DDI_ARG_2010_PHC_v01_M_v7.5_A_IPUMS National Population, Households, and Dwellings Census, 2010 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, Households, and Dwellings Census, 2010 - IPUMS Subset PHC Argentina 2010 (IPUMS Harmonized Subset) ARG_2010_PHC_v01_M_v7.5_A_IPUMS Argentine National Institute of Statistics and Censuses IPUMS (c) Copyright 2010, 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 Dwelling Characteristics Variables -- HOUSEHOLD Nativity and Birthplace Variables -- PERSON Utilities Variables -- HOUSEHOLD Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD Technical Household Variables -- HOUSEHOLD Education Variables -- PERSON Work Variables -- PERSON Geography: A-E Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Constructed Household Variables -- HOUSEHOLD Household Economic Variables -- HOUSEHOLD Technical Person Variables -- PERSON Other Person Variables -- PERSON Education Variables -- PERSON Argentina Department Persons and households UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no UNIT DESCRIPTIONS: - Dwellings: A dwelling is a space where persons live. It should be a place that is separated and independent: surrounded by walls or another element of separation, covered by a roof (to be able to sleep, feed oneself, and protect oneself from the environment); and have an independent entrance so that its occupants can enter and leave without passing through the interior of other dwellings. For the Census, dwellings are the spaces that have been built or adapted to be inhabited and are also the spaces that not originally designated to house persons but that on the day of the Census are used for this purpose (for example, a business space or a warehouse where a person spent the night of reference). - Households: A person or group of persons, related or not, who live in the same dwelling and share the expenses of food. The persons who live alone also form a household. - Group quarters: A dwelling occupied by a group of people that share the dwelling under a non-family arrangement due to the following reasons: work, military, health, religion, discipline, etc. All people who spent the night before October 27, 2010 in a specific dwelling, including visitors. Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Argentina 2010 MICRODATA SOURCE: Argentine National Institute of Statistics and Censuses SAMPLE SIZE (person records): 3966245. SAMPLE DESIGN: Systematic sample of every 10th private household, drawn by INDEC from the microdata of 100 percent of households. The sample is drawn from the short (basic) form census questionnaire responses. Face-to-face [f2f] (1) Private household questionnaire - basic and long form (2) Collective household questionnaire. de facto, CENSUS DAY: October 27, 2010 Self-weighting (expansion factor = 10) IPUMS International distributes integrated microdata of individuals and households only by agreement of collaborating national statistical offices and under the strictest of confidence. Before data may be distributed to an individual researcher, an electronic license agreement must be signed and approved. To gain access to the data, a researcher must agree to the following: (1) Implement security measures to prevent unauthorized access to census microdata. Under IPUMS International agreements with collaborating agencies, redistribution of the data to third parties is prohibited. (2) Use the microdata for the exclusive purposes of scholarly research and education. Researchers must explicitly agree to not use microdata acquired for any commercial or income-generating venture. (3) Maintain the confidentiality of persons, households, and other entities. Any attempt to ascertain the identity of persons or households from the microdata is prohibited. Alleging that a person or household has been identified is also prohibited. (4) Report all publications based on these data to IPUMS International, which will in turn pass the information on to the relevant national statistical agencies. Once a project is approved, a password is issued and data may be acquired through the Internet. Penalties for violating the license include: revocation of the license, recall of all microdata acquired, filing of a motion of censure to the appropriate professional organizations, and civil prosecution under the relevant national or international statutes. These safeguards mirror the principles from the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. Employees of the Minnesota Population Center who work with the census microdata to produce the harmonized database also sign agreements to respect the confidentiality of the data. IPUMS International works with each country's statistical office to minimize the risk of disclosure of respondent information. The details of the confidentiality protections vary across countries, but in all cases, names and detailed geographic information are suppressed and top-codes are imposed on variables such as income that might identify specific persons. In addition, IPUMS International uses a variety of technical procedures to enhance confidentiality protection. These include the following: (1) Swapping an undisclosed fraction of records from one administrative district to another to make positive identification of individuals impossible. (2) Randomizing the placement of households within districts to disguise the order in which individuals were enumerated or the data processed. (3) Aggregating codes of sensitive characteristics (e.g., grouping together very small ethnic categories) (4) Top- and bottom-coding continuous variables to prevent identification of extreme cases. The safety record for public-use census microdata is apparently perfect. In almost four decades of use, there has not been a single verified breach of statistical confidentiality. The measures implemented by the IPUMS International are designed to extend this record. 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, Households, and Dwellings Census, 2010 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. ARG2010_PHC-H-H Household records 1 56 ARG2010_PHC-P-H Person records 3966245 39 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 Number of unrelated persons 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9+ UNREL indicates the number of persons in the household who are unrelated to the head as defined in the variable RELATE. IPUMS Continent and region of country 11 Eastern Africa 12 Middle Africa 13 Northern Africa 14 Southern Africa 15 Western Africa 21 Caribbean 22 Central America 23 North America 24 South America 31 Central Asia 32 Eastern Asia 33 Southern Asia 34 South-Eastern Asia 35 Western Asia 41 Eastern Europe 42 Northern Europe 43 Southern Europe 44 Western Europe 51 Australia and New Zealand 52 Melanesia 53 Micronesia 54 Polynesia REGIONW identifies the continent and region of each country. IPUMS 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 2010 [Level 1, GIS] 002 City of Buenos Aires 006 Buenos Aires province 010 Catamarca 014 Córdoba 018 Corrientes 022 Chaco 026 Chubut 030 Entre Ríos 034 Formosa 038 Jujuy 042 La Pampa 046 La Rioja 050 Mendoza 054 Misiones 058 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_AR2010 identifies the household's province within Argentina in 2010. Provinces are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_AR2010 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 2010 [Level 2, GIS] 002001 Distrito Escolar XIII 002002 Distrito Escolar XIV 002003 Distrito Escolar VII 002004 Distrito Escolar IV 002005 Distrito Escolar XII 002006 Distrito Escolar I 002007 Distrito Escolar XI 002008 Distrito Escolar III 002009 Distrito Escolar VIII 002010 Distrito Escolar XV 002011 Distrito Escolar V 002012 Distrito Escolar VI 002013 Distrito Escolar X 002014 Distrito Escolar IX 002015 Distrito Escolar II 006001 Mar Chiquita 006002 Carlos Casares 006003 Colón 006004 Adolfo Alsina, Salliqueló 006005 Brandsen 006006 Daireaux, Hipólito Yrigoyen 006007 San Andrés de Giles 006008 Rojas 006009 Carlos Tejedor, Florentino Ameghino, General Pinto 006010 Las Flores 006011 Morón 006012 Hurlingham 006013 Ituzaingo 006014 Berazategui 006015 San Isidro 006016 Esteban Echeverría 006017 Ezeiza 006018 Avellaneda 006019 Tigre 006020 Malvinas Argentinas 006021 San Miguel 006022 Jose C. Paz 006023 Tres de Febrero 006024 San Fernando 006025 Vicente López 006026 General Lavalle, La Costa 006027 Azul 006028 General Rodríguez 006029 Presidente Perón 006030 San Vicente 006031 Junín 006032 Berisso 006033 Campana 006034 Necochea 006035 Pergamino 006036 Luján 006037 Olavarría 006038 Zárate 006039 Escobar 006040 Bahía Blanca 006041 Tandil 006042 San Nicolás 006043 La Plata 006044 Pilar 006045 General Pueyrredón 006046 Trenque Lauquen 006047 Ayacucho, General Guido, Pila, Rauch 006048 Bolívar, General Alvear, Tapalqué 006049 Cañuelas 006050 Marcos Paz 006051 General Las Heras, Lobos 006052 Coronel Pringles, Tornquist 006053 Coronel Dorrego, Monte Hermoso 006054 Chacabuco 006055 9 de Julio 006056 Puan, Villarino 006057 San Pedro 006058 Alberti, Roque Pérez, Veinticinco de Mayo 006059 Chascomus 006060 Magdalena, Punta Indio 006061 General Viamonte, Lincoln 006062 Coronel de Marine L. Rosales 006063 Balcarce, Lobería 006064 Mercedes 006065 Chivilcoy 006066 Ensenada 006067 Tres Arroyos 006068 General Arenales, Leandro N. Alem 006069 General Villegas 006070 Saladillo 006071 Baradero 006072 Arrecifes 006073 Exaltación de la Cruz 006074 General Juan Madariaga, Villa Gesell 006075 Pinamar 006076 Patagones 006077 General Belgrano, General Paz 006078 Dolores 006079 General Alvarado 006080 Carmen de Areco, Navarro, Suipacha 006081 Adolfo González Chaves, Benito Juárez, San Cayetano 006082 Bragado 006083 Coronel Suárez 006084 Pehuajó 006085 Capitán Sarmiento, San Antonio de Areco 006086 Guaminí, Pellegrini, Rivadavia, Tres Lomas 006087 Salto 006088 Ramallo 006089 Monte 006090 General La Madrid, Laprida 006091 Saavedra 006092 Castelli, Maipú, Tordillo 006093 Lanús 006094 Moreno 006095 Almirante Brown 006096 Merlo 006097 Lomas de Zamora 006098 Quilmes 006099 La Matanza 006100 Florencio Varela 006101 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, 2 de Abril, Fray Justo Sta. María de Oro 022003 Chacabuco 022004 Almirante Brown 022005 Quitilipi 022006 Bermejo 022007 Maipú 022008 Libertador General San Martín, 1 de Mayo 022009 Comandante Fernandez 022010 San Lorenzo, Sargento Cabral, 25 de Mayo 022011 General Güemes 022012 9 de Julio, O'Higgins 022013 Mayor Luis J. Fontana 022014 General Belgrano, Independencia 022015 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 San Salvador, Villaguay 030008 Colón 030009 Federal, Feliciano 030010 Parana 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 Cainguas 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, Pichi Mahuida 062005 General Roca 062006 Bariloche 062007 Adolfo Alsina 066001 Cerrillos 066002 Guachipas, La Candelaria, Rosario de la Frontera 066003 Cafayate, La Vina 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, Ullúm, 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, Guasayan 086013 Robles 086014 Aguirre, Belgrano, Mitre, Rivadavia 086015 Ojo de Agua, Quebrachos 090001 Chicligasta 090002 Monteros 090003 Río Chico 090004 Granero, La Cocha 090005 Juan Bautista Alberdi 090006 Simoca 090007 Leales 090008 Burruyacú 090009 Cruz Alta 090010 Capital 090011 Tafí Viajo 090012 Yerba Buena 090013 Lules 090014 Famaillá 090015 Tafí del Valle, Trancas 094001 Ushuaia 094002 Río Grande GEO2_ AR2010 identifies the household's department within Argentina in 2010. Departments are the second level administrative units of the country, after provinces. A GIS map (in shapefile format), corresponding to GEO2_ AR2010 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 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 Telephone availability NIU (not in universe) 1 No 2 Yes 9 Unknown/missing PHONE indicates the availability of a telephone in the dwelling. IPUMS Cellular phone availability NIU (not in universe) 1 Yes 2 No 9 Unknown CELL indicates the availability of a cellular phone in the household. IPUMS Computer NIU (not in universe) 1 No 2 Yes 9 Unknown/missing COMPUTER indicates whether the household had a personal computer. IPUMS Refrigerator NIU (not in universe) 1 No 2 Yes 9 Unknown/missing REFRIG indicates whether the household had a refrigerator. 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 Number of bedrooms 00 No bedrooms 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+ 98 Unknown 99 NIU (not in universe) BEDROOMS indicates the number of rooms available to members of the household for sleeping. IPUMS Toilet 00 NIU (not in universe) 10 No toilet 11 No flush toilet 20 Have toilet, type not specified 21 Flush toilet 22 Non-flush, latrine 23 Non-flush, other and unspecified 99 Unknown TOILET indicates whether the household had access to a toilet and, in most cases, whether it was a flush toilet or other type of installation. IPUMS Bathing facilities NIU (not in universe) 1 No bathing facility 2 Have bathing facility, exclusivity not specified 3 Have bathing facility, exclusive use 4 Have bathing facility, shared use 9 Unknown BATH indicates whether the household had access to bathing facilities and, in most cases, whether it had exclusive access. IPUMS 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 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 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 2010: All records 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 2010: All records 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 34 34 40 40 This variable indicates the number of persons in the household. IPUMS Dwelling type <svar a="all" v="AR10A024"><span class="h3"> 1. Type of Dwelling </span> [Write down by observation.]<br /><div class="i1">[] House<br />[] Ranch house<br />[] Small house<br />[] Apartment<br />[] Room in a boarding house<br />[] Room in a family house or pension<br />[] Mobile dwelling<br />[] Persons living in the street (continue with 4)</div><br /></svar> Argentina 2010: All households 1 House 2 Ranch house 3 Shack 4 Apartment 5 Room in a boarding house 6 Room in a family house or pension 7 Mobile dwelling 8 Persons living in the street This variable indicates the dwelling type. IPUMS Floors material <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A027"><span class="h3">5. What is the predominant material of the floors...</span><br /><div class="i1">[] Ceramic, tile, mosaic, marble, wood, or carpet?<br />[] Cement or attached bricks?<br />[] Dirt or loose brick?<br />[] Other?</div><br /></svar> Argentina 2010: All households 1 Ceramic, tile, mosaic, marble, wood, or carpet 2 Cement or attached bricks 3 Dirt or loose brick 4 Other This variable indicates the predominant material of the floors. IPUMS Roof material <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A028"><span class="h3">6. What is the predominant material of the exterior covering of the roof...</span><br /><div class="i1">If the dwelling is part of an apartment building, you should consider the roof of the last floor on the building.<br /><br />[] Asphalt coating or membrane?<br />[] Tile or clay tiles (without coating)?<br />[] Clay or clay tiles?<br />[] Metal sheeting (without coating)?<br />[] Fiber-cement or plastic sheeting?<br />[] Cardboard sheeting?<br />[] Cane, palm, boards, or straw with or without clay?<br />[] Other</div><br /></svar> Argentina 2010: All households 1 Asphalt coating or membrane 2 Tile or clay tiles (without coating) 3 Clay or clay tiles 4 Metal sheeting (without coating) 5 Fiber-cement or plastic sheeting 6 Cardboard sheeting 7 Cane, palm, boards, or straw with or without clay 8 Other This variable indicates the predominant material of the roof. IPUMS Interior ceiling <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A029"><span class="h3">7. Does the roof have an interior coating or ceiling?</span><br /><div class="i1">[] Yes<br />[] No</div><br /></svar> Argentina 2010: All households 1 Yes 2 No This variable indicates the predominant material of the interior ceiling. IPUMS Water location <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A030"><span class="h3">8. Does the dwelling have water?</span><br /><div class="i1">[] Supplied through pipes inside the dwelling?<br />[] Outside the dwelling but within the property?<br />[] Outside the property?</div><br /></svar> Argentina 2010: All households 1 Supplied through pipes inside the dwelling 2 Outside the dwelling but within the property 3 Outside the property This variable indicates the water location. IPUMS Water source <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A031"><span class="h3">9. The water that is used comes from?</span><br /><div class="i1">[] A public network?<br />[] A drilled-well with a motorized pump?<br />[] A drilled-well with a hand pump?<br />[] A well?<br />[] Transported on a cistern?<br />[] Rain water, river, channel, ditch or spring?</div><br /></svar> Argentina 2010: All households 1 Public network 2 Drilled-well with a motorized pump 3 Drilled-well with a hand pump 4 Well 5 Transported on a cistern 6 Rain water, river, channel, ditch or spring This variable indicates the water source. IPUMS Household have bathroom <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a=" AR10A032" v="AR10A032 AR10A033 AR10A034 AR10A035"><span class="h3">10. Does this household have a bathroom or latrine?</span><br /><div class="i1">[] Yes<br />[] No - continue with 14</div><br /></svar> Argentina 2010: All households 1 Yes 2 No This variable indicates the type of bath. IPUMS Household have toilet <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a=" AR10A032" v="AR10A032 AR10A033 AR10A034 AR10A035"><span class="h3">10. Does this household have a bathroom or latrine?</span><br /><div class="i1">[] Yes<br />[] No - continue with 14</div><br /></svar></p> <p><svar a="all" v="AR10A033"><span class="h3">11. In the bathroom, is there a button, chain, or tank for cleaning the toilet?</span><br /><div class="i1">[] Yes<br />[] No</div><br /></svar> Argentina 2010: Households with a bathroom [discrepancies: none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates if the household has a toilet facility. IPUMS Sewage disposal <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a=" AR10A032" v="AR10A032 AR10A033 AR10A034 AR10A035"><span class="h3">10. Does this household have a bathroom or latrine?</span><br /><div class="i1">[] Yes<br />[] No - continue with 14</div><br /></svar></p> <p><svar a="all" v="AR10A034"><span class="h3">12. The toilet drainage is?</span><br /><div class="i1">[] To a public network (sewer)?<br />[] To a septic tank and septic pit?<br />[] Only to a septic pit?<br />[] To a hole or excavation in the dirt, etc.?</div><br /></svar> Argentina 2010: Households with a bathroom [discrepancies: none] 1 Public network (sewer) 2 Septic tank and septic well 3 Only to a septic well 4 Hole or excavation in the dirt, etc. 9 NIU (not in universe) This variable indicates the type of sewage disposal. IPUMS Private bath <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a=" AR10A032" v="AR10A032 AR10A033 AR10A034 AR10A035"><span class="h3">10. Does this household have a bathroom or latrine?</span><br /><div class="i1">[] Yes<br />[] No - continue with 14</div><br /></svar></p> <p><svar a="all" v="AR10A035"><span class="h3">13. The bathroom or latrine is?</span><br /><div class="i1">[] Only used by this household?<br />[] Shared with other households?</div><br /></svar> Argentina 2010: Households with a bathroom [discrepancies: none] 1 Only used by this household 2 Shared with other households 9 NIU (not in universe) This variable indicates if the household has a private bath. IPUMS Cooking fuel <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A036"><span class="h3">14. To cook, you use primarily?</span><br /><div class="i1">[] Gas from a network?<br />[] Gas from a tank (zeppelin tank)?<br />[] Gas in a tube?<br />[] Gas in a jar?<br />[] Electricity?<br />[] Firewood or charcoal?<br />[] Other?</div><br /></svar> Argentina 2010: All households 1 Gas from a network 2 Gas from a tank (zeppelin tank) 3 Gas in a cylinder 4 Gas in a canister 5 Electricity 6 Firewood or charcoal 7 Other This variable indicates the type of cooking fuel. IPUMS Number of bedrooms <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A037"><span class="h3">15. How many bedrooms or rooms for sleeping are in this household?</span><br /><div class="i1">Number of bedrooms or rooms for sleeping: _ _</div><br /></svar> Argentina 2010: All households 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10+ This variable indicates the number of bedrooms. IPUMS Number of rooms <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A038"><span class="h3">16. In total, how many rooms does this household have? </span> (without counting bathroom(s) and kitchen(s))<br /><div class="i1">Number of total rooms: _ _</div><br /></svar> Argentina 2010: 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+ This variable indicates the number of rooms. IPUMS Ownership of dwelling <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A039"><span class="h3">17. The dwelling that this household occupies is?</span><br /><div class="i1">[] Owned?<br />[] Rented? - continue with 19<br />[] Lent? - continue with 19<br />[] Ceded for work? - continue with 19<br />[] Other situation - continue with 19</div><br /></svar> Argentina 2010: All households 1 Owned 2 Rented 3 Loaned 4 Ceded for work 5 Other situation This variable indicates the ownership of dwelling. IPUMS Land ownership <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar a="all" v="AR10A040"><span class="h3">18. Is the land owned by the household?</span><br /><div class="i1">[] Yes<br />[] No</div><br /></svar> Argentina 2010: Households who own the dwelling [discrepancies: none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates the land ownership. IPUMS Refrigerator <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar v="AR10A041 AR10A042 AR10A043 AR10A044"><span class="h3">19. Does this household have?</span><br /></svar></p> <p><svar a="all" v="AR10A041"><div class="i1"><span class="em">A refrigerator?</span></div><br /><div class="i2">[] Yes<br />[] No</div><br /></svar> Argentina 2010: All households 1 Yes 2 No This variable indicates if the household owns a refrigerator. IPUMS Computer <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar v="AR10A041 AR10A042 AR10A043 AR10A044"><span class="h3">19. Does this household have?</span><br /></svar></p> <p><svar a="all" v="AR10A042"><div class="i1"><span class="em">A computer?</span></div><br /><div class="i2">[] Yes<br />[] No</div><br /></svar> Argentina 2010: All households 1 Yes 2 No This variable indicates if the household owns a computer. IPUMS Cellular telephone <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar v="AR10A041 AR10A042 AR10A043 AR10A044"><span class="h3">19. Does this household have?</span><br /></svar></p> <p><svar a="all" v="AR10A043"><div class="i1"><span class="em">A cellular telephone?</span></div><br /><div class="i2">[] Yes<br />[] No</div><br /></svar> Argentina 2010: All households 1 Yes 2 No This variable indicates if the household owns a cellular telephone. IPUMS Landline telephone <svar v="AR10A026 AR10A027 AR10A028 AR10A029 AR10A030 AR10A031 AR10A032 AR10A033 AR10A034 AR10A035 AR10A036 AR10A037 AR10A038 AR10A039 AR10A040 AR10A041 AR10A042 AR10A043 AR10A044">[Questions 3 to 19 were asked of inhabited households with persons present]<br /></svar></p> <p><svar v="AR10A041 AR10A042 AR10A043 AR10A044"><span class="h3">19. Does this household have?</span><br /></svar></p> <p><svar a="all" v="AR10A044"><div class="i1"><span class="em">A landline?</span></div><br /><div class="i2">[] Yes<br />[] No</div><br /></svar> Argentina 2010: All households 1 Yes 2 No This variable indicates if the household owns a landline. IPUMS Strata Argentina 2010: 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 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 Year of birth 0000 NIU (not in universe) 1628 1628 1629 1629 1630 1630 1631 1631 1634 1634 1635 1635 1636 1636 1637 1637 1638 1638 1639 1639 1640 1640 1641 1641 1642 1642 1643 1643 1644 1644 1645 1645 1646 1646 1647 1647 1648 1648 1649 1649 1650 1650 1651 1651 1652 1652 1653 1653 1654 1654 1655 1655 1656 1656 1657 1657 1658 1658 1659 1659 1660 1660 1661 1661 1662 1662 1663 1663 1664 1664 1665 1665 1666 1666 1667 1667 1668 1668 1669 1669 1670 1670 1671 1671 1672 1672 1673 1673 1674 1674 1675 1675 1676 1676 1677 1677 1678 1678 1679 1679 1680 1680 1681 1681 1682 1682 1683 1683 1684 1684 1685 1685 1686 1686 1687 1687 1688 1688 1689 1689 1690 1690 1691 1691 1692 1692 1693 1693 1694 1694 1695 1695 1696 1696 1697 1697 1698 1698 1699 1699 1700 1700 1701 1701 1702 1702 1703 1703 1704 1704 1705 1705 1706 1706 1707 1707 1708 1708 1709 1709 1710 1710 1711 1711 1712 1712 1713 1713 1714 1714 1715 1715 1716 1716 1717 1717 1718 1718 1719 1719 1720 1720 1721 1721 1722 1722 1723 1723 1724 1724 1725 1725 1726 1726 1727 1727 1728 1728 1729 1729 1730 1730 1731 1731 1732 1732 1733 1733 1734 1734 1735 1735 1736 1736 1737 1737 1738 1738 1739 1739 1740 1740 1741 1741 1742 1742 1743 1743 1744 1744 1745 1745 1746 1746 1747 1747 1748 1748 1749 1749 1750 1750 1751 1751 1752 1752 1753 1753 1754 1754 1755 1755 1756 1756 1757 1757 1758 1758 1759 1759 1760 1760 1761 1761 1762 1762 1763 1763 1764 1764 1765 1765 1766 1766 1767 1767 1768 1768 1769 1769 1770 1770 1771 1771 1772 1772 1773 1773 1774 1774 1775 1775 1776 1776 1777 1777 1778 1778 1779 1779 1780 1780 1781 1781 1782 1782 1783 1783 1784 1784 1785 1785 1786 1786 1787 1787 1788 1788 1789 1789 1790 1790 1791 1791 1792 1792 1793 1793 1794 1794 1795 1795 1796 1796 1797 1797 1798 1798 1799 1799 1800 1800 1801 1801 1802 1802 1803 1803 1804 1804 1805 1805 1806 1806 1807 1807 1808 1808 1809 1809 1810 1810 1811 1811 1812 1812 1813 1813 1814 1814 1815 1815 1816 1816 1817 1817 1818 1818 1819 1819 1820 1820 1821 1821 1822 1822 1823 1823 1824 1824 1825 1825 1826 1826 1827 1827 1828 1828 1829 1829 1830 1830 1831 1831 1832 1832 1833 1833 1834 1834 1835 1835 1836 1836 1837 1837 1838 1838 1839 1839 1840 1840 1841 1841 1842 1842 1843 1843 1844 1844 1845 1845 1846 1846 1847 1847 1848 1848 1849 1849 1850 1850 1851 1851 1852 1852 1853 1853 1854 1854 1855 1855 1856 1856 1857 1857 1858 1858 1859 1859 1860 1860 1861 1861 1862 1862 1863 1863 1864 1864 1865 1865 1866 1866 1867 1867 1868 1868 1869 1869 1870 1870 1871 1871 1872 1872 1873 1873 1874 1874 1875 1875 1876 1876 1877 1877 1878 1878 1879 1879 1880 1880 1881 1881 1882 1882 1883 1883 1884 1884 1885 1885 1886 1886 1887 1887 1888 1888 1889 1889 1890 1890 1891 1891 1892 1892 1893 1893 1894 1894 1895 1895 1896 1896 1897 1897 1898 1898 1899 1899 1900 1900 1901 1901 1902 1902 1903 1903 1904 1904 1905 1905 1906 1906 1907 1907 1908 1908 1909 1909 1910 1910 1911 1911 1912 1912 1913 1913 1914 1914 1915 1915 1916 1916 1917 1917 1918 1918 1919 1919 1920 1920 1921 1921 1922 1922 1923 1923 1924 1924 1925 1925 1926 1926 1927 1927 1928 1928 1929 1929 1930 1930 1931 1931 1932 1932 1933 1933 1934 1934 1935 1935 1936 1936 1937 1937 1938 1938 1939 1939 1940 1940 1941 1941 1942 1942 1943 1943 1944 1944 1945 1945 1946 1946 1947 1947 1948 1948 1949 1949 1950 1950 1951 1951 1952 1952 1953 1953 1954 1954 1955 1955 1956 1956 1957 1957 1958 1958 1959 1959 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1965 1965 1966 1966 1967 1967 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1988 1988 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 9999 Unknown BIRTHYR gives the person's year of birth. IPUMS Month of birth 01 January 02 February 03 March 04 April 05 May 06 June 07 July 08 August 09 September 10 October 11 November 12 December 98 Unknown 99 NIU (not in universe) BIRTHMO indicates the person's month of birth. 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 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 Person number (within household) Argentina 2010: All records 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 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 This variable indicates the person number (within household). IPUMS Relationship to household head <svar a="all" v="AR10A400"><span class="h3">1. What is the relationship of this person to the head of household?</span><br /><div class="i1">[] Head of household<br />[] Spouse or partner<br />[] Son/daughter / stepson/daughter<br />[] Son/daughter-in-law<br />[] Grandchild<br />[] Father/mother, father/mother-in-law<br />[] Other relative<br />[] Other non-relatives<br />[] Domestic servants and their relatives</div><br /></svar> Argentina 2010: All persons 1 Head 2 Spouse or partner 3 Son/daughter or stepson/daughter 4 Son-in-law or daughter-in-law 5 Grandson/granddaughter 6 Father/mother/parent-in-law 7 Other family members 8 Other non-family members 9 Domestic servants and their families This variable indicates the relationship to household head. IPUMS Sex <svar a="all" v="AR10A401"><span class="h3">2. Is this person male or female?</span><br /><div class="i1">[] Male<br />[] Female</div><br /></svar> Argentina 2010: All persons 1 Male 2 Female This variable indicates the sex of the person. IPUMS Age <svar a="all" v="AR10A402"><span class="h3">3. How old is this person (in completed years)?</span><br /><div class="i1">If the person has not completed one year of age, write 000.<br /><br />Years: _ _ _</div><br /></svar> Argentina 2010: All persons 000 Less than 1 year old 001 1 002 2 003 3 004 4 005 5 006 6 007 7 008 8 009 9 010 10 011 11 012 12 013 13 014 14 015 15 016 16 017 17 018 18 019 19 020 20 021 21 022 22 023 23 024 24 025 25 026 26 027 27 028 28 029 29 030 30 031 31 032 32 033 33 034 34 035 35 036 36 037 37 038 38 039 39 040 40 041 41 042 42 043 43 044 44 045 45 046 46 047 47 048 48 049 49 050 50 051 51 052 52 053 53 054 54 055 55 056 56 057 57 058 58 059 59 060 60 061 61 062 62 063 63 064 64 065 65 066 66 067 67 068 68 069 69 070 70 071 71 072 72 073 73 074 74 075 75 076 76 077 77 078 78 079 79 080 80 081 81 082 82 083 83 084 84 085 85 086 86 087 87 088 88 089 89 090 90 091 91 092 92 093 93 094 94 095 95 096 96 097 97 098 98 099 99 100 100+ This variable indicates the age of the person. IPUMS Birth month <svar a="all" v="AR10A403 AR10A404 AR10A405"><span class="h3">4. Date of birth</span><br /><div class="i1">Day: _ _<br />Month: _ _<br />Year: _ _ _ _</div><br /></svar> Argentina 2010: All persons 01 January 02 February 03 March 04 April 05 May 06 June 07 July 08 August 09 September 10 October 11 November 12 December 98 Unknown This variable indicates the birth month of the person. IPUMS Birth year <svar a="all" v="AR10A403 AR10A404 AR10A405"><span class="h3">4. Date of birth</span><br /><div class="i1">Day: _ _<br />Month: _ _<br />Year: _ _ _ _</div><br /></svar> Argentina 2010: All persons 1900 1900 1901 1901 1902 1902 1903 1903 1904 1904 1905 1905 1906 1906 1907 1907 1908 1908 1909 1909 1910 1910 1911 1911 1912 1912 1913 1913 1914 1914 1915 1915 1916 1916 1917 1917 1918 1918 1919 1919 1920 1920 1921 1921 1922 1922 1923 1923 1924 1924 1925 1925 1926 1926 1927 1927 1928 1928 1929 1929 1930 1930 1931 1931 1932 1932 1933 1933 1934 1934 1935 1935 1936 1936 1937 1937 1938 1938 1939 1939 1940 1940 1941 1941 1942 1942 1943 1943 1944 1944 1945 1945 1946 1946 1947 1947 1948 1948 1949 1949 1950 1950 1951 1951 1952 1952 1953 1953 1954 1954 1955 1955 1956 1956 1957 1957 1958 1958 1959 1959 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1965 1965 1966 1966 1967 1967 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1988 1988 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 9999 Unknown This variable indicates the birth year of the person. IPUMS Country of birth (Argentina or other) <svar a="all" v="AR10A406 AR10A407"><span class="h3">5. In what country was this person born?</span><br /><div class="i1">[] Argentina. If the person is three years old and over, continue with 7; if less than three years old, end of interview<br />[] Another country</div><br /><br /><span class="h3">6. Which country?</span><br /><div class="i1">________</div><br /></svar> Argentina 2010: All persons 1 Argentina 2 Other country This variable indicates the country of birth (Argentina or other). IPUMS Country of birth outside Argentina <svar a="all" v="AR10A406 AR10A407"><span class="h3">5. In what country was this person born?</span><br /><div class="i1">[] Argentina. If the person is three years old and over, continue with 7; if less than three years old, end of interview<br />[] Another country</div><br /><br /><span class="h3">6. Which country?</span><br /><div class="i1">________</div><br /></svar> Argentina 2010: Persons born outside Argentina [discrepancies: none] 004 Germany 009 Armenia 011 Australia 012 Austria 016 Belgium 020 Bolivia 022 Brazil 023 Bulgaria 030 Canada 032 Chile 033 China 034 Cyprus 035 Colombia 037 North Korea 038 South Korea 039 Costa Rica 040 Croatia 041 Cuba 042 Denmark 043 Ecuador 044 Egypt 045 El Salvador 047 Slovenia 048 United States 053 France 058 Greece 060 Guatemala 063 Haiti 066 Hungary 067 India 069 Iran 073 Israel 074 Italy 076 Japan 081 Lao 083 Lebanon 086 Lithuania 092 Mexico 095 Montenegro 100 Norway 103 Nethelands 104 Panama 106 Paraguay 107 Peru 108 Poland 109 Portugal 110 Puerto Rico 111 United Kingdom 112 Czech Republic 113 Dominican Republic 114 Romania 115 Russia 117 Senegal 121 Syria 122 South Africa 123 Sweden 124 Switzerland 126 Taiwan 128 Palestina 132 Turkey 133 Ukraine 134 Uruguay 136 Venezuela 140 Spain 900 Response suppressed 999 NIU (not in universe) This variable indicates the country of birth outside Argentina. IPUMS Literacy <svar v="AR10A408 AR10A409 AR10A410 AR10A411 AR10A412 AR10A413 AR10A414 AR10A415 AR10A416">From here, all persons over three years old answer.<br />[Questions 7 to 12 were asked of persons 3 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A408"><span class="h3">7. Does this person know how to read and write?</span><br /><div class="i1">[] Yes<br />[] No</div><br /></svar> Argentina 2010: Persons age 3+ [discrepancies: none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates if the person knows how to read and write (literacy). IPUMS Attend school <svar v="AR10A408 AR10A409 AR10A410 AR10A411 AR10A412 AR10A413 AR10A414 AR10A415 AR10A416">From here, all persons over three years old answer.<br />[Questions 7 to 12 were asked of persons 3 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A409"><span class="h3">8. Does this person attend an educational establishment?</span><br /><div class="i1">[] Attending<br />[] Attended<br />[] Never attended (continue with 12)</div><br /></svar> Argentina 2010: Persons age 3+ [discrepancies: type I 0.9%; type II none] 1 Attends 2 Attended 3 Never attended 9 NIU (not in universe) This variable indicates if the person attends school. IPUMS Educational level <svar v="AR10A408 AR10A409 AR10A410 AR10A411 AR10A412 AR10A413 AR10A414 AR10A415 AR10A416">From here, all persons over three years old answer.<br />[Questions 7 to 12 were asked of persons 3 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A410 AR10A412"><span class="h3">9. What educational level does/did this person attend?</span><br /><div class="i1">[] Initial (kindergarten, pre-school) -- Continue with 12<br />[] Elementary<br />[] EGB [General Basic Education]<br />[] Secondary</div><br /><div class="i2">Attended:<br />[] 6-year elementary<br />[] 7-year elementary</div><br /><div class="i1">[] Polymodal (Secondary)<br />[] Higher education, non-university<br />[] University<br />[] Graduate<br />[] Special education (for persons with disabilities) -- Continue with 12</div><br /></svar> Argentina 2010: Persons age 3+ who are attending or ever attended school [discrepancies: type I 4.3%; type II none] 01 Initial (kindergarten, pre-school) 02 Elementary 03 EGB [General Basic Education] 04 Secondary 05 Polimodal (Secondary) 06 Higher education, non-university 07 University 08 Post-graduate 09 Special education (for persons with disabilities) 99 NIU (not in universe) This variable indicates the educational level of the person. IPUMS Educational attainment <svar v="AR10A408 AR10A409 AR10A410 AR10A411 AR10A412 AR10A413 AR10A414 AR10A415 AR10A416">From here, all persons over three years old answer.<br />[Questions 7 to 12 were asked of persons 3 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A410 AR10A412"><span class="h3">9. What educational level does/did this person attend?</span><br /><div class="i1">[] Initial (kindergarten, pre-school) -- Continue with 12<br />[] Elementary<br />[] EGB [General Basic Education]<br />[] Secondary</div><br /><div class="i2">Attended:<br />[] 6-year elementary<br />[] 7-year elementary</div><br /><div class="i1">[] Polymodal (Secondary)<br />[] Higher education, non-university<br />[] University<br />[] Graduate<br />[] Special education (for persons with disabilities) -- Continue with 12</div><br /></svar> Argentina 2010: Persons who are attending or ever attended school (excludes Initial and Special education) [discrepancies: type I 4.3%; type II none] 100 Initial (kindergarten, pre-school) 200 Elementary - 0 years 201 Elementary - 1 year 202 Elementary - 2 years 203 Elementary - 3 years 204 Elementary - 4 years 205 Elementary - 5 years 206 Elementary - 6 years 207 Elementary - 7 years or more 209 Elementary - Unknown years 300 EGB - 0 years [General Basic Education] 301 EGB - 1 year 302 EGB - 2 years 303 EGB - 3 years 304 EGB - 4 years 305 EGB - 5 years 306 EGB - 6 years 307 EGB - 7 years 308 EGB - 8 years 309 EGB - 9 years 310 EGB - Unknown years 400 Secondary - 0 years 401 Secondary - 1 year 402 Secondary - 2 years 403 Secondary - 3 years 404 Secondary - 4 years 405 Secondary - 5 years 406 Secondary - 6 years or more 409 Secondary - Unknown years 500 Polimodal - 0 years 501 Polimodal - 1 year 502 Polimodal - 2 years 503 Polimodal - 3 years or more 509 Polimodal - Unknown years 600 Higher education, non-university - 0 years 601 Higher education, non-university - 1 year 602 Higher education, non-university - 2 years 603 Higher education, non-university - 3 years 604 Higher education, non-university - 4 years 605 Higher education, non-university - 5 years 606 Higher education, non-university - 6 years 607 Higher education, non-university - 7 years 608 Higher education, non-university - 8 years or more 609 Higher education, non-university - Unknown years 700 University - 0 years 701 University - 1 year 702 University - 2 years 703 University - 3 years 704 University - 4 years 705 University - 5 years 706 University - 6 years 707 University - 7 years 708 University - 8 years or more 709 University - Unknown years 800 Post-graduate - 0 years 801 Post-graduate - 1 year 802 Post-graduate - 2 years 803 Post-graduate - 3 years 804 Post-graduate - 4 years 805 Post-graduate - 5 years 806 Post-graduate - 6 years or more 809 Post-graduate - Unknown years 900 Special education (for persons with disabilities) 999 NIU (not in universe) This variable indicates the person's educational attainment. IPUMS Type of primary school <svar v="AR10A408 AR10A409 AR10A410 AR10A411 AR10A412 AR10A413 AR10A414 AR10A415 AR10A416">From here, all persons over three years old answer.<br />[Questions 7 to 12 were asked of persons 3 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A410 AR10A412"><span class="h3">9. What educational level does/did this person attend?</span><br /><div class="i1">[] Initial (kindergarten, pre-school) -- Continue with 12<br />[] Elementary<br />[] EGB [General Basic Education]<br />[] Secondary</div><br /><div class="i2">Attended:<br />[] 6-year elementary<br />[] 7-year elementary</div><br /><div class="i1">[] Polymodal (Secondary)<br />[] Higher education, non-university<br />[] University<br />[] Graduate<br />[] Special education (for persons with disabilities) -- Continue with 12</div><br /></svar> Argentina 2010: Persons age 3+ who are attending or attended secondary school [discrepancies: type I trace; type II 9.3%] 1 6-year primary school 2 7-year primary school 8 Unknown 9 NIU (not in universe) This variable indicates the type of primary school that the person attended. IPUMS Completed educational level <svar v="AR10A408 AR10A409 AR10A410 AR10A411 AR10A412 AR10A413 AR10A414 AR10A415 AR10A416">From here, all persons over three years old answer.<br />[Questions 7 to 12 were asked of persons 3 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A413 AR10A414 AR10A415"><span class="h3">10. Did this person complete this level?</span><br /><div class="i1">[] Yes<br />[] No<br />[] Do not know</div><br /></svar> Argentina 2010: Persons age 3+ who are attending or ever attended school (excludes initial and special education) [discrepancies: type I trace; type II trace] 1 Yes 2 No 8 Unknown 9 NIU (not in universe) This variable indicates if the person completed the level of school previously indicated. IPUMS Uses computer <svar v="AR10A408 AR10A409 AR10A410 AR10A411 AR10A412 AR10A413 AR10A414 AR10A415 AR10A416">From here, all persons over three years old answer.<br />[Questions 7 to 12 were asked of persons 3 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A416"><span class="h3">12. Does this person use a computer?</span><br /><div class="i1">[] Yes<br />[] No</div><br /></svar> Argentina 2010: Persons age 3+ [discrepancies: none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates if the person uses a computer. IPUMS Worked at least 1 hour last week <svar v="AR10A417 AR10A418 AR10A419 AR10A420">From here, all persons 14 years old or more answer<br />[Questions 13 to 16 were asked of persons 14 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A417"><span class="h3">13. During the last week, did this person work for at least one hour? </span> <br /><div class="i1">(Without counting household task in your own household)<br /><br />[] Yes - End of the interview for this person<br />[] No</div><br /></svar> Argentina 2010: Persons age 14+ [discrepancies: type I trace; type II none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates if the person worked at least 1 hour last week. IPUMS Informal work last week <svar v="AR10A417 AR10A418 AR10A419 AR10A420">From here, all persons 14 years old or more answer<br />[Questions 13 to 16 were asked of persons 14 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A418"><span class="h3">14. In the last week, did this person do any odd-job, do something to sell outside the household or help any relative/friend in a job or business?</span><br /><div class="i1">[] Yes -- End of the interview for this person<br />[] No</div><br /></svar> Argentina 2010: Persons age 14+ who did not work at least 1 hour last week [discrepancies: none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates if the person had informal work last week. IPUMS Did not work but had work <svar v="AR10A417 AR10A418 AR10A419 AR10A420">From here, all persons 14 years old or more answer<br />[Questions 13 to 16 were asked of persons 14 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A419"><span class="h3">15. In this week, did this person have a job but were on leave with permission for vacations or illness; suspension; labor conflict; etc.?</span><br /><div class="i1">[] Yes -- End of the interview for this person<br />[] No</div><br /></svar> Argentina 2010: Persons age 14+ who did not worked at least 1 hour last week or in an informal job [discrepancies: none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates if the person did not work last week but had work. IPUMS Looked for work last 4 weeks <svar v="AR10A417 AR10A418 AR10A419 AR10A420">From here, all persons 14 years old or more answer<br />[Questions 13 to 16 were asked of persons 14 years old and over]<br /></svar></p> <p><svar a="all" v="AR10A420"><span class="h3">16. In the last four weeks, were this person looking for work: answered ads, asked among friends/relatives, put up signs, did something to set up in business himself/herself?</span><br /><div class="i1">[] Yes<br />[] No -- End of the interview for this person</div><br /></svar> Argentina 2010: Persons age 14+ who did not worked at least 1 hour last week or in an informal job or were on leave or vacations [discrepancies: none] 1 Yes 2 No 9 NIU (not in universe) This variable indicates if the person looked for work during the last 4 weeks. IPUMS