DDI_ARG_1991_PHC_v01_M_v7.5_A_IPUMS National Population and Housing Census, 1991 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. National Population and Housing Census, 1991 - IPUMS Subset PHC Argentina 1991 (IPUMS Harmonized Subset) ARG_1991_PHC_v01_M_v7.5_A_IPUMS Argentine National Institute of Statistics and Censuses IPUMS (c) Copyright 1991, Argentine National Institute of Statistics and Censuses and Minnesota Population Center MetadataEditor Argentine National Institute of Statistics and Censuses 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 Fertility and Mortality Variables -- PERSON Nativity and Birthplace Variables -- PERSON Work Variables -- PERSON Technical Household Variables -- HOUSEHOLD Education Variables -- PERSON Constructed Family Interrelationship Variables -- PERSON Utilities Variables -- HOUSEHOLD Dwelling Characteristics Variables -- HOUSEHOLD Geography: A-E Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Constructed Household Variables -- HOUSEHOLD Household Economic Variables -- HOUSEHOLD Migration: Global Variables -- PERSON Income Variables -- PERSON Technical Person Variables -- PERSON Utilities Variables -- HOUSEHOLD Dwelling Characteristics Variables -- HOUSEHOLD Household Economic Variables -- HOUSEHOLD Technical Household Variables -- HOUSEHOLD Constructed Household Variables -- HOUSEHOLD Geography: A-E Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Demographic Variables -- PERSON Income Variables -- PERSON Migration: Global Variables -- PERSON Nativity and Birthplace Variables -- PERSON Education Variables -- PERSON Work Variables -- PERSON Work: Industry Variables -- PERSON Work: Occupation Variables -- PERSON Fertility and Mortality Variables -- PERSON Other Person Variables -- PERSON Technical Person Variables -- PERSON Argentina Department Persons, households, and dwellings Missing data for several key variables requires use of alternative weight UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes UNIT DESCRIPTIONS: - Dwellings: Any place designed to lodge people or a place not originally designed to lodge people but on the census day it was used for that purpose. Private dwellings are those occupied by one or more private households. The Inquilinato houses are those which made up a single dwelling in which one or more households are situated. Non-touristic hotels with a housing capacity inferior to 15 rooms in the federal capital and 10 rooms for the rest of the country are considered private dwellings. - Households: A person or group of people, relatives or not that live under the same roof according to a family arrangement, meaning they share their food expenses. - Group quarters: A place for lodging groups, or one that was not primarily designed for that purpose but used for that purpose on the census day. The following are considered group quarters: a home for the elderly, a home for the underaged, boarding schools, camping/working sites, hospitals, prisons, policemen's headquarters, firemen's headquarters, and military headquarters, religious households and hotels. All the population in the national territory at the moment the census is carried out. Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Argentina 1991 Note: Missing data for several key variables requires use of alternative weight MICRODATA SOURCE: Argentine National Institute of Statistics and Censuses SAMPLE SIZE (person records): 4143727. SAMPLE DESIGN: The microdatabase was obtained in the following way. In those locations in which the sample fraction was 10 percent, 100 percent of the households were chosen. In locations in which the sample fraction was 20 percent, a sample of 1 out of two households was taken from random start. For the remaining locations were there was not a sample, one of every 10 households was taken. In group quarters the sample was not applied in the enumeration. A systematic sample of every 10th person was taken with random start. The sample was drawn by INDEC. NOTE: Roughly half the population lacks data for several important write-in variables, including occupation, industry, birthplace, and previous residence. An alternative weight variable, AR1991A_0434 (WTPERC), properly adjusts the statistics, and it should be applied to any analyses involving the affected variables. Face-to-face [f2f] Short form questionnaire: (1) Dwelling questionnaire (2) Population questionnaire (both questionnaires made up a single booklet). Long form questionnaire: (1) Dwelling questionnaire (2) Population questionnaire (both questionnaires make up a single booklet). de facto, CENSUS DAY: May 15, 1991 Computed by census agency and should be used for most types of analysis. 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. Argentine National Institute of Statistics and Censuses 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: Argentina, Argentine National Institute of Statistics and Censuses. National Population and Housing Census, 1991 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. ARG1991_PHC-H-H Household records 1 62 ARG1991_PHC-P-H Person records 4143727 79 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 Strata identifier This variable is the strata identifier for the sample. The STRATA variable provides information about the sample design that can be used to improve estimation. STRATA is a 12-digit numeric variable. 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 Group quarters type 100 Institutional 110 Prisons, reformatories, or correctional institutions 120 Hospital, nursing home, hospice, or instutions for persons with disabilities 121 Institutions for persons with disabilities 122 Sanatorium or mental institutions 130 Homes for the elderly or orphanage 131 Retirement home 132 Orphanage, children's home 140 Shelter for homeless, youth, or others 150 Military or police institution 160 Boarding school or student housing 170 Religious institution, monastery, seminary, or convent 199 Other institutional n.e.c. 200 Non-institutional 210 Camps (refugees, workers, or others) 220 Hotel, pension, lodging, or boarding house 230 Floating population 299 Other non-institutional n.e.c. 300 Other group quarters 399 1-person unit created by splitting large household 998 Unknown 999 NIU (not in universe) GQTYPE identifies the type of group quarters -- collective dwellings -- which are broadly classified into institutional and non-institutional types. Institutions are a place of residence where people are subject to a common authority or bound by a common objective or personal interest. The definition encompasses correctional facilities, health institutions, retirement homes, orphanages, shelters (social welfare institutions), military or police establishments, boarding schools, and religious group dwellings. Non-institutional group quarters comprise refugee and workers' camps (temporary accommodation), hotels, pensions, and all types of boarding or lodging houses. The "floating population" is included as a category within the non-institutional group quarters, when identified by the corresponding sample. This group refers to outdoor sleepers, homeless persons, travelers, and persons in ships, boats, or other mobile dwellings. A more general classification of households between private and group quarters is available in GQ. IPUMS Number of unrelated persons 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9+ UNREL indicates the number of persons in the household who are unrelated to the head as defined in the variable RELATE. IPUMS Urban-rural status 1 Rural 2 Urban 9 Unknown URBAN indicates whether the household was located in a place designated as urban or as rural. 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 2nd subnational geographic level, world [consistent boundaries over time] GEOLEV2 indicates the second 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. Second administrative units in GEOLEV2 have been spatio-temporally harmonized to provide spatially consistent boundaries across samples in each country. GEOLEV2 is a 9-digit numeric variable. GEOLEV2 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. GEO2_BR. 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 Population density of GEOLEV2 unit, in persons per square kilometer POPDENSGEO2 indicates the population density in persons per square kilometer of the second major administrative unit in which the household was enumerated. The second major administrative unit of the household is identified by the GEOLEV2 variable. The area of units in GEOLEV2 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. POPDENSGEO2 is a 12-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 Area of GEOLEV2 unit in square kilometers AREAMOLLWGEO2 indicates the area in square kilometers of the second major administrative unit in which the household was enumerated. The second major administrative unit of the household is identified by the GEOLEV2 variable. The area of units in GEOLEV2 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. AREAMOLLWGEO2 is a 10-digit string variable listing the area in square kilometers. IPUMS Argentina, Province 1970 - 2010 [Level 1; consistent boundaries, GIS] 032002 City of Buenos Aires 032006 Buenos Aires province 032010 Catamarca 032014 Córdoba 032018 Corrientes 032022 Chaco 032026 Chubut 032030 Entre Ríos 032034 Formosa 032038 Jujuy 032042 La Pampa 032046 La Rioja 032050 Mendoza 032054 Misiones 032058 Neuquén 032062 Río Negro 032066 Salta 032070 San Juan 032074 San Luis 032078 Santa Cruz 032082 Santa Fe 032086 Santiago del Estero 032090 Tucumán 032094 Tierra del Fuego 239094 South Georgia and South Sandwich Islands 238094 Falkland Islands 032099 Unknown GEO1_AR identifies the household's province within Argentina in all sample years. Provinces are the first level administrative units of the country. GEO1_AR 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_AR can be downloaded from the GIS Boundary Files page in the IPUMS International web site. The full set of geography variables for Argentina 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 Argentina, Province 1991 [Level 1, GIS] 002 Capital Federal 006 Buenos Aires province 010 Catamarca 014 Córdoba 018 Corrientes 022 Del Chaco 026 Del Chubut 030 Entre Ríos 034 Formosa 038 Jujuy 042 La Pampa 046 La Rioja 050 Mendoza 054 Misiones 058 Del Neuquén 062 Río Negro 066 Salta 070 San Juan 074 San Luis 078 Santa Cruz 082 Santa Fe 086 Santiago del Estero 090 Tucumán 094 Tierra del Fuego GEO1_AR1991 identifies the household's province within Argentina in 1991. Provinces are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_AR1991 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Argentina 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 Argentina, Department 1970 - 2010 [Level 2; consistent boundaries, GIS] 032002001 City of Buenos Aires 032006001 La Plata 032006002 General Pueyrredón 032006003 Pilar 032006004 Bahía Blanca 032006005 Escobar 032006006 San Nicolás 032006007 Tandil 032006008 Zárate 032006009 Olavarría 032006010 Pergamino 032006011 Luján 032006012 Campana 032006013 Necochea 032006014 Junín 032006015 Berisso 032006016 General Rodríguez 032006017 Presidente Perón, San Vicente 032006018 General Lavalle, La Costa 032006019 Azul 032006020 Chivilcoy 032006021 Mercedes 032006022 Balcarce, Lobería 032006023 Coronel de Marine L. Rosales 032006024 General Viamonte, Lincoln 032006025 Chascomus, Magdalena, Punta Indio 032006026 Alberti, Roque Pérez, 25 de Mayo 032006027 San Pedro 032006028 Tres Arroyos 032006029 Ensenada 032006030 Bolívar, General Alvear, Tapalqué 032006031 Cañuelas 032006032 Marcos Paz 032006033 General Las Heras, Lobos 032006034 Coronel Dorrego, Coronel Pringles, Monte Hermoso, Tornquist 032006035 Chacabuco 032006036 9 de Julio 032006037 Puan, Villarino 032006038 Trenque Lauquen 032006039 Ayacucho, General Guido, Pila, Rauch 032006040 Carmen de Areco, Navarro, Suipacha 032006041 General Alvarado 032006042 Bragado 032006043 Adolfo González Chaves, Benito Juárez, San Cayetano 032006044 Pehuajó 032006045 Coronel Suárez 032006046 Guaminí, Pellegrini, Rivadavia, Tres Lomas 032006047 Capitán Sarmiento, San Antonio de Areco 032006048 Ramallo 032006049 Salto 032006050 Saladillo 032006051 Baradero 032006052 General Arenales, Leandro N. Alem 032006053 General Villegas 032006054 General Juan Madariaga, Pinamar, Villa Gesell 032006055 Patagones 032006056 Arrecifes 032006057 Exaltación de la Cruz 032006058 General Belgrano, General Paz 032006059 Dolores 032006060 Daireaux, Hipólito Yrigoyen 032006061 Brandsen 032006062 Adolfo Alsina, Salliqueló 032006063 Colón 032006064 Las Flores 032006065 Carlos Tejedor, Florentino Ameghino, General Pinto 032006066 Rojas 032006067 San Andrés de Giles 032006068 Carlos Casares 032006069 Mar Chiquita 032006070 Monte 032006071 General La Madrid, Laprida 032006072 Saavedra 032006073 Castelli, Maipú, Tordillo 032006074 La Matanza 032006075 Lomas de Zamora 032006076 Quilmes 032006077 Almirante Brown 032006078 Merlo 032006079 Lanus 032006080 Moreno 032006081 Florencio Varela 032006082 General San Martín 032006083 Tigre 032006084 Avellaneda 032006085 Tres de Febrero 032006086 General Sarmiento, Jose C. Paz, Malvinas Argentinas, San Miguel 032006087 Berazategui 032006088 Hurlingham, Ituzaingo, Morón 032006089 Esteban Echeverría, Ezeiza 032006090 San Isidro 032006091 Vicente López 032006092 San Fernando 032010001 Capital 032010002 Andalgalá, Santa María 032010003 Fray Mamerto Esquiú, Valle Viejo 032010004 Belén, Pomán 032010005 Ambato, Capayán, Paclín, Santa Rosa 032010006 Ancasti, El Alto, La Paz 032010007 Antofagasta de la Sierra, Tinogasta 032014001 Capital 032014002 Río Cuarto 032014003 Colón 032014004 San Justo 032014005 Punilla 032014006 General San Martín 032014007 Tercero Arriba 032014008 Río Segundo 032014009 Unión 032014010 Marcos Juárez 032014011 Santa María 032014012 Cruz del Eje, Minas, Pocho 032014013 Juárez Celman 032014014 San Javier 032014015 Calamuchita 032014016 Ischilín, Totoral 032014017 Río Primero 032014018 Presidente Roque Saénz Peña 032014019 San Alberto 032014020 General Roca 032014021 Río Seco, Sobremonte, Tulumba 032018001 Capital 032018002 Goya 032018003 Santo Tomé 032018004 Berón de Astrada, Concepción, General Paz, San Roque 032018005 Curuzú Cuatiá, Sauce 032018006 Paso de los Libres 032018007 Ituzaingó, San Miguel 032018008 Empedrado, Mburucuyá, San Luis del Palmar 032018009 Mercedes 032018010 Bella Vista 032018011 Monte Caseros 032018012 Esquina 032018013 Lavalle 032018014 Saladas 032018015 Itatí, San Cosme 032018016 General Alvear, San Martín 032022001 San Fernando 032022002 Comandante Fernandez 032022003 Libertador General San Martín, 1 de Mayo 032022004 General Güemes 032022005 San Lorenzo, Sargento Cabral, 25 de Mayo 032022006 Mayor Luis J. Fontana 032022007 9 de Julio, O'Higgins 032022008 General Donovan, Libertad, Presidencia de La Plaza, Tapenaga 032022009 General Belgrano, Independencia 032022010 Almirante Brown 032022011 Quitilipi 032022012 12 de Octubre, 2 de Abril, Fray Justo Sta. María de Oro 032022013 Chacabuco 032022014 Bermejo 032022015 Maipú 032026001 Escalante 032026002 Rawson 032026003 Biedma 032026004 Futaleufú 032026005 Florentino Ameghino, Gaimán, Gastre, Mártires, Paso de los Indios, Río Senguer, Sarmiento, Tehuelches, Telsen 032026006 Cushamen, Languiñeo 032030000 Entre Rios [district unknown] 032030001 Parana 032030002 Colón, Concordia, Federal, Feliciano, San Salvador, Villaguay 032030003 Gualeguaychú, Islas del Ibicuy 032030004 Uruguay 032030005 Federación 032030006 La Paz 032030007 Gualeguay 032030008 Diamante 032030009 Nogoyá 032030010 Victoria 032030011 Tala 032034001 Formosa 032034002 Laishi, Pilagás, Pirané 032034003 Pilcomayo 032034004 Bermejo, Patiño 032034005 Matacos, Ramón Lista 032038001 Dr. Manuel Belgrano, Palpalá 032038002 El Carmen, San Antonio 032038003 Ledesma, Santa Bárbara 032038004 San Pedro 032038005 Cochinoca, Rinconada, Santa Catalina, Susques, Tumbaya, Yaví 032038006 Humahuaca, Tilcara, Valle Grande 032042001 Capital 032042002 Maracó 032042003 Conhello, Rancul, Toay, Trenel 032042004 Atreucó, Catriló, Guatrache, Quemú-Quemú 032042005 Chapaleufú, Realicó 032042006 Caleu-Caleu, Cura-Co, Chalileo, Chical-Có, Lihuel Calel, Limay Mahuida, Leventué, Puelén 032042007 Hucal, Utracán 032046001 Capital 032046002 Chilecito 032046003 Coronel Felipe Varela, Famatina, General Angel V. Peñaloza, General Juan F. Quiroga, General Lamadrid, Vinchina, Independencia, Sanagasta 032046004 General Ocampo, General San Martín, Rosario Vera Peñaloza 032046005 Arauco, Castro Barros, San Blas de los Sauces 032046006 Chamical, General Belgrano 032050001 Guaymallén 032050002 Las Heras 032050003 Godoy Cruz 032050004 San Rafael 032050005 Maipú 032050006 San Martín 032050007 Luján de Cuyo 032050008 Capital 032050009 Rivadavia 032050010 Tunuyán 032050011 General Alvear 032050012 Junín 032050013 Lavalle 032050014 San Carlos 032050015 Tupungato 032050016 Malargue 032050017 La Paz, Santa Rosa 032054001 Capital, Leandro N. Alem 032054002 Oberá 032054003 Iguazú 032054004 El Dorado 032054005 Guaraní 032054006 San Ignacio 032054007 Cainguas 032054008 Libertador General San Martín 032054009 Apóstoles 032054010 General Manuel Belgrano 032054011 Montecarlo 032054012 Concepción, San Javier 032054013 San Pedro 032054014 25 de Mayo 032054015 Candelaria 032058001 Confluencia 032058002 Aluminé, Añelo, Catán-Lil, Collón-Cura, Loncopué, Minas, Ñorquin, Picún-Leufú, Picunches 032058003 Huiliches, Lácar, Los Lagos 032058004 Chos Malal, Pehuenches 032058005 Zapala 032062001 General Roca 032062002 Bariloche 032062003 Adolfo Alsina 032062004 El Cuy, 9 de Julio, Ñorquincó, Pilcaniyeu, Valcheta, 25 de Mayo 032062005 Avellaneda 032062006 San Antonio 032062007 Conesa, Pichi Mahuida 032066001 Capital 032066002 General Jose de San Martín 032066003 Iruya, Orán, Santa Victoria 032066004 Anta 032066005 La Caldera, Rosario de Lerma 032066006 General Güemes 032066007 Metán 032066008 Guachipas, La Candelaria, Rosario de la Frontera 032066009 Cerrillos 032066010 Rivadavia 032066011 Cachí, La Poma, Los Andes, Molinos, San Carlos 032066012 Cafayate, La Vina 032066013 Chicoana 032070001 Rawson 032070002 Capital 032070003 Chimbas 032070004 Rivadavia 032070005 Angaco, Caucete, 25 de Mayo 032070006 Pocito 032070007 Santa Lucía 032070008 Jachal, Valle Fértil 032070009 Albardón 032070010 Calingasta, Iglesia, Ullúm, Zonda 032070011 Sarmiento 032070012 9 de Julio, San Martín 032074001 La Capital 032074002 General Pedernera, Gobernador Dupuy 032074003 Ayacucho, Belgrano, Coronel Pringles, Libertador General San Martín 032074004 Junín 032074005 Chacabuco 032078001 Guer Aike 032078002 Deseado 032078003 Corpen Aike, Lago Argentino 032078004 Lago Buenos Aires, Magallanes, Río Chico 032082001 Rosario 032082002 La Capital 032082003 General López 032082004 Castellanos 032082005 General Obligado 032082006 San Lorenzo 032082007 Las Colonias 032082008 Constitución 032082009 San Jerónimo 032082010 Caseros 032082011 San Cristóbal 032082012 Iriondo 032082013 San Martín 032082014 Vera 032082015 Belgrano 032082016 San Justo 032082017 San Javier 032082018 9 de Julio 032082019 Garay 032086001 Capital 032086002 Banda 032086003 Alberdi, Juan F. Ibarra, Moreno 032086004 Río Hondo 032086005 Robles 032086006 Choya, Guasayan 032086007 General Taboada 032086008 Jiménez, Pellegrini 032086009 Atamisqui, Salavina, San Martín 032086010 Copo 032086011 Loreto, Silipica 032086012 Ojo de Agua, Quebrachos 032086013 Aguirre, Belgrano, Mitre, Rivadavia 032086014 Figueroa, Sarmiento 032086015 Avellaneda 032090001 Famaillá, Lules, Capital, Tafí del Valle, Tafí Viajo, Trancas, Yerba Buena 032090002 Cruz Alta 032090003 Chicligasta, Granero, Juan Bautista Alberdi, La Cocha, Monteros, Río Chico, Simoca 032090004 Leales 032090005 Burruyacú 032094001 Río Grande, Antarctic Argentina 032094002 Ushuaia 032099999 Unknown 239094003 South Georgia and South Sandwich Islands 238094004 Falkland Islands GEO2_AR identifies the household's department within Argentina in all sample years. Departments are the second level administrative units of the country, after provinces. GEO2_AR 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 GEO2_AR can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Argentina 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 Argentina, Department 1991 [Level 2, GIS] 002001 Distrito Escolar I 002002 Distrito Escolar IX 002003 Distrito Escolar II 002004 Distrito Escolar X 002005 Distrito Escolar VII 002006 Distrito Escolar VI 002007 Distrito Escolar VIII 002008 Distrito Escolar III 002009 Distrito Escolar XVII 002010 Distrito Escolar XII 002011 Distrito Escolar XIII 002012 Distrito Escolar XI 002013 Distrito Escolar XV 002014 Distrito Escolar XVIII 002015 Distrito Escolar XIV 002016 Distrito Escolar V 002017 Distrito Escolar IV 002018 Distrito Escolar XXI 002019 Distrito Escolar XX 002020 Distrito Escolar XVI 002021 Distrito Escolar IXX 006001 Mar Chiquita 006002 Carlos Casares 006003 Colón 006004 Adolfo Alsina, Saliqueló 006005 Brandsen 006006 Daireaux, Hipólito Yrigoyen 006007 San Andrés de Giles 006008 Rojas 006009 Carlos Tejedor, Florentino Ameghino, General Pinto 006010 Las Flores 006011 General Lavalle, La Costa 006012 Azul 006013 General Rodríguez 006014 San Vicente 006015 Junín 006016 Berisso 006017 Campana 006018 Necochea 006019 Pergamino 006020 Luján 006021 Olavarría 006022 Zárate 006023 Escobar 006024 Bahía Blanca 006025 Tandil 006026 San Nicolás 006027 La Plata 006028 Pilar 006029 General Pueyrredón 006030 Trenque Lauquen 006031 Ayacucho, General Guido, Pila, Rauch 006032 Bolívar, General Alvear, Tapalqué 006033 Cañuelas 006034 Marcos Paz 006035 General Las Heras, Lobos 006036 Coronel Pringles, Tornquist 006037 Coronel Dorrego, Monte Hermoso 006038 Chacabuco 006039 Nueve de Julio 006040 Puan, Villarino 006041 San Pedro 006042 Alberti, Roque Pérez, Veinticinco de Mayo 006043 Chascomus 006044 Magdalena 006045 General Viamonte, Lincoln 006046 Coronel de Marine Leonardo Rosales 006047 Balcarce, Lobería 006048 Mercedes 006049 Chivilcoy 006050 Ensenada 006051 Tres Arroyos 006052 General Arenales, Leandro N. Alem 006053 General Villegas 006054 Saladillo 006055 Baradero 006056 Bartolomé Mitre 006057 Exaltación de la Cruz 006058 General Juan Madariaga, Pinamar, Villa Gesell 006059 Patagones 006060 General Belgrano, General Paz 006061 Dolores 006062 General Alvarado 006063 Carmen de Areco, Navarro, Suipacha 006064 Adolfo González Chaves, San Cayetano 006065 Benito Juárez 006066 Bragado 006067 Coronel Suárez 006068 Pehuajó 006069 Capitán Sarmiento, San Antonio de Areco 006070 Pellegrini, Rivadavia 006071 Guaminí, Tres Lomas 006072 Salto 006073 Ramallo 006074 Monte 006075 General La Madrid, Laprida 006076 Saavedra 006077 Castelli, Maipú, Tordillo 006078 Morón 006079 Berazategui 006080 San Isidro 006081 Esteban Echeverría 006082 Avellaneda 006083 Tigre 006084 General Sarmiento 006085 Tres de Febrero 006086 San Fernando 006087 Vicente López 006088 Lanús 006089 Moreno 006090 Almirante Brown 006091 Merlo 006092 Lomas de Zamora 006093 Quilmes 006094 La Matanza 006095 Florencio Varela 006096 General San Martín 010001 Capital 010002 Andalgalá, Santa María 010003 Fray Mamerto Esquiú, Valle Viejo 010004 Belén, Pomán 010005 Ambato, Capayán, Paclín, Santa Rosa 010006 Ancasti, El Alto, La Paz 010007 Antofagasta de la Sierra, Tinogasta 014001 Tercero Arriba 014002 General San Martín 014003 Punilla 014004 San Justo 014005 Colón 014006 Río Cuarto 014007 Capital 014008 Unión 014009 Río Segundo 014010 Marcos Juárez 014011 Santa María 014012 Río Seco, Sobremonte, Tulumba 014013 General Roca 014014 San Alberto 014015 Cruz del Eje, Minas, Pocho 014016 Juárez Celman 014017 San Javier 014018 Calamuchita 014019 Ischilín, Totoral 014020 Río Primero 014021 Presidente Roque Saénz Peña 018001 Empedrado, Mburucuyá, San Luis del Palmar 018002 Mercedes 018003 Goya 018004 Santo Tomé 018005 Capital 018006 Paso de los Libres 018007 Ituzaingó, San Miguel 018008 Berón de Astrada, Concepción, General Paz, San Roque 018009 Curuzú Cuatiá, Sauce 018010 Monte Caseros 018011 Bella Vista 018012 Lavalle 018013 Esquina 018014 Itatí, San Cosme 018015 Saladas 018016 General Alvear, San Martín 022001 San Fernando 022002 12 de Octubre, Fray Justo Sta. María de Oro 022003 Chacabuco 022004 Almirante Brown 022005 Quitilipi 022006 Bermejo 022007 Maipú 022008 Libertador Gral. San Martín, 1 de Mayo 022009 Comandante Fernández 022010 San Lorenzo, Sargento Cabral, 25 de Mayo 022011 General Güemes 022012 9 de Julio 022013 O'Higgins 022014 Mayor Luis J. Fontana 022015 General Belgrano, Independencia 022016 General Donovan, Libertad, Presidencia de La Plaza, Tapenaga 026001 Futaleufú 026002 Florentino Ameghino, Gaimán, Gastre, Mártires, Paso de los Indios, Río Senguer, Sarmiento, Tehuelches, Telsen 026003 Cushamen, Languiñeo 026004 Escalante 026005 Rawson 026006 Biedma 030001 Victoria 030002 Tala 030003 Nogoyá 030004 Diamante 030005 Gualeguaychú, Islas del Ibicuy 030006 Concordia 030007 Colón 030008 Villaguay 030009 Federal, Feliciano 030010 Paraná 030011 Gualeguay 030012 La Paz 030013 Federación 030014 Uruguay 034001 Bermejo, Patiño 034002 Matacos, Ramón Lista 034003 Laishi, Pilagás, Pirané 034004 Pilcomayo 034005 Formosa 038001 Cochinoca, Rinconada, Santa Catalina, Susques, Tumbaya, Yaví 038002 San Pedro 038003 Humahuaca, Tilcara, Valle Grande 038004 Dr. Manuel Belgrano 038005 Palpalá 038006 Ledesma, Santa Bárbara 038007 El Carmen, San Antonio 042001 Hucal, Utracán 042002 Caleu-Caleu, Cura-Co, Chalileo, Chical-Có, Lihuel Calel, Limay Mahuida, Leventué, Puelén 042003 Conhello, Rancul, Toay, Trenel 042004 Maracó 042005 Capital 042006 Chapaleufú, Realicó 042007 Atreucó, Catriló, Guatrache, Quemú-Quemú 046001 Capital 046002 Chilecito 046003 Coronel Felipe Varela, Famatina, General Angel V. Peñaloza, General Juan F. Quiroga, General Lamadrid, Vinchina, Independencia, Sanagasta 046004 General Ocampo, General San Martín, Rosario Vera Peñaloza 046005 Arauco, Castro Barros, San Blas de los Sauces 046006 Chamical, General Belgrano 050001 Malargue 050002 La Paz, Santa Rosa 050003 San Carlos 050004 Tupungato 050005 Junín 050006 Lavalle 050007 Tunuyán 050008 General Alvear 050009 Rivadavia 050010 Capital 050011 Maipú 050012 San Rafael 050013 Luján de Cuyo 050014 San Martín 050015 Guaymallén 050016 Godoy Cruz 050017 Las Heras 054001 San Ignacio 054002 Cainguás 054003 El Dorado 054004 Guaraní 054005 Oberá 054006 Iguazú 054007 Capital 054008 Leandro N. Alem 054009 Libertador General San Martín 054010 Apóstoles 054011 Candelaria 054012 25 de Mayo 054013 Montecarlo 054014 General Manuel Belgrano 054015 San Pedro 054016 Concepción, San Javier 058001 Huiliches, Lácar, Los Lagos 058002 Aluminé, Añelo, Catán-Lil, Collón-Cura, Loncopué, Minas, Ñorquin, Picún-Leufú, Picunches 058003 Confluencia 058004 Zapala 058005 Chos Malal, Pehuenches 062001 El Cuy, 9 de Julio, Ñorquincó, Pilcaniyeu, Valcheta, 25 de Mayo 062002 Avellaneda 062003 San Antonio 062004 Conesa, Pichí Mahuida 062005 General Roca 062006 Bariloche 062007 Adolfo Alsina 066001 Cerrillos 066002 Guachipas, La Candelaria, Rosario de la Frontera 066003 Cafayate, La Viña 066004 La Caldera, Rosario de Lerma 066005 Chicoana 066006 Anta 066007 Rivadavia 066008 Metán 066009 Capital 066010 Iruya, Orán, Santa Victoria 066011 General José de San Martín 066012 General Güemes 066013 Cachí, La Poma, Los Andes, Molinos, San Carlos 070001 Albardón 070002 Jachal, Valle Fértil 070003 Rivadavia 070004 Capital 070005 Chimbas 070006 Rawson 070007 Pocito 070008 Santa Lucía 070009 Sarmiento 070010 Calingasta, Iglesia, Ullún, Zonda 070011 Angaco, Caucete, 25 de Mayo 070012 9 de Julio, San Martín 074001 General Pedernera, Gobernador Dupuy 074002 Junín 074003 La Capital 074004 Chacabuco 074005 Ayacucho, Belgrano, Coronel Pringles, Libertador General San Martín 078001 Guer Aike 078002 Deseado 078003 Corpen Aike, Lago Argentino 078004 Lago Buenos Aires, Magallanes, Río Chico 082001 San Jerónimo 082002 San Martín 082003 Castellanos 082004 San Javier 082005 General Obligado 082006 San Lorenzo 082007 9 de Julio 082008 Caseros 082009 Iriondo 082010 Belgrano 082011 Vera 082012 Garay 082013 San Justo 082014 Las Colonias 082015 Rosario 082016 La Capital 082017 General López 082018 Constitución 082019 San Cristóbal 086001 Figueroa, Sarmiento 086002 Avellaneda 086003 Río Hondo 086004 Copo 086005 Loreto, Silipica 086006 Capital 086007 Atamisqui, Salavina, San Martín 086008 Jiménez, Pellegrini 086009 Alberdi, Juan F. Ibarra, Moreno 086010 Banda 086011 General Taboada 086012 Choya, Guasayán 086013 Robles 086014 Aguirre, Belgrano, Mitre, Rivadavia 086015 Ojo de Agua, Quebrachos 090001 Chicligasta 090002 Monteros 090003 Río Chico 090004 Simoca 090005 Granero, La Cocha 090006 Juan B. Alberdi 090007 Leales 090008 Burruyacú 090009 Cruz Alta 090010 Capital 090011 Tafí Viajo 090012 Lules 090013 Yerba Buena 090014 Famaillá 090015 Tafí del Valle, Trancas 094001 Ushuaia 094002 Río Grande, Falkland Islands, South Georgia and South Sandwich Islands GEO2_AR1991 identifies the household's department within Argentina in 1991. Departments are the second level administrative units of the country, after provinces. A GIS map (in shapefile format), corresponding to GEO2_AR1991 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Argentina 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 Land ownership 00 NIU (not in universe) 10 Land owned by a resident 11 Owned, fully paid for 12 Owned, still paying 13 Freehold tenure 14 Customary land 20 Land not owned 21 Rented 22 Ceded 23 Mailo land tenure 24 Free, occupied with consent 25 Free, occupied without consent 26 Free, consent not specified 27 Communal 28 Right to build 29 Right to use 30 Other, not specified 99 Unknown This variable indicates whether the land on which the dwelling was located was owned, either in full or in part, by one of the dwelling's residents. Conceivably, a resident might own land but not the dwelling on which it resides. Such a case would be classified as NIU (not in universe): only dwellings indicating that a resident owned the dwelling were asked about land ownership. IPUMS Electricity NIU (not in universe) 1 Yes 2 No 9 Unknown ELECTRIC indicates whether the household had access to electricity. 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 Cooking fuel 00 NIU (not in universe) 10 None 20 Electricity 30 Petroleum gas, unspecified 31 Gas -- piped/utility 32 Gas -- tanked or bottled 33 Propane 34 Liquefied petroleum gas 35 Gas -- piped and bottled 40 Petroleum liquid 41 Oil, kerosene, and other liquid fuels 42 Kerosene/paraffin 43 Kerosene or oil 44 Kerosene or gasoline 45 Gasoline 46 Cocinol 47 Diesel 50 Wood, coal, and other solid fuels 51 Wood and other plant fuels 52 Non-wood plant materials 53 Coal or charcoal 54 Charcoal 55 Coal 56 Wood or charcoal 60 Multiple fuels 61 Bottled gas and wood 62 Propane and electricity 63 Propane, kerosene, and electricity 64 Propane and kerosene 65 Kerosene and electrictiy 66 Other combinations 70 Other 71 Alcohol 72 Biogas 73 Discarded or waste material 74 Dung/manure 75 Other combined organic waste materials 76 Solar energy 77 Candle 99 Unknown/missing FUELCOOK indicates the predominant type of fuel or energy used for cooking. IPUMS Number of rooms 00 Part of a room; no rooms 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+ 98 Unknown 99 NIU (not in universe) ROOMS indicates the number of rooms occupied by the housing unit. 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 Floor material 000 NIU (not in universe) 100 None/unfinished (earth) 110 Sand 120 Dung 200 Finished 201 Cement, tile, or brick 202 Cement 203 Concrete 204 Cement screed 205 Ceramic tile 206 Paving stone, cement tile 207 Stone 208 Brick 209 Brick or stone 210 Brick or cement 211 Block 212 Terrazzo 213 Wood 214 Palm, bamboo 215 Parquet 216 Parquet, tile, vinyl 217 Parquet, tile, marble 218 Ceramic, marble, granite 219 Ceramic, marble, tile, or vinyl 220 Marble 221 Mosaic 222 Tile 223 Tile, linoleum, ceramic, etc 224 Tile, cement 225 Tile, stone 226 Tile, stone, brick 227 Tile, stone, vinyl, brick 228 Tile, vinyl, brick 229 Tile, vinyl 230 Vinyl, linoleum, etc 231 Asphalt sheet, vinyl, etc 232 Synthetic, plastic 233 Cane 234 Carpet, rug 235 Scrap material 236 Other finished, n.e.c. 999 Unknown/missing FLOOR indicates the dwelling's predominant flooring material. IPUMS Wall or building material 000 NIU (not in universe) 100 No walls 200 Cardboard, scrap, and miscellaneous materials 201 Waste, scrap, or discarded material 202 Fabric or discarded material 203 Zinc, fabric, cardboard, tins, and waste material 204 Cardboard sheet 205 Plastic sheeting, cardboard 206 Makeshift, salvaged, or improvised materials 207 Reused materials 300 Wood 310 Rough wood 320 Wood, fibercement or plywood 330 Wood, formica, and other 340 Wood or bamboo 350 Wood or straw 400 Other plant-based materials 401 Plantain leaves and similar material 402 Bamboo or cane 403 Bamboo, sawali, cogon, nipa 404 Straw or bamboo 405 Grass, straw or reed 406 Reed, bamboo, or palm 407 Cane, palm leaves, logs 408 Palm leaves or palm planks 409 Bark, sticks, or cane 500 Masonry, stone, cement, adobe, metal, glass, and other fabricated materials (sometimes mixed with wood) 501 Brick, block, stone, or cement 502 Brick, stone, concrete 503 Brick, stone, or substitutes (dividing panels made of reinforced concrete) 504 Brick, stone, or substitutes (dividing panels made of wood) 505 Brick or tile 506 Brick or stone 507 Brick or cement block 508 Brick with plaster exterior 509 Brick without plaster exterior 510 Burnt or stabilized brick 511 Covered brick 512 Brick 513 Unburnt brick 514 Unburnt brick with cement 515 Unburnt brick with mud 516 Concrete 517 Landcrete, sandcrete 518 Cement blocks 519 Cement blocks or brick 520 Cement blocks or brick, unfinished 521 Cement and adobe bricks 522 Cement and stone block 523 Cement and tiles 524 Reinforced concrete, pre-cast concrete panels, or steel skeleton framed concrete 525 Concrete, reinforced concrete, blocks, panels 526 Fibercement 527 Adobe 528 Adobe walls with plaster exterior 529 Adobe walls without plaster exterior 530 Adobe with cement exterior 531 Wood and earth adobe 532 Wood and cement adobe 533 Mud or adobe 534 Pressed dirt 535 Clay 536 Coated clay/mud with sticks/cane 537 Clay or clay-covered sticks 538 Netted bamboo or cane with mud 539 Bundle of mud, straw, other materials 540 Mud with wood/wattle 541 Pole and mud 542 Mud with cement 543 Unfinished lathe and plaster, stucco, etc. 544 Stone 545 Hand-laid stone 546 Quarried stone 547 Cut stone and concrete 548 Cemented stone 549 Stone with clay 550 Blocks of light material 551 Prefabricated material 552 Asbestos 553 Metal or asbestos sheet 554 Metal or iron sheet 555 Metal or fibercement sheeting 556 Galvanized iron or aluminum 557 Tin 558 Glass 559 Cloth 560 Covintec panels 561 Mixed material 562 Mixed material: part wood; part concrete, brick, or stone 563 Wood plastered with clay, adobe, other materials; wood pressed panels; rolled mud bricks; etc. 564 Mixed material: wood or galvanized metal 570 Mainly permanent materials 600 Other material 601 Partition wall, lined with wood or steel 602 Partition wall, unlined 999 Unknown/missing This variable indicates the primary material used in the construction of the dwelling, particularly the dwelling's exterior walls. IPUMS Roof material 00 NIU (not in universe) 10 Masonry, concrete, clay tile, or tiles of unspecified type 11 Concrete or cement 12 Reinforced concrete (slab) 13 Cement or sheet metal 14 Tile, unspecified material 15 Clay tile 16 Tile or cement 17 Modern tiles, industrial 18 Traditional tiles, locally made 19 Tile or flat stone 20 Tile, unspecified or mixed materials 21 Fibercement or plastic 22 Fibercement or metal sheets 23 Asphalt or laminate cover 24 Tile, cement, asphalt 25 Asphalt tile 26 Slate or tile 27 Slate or asbestos 28 Asbestos 29 Adobe 30 Tiles or wood planks 31 Roofing shingles 32 Tar paper 33 Metal 34 Sheet metal 35 Zinc or tin 36 Tin 37 Sheet metal or other sheet material 38 Sheet metal, tile, slate 40 Wood and other plant materials 41 Wood 42 Wood, including bamboo 43 Bamboo 44 Cogon, nipa, anahaw 45 Thatch (straw, grass, leaves, palm, etc.) 46 Cane, wood, straw 47 Grass or straw 48 Papyrus 49 Banana leaves or fiber 50 Palm or makuti 51 Straw, bamboo, polythene 52 Wood with clay 53 Grass and mud 54 Rustic mat 60 Mud or earth 61 Clay 70 Cardboard, scrap, and miscellaneous materials 71 Discarded or scrap material 72 Cardboard 73 Plastic, tarpaulin 80 Other, unspecified 90 No roof 99 Unknown/missing This variable indicates the dwelling's predominant roofing material. IPUMS Household classification 00 Vacant household 01 One-person household 02 Married/cohab couple, no children 03 Married/cohab couple with children 04 Single-parent family 05 Polygamous family 06 Extended family, relatives only 07 Composite household, family and non-relatives 08 Non-family household 09 Unclassified subfamily 10 Other relative or non-relative household 11 Group quarters 99 Unclassifiable HHTYPE is a constructed variable that describes the composition of households. HHTYPE is constructed from information in RELATE (relationship to head), from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father), and from information on group quarters status, GQ. IPUMS Number of families in household Vacant household 1 1 family 2 2 families 3 3 families 4 4 families 5 5 families 6 6 families 7 7 families 8 8 families 9 9 or more families NFAMS is a constructed variable that indicates the number of families within each household. Family membership is defined by FAMUNIT. A "family" is any group of persons related by blood, adoption, or marriage. An unrelated individual within the household is considered a separate family. Thus, a household consisting of a widow and a domestic employee contains two families; a household consisting of a large, multi-generation extended family with no persons unrelated to the head counts as a single family. NFAMS is constructed from information in RELATE (relationship to head) and from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). See those variable descriptions for more detail. IPUMS Number of married couples in household No married couples in household 1 1 couple 2 2 couples 3 3 couples 4 4 couples 5 5 couples 6 6 couples 7 7 couples 8 8 couples 9 9 or more couples NCOUPLES is a constructed variable indicating the number of married/in-union couples within a household. NCOUPLES is constructed using the IPUMS-International pointer variable SPLOC (spouse's location in the household). IPUMS Number of mothers in household No mothers in household 1 1 mother 2 2 mothers 3 3 mothers 4 4 mothers 5 5 mothers 6 6 mothers 7 7 mothers 8 8 mothers 9 9 or more mothers in household NMOTHERS is a constructed variable indicating the number of mothers -- of persons of any age -- within a household. NMOTHERS is constructed using the IPUMS-International pointer variable MOMLOC (mother's location in the household). IPUMS Number of fathers in household No fathers in household 1 1 father 2 2 fathers 3 3 fathers 4 4 fathers 5 5 fathers 6 6 fathers 7 7 fathers 8 8 fathers 9 9 or more fathers in household NFATHERS is a constructed variable indicating the number of fathers -- of persons of any age -- within a household. NFATHERS is constructed using the IPUMS-International pointer variable POPLOC (father's location in the household). IPUMS Head's location in household HEADLOC gives the person number (PERNUM) of the head of household in samples in which persons are organized into households. HEADLOC is a 3-digit numeric variable. IPUMS Dwelling number Argentina 1991: 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 Argentina 1991: 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 30 30 This variable indicates the number of persons in the household. IPUMS Dwelling created by splitting apart a large dwelling or household Argentina 1991: All households No problem 1 Yes: households within a large dwelling were split apart into separate dwellings 2 Yes: persons within a large household were split apart into separate dwellings This variable indicates that a dwelling was created by splitting apart a large dwelling or household. IPUMS Number of persons in large household before it was split Argentina 1991: All households 00 Household was not split 31 31 32 32 35 35 42 42 60 60 This variable indicates the number of persons in a large household before it was split. IPUMS Type of private dwelling <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A027 AR91A046">2. Type of dwelling<br /><div class="i1">Collective - continue with population [skip questions 3-16]</div><br /><div class="i2">[] Home for the elderly<br />[] Home for children<br />[] Boarding school<br />[] Labor camp<br />[] Hospital<br />[] Prison<br />[] Barracks<br />[] Religious household<br />[] Hotel</div><br /><div class="i1">Individual</div><br /><div class="i2">[] House<br />[] Rural dwelling or cabin<br />[] Apartment<br />[] Rented room/s<br />[] Pension or hotel<br />[] Space not built for occupation<br />[] Mobile dwelling</div><br /></svar> Argentina 1991: All households House type A 1 House type B 2 Shack 3 Apartment 4 Rented room 5 Boarding house 6 Space not built for occupation 7 Mobile dwelling 8 Unknown private 9 Collective dwelling This variable indicates the type of private dwelling. Housing type B refers to a home that lacks at least one of the following: internal plumbing; plumbing to evacuate black water from the dwelling; or a floor not made of constructed material (for example, a dirt floor). Housing type A refers to a home that does not lack any of those conveniences. IPUMS Wall material <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A028">4. What is the predominant material of the outer walls?<br /><div class="i1">[] Brick, stone, cinder block, reinforced concrete<br />[] Adobe<br />[] Wood<br />[] Metal sheeting or cement/fiber sheeting<br />[] Plant material, cardboard, discarded materials<br />[] Other</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 Brick, stone, cinder block, reinforced concrete 2 Adobe 3 Wood 4 Metal sheeting or cement/fiber sheeting 5 Bundle of mud and straw, cardboard, discarded materials 6 Others 9 NIU (not in universe) This variable indicates the main wall material of the dwelling. IPUMS Roof material <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A029">5. What is the predominant material of the outer covering of the roof?<br /><div class="i1">[] Tile, asphalt, or membrane.<br />[] Clay roofing tile<br />[] Metal sheeting<br />[] Cement fiber sheeting<br />[] Cardboard<br />[] Cane, wood (boards), straw with clay, straw<br />[] Other<br />[] Unanswered</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 Tile 2 Clay roofing tile 3 Metal sheeting 4 Cement fiber sheeting 5 Cardboard 6 Cane, wood (boards), straw with clay, straw 7 Other 8 Unknown 9 NIU (not in universe) This variable indicates the main roof material in the dwelling. IPUMS Floor material <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A030">6. What is the predominant material of the floors?<br /><div class="i1">[] Ceramic, tile, mosaic, wood, carpet, or plastic<br />[] Cement or permanent brick<br />[] Dirt<br />[] Other</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 Ceramic, tile, mosaic, wood, carpet, or plastic 2 Cement or permanent brick 3 Earth 4 Others 8 Unknown 9 NIU (not in universe) This variable indicates the main floor material in the dwelling. IPUMS Electricity <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A031">7. Electrical service?<br /><div class="i1">[] With electricity from a public or private network<br />[] With electricity generated by household<br />[] No electricity</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 From a public or private system 2 Generated within property 3 Does not have 9 NIU (not in universe) This variable indicates the source of electricity in the dwelling. IPUMS Water service <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A032">8. Water service?<br /><div class="i1">[] With tubing inside the dwelling<br />[] Outside the dwelling, but inside the property<br />[] Outside the property</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 With tubing within the dwelling 2 Outside the dwelling, but within the property 3 Outside the property 8 Unknown 9 NIU (not in universe) This variable indicates the way water is transported to the dwelling. IPUMS Source of water <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A033">9. Household obtains water through?<br /><div class="i1">[] Running water from a public system<br />[] Perforated well with a motorized pump<br />[] Perforated well with a hand pump<br />[] Well<br />[] Rain water<br />[] Supplied by a water-tank truck<br />[] River, canal, ditch, etc.</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 From a public system 2 Drilled well with a motorized pump 3 Drilled well with a hand pump 4 Well 5 Rain water 6 Transported by tank 7 River, canal, stream, etc. 8 Unknown 9 NIU (not in universe) This variable indicates the source of water in the dwelling. IPUMS Number of rooms <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><span class="h2">II. Habitational Characteristics of the Dwelling- for each household</span> <br />[Questions 10-16]</p> <p><svar a="all" v="AR91A034">10. How many rooms are for the exclusive use of the household, excluding the bathroom or kitchen?<br /><div class="i1">[] None<br />[] 1<br />[] 2<br />[] 3<br />[] 4<br />[] 5<br />[] 6<br />[] 7<br />[] 8 or more</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8+ 98 Unknown 99 NIU (not in universe) This variable indicates the number of rooms in the dwelling. IPUMS Cooking fuel <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><span class="h2">II. Habitational Characteristics of the Dwelling- for each household</span> <br />[Questions 10-16]</p> <p><svar a="all" v="AR91A035">11. What is the principal combustible used for cooking?<br /><div class="i1">[] Gas from a network<br />[] Packaged gas (tank, tube, etc)<br />[] Kerosene<br />[] Electricity<br />[] Wood or charcoal<br />[] Other</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 Gas from a system 2 Gas from a cylinder 3 Kerosene 4 Electricity 5 Fire wood or coal 6 Other 9 NIU (not in universe) This variable indicates the cooking fuel used in the dwelling. IPUMS Toilet <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><span class="h2">II. Habitational Characteristics of the Dwelling- for each household</span> <br />[Questions 10-16]</p> <p><svar a="all" v="AR91A036">12. In the bathroom, does the dwelling have a toilet or lavatory with flushing water (with a button, chain, etc)?<br /><div class="i1">[] For the exclusive use of the members of the household<br />[] Shared with another household<br />[] Does not have a toilet or lavatory with flushing water [skip question 13]</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 Has toilet for the exclusive use of the members of the household 2 Has toilet shared with another household 3 Does not have a toilet or lavatory with flushing water 8 Unknown 9 NIU (not in universe) This variable indicates toilet availability in the dwelling. IPUMS Drain from toilet <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><span class="h2">II. Habitational Characteristics of the Dwelling- for each household</span> <br />[Questions 10-16]</p> <p><svar a="all" v="AR91A037">13. The drain for the flush toilet has:<br /><div class="i1">[Question 13 was asked only in the case of a household with toilet, per question 12.]<br /><br />[] With drainage to a public system<br />[] A septic tank or pit<br />[] Only a pit<br />[] Unanswered</div><br /></svar> Argentina 1991: Private dwellings with a flush toilet [discrepancies: none] 1 With drainage to a public system 2 Septic tank 3 Septic pit 8 Unknown 9 NIU (not in universe) This variable indicates the type of sewage available in the dwelling. IPUMS Ownership <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><span class="h2">II. Habitational Characteristics of the Dwelling- for each household</span> <br />[Questions 10-16]</p> <p><svar a="all" v="AR91A038">14. The occupants:<br /><div class="i1">[] Own the dwelling and the land<br />[] Own the dwelling only<br />[] Renter<br />[] Other dependant relationship<br />[] Occupancy through loan, grant or permission<br />[] De facto occupation<br />[] Other<br />[] Unanswered</div><br /></svar> Argentina 1991: Private dwellings [discrepancies: none] 1 Owner of the dwelling and the land 2 Owner of the dwelling only 3 Renter 4 Other dependant relationship 5 Occupant through loan 6 De facto occupant 7 Other 8 Unknown 9 NIU (not in universe) This variable indicates the ownership of the dwelling. IPUMS Number of persons in household, original <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><span class="h2">II. Habitational Characteristics of the Dwelling- for each household</span> <br />[Questions 10-16]</p> <p><svar a="all" v="AR91A039">16. What is the total number of persons in the household? _ _<br /></svar> Argentina 1991: Private dwellings [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 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 30 30 31 31 32 32 35 35 42 42 60 60 99 NIU (not in universe) This variable indicates the number of people in the household. IPUMS Number of people per room Argentina 1991: Private dwellings [discrepancies: none] 0000 NIU (not in universe) 0011 11 0014 14 0016 16 0020 20 0022 22 0025 25 0028 28 0033 33 0040 40 0042 42 0044 44 0050 50 0055 55 0057 57 0060 60 0066 66 0071 71 0075 75 0077 77 0080 80 0083 83 0085 85 0088 88 0100 100 0111 111 0114 114 0116 116 0120 120 0122 122 0125 125 0128 128 0133 133 0140 140 0142 142 0144 144 0150 150 0155 155 0157 157 0160 160 0166 166 0171 171 0175 175 0177 177 0180 180 0183 183 0185 185 0188 188 0200 200 0214 214 0216 216 0220 220 0225 225 0228 228 0233 233 0240 240 0242 242 0244 244 0250 250 0255 255 0257 257 0260 260 0266 266 0275 275 0277 277 0280 280 0283 283 0288 288 0300 300 0316 316 0320 320 0325 325 0333 333 0340 340 0344 344 0350 350 0360 360 0366 366 0375 375 0380 380 0383 383 0400 400 0416 416 0420 420 0425 425 0433 433 0450 450 0460 460 0466 466 0475 475 0480 480 0500 500 0525 525 0533 533 0550 550 0566 566 0600 600 0625 625 0633 633 0650 650 0666 666 0675 675 0700 700 0733 733 0750 750 0800 800 0833 833 0850 850 0900 900 0950 950 1000 1000 1100 1100 1150 1150 1200 1200 1300 1300 1400 1400 1500 1500 1600 1600 1700 1700 1800 1800 2000 2000 2200 2200 2400 2400 2600 2600 2800 2800 3200 3200 4000 4000 9999 Unknown This variable indicates the number of people per room in the household. IPUMS Unsatisfied basic needs Argentina 1991: Private dwellings [discrepancies: none] No unsatisfied basic needs 1 1 unsatisfied need 2 2 3 3 4 4 5 5 9 NIU (not in universe) This variable indicates the number of unsatisfied basic needs in the dwelling. The unsatisfied basic needs include: without public or private medical insurance, without water access, dirt floor in the dwelling, and without a telephone. IPUMS Type of family 1 Argentina 1991: Private dwellings [discrepancies: none] 1 Single 2 Compound nuclear 3 Compound nuclear home 4 Non-compound nuclear 5 Without nuclear 9 NIU (not in universe) This variable indicates the type of household nucleus. [constructed variable] IPUMS Type of family 2 Argentina 1991: Private dwellings [discrepancies: none] 00 Pure [traditional] 01 Parent 02 Parent, son/daughter-in-law 03 Parent, son/daughter-in-law and mother/father-in-law 04 Parent, son/daughter-in-law, mother/father-in-law and other 05 Parent, son/daughter-in-law, mother/father-in-law, other and non family member 06 Parent, son/daughter-in-law, mother/father-in-law, non family member 07 Parent, son/daughter-in-law, other family member and non family member 08 Parent, son/daughter-in-law and other family member 09 Parent, son/daughter-in-law and non family member 10 Parent and non family member 11 Parent, mother/father-in-law and other family member 12 Parent, mother/father-in-law, other family member and non family member 13 Parent, mother/father-in-law and non family member 14 Parent and other family member 15 Parent, other family member and non family member 16 Parent and non family member 17 Son/daughter-in-law 18 Son/daugher-in-law and mother/father-in-law 19 Son/daughter-in-law, mother/father-in-law and other family member 20 Son/daughter-in-law, mother/father-in-law, other family member and non family member 21 Son/daughter-in-law, mother/father-in-law and non family member 22 Son/daughter-in-law and other family member 23 Son/daughter-in-law, other family member and non family member 24 Son/daughter-in-law and non family member 25 Mother/father-in-law 26 Mother/father-in-law and other family member 27 Mother/father-in-law, other family member and non family member 28 Mother/father-in-law and non family member 29 Other family member 30 Other family member and non family member 31 Non family member 99 NIU (not in universe) This variable indicates the type and composition of the household. [constructed variable] IPUMS Size of place Argentina 1991: Private dwellings [discrepancies: none] 01 Rural dispersed 02 Rural, less than 100 03 Rural, between 100 and 500 04 Rural, between 500 and 1000 05 Rural, between 1000 and 2000 06 Less 20000 07 Between 20000 and 50000 08 Between 50000 and 100000 09 Between 100000 and 1000000 10 Larger than 1000000 99 NIU (not in universe) This variable indicates the size of the locality where the household resides. IPUMS Collective dwelling type <span class="h3">I. Characteristics of the dwelling for the only household or for the first household</span> <br />[Questions 1-9]</p> <p><svar v="AR91A027 AR91A046 AR91A028 AR91A029 AR91A030 AR91A031 AR91A032 AR91A033 AR91A034 AR91A035 AR91A036 AR91A037 AR91A038 AR91A039">[Questions 2-16 were addressed only in case of an occupied dwelling, with occupants present, per Question 1.]<br /></svar></p> <p><svar a="all" v="AR91A027 AR91A046">2. Type of dwelling<br /><div class="i1">Collective - continue with population [skip questions 3-16]</div><br /><div class="i2">[] Home for the elderly<br />[] Home for children<br />[] Boarding school<br />[] Labor camp<br />[] Hospital<br />[] Prison<br />[] Barracks<br />[] Religious household<br />[] Hotel</div><br /><div class="i1">Individual</div><br /><div class="i2">[] House<br />[] Rural dwelling or cabin<br />[] Apartment<br />[] Rented room/s<br />[] Pension or hotel<br />[] Space not built for occupation<br />[] Mobile dwelling</div><br /></svar> Argentina 1991: Collective dwellings [discrepancies: none] 01 Old persons' home 02 Orphanage 03 Educational institution 04 Camp 05 Hospital 06 Prison 07 Military base 08 Religious home 09 Hotel 98 Unknown dwelling type 99 NIU (not in universe) This variable indicates the type of collective dwelling. IPUMS Household weight Argentina 1991: Private dwellings [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 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 50 50 51 51 52 52 54 54 57 57 58 58 62 62 63 63 67 67 73 73 77 77 80 80 99 NIU (not in universe) This variable indicates the household weight in the sample. IPUMS Strata Argentina 1991: All households This variable is the strata identifier for the sample. Strata is a constructed variable that captures implicit geographic stratification resulting from the sample design. It is created by assigning a unique identifier to groups of between 10 and 19 adjacent households. Additional documentation is available on the Variance Estimation page. This is a 6-digit numeric variable with 0 implied decimal places IPUMS Person number PERNUM numbers all persons within each household consecutively (starting with "1" for the first person record of each household). When combined with SAMPLE and SERIAL, PERNUM uniquely identifies each person in the IPUMS-International database. PERNUM is a 4-digit numeric variable. IPUMS Person weight PERWT indicates the number of persons in the actual population represented by the person in the sample. For the samples that are truly weighted (see the comparability discussion), PERWT must be used to yield accurate statistics for the population. NOTE: PERWT has 2 implied decimal places. That is, the last two digits of the eight-digit variable are decimal digits, but there is no actual decimal in the data. PERWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description. IPUMS Mother's location in household MOMLOC is a constructed variable that indicates whether or not the person's mother lived in the same household and, if so, gives the person number of the mother (see PERNUM). MOMLOC makes it easy for researchers to link the characteristics of children and their (probable) mothers. The method by which probable child-mother links are identified is described in PARRULE. The general design of MOMLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly. For more details on the construction of MOMLOC, see the Comparability section of PARRULE and this paper on IPUMSI family linking methodology. Note: MOMLOC identifies social relationships (such as stepmother and adopted mother) as well as biological relationships. The variable STEPMOM is designed to identify some of these social relationships. To restrict MOMLOC to biological mothers, such as for own children fertility estimation, MOMLOC should be reset to zero when STEPMOM is greater than zero. MOMLOC is a 3-digit numeric variable. Codes0 = No mother of this person present in the household. 1 or higher = The person number of this person's mother IPUMS Father's location in household POPLOC is a constructed variable that indicates whether or not the person's father lived in the same household and, if so, gives the person number of the father (see PERNUM). POPLOC makes it easy for researchers to link the characteristics of children and their (probable) fathers. The method by which probable child-father links are identified is described in PARRULE. The general design of POPLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly. For more details on the construction of POPLOC, see the Comparability section of PARRULE and this paper on IPUMSI family linking methodology. Note: POPLOC identifies social relationships (such as stepfather and adopted father) as well as biological relationships. The variable STEPPOP is designed to identify some of these social relationships. To restrict POPLOC to biological mothers, such as for own children fertility estimation, POPLOC should be reset to zero when STEPPOP is greater than zero. POPLOC is a 3-digit numeric variable. Codes0 = No father of this person present in the household. 1 or higher = The person number of this person's father IPUMS Spouse's location in household SPLOC is a constructed variable that indicates whether or not the person's spouse lived in the same household and, if so, gives the person number (PERNUM) of the spouse. SPLOC makes it easy for researchers to link the characteristics of (probable) spouses. The method by which probable spouse-spouse links are identified is described in SPRULE. The general design of SPLOC and other constructed variables is modeled on the methods developed for IPUMS-USA "Family Interrelationships", but the details vary significantly. For more details on the construction of SPLOC, see the Comparability section of SPRULE and this paper on IPUMSI family linking methodology. SPLOC is a 3-digit numeric variable. Codes0 = No spouse of this person present in the household. 1 or higher = The person number of this person's spouse IPUMS Rule for linking parent 00 No parent of person in household 11 Link to head or spouse, unambiguous 12 Link to head or spouse, ambiguous 21 Child-Grandchild, within empirical child cap 22 Child-Grandchild, within constructed child cap 23 Child-Grandchild, exceeds child cap 31 Specified Other Relatives, within empirical child cap 32 Specified Other Relatives, within constructed child cap 33 Specified Other Relatives, exceeds child cap 41 Other Relatives, within empirical child cap 42 Other Relatives, within constructed child cap 51 Non-Relatives, within empirical child cap 52 Non-Relatives, within constructed child cap PARRULE describes the criteria by which the IPUMS International variables MOMLOC and POPLOC linked the person to a probable mother and/or father. IPUMS International establishes child-parent links according to five basic rules, and PARRULE gives the number of the rule that applied to the link in question. A link to any parent automatically generates a second link to that parent's spouse or partner, so only one rule is needed to describe both MOMLOC and POPLOC. The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology. IPUMS Rule for linking spouse 00 No spouse present 01 Rule 1: strong relationship pairing, couple adjacent 02 Rule 2: strong relationship pairing, couple not adjacent 03 Rule 3: weak relationship pairing, couple adjacent 04 Rule 4: weak relationship pairing, couple not adjacent 05 Rule 5: weak consensual union pairings 06 Rule 6: sample-specific rules (usually child-to-child) SPRULE explains the criteria by which the IPUMS-International variable SPLOC linked the person to his/her probable spouse. IPUMS International establishes spouse-spouse links according to five basic rules, and SPRULE gives the number of the rule that applied to the link in question. A sixth rule identifies sample-specific linking procedures only imposed in selected instances. The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology. IPUMS Probable stepmother Biological mother or no mother present 1 Mother has no children born or surviving 2 Child reports mother is deceased 3 Explicitly identified step relationship 4 Mother reports no children in the home 5 Age difference implausible 6 Child exceeds known fertility of mother STEPMOM indicates whether a person's mother, as identified by MOMLOC, was most probably not the person's biological mother. Non-zero values of STEPMOM explain why it is probable that the person's mother was a step- or adopted mother. A value of 0 indicates no likely stepmother because (1) the mother identified in MOMLOC was probably the biological mother or (2) there is no mother of this person present in the household. The codes for STEPMOM are as follows: 0 = Biological mother or no mother of this person present in household. 1 = Mother has no children born or surviving. 2 = Child reports mother is deceased. 3 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner, stepchild/child-in-law). 4 = Mother reports no children in the home. 5 = Age difference between mother and child was less than 12 or greater than 54 years. 6 = Child exceeds known fertility of mother. In cases where more than one criterion for a likely stepmother is met, STEPMOM will take the value of the criterion with the lowest code. See PARRULE for a description of the linking process. Users should note that there are many stepmothers and adopted mothers in the population that cannot be identified with information available in the censuses. Therefore, STEPMOM will always under-represent their actual number in the population. IPUMS Probable stepfather Biological father or no father present 1 Child reports father is deceased 2 Explicitly identified step relationship 3 Age difference implausible 4 Spouse of mother 5 Identified as adopted 6 Surname difference -- male child or never-married female STEPPOP indicates whether a person's father, as identified by POPLOC, was most probably not the person's biological father. Non-zero values of STEPPOP explain why it is probable that the person's father was a step- or adopted father. A value of 0 indicates no likely stepfather because (1) the father identified in POPLOC was probably the biological father or (2) there is no father of this person present in the household. The codes for STEPPOP are as follows: 0 = Biological father or no father of this person present in household. 1 = Child reports father is deceased. 2 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner; stepchild/child-in-law). 3 = Age difference between father and child was less than 12 or greater than 54 years. In cases where more than one criterion for a likely stepfather is met, STEPPOP will take the value of the criterion with the lowest code. See PARRULE for a description of the linking process. Users should note that there are many stepfathers and adopted fathers in the population that cannot be identified with information available in the censuses. Therefore, STEPPOP will always under-represent their actual number in the population. IPUMS Man with more than one wife linked No more than one wife linked via SPLOC 1 More than one wife linked via SPLOC POLYMAL indicates if a man had more than one wife linked to him in the constructed IPUMS variable SPLOC -- Spouse's Location in Household. The point of POLYMAL is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record. IPUMS Woman is second or higher order wife Person is not the 2nd or higher order wife linked via SPLOC 1 Person is the 2nd or higher order wife linked via SPLOC POLY2ND indicates if a woman was the second or higher order wife linked to a husband in the constructed IPUMS variable SPLOC -- Spouse's Location in Household. The variable does not suggest the actual marital order of wives, only their relative positions in the person order of the household as it was enumerated. The point of POLY2ND is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record. IPUMS Family unit membership FAMUNIT is a constructed variable indicating to which family within the household a person belongs. All persons related to the household head receive a 1 (see RELATE). Each secondary family or secondary individual receives a higher code. For purposes of FAMUNIT, secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). FAMUNIT is a 4-digit numeric variable. CodesIf there is only one group of related individuals within the household, all of them will be coded "1;" if there is a second, separate such group listed on the form, all of them will be coded "2," and so on. IPUMS Number of own family members in household 0001 1 family member present 0002 2 family members present 0003 3 family members present 0004 4 0005 5 0006 6 0007 7 0008 8 0009 9 0010 10 0011 11 0012 12 0013 13 0014 14 0015 15 0016 16 0017 17 0018 18 0019 19 0020 20 0021 21 0022 22 0023 23 0024 24 0025 25 0026 26 0027 27 0028 28 0029 29 0030 30 0031 31 0032 32 0033 33 0034 34 0035 35 0036 36 0037 37 0038 38 0039 39 0040 40 0041 41 0042 42 0043 43 0044 44 0045 45 0046 46 0047 47 0048 48 0049 49 0050 50 0051 51 0052 52 0053 53 0054 54 0055 55 0056 56 0057 57 0058 58 0059 59 0060 60 0061 61 0062 62 0063 63 0064 64 0065 65 0066 66 0067 67 0068 68 0069 69 0070 70 0071 71 0072 72 0073 73 0074 74 0075 75 0076 76 0077 77 0078 78 0079 79 0080 80 0081 81 0082 82 0083 83 0084 84 0085 85 0086 86 0087 87 0088 88 0089 89 0090 90 0091 91 0092 92 0093 93 0094 94 0095 95 0096 96 0097 97 0098 98 0099 99 or more persons FAMSIZE counts the number of the person's own family members living in the household with her/him, including the person her/himself. These include all persons related to the person by blood, adoption, or marriage as indicated by the census forms or inferred from them. FAMSIZE is calculated from the units identified in the IPUMS constructed variable FAMUNIT (family unit membership). The primary family is defined as all persons related to the head in the RELATE variable. Secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). IPUMS Number of own children in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 or more children in household NCHILD provides a count of the person's own children living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. IPUMS Number of own children under age 5 in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 or more own children under age 5 in household 98 One or more children have unknown age NCHLT5 provides a count of the person's own children under age five living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. IPUMS Age of eldest own child in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 or older 98 One or more children have unknown age 99 No own child in household ELDCH gives the age of the person's oldest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. ELDCH is top-coded at age 50 or older. IPUMS Age of youngest own child in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 or older 98 One or more children have unknown age 99 No own child in household YNGCH gives the age of the person's youngest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. YNGCH is top-coded at age 50 or older. IPUMS Relationship to household head [general version] 1 Head 2 Spouse/partner 3 Child 4 Other relative 5 Non-relative 6 Other relative or non-relative 9 Unknown RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). IPUMS Relationship to household head [detailed version] 1000 Head 2000 Spouse/partner 2100 Spouse 2200 Unmarried partner 2210 Civil union 2300 Same-sex spouse/partner 3000 Child 3100 Biological child 3200 Adopted child 3300 Stepchild 3400 Child/child-in-law 3500 Child/child-in-law/grandchild 3600 Child of unmarried partner 4000 Other relative 4100 Grandchild 4110 Grandchild or great grandchild 4120 Great grandchild 4130 Great-great grandchild 4200 Parent/parent-in-law 4210 Parent 4211 Stepparent 4220 Parent-in-law 4300 Child-in-law 4301 Daughter-in-law 4302 Spouse/partner of child 4310 Unmarried partner of child 4400 Sibling/sibling-in-law 4410 Sibling 4420 Stepsibling 4430 Sibling-in-law 4431 Sibling of spouse/partner 4432 Spouse/partner of sibling 4500 Grandparent 4510 Great grandparent 4600 Parent/grandparent/ascendant 4700 Aunt/uncle 4800 Other specified relative 4810 Nephew/niece 4820 Cousin 4830 Sibling's sibling-in-law 4900 Other relative, not elsewhere classified 4910 Other relative with same family name 4920 Other relative with different family name 4930 Other relative, not specified (secondary family) 5000 Non-relative 5100 Friend/guest/visitor/partner 5110 Partner/friend 5111 Friend 5112 Partner/roommate 5113 Housemate/roommate 5120 Visitor 5130 Ex-spouse 5140 Godparent 5150 Godchild 5200 Employee 5210 Domestic employee 5220 Relative of employee, n.s. 5221 Spouse of servant 5222 Child of servant 5223 Other relative of servant 5300 Roomer/boarder/lodger/foster child 5310 Boarder 5311 Boarder or guest 5320 Lodger 5330 Foster child 5340 Tutored/foster child 5350 Tutored child 5400 Employee, boarder, or guest 5500 Other specified non-relative 5510 Agregado 5520 Temporary resident, guest 5600 Group quarters 5610 Group quarters, non-inmates 5620 Institutional inmates 5900 Non-relative, n.e.c. 6000 Other relative or non-relative 9999 Unknown RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). IPUMS 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 Consensual union 1 Yes, in consensual union 2 No, married 8 Unknown 9 NIU (not in universe) CONSENS indicates whether the respondent was in a consensual union -- a de facto marriage. 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 Children surviving 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) CHSURV reports the number of children born to a woman who were still living at the time of the census. IPUMS Number of births last year None 1 1 (1 or more) 2 2 3 3 4 4+ 8 Unknown 9 NIU (not in universe) BIRTHSLYR indicates whether any -- and in most cases how many -- children were born to a woman in the past twelve months. IPUMS Nativity status NIU (not in universe) 1 Native-born 2 Foreign-born 9 Unknown/missing NATIVITY indicates whether the person was native-born or foreign-born. IPUMS Country of birth 00000 NIU (not in universe) 10000 Africa 11000 Eastern Africa 11005 British Indian Ocean Territory 11010 Burundi 11020 Comoros 11030 Djibouti 11040 Eritrea 11050 Ethiopia 11051 Ethiopia (including Eritrea) 11060 Kenya 11070 Madagascar 11080 Malawi 11090 Mauritius 11100 Mozambique 11110 Reunion 11120 Rwanda 11130 Seychelles 11140 Somalia 11150 South Sudan 11160 Uganda 11170 Tanzania 11180 Zambia 11190 Zimbabwe 11999 Eastern Africa, other or n.s. 12000 Middle Africa 12010 Angola 12020 Cameroon 12030 Central African Republic 12040 Chad 12050 Congo (Republic of) 12060 Democratic Republic of Congo 12070 Equatorial Guinea 12080 Gabon 12090 Sao Tome and Principe 12999 Middle Africa, other or n.s. 13000 Northern Africa 13010 Algeria 13011 Algeria/Tunisia 13020 Egypt 13021 Egypt/Sudan 13030 Libya 13040 Morocco 13050 Sudan 13060 Tunisia 13070 Western Sahara 13999 Northern Africa, other or n.s. 14000 Southern Africa 14010 Botswana 14020 Lesotho 14030 Namibia 14040 South Africa 14050 Swaziland 14999 Southern Africa, other or n.s. 15000 Western Africa 15010 Benin 15020 Burkina Faso 15021 Upper Volta 15030 Cape Verde 15040 Ivory Coast 15050 Gambia 15060 Ghana 15070 Guinea 15080 Guinea-Bissau 15081 Guinea-Bissau and Cape Verde 15090 Liberia 15100 Mali 15110 Mauritania 15120 Niger 15130 Nigeria 15140 St. Helena and Ascension 15150 Senegal 15160 Sierra Leone 15170 Togo 15180 Canary Islands 15999 West Africa, other or n.s. 19999 Africa, other or n.s. 20000 Americas 21000 Caribbean 21010 Anguilla 21020 Antigua-Barbuda 21030 Aruba 21040 Bahamas 21050 Barbados 21060 British Virgin Islands 21070 Cayman Isles 21080 Cuba 21090 Dominica 21100 Dominican Republic 21110 Grenada 21120 Guadeloupe 21130 Haiti 21140 Jamaica 21150 Martinique 21160 Montserrat 21170 Netherlands Antilles 21180 Puerto Rico 21190 St. Kitts-Nevis 21200 St. Croix 21210 St. John 21220 St. Lucia 21230 St Thomas 21240 St. Vincent 21250 Trinidad and Tobago 21260 Turks and Caicos 21270 U.S. Virgin Islands 21991 Caribbean commonwealth, n.s. 21999 Caribbean, other or n.s. 22000 Central America 22010 Belize/British Honduras 22020 Costa Rica 22030 El Salvador 22040 Guatemala 22050 Honduras 22060 Mexico 22070 Nicaragua 22080 Panama 22081 Panama Canal Zone 22999 Central America, other or n.s. 23000 South America 23010 Argentina 23020 Bolivia 23030 Brazil 23040 Chile 23050 Colombia 23060 Ecuador 23070 Falkland Islands 23080 French Guiana 23090 Guyana/British Guiana 23100 Paraguay 23110 Peru 23120 Suriname 23130 Uruguay 23140 Venezuela 23999 South America, other or n.s. 24000 North America 24010 Bermuda 24020 Canada 24030 Greenland 24040 United States 24999 North America, other or n.s. 29999 Americas, other or n.s. 30000 Asia 31000 Eastern Asia 31010 China 31011 Hong Kong 31012 Macau 31013 Taiwan 31020 Japan 31030 Korea 31031 Korea, DPR (North) 31032 Korea, RO (South) 31040 Mongolia 31999 Eastern Asia, other or n.s. 32000 South-Central Asia 32010 Afghanistan 32020 Bangladesh 32030 Bhutan 32040 India 32041 India/Pakistan 32042 India/Pakistan/Bangladesh/Sri Lanka 32050 Iran 32060 Kazakhstan 32070 Kyrgyzstan 32080 Maldives 32090 Nepal 32100 Pakistan 32101 Pakistan/Bangladesh 32110 Sri Lanka (Ceylon) 32120 Tajikistan 32130 Turkmenistan 32140 Uzbekistan 32999 South-Central Asia, other or n.s. 33000 South-Eastern Asia 33010 Brunei 33020 Cambodia (Kampuchea) 33030 East Timor 33040 Indonesia 33050 Laos 33060 Malaysia 33070 Myanmar (Burma) 33080 Philippines 33090 Singapore 33100 Thailand 33110 Vietnam 33999 South-Eastern Asia, other or n.s. 34000 Western Asia 34010 Armenia 34020 Azerbaijan 34030 Bahrain 34040 Cyprus 34050 Georgia 34051 Abkhazia 34052 South Ossetia 34060 Iraq 34070 Israel 34071 Israel/Palestine 34080 Jordan 34090 Kuwait 34100 Lebanon 34110 Palestine 34111 West Bank 34112 Gaza Strip 34120 Oman 34130 Qatar 34140 Saudi Arabia 34150 Syria 34151 Syria/Lebanon 34160 Turkey 34170 United Arab Emirates 34180 Yemen 34991 Middle East 34999 Western Asia, other or n.s. 39999 Asia, other or n.s. 40000 Europe 41000 Eastern Europe 41010 Belarus 41020 Bulgaria 41021 Bulgaria/Greece 41030 Czech Republic/Czechoslovakia 41040 Hungary 41050 Poland 41060 Moldova 41070 Romania 41080 Russia/USSR 41090 Slovakia 41100 Ukraine 41991 Albania, Bulgaria, Czech, Hungary, Romania, Yugoslavia 41992 Central-Eastern Europe 41999 Eastern Europe, other or n.s. 42000 Northern Europe 42010 Denmark 42020 Estonia 42030 Faroe Islands 42040 Finland 42050 Iceland 42060 Ireland 42070 Latvia 42080 Lithuania 42090 Norway 42100 Svalbard and Jan Mayen Islands 42110 Sweden 42120 United Kingdom 42999 Northern Europe, other or n.s. 43000 Southern Europe 43010 Albania 43020 Andorra 43030 Bosnia and Herzegovina 43040 Croatia 43050 Gibraltar 43060 Greece 43070 Italy 43071 Vatican City 43080 Malta 43090 Portugal 43100 San Marino 43110 Slovenia 43120 Spain 43121 Spain/Portugal 43130 Macedonia 43140 Yugoslavia 43141 Montenegro 43142 Serbia 43143 Kosovo 43144 Serbia and Montenegro 43991 Gibraltar/Malta 43992 Portugal/Greece 43993 Italy, Holy See, San Marino 43999 Southern Europe, other or n.s. 44000 Western Europe 44010 Austria 44020 Belgium 44021 Belgium/Luxemburg 44022 Belgium/Netherlands/Luxemburg 44030 France 44040 Germany 44042 West Germany 44043 Germany/Austria 44044 Mecklenburg-Schwerin 44050 Liechtenstein 44060 Luxembourg 44070 Monaco 44080 Netherlands 44090 Switzerland 44991 Belgium, Denmark, Luxembourg, Netherlands 44999 Western Europe, other or n.s. 49992 European Union 49993 European Union (original 15) 49994 Other European Union (not original 15) 49999 Europe, other or n.s. 50000 Oceania 51000 Australia and New Zealand 51010 Australia 51020 New Zealand 51030 Norfolk Islands 51999 Australia and New Zealand, n.s. 52000 Melanesia 52010 Fiji 52020 New Caledonia 52030 Papua New Guinea 52040 Solomon Islands 52050 Vanuatu (New Hebrides) 52999 Melanesia, n.s. 53000 Micronesia 53010 Kiribati 53020 Marshall Islands 53030 Nauru 53040 Northern Mariana Isls. 53050 Palau 53060 Federated States of Micronesia 53999 Micronesia, other or n.s. 54000 Polynesia 54010 Cook Islands 54020 French Polynesia 54030 Niue 54040 Pitcairn Island 54050 Western Samoa 54060 Eastern Samoa 54070 Tokelau 54080 Tonga 54090 Tuvalu 54100 Wallis and Futuna Isls. 54999 Polynesia, other or n.s. 55000 U.S. Pacific Possessions 55010 American Samoa 55020 Baker Island 55030 Guam 55040 Howland Island 55050 Johnston Atoll 55060 Kingman Reef 55070 Midway Islands 55080 Wake Island 55999 US Pacific, other or n.s. 59999 Oceania, other or n.s. 80000 AT SEA 90000 Other countries n.s. 99999 Unknown BPLCOUNTRY indicates the person's country of birth. IPUMS Province of birth, Argentina 02 City of Buenos Aires 06 Buenos Aires province 10 Catamarca 14 Córdoba 18 Corrientes 22 Chaco 26 Chubut 30 Entre Ríos 34 Formosa 38 Jujuy 42 La Pampa 46 La Rioja 50 Mendoza 54 Misiones 58 Neuquén 62 Río Negro 66 Salta 70 San Juan 74 San Luis 78 Santa Cruz 82 Santa Fe 86 Santiago del Estero 90 Tucumán 94 Tierra del Fuego 97 Argentina, not specified 98 Foreign country 99 Unknown BPLAR indicates the person's province of birth within Argentina. 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 Literacy NIU (not in universe) 1 No, illiterate 2 Yes, literate 9 Unknown/missing LIT indicates whether or not the respondent could read and write in any language. A person is typically considered literate if he or she can both read and write. All other persons are illiterate, including those who can either read or write but cannot do both. 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 Years of schooling 00 None or pre-school 01 1 year 02 2 years 03 3 years 04 4 years 05 5 years 06 6 years 07 7 years 08 8 years 09 9 years 10 10 years 11 11 years 12 12 years 13 13 years 14 14 years 15 15 years 16 16 years 17 17 years 18 18 years or more 90 Not specified 91 Some primary 92 Some technical after primary 93 Some secondary 94 Some tertiary 95 Adult literacy 96 Special education 98 Unknown/missing 99 NIU (not in universe) YRSCHOOL indicates the highest grade/level of schooling the person had completed, in years. Only formal schooling is counted. YRSCHOOL accounts for the number of years of study, regardless of the track or kind of study. Information on degree and/or technical track is available in EDATTAIN. Years of schooling for Israel, categorized into intervals, are given in YRSCHOOL2. Users should pay close attention to the top-codes in each sample, as discussed in the comparability section. IPUMS Educational attainment, Argentina 000 NIU (not in universe) 110 Never attended 120 Pre-school 130 Primary, no years completed 201 Primary, grade 1 202 Primary, grade 2 203 Primary, grade 3 204 Primary, grade 4 205 Primary, grade 5 206 Primary, grade 6 207 Primary, grade 7+ 208 Primary, complete 209 Primary, grade unknown 311 Secondary general, grade 1 312 Secondary general, grade 2 313 Secondary general, grade 3 314 Secondary general, grade 4 315 Secondary general, grade 5 318 Secondary general, complete 319 Secondary general, grade unknown 321 Commercial school, grade 1 322 Commercial school, grade 2 323 Commercial school, grade 3 324 Commercial school, grade 4 325 Commercial school, grade 5 328 Commercial school, complete 329 Commercial school, grade unknown 331 Teaching school, grade 1 332 Teaching school, grade 2 333 Teaching school, grade 3 334 Teaching school, grade 4 335 Teaching school, grade 5 338 Teaching school, complete 339 Teaching school, grade unknown 341 Secondary technical, grade 1 342 Secondary technical, grade 2 343 Secondary technical, grade 3 344 Secondary technical, grade 4 345 Secondary technical, grade 5 348 Secondary technical, complete 349 Secondary technical, grade unknown 351 Other secondary, grade 1 352 Other secondary, grade 2 353 Other secondary, grade 3 354 Other secondary, grade 4 355 Other secondary, grade 5 358 Degree complete 359 Other secondary, grade unknown 410 Higher education non-university, year 1 411 Higher education non-university, year 2 412 Higher education non-university, year 3 413 Higher education non-university, year 4 414 Higher education non-university, year 5 415 Higher education non-university, year 6 416 Higher education non-university, year 7 417 Higher education non-university, year 8+ 418 Higher education non-university, complete 419 Higher education non-university, year unknown 430 University, year 1 431 University, year 2 432 University, year 3 433 University, year 4 434 University, year 5 435 University, year 6 436 University, year 7 437 University, year 8+ 438 University, complete 439 University, year unknown 450 Post-graduate, year 1 451 Post-graduate, year 2 452 Post-graduate, year 3 453 Post-graduate, year 4 454 Post-graduate, year 5 455 Post-graduate, year 6+ 458 Post-graduate, complete 459 Post-graduate, year unknown 500 Special education, years not specified 998 Attended, level unknown 999 Unknown EDUCAR indicates the person's educational attainment in Argentina in terms of the level and grade of schooling completed. IPUMS Activity status (employment status) [general version] NIU (not in universe) 1 Employed 2 Unemployed 3 Inactive 9 Unknown/missing EMPSTAT indicates whether or not the respondent was part of the labor force -- working or seeking work -- over a specified period of time. Depending on the sample, EMPSTAT can also convey further information. The first digit of EMPSTAT is fully comparable, and classifies the population into three groups: employed, unemployed, and inactive. The combination of employed and unemployed yields the total labor force. The second and third digits of EMPSTAT preserve additional information available for some countries and census years but not for others. Employment status is sometimes referred to in other sources as "activity status". IPUMS Activity status (employment status) [detailed version] 000 NIU (not in universe) 100 Employed, not specified 110 At work 111 At work, and 'student' 112 At work, and 'housework' 113 At work, and 'seeking work' 114 At work, and 'retired' 115 At work, and 'no work' 116 At work, and other situation 117 At work, family holding, not specified 118 At work, family holding, not agricultural 119 At work, family holding, agricultural 120 Have job, not at work in reference period 130 Armed forces 131 Armed forces, at work 132 Armed forces, not at work in reference period 133 Military trainee 140 Marginally employed 200 Unemployed, not specified 201 Unemployed 6 or more months 202 Worked fewer than 6 months, permanent job 203 Worked fewer than 6 months, temporary job 210 Unemployed, experienced worker 220 Unemployed, new worker 230 No work available 240 Inactive unemployed 300 Inactive (not in labor force) 310 Housework 320 Unable to work, disabled or health reasons 321 Permanent disability 322 Temporary illness 323 Disabled or imprisoned 330 In school 340 Retirees and living on rent 341 Living on rents 342 Living on rents or pension 343 Retirees/pensioners 344 Retired 345 Pensioner 346 Non-retirement pension 347 Disability pension 348 Retired without benefits 350 Elderly 351 Elderly or disabled 360 Institutionalized 361 Prisoner 370 Intermittent worker 371 Not working, seasonal worker 372 Not working, occasional worker 380 Other income recipient 390 Inactive, other reasons 391 Too young to work 392 Dependent 999 Unknown/missing EMPSTAT indicates whether or not the respondent was part of the labor force -- working or seeking work -- over a specified period of time. Depending on the sample, EMPSTAT can also convey further information. The first digit of EMPSTAT is fully comparable, and classifies the population into three groups: employed, unemployed, and inactive. The combination of employed and unemployed yields the total labor force. The second and third digits of EMPSTAT preserve additional information available for some countries and census years but not for others. Employment status is sometimes referred to in other sources as "activity status". IPUMS Labor force participation 1 No, not in the labor force 2 Yes, in the labor force 8 Unknown 9 NIU (not in universe) LABFORCE is a dichotomous variable identifying whether a person participated in the labor force. Labor force participation generally means working or seeking work within a specified reference period. For most samples LABFORCE is a recode of EMPSTAT (employment status). A consistent lower age universe of 15 or older has been applied to increase comparability across samples. Full detail is retained in EMPSTAT, which should be used for any study of child labor. IPUMS Occupation, 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 Size of work establishment 00 None 01 1 employee 02 1 to 5 03 1 to 9 04 2 to 4 05 2 to 5 06 2 to 9 07 5 08 5 to 9 09 6 to 9 10 6 to 10 11 6 to 39 12 10 to 19 13 10 to 24 14 10 to 49 15 10 to 99 16 11 to 15 17 11 to 20 18 11 to 50 19 16 to 19 20 16 to 50 21 20 to 49 22 21 to 100 23 25 to 499 24 50 to 249 25 100 to 449 30 6 or more 31 10 or more 32 20 or more 33 40 or more 34 50 or more 35 51 or more 36 100 or more 37 250 or more 38 500 or more 98 Unknown 99 NIU (not in universe) ESTABSZ reports the number of people employed in the respondent's work establishment. 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 Migration status, 5 years 00 NIU (not in universe) 10 Same major administrative unit 11 Same major, same minor administrative unit 12 Same major, different minor administrative unit 20 Different major administrative unit 30 Abroad 99 Unknown/missing MIGRATE5 indicates the person's place of residence 5 years ago. The first digit records movement across major administrative divisions and countries. The second digit reports movement across minor administrative divisions, for samples in which that detail is available. IPUMS Person number (within household) Argentina 1991: 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 the household). IPUMS Relationship <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A401">1. What is the relationship with the head of household?<br /><div class="i1">[] Spouse or companion<br />[] Son, daughter; step-son, step-daughter<br />[] Parent or parent-in-law<br />[] Son-in-law, daughter-in-law<br />[] Grandson, granddaughter<br />[] Other relative<br />[] Domestic servants and their relatives<br />[] Other non-relatives<br />[] Resident in a collective dwelling</div><br /></svar> Argentina 1991: All persons Person from collective dwelling 1 Head 2 Spouse 3 Child 4 Parent/parent-in-law 5 Child-in-law 6 Grandchild 7 Other family member 8 Domestic servant 9 Other non-family member This variable indicates kin relationship. IPUMS Sex <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A402">2. Sex<br /><div class="i1">[] Male<br />[] Female</div><br /></svar> Argentina 1991: All persons 1 Male 2 Female This variable indicates the person's sex. IPUMS Age <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A403">3. What is his or her current age in completed years? _ _<br /><div class="i1">For those younger than one year old mark 00. For those older than 99 years old mark 99.</div><br /></svar> Argentina 1991: 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 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 96 96 97 97 98 98 99 99+ This variable indicates the person's age. IPUMS Retirement payment <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A404">4. Does the person receive a pension or retirement payment?<br /><div class="i1">[] Yes<br />[] No<br />[] Unanswered</div><br /></svar> Argentina 1991: All persons 1 Yes 2 No 9 Unknown This variable indicates whether the person receives a retirement payment or pension. IPUMS Place of current residence <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A405 AR91A406">5. Where does the person usually live?<br /><div class="i1">The place where the person has lived for the last six months or more or has decided to reside.<br /><br />[] In this locality<br />[] In another locality in Argentina</div><br /><div class="i2">____ Locality or place<br />____ Department or Fraction<br />____ Province (consider the Federal Capital as a province)</div><br /><div class="i1">[] In another country: name of the country ____<br />[] Unanswered</div><br /></svar> Argentina 1991: All persons 1 Lives here 2 Lives in another Argentinian province 3 Lives abroad 8 Unknown 9 No response This variable indicates the place of current residence. The sample lacks data for this and several other variables for roughly half the cases. Users should apply the alternative weight variable AR91A434 when using one of these variables. IPUMS Residence 5 years ago <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A407 AR91A408">6. Where did the person usually live five years ago?<br /><div class="i1">[] In this locality<br />[] In another locality in Argentina</div><br /><div class="i2">____ Locality or place<br />____ Department or Fraction<br />____ Province (consider the Federal Capital as a province)</div><br /><div class="i1">[] In another country: name of the country ____<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 5+ [discrepancies: none] No response 1 Lived here 2 Lived in another Argentinian province 3 Lived abroad 8 Unkown 9 NIU (not in universe) This variable indicates the place of residence 5 years ago. The sample lacks data for this and several other variables for roughly half the cases. Users should apply the alternative weight variable AR91A434 when using one of these variables. IPUMS Place of birth <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A409 AR91A410">7. Where he or she was born?<br /><div class="i1">[] In this province<br />[] In another province in Argentina: Province (consider the Federal Capital as a province) ____<br />[] In Bolivia, Brazil, Chile, Paraguay, or Uruguay</div><br /><div class="i2">Name of the country ____<br />Year of arrival in Argentina (if before 1900, write 00) 19_ _ <br />[] Unanswered</div><br /><div class="i1">[] In another country</div><br /><div class="i2">Name of the country ____<br />Year of arrival in Argentina (if before 1900, write 00) 19_ _ <br />[] Unanswered</div><br /><div class="i1">[] Unanswered</div><br /></svar> Argentina 1991: All persons 1 This province 2 Another province 3 Neighboring country 4 Other country 8 Unknown 9 No response This variable indicates the person's place of birth. The sample lacks data for this and several other variables for roughly half the cases. Users should apply the alternative weight variable AR91A434 when using one of these variables. IPUMS Province or country of birth <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A409 AR91A410">7. Where he or she was born?<br /><div class="i1">[] In this province<br />[] In another province in Argentina: Province (consider the Federal Capital as a province) ____<br />[] In Bolivia, Brazil, Chile, Paraguay, or Uruguay</div><br /><div class="i2">Name of the country ____<br />Year of arrival in Argentina (if before 1900, write 00) 19_ _ <br />[] Unanswered</div><br /><div class="i1">[] In another country</div><br /><div class="i2">Name of the country ____<br />Year of arrival in Argentina (if before 1900, write 00) 19_ _ <br />[] Unanswered</div><br /><div class="i1">[] Unanswered</div><br /></svar> Argentina 1991: All persons 002 Capital Federal 004 Buenos Aires 006 Buenos Aires 010 Catamarca 014 Cordoba 018 Corrientes 022 Del Chaco 026 Del Chubut 030 Entre Ríos 034 Formosa 038 Jujuy 042 La Pampa 046 La Rioja 050 Mendoza 054 Misiones 058 Del Neuquen 062 Rio Negro 066 Salta 070 San Juan 074 San Luis 078 Santa Cruz 082 Santa Fe 086 Santiago del Estero 090 Tucuman 094 Tierra del Fuego, Antartida e Islas del Atlantico Sur 099 Argentina, not specified 109 Zaire 132 Zimbabwe 138 Sudan 139 Tanzania 141 Tunisia 159 South Africa 202 Bolivia 203 Brazil 204 Canada 205 Colombia 206 Costa Rica 207 Cuba 208 Chile 209 Dominican Republic 210 Ecuador 211 El Salvador 212 United States of America 213 Guatemala 215 Haiti 216 Honduras 217 Jamaica 218 Mexico 219 Nicaragua 220 Panama 221 Paraguay 222 Peru 223 Puerto Rico 224 Trinidad and Tobago 225 Uruguay 226 Venezuela 227 American Protectorates 228 Greenland 230 Aruba 232 Surinam 233 Dominica 240 Grenada 289 Unknown - neighbor country 299 Unknown - non-neighbor country 301 Afghanistan 302 Saudi Arabia 303 Bahrain 304 Myanmar 306 Kampuchea (Cambodia) 307 Sri Lanka 308 South Korea 309 North Korea 310 China 312 Philippines 313 Taiwan 315 India 316 Indonesia 317 Iraq 318 Iran 319 Israel 320 Japan 321 Jordan 324 Laos 325 Lebanon 333 Singapore 334 Syria 335 Thailand 341 Hong Kong 347 Former Russian Republics 381 Korea (unspecified) 382 China (unspecified) 401 Albania 402 West Germany (BRD) 403 East Germany (DDR) 405 Austria 406 Belgium 407 Bulgaria 408 Checkoslovakia 409 Denmark 410 Spain 411 Finland 412 France 413 Greece 414 Hungary 415 Ireland 416 Iceland 417 Italy 418 Liechtenstein 419 Luxemburg 420 Malta 421 Monaco 422 Norway 423 Netherlands 424 Poland 425 Portugal 426 Great Britain 427 Romania 428 San Marino 429 Sweden 430 Switzerland 431 Vatican City 432 Yugoslavia 433 Gibraltar 435 Cyprus 436 Turkey 437 Russia 438 Germany (unspecified) 481 Estonia 482 Lithuania 501 Australia 504 New Zealand 505 Vanatu 510 New Zealand Protectorates 511 Oceania, US Dependencies 515 Micronesia 601 USSR (unspecified) 998 Other country, unspecified 999 Unknown This variable indicates the province of birth. The sample lacks data for this and several other variables for roughly half the cases. Users should apply the alternative weight variable AR91A434 when using one of these variables. IPUMS Literacy <svar v="AR91A401 AR91A402 AR91A403 AR91A404 AR91A405 AR91A406 AR91A407 AR91A408 AR91A409 AR91A410 AR91A411">[Questions 1-10 were asked of all persons.]<br /></svar></p> <p><svar a="all" v="AR91A411">10. Does the person know how to read and write?<br /><div class="i1">[] Yes<br />[] No<br />[] Unanswered</div><br /></svar> Argentina 1991: All persons 1 Yes - read and write 2 No - read and write 9 Unknown This variable indicates whether the person knows how to read and write. IPUMS Attending school <svar v="AR91A412 AR91A413 AR91A414 AR91A415 AR91A416 AR91A417 AR91A418">For all persons 3 years old and more<br />[Questions 11- 16 were asked of persons age 3 years or older.]<br /></svar></p> <p><svar a="all" v="AR91A412">11. Do you attend some educational establishment?<br /><div class="i1">[] Attends a public establishment<br />[] Attends a private institution<br />[] Does not attend, but attended [skip questions 12-13]<br />[] Never attended [skip questions 12-16]<br />[] Unanswered [skip questions 12-16]</div><br /></svar> Argentina 1991: Persons age 3+ [discrepancies: none] 1 Attends - public 2 Attends - private 3 Attended in past 4 Never attended 8 Unknown 9 NIU (not in universe) This variable indicates whether the person attends school. IPUMS Education level attends <svar v="AR91A412 AR91A413 AR91A414 AR91A415 AR91A416 AR91A417 AR91A418">For all persons 3 years old and more<br />[Questions 11- 16 were asked of persons age 3 years or older.]<br /></svar></p> <p><svar a="all" v="AR91A413 AR91A418">12. What level does the person attend?<br /><div class="i1">[Question 12 was asked of persons age 3+ who currently attended an educational establishment, per question 11.]<br /><br />[] Preschool or kindergarten [finished, skip questions 13-28]<br />[] Elementary<br />[] Secondary<br />[] Tertiary<br />[] University<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 3+ who attend an education establishment [discrepancies: none] 1 Kindergarten 2 Primary 3 Secondary 4 Tertiary 5 University 8 Unknown 9 NIU (not in universe) This variable indicates the person's education level attended. IPUMS Education grade attends <svar v="AR91A412 AR91A413 AR91A414 AR91A415 AR91A416 AR91A417 AR91A418">For all persons 3 years old and more<br />[Questions 11- 16 were asked of persons age 3 years or older.]<br /></svar></p> <p><svar a="all" v="AR91A414">13. What year or grade is the person attending?<br /><div class="i1">[Question 13 was asked of persons age 3+ who currently attend an educational establishment at a level higher than preschool or kindergarten, per questions 11-12.]<br /><br />[After responding to question 13, skip questions 14-16.]<br /><br />[] 1st<br />[] 2nd<br />[] 3rd<br />[] 4th<br />[] 5th<br />[] 6th<br />[] 7th<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 3+ who attend higher than kindergarten [discrepancies: none] NIU (not in universe) 1 1 2 2 3 3 4 4 5 5 6 6 7 7+ 9 Unknown This variable indicates the education grade that the person currently attends. IPUMS Highest education level attended <svar v="AR91A412 AR91A413 AR91A414 AR91A415 AR91A416 AR91A417 AR91A418">For all persons 3 years old and more<br />[Questions 11- 16 were asked of persons age 3 years or older.]<br /></svar></p> <p><svar a="all" v="AR91A415">14. What is the highest level that the person attended?<br /><div class="i1">[Question 14 was asked of persons age 3+ who do not currently attend an educational establishment but attended one in the past, per question 11.]<br /><br />[] Preschool or kindergarten - continue on the other side [skip questions 15-16]<br />[] Elementary<br />[] Secondary<br />[] Tertiary<br />[] University<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 3+ not in school but who attended previously [discrepancies: none] NIU (not in universe) 1 Kindergarten 2 Primary 3 Secondary 4 Tertiary 5 University 9 Unknown This variable indicates the highest educational level attended. IPUMS Highest grade passed <svar v="AR91A412 AR91A413 AR91A414 AR91A415 AR91A416 AR91A417 AR91A418">For all persons 3 years old and more<br />[Questions 11- 16 were asked of persons age 3 years or older.]<br /></svar></p> <p><svar a="all" v="AR91A416">15. What was the highest year or grade that the person passed at this level?<br /><div class="i1">[Question 15 was asked of persons age 3+ who did not currently attend an educational establishment but attended one in the past, at a level higher than preschool or kindergarten, per questions 11 and 14.]<br /><br />[] Did not complete 1st - continue on the other side [skip question 16]<br />[] 1st<br />[] 2nd<br />[] 3rd<br />[] 4th<br />[] 5th<br />[] 6th<br />[] 7th<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 3+ not in school who attended higher than kindergarten in the past [discrepancies: type I: trace, type II: none] Not first 1 1 2 2 3 3 4 4 5 5 6 6 7 7+ 8 Unknown 9 NIU (not in universe) This variable indicates the highest school grade passed. IPUMS Completed education level <svar v="AR91A412 AR91A413 AR91A414 AR91A415 AR91A416 AR91A417 AR91A418">For all persons 3 years old and more<br />[Questions 11- 16 were asked of persons age 3 years or older.]<br /></svar></p> <p><svar a="AR91A417" v="AR91A417 AR91A418">16. Did the person complete this level?<br /><div class="i1">[Question 16 was asked of persons age 3+ who do not currently attend an educational establishment but attended one in the past, at a level higher than preschool or kindergarten, and completed at least the 1st year at that level, per questions 11, 14, and 15.]<br /><br />[] Yes<br />[] No<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 3+ not in school who completed 1 or more years above kindergarten in past [discrepancies: none] NIU (not in universe) 1 Yes, completed 2 No, did not complete 9 Unknown This variable indicates whether the person completed a course when he/she was attending an educational establishment. IPUMS Highest level of education <svar v="AR91A412 AR91A413 AR91A414 AR91A415 AR91A416 AR91A417 AR91A418">For all persons 3 years old and more<br />[Questions 11- 16 were asked of persons age 3 years or older.]<br /></svar></p> <p><svar a="all" v="AR91A413 AR91A418">12. What level does the person attend?<br /><div class="i1">[Question 12 was asked of persons age 3+ who currently attended an educational establishment, per question 11.]<br /><br />[] Preschool or kindergarten [finished, skip questions 13-28]<br />[] Elementary<br />[] Secondary<br />[] Tertiary<br />[] University<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 3+ [discrepancies: none] 00 NIU (not in universe) 01 Never attended 02 Preschool 03 Primary incomplete 04 Primary complete 05 Seconday incomplete 06 Secondary complete 07 Tertiary incomplete 08 Tertiary complete 09 University incomplete 10 University complete 99 Unknown This variable indicates the highest level of education completed. IPUMS Labor relation <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar v="AR91A419 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426">[Questions 21-24 were asked of persons age 14+ who had a job last week (did formal or informal work or did not work due to sickness, vacation, etc.), per questions 17-19.]<br /></svar></p> <p><svar a="all" v="AR91A419 AR91A421 AR91A422">21. In your main job, the one where you work the most hours, what position did you have in this occupation?<br /><div class="i1">[] Owner or partner (has employees)</div><br /><div class="i2">How many workers or employers are there in the establishment where you work?</div><br /><div class="i3">[] 1 to 5<br />[] 6 or more<br />[] Unanswered</div><br /><div class="i1">[] Employee or worker in the private sector</div><br /><div class="i2">How many workers or employers are there in the establishment where you work?</div><br /><div class="i3">[] 1 to 5<br />[] 6 or more<br />[] Unanswered</div><br /><div class="i1">[] Employee or worker in the public sector</div><br /><div class="i2">[] National<br />[] Provincial<br />[] Municipal<br />[] Unanswered</div><br /><div class="i1">[] Domestic employee</div><br /><div class="i2">[] Self-employed (does not have employer)<br />[] Family worker without regular income<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 14+ who had a job last week [discrepancies: none] NIU (not in universe) 1 Employer (private) 2 Private employee 3 Public employee 4 Domestic servant 5 Own account 6 Family worker 9 Unknown This variable indicates employment status. IPUMS Activity status <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar a="all" v="AR91A420">17. During the past week, did you work, even if it was for just a few hours?<br /><div class="i1">[] Yes [skip questions 18-20]<br />[] No<br />[] Unanswered [skip questions 18-24]</div><br /><br />18. Did you do anything in the house that was for outside or help anyone in a business, small farm, or work?<br /><div class="i1">[Question 18 was asked of persons age 14+ who did not formally work last week, per question 17.]<br /><br />[] Yes [skip questions 19-20]<br />[] No<br />[] Unanswered [skip questions 19-24]</div><br /><br />19. Were you off work because of illness, vacation, etc?<br /><div class="i1">[Question 19 was asked of persons age 14+ who did not formally work last week, per question 17, and did not do any informal work last week, per question 18.]<br /><br />[] Yes [skip question 20]<br />[] No<br />[] Unanswered [skip questions 20-24]</div><br /><br />20. During the past four weeks, did you look for work?<br /><div class="i1">[Question 20 was asked of persons age 14+ who did not do any work last week due to reasons other then sickness, vacation, etc., per questions 17-19.]<br /><br />[] Yes [skip questions 21-24]<br />[] No [skip questions 21-24]<br />[] Unanswered [skip questions 21-24]</div><br /></svar> Argentina 1991: Persons age 14+ [discrepancies: none] NIU (not in universe) 1 Active - employed 2 Active - unemployed 3 Inactive - retired 4 Inactive - student 5 Inactive - other situation 9 Unknown This variable indicates whether the person is economically active. IPUMS Number of employees in work establishment <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar v="AR91A419 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426">[Questions 21-24 were asked of persons age 14+ who had a job last week (did formal or informal work or did not work due to sickness, vacation, etc.), per questions 17-19.]<br /></svar></p> <p><svar a="all" v="AR91A419 AR91A421 AR91A422">21. In your main job, the one where you work the most hours, what position did you have in this occupation?<br /><div class="i1">[] Owner or partner (has employees)</div><br /><div class="i2">How many workers or employers are there in the establishment where you work?</div><br /><div class="i3">[] 1 to 5<br />[] 6 or more<br />[] Unanswered</div><br /><div class="i1">[] Employee or worker in the private sector</div><br /><div class="i2">How many workers or employers are there in the establishment where you work?</div><br /><div class="i3">[] 1 to 5<br />[] 6 or more<br />[] Unanswered</div><br /><div class="i1">[] Employee or worker in the public sector</div><br /><div class="i2">[] National<br />[] Provincial<br />[] Municipal<br />[] Unanswered</div><br /><div class="i1">[] Domestic employee</div><br /><div class="i2">[] Self-employed (does not have employer)<br />[] Family worker without regular income<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 14+ who had a job in the private sector last week [discrepancies: none] NIU (not in universe) 1 1 to 5 2 6 or more 9 Unknown This variable indicates the number of employees in the person's working establishment. IPUMS Jurisdiction [of public employment] <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar v="AR91A419 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426">[Questions 21-24 were asked of persons age 14+ who had a job last week (did formal or informal work or did not work due to sickness, vacation, etc.), per questions 17-19.]<br /></svar></p> <p><svar a="all" v="AR91A419 AR91A421 AR91A422">21. In your main job, the one where you work the most hours, what position did you have in this occupation?<br /><div class="i1">[] Owner or partner (has employees)</div><br /><div class="i2">How many workers or employers are there in the establishment where you work?</div><br /><div class="i3">[] 1 to 5<br />[] 6 or more<br />[] Unanswered</div><br /><div class="i1">[] Employee or worker in the private sector</div><br /><div class="i2">How many workers or employers are there in the establishment where you work?</div><br /><div class="i3">[] 1 to 5<br />[] 6 or more<br />[] Unanswered</div><br /><div class="i1">[] Employee or worker in the public sector</div><br /><div class="i2">[] National<br />[] Provincial<br />[] Municipal<br />[] Unanswered</div><br /><div class="i1">[] Domestic employee</div><br /><div class="i2">[] Self-employed (does not have employer)<br />[] Family worker without regular income<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 14+ who had a job in the public sector last week [discrepancies: none] NIU (not in universe) 1 National 2 Provincial 3 Municipal 9 Unknown This variable indicates the jurisdiction of public employment. IPUMS Industry <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar v="AR91A419 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426">[Questions 21-24 were asked of persons age 14+ who had a job last week (did formal or informal work or did not work due to sickness, vacation, etc.), per questions 17-19.]<br /></svar></p> <p><svar a="all" v="AR91A423">22. What does the establishment or place where the person works do or produce? ____<br /><div class="i1">For example: stove factory, collective passenger transportation, leather shoe factory, elementary education, preparation and sale of cakes, etc.</div><br /></svar> Argentina 1991: Persons age 14+ who had a job last week [discrepancies: type I: trace, type II: none] 00 Activities not well specified and/or no answer 01 Agriculture, cattle ranching, hunting and related service activities 02 Forestry, lumber extraction and related service activities 05 Fishing, production in fish hatcheries and fish farms; services activities related to fishing 10 Extraction of coal and lignite; peat extraction 11 Extraction of crude petroleum and natural gas; related services activities 12 Extraction of uranium and thorium minerals 13 Extraction of metallic minerals 14 Extraction from other mines and quarries 15 Manufacture of food and beverage products 16 Manufacture of tobacco products 17 Manufacture of textile products 18 Clothing manufacture. Finishing and dying hides 19 Tanning and finishing leathers; manufacture of leather products and footwear 20 Lumber industries, except furniture 21 Paper industry 22 Activities of editing and printing 23 Coke manufacture, products of petroleum refining and nuclear combustion 24 Manufacture of chemical substances and products 25 Manufacture of rubber and plastic goods 26 Manufacture of non-metallic mineral products 27 Manufacture of common metals 28 Manufacture of metallic products, except machinery 29 Manufacture of machinery and equipment 30 Manufacture of office machinery 31 Manufacture of electrical machinery and products 32 Manufacture of radio, TV and communications equipment and appliances 33 Manufacture of medical and precision instruments and watches (clocks) 34 Manufacture of vehicles, automobiles, tractor-trailers and semi-trucks 35 Manufacture of other types of transportation equipment 36 Manufacture or furniture and other industries not listed elsewhere 40 Generation and distribution of electrical energy 41 Collection, purification and distribution of water 42 Manufacture of gas; distribution of gas via pipelines 45 Construction 50 Sale, maintenance and repair of vehicles and motorcycles; retail sales of automobile fuels 51 Wholesale and retail and commission sales, except vehicles, automobiles and motorcycles 52 Repair of personal items ad household goods 53 Undocumented 55 Hotels, camps, and other types of housing accommodations 56 Restaurants, bars, and cantinas 60 Transportation via land and pipelines 61 Transportation via aqueducts 62 Air transportation 63 Activities that support transportation activities 64 Mail and telecommunications 65 Banking activities 66 Insurance and pension plans 67 Support activities for financial intermediation 70 Real estate activities 71 Rental of machinery and equipment and personal items and household goods 72 Informatics and related activities 73 Research and development 74 Other business activities 75 Pubic administration and defense; social security plans with obligatory affiliation 80 Education 85 Social services and health services 90 Removal of waste and sewage 91 Activities of associations not included elsewhere 92 Recreational, cultural, and sports activities 93 Other service activities 95 Private homes with domestic service 96 No response 97 Extraterritorial organizations and organisms 98 Unknown 99 NIU (not in universe) This variable indicates the industry where the respondent worked. The sample lacks data for this and several other variables for roughly half the cases. Users should apply the alternative weight variable AR91A434 when using one of these variables. IPUMS Occupation <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar v="AR91A419 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426">[Questions 21-24 were asked of persons age 14+ who had a job last week (did formal or informal work or did not work due to sickness, vacation, etc.), per questions 17-19.]<br /></svar></p> <p><svar a="all" v="AR91A424">23. What do you do in this job? ____<br /><div class="i1">For example: Administrative: payroll; Chauffeur: drive collective buses; Machine sewing: sew leather shoes with an electric sewing machine; Doorman: clean and take care of school; Pastry chef: makes cakes and pastries to sell.</div><br /></svar> Argentina 1991: Persons age 14+ who had a job last week [discrepancies: none] 000 Non-specified directive occupations 011 Government directive officials 021 Directors of public business and establishments that produce goods and/or services, in a professional specialization 031 Directors of large private business that produce goods and/or services, in a professional specialization 041 Directors of mid-sized private business that produce goods and/or services, in a professional specialization 051 Directors of social, communal, political, labor union and religious institutions, with a professional specialization 100 Occupations of administrative, judicial-legal, planning and informatics processing, not specified 111 Independent providers of services for administrative, judicial-legal, planning and informatics processing, in a professional specialization 112 Independent providers of services for administrative, judicial-legal, planning and informatics processing, in a technical specialization 121 Bosses in administrative, judicial-legal, planning and informatics processing, in a professional specialization 122 Bosses in administrative, judicial-legal, planning and informatics processing, in a technical specialization 123 Bosses in administrative, judicial-legal, planning and informatics processing, in an operative specialization 129 Bosses in administrative, judicial-legal, planning and informatics processing, in an unknown specialization 131 Workers in administrative, judicial-legal, planning and informatics processing, in a professional specialization 132 Workers in administrative, judicial-legal, planning and informatics processing, in a technical specialization 133 Workers in administrative, judicial-legal, planning and informatics processing, in an operative specialization 134 Workers in administrative, judicial-legal, planning and informatics processing, non-specialized 139 Workers in administrative, judicial-legal, planning and informatics processing, in an unknown specialization 141 Informatics workers, professional specialization 142 Informatics workers, technical specialization 143 Informatics workers, operative specialization 149 Informatics workers, unknown specialization 200 Undocumented 211 Independent providers of services for budgeting, accounting and financial processes, in a professional specialization 212 Independent providers of services for budgeting, accounting and financial processes, in a technical specialization 221 Bosses of budgeting, accounting and financial processes, in a professional specialization 222 Bosses of budgeting, accounting and financial processes, in a technical specialization 223 Bosses of budgeting, accounting and financial processes, in an operative specialization 229 Undocumented 231 Workers in budgets, accounting, and finance with professional qualifications 232 Workers in budgets, accounting, and finance with technical qualifications 233 Workers in budgets, accounting, and finance with operational qualifications 234 Workers in budgets, accounting, and finance with no qualifications 239 Undocumented 300 Occupations of commercialization, transportation, storage, and telecommunications, not specified 302 Operators of equipment and systems for telecommunication, in a technical specialization 303 Operators of equipment and systems for telecommunication, in an operative specialization 304 Operators of equipment and systems for telecommunication, non-specialized 309 Operators of equipment and systems for telecommunication, not classified elsewhere 311 Independent businessmen, in a professional specialization 312 Independent businessmen, in a technical specialization 321 Bosses in the commercialization of goods and services, in a professional specialization 322 Bosses in the commercialization of goods and services, in a technical specialization 323 Bosses in the commercialization of goods and services, in an operative specialization 329 Bosses in the commercialization of goods and services, in an unknown specialization 331 Sales and marketing workers with professional qualifications 332 Sales and marketing workers with technical qualifications 333 Sales and marketing workers with operational qualifications 334 Sales and marketing workers with no qualifications 339 Sales and workers in the commercialization of goods and services, in a professional specialization 344 Traveling salesmen, non-specialized 351 Independent providers of transportation, storage, and telecommunications services, in a professional specialization 352 Independent providers of transportation, storage, and telecommunications services, in a technical specialization 361 Bosses of transportation, storage, and telecommunications services, in a professional specialization 362 Bosses of transportation, storage, and telecommunications services, in a technical specialization 363 Bosses of transportation, storage, and telecommunications services, in an operative specialization 369 Bosses of transportation, storage, and telecommunications services, in an unknown specialization 371 Workers in transportation and storage, in a professional specialization 372 Workers in transportation and storage, in a technical specialization 373 Workers in transportation and storage, in an operative specialization 374 Workers in transportation and storage, non-specialized 379 Workers in transportation and storage, in an unknown specialization 381 Drivers and operators of machinery for transportation and storage, in a professional specialization 382 Drivers and operators of machinery for transportation and storage, in a technical specialization 383 Drivers and operators of machinery for transportation and storage, in an operative specialization 384 Drivers and operators of machinery for transportation and storage, non-specialized 389 Drivers and operators of machinery for transportation and storage, in an unknown specialization 391 Workers in telecommunications, in a professional specialization 392 Workers in telecommunications, in a technical specialization 393 Workers in telecommunications, in an operative specialization 394 Workers in telecommunications, non-specialized 399 Workers in telecommunications, in an unknown specialization 400 Basic social service occupations, unspecified 401 Undocumented 402 Undocumented 403 Undocumented 404 Armed Forces, Civil police and police, non-specialized 409 Armed Forces, Civil police and police, in an unknown specialization 411 Independent providers of basic social services, in a professional specialization 412 Independent providers of basic social services, in a technical specialization 421 Bosses of basic social services, in a professional specialization 422 Bosses of basic social services, in a technical specialization 423 Bosses of basic social services, in an operative specialization 429 Bosses of basic social services, in an unknown specialization 431 Workers in health, sanitation and ecology, with professional qualifications 432 Workers in health, sanitation and ecology, with technical qualifications 433 Workers in health, sanitation and ecology, with operational qualifications 434 Workers in health, sanitation and ecology, with no qualifications 439 Undocumented 441 Education workers with professional qualifications 442 Education workers with technical qualifications 443 Education workers with operational qualifications 449 Workers in education, in an unknown qualifications 451 Workers in scientific research, advising and consulting, in a professional specialization 452 Workers in scientific research, advising and consulting, in a technical specialization 453 Workers in scientific research, advising and consulting, in an operative specialization 459 Workers in scientific research, advising and consulting, in an unknown specialization 461 Communications workers, in a professional specialization 462 Communications workers, in a technical specialization 463 Communications workers, in a operational specialization 469 Undocumented 471 Workers in social service, community, political, and religious services with professional qualifications 472 Workers in social service, community, political, and religious services with operational qualifications 473 Workers in social service, community, political, and religious services with technical qualifications 479 Undocumented 481 Monitoring and prevention workers, professionals 482 Monitoring and prevention workers, technical qualifications 483 Monitoring and prevention workers, operational qualifications 484 Monitoring and prevention workers, no qualifications 489 Undocumented 492 Operators of equipment and systems of basic social services, technical qualifications 493 Operators of equipment and systems of basic social services, operational qualifications 500 Occupations of various services, not specified 501 Undocumented 502 Undocumented 503 Undocumented 504 Undocumented 509 Operators of machinery and equipment for various services, in an unknown specialization 511 Independent providers of various services, in a professional specialization 512 Independent providers of various services, in a technical specialization 521 Bosses of various services, in a professional specialization 522 Bosses of various services, in a technical specialization 523 Bosses of various services, in an operative specialization 529 Bosses of various services, in an unknown specialization 531 Workers in sports, body building, art and recreation, in a professional specialization 532 Workers in sports, body building, art and recreation, in a technical specialization 533 Workers in sports, body building, art and recreation, in an operative specialization 534 Workers in sports, body building, art and recreation, non-specialized 539 Workers in sports, body building, art and recreation, in an unknown specialization 542 Workers in food services, in a technical specialization 543 Workers in food services, in an operative specialization 544 Workers in food services, in non-specialized 549 Workers in food services, in an unknown specialization 551 Workers in lodging and tourism services, in a professional specialization 552 Workers in lodging and tourism services, in a technical specialization 553 Workers in lodging and tourism services, in an operative specialization 554 Workers in lodging and tourism services, with no specialization 559 Workers in lodging and tourism services, in an unknown specialization 563 Undocumented 564 Workers in domestic services, non-specialized 573 Workers in non-domestic cleaning services, in an operative specialization 574 Workers in non-domestic cleaning services, non-specialized 579 Workers in non-domestic cleaning services, in an unknown specialization 581 Workers in the care for and attention to persons, in a professional specialization 582 Workers in the care for and attention to persons, in a technical specialization 583 Workers in the care for and attention to persons, in an operative specialization 584 Workers in the care for and attention to persons, non-specialized 589 Workers in the care for and attention to persons, in an unknown specialization 592 Workers in other services, in a technical specialization 593 Workers in other services, in an operative specialization 594 Workers in other services, non-specialized 599 Workers in other services, in an unknown specialization 600 Occupation in agricultural and primary production, not specified 611 Independent producers in agriculture, cattle ranching, and forestry, in a professional specialization 612 Independent producers in agriculture, cattle ranching, and forestry, in a technical specialization 621 Bosses in agriculture, cattle ranching, and forestry production, in a professional specialization 622 Bosses in agriculture, cattle ranching, and forestry production, in a technical specialization 623 Bosses in agriculture, cattle ranching, and forestry production, in an operative specialization 629 Undocumented 631 Workers in agriculture, cattle ranching, and forestry production, in a professional specialization 632 Workers in agriculture, cattle ranching, and forestry production, in a technical specialization 633 Workers in agriculture, cattle ranching, and forestry production, in an operative specialization 634 Workers in agriculture, cattle ranching, and forestry production, non-specialized 639 Workers in agriculture, cattle ranching, and forestry production, in an unknown specialization 642 Operators of machinery and equipment in agriculture, cattle ranching, and forestry production, in a technical specialization 643 Operators of machinery and equipment in agriculture, cattle ranching, and forestry production, in an operative specialization 644 Operators of machinery and equipment in agriculture, cattle ranching, and forestry production, non-specialized 649 Operators of machinery and equipment in agriculture, cattle ranching, and forestry production, in an unknown specialization 652 Independent producers in fishing and hunting, in a technical specialization 662 Bosses in fishing and hunting, in a technical specialization 663 Undocumented 669 Bosses in fishing and hunting, in an unknown specialization 672 Workers in fishing and hunting, in a technical specialization 673 Workers in fishing and hunting, in an operative specialization 674 Workers in fishing and hunting, non-specialized 679 Workers in fishing and hunting, in an unknown specialization 682 Undocumented 683 Operators of machinery and equipment in fishing and hunting, in an operative specialization 684 Operators of machinery and equipment in fishing and hunting, non-specialized 700 Undocumented 711 Independent producers in extractive, energy, construction and infrastructure production, in a professional specialization 712 Independent producers in extractive, energy, construction and infrastructure production, in a technical specialization 721 Bosses in extractive, energy, construction and infrastructure production, in a professional specialization 722 Bosses in extractive, energy, construction and infrastructure production, in a technical specialization 723 Bosses in extractive, energy, construction and infrastructure production, in an operative specialization 729 Bosses in extractive, energy, construction and infrastructure production, in an unknown specialization 731 Workers in extractive production, in a professional specialization 732 Workers in extractive production, in a technical specialization 733 Workers in extractive production, in an operative specialization 734 Workers in extractive production, non-specialized 739 Workers in extractive production, in an unknown specialization 741 Undocumented 742 Operators of machinery and equipment in extractive production, in a technical specialization 743 Operators of machinery and equipment in extractive production, in an operative specialization 744 Operators of machinery and equipment in extractive production, non-specialized 749 Operators of machinery and equipment in extractive production, in an unknown specialization 751 Workers in the production of energy, water, and gas, in a professional specialization 752 Workers in the production of energy, water, and gas, in a technical specialization 753 Workers in the production of energy, water, and gas, in an operative specialization 754 Workers in the production of energy, water, and gas, non-specialized 759 Workers in the production of energy, water, and gas, in an unknown specialization 762 Operators of machinery and equipment in the production of energy, water, and gas, in a technical specialization 763 Operators of machinery and equipment in the production of energy, water, and gas, in an operative specialization 764 Operators of machinery and equipment in the production of energy, water, and gas, non-specialized 769 Operators of machinery and equipment in the production of energy, water, and gas, in an unknown specialization 771 Workers in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, in a professional specialization 772 Workers in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, in a technical specialization 773 Workers in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, in an operative specialization 774 Workers in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, non-specialized 779 Workers in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, in an unknown specialization 782 Operators of machinery and equipment in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, in a technical specialization 783 Operators of machinery and equipment in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, in an operative specialization 784 Operators of machinery and equipment in building construction, infrastructure work, and distribution of energy, potable water, gas and telephone service, with no specialization 789 Undocumented 800 Occupations of industrial production and repair of consumer goods, not specified 811 Independent producers in artisan and industrial production, in a professional specialization 812 Independent producers in artisan and industrial production, in a technical specialization 821 Bosses in industrial production, in a professional specialization 822 Bosses in industrial production, in a technical specialization 823 Bosses in industrial production, with an operational qualification 829 Bosses in industrial production, in an unknown specialization 831 Workers in artisan and industrial production, in a professional specialization 832 Workers in artisan and industrial production, in a technical specialization 833 Workers in artisan and industrial production, in an operative specialization 834 Workers in artisan and industrial production, non-specialized 839 Workers in artisan and industrial production, in an unknown specialization 842 Operators of machinery and equipment in artisan and industrial production, with a technical qualification 843 Operators of machinery and equipment in artisan and industrial production, in an operative specialization 844 Operators of machinery and equipment in industrial production, non-specialized 849 Operators of machinery and equipment in industrial production, in an unknown specialization 851 Undocumented 852 Independent repairmen of consumer goods, in a technical specialization 862 Bosses in repair of consumer goods, in a technical specialization 863 Bosses in repair of consumer goods, in an operative specialization 871 Undocumented 872 Workers in repair of consumer goods, in a technical specialization 873 Workers in repair of consumer goods, in an operative specialization 874 Workers in repair of consumer goods, non-specialized 879 Workers in repair of consumer goods, in an unknown specialization 900 Undocumented 911 Independent producers in auxiliary processes for the production of goods, in a professional specialization 912 Independent producers in auxiliary processes for the production of goods, in a technical specialization 921 Bosses in auxiliary processes for the production of goods, in a professional specialization 922 Bosses in auxiliary processes for the production of goods, in a technical specialization 923 Bosses in auxiliary processes for the production of goods, in an operative specialization 931 Workers in installation and maintenance of machinery and equipment for the production of goods, in a professional specialization 932 Workers in installation and maintenance of machinery and equipment for the production of goods, in a technical specialization 933 Workers in installation and maintenance of machinery and equipment for the production of goods, in an operative specialization 934 Workers in installation and maintenance of machinery and equipment for the production of goods, non-specialized 939 Workers in installation and maintenance of machinery and equipment for the production of goods, in an unknown specialization 941 Workers in technological development, in a professional specialization 942 Workers in technological development, in a technical specialization 943 Workers in technological development, in an operative specialization 949 Workers in technological development, in an unknown specialization 952 Workers in supply and storage of production units, in a technical specialization 953 Workers in supply and storage of production units, in an operative specialization 954 Workers in supply and storage of production units, non-specialized 959 Workers in supply and storage of production units, in an unknown specialization 961 Independent providers in auxiliary processes for service providing, in a professional specialization 962 Independent providers in auxiliary processes for service providing, in a technical specialization 971 Bosses in auxiliary processes for service providing, in a professional specialization 972 Bosses in auxiliary processes for service providing, in a technical specialization 973 Bosses in auxiliary processes for service providing, in an operative specialization 979 Bosses in auxiliary processes for service providing, in an unknown specialization 981 Workers in the installation and maintenance of machinery and equipment for service providing, in a professional specialization 982 Workers in the installation and maintenance of machinery and equipment for service providing, in a technical specialization 983 Workers in the installation and maintenance of machinery and equipment for service providing, in an operative specialization 984 Workers in the installation and maintenance of machinery and equipment for service providing, non-specialized 989 Workers in the installation and maintenance of machinery and equipment for service providing, in an unknown specialization 991 Unknown occupation, in a professional specialization 992 Unknown occupation, in a technical specialization 993 Unknown occupation, in an operative specialization 994 Unknown occupation, non-specialized 995 Occupations with insufficient information 996 Not necessary to code occupation 997 No response 998 Unknown 999 NIU (not in universe) This variable indicates the occupation of the respondent. The sample lacks data for this and several other variables for roughly half the cases. Users should apply the alternative weight variable AR91A434 when using one of these variables. IPUMS Deducted retirement <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar v="AR91A419 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426">[Questions 21-24 were asked of persons age 14+ who had a job last week (did formal or informal work or did not work due to sickness, vacation, etc.), per questions 17-19.]<br /></svar></p> <p><svar a="all" v="AR91A426">24. In this work, do you contribute to a retirement fund?<br /><div class="i1">[] Yes<br />[] No<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 14+ who had a job last week [discrepancies: none] NIU (not in universe) 1 Yes, deducted retirement 2 No, deducted retirement 9 Unknown This variable indicates deducted retirement availability. IPUMS Marital status <svar v="AR91A419 AR91A420 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426 AR91A427">For all women and men fourteen years of age or older.<br />[Questions 17-25 were asked of persons age 14 years +.]<br /></svar></p> <p><svar v="AR91A419 AR91A421 AR91A422 AR91A423 AR91A424 AR91A426">[Questions 21-24 were asked of persons age 14+ who had a job last week (did formal or informal work or did not work due to sickness, vacation, etc.), per questions 17-19.]<br /></svar></p> <p><svar a="all" v="AR91A427">25. [Marital status]<br /><div class="i1">Currently, you are:<br /><br />[] In a consensual union<br />[] Married in a legal union<br />[] Separated from a union or marriage<br />[] Divorced from a marriage<br />[] Widowed from a consensual union or marriage<br />[] Single, never formed a union or married<br />[] Unanswered</div><br /></svar> Argentina 1991: Persons age 14+ [discrepancies: none] NIU (not in universe) 1 United 2 Married 3 Separated 4 Divorced 5 Widowed 6 Single 9 Unknown This variable indicates the person's marital status. IPUMS Children born alive <svar v="AR91A428 AR91A429 AR91A430">For women 14 years old and older (do not forget the single women).<br />[Questions 26-28 were asked of women 14 years of age and older.]<br /></svar></p> <p><svar a="all" v="AR91A428">26. How many children have you had born alive?<br /><div class="i1">[] None [end of questions for this person]<br />[] 1<br />[] 2<br />[] 3<br />[] 4<br />[] 5<br />[] 6<br />[] 7<br />[] 8<br />[] 9<br />[] 10<br />[] 11<br />[] 12<br />[] 13<br />[] 14<br />[] 15<br />[] 16 or more<br />[] Unanswered [end of questions for this person]</div><br /></svar> Argentina 1991: Females age 14+ [discrepancies: none] 00 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 98 Unknown 99 NIU (not in universe) This variable indicates the number of children born alive. IPUMS Children currently living <svar v="AR91A428 AR91A429 AR91A430">For women 14 years old and older (do not forget the single women).<br />[Questions 26-28 were asked of women 14 years of age and older.]<br /></svar></p> <p><svar a="all" v="AR91A429">27. How many are currently alive?<br /><div class="i1">[Question 27 was asked of women age 14+ who ever had children born alive, per question 26.]<br /><br />[] None<br />[] 1<br />[] 2<br />[] 3<br />[] 4<br />[] 5<br />[] 6<br />[] 7<br />[] 8<br />[] 9<br />[] 10<br />[] 11<br />[] 12<br />[] 13<br />[] 14<br />[] 15<br />[] 16 or more<br />[] Unanswered [end of questions for this person]</div><br /></svar> Argentina 1991: Females age 14+ who had children born alive [discrepancies: none] 00 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 98 Unknown 99 NIU (not in universe) This variable indicates the number of children currently living. IPUMS Children born alive last year <svar v="AR91A428 AR91A429 AR91A430">For women 14 years old and older (do not forget the single women).<br />[Questions 26-28 were asked of women 14 years of age and older.]<br /></svar></p> <p><svar a="all" v="AR91A430">28. Have you had any children born alive in the past twelve months?<br /><div class="i1">Only for women ages 14 to 49.<br />[Question 28 was asked of women of age 14-49 who ever had children born alive, per question 26.]<br /><br />[] Yes, 1<br />[] Yes, 2 or more<br />[] No<br />[] Unanswered</div><br /></svar> Argentina 1991: Females age 14 to 49 who had children born alive [discrepancies: none] NIU (not in universe) 1 1 2 2+ 3 No children 9 Unknown This variable indicates the number of children born alive last year. IPUMS Contribution to retirement fund Argentina 1991: All persons 1 Social plan 2 Medical plan 3 Social plan and medical plan 4 Without social plan or medical plan 5 With social plan "I" medical plan 6 With medical plan "I" social plan 7 Without social plan "I" medical plan 8 Without medical plan "I" social plan 9 Unknown This variable indicates the type of health coverage. IPUMS Person weight Argentina 1991: All persons 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 28 28 30 30 32 32 34 34 36 36 50 50 60 60 27 27 29 29 31 31 38 38 40 40 42 42 44 44 48 48 70 70 80 80 90 90 This variable indicates weight. IPUMS Person weight for occ, ind, migration Argentina 1991: 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 52 059 59 060 60 062 62 068 68 070 70 076 76 078 78 079 79 080 80 081 81 084 84 090 90 100 100 110 110 120 120 130 130 140 140 150 150 160 160 170 170 180 180 190 190 200 200 210 210 230 230 240 240 260 260 290 290 A number of important variables were never fully coded in the microdata. Approximately half of the persons in the sample lack information for any of the variables listed below. AR1991A_0434 is an alternative weight variable that corrects for the missing data. This variable -- not PERWT (person weight) -- should be applied to any analyses that use one of the affected variables. When pooling 1991 Argentina with other samples, users will need to recode PERWT to equal AR1991A_0434. The affected variables requiring the special weight are: OCC -- OccupationOCCISCO -- Occupation, ISCO recodeIND -- IndustryINDGEN -- Industry, general recodeBPLAR -- Province of birthBPLCOUNTRY -- Country of birthNATIVITY -- Nativity statusMIGRATE5 -- Migration status, 5 yearsAR1991A_0405 (AR1991A_RESH) -- Place of current residenceAR1991A_0407 (AR1991A_RESPRH) -- Residence 5 years agoAR1991A_0409 (AR1991A_BORNH) -- Place of birthAR1991A_0410 (AR1991A_BORN) -- Province or country of birthAR1991A_0423 (AR1991A_IND) -- IndustryAR1991A_0424 (AR1991A_OCC) -- OccupationNote: Six provinces actually have full information for all variables, and analyses confined to those provinces require no special treatment: 2 Capital Federal, 4 Buenos Aires, 58 Del Nequen, 62 Rio Negro, 78 Santa Cruz, and 94 Tierra del Fuego. IPUMS