are 13-49 years older than persons listed as grandchildren and who share the grandchild's surname. If there is more than one potential mother, we used relative positions in the household, age and marital status to decide the link. 3 = Other relatives and non-relatives. Link to a preceding person with a plausible relationship pairing of child-to-mother, where the mother is 13- 49 years older, is ever-married, and shares the child's surname. The mother must immediately precede the child or all intervening persons must also be linked to the mother as spouse or child. No links are made for non-relatives over age 17. Permissible relationship pairings are as follows (child to mother): Nephew/niece to sibling or sibling-in-lawCousin to aunt/uncleOther relative to siblingGrandchild to other relativeOther relative to other relative (RELATEH code 1000, 1001, 1061)Non-relative to non-relative (RELATEH code 1100-1303)7= If the child is already linked to a father (see POPLOC and POPRULEH), and that father has a wife present in the household who has not been linked to the child by another rule, the wife is linked to the child as a stepmother, regardless of the age gap between child and the stepmother. Consistency checks: If a husband and wife were both linked to the same mother, historic samples chose the best mother link based on detailed relationship (RELATEH) and proximity within the household. If a person is linked to two parents who are not linked to each other, remove one of the parental links on the basis of proximity within the household. IPUMS Rule for linking father (historical) No father link 1 Unambiguous father link 2 Son/granchild/greatgrandchild link 3 Ambiguous father link 7 Husband of mother becomes stepfather POPRULEH explains the criteria by which the variable POPLOC linked the person to a probable father in pre-1950 datasets. In the pre-1950 datasets, child-father links were established according to four basic rules, and POPRULEH gives the number of the rule that applied to the link in question. POPRULEH works the same way for all countries. A lower-numbered rule (greater than 0) takes precedence over a higher numbered rule. The codes for POPRULEH are as follows: 0 = No father of this person present in the household. 1 = Unambiguous relationship. This rule covers four basic cases, and occurs when the person linked is 10 to 69 years younger than the probable parent. All relationship pairings are not available in every sample. The allowable relationship pairings are as follows: Child linked to head or spouse/unmarried partner of the headHead or sibling linked to parentSpouse or sibling-in-law linked to parent-in-lawChild/child-in-law linked to head or spouse2 = Grandchildren. Potential fathers are males listed as child or child-in- law in RELATE 13-79 years older than persons listed as grandchildren and who share the grandchild's surname. If there is more than one potential father, we used relative positions in the household, age and marital status to decide the link. 3 = Other relatives and nonrelatives. Link to a preceding person with a plausible relationship pairing of child-to-father, where the father is 13-79 years older, is ever-married, and shares the child's surname. The father must immediately precede the child or all intervening persons must also be linked to the father as spouse or child. Permissible relationship pairings are as follows (child to father): Nephew/niece to sibling or sibling-in-lawCousin to aunt/uncleOther relative to siblingGrandchild to other relativeOther relative to other relative (RELATEH code 1000, 1001, 1061)Non-relative to non-relative (RELATEH code 1100-1303)7 = If a child is linked to a mother (see MOMLOC and MOMRULEH), and that mother is linked to a husband who has not been linked to the child by another rule, the husband is linked to the child as a stepfather, regardless of the age gap between child and husband. Consistency checks: If a husband and wife were both linked to the same father, the best father link was chosen based on detailed relationship (RELATEH) and proximity within the household. If a person is linked to two parents who are not linked to each other, remove one of the parental links on the basis of proximity within the household. 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 Number of own siblings 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 or more NSIBS counts the number of the person's own siblings (including half- siblings) living in the household with her/him. NSIBS is generally based on information contained in RELATE (Relationship to household head). Persons with no siblings present are coded "0." IPUMS Number of generations in family unit 1 1 2 2 3 3 4 4 5 5 6 6 or more NUMGEN describes the number of generations in a given family unit. (See the documentation for FAMUNIT). For example, a family unit comprised of parents and children has a NUMGEN of 2. A family unit which has co-resident grandparents, parents and children has a NUMGEN of 3. A family unit with only grandparents and grandchildren has a NUMGEN of 2, as the intervening generation is not resident. IPUMS Number of non-relatives of person in household 0000 0001 1 0002 2 0003 3 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 0091 91 0092 92 0093 93 0094 94 0095 95 0096 96 0098 98 0099 99 0101 101 0100 100 0102 102 0103 103 0104 104 0105 105 0108 108 0109 109 0110 110 0112 112 0115 115 0116 116 0117 117 0118 118 0121 121 0123 123 0124 124 0127 127 0128 128 0129 129 0132 132 0134 134 0135 135 0137 137 0139 139 0140 140 0142 142 0145 145 0147 147 0153 153 0154 154 0155 155 0156 156 0158 158 0162 162 0163 163 0166 166 0167 167 0173 173 0178 178 0179 179 0189 189 0197 197 0201 More than 200 NONRELS is a constructed variable that counts the number of people unrelated to the individual, living in the household. See also FAMUNIT, FAMSIZE and PERSONS. 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 household head, historical 0101 Head/householder 0201 Spouse 0202 2nd/3rd wife (polygamous) 0203 Concubine/mistress 0204 Fiance/fiancee 0301 Child 0302 Adopted Child 0303 Stepchild 0304 Adopted, n.s. 0305 Adopted/fostered child (Great Britain) 0306 Fostered child 0401 Child-in-law 0402 Step child-in-law 0501 Parent 0502 Stepparent 0503 Adoptive parent 0601 Parent-in-law 0602 Stepparent-in-law 0603 Retired people with special benefits from farm (karfolk) 0701 Sibling 0702 Step/half/adopted sibling 0703 Step sibling 0704 Half-sibling 0801 Sibling-in-Law 0802 Step/half sibling-in-law 0901 Grandchild 0902 Adopted grandchild 0903 Step grandchild 0904 Grandchild-in-law 1000 Other Relatives: 1001 Other relatives, n.s. 1011 Grandparent 1012 Step grandparent 1013 Grandparent-in-law 1021 Aunt or uncle 1022 Aunt,uncle-in-law 1023 Step aunt or uncle 1024 Great aunt or uncle 1031 Nephew, niece 1032 Nephew/niece-in-law 1033 Step/adopted nephew/niece 1034 Grand niece/nephew 1035 Step nephew/niece 1036 Adopted nephew/niece 1037 Grand step nephew/niece 1038 Grand nephew/niece in law 1041 Cousin 1042 Cousin-in-law 1043 First cousin 1044 Second cousin 1045 Step cousin 1051 Great grandchild 1061 Other relatives, n.e.c. 1100 Partner, friend, visitor 1110 Partner/friend 1111 Friend 1112 Partner 1113 Partner/roommate (1960/70 residual category for other non-relatives) 1114 Unmarried partner 1115 Housemate/roommate 1120 Relative of partner 1131 Visitor 1132 Companion and companion's family 1139 Allocated partner/friend/visitor 1201 Roomers/boarders/lodgers 1202 Boarders 1203 Lodgers 1204 Roomer 1205 Tenant 1207 Poor or receiver of charity by head of household 1208 Pauper staying with others at expense of municipality 1209 Illicit Vagrant 1210 Employees: 1211 Servant 1212 Housekeeper 1213 Maid 1214 Cook 1215 Nurse 1216 Other probable domestic employee 1217 Other employees 1218 Employees of guests 1219 Relative of employee 1221 Military 1222 Students 1223 Members of religious orders 1224 Harim (Egypt) 1225 Slave (Egypt) 1226 Child of slave (Egypt) 1230 Other non-relatives 1239 Allocated other non-relative 1301 Institutional inmates 1302 Families of inmates 1303 Foundlings/orphans 9999 Unknown RELATEH is the NAPP version of the RELATE variable, which describes an individual's relationship to the head of household. This variable is largely compatible with the IPUMS-International variable RELATE, though some changes have been made. The relationship codes are divided into two categories-relatives (codes 1- 10) and non-relatives (codes 11-12). The codes for relatives are self- explanatory. The non-relative codes are divided into three groups: "Partner, friend, visitor", roughly described as persons who do not pay or work for their accommodations (unless they share ownership); "Other non-relatives", including those persons paying or working for accommodations; and "Institutional inmates". 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 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 Year of birth 0000 NIU (not in universe) 1628 1628 1629 1629 1630 1630 1631 1631 1634 1634 1635 1635 1636 1636 1637 1637 1638 1638 1639 1639 1640 1640 1641 1641 1642 1642 1643 1643 1644 1644 1645 1645 1646 1646 1647 1647 1648 1648 1649 1649 1650 1650 1651 1651 1652 1652 1653 1653 1654 1654 1655 1655 1656 1656 1657 1657 1658 1658 1659 1659 1660 1660 1661 1661 1662 1662 1663 1663 1664 1664 1665 1665 1666 1666 1667 1667 1668 1668 1669 1669 1670 1670 1671 1671 1672 1672 1673 1673 1674 1674 1675 1675 1676 1676 1677 1677 1678 1678 1679 1679 1680 1680 1681 1681 1682 1682 1683 1683 1684 1684 1685 1685 1686 1686 1687 1687 1688 1688 1689 1689 1690 1690 1691 1691 1692 1692 1693 1693 1694 1694 1695 1695 1696 1696 1697 1697 1698 1698 1699 1699 1700 1700 1701 1701 1702 1702 1703 1703 1704 1704 1705 1705 1706 1706 1707 1707 1708 1708 1709 1709 1710 1710 1711 1711 1712 1712 1713 1713 1714 1714 1715 1715 1716 1716 1717 1717 1718 1718 1719 1719 1720 1720 1721 1721 1722 1722 1723 1723 1724 1724 1725 1725 1726 1726 1727 1727 1728 1728 1729 1729 1730 1730 1731 1731 1732 1732 1733 1733 1734 1734 1735 1735 1736 1736 1737 1737 1738 1738 1739 1739 1740 1740 1741 1741 1742 1742 1743 1743 1744 1744 1745 1745 1746 1746 1747 1747 1748 1748 1749 1749 1750 1750 1751 1751 1752 1752 1753 1753 1754 1754 1755 1755 1756 1756 1757 1757 1758 1758 1759 1759 1760 1760 1761 1761 1762 1762 1763 1763 1764 1764 1765 1765 1766 1766 1767 1767 1768 1768 1769 1769 1770 1770 1771 1771 1772 1772 1773 1773 1774 1774 1775 1775 1776 1776 1777 1777 1778 1778 1779 1779 1780 1780 1781 1781 1782 1782 1783 1783 1784 1784 1785 1785 1786 1786 1787 1787 1788 1788 1789 1789 1790 1790 1791 1791 1792 1792 1793 1793 1794 1794 1795 1795 1796 1796 1797 1797 1798 1798 1799 1799 1800 1800 1801 1801 1802 1802 1803 1803 1804 1804 1805 1805 1806 1806 1807 1807 1808 1808 1809 1809 1810 1810 1811 1811 1812 1812 1813 1813 1814 1814 1815 1815 1816 1816 1817 1817 1818 1818 1819 1819 1820 1820 1821 1821 1822 1822 1823 1823 1824 1824 1825 1825 1826 1826 1827 1827 1828 1828 1829 1829 1830 1830 1831 1831 1832 1832 1833 1833 1834 1834 1835 1835 1836 1836 1837 1837 1838 1838 1839 1839 1840 1840 1841 1841 1842 1842 1843 1843 1844 1844 1845 1845 1846 1846 1847 1847 1848 1848 1849 1849 1850 1850 1851 1851 1852 1852 1853 1853 1854 1854 1855 1855 1856 1856 1857 1857 1858 1858 1859 1859 1860 1860 1861 1861 1862 1862 1863 1863 1864 1864 1865 1865 1866 1866 1867 1867 1868 1868 1869 1869 1870 1870 1871 1871 1872 1872 1873 1873 1874 1874 1875 1875 1876 1876 1877 1877 1878 1878 1879 1879 1880 1880 1881 1881 1882 1882 1883 1883 1884 1884 1885 1885 1886 1886 1887 1887 1888 1888 1889 1889 1890 1890 1891 1891 1892 1892 1893 1893 1894 1894 1895 1895 1896 1896 1897 1897 1898 1898 1899 1899 1900 1900 1901 1901 1902 1902 1903 1903 1904 1904 1905 1905 1906 1906 1907 1907 1908 1908 1909 1909 1910 1910 1911 1911 1912 1912 1913 1913 1914 1914 1915 1915 1916 1916 1917 1917 1918 1918 1919 1919 1920 1920 1921 1921 1922 1922 1923 1923 1924 1924 1925 1925 1926 1926 1927 1927 1928 1928 1929 1929 1930 1930 1931 1931 1932 1932 1933 1933 1934 1934 1935 1935 1936 1936 1937 1937 1938 1938 1939 1939 1940 1940 1941 1941 1942 1942 1943 1943 1944 1944 1945 1945 1946 1946 1947 1947 1948 1948 1949 1949 1950 1950 1951 1951 1952 1952 1953 1953 1954 1954 1955 1955 1956 1956 1957 1957 1958 1958 1959 1959 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1965 1965 1966 1966 1967 1967 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 1988 1988 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 9999 Unknown BIRTHYR gives the person's year of birth. IPUMS Labor force participation 1 No, not in the labor force 2 Yes, in the labor force 8 Unknown 9 NIU (not in universe) LABFORCE is a dichotomous variable identifying whether a person participated in the labor force. Labor force participation generally means working or seeking work within a specified reference period. For most samples LABFORCE is a recode of EMPSTAT (employment status). A consistent lower age universe of 15 or older has been applied to increase comparability across samples. Full detail is retained in EMPSTAT, which should be used for any study of child labor. IPUMS Occupation, ISCO general 01 Legislators, senior officials and managers 02 Professionals 03 Technicians and associate professionals 04 Clerks 05 Service workers and shop and market sales 06 Skilled agricultural and fishery workers 07 Crafts and related trades workers 08 Plant and machine operators and assemblers 09 Elementary occupations 10 Armed forces 11 Other occupations, unspecified or n.e.c. 97 Response suppressed 98 Unknown 99 NIU (not in universe) OCCISCO records the person's primary occupation, coded according to the major categories in the International Standard Classification of Occupations (ISCO) scheme for 1988. For someone with more than one job, the primary occupation is typically the one in which the person had spent the most time or earned the most money. IPUMS Occupation, ISCO-1968, 3-digit 011 Chemists 012 Physicists 013 Physical scientists not elsewhere classified 014 Physical science technicians 021 Architects and town planners 022 Civil engineers 023 Electrical and electronics engineers 024 Mechanical engineers 025 Chemical engineers 026 Metallurgists 027 Mining engineers 028 Industrial engineers 029 Engineers not elsewhere classified 031 Surveyors 032 Draughtsmen 033 Civil engineering technicians 034 Electrical and electronics engineering technicians 035 Mechanical engineering technicians 036 Chemical engineering technicians 037 Metallurgical technicians 038 Mining technicians 039 Engineering technicians not elsewhere classified 041 Aircraft pilots, navigators and flight engineers 042 Ships' deck officers and pilots 043 Ships' engineers 049 Aircraft and ships officers, n.e.c. 051 Biologists, zoologists and related scientists 052 Bacteriologists, pharmacologists and related scientists 053 Agronomists and related scientists 054 Life sciences technicians 059 Life sciences technicians and related technicians, n.e.c. 061 Medical doctors 062 Medical assistants 063 Dentists 064 Dental assistants 065 Veterinarians 066 Veterinary assistants 067 Pharmacists 068 Pharmaceutical assistants 069 Dietitians and public health nutritionists 071 Professional nurses 072 Nursing personnel not elsewhere classified 073 Professional midwives 074 Midwifery personnel not elsewhere classified 075 Optometrists and opticians 076 Physiotherapists and occupational therapists 077 Medical Xray technicians 079 Medical, dental, veterinary and related workers not elsewhere classified 081 Statisticians 082 Mathematicians and actuaries 083 Systems Analysts 084 Statistical and mathematical technicians 089 Statisticians, mathematicians, systems analysts and related technicians, n.e.c. 090 Economists 099 Other social scientists, n.e.c. 110 Accountants 121 Lawyers 122 Judges 129 Jurists not elsewhere classified 131 University and higher education teachers 132 Secondary education teachers 133 Primary education teachers 134 Preprimary education teachers 135 Special education teachers 139 Teachers not elsewhere classified 141 Ministers of religion and related members of religious orders 149 Workers in religion not elsewhere classified 151 Authors and critics 159 Authors, journalists and related writers not elsewhere classified 161 Sculptors, painters and related artists 162 Commercial artists and designers 163 Photographers and cameramen 169 Sculptors, painters and related artists, n.e.c. 171 Composers, musicians and singers 172 Choreographers and dancers 173 Actors and stage directors 174 Producers, performing arts 175 Circus performers 179 Performing artists not elsewhere classified 180 Athletes, sportsmen and related workers 191 Librarians, archivists and curators 192 Sociologists, anthropologists and related scientists 193 Social workers 194 Personnel and occupational specialists 195 Philologists, translators and interpreters 199 Other professional, technical and related workers 201 Legislative officials 202 Government administrators 211 General managers 212 Production managers (except farm) 219 Managers not elsewhere classified 299 Administrative and managerial, n.e.c. 300 Clerical supervisors 310 Government executive officials 321 Stenographers, typists and teletypists 322 Card and tapepunching machine operators 323 Telex operators 329 Stenegraphers, typists and teletypists, n.e.d. 331 Bookkeepers and cashiers 339 Bookkeepers, cashiers and related workers not elsewhere classified 341 Bookkeeping and calculating machine operators 342 Automatic dataprocessing machine operators 349 Computing machine operators, n.e.c. 351 Railway station masters 352 Postmasters 359 Transport and communications supervisors not elsewhere classified 360 Transport conductors 370 Mail distribution clerks 380 Telephone and telegraph operators 391 Stock clerks 392 Material and production planning clerks 393 Correspondence and reporting clerks 394 Receptionists and travel agency clerks 395 Library and filing clerks 399 Clerks not elsewhere classified 400 Managers (wholesale and retail trade) 410 Working proprietors (wholesale and retail trade) 421 Sales supervisors 422 Buyers 431 Technical salesmen and service advisers 432 Commercial travellers and Manufacturers' agents 439 Technical salesmen, commercial travellers and manufacturers' agents, n.e.c. 441 Insurance, real estate and securities salesmen 442 Business services salesmen 443 Auctioneers 451 Salesmen, shop assistants and demonstrators 452 Street vendors, canvassers and newsvendors 454 Itinerant traders 459 Salesmen, shop assistants and demonstrators, n.e.c. 490 Sales workers not elsewhere classified 500 Managers (catering and lodging services) 510 Working proprietors (catering and lodging services) 520 Housekeeping and related service supervisors 531 Cooks 532 Waiters, bartenders and related workers 540 Maids and related housekeeping service workers not elsewhere classified 551 Building caretakers 552 Charworkers, cleaners and related workers 560 Launderers, drycleaners and pressers 570 Hairdressers, barbers, beauticians and related workers 581 Firefighters 582 Policemen and detectives 589 Protective service workers not elsewhere classified 591 Guides 592 Undertakers and embalmers 599 Other service workers 600 Farm managers and supervisors 611 General farmers 612 Specialised farmers 621 General farm workers 622 Field crop and vegetable farm workers 623 Orchard, vineyard and related tree and shrub crop workers 624 Livestock workers 625 Dairy farm workers 626 Poultry farm workers 627 Nursery workers and gardeners 628 Farm machinery operators 629 Agricultural and animal husbandry workers not elsewhere classified 631 Loggers 632 Forestry workers (except logging) 639 Forestry and loggers, n.e.c. 641 Fishermen 649 Fishermen, hunters and related workers not elsewhere classified 700 Production supervisors and general foremen 711 Miners and quarrymen 712 Mineral and stone treaters 713 Well drillers, borers and related workers 721 Metal smelting, converting and refining furnacemen 722 Metal rollingmill workers 723 Metal melters and reheaters 724 Metal casters 725 Metal moulders and coremakers 726 Metal annealers, temperers and casehardeners 727 Metal drawers and extruders 728 Metal platers and coaters 729 Metal processers not elsewhere classified 731 Wood treaters 732 Sawyers, plywood makers and related woodprocessing workers 733 Paper pulp preparers 734 Paper makers 739 Wood preparation workers and paper makers, n.e.c. 741 Crushers, grinders and mixers 742 Cookers, roasters and related heattreaters 743 Filter and separator operators 744 Still and reactor operators 745 Petroleum refining workers 749 Chemical processers and related workers not elsewhere classified 751 Fibre preparers 752 Spinners and winders 753 Weaving and knittingmachine setters and patterncard preparers 754 Weavers and related workers 755 Knitters 756 Bleachers, dyers and textile product finishers 759 Spinners, weavers, knitters, dyers and related workers not elsewhere classified 761 Tanners and fellmongers 762 Pelt dressers 769 Tanners, fellmongers and pelt dressers, n.e.c. 771 Grain millers and related workers 772 Sugar processers and refiners 773 Butchers and meat preparers 774 Food preservers 775 Dairy product processers 776 Bakers, pastrycooks and confectionery makers 777 Tea, coffee and cocoa preparers 778 Brewers, wine and beverage makers 779 Food and beverage processers not elsewhere classified 781 Tobacco preparers 782 Cigar makers 783 Cigarette makers 789 Tobacco preparers and tobacco product makers not elsewhere classified 791 Tailors and dressmakers 792 Fur tailors and related workers 793 Milliners and hatmakers 794 Patternmakers and cutters 795 Sewers and embroiderers 796 Upholsterers and related workers 799 Tailors, dressmakers, sewers, upholsterers and related workers not elsewhere classified 801 Shoemakers and shoe repairers 802 Shoe cutters, lasters, sewers and related workers 803 Leather goods makers 811 Cabinetmakers 812 Woodworking machine operators 819 Cabinetmakers and related woodworkers not elsewhere classified 820 Stone cutters and carvers 831 Blacksmiths, hammersmiths and forgingpress operators 832 Toolmakers, metal patternmakers and metal markers 833 Machinetool setteroperators 834 Machinetool operators 835 Metal grinders, polishers and tool sharpeners 839 Blacksmiths, toolmakers and machinetool operators not elsewhere classified 841 Machinery fitters and machine assemblers 842 Watch, clock and precision instrument makers 843 Motor vehicle mechanics 844 Aircraft engine mechanics 849 Machinery fitters, machine assemblers and precision instrument makers (except electrical) not elsewhere classified 851 Electrical fitters 852 Electronics fitters 853 Electrical and electronics equipment assemblers 854 Radio and television repairmen 855 Electrical wiremen 856 Telephone and telegraph installers 857 Electric linemen and cable jointers 859 Electrical fitters and related electrical and electronics workers not elsewhere classified 861 Broadcasting station operators 862 Sound equipment operators and cinema projectionists 871 Plumbers and pipe fitters 872 Welders and flamecutters 873 Sheetmetal workers 874 Structural metal preparers and erectors 879 Other plumbers, welders, sheet metal and structural metal preparers and erectors 880 Jewellery and precious metal workers 891 Glass formers, cutters, grinders and finishers 892 Potters and related clay and abrasive formers 893 Glass and ceramics kilnmen 894 Glass engravers and etchers 895 Glass and ceramics painters and decorators 899 Glass formers, potters and related workers not elsewhere classified 901 Rubber and plastics product makers (except tire makers and tire vulcanisers) 902 Tire makers and vulcanisers 910 Paper and paperboard products makers 921 Compositors and typesetters 922 Printing pressmen 923 Stereotypers and electrotypers 924 Printing engravers (except photoengravers) 925 Photoengravers 926 Bookbinders and related workers 927 Photographic darkroom workers 929 Printers and related workers not elsewhere classified 931 Painters, construction 939 Painters not elsewhere classified 941 Musical instrument makers and tuners 942 Basketry weavers and brush makers 943 Nonmetallic mineral product makers 949 Other production and related workers 951 Bricklayers, stonemasons and tile setters 952 Reinforced concreters, cement finishers and terrazzo workers 953 Roofers 954 Carpenters, joiners and parquetry workers 955 Plasterers 956 Insulators 957 Glaziers 959 Construction workers not elsewhere classified 961 Power generating machinery operators 969 Stationary engine and related equipment operators not elsewhere classified 971 Dockers and freight handlers 972 Riggers and cable splicers 973 Crane and hoist operators 974 Earthmoving and related machinery operators 979 Material handling equipment operators not elsewhere classified 981 Ships' deck ratings, barge crews and boatmen 982 Ships' engineroom ratings 983 Railway engine drivers and firemen 984 Railway brakemen, signalmen and shunters 985 Motor vehicle drivers 986 Animal and animaldrawn vehicle drivers 989 Transport equipment operators not elsewhere classified 990 Labourers not elsewhere classified 995 Armed forces 997 Response suppressed 998 Unknown 999 NIU (not in universe) ISCO68A provides the 3-digit occupation code for the respondent using the ISCO-1968 occupation classification. IPUMS Occupation, HISCO classification 01100 Chemists 01200 Physicists 01300 Physical scientists nec 01400 Physical science assistants and technicians 02000 Professional engineers, specialisation unknown 02100 Architects 02200 Civil engineers 02300 Electrical engineers 02410 Mechanical engineers, general or nfs 02420 Industrial machinery or tools engineers 02430 Mechanical engineer (motors and engines, except marine) 02440 Marine engineers 02450 Ship construction engineers 02460 Heating, ventilation and refrigeration engineers 02490 Other mechanical engineers 02500 Chemical engineers 02600 Metallurgists and assayers 02700 Mining engineers 02800 Industrial engineers 02900 Professional engineers nec 03010 Surveyors, general and nfs 03020 Land surveyors 03030 Mine surveyors 03040 Hydrographic surveyors 03090 Other surveyors 03100 Draughtsmen 03200 Technician, specialisation unknown 03210 Technicians 03220 Civil engineering technicians 03230 Electrical engineering technicians 03240 Mechanical engineering technicians 03250 Chemical engineering technicals 03260 Metallurgical technicians 03270 Mining technicians 03290 Engineering technicians nec 04200 Ship's deck officer or pilot 04210 Ships officers nfs 04220 Ship's masters and captains (sea) 04230 Ship's masters and captains (sea or inland waterways) 04240 Ship's masters and captains (inland waterways) 04250 Ship's navigating officers and ship's mates 04260 Ship and boat pilots 04270 Marine superintendents (deck) 04290 Other ship deck officers and pilots 04300 Ship's engineers 05100 Biologists, botanists, zoologists and related scientists 05200 Medical scientists 05300 Agronomists and related scientists 05400 Life sciences technicians 06100 Medical doctors and surgeons 06110 Medical doctors and physicians (UK: Medical doctors only) 06130 Surgeons 06140 Specialised physicians 06150 Specialised surgeons 06160 Public health physicians 06170 Oculist 06190 Medical doctors, nec 06200 Dentists 06300 Veterinarians 06400 Pharmacists 06500 Medical and related assistants 07110 Trained nurses 07210 Nurses nfs 07220 Medical nurses, untrained or level of training unknown 07320 Midwives 07330 Monthly nurses 07500 Opticians and optometrists 07600 Healers and therapists nec 07920 Osteopaths 07930 Chiropractors 07940 Sanitarians 07990 Other medical, dental, veterinary and related workers 08000 Mathematicians and actuaries, etc. 09000 Economists 11010 Accountants 11020 Auditors 11090 Accountants nec 12110 Lawyers 12120 Barristers 12130 Solicitors 12200 Judge 12220 Justice of the peace 12300 Notary 12900 Jurists (except lawyer, judge or solicitor) 13100 Teachers in higher education 13200 Teachers not in higher education 13210 Teachers (unspecified) 13220 Teachers (secondary) 13230 Teachers (primary) 13240 Teachers (pre-primary) 13250 Teachers (of the disabled) 13300 School administrators and principals 13920 Governesses 13990 Other teaching personnel 14120 Ministers of religion 14130 Missionaries 14140 Members of religious orders 14190 Other ministers of religion 14900 Workers in religion nec 15100 Authors 15220 Editors 15230 Journalists, reporters and correspondents 15900 Others writers 16110 Artists nfs and art teachers 16120 Sculptors 16130 Artistic painters 16140 Cartoonists 16150 Engravers and etchers (artistic) 16190 Other specified creative artists 16200 Designers 16300 Photographers 16400 Engravers 17120 Musical performers and musicians nfs 17130 Music teachers 17190 Others in music 17200 Dancers and dancing teachers 17300 Actors and related workers 17400 Producers, performing arts 17900 Performing artists nec 18000 Athletes, sportsmen and related workers 19100 Librarians, archivists and curators 19200 Social scientists and historians 19300 Social workers 19500 Interpreters, translators and philologists 19920 Patent agents 19930 Underwriters 19990 Other professional, technical and related workers nec 20100 Legislative officials and heads of government 20200 Government administrators 21110 Proprietors, nfs 21112 Executive officers, nfs (U.S. only) 21120 Manufacturers nfs 21122 Executive officers of manufacturing concerns nfs 21130 Manufacturers or proprietors of specified manufacturing concerns 21132 Executive officers of specified manufacturing concerns 21140 Proprietor, mining, quarrying, oil and natural gas extraction 21141 Oil producer 21142 Executive officers of extractive industry 21150 Proprietor, railway company 21152 Executive officers of railway concerns 21160 Proprietor, other transport and communications 21162 Executive officer of other transport and communications 21170 Proprietor, public utilities (i.e., water, gas, etc.) 21172 Executive officer of public utilities 21180 Proprietor, finance 21182 Executive officer finance company 21190 Other owners of large scale productive enterprises 21192 Executive officers of other large scale productive enterprises 21194 Publisher 21210 Contractors nfs 21220 Construction contractors 21230 Builders 21290 Other specified contractors 21330 Capitalists 21340 Speculators 21390 Investors nec 22110 Superintendents and managers nfs 22120 Superintendents and managers, manufacturing 22130 Superintendents and managers, mining, quarrying, etc. 22140 Superintendents and managers, railways 22150 Superintendents and managers, other transport and communications 22160 Superintendents and managers, public utilities 22170 Superintendents and managers, construction, except railway construction 22180 Superintendents and managers, commerce 22190 Superintendents and managers, nec 23110 Foremen and supervisors nfs 23120 Foremen and supervisors, manufacturing 23130 Foremen and supervisors, mining 23140 Foremen and supervisors, railways 23150 Foremen and supervisors, other transport and communications 23160 Foremen and supervisors, public utilities 23170 Foremen and supervisors, construction, except railway construction 23180 Foremen and supervisors, commerce 23190 Foremen and supervisors, nec 24100 Inspectors of manufactories, companies etc. 31010 Civil servants, title unknown 31020 Tax collectors 31030 Tax assessors 31040 Customs officers 31050 Postmasters or postmistresses 31060 Clerks of court and other court clerks 31090 Government functionaries, nec 32000 Typists, stenographers and secretaries 33120 Bookkeepers and bookkeeping clerks 33130 Cashiers, bank or office 33140 Bank clerks 33142 Bank tellers (US) 33150 Money collector 33160 Cash desk cashier 33170 Toll collectors 33180 Collector (cash or account) 33190 Bookkeeping, account and cash handling workers nec 33210 Railway clerks (UK) and railway agents (CA/US) 33220 Baggage agents and baggage men 33320 Agent, water transport (principally steamship and steamboat) 33330 Agent, land transport (non-rail) 34000 Office machine operators 36010 Conductors, nfs 36020 Railway guards (UK) and railroad conductors (US/CAN) 36030 Sleeping- or pullman-car attendants 36040 Bus, tram or streetcar conductors 36090 Transport conductors, nec 37020 Postal, mail and telegraph clerks 37030 Mail carriers, aka postmen (UK) 37040 Messengers 37050 Mail sorters 37090 Post office, mail, and message workers nec 38010 Tel. operator 38020 Telephone operator 38030 Telegraph operator 38090 Other telephone/telegraph operators 39000 Clerk, nfs 39100 Stock, weight and shipping clerks 39130 Stock records clerk 39310 Office clerks, specialisation unknown 39320 Correspondence clerks 39340 Legal clerks 39350 Insurance clerks 39390 Other correspondence and reporting clerks 39400 Hotel clerk or other receptionist 39500 Library and filing clerks 39600 Railroad, railway clerk 39720 Timekeepers, railway 39730 Timekeepers, factory 39790 Timekeepers, undefined and nec 39900 Other specified clerks 39990 Proofreader 40000 Sales workers, nfs 41000 Working proprietors, nfs 41010 Dealer, merchant etc. (wholesale and retail trade) 41015 Merchants, specified large-scale wholesaleing 41020 Hirers out 42000 Buyers 42200 Buyers nec 43010 Agents nfs 43020 Commercial travellers 43030 Manufacturers and sales agents 43090 Other specialized agents 44110 Insurance, real state or securities salesmen, nfs 44120 Insurance salesmen and agents 44130 Real estate agents 44140 Brokers 44150 Stockbrokers 44160 Patent Agents 44190 Insurance, real estate and securities salesmen nec 44200 Advertising salesmen 44320 Auctioneers 44330 Appraisers 45120 Salespeople, wholesale or retail trade 45130 Clerks in shops and stores 45140 Fashion models 45190 Other sales personnel men, shop assistants and demonstrators 45220 Street sellers, pedlars and hawkers, non-food items 45230 Street sellers, pedlars and hawkers, food items 45240 Newsvendors 45250 Canvassers 45290 Other street traders for coffee and food 49020 Pawnbrokers 49030 Scrap and junk dealers, scavengers 49040 Street vendors, canvassers and news vendors, newsvendors 49090 Dealers and salesworkers nec 51020 Hotel keepers and managers 51030 Restaurant keepers and managers 51040 Boarding and lodging house keepers and managers 51050 Pub, saloon, tavern and coffee house keepers 51060 Ship's pursers 51090 Other hospitality and entertainment keepers and managers 52020 Housekeeper 52030 Stewards 52040 Matrons 52050 Butlers 52090 Other housekeeping service supervisors 53100 Cooks 53101 Cook (domestic) UK only 53102 Cook (not domestic) 53103 Ship's cooks 53220 Waiter or waitress 53230 Bartender 53290 Other food and drink service workers 54010 Servants nfs 54020 House servants nfs and maids 54030 Personal servants and valets 54040 Nursemaids 54050 Companions 54060 Chambermaids and room attendants 54090 Other specified servants 55100 Caretakers and janitors 55200 Charworkers 55230 Window cleaners 55240 Chimney sweeps 55250 Refuse collectors and removers 55290 Other charworkers, cleaners and related workers 56000 Washing and laundry services 57020 Hairdressers 57030 Barbers 57040 Bath attendants 57090 Other barbers, hairdressers and related workers 58100 Firefighters 58210 Policemen and detectives, employer unknown 58220 Policemen and detectives, public service 58230 Policemen and detectives, private service 58240 Sheriffs and their deputies 58290 Other law enforcement officers 58300 Watchmen and guards 58410 Military, rank unknown 58420 Military officers 58430 Other members of the armed forces 58500 Prison keepers and guards 58900 Other protective service workers 59100 Guides 59200 Undertakers and embalmers 59300 Prostitutes 59920 Bookmakers (sport) 59930 Croupiers 59940 Nursing aids and attendents 59950 Errand boys and errand girls 59960 Practical aid (veterinary) 59990 Other service workers nec 61110 General farmers and farmers nfs 61115 Husbandmen or cottars 61117 Female farmer 61120 Major landowner 61220 Field crop farmers 61230 Orchardists and fruit farmers 61240 Market gardeners 61250 Nurserymen and florists 61260 Livestock farmers 61270 Dairy farmers 61280 Poultry farmers 61290 Other specialised farmers 61320 Farmer and fisherman 61330 Cottar and fisherman 61400 Farm managers, foremen and supervisors nfs 62110 Farm workers, specialisation unknown 62113 Farmer's sons and other male relatives 62200 Farm workers (field crop and vegetables) 62300 Farm labourers, orchard and fruit farm workers 62410 Livestock workers, general or nfs 62420 Cattle workers, except specified dairy 62430 Sheep workers 62440 Pig workers 62450 Fur-bearing animal workers 62460 Horse workers 62461 Grooms and horse keepers (domestic) 62463 Grooms and horse keepers (not domestic) 62470 Horse breaker 62490 Livestock workers, nec 62500 Farm workers (dairy) 62600 Farm workers (poultry) 62710 Gardeners nfs 62711 Gardener (domestic) 62713 Gardener (not domestic) 62720 Market garden labourers 62730 Nursery labourers 62740 Landscape gardeners 62790 Other nursery workers and gardeners 62800 Farm machinery operators 62920 Other animal workers e.g., dogs, elephants 62930 Other plant workers e.g., gathers of herbs etc. 62940 Groundskeepers, etc. 62990 Others in agricultural or husbandry 63110 Woodsmen and workers in the woods, nfs 63120 Lumbermen, loggers and kindred workers 63130 River drivers 63140 Wood cutters and choppers 63190 Others in logging and wood cutting 63220 Foresters and wood wardens 63230 Forestry workers 63240 Timber cruisers 63250 Forest fire-fighters 63290 Other forestry workers 64100 Fishermen 64920 Fish farm workers 64930 Oyster farm workers 64940 Whale hunters 64950 Whale or seal hunters 64960 Seal hunters 64970 Trappers or hunters 64990 Other fishermen, hunters and related workers 71120 Miners 71130 Quarrymen 71190 Others working in mines and quarries 71200 Mineral or stone treaters 71300 Well drillers, borers and related workers 72000 Metal workers, specialisation unknown 72100 Metal smelter and furnacemen 72200 Metal rollers 72300 Metal melters and reheaters, and workers in metal melting mills 72400 Metal casters and workers in metal casting plants 72500 Metal moulders and coremakers 72600 Metal annealers, temperers and hardeners 72700 Metal drawers and workers in metal drawing 72800 Metal platers and coaters 72900 Other (specialised) metal workers 73100 Wood treaters 73200 Sawyers and other titled wood/sawmill operatives 73300 Papermill machine operators and paper makers 74000 Chemical workers, product nfs 74100 Drug workers 74200 Gunpowder and explosive makers 74320 Charcoal burners 74330 Coal gas makers 74340 Tar makers 74390 Other coal product makers e.g., coke 74420 Salt makers 74430 Alkali and soda makers 74500 Oil mill workers 74620 Paint and varnish makers 74630 Dye makers 74640 Ink, blacking, colouring, etc., makers 74720 Tallow chandlers, candle makers and grease makers 74730 Soap and perfume makers 74740 Glue, size and gelatine makers 74750 Wax and polish makers 74920 Fertilizer and manure makers 74930 Starch makers 74940 Turpentine makers 74990 Specified chemical workers nec 75000 Textile workers, specialisation unknown 75100 Fibre preparers 75200 Spinners, doublers, twisters and winders 75300 Weaving and knitting-machine setters and pattern-card preparers 75400 Weavers 75500 Knitters 75600 Bleachers, dyer or textile product finisher 75700 Rope makers 75920 Net maker 75990 Other specialised textile workers nec 76110 Tanners or fellmongers, specialisation unknown 76120 Fellmongers 76130 Tanners 76140 Leather curriers and finishers 76190 Other specified leather curers 76200 Fur and pelt dressers 77100 Millers and related workers 77200 Sugar refiners 77310 Butchers 77320 Pork butchers 77340 Slaughterer, meat cutters or meat packer 77350 Sausage makers 77360 Fish butchers 77380 Meat cannery workers and other meat preservers (not sausages) 77390 Other workers in meat preparation 77400 Cannery workers and other food preservers 77500 Butter, cheese and dairy product makers 77610 Baker and confectioner 77620 Bakers 77630 Confectioners and pastry makers 77640 Candy makers 77650 Chocolate makers 77690 Other baked goods makers 77700 Tea, coffee and cocoa preparers 77820 Brewers 77830 Maltsters 77840 Wine workers 77850 Vinegar makers and workers 77860 Distillers 77870 Makers of soft drinks 77880 Bottlers 77890 Other drinks makers and workers 77990 Other food and beverage processors 78100 Tobacco preparers and tobacco factory workers 78200 Cigar makers 78300 Cigarette makers 78400 Other tobacco product makers 79120 Tailors and tailoresses 79130 Dressmakers 79190 Other garment makers 79200 Furriers and fur workers 79320 Milliners 79330 Hat makers 79340 Straw hat makers 79390 Other hat makers 79400 Glove makers and related workers 79520 Seamstresses 79530 Sewers and sewing machine operators 79540 Embroiderers 79590 Others hand sewers 79620 Upholsterers (except vehicle) 79630 Vehicle trimmers and upholsterers 79640 Mattress makers 79690 Others working in upholstery jobs 79920 Sail, tent and awning makers 79930 Umbrella makers 79940 Artificial flower makers 79990 Other cloth and related product manufacturing workers 80100 Boot and shoe makers and repairers 80200 Specialised occupations in the boot and shoe making industry 80320 Saddlers and harness makers 80330 Trunk or bag maker 80390 Other leather goods makers 81120 Cabinet makers 81130 Chair makers 81140 Wicker furniture makers 81190 Other furniture makers 81210 Woodworkers 81220 Sawyers, woodworking 81230 Wood turners and handle makers 81240 Wood planers 81250 Wood carvers 81290 Other woodworking machine operators 81300 Coach, carriage and wagon makers 81400 Wheelwrights and cartwrights 81500 Coopers, hoop makers and benders 81600 Clog makers 81710 Box makers nfs 81720 Wooden box makers 81730 Paper box makers 81790 Box makers nec 81920 Wooden tool makers 81930 Ski makers 81990 Other makers of wooden products 82000 Stone carvers or cutters and stone yard workers 83120 Blacksmiths 83130 Hammersmiths 83140 Forgemen 83150 Farriers or horseshoers 83160 Specialised makers of forged metal products 83190 Other blacksmiths, hammermen and forgeing-press operators 83210 Tool makers nfs 83220 Tool and die makers 83230 Pattern makers nfs and nec 83290 Other tool makers and metal markers 83310 Metalworking machine operators 83320 Metal turners 83390 Other metal working machine operators 83400 Machinists 83520 Metal polishers and finishers 83530 Metal grinders and sharpeners 83540 Cutlers and cutting instrument makers 83550 Cutler (knives, forks, spoon) 83590 Other metal grinders, polishers and tool sharpeners nec 83600 Gunsmiths 83700 Locksmiths 83820 Saw makers 83830 Saw, spade, shovel and half wood/half metal agricultural instrument type things 83840 Chain makers 83850 Nailer and nail makers 83860 Wire makers 83890 Other metal workers nec 84110 Mechanics 84120 Millwrights 84130 Machine makers, builders and fitters 84140 Engine and locomotive builders 84150 Sewing machine makers and builders 84190 Other machinery fitters and machine assemblers 84220 Watch and clock makers 84230 Optical instrument makers and workers (not opticians) 84290 Other specialized instrument makers (not musical instrument makers) 84300 Bicycle makers 84400 Motor vehicle manufacturing and repair workers, specialization unknown 84460 Automobile manufacturing workers 84900 Others in machinery 85100 Electrical fitters and electrical equipment assemblers 85500 Electricians and wiremen 85600 Telephone and telegraph installers 85700 Linesmen: telephone, telegraph and electric 85920 Electrical products inspector or tester 85990 Other electrical fitters and related electrical workers 87120 Plumbers 87130 Pipe fitters 87220 Welders 87230 Braziers 87250 Flamecutter 87290 Other welders, braziers, solderers 87320 Sheet metal worker, general 87330 Coppersmith or copperware maker 87340 Tinsmith, tinner or tinker 87350 Boilermaker 87390 Other sheet metal workers 87420 Structural metal workers 87430 Rivetters nec 87490 Other structural metal preparers and erectors 87520 Ship and boat builders 87530 Shipwrights and ship joiners 87540 Ship's carpenters 87550 Block, mast and tackle makers 87560 Caulker 87590 Other ship builders 88020 Jewellers 88030 Goldsmith 88040 Silversmith 88050 Working with gems and stones 88090 Other jewellery and precious metal workers 89120 Glass makers 89130 Glass bottle makers 89190 Other specific glass makers 89200 Pottery and porcelain makers and workers 89300 Brick and tile makers 89400 Glass engravers, etchers and finishers 89500 Pottery and porcelain painters 89620 Cement makers 89630 Lime burners 89700 Clay-based products makers 90100 Rubber product makers 90200 Tire makers and vulcanisers 91020 Paper and paperboard products makers, except boxes 91090 Other paper product makers 91100 Paper and paper board product makers 92000 Printers nec 92110 Printers nfs 92120 Compositors 92190 Other compositors and typesetters 92200 Lithographic printers 92300 Stereotypers and electrotypers 92400 Lithographers 92500 Photo-engravers 92600 Bookbinders and related workers 92800 Textile printers 92900 Other printers and related workers 93110 Painters, nfs 93120 Painters, construction 93130 Painters, ship 93190 Other specialised painters nec 93200 Decorators 93320 Lacquerers and enamellers 93330 Japanners 93400 Gilders 94110 Musical instrument maker nfs 94120 Piano maker 94130 Piano tuner 94140 Organ builder 94190 Other musical instrument makers and tuner 94220 Basket makers 94230 Brush makers 94240 Broom makers 94290 Bristle and straw workers nec 94300 Non-metallic mineral product makers 94910 Other production or related worker, specialisation unknown 94920 Animal stuffers, taxidermists and shell workers 94930 Linoleum makers and floor cloth makers 94940 Toy and doll makers 94990 Production and related workers nec 95110 Mason nfs or combined 95120 Bricklayers 95130 Stone masons 95140 Plasterers 95150 Paviours 95160 Marble mason 95190 Others in construction 95200 Cement masons and finishers 95310 Roofer, specialisation unknown 95320 Slate and tile roofers 95330 Thatchers 95390 Other roofers 95410 Carpenter and joiner 95420 Carpenters 95430 Joiners 95440 Scaffolders 95490 Other wooden construction workers 95600 Pipe coverers and insulators 95700 Glaziers 95910 Construction workers nec 95920 Paperhangers 95930 Carpet planners 95940 Whitewashers 95950 Demolition workers 95960 Marblers 95990 Others in building construction 96100 Electricity generators 96220 Engineers (US) nfs 96230 Stationary engineers and engine men 96240 Stationary firemen and firemen nfs 96900 Other stationary engine and related equipment operators 97120 Ship boat loaders and dock workers 97130 Railway freight handlers 97140 Porters 97150 Packers 97190 Other freight handlers 97210 Riggers nec or nfs 97220 Ship riggers 97300 Crane and hoist operators 97410 Navvy, excavator and diggers, nfs 97420 Road builders, workers and labourers 97430 Railway builders, workers and labourers 97440 Waterway and harbour builders, workers and labourers 97490 Other road, railway and related construction labourers 97900 Material handling equipment operators nec 98120 Seamen 98130 Boatmen and canalmen 98190 Other crew of boats, ships, etc. 98200 Ship's engine men 98300 Railway/railroad locomotive operators 98320 Railway/railroad engineers and enginemen 98330 Railway/railroad firemen and stokers 98420 Railway brakemen 98430 Railway signallers 98440 Railway switchmen and shunters 98490 Others skilled railway workers 98510 Drivers, nec 98520 Drivers (private) 98530 Drivers (personal, for hire) 98540 Drivers (public) 98550 Delivery men and drivers of goods 98560 Drivers (general haulage) 98562 Carter (agricultural) 98570 Teamsters (inc. mine driver, etc.) 98590 Others drivers 98700 Lock and gate keepers 98720 Lock keepers 98730 Gate keepers 98900 Other transport equipment operators 99120 Labourers nfs 99130 Common labourers or general labourers 99140 Day labourers (e.g., journalier) 99150 Worker nfs 99200 Factory labourers (unspecified) 99300 Occupational title unclassifiable 99420 Assistants nfs 99430 Helpers nfs 99440 Helpers of relative or helping at home 99450 Apprentices nfs 99500 Ambiguous responses 99998 Alternative occupation (Sweden) 99999 No occupation/unknown OCCHISCO is a uniformly coded classification of occupations that is consistent across all countries. It is the most complicated variable to code consistently in the pre-1950 samples, and the present documentation does not fully cover the issues addressed. We will be progressively adding to the level of detail available on this variable. For example, we will occasionally provide examples of common responses below each code so that users will be able to see what responses were coded where. Users with queries about the coding of specific occupations should contact the IPUMS staff in the first instance. Users interested in variation in occupational responses below the level of detail provided in the codes (e.g., distinguishing "caretakers" from "janitors") should look at the OCCSTRNG variable. The codes for OCC were adapted from the Historical International Standard Classification of Occupations (HISCO) coding scheme. More information on HISCO is available in M.H.D. van Leeuwen, I. Maas and A. Miles. (2002) HISCO: Historical International Standard Classification of Occupations. Leuven: Leuven University Press. HISCO in turn is based on the International Standard Classification of Occupations from 1968, commonly known as ISCO-68. Information on HISCO can also be located at the HISCO database of occupational titles, and the International Institute of Social History. More information on the adaptations IPUMS made to HISCO can be found in the article "Occupational Classification in the North Atlantic Population Project" (2003), Historical Methods, Vol. 36, No. 2 (Part 2), pp.89-96. Structure of the classification scheme The classification scheme is hierarchical, in the sense that each digit in the 5 digit codes introduces a new level of detail. Codes sharing the same first 1, 2 or 3 digits are considered to be increasingly similar. For example, all people working in agriculture have the first digit 6. The first digit of a code indicates the "Major group" a person's occupation is in. The second digit indicates a "Minor group" distinction. Continuing the previous example, people who have the first two digits "61" are farmers - who may specify what they are cultivating or tending - and farm managers. Thus, as well as sharing the characteristic of working in agriculture (6) they also share the characteristic of being owners or managers (61). The first 3 digits denote the "Unit group" of an occupation. At the third digit level, we introduce more detail. For example, the unit group "612" indicates "Specialized farmers". Within this unit group, 4th and 5th digit distinctions known as "titles" or "headings" are made. For example, "61220" indicates "Field crop farmers," and "61230" indicates "Orchardists and fruit farmers." Understanding headings In general, if the last two digits of a code are "00" the heading is reserved for general titles. For example, in unit group "721" for "Metal smelter and furnace workers", the heading "72100" is reserved for responses such as "Metal smelter" and "Furnace men." If the last two digits of a code are "10" the heading is reserved for "not further specified" titles (often abbreviated "n.f.s." or "nfs" in syntax files), and codes ending in "20", "30" and higher multiples of 10 are reserved for responses with more detail on some aspect of the occupation. For example, "58210" is the code for "Policemen and detectives, employer unknown," whereas "58220" and "58230" are the codes for "Policemen and detectives, public service," and "Policemen and detectives, private service" respectively. Headings ending in digits other than "0" (e.g., 2, 3, 4, 9) are generally reserved for frequent responses specific to a particular country that probably belong with responses sharing the same first four digits of the heading. For example, "61115" ("Husbandman or cottar") in Norway and Sweden and "61117" ("Female farmer") in Canada could be classified with "Other general farmers" ("61110"), but occurred frequently enough that we felt they should be given a separate code for easier identification. If the last two digits of a code are "90", the heading is reserved for "not elsewhere classified" responses. For example, "58290" is the code for "Other law enforcement officers". The difference between "n.f.s." and "n.e.c." responses is that "n.f.s." responses are quite general, and may not offer much detail on the tasks and duties of the job. Conversely, "n.e.c." responses are typically quite detailed in the information provided, but there are not enough similar responses to justify a separate heading. Alterations to HISCO Compared to HISCO we have reduced the overall number of headings, while still introducing new ones, and retaining more detail from vaguely specified occupations. In general, we made the fewest changes to the structure of the HISCO codes in major groups 0 and 1 (professional workers). We re-organized the codes within major groups 2 (administrative and managerial workers), 3 (clerical workers), 4 (sales workers), 5 (service workers), and 6 (workers in agriculture). We made the most substantial revisions in moving people between major groups in the manufacturing and transport major groups (7, 8, and 9). In particular, we created codes for vague responses of the form "Works in [specified type of] factory," such as "Works in cotton mill." We grouped these workers with "Laborers" and titled, skilled workers in the same general industry. We eliminated codes where HISCO made distinctions that were not consistently made in nineteenth century census data. For example, HISCO distinguished between hand and machine spinners. We found that most spinners did not specify whether they were spinning by hand or machine, more often giving information on what they were spinning (e.g., cotton, wool, silk), or a firm's name. We introduced codes to handle vague occupations, as discussed above. We also created codes to retain linguistic and nominal distinctions above a rough frequency level. These distinctions are generally made in the fourth and fifth digit of the codes. For example, we distinguish between "Farm workers, specialisation unknown" (62110) and "Farmer's sons and other male relatives" (62113), because "farmer's son" was a numerically significant response in Canada. Both are considered part of the "Farm labourers and helpers, general farming and n.f.s" unit group (621). Other variables Responses to the occupational questions also returned information that cannot be classified within an occupational classification scheme, such as relationship to workers, or indications of status. Please see the documentation for the variables OCRELATE and OCSTATUS. Information on the products sold by retail workers (Major Group 4) is available in the variable PRODUCT. Occupations were also coded into domestic occupational classification schemes in Great Britain, Norway, and the United States. See the documentation for the following variables for more information. Norway OC1HIRNO OC1TRDNO OC2HIRNO OC2TRDNO United Kingdom OCCICEM United States OCC95US IPUMS Status information in occupation string 00 None 11 Retired 12 Unemployed 13 Pauper 14 Owner 15 Leaseholder 21 Master 22 Journeyman 23 Apprentice, learner 24 Artisan 31 Principal 32 Worker or works in 33 Labourer 34 Assistant or helper 40 Attending school or college 51 Nobility 52 Prestige 99 No occupation, unknown OCSTATUS preserves inconsistently available information in responses to occupational questions. It is similar to the HISCO subsidiary coding scheme "STATUS". The purpose of OCSTATUS is to preserve information about the social status of an occupation. Correct usage of OCSTATUS is critical to proper interpretation of the occupation codes. In modifying the HISCO system we faced the challenge of classifying industrial workers who often gave information on the industry they were employed in and less specific information on the tasks and functions they performed. We have grouped people of varying statuses in similar occupational codes. To differentiate between skilled workers, people who "worked in" a particular industry (skill level indeterminate), and laborers, you must use OCSTATUS. OCSTATUS has been applied to all occupations. Thus, a person indicating that they are an "Assistant Superintendent" receives the occupational code for Superintendent (22xxx), and the OCSTATUS code 34 for assistant. Changes from the HISCO STATUS codes We have changed the HISCO STATUS codes for ownership to 11Retired 12Unemployed 13Pauper 14Owner 15Leaseholder Changes from the HISCO RELATION codes We have removed the "temporal relationships" category from the OCRELATE codes and moved them to OCSTATUS. We felt that "former or retired" and "future" relationships to work were better described as status designators. Future relationships to an occupation were often people describing themselves as students or apprentices in specified professions and trades. IPUMS Surname similarity 001 1st surname in household 002 2 003 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 SURSIM assigns the same code to all persons within each household who had the same surname. Persons coded 1, including the household head, had the same surname as the household head. Other surnames are assigned codes in the order in which they appear within the household on the census form. Persons within a household who shared a surname will have the same code for this variable. IPUMS Last name NAMELAST is an alphabetic variable giving a person's full last name. It will primarily be of use to researchers linking the IPUMS historical census data to other sources of demographic data, or researchers whose research topic involves naming patterns. Because of the length of this variable (32 columns), and the size of the dataset, other researchers should not select this variable. NAMELAST is used to produce the variable SURSIM which assigns the same code to all persons within each household who had a shared surname. This is a 32-digit numeric variable with 0 implied decimal places IPUMS First name NAMEFRST is an alphabetic variable giving a person's full first name. It will primarily be of use to researchers linking the historical census data to other sources of demographic data, or researchers whose research topic involves naming patterns. Because of the length of this variable (32 columns), and the size of the dataset other researchers should not select this variable. This is a 32-digit numeric variable with 0 implied decimal places IPUMS Person number (within household) Iceland 1729: All persons 000 Household record 001 1 002 2 003 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 99 100 100 101 101 102 102 103 103 104 104 105 105 106 106 107 107 108 108 109 109 110 110 111 111 112 112 113 113 114 114 115 115 116 116 117 117 118 118 119 119 120 120 121 121 122 122 123 123 124 124 125 125 126 126 127 127 128 128 129 129 130 130 131 131 132 132 133 133 134 134 135 135 136 136 137 137 138 138 139 139 140 140 141 141 142 142 143 143 144 144 145 145 146 146 147 147 148 148 149 149 150 150 151 151 152 152 153 153 154 154 155 155 156 156 157 157 158 158 159 159 This constructed variable indicates the person number within the household. IPUMS Sequence Iceland 1729: All persons This variable indicates a person's sequence number. This is a 4-digit numeric variable with 0 implied decimal places IPUMS Page number in the original source Iceland 1729: All persons 563 563 564 564 565 565 566 566 567 567 568 568 569 569 570 570 571 571 572 572 573 573 574 574 575 575 576 576 577 577 578 578 579 579 580 580 581 581 582 582 583 583 584 584 585 585 586 586 587 587 588 588 589 589 590 590 591 591 592 592 593 593 594 594 595 595 596 596 597 597 598 598 599 599 600 600 601 601 602 602 603 603 604 604 605 605 606 606 607 607 608 608 609 609 610 610 611 611 612 612 613 613 614 614 615 615 616 616 617 617 618 618 619 619 620 620 621 621 622 622 623 623 624 624 625 625 626 626 627 627 628 628 629 629 630 630 631 631 632 632 633 633 634 634 635 635 636 636 637 637 638 638 639 639 640 640 641 641 642 642 643 643 This variable indicates the page number in the original source document. IPUMS Full name Iceland 1729: All persons This variable indicates a person's full name. This is a 31-digit numeric variable with 0 implied decimal places IPUMS First name Iceland 1729: All persons This variable indicates a person's first name. This is a 20-digit numeric variable with 0 implied decimal places IPUMS Last name Iceland 1729: All persons This variable indicates a person's last name. This is a 18-digit numeric variable with 0 implied decimal places IPUMS Occupational status as transcribed Iceland 1729: All persons This variable indicates a person's occupational status (string). This is a 109-digit numeric variable with 0 implied decimal places IPUMS Relate, Iceland Iceland 1729: All persons 010 Head of household 020 Spouse 030 Cohabiting partner of head (not married to head of household) 031 Cohabiting partner with child 034 Child of cohabiting partner (stepchild) 040 Child 041 Child with family 042 Spouse of child (child-in-law) 044 Child of child (grandchild) 070 Grandchild 080 Foster-child 090 Parent/stepparent/parent-in-law 091 Parent/parent-in-law with family 092 Spouse of parent/parent-in-law 094 Child of parent/parent-in-law i.e. sibling or sibling-in-law to head of household 100 Other relative 101 Relative with family 102 Spouse of relative 104 Child or stepchild of relative and or partner 110 Pauper 111 Pauper with family 112 Spouse of pauper 120 Young persons working in the household (non-relatives) Léttadrengur or stúlka, smali, matvinnungur 130 Household servant 131 Household servant with family 132 Spouse of household servant 134 Child or stepchild of servant 140 Boarder or lodger 150 Other (includes unknown) 151 Other with family 152 Spouse of other 154 Child or stepchild of other 999 Unknown This variable indicates a person's relationship to the household head using classification codes provided by the University of Iceland. IPUMS Relate, NAPP Iceland 1729: All persons 0101 Head or householder 0201 Spouse 0202 Second or third wife (polygamous) 0301 Child 0302 Adopted Child 0303 Stepchild 0306 Fostered child 0401 Child-in-law 0501 Parent 0601 Parent-in-law 0701 Sibling 0801 Sibling-in-Law 0901 Grandchild 1011 Grandparent 1012 Step grandparent 1021 Aunt or uncle 1031 Nephew, niece 1202 Boarders 1203 Lodgers 1208 Pauper staying with others at expense of municipality 1211 Servant 1212 Housekeeper 1215 Nurse 1219 Relative of employee 9996 Unclassifiable 9998 Missing 9999 Unknown This variable indicates a person's relationship to the household head (using NAPP codes). IPUMS Age Iceland 1729: All persons 000 001 1 002 2 003 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 95 098 98 099 99 100 100 101 101 999 Unknown This variable indicates a person's age. IPUMS Imputation rule for age Iceland 1729: All persons 1 Explicit age in other sources (most commonly 1703 census) 2 Explicit information not given, age obtained indirectly 9 Unknown This variable indicates the imputation rule used for determining a person's age. IPUMS Marital status Iceland 1729: All persons 1 Unmarried 3 Married 4 Widowed 9 Unknown This variable indicates a person's marital status. IPUMS Imputation rule for marital status Iceland 1729: All persons 01 Heads of household with own children [married (if with a spouse), widowed (if without a spouse)] 02 Parents to heads of household widowed or married depending on presence of spouse 03 Heads of household without children at home and 50 years and older married or widowed 05 Given to head in 1729 if all household members are absent 06 If other than heads of household have own children in the household it is assumed that they are married (if both parents present), widowed if only one 07 If individuals are listed in census as if they were married (position 1 and 2) and only children of one of them is present it is assumed that they are married 09 Needs further checking 10 Relationship to head of household imputed (see documentation) 99 Unknown This variable indicates the imputation rule for a person's marital status. IPUMS Occupation, HISCO Iceland 1729: All persons 14120 Ministers of religion 20100 Legislative officials and heads of government 54010 Servants nfs 54020 House servants nfs and maids 54040 Nursemaids 61110 General farmers and farmers nfs 61115 Husbandmen or cottars 61330 Cottar and fisherman 61400 Farm managers, formen and supervisors nfs 99150 Worker nfs 99430 Helpers nfs 99999 No occupation/unknown This variable indicates a person's occupation (using HISCO codes). IPUMS Occupational status Iceland 1729: All persons 11 Retired 13 Pauper 40 Attending school or college 99 Unknown This variable indicates a person's occupational status. IPUMS Identity number of individual used by decode Iceland 1729: All persons This variable indicates a person's identification code. This is a 8-digit numeric variable with 0 implied decimal places IPUMS Identity number of father Iceland 1729: All persons This variable indicates the identification number of a person's father. This is a 6-digit numeric variable with 0 implied decimal places IPUMS Identity number of mother Iceland 1729: All persons This variable indicates the identification number of a person's mother. This is a 6-digit numeric variable with 0 implied decimal places IPUMS Sex Iceland 1729: All persons 1 Male 2 Female 9 Unknown This variable indicates the person's sex. IPUMS