DDI_DEU_1981_PHC_v01_M_v7.5_A_IPUMS The 1981 Census of Population, Occupation, Dwellings and Buildings of the German Democratic Republic IPUMS May 20, 2024 MetadataEditor Version 7.5 October 2024. NEW FEATURES. --Historical data from NAPP project now available from IPUMS-International. --Historical census data from Canada, Denmark, the United Kingdom, Germany, Iceland, Norway, Sweden, and the United States for the period 1703 to 1911 are now available from IPUMS-International. The complete count and sample datasets were previously disseminated by the North Atlantic Population Project (NAPP). Where possible, the data have been integrated into existing IPUMS-International variable coding schema. Some new variables have been created that are available only for these pre-1960 datasets. NAPP data users should note that many NAPP variables are available from IPUMS-International by different names. For a complete list of NAPP variables that have been renamed in IPUMS-Interational, refer to the crosswalk. --Individual country shapefiles for the third-level administrative level of geography are now available for a few IPUMS samples. --New spatially harmonized previous-residence variables at the second administrative level of geography are available for several samples in this data release. More information is available here. Users should note that many older migration variables are available by different names. Refer to this table for a crosswalk of old and corresponding new migration variables. --IPUMS now hosts the Census Mosaic data collection. Census Mosaic identifies, gathers, harmonizes, and distributes surviving historical census microdata from regions of Continental Europe where complete centralized records are not available. The Mosaic project was founded by a consortium of historical social scientists in Europe. Data can be downloaded as static files from the Census Mosaic website. Although the data are not yet integrated fully into IPUMS International, variables have been standardized and harmonized to be roughly compatible with IPUMS coding structures. NEW SAMPLES. --Full-count datasets for Great Britain 1851, 1861, 1871 (Scotland only), 1891, and 1901. --Full-count dataset for Sweden 1910. Denmark (1845, 1880, and 1885) --Labor force surveys from Spain and eight new labor force surveys from Italy added to the series. Newly added countries: Benin, Cote d'Ivoire, Finland, Guatemala, Honduras, Laos, Lesotho, Mauritius, Myanmar, Papua New Guinea, Russia, Slovak Republic, Suriname, Togo, and Zimbabwe New samples for: Bolivia, Cambodia, Cambodia, Chile, Cuba, Cote d'Ivoire, Egypt (1848 and 1868, historical samples), Fiji, Guinea, Ireland, Israel, Italy, Lao PDR, Mexico, Morocco, Nepal, Netherlands, Palestine, Peru, Philippines, Puerto Rico, Rwanda, Senegal, Sierra Leone, South Africa, Switzerland, Uganda, United States, United Kingdom, United States, Vietnam, and Zimbabwe SUPPLEMENTAL DATA. Data from censuses from Benin and Lesotho that record individual fertility and/or mortality events were made available in IPUMS-International. These files can be downloaded and linked to data produced by the extract system. NEW VARIABLES. --IPUMS-International now provides harmonized and year-specific geography variables for all countries including 13 new samples from Dominican Republic, Germany, Indonesia, Israel, Malaysia, Mongolia, Nicaragua, Nigeria, Palestine, Paraguay, Thailand, United Kingdom, and Uruguay. First-level and second-level year specific geography variables are also available for all countries. IPUMS provides corresponding, downloadable GIS boundary files for all harmonized and year specific geography variables. More information about IPUMS geography variables is available here. --IPUMS International now provides spatially harmonized previous-residence variables at the first administrative level of geography. The codes for the spatially harmonized previous-residence variables match the spatially harmonized place of current residence. More information is available here. --IPUMS International provides spatially harmonized previous-residence variables at the first administrative level of geography for all samples; previously available country-specific migration variables at the first administrative level that were not fully harmonized spatially have been phased out. Spatially harmonized previous-residence variables at the second administrative level of geography are available for selected samples. More information is available here. Users should note that many older migration variables are available by different names. Refer to this table for a crosswalk of old and corresponding new migration variables. --IPUMS International now provides spatially harmonized previous-residence variables at the first administrative level of geography for all samples. Spatially harmonized previous-residence variables at the second administrative level of geography are available for several samples in this data release. More information is available here. Users should note that many older migration variables are available by different names. Refer to this table for a crosswalk of old and corresponding new migration variables. --Lower (third) level geography codes and GIS files have been added for Bangladesh, China, Ethiopia, Mali, Rwanda, and Zimbabwe. Some geography codes and labels might have changed for these countries to accommodate the newer lower level geography. --Added more detailed 3-digit industry and occupation variables for China 2000. EDITED SAMPLES. --Revised full-count data for Great Britain 1881 --Revised full-count datasets for Sweden 1890 and 1900. The revision includes the following changes that improve comparability across Sweden datasets: --Revisions to certain ethnicity and work variables (and the underlying source data): ORIGIN, LABFORCE, OCCHISCO, OCRELATE, OCSTATUS. --Revisions to unharmonized source variables: SE1890A_HISCOSE, SE1890A_HISCRELSE, SE1890A_HISCSTATSE, SE1890A_OCCMULTISE, SE1900A_HISCOSE, SE1900A_HISCRELSE, SE1900A_HISCSTATSE, SE1900A_OCCMULTISE. --A new United States 1850 full-count dataset now matches the corresponding dataset distributed by the USA IPUMS data project. The source variable US1850A_0502 (HISTID) provides a linking key to match person records to the USA version of the data. The IPUMS International version of the data contains names, which the USA version cannot distribute. EDITED VARIABLES. An error affecting HHWT for South Africa 2007 was corrected. The existing values were adjusted by a factor of 0.01. AGEMARR was edited to add data for Hungary 1980 and 1990. Harmonized and year-specific geography variables for Brazil and Colombia have been edited to accommodate for the availability of refined municipal boundaries. Users should be aware that codes and labels have changed in all harmonized and year specific geography varaibles for these two countries. Errors affecting BPLSE2 (formerly BPLPARSE) for Sweden 1890 and the underlying source variable were corrected. Several thousand cases were incorrectly coded as 258101000. These cases have been updated with the correct code: 258171000. Harmonized geography variables for Italy, Philippines, Rwanda, and United States have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year specific geography varaibles for these countries. More information about IPUMS geography variables is available here. The codes for the source variable RW2002A_0419 were corrected to include 0 and 8 as possible responses, which were previously identified as 'unknown years' within primary education. Errors affecting EDUCFJ for Fiji 2006 were corrected. A problem with PERWT for Tanzania 2012 was corrected. The previous weights were adjusted to properly reflect population totals. MOMLOC, POPLOC, and PARRULE were updated for the United States 2010 and 2015 samples to include additional information on subfamilies. Prior to this correction, persons above age 17 were not receiving links to their co-resident mothers and fathers. An error affecting codes for the URBAN variable in Egypt 1986 for Cairo, Alexandria, Port-Said, and Suez was corrected. An error in INCEARN affecting Venezuela 2001 was corrected. Earned income in the source variable VE2001A_0440 is interpreted as a monthly amount, thus adjustments previously applied to convert data from daily or weekly income were supressed. All the six Brazil samples in IPUMS International were replaced with higher density samples. An edited version of the Chile 2017 sample was introduced to correct an error in household breaks. Errors affecting codes for GEO1_ZA in South Africa 2011 and ENUTS1 in United Kingdom 1991 were corrected. Harmonized geography variables for Cambodia, Fiji, and Nepal have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography varaibles for these countries. More information about IPUMS geography variables is available here. An error in PERWT affecting Nepal 2001 was corrected. Errors affecting a code in GQ for Brazil 2010 and Indonesia 2010 were corrected. Both census samples now identify 1-person units created by splitting a large household. An error in MARRNUM affecting Indonesia 1976 was corrected. Some codes for GEO1_EG2006 and GEO2_EG2006 were edited. Harmonized geography variables for Bolivia, Cuba, Guinea, Ireland, Morocco, Palestine, Senegal, South Africa, and Uganda have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography variables for these countries. More information about IPUMS geography variables is available here. An error in INCEARN affecting Brazil 1980 was corrected. An error in EDATTAIN affecting Ireland 1971 and 1981 was corrected. A small proportion of person records in Mexico 1960 were re-classified in MIGRATEP based on information about their current and previous residence. These were previously coded to 'different major administrative unit', even though their place of residence suggests that their last move was within the same major administrative unit. The second-level technician (higher) degrees for Spain 1991, 2001, and 2011 were re-classified into post-secondary technical education in EDATTAIN. An error affecting codes for SEX for Egypt 1848 and 1868 was corrected. The values for male and female had been reversed. A problem with HHWT and PERWT for Canada 2011 was corrected. The previous weights were adjusted to properly reflect population totals. Harmonized geography variables for Cambodia, Lao PDR, Mexico, Peru, Switzerland, Vietnam, Puerto Rico, United Kingdom, and United States have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography variables for these countries. More information about IPUMS geography variables is available here. Harmonized geography variables for Chile and Sierra Leone have been edited to accommodate new samples. Users should be aware that codes and labels have changed in all harmonized and year-specific geography variables for these countries. More information about IPUMS geography variables is available here. An error affecting codes for COMPUTER for Senegal 2013 was corrected. An error affecting labels available in IND for Peru 1993 was corrected. An error affecting codes for persons previously residing abroad for MIG1_5_BO in Bolivia 2001 and 2012 was corrected. EDUCAR, EDATTAIN, and YRSCHOOL were adjusted in the Argentina samples to incorporate information on completion of education levels in the data harmonization. HHWT and PERWT were calibrated in Kenya 1979 to properly reflect the population distribution by province. In GQ (group quarters status), persons residing in hospitals of all types were reclassified to 'institutional group quarters' from 'other group quarters,' making their treatment consistent with GQTYPE. Errors affecting codes for BPLBJ2 in Benin 1979, 1992, and 2002 were corrected. Errors affecting codes for GEO2_BR1970 in Brazil 1970 were corrected. The 1981 Census of Population, Occupation, Dwellings and Buildings of the German Democratic Republic - IPUMS Subset PHC Germany 1981 (East) (IPUMS Harmonized Subset) DEU_1981_PHC_v01_M_v7.5_A_IPUMS Central State Office for Statistics IPUMS (c) Copyright 1981, Central State Office for Statistics and Minnesota Population Center MetadataEditor Central State Office for Statistics Population and Housing Census [hh/popcen] IPUMS International DOI:10.18128/D020.V7.5 Version 7.5. The datasets contain selected variables from the original census microdata plus harmonized variables from the IPUMS-International database. Demographic Variables -- PERSON Geography: Global Variables -- HOUSEHOLD Dwelling Characteristics Variables -- HOUSEHOLD Fertility and Mortality Variables -- PERSON Work Variables -- PERSON Technical Household Variables -- HOUSEHOLD Education Variables -- PERSON Geography: F-N Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD Income Variables -- PERSON Constructed Family Interrelationship Variables -- PERSON Constructed Household Variables -- HOUSEHOLD Household Economic Variables -- HOUSEHOLD Technical Person Variables -- PERSON Utilities Variables -- HOUSEHOLD Other Household Variables -- HOUSEHOLD Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD Other Household Variables -- HOUSEHOLD Dwelling Characteristics Variables -- HOUSEHOLD Constructed Household Variables -- HOUSEHOLD Household Economic Variables -- HOUSEHOLD Technical Person Variables -- PERSON Constructed Family Interrelationship Variables -- PERSON Other Person Variables -- PERSON Demographic Variables -- PERSON Fertility and Mortality Variables -- PERSON Income Variables -- PERSON Work Variables -- PERSON Education Variables -- PERSON Work: Occupation Variables -- PERSON Work: Industry Variables -- PERSON Germany State Persons, households, and dwellings East Germany UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes UNIT DESCRIPTIONS: - Dwellings: A dwelling is defined as a group of connected rooms, which are built as living quarters, have a separate entrance directly from a stair well or directly from the outside, and have a kitchen or a kitchenette. - Households: A household is a group of persons who live and keep house together. A person living alone forms a household. Persons occupying several dwellings are assigned to one household in each dwelling. - Group quarters: Collective dwellings, for the purpose of the census, are hostels, establishments for the care of children and youth, for health services, or for social welfare to provide care and accommodations to persons who lived there for reasons of working, occupational training, studying or for special education and treatment. Persons who live permanently in the GDR, excluding members of foreign embassies and consular offices, and military and civil members of the Soviet Union military and their families Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Germany 1981 (East) Note: East Germany MICRODATA SOURCE: Central State Office for Statistics SAMPLE SIZE (person records): 4278563. SAMPLE DESIGN: 25% sample of households drawn by the Federal Statistical Office. Sample method unknown Face-to-face [f2f] There are 3 forms: (1) household list; (2) person list which contains information on all persons living in the household; and (3) housing list which contains information on the rooms of residence de jure, CENSUS DAY: December 31, 1981 Self-weighting (expansion factor=4) IPUMS International distributes integrated microdata of individuals and households only by agreement of collaborating national statistical offices and under the strictest of confidence. Before data may be distributed to an individual researcher, an electronic license agreement must be signed and approved. To gain access to the data, a researcher must agree to the following: (1) Implement security measures to prevent unauthorized access to census microdata. Under IPUMS International agreements with collaborating agencies, redistribution of the data to third parties is prohibited. (2) Use the microdata for the exclusive purposes of scholarly research and education. Researchers must explicitly agree to not use microdata acquired for any commercial or income-generating venture. (3) Maintain the confidentiality of persons, households, and other entities. Any attempt to ascertain the identity of persons or households from the microdata is prohibited. Alleging that a person or household has been identified is also prohibited. (4) Report all publications based on these data to IPUMS International, which will in turn pass the information on to the relevant national statistical agencies. Once a project is approved, a password is issued and data may be acquired through the Internet. Penalties for violating the license include: revocation of the license, recall of all microdata acquired, filing of a motion of censure to the appropriate professional organizations, and civil prosecution under the relevant national or international statutes. These safeguards mirror the principles from the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. Employees of the Minnesota Population Center who work with the census microdata to produce the harmonized database also sign agreements to respect the confidentiality of the data. IPUMS International works with each country's statistical office to minimize the risk of disclosure of respondent information. The details of the confidentiality protections vary across countries, but in all cases, names and detailed geographic information are suppressed and top-codes are imposed on variables such as income that might identify specific persons. In addition, IPUMS International uses a variety of technical procedures to enhance confidentiality protection. These include the following: (1) Swapping an undisclosed fraction of records from one administrative district to another to make positive identification of individuals impossible. (2) Randomizing the placement of households within districts to disguise the order in which individuals were enumerated or the data processed. (3) Aggregating codes of sensitive characteristics (e.g., grouping together very small ethnic categories) (4) Top- and bottom-coding continuous variables to prevent identification of extreme cases. The safety record for public-use census microdata is apparently perfect. In almost four decades of use, there has not been a single verified breach of statistical confidentiality. The measures implemented by the IPUMS International are designed to extend this record. Central State Office for Statistics 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: Germany, Central State Office for Statistics. The 1981 Census of Population, Occupation, Dwellings and Buildings of the German Democratic Republic 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. DEU1981_PHC-H-H Household records 1 79 DEU1981_PHC-P-H Person records 4278563 76 Record type H Household P Person RECTYPE identifies the type of record for the case: household or person. NOTE: RECTYPE is an alphabetic (character string) variable with a value of 'H' for household records and 'P' for person records. RECTYPE will not appear as a variable in the default rectangular extracts produced by the data extract system. It is only available in hierarchical extracts, to distinguish between the two record types. IPUMS Country 032 Argentina 051 Armenia 040 Austria 050 Bangladesh 112 Belarus 204 Benin 068 Bolivia 072 Botswana 076 Brazil 854 Burkina Faso 116 Cambodia 120 Cameroon 124 Canada 152 Chile 156 China 170 Colombia 188 Costa Rica 192 Cuba 208 Denmark 214 Dominican Republic 218 Ecuador 818 Egypt 222 El Salvador 231 Ethiopia 242 Fiji 246 Finland 250 France 276 Germany 288 Ghana 300 Greece 320 Guatemala 324 Guinea 332 Haiti 340 Honduras 348 Hungary 352 Iceland 356 India 360 Indonesia 364 Iran 368 Iraq 372 Ireland 376 Israel 380 Italy 384 Ivory Coast 388 Jamaica 400 Jordan 404 Kenya 417 Kyrgyz Republic 418 Laos 426 Lesotho 430 Liberia 454 Malawi 458 Malaysia 466 Mali 480 Mauritius 484 Mexico 496 Mongolia 504 Morocco 508 Mozambique 104 Myanmar 524 Nepal 528 Netherlands 558 Nicaragua 566 Nigeria 578 Norway 586 Pakistan 275 Palestine 591 Panama 598 Papua New Guinea 600 Paraguay 604 Peru 608 Philippines 616 Poland 620 Portugal 630 Puerto Rico 642 Romania 643 Russia 646 Rwanda 662 Saint Lucia 686 Senegal 694 Sierra Leone 703 Slovak Republic 705 Slovenia 710 South Africa 728 South Sudan 724 Spain 729 Sudan 740 Suriname 752 Sweden 756 Switzerland 834 Tanzania 764 Thailand 768 Togo 780 Trinidad and Tobago 792 Turkey 800 Uganda 804 Ukraine 826 United Kingdom 840 United States 858 Uruguay 862 Venezuela 704 Vietnam 894 Zambia 716 Zimbabwe COUNTRY gives the country from which the sample was drawn. The codes assigned to each country are those used by the UN Statistics Division and the ISO (International Organization for Standardization). IPUMS Year 1703 1703 1729 1729 1787 1787 1801 1801 1819 1819 1845 1845 1848 1848 1850 1850 1851 1851 1852 1852 1860 1860 1861 1861 1865 1865 1868 1868 1870 1870 1871 1871 1875 1875 1880 1880 1881 1881 1885 1885 1890 1890 1891 1891 1900 1900 1901 1901 1910 1910 1911 1911 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1966 1966 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 YEAR gives the year in which the census or survey was taken. For samples that span years, the midpoint or first year of the interval is reported. IPUMS IPUMS sample identifier 032197001 Argentina 1970 032198001 Argentina 1980 032199101 Argentina 1991 032200101 Argentina 2001 032201001 Argentina 2010 051200101 Armenia 2001 051201101 Armenia 2011 040197101 Austria 1971 040198101 Austria 1981 040199101 Austria 1991 040200101 Austria 2001 040201101 Austria 2011 050199101 Bangladesh 1991 050200101 Bangladesh 2001 050201101 Bangladesh 2011 112199901 Belarus 1999 112200901 Belarus 2009 204197901 Benin 1979 204199201 Benin 1992 204200201 Benin 2002 204201301 Benin 2013 068197601 Bolivia 1976 068199201 Bolivia 1992 068200101 Bolivia 2001 068201201 Bolivia 2012 072198101 Botswana 1981 072199101 Botswana 1991 072200101 Botswana 2001 072201101 Botswana 2011 076196001 Brazil 1960 076197001 Brazil 1970 076198001 Brazil 1980 076199101 Brazil 1991 076200001 Brazil 2000 076201001 Brazil 2010 854198501 Burkina Faso 1985 854199601 Burkina Faso 1996 854200601 Burkina Faso 2006 116199801 Cambodia 1998 116200401 Cambodia 2004 116200801 Cambodia 2008 116201301 Cambodia 2013 116201901 Cambodia 2019 120197601 Cameroon 1976 120198701 Cameroon 1987 120200501 Cameroon 2005 124185201 Canada 1852 124187101 Canada 1871 124188101 Canada 1881 124189101 Canada 1891 124190101 Canada 1901 124191101 Canada 1911 124197101 Canada 1971 124198101 Canada 1981 124199101 Canada 1991 124200101 Canada 2001 124201101 Canada 2011 152196001 Chile 1960 152197001 Chile 1970 152198201 Chile 1982 152199201 Chile 1992 152200201 Chile 2002 152201701 Chile 2017 156198201 China 1982 156199001 China 1990 156200001 China 2000 170196401 Colombia 1964 170197301 Colombia 1973 170198501 Colombia 1985 170199301 Colombia 1993 170200501 Colombia 2005 188196301 Costa Rica 1963 188197301 Costa Rica 1973 188198401 Costa Rica 1984 188200001 Costa Rica 2000 188201101 Costa Rica 2011 192200201 Cuba 2002 192201201 Cuba 2012 208178701 Denmark 1787 208180101 Denmark 1801 208184501 Denmark 1845 208188001 Denmark 1880 208188501 Denmark 1885 214196001 Dominican Republic 1960 214197001 Dominican Republic 1970 214198101 Dominican Republic 1981 214200201 Dominican Republic 2002 214201001 Dominican Republic 2010 218196201 Ecuador 1962 218197401 Ecuador 1974 218198201 Ecuador 1982 218199001 Ecuador 1990 218200101 Ecuador 2001 218201001 Ecuador 2010 818184801 Egypt 1848 818186801 Egypt 1868 818198601 Egypt 1986 818199601 Egypt 1996 818200601 Egypt 2006 222199201 El Salvador 1992 222200701 El Salvador 2007 231198401 Ethiopia 1984 231199401 Ethiopia 1994 231200701 Ethiopia 2007 242196601 Fiji 1966 242197601 Fiji 1976 242198601 Fiji 1986 242199601 Fiji 1996 242200701 Fiji 2007 242201401 Fiji 2014 246201001 Finland 2010 250196201 France 1962 250196801 France 1968 250197501 France 1975 250198201 France 1982 250199001 France 1990 250199901 France 1999 250200601 France 2006 250201101 France 2011 276181901 Germany 1819 (Mecklenburg) 276197001 Germany 1970 (West) 276197101 Germany 1971 (East) 276198101 Germany 1981 (East) 276198701 Germany 1987 (West) 288198401 Ghana 1984 288200001 Ghana 2000 288201001 Ghana 2010 300197101 Greece 1971 300198101 Greece 1981 300199101 Greece 1991 300200101 Greece 2001 300201101 Greece 2011 320196401 Guatemala 1964 320197301 Guatemala 1973 320198101 Guatemala 1981 320199401 Guatemala 1994 320200201 Guatemala 2002 324198301 Guinea 1983 324199601 Guinea 1996 324201401 Guinea 2014 332197101 Haiti 1971 332198201 Haiti 1982 332200301 Haiti 2003 340196101 Honduras 1961 340197401 Honduras 1974 340198801 Honduras 1988 340200101 Honduras 2001 348197001 Hungary 1970 348198001 Hungary 1980 348199001 Hungary 1990 348200101 Hungary 2001 348201101 Hungary 2011 352170301 Iceland 1703 352172901 Iceland 1729 352180101 Iceland 1801 352190101 Iceland 1901 352191001 Iceland 1910 356198341 India 1983 356198741 India 1987 356199341 India 1993 356199941 India 1999 356200441 India 2004 356200941 India 2009 360197101 Indonesia 1971 360197601 Indonesia 1976 360198001 Indonesia 1980 360198501 Indonesia 1985 360199001 Indonesia 1990 360199501 Indonesia 1995 360200001 Indonesia 2000 360200501 Indonesia 2005 360201001 Indonesia 2010 364200601 Iran 2006 364201101 Iran 2011 368199701 Iraq 1997 372190101 Ireland 1901 372191101 Ireland 1911 372197101 Ireland 1971 372197901 Ireland 1979 372198101 Ireland 1981 372198601 Ireland 1986 372199101 Ireland 1991 372199601 Ireland 1996 372200201 Ireland 2002 372200601 Ireland 2006 372201101 Ireland 2011 372201601 Ireland 2016 376197201 Israel 1972 376198301 Israel 1983 376199501 Israel 1995 376200801 Israel 2008 380200101 Italy 2001 380201101 Italy 2011 380201121 Italy 2011 Q1 LFS 380201221 Italy 2012 Q1 LFS 380201321 Italy 2013 Q1 LFS 380201421 Italy 2014 Q1 LFS 380201521 Italy 2015 Q1 LFS 380201621 Italy 2016 Q1 LFS 380201721 Italy 2017 Q1 LFS 380201821 Italy 2018 Q1 LFS 380201921 Italy 2019 Q1 LFS 380202021 Italy 2020 Q1 LFS 384198801 Ivory Coast 1988 384199801 Ivory Coast 1998 388198201 Jamaica 1982 388199101 Jamaica 1991 388200101 Jamaica 2001 400200401 Jordan 2004 404196901 Kenya 1969 404197901 Kenya 1979 404198901 Kenya 1989 404199901 Kenya 1999 404200901 Kenya 2009 417199901 Kyrgyz Republic 1999 417200901 Kyrgyz Republic 2009 418199501 Laos 1995 418200501 Laos 2005 418201501 Laos 2015 426199601 Lesotho 1996 426200601 Lesotho 2006 430197401 Liberia 1974 430200801 Liberia 2008 454198701 Malawi 1987 454199801 Malawi 1998 454200801 Malawi 2008 458197001 Malaysia 1970 458198001 Malaysia 1980 458199101 Malaysia 1991 458200001 Malaysia 2000 466198701 Mali 1987 466199801 Mali 1998 466200901 Mali 2009 480199001 Mauritius 1990 480200001 Mauritius 2000 480201101 Mauritius 2011 484196001 Mexico 1960 484197001 Mexico 1970 484199001 Mexico 1990 484199501 Mexico 1995 484200001 Mexico 2000 484200501 Mexico 2005 484201001 Mexico 2010 484201501 Mexico 2015 484202001 Mexico 2020 484200521 Mexico 2005 Q1 LFS 484200522 Mexico 2005 Q2 LFS 484200523 Mexico 2005 Q3 LFS 484200524 Mexico 2005 Q4 LFS 484200621 Mexico 2006 Q1 LFS 484200622 Mexico 2006 Q2 LFS 484200623 Mexico 2006 Q3 LFS 484200624 Mexico 2006 Q4 LFS 484200721 Mexico 2007 Q1 LFS 484200722 Mexico 2007 Q2 LFS 484200723 Mexico 2007 Q3 LFS 484200724 Mexico 2007 Q4 LFS 484200821 Mexico 2008 Q1 LFS 484200822 Mexico 2008 Q2 LFS 484200823 Mexico 2008 Q3 LFS 484200824 Mexico 2008 Q4 LFS 484200921 Mexico 2009 Q1 LFS 484200922 Mexico 2009 Q2 LFS 484200923 Mexico 2009 Q3 LFS 484200924 Mexico 2009 Q4 LFS 484201021 Mexico 2010 Q1 LFS 484201022 Mexico 2010 Q2 LFS 484201023 Mexico 2010 Q3 LFS 484201024 Mexico 2010 Q4 LFS 484201121 Mexico 2011 Q1 LFS 484201122 Mexico 2011 Q2 LFS 484201123 Mexico 2011 Q3 LFS 484201124 Mexico 2011 Q4 LFS 484201221 Mexico 2012 Q1 LFS 484201222 Mexico 2012 Q2 LFS 484201223 Mexico 2012 Q3 LFS 484201224 Mexico 2012 Q4 LFS 484201321 Mexico 2013 Q1 LFS 484201322 Mexico 2013 Q2 LFS 484201323 Mexico 2013 Q3 LFS 484201324 Mexico 2013 Q4 LFS 484201421 Mexico 2014 Q1 LFS 484201422 Mexico 2014 Q2 LFS 484201423 Mexico 2014 Q3 LFS 484201424 Mexico 2014 Q4 LFS 484201521 Mexico 2015 Q1 LFS 484201522 Mexico 2015 Q2 LFS 484201523 Mexico 2015 Q3 LFS 484201524 Mexico 2015 Q4 LFS 484201621 Mexico 2016 Q1 LFS 484201622 Mexico 2016 Q2 LFS 484201623 Mexico 2016 Q3 LFS 484201624 Mexico 2016 Q4 LFS 484201721 Mexico 2017 Q1 LFS 484201722 Mexico 2017 Q2 LFS 484201723 Mexico 2017 Q3 LFS 484201724 Mexico 2017 Q4 LFS 484201821 Mexico 2018 Q1 LFS 484201822 Mexico 2018 Q2 LFS 484201823 Mexico 2018 Q3 LFS 484201824 Mexico 2018 Q4 LFS 484201921 Mexico 2019 Q1 LFS 484201922 Mexico 2019 Q2 LFS 484201923 Mexico 2019 Q3 LFS 484201924 Mexico 2019 Q4 LFS 484202021 Mexico 2020 Q1 LFS 484202023 Mexico 2020 Q3 LFS 496198901 Mongolia 1989 496200001 Mongolia 2000 504198201 Morocco 1982 504199401 Morocco 1994 504200401 Morocco 2004 504201401 Morocco 2014 508199701 Mozambique 1997 508200701 Mozambique 2007 104201401 Myanmar 2014 524200101 Nepal 2001 524201101 Nepal 2011 528196001 Netherlands 1960 528197101 Netherlands 1971 528200101 Netherlands 2001 528201101 Netherlands 2011 558197101 Nicaragua 1971 558199501 Nicaragua 1995 558200501 Nicaragua 2005 566200621 Nigeria 2006 566200721 Nigeria 2007 566200821 Nigeria 2008 566200921 Nigeria 2009 566201021 Nigeria 2010 578180101 Norway 1801 578186501 Norway 1865 578187501 Norway 1875 578190001 Norway 1900 578191001 Norway 1910 586197301 Pakistan 1973 586198101 Pakistan 1981 586199801 Pakistan 1998 275199701 Palestine 1997 275200701 Palestine 2007 275201701 Palestine 2017 591196001 Panama 1960 591197001 Panama 1970 591198001 Panama 1980 591199001 Panama 1990 591200001 Panama 2000 591201001 Panama 2010 598198001 Papua New Guinea 1980 598199001 Papua New Guinea 1990 598200001 Papua New Guinea 2000 600196201 Paraguay 1962 600197201 Paraguay 1972 600198201 Paraguay 1982 600199201 Paraguay 1992 600200201 Paraguay 2002 604199301 Peru 1993 604200701 Peru 2007 604201701 Peru 2017 608199001 Philippines 1990 608199501 Philippines 1995 608200001 Philippines 2000 608201001 Philippines 2010 616197801 Poland 1978 616198801 Poland 1988 616200201 Poland 2002 616201101 Poland 2011 620198101 Portugal 1981 620199101 Portugal 1991 620200101 Portugal 2001 620201101 Portugal 2011 630197001 Puerto Rico 1970 630198001 Puerto Rico 1980 630199001 Puerto Rico 1990 630200001 Puerto Rico 2000 630200501 Puerto Rico 2005 630201001 Puerto Rico 2010 630201501 Puerto Rico 2015 630202001 Puerto Rico 2020 642197701 Romania 1977 642199201 Romania 1992 642200201 Romania 2002 642201101 Romania 2011 643200201 Russia 2002 643201001 Russia 2010 646199101 Rwanda 1991 646200201 Rwanda 2002 646201201 Rwanda 2012 662198001 Saint Lucia 1980 662199101 Saint Lucia 1991 686198801 Senegal 1988 686200201 Senegal 2002 686201301 Senegal 2013 694200401 Sierra Leone 2004 694201501 Sierra Leone 2015 703199101 Slovak Republic 1991 703200101 Slovak Republic 2001 703201101 Slovak Republic 2011 705200201 Slovenia 2002 710199601 South Africa 1996 710200101 South Africa 2001 710200701 South Africa 2007 710201101 South Africa 2011 710201601 South Africa 2016 728200801 South Sudan 2008 724198101 Spain 1981 724199101 Spain 1991 724200101 Spain 2001 724201101 Spain 2011 724200521 Spain 2005 Q1 LFS 724200522 Spain 2005 Q2 LFS 724200523 Spain 2005 Q3 LFS 724200524 Spain 2005 Q4 LFS 724200621 Spain 2006 Q1 LFS 724200622 Spain 2006 Q2 LFS 724200623 Spain 2006 Q3 LFS 724200624 Spain 2006 Q4 LFS 724200721 Spain 2007 Q1 LFS 724200722 Spain 2007 Q2 LFS 724200723 Spain 2007 Q3 LFS 724200724 Spain 2007 Q4 LFS 724200821 Spain 2008 Q1 LFS 724200822 Spain 2008 Q2 LFS 724200823 Spain 2008 Q3 LFS 724200824 Spain 2008 Q4 LFS 724200921 Spain 2009 Q1 LFS 724200922 Spain 2009 Q2 LFS 724200923 Spain 2009 Q3 LFS 724200924 Spain 2009 Q4 LFS 724201021 Spain 2010 Q1 LFS 724201022 Spain 2010 Q2 LFS 724201023 Spain 2010 Q3 LFS 724201024 Spain 2010 Q4 LFS 724201121 Spain 2011 Q1 LFS 724201122 Spain 2011 Q2 LFS 724201123 Spain 2011 Q3 LFS 724201124 Spain 2011 Q4 LFS 724201221 Spain 2012 Q1 LFS 724201222 Spain 2012 Q2 LFS 724201223 Spain 2012 Q3 LFS 724201224 Spain 2012 Q4 LFS 724201321 Spain 2013 Q1 LFS 724201322 Spain 2013 Q2 LFS 724201323 Spain 2013 Q3 LFS 724201324 Spain 2013 Q4 LFS 724201421 Spain 2014 Q1 LFS 724201422 Spain 2014 Q2 LFS 724201423 Spain 2014 Q3 LFS 724201424 Spain 2014 Q4 LFS 724201521 Spain 2015 Q1 LFS 724201522 Spain 2015 Q2 LFS 724201523 Spain 2015 Q3 LFS 724201524 Spain 2015 Q4 LFS 724201621 Spain 2016 Q1 LFS 724201622 Spain 2016 Q2 LFS 724201623 Spain 2016 Q3 LFS 724201624 Spain 2016 Q4 LFS 724201721 Spain 2017 Q1 LFS 724201722 Spain 2017 Q2 LFS 724201723 Spain 2017 Q3 LFS 724201724 Spain 2017 Q4 LFS 724201821 Spain 2018 Q1 LFS 724201822 Spain 2018 Q2 LFS 724201823 Spain 2018 Q3 LFS 724201824 Spain 2018 Q4 LFS 724201921 Spain 2019 Q1 LFS 724201922 Spain 2019 Q2 LFS 724201923 Spain 2019 Q3 LFS 724201924 Spain 2019 Q4 LFS 724202021 Spain 2020 Q1 LFS 724202022 Spain 2020 Q2 LFS 724202023 Spain 2020 Q3 LFS 724202024 Spain 2020 Q4 LFS 729200801 Sudan 2008 740200401 Suriname 2004 740201201 Suriname 2012 752188001 Sweden 1880 752189001 Sweden 1890 752190001 Sweden 1900 752191001 Sweden 1910 756197001 Switzerland 1970 756198001 Switzerland 1980 756199001 Switzerland 1990 756200001 Switzerland 2000 756201101 Switzerland 2011 834198801 Tanzania 1988 834200201 Tanzania 2002 834201201 Tanzania 2012 764197001 Thailand 1970 764198001 Thailand 1980 764199001 Thailand 1990 764200001 Thailand 2000 768196001 Togo 1960 768197001 Togo 1970 768201001 Togo 2010 780197001 Trinidad and Tobago 1970 780198001 Trinidad and Tobago 1980 780199001 Trinidad and Tobago 1990 780200001 Trinidad and Tobago 2000 780201101 Trinidad and Tobago 2011 792198501 Turkey 1985 792199001 Turkey 1990 792200001 Turkey 2000 800199101 Uganda 1991 800200201 Uganda 2002 800201401 Uganda 2014 804200101 Ukraine 2001 826185101 United Kingdom 1851 (England and Wales) 826185102 United Kingdom 1851 (Scotland) 826185103 United Kingdom 1851 (2% sample) 826186101 United Kingdom 1861 (England and Wales) 826186102 United Kingdom 1861 (Scotland) 826187101 United Kingdom 1871 (Scotland) 826188101 United Kingdom 1881 (England and Wales) 826188102 United Kingdom 1881 (Scotland) 826189101 United Kingdom 1891 (England and Wales) 826189102 United Kingdom 1891 (Scotland) 826190101 United Kingdom 1901 (England and Wales) 826190102 United Kingdom 1901 (Scotland) 826191101 United Kingdom 1911 (England and Wales) 826196101 United Kingdom 1961 826197101 United Kingdom 1971 826199101 United Kingdom 1991 826200101 United Kingdom 2001 840185001 United States 1850 (100%) 840185002 United States 1850 (1%) 840186001 United States 1860 (1%) 840187001 United States 1870 (1%) 840188001 United States 1880 (100%) 840188002 United States 1880 (10%) 840190001 United States 1900 (5%) 840191001 United States 1910 (1%) 840196001 United States 1960 840197001 United States 1970 840198001 United States 1980 840199001 United States 1990 840200001 United States 2000 840200501 United States 2005 840201001 United States 2010 840201501 United States 2015 840202001 United States 2020 858196301 Uruguay 1963 858196302 Uruguay 1963 (full count) 858197501 Uruguay 1975 858197502 Uruguay 1975 (full count) 858198501 Uruguay 1985 858198502 Uruguay 1985 (full count) 858199601 Uruguay 1996 858199602 Uruguay 1996 (full count) 858200621 Uruguay 2006 858201101 Uruguay 2011 858201102 Uruguay 2011 (full count) 862197101 Venezuela 1971 862198101 Venezuela 1981 862199001 Venezuela 1990 862200101 Venezuela 2001 704198901 Vietnam 1989 704199901 Vietnam 1999 704200901 Vietnam 2009 704201901 Vietnam 2019 894199001 Zambia 1990 894200001 Zambia 2000 894201001 Zambia 2010 716201201 Zimbabwe 2012 SAMPLE identifies the IPUMS sample from which the case is drawn. Each sample receives a unique 9-digit code. The code is structured as follows: The first 3 digits are the ISO/UN codes used in COUNTRY The next 4 digits are the year of the census/survey The final 2 digits identify the sample within the year. For the last two digits, censuses or large census-like surveys have a value "0" (e.g, 01) in the second-to-last digit, household surveys have a value of "2" (e.g., 21), and employment surveys have a value of "4" (e.g., 41). IPUMS Household serial number SERIAL is an identifying number unique to each household in a given sample. All person records are assigned the same serial number as the household record that they follow. (Person records also have their own unique identifiers -- see PERNUM.) The combination of SAMPLE and SERIAL provides a unique identifier for every household in the IPUMS-International database; SAMPLE, SERIAL and PERNUM uniquely identify every person in the database. SERIAL can be used to identify dwellings in some samples. In these samples, the first 7 digits of SERIAL provide the dwelling number common to all households that were sampled from the same structure. The last three digits give the sequence of the household within the dwelling. The following is a list of samples in which dwellings can be inferred: Chile 1970, 1992, 2002Colombia 1993, 2005Costa Rica 1984, 2000Cuba 2002Dominican Republic 1981, 2002, 2010Ecuador 1990, 2001Germany 1971Hungary 1980, 1990, 2001Jamaica 1982, 1991, 2001Malaysia 1970, 1991, 2000Mexico 1995, 1990, 2000, 2005Nigeria 2006Panama 2000Peru 1993, 2007Portugal 1981, 1991, 2001Spain 1991Uruguay 2011Venezuela 1990, 2001Vietnam 1989In all other samples, the last 3 digits are always zeroes. SERIAL was constructed for IPUMS-International, and has no relation to the serial number in the original datasets. The U.S. 1900 sample and 1880 10% sample have multi-household dwellings that can be identified using the last 3 digits of SERIAL. SERIAL is a 10-digit numeric variable. The last 3 digits of SERIAL indicate household number within dwelling for selected samples noted in the variable description. In all other samples, the last 3 digits are always zeroes. IPUMS Number of person records in the household PERSONS indicates how many person records are included in the household (i.e., the number of person records associated with the household record in the sample). These person records will all have the same serial number (SERIAL) as the household record. The information contained in the household record will normally apply to all of these persons. PERSONS is a 4-digit numeric variable. IPUMS Household weight HHWT indicates the number of households in the population represented by the household in the sample. For the samples that are truly weighted (see the comparability discussion), HHWT must be used to yield accurate household-level statistics. NOTE: HHWT has 2 implied decimal places. That is, the last two digits of the eight-digit variable are decimal digits, but there is no actual decimal in the data. HHWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description. IPUMS Subsample number 00 1st 1% subsample 01 2nd 1% subsample 02 3rd 1% subsample 03 4th 1% subsample 04 5th 1% subsample 05 6th 1% subsample 06 7th 1% subsample 07 8th 1% subsample 08 9th 1% subsample 09 10th 1% subsample 10 11th 1% subsample 11 12th 1% subsample 12 13th 1% subsample 13 14th 1% subsample 14 15th 1% subsample 15 16th 1% subsample 16 17th 1% subsample 17 18th 1% subsample 18 19th 1% subsample 19 20th 1% subsample 20 21st 1% subsample 21 22nd 1% subsample 22 23rd 1% subsample 23 24th 1% subsample 24 25th 1% subsample 25 26th 1% subsample 26 27th 1% subsample 27 28th 1% subsample 28 29th 1% subsample 29 30th 1% subsample 30 31st 1% subsample 31 32nd 1% subsample 32 33rd 1% subsample 33 34th 1% subsample 34 35th 1% subsample 35 36th 1% subsample 36 37th 1% subsample 37 38th 1% subsample 38 39th 1% subsample 39 40th 1% subsample 40 41st 1% subsample 41 42nd 1% subsample 42 43rd 1% subsample 43 44th 1% subsample 44 45th 1% subsample 45 46th 1% subsample 46 47th 1% subsample 47 48th 1% subsample 48 49th 1% subsample 49 50th 1% subsample 50 51st 1% subsample 51 52nd 1% subsample 52 53rd 1% subsample 53 54th 1% subsample 54 55th 1% subsample 55 56th 1% subsample 56 57th 1% subsample 57 58th 1% subsample 58 59th 1% subsample 59 60th 1% subsample 60 61st 1% subsample 61 62nd 1% subsample 62 63rd 1% subsample 63 64th 1% subsample 64 65th 1% subsample 65 66th 1% subsample 66 67th 1% subsample 67 68th 1% subsample 68 69th 1% subsample 69 70th 1% subsample 70 71st 1% subsample 71 72nd 1% subsample 72 73rd 1% subsample 73 74th 1% subsample 74 75th 1% subsample 75 76th 1% subsample 76 77th 1% subsample 77 78th 1% subsample 78 79th 1% subsample 79 80th 1% subsample 80 81st 1% subsample 81 82nd 1% subsample 82 83rd 1% subsample 83 84th 1% subsample 84 85th 1% subsample 85 86th 1% subsample 86 87th 1% subsample 87 88th 1% subsample 88 89th 1% subsample 89 90th 1% subsample 90 91st 1% subsample 91 92nd 1% subsample 92 93rd 1% subsample 93 94th 1% subsample 94 95th 1% subsample 95 96th 1% subsample 96 97th 1% subsample 97 98th 1% subsample 98 99th 1% subsample 99 100th 1% subsample SUBSAMP allocates each case to one of 100 subsample replicates, randomly numbered from 0 to 99. Each subsample is nationally representative and preserves any stratification of the sample from which it is drawn. Users who need a representative subset of a sample can use SUBSAMP to select their cases. For example, to randomly extract 10% of the cases from a sample, select any 10 of the 100 subsamples. IPUMS Group quarters (collective dwelling) status 00 Vacant 10 Households 20 Group quarters (collective), n.s. 21 Institutions 22 Other group quarters 29 1-person unit created by splitting large household 99 Unknown/group quarters not identified GQ identifies households as vacant dwellings, group quarters, or private households. Group quarters -- collective dwellings -- are generally institutions and other group living arrangements such as rooming houses and boarding schools. Institutions often retain persons under formal supervision or custody, such as correctional institutions, military barracks, asylums, or nursing homes. Educational and religious group dwellings (e.g., boarding schools, convents, monasteries, etc.) are also included in the institutional classification. Group quarter designations are often useful for understanding the universe of households that answered questions about household characteristics. Censuses will often exclude group quarters from such questions. IPUMS Continent and region of country 11 Eastern Africa 12 Middle Africa 13 Northern Africa 14 Southern Africa 15 Western Africa 21 Caribbean 22 Central America 23 North America 24 South America 31 Central Asia 32 Eastern Asia 33 Southern Asia 34 South-Eastern Asia 35 Western Asia 41 Eastern Europe 42 Northern Europe 43 Southern Europe 44 Western Europe 51 Australia and New Zealand 52 Melanesia 53 Micronesia 54 Polynesia REGIONW identifies the continent and region of each country. IPUMS 1st subnational geographic level, world [consistent boundaries over time] GEOLEV1 indicates the major administrative unit in which the household was enumerated. The variable incorporates the geographies for every country, to enable cross-national geographic analysis over time. First administrative units in GEOLEV1 have been spatiotemporally harmonized to provide spatially consistent boundaries across samples in each country. GEOLEV1 is a 6-digit numeric variable. GEOLEV1 codes and labels can be found here. Codes, labels, frequencies, and information about boundary changes for each country can be found in the country specific harmonized variable e.g. GEO1_BR. IPUMS Nomenclature of Territorial Units for Statistics 1, Europe 0101 AT1 / Ostösterreich 0102 AT2 / Südösterreich 0103 AT3 / Westösterreich 0601 DE1 / Baden-Württemberg 0602 DE2 / Bayern 0603 DE3 / Berlin 0604 DE4 / Brandenburg 0605 DE5 / Bremen 0606 DE6 / Hamburg 0607 DE7 / Hessen 0608 DE8 / Mecklenburg-Vorpommern 0609 DE9 / Niedersachsen 0610 DEA / Nordrhein-Westfalen 0611 DEB / Rheinland-Pfalz 0612 DEC / Saarland 0613 DED / Sachsen 0614 DEE / Sachsen-Anhalt 0615 DEF / Schleswig-Holstein 0616 DEG / Thüringen 0901 ES1 / Noroeste 0902 ES2 / Noreste 0903 ES3 / Comunidad de Madrid 0904 ES4 / Centro (E) 0905 ES5 / Este 0906 ES6 / Sur 0907 ES7 / Canarias 0909 ES / Unknown 1101 FR1 / Île de France 1102 FR2 / Bassin Parisien 1103 FR3 / Nord - Pas-de-Calais 1104 FR4 / Est 1105 FR5 / Ouest 1106 FR6 / Sud-Ouest 1107 FR7 / Centre-Est 1108 FR8 / Méditerranée 1109 FR9 / Département d’Outre-Mer 1199 FR99 / Unknown 1201 EL1 / Voreia Ellada 1202 EL2 / Kentriki Ellada 1203 EL3 / Attiki 1204 EL4 / Nisia Aigaiou, Kriti 1400 IE0 / Republic of Ireland 1503 ITC / Nord-Ovest 1506 ITF / Sud 1507 ITG / Isole 1508 ITH / Nord-Est 1509 ITI / Centro 2101 PL1 / Centralny 2102 PL2 / Południowy 2103 PL3 / Wschodni 2104 PL4 / Północno-zachodni 2105 PL5 / Południowo-zachodni 2106 PL6 / Północny 2201 PT1 / Continente 2202 PT2 / Região Autónoma dos Açores 2203 PT3 / Região Autónoma da Madeira 2301 RO1 / Macroregiunea Unu 2302 RO2 / Macroregiunea Doi 2303 RO3 / Macroregiunea Trei 2304 RO4 / Macroregiunea Patru 2501 SI0 / Slovenia 2600 SK0 / Slovensko 2701 UKC / North East (England) 2702 UKD / North West (England) 2703 UKE / Yorkshire and the Humber (England) 2704 UKF / East Midlands (England) 2705 UKG / West Midlands (England) 2706 UKH / East of England (England) 2707 UKI / LONDON (England) 2708 UKJ / South East (England) 2709 UKK / South West (England) 2710 UKL / WALES 2711 UKM / SCOTLAND 2712 UKN / NORTHERN IRELAND 3400 CH0/Switzerland 3901 TR1 / Istanbul 3902 TR2 / Bati Marmara 3903 TR3 / Ege 3904 TR4 / Dogu Marmara 3905 TR5 / Bati Anadolu 3906 TR6 / Akdeniz 3907 TR7 / Orta Anadolu 3908 TR8 / Bati Karadeniz 3909 TR9 / Dogu Karadeniz 3911 TRA / Kuzeydogu Anadolu 3912 TRB / Ortadogu Anadolu 3913 TRC / Güneydogu Anadolu 9999 UNKNOWN ENUTS1 identifies the Nomenclature of Territorial Units for Statistics (NUTS) within Europe in which the household was enumerated. NUTS1 is the first level territorial units within countries. NUTS is a standard administrative division of the European Union, and was developed by the EU. The European Free Trade Association extends the NUTS system to several additional countries outside of the EU, and they are also incorporated into this variable. ENUTS1 corresponds to the 2010 version of NUTS1 released by Eurostat. IPUMS has added ENUTS1_2013 for the more recent samples. ENUTS1_2013 is an amendment to the annexes to the ENUTS1 classification. The last 2-digits of the ENUTS1 variable provide the NUTS1 code. The labels include the standard code for the NUTS1 system and the name of the NUTS1 region, separated by a slash. Smaller sub-national units are available for most countries in ENUTS2 and ENUTS3. The full set of geography variables for the countries can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Population density of GEOLEV1 unit, in persons per square kilometer POPDENSGEO1 indicates the population density in persons per square kilometer of the major administrative unit in which the household was enumerated. The major administrative unit of the household is identified by the GEOLEV1 variable. The area of units in GEOLEV1 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. POPDENSGEO1 is an 8-digit string variable listing the population density in persons per square kilometer. IPUMS Area of GEOLEV1 unit in square kilometers AREAMOLLWGEO1 indicates the area in square kilometers of the major administrative unit in which the household was enumerated. The major administrative unit of the household is identified by the GEOLEV1 variable. The area of units in GEOLEV1 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. AREAMOLLWGEO1 is a 10-digit string variable listing the area in square kilometers. IPUMS Germany, State 1970 - 1987 [Level 1; consistent boundaries, GIS] 276001 Schleswig-Holstein 276002 Hamburg 276003 Niedersachsen 276004 Bremen 276005 Nordrhein-Westfalen 276006 Hessen 276007 Rheinland-Pfalz 276008 Baden-Württemberg 276009 Bayern 276010 Saarland 276011 West Berlin 276012 Brandenburg 276013 Mecklenburg-West Pomerania 276014 Saxony 276015 Saxony-Anhalt 276016 Thuringia 276017 East Berlin GEO1_DE identifies the household's state within Germany in all sample years. States are the first level administrative units of the country. GEO1_DE is spatially harmonized to account for political boundary changes across census years. Some detail is lost in harmonization; see the comparability discussion. A GIS map (in shapefile format), corresponding to GEO1_DE can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Germany can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Germany, State 1981 [Level 1, GIS] 001 Berlin (East) 002 Brandenburg 003 Mecklenburg-Vorpommern 004 Saxony 005 Saxony-Anhalt 006 Thuringia 097 West Germany GEO1_DE1981 identifies the household’s state within Germany in 1981. States are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_DE1981 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Germany can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level of any country refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here. IPUMS Ownership of dwelling [general version] NIU (not in universe) 1 Owned 2 Not owned 9 Unknown OWNERSHIP indicates whether a member of the household owned the housing unit. Households that acquired their unit with a mortgage or other lending arrangement were understood to "own" their unit even if they had not yet completed repayment. For those that did not own their housing unit, several options were possible: renting (from various types of owners), subletting, usufruct, and de facto occupation. IPUMS Ownership of dwelling [detailed version] 000 NIU (not in universe) 100 Owned 110 Owned, already paid 120 Owned, still paying 130 Owned, constructed 140 Owned, inherited 190 Owned, other 191 Owned, house 192 Owned, condominium 193 Apartment proprietor 194 Shared ownership 200 Not owned 210 Renting, not specified 211 Renting, government 212 Renting, local authority 213 Renting, parastatal 214 Renting, private 215 Renting, private company 216 Renting, individual 217 Renting, collective 218 Renting, joint state and individual 219 Renting, public subsidized 220 Renting, private subsidized 221 Renting, co-tenant 222 Renting, relative of tenant 223 Renting, cooperative 224 Renting, with a job or business 225 Renting, loan-backed habitation 226 Renting, mixed contract 227 Furnished dwelling 228 Sharecropping 230 Subletting 231 Rent to own 239 Renting, other 240 Occupied de facto/squatting 250 Free/usufruct (no cash rent) 251 Free, provided by employer 252 Free, without work or services 253 Free, provided by family or friend 254 Free, private 255 Free, public 256 Free, condemned 257 Free, other 260 Endowment, Waqf (Egypt historical) 290 Not owned, other 999 Unknown OWNERSHIP indicates whether a member of the household owned the housing unit. Households that acquired their unit with a mortgage or other lending arrangement were understood to "own" their unit even if they had not yet completed repayment. For those that did not own their housing unit, several options were possible: renting (from various types of owners), subletting, usufruct, and de facto occupation. IPUMS Water supply 00 NIU (not in universe) 10 Yes, piped water 11 Piped inside dwelling 12 Piped, exclusively to this household 13 Piped, shared with other households 14 Piped outside the dwelling 15 Piped outside dwelling, in building 16 Piped within the building or plot of land 17 Piped outside the building or lot 18 Have access to public piped water 20 No piped water 99 Unknown WATSUP describes the physical means by which the housing unit receives its water. The primary distinction is whether or not the household had piped (running) water. IPUMS Sewage 00 NIU (not in universe) 10 Connected to sewage system or septic tank 11 Sewage system (public sewage disposal) 12 Septic tank (private sewage disposal) 20 Not connected to sewage disposal system 99 Unknown SEWAGE indicates whether the household has access to a sewage system or septic tank. IPUMS Hot water heater NIU (not in universe) 1 No 2 Yes 9 Unknown/missing HOTWATER indicates whether the housing unit had a water heater. IPUMS Central heating NIU (not in universe) 1 No heating 2 Central heating, not specified 3 Collective central heating 4 Individual central heating 5 Other heating, not central 6 Heating, unspecified 7 No central heating/heating unknown 9 Unknown HEAT indicates the type of heating in the dwelling: individual or collective central heating, non-central heating, or none. IPUMS Kitchen or cooking facilities 00 NIU (not in universe) 10 No kitchen 11 Food is prepared in a non-kitchen room 13 Does not prepare food in the dwelling 20 Yes, have a kitchen 21 Kitchen located inside the dwelling 22 Indoor kitchen, exclusive use 23 Indoor kitchen, shared use 24 Exclusive use of kitchen (indoor/outdoor status not specified) 25 Shared use of kitchen with another household (indoor/outdoor status not specified) 26 Kitchen located outside the dwelling 27 Outdoor kitchen, exclusive use 28 Outdoor kitchen, shared use 99 Unknown/missing KITCHEN indicates whether the household had a kitchen, cooking facilities, or room dedicated to food preparation. IPUMS Toilet 00 NIU (not in universe) 10 No toilet 11 No flush toilet 20 Have toilet, type not specified 21 Flush toilet 22 Non-flush, latrine 23 Non-flush, other and unspecified 99 Unknown TOILET indicates whether the household had access to a toilet and, in most cases, whether it was a flush toilet or other type of installation. IPUMS Bathing facilities NIU (not in universe) 1 No bathing facility 2 Have bathing facility, exclusivity not specified 3 Have bathing facility, exclusive use 4 Have bathing facility, shared use 9 Unknown BATH indicates whether the household had access to bathing facilities and, in most cases, whether it had exclusive access. IPUMS Stories in structure 00 NIU (not in universe) 01 1 story 02 2 stories 03 3 stories 04 4 stories 05 5 stories 06 6 stories 07 7 stories 08 8 stories 09 9 stories 10 10 stories 11 11 stories 12 12 stories 13 13 stories 14 14 stories 15 15 stories 16 16 stories 17 17 stories 18 18 stories 19 19 stories 20 20 stories 21 21 stories 22 22 stories 23 23 stories 24 24 stories 25 25+ stories 99 Unknown STORIES indicates the number of floors or levels in the building containing the responding housing unit. IPUMS Living area in square meters LIVEAREA describes the total living area in the dwelling inhabited by the household. LIVEAREA is a 3-digit numeric variable. Codes000 = NIU (not in universe) 999 = Unknown Top codes:Unless otherwise specified: 998+ Austria 1991-2001: 150+ Belarus 1999: 201+ Belarus 2009: 250+ Germany 1987: 361+ Hungary 2001: 260+ Hungary 2011: 301+ Iran 2006: 501+ Italy 2001: 150+ Italy 2011: 145+ Laos 2005: 200+ Philippines 1990-2010: 200+ Poland 2002: 200+ Romania 2002: 221+ Romania 2011: 500+ Slovenia 2002: 101+ Spain 1991: 181+ Spain 2001-2011: 900+ Switzerland 1980-1990: 400+ Switzerland 2000: 500+ 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 Dwelling number Germany 1981: All households This variable indicates the dwelling number. This is a 7-digit numeric variable with 0 implied decimal places IPUMS Number of persons in household Germany 1981: 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 24 24 25 25 26 26 27 27 28 28 29 29 30 30 This variable indicates the number of persons in a household. IPUMS Dwelling created by splitting apart a large dwelling or household Germany 1981: All households No problem 2 Yes: persons within a large household were split apart into separate dwellings This variable indicates that the dwelling was created by splitting apart a large dwelling or household. IPUMS Number of persons in large household before it was split Germany 1981: All households 000 031 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 99 100 100 101 101 102 102 103 103 104 104 105 105 106 106 107 107 108 108 109 109 110 110 111 111 112 112 113 113 114 114 115 115 116 116 117 117 119 119 120 120 121 121 122 122 123 123 124 124 125 125 126 126 127 127 128 128 129 129 130 130 131 131 132 132 133 133 134 134 135 135 136 136 137 137 138 138 141 141 142 142 146 146 147 147 150 150 151 151 153 153 158 158 162 162 164 164 168 168 169 169 172 172 174 174 178 178 180 180 183 183 193 193 196 196 197 197 201 201 205 205 214 214 219 219 234 234 236 236 247 247 This variable indicates the number of persons in large household before it was split. IPUMS Number of households in the dwelling Germany 1981: All households 1 1 2 2 3 3 4 4 5 5 This variable indicates the running number of households in a dwelling. IPUMS Type of household Germany 1981: All households 1 Private household 2 Collective household This variable indicates the type of household. IPUMS Number of families in household Germany 1981: Private households [discrepancies: none] 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 99 NIU (not in universe) This variable indicates the number of families in a household. IPUMS Number of persons in household Germany 1981: Private households [discrepancies: none] 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 18 18 99 NIU (not in universe) Number of persons in household. IPUMS Type of family Germany 1981: Private households [discrepancies: none] 1 Core family only 2 Core family w/other persons not part of family 3 Household with several core families 4 Household without core family 9 NIU (not in universe) This variable indicates the type of family. IPUMS Social group of household Germany 1981: Private households [discrepancies: none] 00 No principal resident in household 01 Worker (not in production) 02 Worker in production 03 White-collar worker 04 Intellectual worker 05 Cooperative farmer 06 Agricultural specialist 07 Member of other cooperatives 08 Self-employed 09 Other/unknown 99 NIU (not in universe) This variable indicates the social group of a household. IPUMS Household and dwelling Germany 1981: Private households [discrepancies: none] 1 More than one household in dwelling 2 Only household in dwelling 9 NIU (not in universe) This variable indicates the number of households in a dwelling. IPUMS Number of principal residents in household Germany 1981: Private households [discrepancies: none] 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 18 18 99 NIU (not in universe) This variable indicates the number of principal residents in a household. IPUMS Number of principal residents of retirement age in household Germany 1981: Private households [discrepancies: none] 1 1 2 2 3 3 4 4 5 5 9 NIU (not in universe) This variable indicates the number of principal residents of retirement age in household. IPUMS Type of building Germany 1981: Occupied households [discrepancies: none] 1 Residential building 2 Non-residential building 3 Temporary shelter 9 NIU (not in universe) This variable indicates the household's type of building for occupied households. IPUMS Ownership of building Germany 1981: Occupied households [discrepancies: none] 1 State owned 2 Cooperative 3 Private 4 Other ownership 9 NIU (not in universe) This variable indicates the ownership of a building. IPUMS Owner occupied building Germany 1981: Occupied households [discrepancies: none] Not private building/not occupied private building 1 Building occupied by owner only 2 Building rented partly or totally 9 NIU (not in universe) This variable indicates the household's ownership status of the building. IPUMS Number of floors in building Germany 1981: Occupied households in residential buildings[discrepancies: none] 1 1 floor 2 2 floors 3 3 floors 4 4 floors 5 5 floors 6 6 floors 7 7-11 floors 8 12+ floors 9 NIU (not in universe) This variable indicates the number of floors in a building. IPUMS Condition of building Germany 1981: Occupied households [discrepancies: none] 1 Well-preserved 2 Light damage 3 Heavy damage 4 Unsuitable as residential building 5 Not stated 9 NIU (not in universe) This variable indicates the condition of a building. IPUMS Year of reconstruction of building Germany 1981: Occupied households [discrepancies: none] Not reconstructed 1 1946-1960 2 1961-1965 3 1966-1970 4 1971-1975 5 1976-1980 6 1981 9 NIU (not in universe) This variable indicates the year of construction of a building. IPUMS Reconstruction of building Germany 1981: Occupied households [discrepancies: none] 1 Reconstructed 2 Not reconstructed 9 NIU (not in universe) This variable indicates any possible reconstruction of a building. IPUMS Type of sewage disposal for building Germany 1981: Occupied households in residential buildings [discrepancies: none] 1 Connected to public sewage system 2 Building has own sewage disposal 3 Other methods 9 NIU (not in universe) This variable indicates the type of sewage disposal for a building. IPUMS Type of water supply system for building Germany 1981: Occupied households in residential buildings[discrepancies: none] 1 Connected to public water network 2 Building has own water supply system 3 Other methods 9 NIU (not in universe) This variable indicates the type of water supply system for the building. IPUMS Number of dwellings in building Germany 1981: Occupied households [discrepancies: none] 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 96 098 98 099 99 100 100 101 101 102 102 103 103 104 104 105 105 106 106 107 107 108 108 109 109 110 110 111 111 112 112 113 113 114 114 115 115 116 116 117 117 118 118 119 119 120 120 121 121 122 122 123 123 124 124 125 125 126 126 127 127 128 128 129 129 130 130 131 131 132 132 133 133 134 134 135 135 136 136 138 138 139 139 140 140 143 143 144 144 145 145 148 148 150 150 151 151 153 153 154 154 155 155 156 156 157 157 158 158 159 159 160 160 161 161 168 168 170 170 171 171 172 172 176 176 179 179 180 180 183 183 186 186 188 188 190 190 193 193 194 194 195 195 196 196 208 208 213 213 216 216 228 228 236 236 239 239 240 240 241 241 999 NIU (not in universe) This variable indicates the number of dwellings in a building. IPUMS Use of dwelling Germany 1981: All households 1 Occupied 2 Unoccupied This variable indicates the use of dwelling (whether or not it is occupied). IPUMS Number of rooms in dwelling Germany 1981: Occupied households [discrepancies: none] 1 1 room 2 2 rooms 3 3 rooms 4 4 rooms 5 5 rooms 6 6 rooms 7 7+ rooms 9 NIU (not in universe) This variable indicates the number of rooms in dwelling. IPUMS Floor space of all rooms (square meters) Germany 1981: Occupied households [discrepancies: none] This variable indicates the total floor space of all rooms (square meters). This is a 3-digit numeric variable with 0 implied decimal places IPUMS Floor space of kitchen (square meters) Germany 1981: Occupied households [discrepancies: none] 00 No kitchen 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 99 NIU (not in universe) This variable indicates the total floor space of the kitchen (square meters). IPUMS Floor space of all auxiliary rooms (square meters) Germany 1981: Occupied households [discrepancies: none] 000 No auxiliary rooms 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 99 999 NIU (not in universe) This variable indicates the total floor space of all auxiliary rooms (square meters). IPUMS Stove heating Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates whether or not the household has stove heating. IPUMS Tele (district) heating Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates the household tele (district) heating. IPUMS Central/floor heating Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates if the household has central/floor heating. IPUMS Gas heating Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates whether or not the household has gas heating. IPUMS Electric boiler heating Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates that the household has an electric boiler heating. IPUMS Type of water supply in dwelling Germany 1981: Occupied households [discrepancies: none] 1 Within dwelling 2 Outside of dwelling but within building 3 Other method 9 NIU (not in universe) This variable indicates the type of water supply in a dwelling. IPUMS Type and location of toilet Germany 1981: Occupied households [discrepancies: none] 1 Toilet within dwelling 2 Toilet outside of dwelling but within building 3 Toilet outside of building 4 Dry toilet in dwelling 5 Dry toilet outside of dwelling but within building 6 Dry toilet outside of building 9 NIU (not in universe) This variable indicates the type and location of the household toilet. IPUMS Location of bath/shower Germany 1981: Occupied households [discrepancies: none] 1 Within dwelling 2 Outside of dwelling but within building 3 Not available 9 NIU (not in universe) This variable gives the location of the household bath or shower. IPUMS Type of gas supply Germany 1981: Occupied households [discrepancies: none] 1 Connected to public gas network 2 Propane 3 Not available 9 NIU (not in universe) This variable indicates the type of gas supply for a household. IPUMS Gas-flow heater for hot water Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates whether or not the household has a gas-flow heater that is used for hot water. IPUMS Electric boiler for hot water Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates whether or not the household has an electric boiler that is used for hot water. IPUMS Central hot water system Germany 1981: Occupied households [discrepancies: none] 1 No 2 Yes 9 NIU (not in universe) This variable indicates that the household has a central hot water system. IPUMS Hot water not available Germany 1981: Occupied households [discrepancies: none] 1 Has hot water 2 No hot water 9 NIU (not in universe) This variable indicates that the household does not have access to hot water. IPUMS Facility level A Germany 1981: Occupied households [discrepancies: none] 1 Running water, toilet within dwelling 2 Running water, bath/shower, toilet within dwelling, hot water 3 Modern heating system, running water, bath/shower, WC within dwelling, hot water 4 Other combinations 9 NIU (not in universe) This variable indicates the facility level A (type of sanitary and water system). IPUMS Facility level B Germany 1981: Occupied households [discrepancies: none] 1 Modern heating system, running water, bath/shower, inside toilet, hot water 2 Modern heating system, running water, bath/shower, inside toilet 3 Running water, bath/shower, inside toilet, hot water 4 Running water, bath/shower, inside toilet 5 Running water, inside toilet 6 Running water 7 Other combinations 8 No facilities 9 NIU (not in universe) This variable indicates facility level B (heating and sanitation availability). IPUMS Number of households in dwelling Germany 1981: Occupied households [discrepancies: none] 1 1 2 2 3 3 4 4 5 5 9 NIU (not in universe) This variable indicates the number of households in dwelling. IPUMS Number of principal residents in dwelling Germany 1981: Occupied households [discrepancies: none] 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 99 NIU (not in universe) This variable indicates the number of principal residents in dwelling. IPUMS Number of secondary residents in dwelling Germany 1981: Occupied households [discrepancies: none] 1 1 2 2 3 3 4 4 5 5 6 6+ 9 NIU (not in universe) This variable indicates the number of secondary residents in a dwelling. IPUMS Tenancy status of 1st household Germany 1981: Occupied households [discrepancies: none] Dwelling vacant 1 Tenant 2 Member of a housing cooperative 3 Owner 4 Sub-tenant 9 NIU (not in universe) This variable indicates the rental status of the 1st household. IPUMS Tenancy status of 2nd household Germany 1981: Occupied households [discrepancies: none] No 2nd household in dwelling 1 Tenant 2 Member of a housing cooperative 3 Owner 4 Sub-tenant 9 NIU (not in universe) This variable indicates the rental status of the 2nd household. IPUMS Number of principal residents in 1st household Germany 1981: Occupied households [discrepancies: none] NIU (not in universe) 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9+ This variable indicates the number of principal residents in the 1st household. IPUMS Number of principal residents in 2nd household Germany 1981: Occupied households [discrepancies: none] 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9+ 99 NIU (not in universe) This variable indicates the number of principal residents in the 2nd household. IPUMS Number of principal residents in 3rd household Germany 1981: Occupied households [discrepancies: none] 00 01 1 02 2 03 3 04 4 05 5 06 6 09 9+ 99 NIU (not in universe) This variable indicates the number of principal residents in the 3rd household. IPUMS Number of children under age 17 in 1st household Germany 1981: Occupied households [discrepancies: none] 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9+ 99 NIU (not in universe) This variable indicates the number of children under age 17 in the 1st household. IPUMS Size of kitchen Germany 1981: Occupied households [discrepancies: none] Dwelling has no kitchen 1 Dwelling has kitchen 4+ square meters 2 Dwelling has kitchen under 4 square meters 9 NIU (not in universe) This variable indicates the size of a household's kitchen. IPUMS Statistical number of rooms Germany 1981: Occupied households [discrepancies: none] 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8+ 9 NIU (not in universe) This variable indicates the statistical number of rooms in a household. 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 00 No spouse present 01 Rule 1: strong relationship pairing, couple adjacent 02 Rule 2: strong relationship pairing, couple not adjacent 03 Rule 3: weak relationship pairing, couple adjacent 04 Rule 4: weak relationship pairing, couple not adjacent 05 Rule 5: weak consensual union pairings 06 Rule 6: sample-specific rules (usually child-to-child) SPRULE explains the criteria by which the IPUMS-International variable SPLOC linked the person to his/her probable spouse. IPUMS International establishes spouse-spouse links according to five basic rules, and SPRULE gives the number of the rule that applied to the link in question. A sixth rule identifies sample-specific linking procedures only imposed in selected instances. The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology. IPUMS Age 000 Less than 1 year 001 1 year 002 2 years 003 3 004 4 005 5 006 6 007 7 008 8 009 9 010 10 011 11 012 12 013 13 014 14 015 15 016 16 017 17 018 18 019 19 020 20 021 21 022 22 023 23 024 24 025 25 026 26 027 27 028 28 029 29 030 30 031 31 032 32 033 33 034 34 035 35 036 36 037 37 038 38 039 39 040 40 041 41 042 42 043 43 044 44 045 45 046 46 047 47 048 48 049 49 050 50 051 51 052 52 053 53 054 54 055 55 056 56 057 57 058 58 059 59 060 60 061 61 062 62 063 63 064 64 065 65 066 66 067 67 068 68 069 69 070 70 071 71 072 72 073 73 074 74 075 75 076 76 077 77 078 78 079 79 080 80 081 81 082 82 083 83 084 84 085 85 086 86 087 87 088 88 089 89 090 90 091 91 092 92 093 93 094 94 095 95 096 96 097 97 098 98 099 99 100 100+ 999 Not reported/missing AGE gives age in years as of the person's last birthday prior to or on the day of enumeration. IPUMS Age, grouped into intervals 01 0 to 4 02 5 to 9 03 10 to 14 04 15 to 19 05 0 to 5 06 6 to 10 07 10 to 15 08 11 to 14 09 15 to 17 10 16 to 19 11 18 to 24 12 20 to 24 13 25 to 29 14 30 to 34 15 35 to 39 16 40 to 44 17 45 to 49 18 50 to 54 19 55 to 59 20 60 to 64 21 65 to 69 22 70 to 74 23 75 to 79 24 80 to 84 25 85+ 98 Unknown AGE2 gives computed years of age grouped into intervals. IPUMS Sex 1 Male 2 Female 9 Unknown SEX reports the sex (gender) of the respondent. IPUMS Marital status [general version] NIU (not in universe) 1 Single/never married 2 Married/in union 3 Separated/divorced/spouse absent 4 Widowed 9 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS Marital status [detailed version] 000 NIU (not in universe) 100 Single/never married 110 Engaged 111 Never married and never cohabited 200 Married or consensual union 210 Married, formally 211 Married, civil 212 Married, religious 213 Married, civil and religious 214 Married, civil or religious 215 Married, traditional/customary 216 Married, monogamous 217 Married, polygamous 219 Married, spouse absent (historical samples) 220 Consensual union 300 Separated/divorced/spouse absent 310 Separated or divorced 320 Separated or annulled 330 Separated 331 Separated legally 332 Separated de facto 333 Separated from marriage 334 Separated from consensual union 335 Separated from consensual union or marriage 340 Annulled 350 Divorced 400 Widowed 410 Widowed or divorced 411 Widowed from consensual union or marriage 412 Widowed from marriage 413 Widowed from consensual union 420 Widowed, divorced, or separated 999 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS Marital status, Europe NIU (not in universe) 1 Never married 2 Married 3 Widowed and not remarried 4 Divorced/separated and not remarried 5 Widowed or divorced 9 Unknown / missing EMARST describes for the European samples the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. European census instructions generally limit marital status to legal unions, but there are exceptions. EMARST has been classified according to the recommendations given by the Conference of European Statisticians for the 2010 Population and Housing Censuses. IPUMS Duration of current marriage or union 00 Less than 1 year 01 1 year 02 2 years 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 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95+ 98 Unknown 99 NIU (not in universe) DURMARR reports the duration of the respondent's current marriage or union, or, in some cases, the total years spent within marriage. IPUMS Subfamily membership number 0000 Non-family or sub-family not identified 0001 1st subfamily 0002 2nd subfamily 0003 3rd subfamily 0004 4th subfamily 0005 5th subfamily 0006 6th subfamily 0007 7th subfamily 0008 8th subfamily 0009 9th subfamily 0010 10th subfamily 0011 11th subfamily 0012 12th subfamily 0013 13th subfamily 0099 Unknown SUBFNUM gives the number of the subfamily to which the person belongs within the household (1 = first subfamily, 2 = second subfamily, etc.). SUBFNUM records the identification of subfamilies in the original dataset, which generally correspond to conjugal units and their offspring. IPUMS Children ever born 00 No children 01 1 child 02 2 children 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+ 98 Unknown 99 NIU (not in universe) CHBORN reports the number of children ever born to each woman of whom the question was asked. In most samples, women were to report all live births by all fathers, whether or not the child was still living. IPUMS School attendance NIU (not in universe) 1 Yes 2 No, not specified 3 No, attended in the past 4 No, never attended 9 Unknown/missing SCHOOL indicates whether or not the person attended school at the time of the census or within some specified period of time prior to the census. IPUMS Educational attainment, international recode [general version] NIU (not in universe) 1 Less than primary completed 2 Primary completed 3 Secondary completed 4 University completed 9 Unknown EDATTAIN records the person's educational attainment in terms of the level of schooling completed (degree or other milestone). The emphasis on level completed is critical: a person attending the final year of secondary education receives the code for having completed lower secondary only -- and in some samples only primary. EDATTAIN does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. In addition to EDATTAIN, a country-specific education classification is provided which loses no information and reflects the particular educational system of that country (for example EDUCBR for Brazil, EDUCCL for Chile, and EDUCUS for the United States). As always, users can refer to the original education source variables for each sample, if they wish. Many samples also give single years of schooling completed, recorded in YRSCHOOL. Some samples provide educational information in a form that could not be incorporated into EDATTAIN. IPUMS Educational attainment, international recode [detailed version] 000 NIU (not in universe) 100 Less than primary completed (n.s.) 110 No schooling 120 Some primary completed 130 Primary (4 yrs) completed 211 Primary (5 yrs) completed 212 Primary (6 yrs) completed 221 Lower secondary general completed 222 Lower secondary technical completed 311 Secondary, general track completed 312 Some college completed 320 Secondary or post-secondary technical completed 321 Secondary, technical track completed 322 Post-secondary technical education 400 University completed 999 Unknown/missing EDATTAIN records the person's educational attainment in terms of the level of schooling completed (degree or other milestone). The emphasis on level completed is critical: a person attending the final year of secondary education receives the code for having completed lower secondary only -- and in some samples only primary. EDATTAIN does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. In addition to EDATTAIN, a country-specific education classification is provided which loses no information and reflects the particular educational system of that country (for example EDUCBR for Brazil, EDUCCL for Chile, and EDUCUS for the United States). As always, users can refer to the original education source variables for each sample, if they wish. Many samples also give single years of schooling completed, recorded in YRSCHOOL. Some samples provide educational information in a form that could not be incorporated into EDATTAIN. IPUMS Educational attainment, Germany 00 NIU (not in universe) 10 Some elementary education (grade 8 or less) 20 Completed primary 21 Completed grade 10 30 Secondary with access to university 31 Part-time vocational 32 Full-time vocational 33 Technical school 34 Semi-skilled worker certificate 35 Skilled worker certificate 36 Master craftsman certificate 40 Engineering or technical college 50 University 99 Unknown EDUCDE indicates the person's educational attainment in Germany in terms of the level of schooling completed. IPUMS Educational attainment, Europe 00 NIU (not in universe) 10 Less than primary 20 Primary (first stage of basic education) 30 Lower secondary (second stage of basic education) 40 Upper secondary 50 Post-secondary non-tertiary education 60 University completed 99 Unknown/missing EEDATTAIN records the person's educational attainment in terms of the level of schooling completed (degree or other milestone) for the European samples. The emphasis on level completed is critical: a person attending the final year of secondary education receives the code for having completed lower secondary only -- and in some samples only primary. All education that was relevant to the completion of a level should be taken into account even if it was provided outside of schools and universities. EEDATTAIN does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EEDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. In addition to EEDATTAIN, a country-specific education classification is provided which loses no information and reflects the particular educational system of that country. Hungary 1980 and 1990 also give single years of schooling completed, recorded in YRSCHOOL. EEDATTAIN has been classified according to the recommendations of the Conference of European Statisticians for the 2010 Population and Housing Censuses. EEDATTAIN presents a less detailed version of EDATTAIN for the European Samples. 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, unrecoded OCC records the person's primary occupation, classified according to the system used by the respective national census office at the time. For someone with more than one job, the primary occupation is usually the one in which the person spent the most time or earned the most money, although this may not have been explicit in the instructions for a specific census. To ensure confidentiality, very small occupations are recoded to a residual category indicating the persons had an occupation, but the job title is not identified. The number of cases recoded should be too small to affect analyses. OCC is a 4-digit numeric variable. Some samples use fewer than 4 digits. In those cases, the data are right-justified, and the extra leading digits are padded with zeroes. CodesArgentina 1970 - Spanish Argentina 1980 - Spanish Argentina 1991 - Spanish Argentina 2001 - Spanish Armenia 2011 Austria 1971-2001 - German Belarus 1999 - Russian Belarus 2009 Benin 1979 Benin 1992 Benin 2002 Benin 2013 Bolivia 1976 Bolivia 1992 Bolivia 2001 Bolivia 2012 Botswana 1981 Botswana 1991 Botswana 2001 Botswana 2011 Brazil 1960 - Portuguese Brazil 1970 - Portuguese Brazil 1980 - Portuguese Brazil 1991 - Portuguese Brazil 2000 - Portuguese Brazil 2010 Burkina Faso 1985 Burkina Faso 1996 Cambodia 1998 Cambodia 2004 Cambodia 2008 Cambodia 2013 Cambodia 2019 Cameroon 1976 Cameroon 2005 Canada 1971 Canada 1981-1991 Canada 2001 Canada 2011 Chile 1960 Chile 1970 Chile 1982 Chile 1992 Chile 2002 China 1982 China 1990 China 2000 Colombia 1964 Colombia 1973 - Spanish Costa Rica 1973 Costa Rica 1984 Costa Rica 2000 Costa Rica 2011 Cote d'Ivoire 1988 Cote d'Ivoire 1998 Cuba 2002 Cuba 2012 Denmark 1845 Denmark 1880 Denmark 1885 Dominican Republic 1960 Dominican Republic 1970 Dominican Republic 1981 Dominican Republic 2002 Dominican Republic 2010 Ecuador 1962 Ecuador 1974 Ecuador 1982 Ecuador 1990 Ecuador 2001 Ecuador 2010 Egypt 1986 Egypt 2006 El Salvador 1992 El Salvador 2007 Ethiopia 1984 Ethiopia 1994 Fiji 1976 Fiji 1986 Fiji 1996 Fiji 2007 Fiji 2014 Finland 2010 France 1962-1990 - French France 1999 France 2006 France 2011 Germany 1970 Germany 1981 Germany 1987 Ghana 1984 Ghana 2000 Ghana 2010 Greece 1971-1991 - Greek Greece 2001 - Greek Greece 2011 Guatemala 1964 Guatemala 1973 Guatemala 1981 Guatemala 1994 Guatemala 2002 Guinea 1983 Guinea 1996 Guinea 2014 Haiti 1982 Haiti 2003 Honduras 1961 Honduras 1974 Honduras 1988 Honduras 2001 Hungary 1970-1990 Hungary 2001 Hungary 2011 India 1983-2004 India 2009 Indonesia 1971 Indonesia 1976 Indonesia 1980 Indonesia 1985 Indonesia 1990 Indonesia 1995 Indonesia 2005 Iran 2006 Iran 2011 Iraq 1997 Ireland 1901 Ireland 1911 Ireland 1971 Ireland 1981 Ireland 1986 Ireland 1991 Ireland 1996 Ireland 2002 Ireland 2006 Ireland 2011 Ireland 2016 Israel 1972 Israel 1983 Israel 1995 Israel 2008 Italy 2001 Italy 2011 Italy Surveys 2011-2020 Jamaica 1982 Jamaica 1991 Jamaica 2001 Jordan 2004 Kenya 1989 Kyrgyz Republic 1999 Laos 1995 Lesotho 1996 Lesotho 2006 Liberia 1974 Liberia 2008 Malawi 1987 Malawi 1998 Malawi 2008 Malaysia 1970 Malaysia 1980-1991 Malaysia 2000 Mali 1987 Mali 1998 Mali 2009 Mauritius 1990 Mauritius 2000 Mauritius 2011 Mexico 1960 - Spanish Mexico 1970 - Spanish Mexico 1990 - Spanish Mexico 1995 - Spanish Mexico 2000 - Spanish Mexico 2010 Mexico 2015 Mexico 2020 Mexico Surveys 2005-2020 Mongolia 2000 Morocco 1982 Morocco 1994 Morocco 2004 Morocco 2014 Mozambique 1997 Mozambique 2007 Myanmar 2014 Nepal 2001 Nepal 2011 Netherlands 1960 Netherlands 1971 Netherlands 2001 Netherlands 2011 Nicaragua 1971 Nicaragua 1995 Nicaragua 2005 Nigeria 2008 Nigeria 2009 Nigeria 2010 Pakistan 1973 Palestine 1997 Palestine 2007 Palestine 2017 Panama 1960 - Spanish Panama 1970 - Spanish Panama 1980 - Spanish Panama 1990 - Spanish Panama 2000 - Spanish Panama 2010 Papua New Guinea 1980 Papua New Guinea 1990 Papua New Guinea 2000 Paraguay 1962 Paraguay 1972 Paraguay 1982 Paraguay 1992 Paraguay 2002 Peru 1993 Peru 2007 Peru 2017 Philippines 1990 Philippines 2000 Philippines 2010 Poland 1978 Poland 1988 Poland 2002 Portugal 1981 - Portuguese Portugal 1991 - Portuguese Portugal 2001 - Portuguese Portugal 2011 Puerto Rico 1970 Puerto Rico 1980 Puerto Rico 1990 Puerto Rico 2000-2005 Puerto Rico 2010 Puerto Rico 2015 Puerto Rico 2020 Romania 1977 Romania 1992 Romania 2002 Romania 2011 Rwanda 2002 - French Rwanda 2012 Saint Lucia 1991 Senegal 1988 Senegal 2002 Senegal 2013 Slovak Republic 1991 Slovak Republic 2001 Slovak Republic 2011 Sierra Leone 2004 Sierra Leone 2015 Slovenia 2002 South Africa 1996 South Africa 2001 South Africa 2007 South Sudan 2008 Spain 1981 - Spanish Spain 1991 - Spanish Spain 2001 - Spanish Spain 2011 Spain Surveys 2005-2020 Sudan 2008 Suriname 2004 Suriname 2012 Switzerland 1970 Switzerland 1980 Switzerland 1990 Switzerland 2000 Switzerland 2011 Tanzania 1988 Tanzania 2002 Tanzania 2012 Thailand 1970 Thailand 1980 Thailand 1990 Thailand 2000 Togo 1960 Togo 1970 Togo 2010 Trinidad and Tobago 1990 Trinidad and Tobago 2000 Trinidad and Tobago 2011 Turkey 1985 Turkey 1990 Turkey 2000 Uganda 1991 Uganda 2002 Uganda 2014 United Kingdom 1961 United Kingdom 1971 United Kingdom 1991 United Kingdom 2001 United States 1960 United States 1970 United States 1980 United States 1990 United States 2000-2005 United States 2010 United States 2015 United States 2020 Uruguay 1963 Uruguay 1975 Uruguay 1996 Uruguay 2006 Venezuela 1981 Venezuela 1990 Venezuela 2001 - Spanish Vietnam 1989 Vietnam 1999 Vietnam 2009 Vietnam 2019 Zambia 1990 Zambia 2000 Zambia 2010 Zimbabwe 2012 IPUMS Industry, general recode 000 NIU (not in universe) 010 Agriculture, fishing, and forestry 020 Mining and extraction 030 Manufacturing 040 Electricity, gas, water and waste management 050 Construction 060 Wholesale and retail trade 070 Hotels and restaurants 080 Transportation, storage, and communications 090 Financial services and insurance 100 Public administration and defense 110 Services, not specified 111 Business services and real estate 112 Education 113 Health and social work 114 Other services 120 Private household services 130 Other industry, n.e.c. 998 Response suppressed 999 Unknown INDGEN recodes the industrial classifications of the various samples into twelve groups that can be fairly consistently identified across all available samples. The groupings roughly conform to the International Standard Industrial Classification (ISIC). The third digit of INDGEN retains important detail among the service industries that could not be consistently distinguished in all samples. "Industry" refers to the activity or product of the establishment or sector in which a person worked. IPUMS Industry, unrecoded "Industry" refers to the activity or product of the establishment or sector in which the person worked. IND is classified according to the system used by the respective national census office at the time, and is not recoded by IPUMS-International. IND is a 5-digit numeric variable. Some samples use fewer than 5 digits. In those cases, the data are right-justified, and the extra leading digits are padded with zeroes. CodesArgentina 1970 - Spanish Argentina 1980 - Spanish Argentina 1991 - Spanish Argentina 2001 - Spanish Armenia 2001 Armenia 2011 Austria 1971-2001 - German Austria 2011 Bangladesh 1991 Bangladesh 2001 Bangladesh 2011 Belarus 2009 Benin 1979 Benin 1992 Benin 2002 Benin 2013 Bolivia 1976 Bolivia 1992 Bolivia 2001 Bolivia 2012 Botswana 1981 Botswana 1991 Botswana 2001 Botswana 2011 Brazil 1960 - Portuguese Brazil 1970 - Portuguese Brazil 1980 - Portuguese Brazil 1991 - Portuguese Brazil 2000 - Portuguese Brazil 2010 Burkina Faso 1996 Cambodia 1998 Cambodia 2004 Cambodia 2008 Cambodia 2013 Cambodia 2019 Cameroon 2005 Canada 1971 Canada 1981 Canada 1991-2001 Canada 2011 Chile 1960 Chile 1970 Chile 1982 Chile 1992 Chile 2002 Chile 2017 China 1982 China 1990 China 2000 Colombia 1964 - Spanish Colombia 1973 - Spanish Colombia 1993 - Spanish Colombia 2005 - Spanish Costa Rica 1963 Costa Rica 1973 Costa Rica 1984 Costa Rica 2000 Costa Rica 2011 Cote d’Ivoire 1988 Cote d’Ivoire 1998 Cuba 2002 Cuba 2012 Dominican Republic 1960 Dominican Republic 1970 Dominican Republic 1981 Dominican Republic 2002 Dominican Republic 2010 Ecuador 1962 Ecuador 1982 Ecuador 1990 Ecuador 2001 Ecuador 2010 Egypt 1986 Egypt 1996 Egypt 2006 El Salvador 1992 El Salvador 2007 Ethiopia 1984 Ethiopia 1994 Fiji 1966 Fiji 1976 Fiji 1986 Fiji 1996 Fiji 2007 Fiji 2014 Finland 2010 France 1962-1968 - French France 1975-1982 - French France 1990 - French France 1999 France 2006 France 2011 Germany 1970 Germany 1971 Germany 1981 Germany 1987 Ghana 1984 Ghana 2000 Ghana 2010 Greece 1971 Greece 1981 Greece 1991-2001 Greece 2011 Guatemala 1964 Guatemala 1973 Guatemala 1981 Guatemala 1994 Guatemala 2002 Guinea 1983 Guinea 2014 Haiti 1971 Haiti 1982 Haiti 2003 Honduras 1961 Honduras 1974 Honduras 2001 Hungary 2001 Hungary 2011 India 1983 India 1987 India 1993 India 1999 India 2004 India 2009 Indonesia 1971 Indonesia 1976 Indonesia 1980 Indonesia 1985 Indonesia 1990 Indonesia 1995 Indonesia 2000 Indonesia 2005 Indonesia 2010 Iran 2006 Iran 2011 Iraq 1997 Ireland 1971 Ireland 1981 Ireland 1986 Ireland 1991 Ireland 1996 Ireland 2002 Ireland 2006 Ireland 2011 Ireland 2016 Israel 1972 Israel 1983 Israel 1995 Israel 2008 Italy 2001 Italy 2011 Italy Surveys 2011-2013 Italy Surveys 2014-2020 Jamaica 1982 Jamaica 1991 Jamaica 2001 Jordan 2004 Kyrgyz Republic 1999 Kyrgyz Republic 2009 Laos 1995 Laos 2005 Laos 2015 Lesotho 2006 Liberia 1974 Liberia 2008 Malawi 1987 Malawi 1998 Malawi 2008 Malaysia 1970 Malaysia 1980-1991 Malaysia 2000 Mali 1987 Mali 1998 Mali 2009 Mauritius 1990 Mauritius 2000 Mauritius 2011 Mexico 1960 - Spanish Mexico 1970 - Spanish Mexico 1990 - Spanish Mexico 1995 - Spanish Mexico 2000 - Spanish Mexico 2010 Mexico 2015 Mexico 2020 Mexico surveys 2005-2019 Morocco 1982 Morocco 1994 Morocco 2004 Morocco 2014 Mozambique 1997 Mozambique 2007 Myanmar 2014 Nepal 2001 Nepal 2011 Netherlands 1960 Netherlands 1971 Netherlands 2001 Netherlands 2011 Nicaragua 1971 Nicaragua 1995 Nicaragua 2005 Nigeria 2006 Nigeria 2007 Nigeria 2008 Nigeria 2009 Nigeria 2010 Pakistan 1973 Palestine 1997 Palestine 2007 Palestine 2017 Panama 1960 - Spanish Panama 1970-1980 - Spanish Panama 1990-2000 - Spanish Panama 2010 Papua New Guinea 1980 Papua New Guinea 2000 Paraguay 1962 Paraguay 1972 Paraguay 1982 Paraguay 1992 Paraguay 2002 Peru 1993 Peru 2007 Peru 2017 Philippines 1990 Philippines 1995 Philippines 2000 Philippines 2010 Poland 1978 Poland 2002 Portugal 1981 - Portuguese Portugal 1991-2001 - Portuguese Portugal 2011 Puerto Rico 1970-2005 Puerto Rico 2010 Puerto Rico 2015 Puerto Rico 2020 Romania 1977 Romania 1992 Romania 2002 Romania 2011 Rwanda 2002 - French Rwanda 2012 Saint Lucia 1991 Senegal 1988 Senegal 2013 Sierra Leone 2004 South Africa 1996 South Africa 2001-2007 South Sudan 2008 Spain 1981 - Spanish Spain 1991 - Spanish Spain 2001 - Spanish Spain 2011 Spain Surveys 2005-2020 Sudan 2008 Suriname 2004 Suriname 2012 Switzerland 1970-2000 Switzerland 2011 Tanzania 2002 Tanzania 2012 Thailand 1970 Thailand 1980 Thailand 1990 Thailand 2000 Togo 1970 Togo 2010 Trinidad and Tobago 1980 Trinidad and Tobago 1990 Trinidad and Tobago 2000 Turkey 1985 Turkey 1990 Turkey 2000 Uganda 2002 United Kingdom 1961 United Kingdom 1971 United Kingdom 1991 United Kingdom 2001 United States 1960 United States 1970 United States 1980 United States 1990 United States 2000-2005 United States 2010 United States 2015 United States 2020 Uruguay 1963 Uruguay 1985 Uruguay 1996 Uruguay 2006 Venezuela 1981 Venezuela 1990 Venezuela 2001 - Spanish Vietnam 1989 Vietnam 1999 Vietnam 2009 Vietnam 2019 Zambia 1990 Zambia 2000 Zambia 2010 IPUMS Status in employment (class of worker) [general version] NIU (not in universe) 1 Self-employed 2 Wage/salary worker 3 Unpaid worker 4 Other 9 Unknown/missing CLASSWK refers to the status of an economically active person with respect to his or her employment -- that is, the type of explicit or implicit contract of employment with other persons or organizations that the person has in his/her job. In general, the variable indicates whether a person was self-employed, or worked for someone else, either for pay or as an unpaid family worker. CLASSWK is related to EMPSTAT, which is used to define the universe in many samples. Class of worker is often referred to as "status in employment" in other sources. IPUMS Status in employment (class of worker) [detailed version] 000 NIU (not in universe) 100 Self-employed 101 Self-employed, unincorporated 102 Self-employed, incorporated 110 Employer 111 Sharecropper, employer 120 Working on own account 121 Own account, agriculture 122 Domestic worker, self-employed 123 Subsistence worker, own consumption 124 Own account, other 125 Own account, without temporary/unpaid help 126 Own account, with temporary/unpaid help 130 Member of cooperative 140 Sharecropper 141 Sharecropper, self-employed 142 Sharecropper, employee 150 Kibbutz member 199 Self-employed, not specified 200 Wage/salary worker 201 Management 202 Non-management 203 White collar (non-manual) 204 Blue collar (manual) 205 White or blue collar 206 Day laborer 207 Employee, with a permanent job 208 Employee, occasional, temporary, contract 209 Employee without legal contract 210 Wage/salary worker, private employer 211 Apprentice 212 Religious worker 213 Wage/salary worker, non-profit, NGO 214 White collar, private 215 Blue collar, private 216 Paid family worker 217 Cooperative employee 220 Wage/salary worker, government 221 Federal, government employee 222 State government employee 223 Local government employee 224 White collar, public 225 Blue collar, public 226 Public companies 227 Civil servants, local collectives 230 Domestic worker (work for private household) 240 Seasonal migrant 241 Seasonal migrant, no broker 242 Seasonal migrant, uses broker 250 Other wage and salary 251 Canal zone/commission employee 252 Government employment/training program 253 Mixed state/private enterprise/parastatal 254 Government public work program 255 State enterprise employee 256 Coordinated and continuous collaboration job 300 Unpaid worker 310 Unpaid family worker 320 Apprentice, unpaid or unspecified 330 Trainee 340 Apprentice or trainee 350 Works for others without wage 400 Other 999 Unknown/missing CLASSWK refers to the status of an economically active person with respect to his or her employment -- that is, the type of explicit or implicit contract of employment with other persons or organizations that the person has in his/her job. In general, the variable indicates whether a person was self-employed, or worked for someone else, either for pay or as an unpaid family worker. CLASSWK is related to EMPSTAT, which is used to define the universe in many samples. Class of worker is often referred to as "status in employment" in other sources. IPUMS Status in employment (class of worker), Europe NIU (not in universe) 1 Employees 2 Employers 3 Own-account worker 4 Contributing family workers 5 Members of producers' co-operatives 6 Persons not classificable by status 9 Unknown ECLASSWK refers in European Samples to the status of an economically active person with respect to his or her employment -- that is, the type of explicit or implicit contract of employment with other persons or organizations that the person has in his/her job. In general, the variable indicates whether a person was self-employed, or worked for someone else, either for pay or as an unpaid family worker. ECLASSWK is related to EEMPSTAT (employment status), which is used to define the universe for the variable in many samples. ECLASSWK has been classified according to the recommendations given by the Conference of European Statisticians for the 2010 Population and Housing Censuses. "Class of worker" is referred to as "Status in Employment" in the CES recommendations. The former term is used to maintain concordance with IPUMS practice. IPUMS Sector of employment 00 NIU (not in universe) 10 Public 20 Private 21 Private, not elsewhere classified 22 Individual/family enterprise, and self-employed 23 Foreign 30 Mixed: public-private or parastatal 40 Collective or cooperative 50 Foreign government or non-governmental organization 60 Other, unspecified 61 Canal zone 62 Faith-based organization 63 Informal sector 99 Unknown EMPSECT indicates the economic sector in which the person was employed. Economic sector is defined in terms of ownership or control of the enterprise in which the person worked. IPUMS Source of livelihood 01 Work 02 Property or entrepreneurial income 03 Pension 04 Unemployment benefits 05 Illness or disability benefits 06 Other social support 07 Under state security or care 08 Scholarship 09 Apprenticeship 10 Credits, savings, or capital sale 11 Household support (dependent) 12 Assistance from relatives in country 13 Assistance from relatives, acquintances abroad 14 Multiple sources 15 Other 98 Unknown INCSRC indicates the respondent's primary source of livelihood, whether from work, benefits, or various other categories. IPUMS Receives pension or similar benefit 10 Yes, receives a pension 11 Retirement 12 Retirement and other 13 Disability 14 Disability and other 15 Other or type not specified 20 No, does not receive a pension 98 Unknown 99 NIU (not in universe) PENSION indicates whether the respondent received a pension or similar benefits. IPUMS Person number (within household) Germany 1981: All persons 00 Household record 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 This variable indicates the person number (within a household). IPUMS Number of persons in household <svar a="all" v="DE81A403 DE91A002">Household/person number _ _ _<br /></svar> Germany 1981: Persons in private households [discrepancies: none] 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 and higher 99 NIU (not in universe) This variable indicates the running number of persons in household. IPUMS Number of families in household Germany 1981: Persons in private households [discrepancies: none] 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 11 11 99 NIU (not in universe) This variable indicates the running number of families in household, for each person. IPUMS Number of persons in family Germany 1981: Persons in private households [discrepancies: none] 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 18 18 99 NIU (not in universe) This variable indicates the number of persons in family, for each person. IPUMS Number of the person in the family Germany 1981: Persons in private households [discrepancies: none] 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 and higher 99 NIU (not in universe) This variable indicates the running number of persons in a family, for each person, for each person. IPUMS Number of children (of the principal resident) aged under 17 years Germany 1981: Persons in private households [discrepancies: none] 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 99 NIU (not in universe) This variable indicates the number of children aged under 17 in family, for persons in private households. IPUMS Age of children of principal residents <svar a="all" v="DE81A409 DE81A411">3. Date of birth<br /><div class="i1">Day _ _<br />Month _ _<br />Year _ _ _ _</div><br /></svar> Germany 1981: Persons in private households [discrepancies: none] Without children under 17 1 Age under 3 2 Age 3 to under 6 3 Age under 3, and age 3 to under 6 4 Age 6 to under 17 5 Age under 3, and age 6 to under 17 6 Age under 3 to 6, and age 6 to under 17 7 Age under 3, and age 3 to under 6, and age 6 to under 17 9 NIU (not in universe) This variable indicates the age of children in a family, for persons in private households. IPUMS Type of occupant Germany 1981: All persons Principal resident 1 Secondary resident This variable indicates the type of occupant (primary or secondary) for each person. IPUMS Age <svar a="all" v="DE81A409 DE81A411">3. Date of birth<br /><div class="i1">Day _ _<br />Month _ _<br />Year _ _ _ _</div><br /></svar> Germany 1981: 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 95 096 96 097 97 098 98 099 99 100 100 101 101 102 102 103 103 104 104 105 105 106 106 This variable indicates a person's age. IPUMS Sex <svar a="all" v="DE81A412">2. Gender<br /><div class="i1">[] 1 Male<br />[] 2 Female</div><br /></svar> Germany 1981: All persons 1 Male 2 Female This variable indicates a person's sex. IPUMS Marital status <svar a="all" v="DE81A413 DE81A414">5. Marital status<br /><div class="i1">[] 1 Married</div><br /><div class="i2">Year of marriage: _ _ _ _</div><br /><div class="i1">[] 2 Single<br />[] 3 Widowed<br />[] 4 Divorced</div><br /></svar> Germany 1981: All persons 1 Single 2 Married 3 Widowed 4 Divorced This variable indicates a person's marital status. IPUMS Duration of marriage (years) <svar a="all" v="DE81A413 DE81A414">5. Marital status<br /><div class="i1">[] 1 Married</div><br /><div class="i2">Year of marriage: _ _ _ _</div><br /><div class="i1">[] 2 Single<br />[] 3 Widowed<br />[] 4 Divorced</div><br /></svar> Germany 1981: Persons who are married 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 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 99 NIU (not in universe) This variable indicates a person's duration of marriage (in years). IPUMS Common law relationship Germany 1981: All persons Not common law relationship 1 Partner in the 1st common law relationship in household 2 Partner in the 2nd common law relationship in household This variable indicates the common law relationship for a person. IPUMS Number of children born <svar a="all" v="DE81A416">6. Number of children born (only to be filled out for women): _ _<br /></svar> Germany 1981: Females 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 99 NIU (not in universe) This variable indicates the number of children born to a female. IPUMS Type of income <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons 01 Income from employment 02 Income from employment and old age pension 03 Income from employment and disability pension 04 Income from employment and other pension 05 Income from old age pension 06 Income from disability pension 07 Income from other pension 08 Income from apprenticeship 09 Income from scholarship 10 Other income 11 Without own income 99 Unknown This variable indicates a person's type of income. IPUMS Source of livelihood <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons Person has own income 1 Person (not child, not spouse) is supported by economically active persons 2 Person is child and is supported by economically active persons 3 Person is spouse and is supoprted by economically active persons 4 Person (not child, not spouse) is supported by economically non-active persons 5 Person is child and is supported by economically non-active persons 6 Person is spouse and is supoprted by economically non-active persons 9 Person is secondary resident This variable indicates a person's source of livelihood. IPUMS Formerly working Germany 1981: All persons Not formerly working 1 Person is no longer working This variable indicates that a person is a former worker. IPUMS Education status <svar a="all" v="DE81A420">8. Are you currently:<br /><div class="i1">[] 1 Pupil<br />[] 2 Apprentice<br />[] 3 Full-time university student</div><br /></svar> Germany 1981: All persons 1 Pupil 2 Apprentice 3 Student 4 Person not in education process This variable indicates a person's education status. IPUMS Highest school grade obtained <svar a="all" v="DE81A421">9. Education completed<br /><div class="i1">[] 1 Eighth grade<br />[] 2 Tenth grade<br />[] 3 Abitur [qualification for university entrance]</div><br /></svar> Germany 1981: All persons 1 Grade 8 2 Grade 10 3 Abitur (access to university) 4 Without stated grade This variable indicates the highest school grade obtained for a person. IPUMS Semi-craftsman certificate <svar a="all" v="DE81A422 DE81A423 DE81A424 DE81A425 DE81A426">10. Vocational education completed (please mark all applicable circles)<br /><div class="i1">[] 1 Semi-skilled worker; occupation ____<br />[] 2 Skilled worker; occupation ____<br />[] 3 Master; occupation ____<br />[] 4 Vocational college degree; field ____<br />[] 5 University degree, field ____<br />What other education? ____</div><br /></svar> Germany 1981: All persons 1 No 2 Yes This variable indicates that a person has a semi-craftsman certificate. IPUMS Craftsman certificate <svar a="all" v="DE81A422 DE81A423 DE81A424 DE81A425 DE81A426">10. Vocational education completed (please mark all applicable circles)<br /><div class="i1">[] 1 Semi-skilled worker; occupation ____<br />[] 2 Skilled worker; occupation ____<br />[] 3 Master; occupation ____<br />[] 4 Vocational college degree; field ____<br />[] 5 University degree, field ____<br />What other education? ____</div><br /></svar> Germany 1981: All persons 1 No 2 Yes This variable indicates that a person has a craftsman certificate. IPUMS Master craftsman certificate <svar a="all" v="DE81A422 DE81A423 DE81A424 DE81A425 DE81A426">10. Vocational education completed (please mark all applicable circles)<br /><div class="i1">[] 1 Semi-skilled worker; occupation ____<br />[] 2 Skilled worker; occupation ____<br />[] 3 Master; occupation ____<br />[] 4 Vocational college degree; field ____<br />[] 5 University degree, field ____<br />What other education? ____</div><br /></svar> Germany 1981: All persons 1 No 2 Yes This variable indicates that a person has a master craftsman certificate. IPUMS Technical college diploma <svar a="all" v="DE81A422 DE81A423 DE81A424 DE81A425 DE81A426">10. Vocational education completed (please mark all applicable circles)<br /><div class="i1">[] 1 Semi-skilled worker; occupation ____<br />[] 2 Skilled worker; occupation ____<br />[] 3 Master; occupation ____<br />[] 4 Vocational college degree; field ____<br />[] 5 University degree, field ____<br />What other education? ____</div><br /></svar> Germany 1981: All persons 1 No 2 Yes This variable indicates that a person has a technical college diploma. IPUMS University degree <svar a="all" v="DE81A422 DE81A423 DE81A424 DE81A425 DE81A426">10. Vocational education completed (please mark all applicable circles)<br /><div class="i1">[] 1 Semi-skilled worker; occupation ____<br />[] 2 Skilled worker; occupation ____<br />[] 3 Master; occupation ____<br />[] 4 Vocational college degree; field ____<br />[] 5 University degree, field ____<br />What other education? ____</div><br /></svar> Germany 1981: All persons 1 No 2 Yes This variable indicates that a person has a university degree. IPUMS Profession <svar v=" DE81A427 DE81A428 DE81A429 DE81A441 DE81A438">Fill out questions 11-13 for employed persons only (not apprentices).<br /></svar></p> <p><svar a="all" v="DE81A427 DE81A438">12. Job held at the place of work: ____<br /></svar> Germany 1981: Persons age 15+ with a profession [discrepancies: unverifiable] 0270 Mathematicians 0410 Physics-technology technician 0450 Meteorology engineering 0470 Physicists 0611 Chemistry technician 0612 Chemical fibers technician 0613 Technician for chemical photography 0614 Plastic processing technician 0621 Laboratory technician (chemicals, photo) 0622 Food chemistry laboratory 0630 Chemistry foreman 0651 Plastic processing engineer 0652 Organic chemistry engineer 0653 Medical laboratory engineer 0654 Laboratory manager 0671 Synthesis process chemist 0672 Process chemical engineer 0811 Biology laboratory worker, preparation 0812 Zoo keeper 0830 Zoo manager 0851 Biotechnology worker 0852 Biomedical engineer 0870 Biologist (HS) 1011 Geology skilled worker 1012 Geology skilled worker - drilling 1020 Mapping, surveying skilled worker 1030 Geoplastic foreman 1050 Geological engineer (FS) 1060 Surveying manager 1071 Geologist 1072 Surveyor, geographer 1270 Psychologist 1411 Child care worker 1412 Nursery worker 1413 Medical worker, technical service 1414 Healthcare worker 1415 Internal consultation 1416 Dental assistant 1417 Practitioner 1418 Health worker 1421 Pharmacy worker 1422 Fine eye optician 1423 Dental techncian 1424 Dietary cooks 1425 Disinfector 1426 Prosthetist 1427 Orthopedic shoemaker 1428 Bandage 1429 Medical worker, technical section 1431 Dental technician - master 1432 Orthopedic shoemaker - craft master 1433 Master prosthetist - bandage craft 1434 Optician 1451 Medical technician assistant 1452 Hygiene specialist 1453 Optician (FS) 1454 Dietitian 1455 Dental technician (FS) 1456 Psysiotherapist 1457 Taxidermist 1461 Nurse / carer 1462 Children's nurse 1463 Surgery nurse 1464 Dental nurse 1465 Midwife 1466 Social worker 1471 Doctor 1472 Dentist 1550 Pharmaceutical engineer (FS) 1570 Pharmacist (HS) 1650 Veterinary surgeon (FS) 1670 Veterinarian 1810 Mining worker 1830 Mining engineer 1850 Mining engineer (FS) 1870 Mining engineer (HS) 2010 Power machinist 2020 Power plant worker 2030 Power plant manager 2050 Energy engineer (FS) 2070 Energy engineer (HS) 2211 Steel mill worker 2212 Foundry worker 2221 Mill worker 2222 Foundry, hardener worker 2223 Model builder 2224 Material tester (metal) 2230 Metallurgy manager 2250 Engineer for metallurgy and resources (FS) 2270 Material engineer (HS) 2411 Machine and plant fitter 2412 Piping elements technician 2413 Machine builder 2414 Steel ship builder 2415 Electrical machinery builder 2416 Engineer designer 2421 Mechanic 2422 Fitter 2423 Locksmith 2424 Automotive locksmith 2425 Aircraft mechanic 2426 Agricultural machine fitter 2427 Plumbing installer 2428 Insulation worker 2431 Machine building manager 2432 Locksmith, master plumber 2433 Mechanic master craftsman 2441 Vehicle repair manager 2442 Machine maintenance manager 2450 Mechanical engineer (FS) 2470 Mechanical engineer (HS) 2611 Resources production worker 2612 Welder 2613 Plant technology worker 2614 Machine cutting worker 2615 Lathe operator 2616 Mill worker 2617 Sander, polisher 2620 Surface finisher 2630 Manifacturing and processing manager 2650 Process engineer (FS) 2670 Process engineer (HS) 2811 Precision engineering engravers 2812 Watch manufacturer 2813 Hunting weapons mechanic 2821 Precision optics 2822 Eyeware optician, frames maker 2830 Precision engineering manager 2850 Precision mechanics engineer (FS) 2870 Precision mechanics engineer (HS) 3011 Electrical, radio mechanic 3012 Electronics worker 3013 Electronics designer 3021 Electrician 3022 Lead mechanic 3030 Electrical engineering manager 3050 Electrical engineer (FS) 3070 Electrical engineer for electronics (HS) 3211 BMSR (Berlin Magnetically Shielded Room) technical worker 3212 Data processing worker 3230 BMSR (Berlin Magnetically Shielded Room) technical manager 3250 Automation engineer (FS) 3270 Automation engineer (HS) 3411 Woodwork and wood products worker 3412 Carpenter, furnitor worker (excluding cabinetmakers) 3420 Other wood worker 3430 Woodwork and wood products manager 3450 Wood processing engineer (FS) 3470 Wood processing engineer (HS) 3611 Wood pulp worker 3612 Paper processing worker 3613 Paper production worker 3630 Pulp / paper manager 3650 Paper processing engineer (FS) 3670 Paper processing engineer (HS) 3811 Printer 3812 Setter 3820 Book binder 3830 Business polygraphy manager 3850 Polygraphy engineer (FS) 3870 Processor/ polygraph engineer 4011 Textile technology worker 4012 Textile fibre worker 4013 Textile worker 4014 Textile manufacturer 4015 Textile artist 4021 Clothing worker 4022 Padding technology worker 4030 Textile technology manager 4041 Light clothing technician 4042 Textile processing master craftsman 4043 Padding technology manager 4050 Textile engineer (FS) 4060 Clothing manager (FS) 4070 Processors engineer (textiles) (HS) 4211 Tanner 4212 Furrier 4221 Leather garments, leather goods and furs worker 4222 Shoe worker 4230 Leather, fur manager 4250 Leather goods processing engineer (FS) 4270 Leather goods processing engineer (HS) 4411 Glass company worker 4412 Glass processor 4421 Ceramic molder, painter 4422 Sintering worker 4423 Fireproof worker 4430 Glass technology manager 4440 Ceramic manager 4450 Glass and ceramic engineer 4470 Glass engineer (FS) 4611 Fish processor 4612 Baker, confectioner 4613 Food and beverage manufacturer 4614 Dairy worker 4615 Butcher, meat product manufacturer 4616 Tobacco worker 4617 Deep sea fishing 4620 Spirits producer 4631 Meat processing manager 4632 Pastry manager 4633 Other food and beverage manufacturer manager 4640 Beverages manager 4650 Food scientist (FS) 4670 Food scientist (HS) 4811 Specialized shop assistant 4812 Waiter, server 4813 Chefs 4821 Chimneysweep, building, vehicle cleaner 4822 Beauty salon, cosmetics [worker] 4823 Custom tailor 4824 Shoe maker 4825 Textile cleaning worker 4826 Other service industry worker 4830 Restaurant manager 4841 Mass production tailoring manager 4842 Master barber 4843 Shoe and leather goods repair manager 4844 Watchmaker 4845 Other service industry manager 4850 Textile engineer (textile only FS) 5011 Plant production mechanization worker 5012 Garderer, winewaker 5013 Animal production mechanization worker 5014 Improvement worker 5015 Agricultural chemist 5021 Forestry worker 5022 Inland fisher 5031 Plant production manager 5032 Animal production manager 5033 Other land economics manager 5041 Forestry manager 5042 Inland fishery manager 5051 Plant production agricultural engineer (FS) 5052 Animal production agricultural engineer (FS) 5061 Forestry engineer (FS) 5062 Fishing engineer (FS) 5071 Agricultural engineer (HS) 5072 Forestry engineer (HS) 5073 Fishing engineer (HS) 5210 Sewage worker 5230 Water resource management manager 5250 Water resource management engineer (FS) 5270 Water resource management engineer (HS) 5411 Binding worker 5412 Materials testing [worker] 5413 Ceramic machinery worker 5430 Building materials production manager 5450 Materials engineer (FS) 5470 Materials engineer (HS) 5611 Joiner 5612 Draughtsman 5613 Industrial furnace worker 5614 Carpenter 5615 Mason 5616 Roofer 5617 Painter 5618 Removal worker 5619 Glazier 5621 Low worker 5622 Road worker 5623 Railroad worker 5624 Water engineering worker 5625 Construction machinist 5626 Stonemason, acid protection worker 5627 Construction worker 5628 Cast worker 5629 Steel worker 5630 Light removal [worker] 5640 Civil, traffic engineering manager 5651 Civil engineer (building) (FS) 5652 Armament technical engineer (FS) 5653 Civil engineer (HS) 5654 Traffic construction engineer (FS) 5655 Water marketing manager (FS) 5671 Civil engineer /architect (FS) 5672 Civil engineer / road construction (HS) 5673 Civil engineer / railways (HS) 5674 Civil engineer / water management (HS) 5675 Other building engineer (HS) 5811 Professional driver 5812 Railway transport technology worker 5813 Trade sailors and inland navigators 5814 Transport, vehicle mechanics worker 5815 Mechanic 5821 Stocks transport worker 5822 Transportation and lifting manager 5830 Ship tour manager 5840 Transport and warehouse manager 5850 Traffic engineer (FS) 5870 Transport engineer (HS) 6011 Technology and telecommunications mechanics worker 6012 Telephone, telex services worker 6020 Post operations worker 6030 Technology manager 6050 Telecommunications manager (FS) 6070 Telecommunications manager (HS) 6210 Marketing, financial consultant 6220 Writing technology worker 6230 Economics engineering manager 6250 Economist (FS) 6270 Economist (HS) 6410 Archive worker 6450 Social scientist (FS) 6461 Archivist 6462 Museum guard 6471 State and legal scholars 6472 Fire, safety engineer 6473 Social scientist (HS) 6474 Historian, folklorist 6611 Photographer 6612 Movie reproduction technology worker 6613 Common use letters and posters' artist 6621 Toy worker 6622 Art goods worker 6623 Musical instruments worker 6630 Crafts, musical compositions, culture manager 6650 Art student (FS) 6670 Art student (HS) 7051 Professional teacher, giving lectures (FS) 7052 High school teacher for lower classes (FS) 7053 Educator (FS) 7054 Kindergarten teacher (FS) 7055 Nursery educator 7070 Teacher for vocational, technical, special schools (HS) 7270 High school teacher (HS) 7470 Education researcher 7810 Library, bookseller worker 7850 Librarian, bookseller (FS) 7860 Journalist, documentator (FS) 7871 Philologist, linguist 7872 Journalist, library scientist (HS) 7950 Linguist (FS) 7970 Linguist (HS) 8070 Theologist 9910 Worker, no details 9920 Worker, no report 9930 Manager, no report 9950 Professional educator, unspecified 9970 High school educator, unspecified 9999 NIU (not in universe) This variable indicates a person's occupation. IPUMS Class of worker <svar v=" DE81A427 DE81A428 DE81A429 DE81A441 DE81A438">Fill out questions 11-13 for employed persons only (not apprentices).<br /></svar></p> <p><svar a="all" v="DE81A428 DE81A429 DE81A441">11. Are you employed as:<br /><div class="i1">[] 1 Worker/employee<br />[] 2 Agricultural production cooperative member<br />[] 3 Other cooperative member<br />[] 4 Person of liberal professions<br />[] 5 Self-employed<br />[] 6 Family worker</div><br /></svar> Germany 1981: All persons Not working, not retired 1 Worker (blue- and white-collar) 2 Member of agricultural cooperative 3 Member of other cooperatives 4 Freelance worker 5 Self-employed 6 Assisting family worker This variable indicates a person's worker class. IPUMS Social group of person <svar v=" DE81A427 DE81A428 DE81A429 DE81A441 DE81A438">Fill out questions 11-13 for employed persons only (not apprentices).<br /></svar></p> <p><svar a="all" v="DE81A428 DE81A429 DE81A441">11. Are you employed as:<br /><div class="i1">[] 1 Worker/employee<br />[] 2 Agricultural production cooperative member<br />[] 3 Other cooperative member<br />[] 4 Person of liberal professions<br />[] 5 Self-employed<br />[] 6 Family worker</div><br /></svar> Germany 1981: All persons 1 Worker (not in production) 2 Worker in production 3 White-collar worker 4 Intellectual worker 5 Farmer in cooperative 6 Agricultural specialist 7 Member of other cooperatives 8 Self-employed 9 Other/unknown This variable indicates the social group of a person. IPUMS Location of work <svar a="all" v="DE81A430">13. Name and address of the place of work<br /><div class="i1">____ Name<br />____ City, street, number<br />____ District</div><br /><br />Location of work (in case this does not coincide with the address of the place of work)<br /><div class="i1">____ Work city, street<br />____ District</div><br /></svar> Germany 1981: All persons Not economically active 1 Economically active, not commuting 2 Economically active, commuting to work outside of community of residence but inside of county 1981 3 Economically active, commuting to work outside of county of residence but inside of district 1981 4 Economically active, commuting to work outside of district of residence 1981 This variable indicates a person's location of work. IPUMS Income from employment <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons No 1 Yes This variable indicates a person's income from employment. IPUMS Income from apprenticeship/scholarship <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: Persons in school or apprentices [discrepancies: none] No 1 Yes This variable indicates a person's income from an apprenticeship or scholarship. IPUMS Income from old age pension/support <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons No 1 Yes This variable indicates a person's income from old age pension or support. IPUMS Income from disability pension/support <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons No 1 Yes This variable indicates a person's income from disability pension or support. IPUMS Income from other pension <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons No 1 Yes This variable indicates a person's income from any other pension. IPUMS No own income <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons Has own income 1 No own income This variable indicates a person does not have his or her own income. IPUMS Income from other sources <svar a="all" v="DE81A417 DE81A418 DE81A431 DE81A432 DE81A433 DE81A434 DE81A435 DE81A436 DE81A437">7. What type of income do you draw? (Please mark all applicable types)<br /><div class="i1">[] 1 Income from employment<br />[] 2 Apprentice's wages/scholarship<br />[] 3 Pension/retirement<br />[] 4 Disability pension/social benefit<br />[] 5 Other types of social benefit<br />[] 6 No individual income</div><br /><div class="i2">Which other types? ____</div><br /></svar> Germany 1981: All persons No 1 Yes This variable indicates a person's income from other sources. IPUMS Occupation <svar v=" DE81A427 DE81A428 DE81A429 DE81A441 DE81A438">Fill out questions 11-13 for employed persons only (not apprentices).<br /></svar></p> <p><svar a="all" v="DE81A427 DE81A438">12. Job held at the place of work: ____<br /></svar> Germany 1981: Persons who are economically active [discrepancies: none] 0270 Mathematicians 0410 Physics-technology technician 0450 Meteorology engineering 0470 Physicists 0611 Chemistry technician 0612 Chemical fibers technician 0613 Technician for chemical photography 0614 Plastic processing technician 0621 Laboratory technician (chemicals, photo) 0622 Food chemistry laboratory 0630 Chemistry foreman 0651 Plastic processing engineer 0652 Organic chemistry engineer 0653 Medical laboratory engineer 0654 Laboratory manager 0671 Synthesis process chemist 0672 Process chemical engineer 0681 Chemical auxiliary worker 0682 Fibers worker 0683 Chemistry laboratory assistant 0684 Photo chemical worker 0685 Plastic, flexible plastic processor 0811 Biology laboratory worker, preparation 0812 Zoo keeper 0830 Zoo manager 0851 Biotechnology worker 0852 Biomedical engineer 0870 Biologist (HS) 1011 Geology skilled worker 1012 Geology skilled worker - drilling 1020 Mapping, surveying skilled worker 1030 Geoplastic foreman 1050 Geological engineer (FS) 1060 Surveying manager 1071 Geologist 1072 Surveyor, geographer 1270 Psychologist 1411 Child care worker 1412 Nursery worker 1413 Medical worker, technical service 1414 Healthcare worker 1415 Internal consultation 1416 Dental assistant 1417 Health practitioner 1418 Health worker 1421 Pharmacy worker 1422 Fine eye optician 1423 Dental technician 1424 Dietary cooks 1425 Disinfector 1426 Prosthetist 1427 Orthopedic shoemaker 1428 Bandage/splint setter 1429 Medical worker, technical section 1431 Dental technician - master 1432 Orthopedic shoemaker - craft master 1433 Master prosthetist - bandage craft 1434 Optician 1451 Medical technician assistant 1452 Hygiene specialist 1453 Optician (FS) 1454 Dietitian 1455 Dental technician (FS) 1456 Psysiotherapist 1457 Taxidermist 1461 Nurse / carer 1462 Children's nurse 1463 Surgery nurse 1464 Dental nurse 1465 Midwife 1466 Social worker 1471 Doctor 1472 Dentist 1480 Medical services assistant 1550 Pharmaceutical engineer (FS) 1570 Pharmacist (HS) 1650 Veterinary surgeon (FS) 1670 Veterinarian 1680 Veterinary assistant 1810 Mining worker 1830 Mining engineer 1850 Mining engineer (FS) 1870 Mining engineer (HS) 1881 Mining auxiliary worker / production 1882 Mining auxiliary worker / processing 1883 Other mining auxiliary worker 2010 Power machinist 2020 Power plant worker 2030 Power plant manager 2050 Energy engineer (FS) 2070 Energy engineer (HS) 2081 Heater (power, gas) 2082 Other energy relief worker 2211 Steel mill worker 2212 Foundry worker 2221 Mill worker 2222 Foundry, hardener worker 2223 Model builder 2224 Material tester (metal) 2230 Metallurgy manager 2250 Engineer for metallurgy and resources (FS) 2270 Material engineer (HS) 2281 Metallurgy helper 2282 Melt and foundry helper 2283 Rolling mill helper 2284 Forge helper 2285 Other metallurgy helper 2411 Machine and plant fitter 2412 Piping elements technician 2413 Machine builder 2414 Steel ship builder 2415 Electrical machinery builder 2416 Engineer designer 2421 Mechanic 2422 Fitter 2423 Locksmith 2424 Automotive locksmith 2425 Aircraft mechanic 2426 Agricultural machine fitter 2427 Plumbing installer 2428 Insulation worker 2431 Machine building manager 2432 Locksmith, master plumber 2433 Mechanic master craftsman 2441 Vehicle repair manager 2442 Machine maintenance manager 2450 Mechanical engineer (FS) 2470 Mechanical engineer (HS) 2481 Installation helper (mechanical engineering) 2482 Mechanics helper 2483 Plumbing, installation helper 2611 Resources production worker 2612 Welder 2613 Plant technology worker 2614 Machine cutting worker 2615 Lathe operator 2616 Mill worker 2617 Sander, polisher 2620 Surface finisher 2630 Manufacturing and processing manager 2650 Process engineer (FS) 2670 Process engineer (HS) 2681 Metal cutting aid worker 2682 Metal connector helper (soldering, riveting, pressing) 2683 Metal deformation helper 2684 Metal coating worker 2685 Other metal worker 2811 Precision engineering engravers 2812 Watch manufacturer 2813 Hunting weapons mechanic 2821 Precision optics 2822 Eyeware optician, frames maker 2830 Precision engineering manager 2850 Precision mechanics engineer (FS) 2870 Precision mechanics engineer (HS) 2881 Fine mechanics auxiliary worker 2882 Optical assistant 2883 Engravers' agent 3011 Electrical, radio mechanic 3012 Electronics worker 3013 Electronics designer 3021 Electrician 3022 Lead mechanic 3030 Electrical engineering manager 3050 Electrical engineer (FS) 3070 Electrical engineer for electronics (HS) 3081 Electrical installation helper 3082 Cable manufacturer 3083 Other electric and electronic aid worker 3211 BMSR (Berlin Magnetically Shielded Room) technical worker 3212 Data processing worker 3230 BMSR (Berlin Magnetically Shielded Room) technical manager 3250 Automation engineer (FS) 3270 Automation engineer (HS) 3411 Woodwork and wood products worker 3412 Carpenter, furniture worker (excluding cabinetmakers) 3420 Other wood worker 3430 Woodwork and wood products manager 3450 Wood processing engineer (FS) 3470 Wood processing engineer (HS) 3481 Wood-based materials worker 3482 Wood technology worker 3483 Carpenter and frame carpenter helper 3484 Wheeler, cooper, boat-building worker 3485 Other wood products worker 3486 Basket, brush, brush-maker helper 3611 Wood pulp worker 3612 Paper processing worker 3613 Paper production worker 3630 Pulp / paper manager 3650 Paper processing engineer (FS) 3670 Paper processing engineer (HS) 3681 Paper production helper 3682 Paper, paper processing helper 3811 Printer 3812 Setter 3820 Book binder 3830 Business polygraphy manager 3850 Polygraphy engineer (FS) 3870 Processor/ polygraph engineer 3881 Printer's helper 3882 Book binder's helper 3883 Copy helper 4011 Textile technology worker 4012 Textile fibre worker 4013 Textile worker 4014 Textile manufacturer 4015 Textile artist 4021 Clothing worker 4022 Padding technology worker 4030 Textile technology manager 4041 Light clothing technician 4042 Textile processing master craftsman 4043 Padding technology manager 4050 Textile engineer (FS) 4060 Clothing manager (FS) 4070 Processors engineer (textiles) (HS) 4081 Spinner and weaver helper 4082 Textile manufacturer helper 4083 Clothing assistant 4084 Finery and jewelry maker's helper 4085 Other textile helper 4211 Tanner 4212 Furrier 4221 Leather garments, leather goods and furs worker 4222 Shoe worker 4230 Leather, fur manager 4250 Leather goods processing engineer (FS) 4270 Leather goods processing engineer (HS) 4281 Leather auxiliary worker 4282 Synthetic leather manufacturer 4283 Leather and synthetic leather worker 4411 Glass company worker 4412 Glass processor 4421 Ceramic molder, painter 4422 Sintering worker 4423 Fireproof worker 4430 Glass technology manager 4440 Ceramic manager 4450 Glass and ceramic engineer 4470 Glass engineer (FS) 4481 Glass maker agent 4482 Ceramics helper 4611 Fish processor 4612 Baker, confectioner 4613 Food and beverage manufacturer 4614 Dairy worker 4615 Butcher, meat product manufacturer 4616 Tobacco worker 4617 Deep sea fishing 4620 Spirits producer 4631 Meat processing manager 4632 Pastry manager 4633 Other food and beverage manufacturer manager 4640 Beverages manager 4650 Food scientist (FS) 4670 Food scientist (HS) 4681 Meat processing helper 4682 Fish processing helper 4683 Oleochemical and milk worker 4684 Cereals, potators, fruit and vegetable processing worker 4685 Sugar and confectionary helper 4686 Beverage adjustment [worker] 4687 Tobacco manufacturer 4688 Other food and beverage manufacturer assistant 4811 Specialized shop assistant 4812 Waiter, server 4813 Chefs 4821 Chimneysweep, building, vehicle cleaner 4822 Beauty salon, cosmetics [worker] 4823 Custom tailor 4824 Shoe maker 4825 Textile cleaning worker 4826 Other service industry worker 4830 Restaurant manager 4841 Mass production tailoring manager 4842 Master barber 4843 Shoe and leather goods repair manager 4844 Watchmaker 4845 Other service industry manager 4850 Textile engineer (textile only FS) 4881 Product helper 4882 Restaurant, kitchen helper 4883 Service assistant 5011 Plant production mechanization worker 5012 Gardener, winemaker 5013 Animal production mechanization worker 5014 Improvement worker 5015 Agricultural chemist 5021 Forestry worker 5022 Inland fisher 5031 Plant production manager 5032 Animal production manager 5033 Other land economics manager 5041 Forestry manager 5042 Inland fishery manager 5051 Plant production agricultural engineer (FS) 5052 Animal production agricultural engineer (FS) 5061 Forestry engineer (FS) 5062 Fishing engineer (FS) 5071 Agricultural engineer (HS) 5072 Forestry engineer (HS) 5073 Fishing engineer (HS) 5081 Crops improvement worker 5082 Horticulture agent 5083 Animal farm helper 5084 Forestry assistant 5210 Sewage worker 5230 Water resource management manager 5250 Water resource management engineer (FS) 5270 Water resource management engineer (HS) 5280 Water industry worker 5411 Binding worker 5412 Materials testing [worker] 5413 Ceramic machinery worker 5430 Building materials production manager 5450 Materials engineer (FS) 5470 Materials engineer (HS) 5480 Construction materials worker 5611 Joiner 5612 Draughtsman 5613 Industrial furnace worker 5614 Carpenter 5615 Mason 5616 Roofer 5617 Painter 5618 Removal worker 5619 Glazier 5621 Low worker 5622 Road worker 5623 Railroad worker 5624 Water engineering worker 5625 Construction machinist 5626 Stonemason, acid protection worker 5627 Construction worker 5628 Cast worker 5629 Steel worker 5630 Light removal [worker] 5640 Civil, traffic engineering manager 5651 Civil engineer (building) (FS) 5652 Armament technical engineer (FS) 5653 Civil engineer (HS) 5654 Traffic construction engineer (FS) 5655 Water marketing manager (FS) 5671 Civil engineer /architect (FS) 5672 Civil engineer / road construction (HS) 5673 Civil engineer / railways (HS) 5674 Civil engineer / water management (HS) 5675 Other building engineer (HS) 5681 Labourer 5682 Civil engineering assistant 5683 Track construction helper 5811 Professional driver 5812 Railway transport technology worker 5813 Trade sailors and inland navigators 5814 Transport, vehicle mechanics worker 5815 Mechanic 5821 Stocks transport worker 5822 Transportation and lifting manager 5830 Ship tour manager 5840 Transport and warehouse manager 5850 Traffic engineer (FS) 5870 Transport engineer (HS) 5881 Vehicle driver guide 5882 Eligibility and lifting central operator 5883 Other traffic assistant 5884 Stock and delivery helper 6011 Technology and telecommunications mechanics worker 6012 Telephone, telex services worker 6020 Post operations worker 6030 Technology manager 6050 Telecommunications manager (FS) 6070 Telecommunications manager (HS) 6081 Telecommunications helper 6082 Telecommunications labourer 6083 Postal service assistant 6210 Marketing, financial consultant 6220 Writing technology worker 6230 Economics engineering manager 6250 Economist (FS) 6270 Economist (HS) 6280 Administrator, office assistant 6410 Archive worker 6450 Social scientist (FS) 6461 Archivist 6462 Museum guard 6471 State and legal scholars 6472 Fire, safety engineer 6473 Social scientist (HS) 6474 Historian, folklorist 6480 Archive, museum helper 6611 Photographer 6612 Movie reproduction technology worker 6613 Common use letters and posters' artist 6621 Toy worker 6622 Art goods worker 6623 Musical instruments worker 6630 Crafts, musical compositions, culture manager 6650 Art student (FS) 6670 Art student (HS) 6680 Creative artist's helper 7051 Professional teacher, giving lectures (FS) 7052 High school teacher for lower classes (FS) 7053 Educator (FS) 7054 Kindergarten teacher (FS) 7055 Nursery educator 7070 Teacher for vocational, technical, special schools (HS) 7080 Youth education helper 7270 High school teacher (HS) 7470 Education researcher 7810 Library, bookseller worker 7850 Librarian, bookseller (FS) 7860 Journalist, documentator (FS) 7871 Philologist, linguist 7872 Journalist, library scientist (HS) 7950 Linguist (FS) 7970 Linguist (HS) 8070 Theologist 9110 Head of state and business organs, social organizations 9120 Director of facilities and operations 9131 Senior worker: health, support 9132 Senior worker: trade, food, service 9133 Senior worker: building 9134 Senior worker: transportation, storage, business 9135 Senior worker: economics 9136 Senior worker: culture, art 9137 Senior worker: education 9140 Other leadership worker 9210 Traffic and transport official 9220 Scientist, employee 9230 Organization management official 9240 State, legal, fire, safety official 9250 Education official 9260 Religious affairs official 9270 Official in other areas 9310 Other professional - mainly intellectual activities 9320 Other professional - mainly physical activities 9910 Worker, no details 9920 Worker, no report 9930 Manager, no report 9950 Professional educator, unspecified 9970 High school educator, unspecified 9990 Unknown 9999 NIU (not in universe) This variable indicates a person's actual occupation, for those who are economically active. IPUMS Industry Germany 1981: Persons age 14+ who are economically active [discrepancies: none] 100 Fuel and energy 110 Chemical industry 120 Metallurgy 130 Construction materials 140 Water management 150 Machinery and transportation equipment 160 Electrical engineering/electronics/instruments 170 Lightweight industry (excluding textiles) 180 Textiles industry 190 Food industry 200 Construction industry 310 Agriculture 320 Inland fisheries 330 Veterinary 332 Agro-chemicals, including plant protection 341 Drying and pelleting industry 350 Forestry 410 Traffic 450 Post and telecommunications 510 Foreign trade 520 In-country trade 530 Refrigerators and warehouses 610 Economic governing bodies 620 Research and development centers of economic institutions 630 Design and construction companies 640 Geological investigations 650 Publishers 660 Combined repairs 670 Textile cleaning 680 Computing enterprises 690 Other areas of production 700 Business services 810 Science and research 821 Pre-school education facilities 822 General schools 823 Other facilities of public education 824 Vocational training 825 Professional and higher education 827 Facilities for adult and youth education 830 Culture and art 840 Health 850 Social work 860 Physical culture and sports 870 Recreation and nature tourism 880 Social security 910 Government, social institutions 999 NIU (not in universe) This variable indicates a person's industry, for those who are economically active. IPUMS Ownership of place of work Germany 1981: Persons who are economically active [discrepancies: none] 01 State owned, including party and government organizations 02 Semi-institutions 03 Agricultural and similar cooperatives 04 Production cooperatives 05 Other cooperatives including consume 06 Private trade 08 Other private 09 Denominational 99 NIU (not in universe) This variable indicates the type of ownership of person's place of work. IPUMS Social group of economically active persons <svar v=" DE81A427 DE81A428 DE81A429 DE81A441 DE81A438">Fill out questions 11-13 for employed persons only (not apprentices).<br /></svar></p> <p><svar a="all" v="DE81A428 DE81A429 DE81A441">11. Are you employed as:<br /><div class="i1">[] 1 Worker/employee<br />[] 2 Agricultural production cooperative member<br />[] 3 Other cooperative member<br />[] 4 Person of liberal professions<br />[] 5 Self-employed<br />[] 6 Family worker</div><br /></svar> Germany 1981: Persons who are economically active [discrepancies: none] 01 Workers in production 02 Other workers 03 Administration and management 04 Intellectual workers 05 Cooperative farmers in production operations 06 Administration and management in agricultural and similar cooperatives 07 Agricultural specialists 08 Other cooperative farmers 09 Members of other cooperatives, non-agricultural 10 Private tradesmen 11 Retail trade 12 Freelance worker 13 Assisting family member 14 Other self-employed 99 NIU (not in universe) This variable indicates the social group of economically active persons. IPUMS Women exemption status Germany 1981: Persons who are economically active [discrepancies: none] Other 1 Exempted women because of child birth 9 NIU (not in universe) This variable indicates a woman's exemption status (for child birth). IPUMS Running number of persons with income in household Germany 1981: Persons who are economically active [discrepancies: none] 00 Secondary residents 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 99 NIU (not in universe) This variable indicates the running number of persons with an income in the household. IPUMS Province of work Germany 1981: Persons who are economically active and commute to work [discrepancies: none] 11 Berlin 12 Brandenburg 13 Mecklenburg-Vorpommern 14 Saxony 15 Saxony-Anhalt 16 Thuringia 99 NIU (not in universe) This variable indicates the province of a person's work. IPUMS 1st mother-child relationship in household Germany 1981: All persons 1 1 2 2 3 3 4 4 6 6 7 7 This variable indicates the number of 1st mother and child relationship in the household. IPUMS 2nd mother-child relationship in household Germany 1981: All persons 2 2 3 3 4 4 This variable indicates the number of 2nd mother-child relationships in household. IPUMS Mother-child relationship in collective dwelling Germany 1981: All persons 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 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 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 60 60 63 63 64 64 67 67 68 68 69 69 This variable indicates the number of mother-child relationships in a collective dwelling. IPUMS Reference person in household Germany 1981: All persons No 1 Yes This variable indicates the reference person in a household. IPUMS