DDI_BFA_1985_PHC_v01_M_v7.5_A_IPUMS Recensement general de la population de 1985 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. Recensement general de la population de 1985 - IPUMS Subset PHC Burkina Faso 1985 (IPUMS Harmonized Subset) BFA_1985_PHC_v01_M_v7.5_A_IPUMS Institut National de la Statistique et de la Démographie IPUMS (c) Copyright 1985, Institut National de la Statistique et de la Démographie and Minnesota Population Center MetadataEditor Institut National de la Statistique et de la Démographie 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 Other Household Variables -- HOUSEHOLD Fertility and Mortality Variables -- PERSON Nativity and Birthplace Variables -- PERSON Work Variables -- PERSON Technical Household Variables -- HOUSEHOLD Disability Variables -- PERSON Education Variables -- PERSON Geography: A-E Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Migration: A-E Variables -- PERSON Migration: Global Variables -- PERSON Technical Person Variables -- PERSON Geography: Global Variables -- HOUSEHOLD Work: Occupation Variables -- PERSON Burkina Faso Province Persons and households UNITS IDENTIFIED: - Dwellings: no - Vacant Units: No - Households: yes - Individuals: yes - Group quarters: no UNIT DESCRIPTIONS: - Dwellings: no - Households: The ordinary household is generally considered the base socio-economic unit within which the members are blood-related or not; they live together in the same house or concession; they combine their resources and work together to satisfy their essential nutritional and vital needs. - Group quarters: A collective household is made up of a group of people who generally share no familial relation but live together under special conditions, that is to say principally using the resources furnished to them by an establishment to provide for their essential needs (food, lodging, care). Persons present in Burkina Faso at the time of the census, as well as Burkinabé persons living abroad (enumerated as emigrants). Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Burkina Faso 1985 MICRODATA SOURCE: Institut National de la Statistique et de la Démographie SAMPLE SIZE (person records): 884797. SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by IPUMS. Face-to-face [f2f] Single enumeration form that requested information on households and individuals. both, CENSUS DAY: December 10-20, 1985 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. Institut National de la Statistique et de la Démographie 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: Burkina Faso, Institut National de la Statistique et de la Démographie. Recensement general de la population de 1985 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. BFA1985_PHC-H-H Household records 145 28 BFA1985_PHC-P-H Person records 884797 54 Record type H Household P Person RECTYPE identifies the type of record for the case: household or person. NOTE: RECTYPE is an alphabetic (character string) variable with a value of 'H' for household records and 'P' for person records. RECTYPE will not appear as a variable in the default rectangular extracts produced by the data extract system. It is only available in hierarchical extracts, to distinguish between the two record types. IPUMS Country 032 Argentina 051 Armenia 040 Austria 050 Bangladesh 112 Belarus 204 Benin 068 Bolivia 072 Botswana 076 Brazil 854 Burkina Faso 116 Cambodia 120 Cameroon 124 Canada 152 Chile 156 China 170 Colombia 188 Costa Rica 192 Cuba 208 Denmark 214 Dominican Republic 218 Ecuador 818 Egypt 222 El Salvador 231 Ethiopia 242 Fiji 246 Finland 250 France 276 Germany 288 Ghana 300 Greece 320 Guatemala 324 Guinea 332 Haiti 340 Honduras 348 Hungary 352 Iceland 356 India 360 Indonesia 364 Iran 368 Iraq 372 Ireland 376 Israel 380 Italy 384 Ivory Coast 388 Jamaica 400 Jordan 404 Kenya 417 Kyrgyz Republic 418 Laos 426 Lesotho 430 Liberia 454 Malawi 458 Malaysia 466 Mali 480 Mauritius 484 Mexico 496 Mongolia 504 Morocco 508 Mozambique 104 Myanmar 524 Nepal 528 Netherlands 558 Nicaragua 566 Nigeria 578 Norway 586 Pakistan 275 Palestine 591 Panama 598 Papua New Guinea 600 Paraguay 604 Peru 608 Philippines 616 Poland 620 Portugal 630 Puerto Rico 642 Romania 643 Russia 646 Rwanda 662 Saint Lucia 686 Senegal 694 Sierra Leone 703 Slovak Republic 705 Slovenia 710 South Africa 728 South Sudan 724 Spain 729 Sudan 740 Suriname 752 Sweden 756 Switzerland 834 Tanzania 764 Thailand 768 Togo 780 Trinidad and Tobago 792 Turkey 800 Uganda 804 Ukraine 826 United Kingdom 840 United States 858 Uruguay 862 Venezuela 704 Vietnam 894 Zambia 716 Zimbabwe COUNTRY gives the country from which the sample was drawn. The codes assigned to each country are those used by the UN Statistics Division and the ISO (International Organization for Standardization). IPUMS Year 1703 1703 1729 1729 1787 1787 1801 1801 1819 1819 1845 1845 1848 1848 1850 1850 1851 1851 1852 1852 1860 1860 1861 1861 1865 1865 1868 1868 1870 1870 1871 1871 1875 1875 1880 1880 1881 1881 1885 1885 1890 1890 1891 1891 1900 1900 1901 1901 1910 1910 1911 1911 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1966 1966 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 YEAR gives the year in which the census or survey was taken. For samples that span years, the midpoint or first year of the interval is reported. IPUMS IPUMS sample identifier 032197001 Argentina 1970 032198001 Argentina 1980 032199101 Argentina 1991 032200101 Argentina 2001 032201001 Argentina 2010 051200101 Armenia 2001 051201101 Armenia 2011 040197101 Austria 1971 040198101 Austria 1981 040199101 Austria 1991 040200101 Austria 2001 040201101 Austria 2011 050199101 Bangladesh 1991 050200101 Bangladesh 2001 050201101 Bangladesh 2011 112199901 Belarus 1999 112200901 Belarus 2009 204197901 Benin 1979 204199201 Benin 1992 204200201 Benin 2002 204201301 Benin 2013 068197601 Bolivia 1976 068199201 Bolivia 1992 068200101 Bolivia 2001 068201201 Bolivia 2012 072198101 Botswana 1981 072199101 Botswana 1991 072200101 Botswana 2001 072201101 Botswana 2011 076196001 Brazil 1960 076197001 Brazil 1970 076198001 Brazil 1980 076199101 Brazil 1991 076200001 Brazil 2000 076201001 Brazil 2010 854198501 Burkina Faso 1985 854199601 Burkina Faso 1996 854200601 Burkina Faso 2006 116199801 Cambodia 1998 116200401 Cambodia 2004 116200801 Cambodia 2008 116201301 Cambodia 2013 116201901 Cambodia 2019 120197601 Cameroon 1976 120198701 Cameroon 1987 120200501 Cameroon 2005 124185201 Canada 1852 124187101 Canada 1871 124188101 Canada 1881 124189101 Canada 1891 124190101 Canada 1901 124191101 Canada 1911 124197101 Canada 1971 124198101 Canada 1981 124199101 Canada 1991 124200101 Canada 2001 124201101 Canada 2011 152196001 Chile 1960 152197001 Chile 1970 152198201 Chile 1982 152199201 Chile 1992 152200201 Chile 2002 152201701 Chile 2017 156198201 China 1982 156199001 China 1990 156200001 China 2000 170196401 Colombia 1964 170197301 Colombia 1973 170198501 Colombia 1985 170199301 Colombia 1993 170200501 Colombia 2005 188196301 Costa Rica 1963 188197301 Costa Rica 1973 188198401 Costa Rica 1984 188200001 Costa Rica 2000 188201101 Costa Rica 2011 192200201 Cuba 2002 192201201 Cuba 2012 208178701 Denmark 1787 208180101 Denmark 1801 208184501 Denmark 1845 208188001 Denmark 1880 208188501 Denmark 1885 214196001 Dominican Republic 1960 214197001 Dominican Republic 1970 214198101 Dominican Republic 1981 214200201 Dominican Republic 2002 214201001 Dominican Republic 2010 218196201 Ecuador 1962 218197401 Ecuador 1974 218198201 Ecuador 1982 218199001 Ecuador 1990 218200101 Ecuador 2001 218201001 Ecuador 2010 818184801 Egypt 1848 818186801 Egypt 1868 818198601 Egypt 1986 818199601 Egypt 1996 818200601 Egypt 2006 222199201 El Salvador 1992 222200701 El Salvador 2007 231198401 Ethiopia 1984 231199401 Ethiopia 1994 231200701 Ethiopia 2007 242196601 Fiji 1966 242197601 Fiji 1976 242198601 Fiji 1986 242199601 Fiji 1996 242200701 Fiji 2007 242201401 Fiji 2014 246201001 Finland 2010 250196201 France 1962 250196801 France 1968 250197501 France 1975 250198201 France 1982 250199001 France 1990 250199901 France 1999 250200601 France 2006 250201101 France 2011 276181901 Germany 1819 (Mecklenburg) 276197001 Germany 1970 (West) 276197101 Germany 1971 (East) 276198101 Germany 1981 (East) 276198701 Germany 1987 (West) 288198401 Ghana 1984 288200001 Ghana 2000 288201001 Ghana 2010 300197101 Greece 1971 300198101 Greece 1981 300199101 Greece 1991 300200101 Greece 2001 300201101 Greece 2011 320196401 Guatemala 1964 320197301 Guatemala 1973 320198101 Guatemala 1981 320199401 Guatemala 1994 320200201 Guatemala 2002 324198301 Guinea 1983 324199601 Guinea 1996 324201401 Guinea 2014 332197101 Haiti 1971 332198201 Haiti 1982 332200301 Haiti 2003 340196101 Honduras 1961 340197401 Honduras 1974 340198801 Honduras 1988 340200101 Honduras 2001 348197001 Hungary 1970 348198001 Hungary 1980 348199001 Hungary 1990 348200101 Hungary 2001 348201101 Hungary 2011 352170301 Iceland 1703 352172901 Iceland 1729 352180101 Iceland 1801 352190101 Iceland 1901 352191001 Iceland 1910 356198341 India 1983 356198741 India 1987 356199341 India 1993 356199941 India 1999 356200441 India 2004 356200941 India 2009 360197101 Indonesia 1971 360197601 Indonesia 1976 360198001 Indonesia 1980 360198501 Indonesia 1985 360199001 Indonesia 1990 360199501 Indonesia 1995 360200001 Indonesia 2000 360200501 Indonesia 2005 360201001 Indonesia 2010 364200601 Iran 2006 364201101 Iran 2011 368199701 Iraq 1997 372190101 Ireland 1901 372191101 Ireland 1911 372197101 Ireland 1971 372197901 Ireland 1979 372198101 Ireland 1981 372198601 Ireland 1986 372199101 Ireland 1991 372199601 Ireland 1996 372200201 Ireland 2002 372200601 Ireland 2006 372201101 Ireland 2011 372201601 Ireland 2016 376197201 Israel 1972 376198301 Israel 1983 376199501 Israel 1995 376200801 Israel 2008 380200101 Italy 2001 380201101 Italy 2011 380201121 Italy 2011 Q1 LFS 380201221 Italy 2012 Q1 LFS 380201321 Italy 2013 Q1 LFS 380201421 Italy 2014 Q1 LFS 380201521 Italy 2015 Q1 LFS 380201621 Italy 2016 Q1 LFS 380201721 Italy 2017 Q1 LFS 380201821 Italy 2018 Q1 LFS 380201921 Italy 2019 Q1 LFS 380202021 Italy 2020 Q1 LFS 384198801 Ivory Coast 1988 384199801 Ivory Coast 1998 388198201 Jamaica 1982 388199101 Jamaica 1991 388200101 Jamaica 2001 400200401 Jordan 2004 404196901 Kenya 1969 404197901 Kenya 1979 404198901 Kenya 1989 404199901 Kenya 1999 404200901 Kenya 2009 417199901 Kyrgyz Republic 1999 417200901 Kyrgyz Republic 2009 418199501 Laos 1995 418200501 Laos 2005 418201501 Laos 2015 426199601 Lesotho 1996 426200601 Lesotho 2006 430197401 Liberia 1974 430200801 Liberia 2008 454198701 Malawi 1987 454199801 Malawi 1998 454200801 Malawi 2008 458197001 Malaysia 1970 458198001 Malaysia 1980 458199101 Malaysia 1991 458200001 Malaysia 2000 466198701 Mali 1987 466199801 Mali 1998 466200901 Mali 2009 480199001 Mauritius 1990 480200001 Mauritius 2000 480201101 Mauritius 2011 484196001 Mexico 1960 484197001 Mexico 1970 484199001 Mexico 1990 484199501 Mexico 1995 484200001 Mexico 2000 484200501 Mexico 2005 484201001 Mexico 2010 484201501 Mexico 2015 484202001 Mexico 2020 484200521 Mexico 2005 Q1 LFS 484200522 Mexico 2005 Q2 LFS 484200523 Mexico 2005 Q3 LFS 484200524 Mexico 2005 Q4 LFS 484200621 Mexico 2006 Q1 LFS 484200622 Mexico 2006 Q2 LFS 484200623 Mexico 2006 Q3 LFS 484200624 Mexico 2006 Q4 LFS 484200721 Mexico 2007 Q1 LFS 484200722 Mexico 2007 Q2 LFS 484200723 Mexico 2007 Q3 LFS 484200724 Mexico 2007 Q4 LFS 484200821 Mexico 2008 Q1 LFS 484200822 Mexico 2008 Q2 LFS 484200823 Mexico 2008 Q3 LFS 484200824 Mexico 2008 Q4 LFS 484200921 Mexico 2009 Q1 LFS 484200922 Mexico 2009 Q2 LFS 484200923 Mexico 2009 Q3 LFS 484200924 Mexico 2009 Q4 LFS 484201021 Mexico 2010 Q1 LFS 484201022 Mexico 2010 Q2 LFS 484201023 Mexico 2010 Q3 LFS 484201024 Mexico 2010 Q4 LFS 484201121 Mexico 2011 Q1 LFS 484201122 Mexico 2011 Q2 LFS 484201123 Mexico 2011 Q3 LFS 484201124 Mexico 2011 Q4 LFS 484201221 Mexico 2012 Q1 LFS 484201222 Mexico 2012 Q2 LFS 484201223 Mexico 2012 Q3 LFS 484201224 Mexico 2012 Q4 LFS 484201321 Mexico 2013 Q1 LFS 484201322 Mexico 2013 Q2 LFS 484201323 Mexico 2013 Q3 LFS 484201324 Mexico 2013 Q4 LFS 484201421 Mexico 2014 Q1 LFS 484201422 Mexico 2014 Q2 LFS 484201423 Mexico 2014 Q3 LFS 484201424 Mexico 2014 Q4 LFS 484201521 Mexico 2015 Q1 LFS 484201522 Mexico 2015 Q2 LFS 484201523 Mexico 2015 Q3 LFS 484201524 Mexico 2015 Q4 LFS 484201621 Mexico 2016 Q1 LFS 484201622 Mexico 2016 Q2 LFS 484201623 Mexico 2016 Q3 LFS 484201624 Mexico 2016 Q4 LFS 484201721 Mexico 2017 Q1 LFS 484201722 Mexico 2017 Q2 LFS 484201723 Mexico 2017 Q3 LFS 484201724 Mexico 2017 Q4 LFS 484201821 Mexico 2018 Q1 LFS 484201822 Mexico 2018 Q2 LFS 484201823 Mexico 2018 Q3 LFS 484201824 Mexico 2018 Q4 LFS 484201921 Mexico 2019 Q1 LFS 484201922 Mexico 2019 Q2 LFS 484201923 Mexico 2019 Q3 LFS 484201924 Mexico 2019 Q4 LFS 484202021 Mexico 2020 Q1 LFS 484202023 Mexico 2020 Q3 LFS 496198901 Mongolia 1989 496200001 Mongolia 2000 504198201 Morocco 1982 504199401 Morocco 1994 504200401 Morocco 2004 504201401 Morocco 2014 508199701 Mozambique 1997 508200701 Mozambique 2007 104201401 Myanmar 2014 524200101 Nepal 2001 524201101 Nepal 2011 528196001 Netherlands 1960 528197101 Netherlands 1971 528200101 Netherlands 2001 528201101 Netherlands 2011 558197101 Nicaragua 1971 558199501 Nicaragua 1995 558200501 Nicaragua 2005 566200621 Nigeria 2006 566200721 Nigeria 2007 566200821 Nigeria 2008 566200921 Nigeria 2009 566201021 Nigeria 2010 578180101 Norway 1801 578186501 Norway 1865 578187501 Norway 1875 578190001 Norway 1900 578191001 Norway 1910 586197301 Pakistan 1973 586198101 Pakistan 1981 586199801 Pakistan 1998 275199701 Palestine 1997 275200701 Palestine 2007 275201701 Palestine 2017 591196001 Panama 1960 591197001 Panama 1970 591198001 Panama 1980 591199001 Panama 1990 591200001 Panama 2000 591201001 Panama 2010 598198001 Papua New Guinea 1980 598199001 Papua New Guinea 1990 598200001 Papua New Guinea 2000 600196201 Paraguay 1962 600197201 Paraguay 1972 600198201 Paraguay 1982 600199201 Paraguay 1992 600200201 Paraguay 2002 604199301 Peru 1993 604200701 Peru 2007 604201701 Peru 2017 608199001 Philippines 1990 608199501 Philippines 1995 608200001 Philippines 2000 608201001 Philippines 2010 616197801 Poland 1978 616198801 Poland 1988 616200201 Poland 2002 616201101 Poland 2011 620198101 Portugal 1981 620199101 Portugal 1991 620200101 Portugal 2001 620201101 Portugal 2011 630197001 Puerto Rico 1970 630198001 Puerto Rico 1980 630199001 Puerto Rico 1990 630200001 Puerto Rico 2000 630200501 Puerto Rico 2005 630201001 Puerto Rico 2010 630201501 Puerto Rico 2015 630202001 Puerto Rico 2020 642197701 Romania 1977 642199201 Romania 1992 642200201 Romania 2002 642201101 Romania 2011 643200201 Russia 2002 643201001 Russia 2010 646199101 Rwanda 1991 646200201 Rwanda 2002 646201201 Rwanda 2012 662198001 Saint Lucia 1980 662199101 Saint Lucia 1991 686198801 Senegal 1988 686200201 Senegal 2002 686201301 Senegal 2013 694200401 Sierra Leone 2004 694201501 Sierra Leone 2015 703199101 Slovak Republic 1991 703200101 Slovak Republic 2001 703201101 Slovak Republic 2011 705200201 Slovenia 2002 710199601 South Africa 1996 710200101 South Africa 2001 710200701 South Africa 2007 710201101 South Africa 2011 710201601 South Africa 2016 728200801 South Sudan 2008 724198101 Spain 1981 724199101 Spain 1991 724200101 Spain 2001 724201101 Spain 2011 724200521 Spain 2005 Q1 LFS 724200522 Spain 2005 Q2 LFS 724200523 Spain 2005 Q3 LFS 724200524 Spain 2005 Q4 LFS 724200621 Spain 2006 Q1 LFS 724200622 Spain 2006 Q2 LFS 724200623 Spain 2006 Q3 LFS 724200624 Spain 2006 Q4 LFS 724200721 Spain 2007 Q1 LFS 724200722 Spain 2007 Q2 LFS 724200723 Spain 2007 Q3 LFS 724200724 Spain 2007 Q4 LFS 724200821 Spain 2008 Q1 LFS 724200822 Spain 2008 Q2 LFS 724200823 Spain 2008 Q3 LFS 724200824 Spain 2008 Q4 LFS 724200921 Spain 2009 Q1 LFS 724200922 Spain 2009 Q2 LFS 724200923 Spain 2009 Q3 LFS 724200924 Spain 2009 Q4 LFS 724201021 Spain 2010 Q1 LFS 724201022 Spain 2010 Q2 LFS 724201023 Spain 2010 Q3 LFS 724201024 Spain 2010 Q4 LFS 724201121 Spain 2011 Q1 LFS 724201122 Spain 2011 Q2 LFS 724201123 Spain 2011 Q3 LFS 724201124 Spain 2011 Q4 LFS 724201221 Spain 2012 Q1 LFS 724201222 Spain 2012 Q2 LFS 724201223 Spain 2012 Q3 LFS 724201224 Spain 2012 Q4 LFS 724201321 Spain 2013 Q1 LFS 724201322 Spain 2013 Q2 LFS 724201323 Spain 2013 Q3 LFS 724201324 Spain 2013 Q4 LFS 724201421 Spain 2014 Q1 LFS 724201422 Spain 2014 Q2 LFS 724201423 Spain 2014 Q3 LFS 724201424 Spain 2014 Q4 LFS 724201521 Spain 2015 Q1 LFS 724201522 Spain 2015 Q2 LFS 724201523 Spain 2015 Q3 LFS 724201524 Spain 2015 Q4 LFS 724201621 Spain 2016 Q1 LFS 724201622 Spain 2016 Q2 LFS 724201623 Spain 2016 Q3 LFS 724201624 Spain 2016 Q4 LFS 724201721 Spain 2017 Q1 LFS 724201722 Spain 2017 Q2 LFS 724201723 Spain 2017 Q3 LFS 724201724 Spain 2017 Q4 LFS 724201821 Spain 2018 Q1 LFS 724201822 Spain 2018 Q2 LFS 724201823 Spain 2018 Q3 LFS 724201824 Spain 2018 Q4 LFS 724201921 Spain 2019 Q1 LFS 724201922 Spain 2019 Q2 LFS 724201923 Spain 2019 Q3 LFS 724201924 Spain 2019 Q4 LFS 724202021 Spain 2020 Q1 LFS 724202022 Spain 2020 Q2 LFS 724202023 Spain 2020 Q3 LFS 724202024 Spain 2020 Q4 LFS 729200801 Sudan 2008 740200401 Suriname 2004 740201201 Suriname 2012 752188001 Sweden 1880 752189001 Sweden 1890 752190001 Sweden 1900 752191001 Sweden 1910 756197001 Switzerland 1970 756198001 Switzerland 1980 756199001 Switzerland 1990 756200001 Switzerland 2000 756201101 Switzerland 2011 834198801 Tanzania 1988 834200201 Tanzania 2002 834201201 Tanzania 2012 764197001 Thailand 1970 764198001 Thailand 1980 764199001 Thailand 1990 764200001 Thailand 2000 768196001 Togo 1960 768197001 Togo 1970 768201001 Togo 2010 780197001 Trinidad and Tobago 1970 780198001 Trinidad and Tobago 1980 780199001 Trinidad and Tobago 1990 780200001 Trinidad and Tobago 2000 780201101 Trinidad and Tobago 2011 792198501 Turkey 1985 792199001 Turkey 1990 792200001 Turkey 2000 800199101 Uganda 1991 800200201 Uganda 2002 800201401 Uganda 2014 804200101 Ukraine 2001 826185101 United Kingdom 1851 (England and Wales) 826185102 United Kingdom 1851 (Scotland) 826185103 United Kingdom 1851 (2% sample) 826186101 United Kingdom 1861 (England and Wales) 826186102 United Kingdom 1861 (Scotland) 826187101 United Kingdom 1871 (Scotland) 826188101 United Kingdom 1881 (England and Wales) 826188102 United Kingdom 1881 (Scotland) 826189101 United Kingdom 1891 (England and Wales) 826189102 United Kingdom 1891 (Scotland) 826190101 United Kingdom 1901 (England and Wales) 826190102 United Kingdom 1901 (Scotland) 826191101 United Kingdom 1911 (England and Wales) 826196101 United Kingdom 1961 826197101 United Kingdom 1971 826199101 United Kingdom 1991 826200101 United Kingdom 2001 840185001 United States 1850 (100%) 840185002 United States 1850 (1%) 840186001 United States 1860 (1%) 840187001 United States 1870 (1%) 840188001 United States 1880 (100%) 840188002 United States 1880 (10%) 840190001 United States 1900 (5%) 840191001 United States 1910 (1%) 840196001 United States 1960 840197001 United States 1970 840198001 United States 1980 840199001 United States 1990 840200001 United States 2000 840200501 United States 2005 840201001 United States 2010 840201501 United States 2015 840202001 United States 2020 858196301 Uruguay 1963 858196302 Uruguay 1963 (full count) 858197501 Uruguay 1975 858197502 Uruguay 1975 (full count) 858198501 Uruguay 1985 858198502 Uruguay 1985 (full count) 858199601 Uruguay 1996 858199602 Uruguay 1996 (full count) 858200621 Uruguay 2006 858201101 Uruguay 2011 858201102 Uruguay 2011 (full count) 862197101 Venezuela 1971 862198101 Venezuela 1981 862199001 Venezuela 1990 862200101 Venezuela 2001 704198901 Vietnam 1989 704199901 Vietnam 1999 704200901 Vietnam 2009 704201901 Vietnam 2019 894199001 Zambia 1990 894200001 Zambia 2000 894201001 Zambia 2010 716201201 Zimbabwe 2012 SAMPLE identifies the IPUMS sample from which the case is drawn. Each sample receives a unique 9-digit code. The code is structured as follows: The first 3 digits are the ISO/UN codes used in COUNTRY The next 4 digits are the year of the census/survey The final 2 digits identify the sample within the year. For the last two digits, censuses or large census-like surveys have a value "0" (e.g, 01) in the second-to-last digit, household surveys have a value of "2" (e.g., 21), and employment surveys have a value of "4" (e.g., 41). IPUMS Household serial number SERIAL is an identifying number unique to each household in a given sample. All person records are assigned the same serial number as the household record that they follow. (Person records also have their own unique identifiers -- see PERNUM.) The combination of SAMPLE and SERIAL provides a unique identifier for every household in the IPUMS-International database; SAMPLE, SERIAL and PERNUM uniquely identify every person in the database. SERIAL can be used to identify dwellings in some samples. In these samples, the first 7 digits of SERIAL provide the dwelling number common to all households that were sampled from the same structure. The last three digits give the sequence of the household within the dwelling. The following is a list of samples in which dwellings can be inferred: Chile 1970, 1992, 2002Colombia 1993, 2005Costa Rica 1984, 2000Cuba 2002Dominican Republic 1981, 2002, 2010Ecuador 1990, 2001Germany 1971Hungary 1980, 1990, 2001Jamaica 1982, 1991, 2001Malaysia 1970, 1991, 2000Mexico 1995, 1990, 2000, 2005Nigeria 2006Panama 2000Peru 1993, 2007Portugal 1981, 1991, 2001Spain 1991Uruguay 2011Venezuela 1990, 2001Vietnam 1989In all other samples, the last 3 digits are always zeroes. SERIAL was constructed for IPUMS-International, and has no relation to the serial number in the original datasets. The U.S. 1900 sample and 1880 10% sample have multi-household dwellings that can be identified using the last 3 digits of SERIAL. SERIAL is a 10-digit numeric variable. The last 3 digits of SERIAL indicate household number within dwelling for selected samples noted in the variable description. In all other samples, the last 3 digits are always zeroes. IPUMS Number of person records in the household PERSONS indicates how many person records are included in the household (i.e., the number of person records associated with the household record in the sample). These person records will all have the same serial number (SERIAL) as the household record. The information contained in the household record will normally apply to all of these persons. PERSONS is a 4-digit numeric variable. IPUMS Household weight HHWT indicates the number of households in the population represented by the household in the sample. For the samples that are truly weighted (see the comparability discussion), HHWT must be used to yield accurate household-level statistics. NOTE: HHWT has 2 implied decimal places. That is, the last two digits of the eight-digit variable are decimal digits, but there is no actual decimal in the data. HHWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description. IPUMS 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 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 Burkina Faso, Region 1985 [Level 1, GIS] 001 Centre-Nord 003 Sud-Ouest 004 Centre-Est 005 Centre-Ouest 008 Est 010 Hauts-Bassins 011 Centre 013 Volta-Naire 019 Sahel 020 Nord GEO1_BF1985 identifies the household's department within Burkina Faso in 1985. Departments were the first level administrative units of the country in 1985 (currently regions). A GIS map (in shapefile format), corresponding to GEO1_BF1996 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Burkina Faso 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 Burkina Faso, Province 1985 [Level 2, GIS] 001001 Bam 001016 Namentenga 001023 Sanmatenga 003003 Bougouriba 003021 Poni 004004 Boulgou 004014 Kouritenga 005005 Boulkiemdé 005020 Passoré 005022 Sanguié 005025 Sissili 008008 Gnagna 008009 Gourma 008028 Tapoa 010006 Comoé 010010 Houet 010012 Kénédougou 011002 Bazèga 011007 Ganzourgou 011011 Kadiogo 011017 Nahouri 011018 Oubritenga 011030 Zoundwéogo 013013 Kossi 013015 Mouhoun 013027 Sourou 019019 Oudalan 019024 Séno 019026 Soum 020029 Yatenga GEO2_BF1985 identifies the household's province within Burkina Faso in 1985. Provinces are the second level administrative units of the country, after regions. A GIS map (in shapefile format), corresponding to GEO2_BF1985 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Burkina Faso 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 Number of deaths in household last year None 1 1 death 2 2 deaths 3 3 deaths 4 4 deaths 5 5 deaths 6 6 deaths 7 7 or more deaths 8 Unknown 9 NIU (not in universe) MORTNUM indicates the number of deaths in the household in the past year. IPUMS Any deaths in household last year 1 Yes 2 No 8 Unknown/missing 9 NIU (not in universe) ANYMORT indicates whether there were any deaths in the household in the past year. IPUMS Dwelling number Burkina Faso 1985: 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 Burkina Faso 1985: 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 26 26 27 27 28 28 29 29 30 30 This variable indicates the number of persons in the household. IPUMS Dwelling created by splitting apart a large dwelling or household Burkina Faso 1985: All records No problem 1 Yes: households within a large dwelling were split apart into separate dwellings 2 Yes: persons within a large household were split apart into separate dwellings This variable indicates that a dwelling was created by splitting apart a large dwelling or household. IPUMS Nationality of household head <svar a="all" v="BF85A030">Nationality of head of household ____<br /></svar> Burkina Faso 1985: All households 00 Burkina 01 Benin 02 Cape Verde 03 Gabon 04 Gambia 05 Ghana 06 Guinea 07 Ivory Coast 08 Mali 09 Mauritania 10 Niger 11 Nigeria 12 Senegal 13 Chad 14 Togo 15 Other African 16 America 17 China 18 Korea 19 France 20 Russia 21 Other non-African 99 Unknown This variable indicates the nationality of the household head. IPUMS Number of deaths in the past 12 months <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: All households 1 1 2 2 3 3 4 4 5 5 6 6 9 Unknown This variable indicates the number of deaths in the household in the past 12 months. IPUMS Sex of first deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting one or more deaths in the past 12 months [discrepancies: none] 1 Male 2 Female 9 NIU (not in universe) This variable indicates the sex of the first person who died in the household in the past 12 months. IPUMS Age of first deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting one or more deaths in the past 12 months [discrepancies: type 1 none; type 2 0.2%] 100 0 months 101 1 month 102 2 months 103 3 months 104 4 month 105 5 months 106 6 months 107 7 months 108 8 months 109 9 months 110 10 months 111 11 months 201 1 year 202 2 years 203 3 years 204 4 years 205 5 years 206 6 years 207 7 years 208 8 years 209 9 years 210 10 years 211 11 years 212 12 years 213 13 years 214 14 years 215 15 years 216 16 years 217 17 years 218 18 years 219 19 years 220 20 years 221 21 years 222 22 years 223 23 years 224 24 years 225 25 years 226 26 years 227 27 years 228 28 years 229 29 years 230 30 years 231 31 years 232 32 years 233 33 years 234 34 years 235 35 years 236 36 years 237 37 years 238 38 years 239 39 years 240 40 years 241 41 years 242 42 years 243 43 years 244 44 years 245 45 years 246 46 years 247 47 years 248 48 years 249 49 years 250 50 years 251 51 years 252 52 years 253 53 years 254 54 years 255 55 years 256 56 years 257 57 years 258 58 years 259 59 years 260 60 years 261 61 years 262 62 years 263 63 years 264 64 years 265 65 years 266 66 years 267 67 years 268 68 years 269 69 years 270 70 years 271 71 years 272 72 years 273 73 years 274 74 years 275 75 years 276 76 years 277 77 years 278 78 years 279 79 years 280 80 years 281 81 years 282 82 years 283 83 years 284 84 years 285 85 years 286 86 years 287 87 years 288 88 years 289 89 years 290 90 years 291 91 years 292 92 years 293 93 years 295 95 years 296 96 years 297 97 years 298 98 years 299 99 years 998 Unknown 999 NIU (not in universe) This variable indicates the age of the first person who died in the household in the past 12 months. IPUMS Sex of second deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting two or more deaths in the past 12 months [discrepancies: type 1 none, type 2 trace] 1 Male 2 Female 8 Unknown 9 NIU (not in universe) This variable indicates the sex of the second person who died in the household in the past 12 months. IPUMS Age of second deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting two or more deaths in the past 12 months [discrepancies: type 1 none, type 2 trace] 100 0 months 101 1 month 102 2 months 103 3 months 104 4 month 105 5 months 106 6 months 107 7 months 108 8 months 109 9 months 110 10 months 111 11 months 201 1 year 202 2 years 203 3 years 204 4 years 205 5 years 206 6 years 207 7 years 208 8 years 209 9 years 210 10 years 211 11 years 212 12 years 213 13 years 214 14 years 215 15 years 216 16 years 217 17 years 218 18 years 219 19 years 220 20 years 221 21 years 222 22 years 223 23 years 224 24 years 225 25 years 226 26 years 227 27 years 228 28 years 229 29 years 230 30 years 231 31 years 232 32 years 234 34 years 235 35 years 237 37 years 238 38 years 239 39 years 240 40 years 241 41 years 242 42 years 243 43 years 245 45 years 247 47 years 249 49 years 250 50 years 251 51 years 252 52 years 254 54 years 255 55 years 256 56 years 257 57 years 258 58 years 259 59 years 260 60 years 261 61 years 262 62 years 263 63 years 264 64 years 265 65 years 266 66 years 267 67 years 268 68 years 269 69 years 270 70 years 271 71 years 272 72 years 273 73 years 275 75 years 276 76 years 277 77 years 278 78 years 280 80 years 281 81 years 282 82 years 284 84 years 285 85 years 290 90 years 293 93 years 295 95 years 298 98 years 998 Unknown 999 NIU (not in universe) This variable indicates the age of the second person who died in the household in the past 12 months. IPUMS Sex of third deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 3 or more deaths in the last 12 months [discrepancies: none] 1 Male 2 Female 9 NIU (not in universe) This variable indicates the sex of the third person who died in the household in the past 12 months. IPUMS Age of third deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 3 or more deaths in the last 12 months [discrepancies: type 1 none; type 2 trace] 100 0 months 101 1 month 102 2 months 103 3 months 104 4 month 105 5 months 106 6 months 107 7 months 108 8 months 109 9 months 110 10 months 111 11 months 201 1 year 202 2 years 203 3 years 204 4 years 205 5 years 206 6 years 207 7 years 208 8 years 210 10 years 211 11 years 212 12 years 215 15 years 219 19 years 220 20 years 222 22 years 224 24 years 225 25 years 230 30 years 231 31 years 232 32 years 234 34 years 240 40 years 249 49 years 250 50 years 252 52 years 254 54 years 265 65 years 267 67 years 270 70 years 278 78 years 280 80 years 290 90 years 298 98 years 998 Unknown 999 NIU (not in universe) This variable indicates the age of the third person who died in the household in the past 12 months. IPUMS Sex of fourth deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 4 or more deaths in the last 12 months [discrepancies: none] 1 Male 2 Female 9 NIU (not in universe) This variable indicates the sex of the fourth person who died in the household in the past 12 months. IPUMS Age of fourth deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 4 or more deaths in the last 12 months [discrepancies: none] 103 3 months 104 4 month 105 5 months 108 8 months 201 1 year 202 2 years 203 3 years 204 4 years 205 5 years 208 8 years 212 12 years 218 18 years 224 24 years 230 30 years 999 NIU (not in universe) This variable indicates the age of the fourth person who died in the household in the past 12 months. IPUMS Sex of fifth deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 5 or more deaths in the last 12 months [discrepancies: none] 1 Male 2 Female 9 NIU (not in universe) This variable indicates the sex of the fifth person who died in the household in the past 12 months. IPUMS Age of fifth deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 5 or more deaths in the last 12 months [discrepancies: none] 103 3 months 104 4 month 201 1 year 202 2 years 203 3 years 205 5 years 215 15 years 231 31 years 999 NIU (not in universe) This variable indicates the age of the fifth person who died in the household in the past 12 months. IPUMS Sex of sixth deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 6 or more deaths in the last 12 months [discrepancies: none] 1 Male 2 Female 9 NIU (not in universe) This variable indicates the sex of the sixth person who died in the household in the past 12 months. IPUMS Age of sixth deceased person <svar a="all" v="BF85A032 BF85A033 BF85A034 BF85A035 BF85A036 BF85A037 BF85A038 BF85A039 BF85A040 BF85A041 BF85A042 BF85A043 BF85A044"><span class="h3">Deaths in the last 12 months</span><br /><div class="i1">[] No<br />[] Yes</div><br /><div class="i2">If yes, how many? _</div><br />First and last name of deceased ____<br />Sex of deceased ____<br />Age at death ____<br /></svar> Burkina Faso 1985: Households reporting 6 or more deaths in the last 12 months [discrepancies: none] 201 1 year 202 2 years 229 29 years 999 NIU (not in universe) This variable indicates the age of the sixth person who died in the household in the past 12 months. IPUMS Strata Burkina Faso 1985: 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 5-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 Residence status: de facto, de jure 1 Present resident 2 Absent resident 3 Visitor/non-resident 4 De facto population (present persons) 9 Unknown/missing RESIDENT identifies whether an enumerated person is a household resident or a visitor and whether she or he was present at the time of enumeration. This variable is available only in samples that enumerated both de facto and de jure residents. It can be used to eliminate the double-counting of persons who were enumerated both at their permanent residence and at the residence they were visiting on census night. De jure population: present residents and absent residents. De facto population: present residents and visitors/non-residents. IPUMS Age 000 Less than 1 year 001 1 year 002 2 years 003 3 004 4 005 5 006 6 007 7 008 8 009 9 010 10 011 11 012 12 013 13 014 14 015 15 016 16 017 17 018 18 019 19 020 20 021 21 022 22 023 23 024 24 025 25 026 26 027 27 028 28 029 29 030 30 031 31 032 32 033 33 034 34 035 35 036 36 037 37 038 38 039 39 040 40 041 41 042 42 043 43 044 44 045 45 046 46 047 47 048 48 049 49 050 50 051 51 052 52 053 53 054 54 055 55 056 56 057 57 058 58 059 59 060 60 061 61 062 62 063 63 064 64 065 65 066 66 067 67 068 68 069 69 070 70 071 71 072 72 073 73 074 74 075 75 076 76 077 77 078 78 079 79 080 80 081 81 082 82 083 83 084 84 085 85 086 86 087 87 088 88 089 89 090 90 091 91 092 92 093 93 094 94 095 95 096 96 097 97 098 98 099 99 100 100+ 999 Not reported/missing AGE gives age in years as of the person's last birthday prior to or on the day of enumeration. IPUMS Age, grouped into intervals 01 0 to 4 02 5 to 9 03 10 to 14 04 15 to 19 05 0 to 5 06 6 to 10 07 10 to 15 08 11 to 14 09 15 to 17 10 16 to 19 11 18 to 24 12 20 to 24 13 25 to 29 14 30 to 34 15 35 to 39 16 40 to 44 17 45 to 49 18 50 to 54 19 55 to 59 20 60 to 64 21 65 to 69 22 70 to 74 23 75 to 79 24 80 to 84 25 85+ 98 Unknown AGE2 gives computed years of age grouped into intervals. IPUMS Sex 1 Male 2 Female 9 Unknown SEX reports the sex (gender) of the respondent. IPUMS Marital status [general version] NIU (not in universe) 1 Single/never married 2 Married/in union 3 Separated/divorced/spouse absent 4 Widowed 9 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS Marital status [detailed version] 000 NIU (not in universe) 100 Single/never married 110 Engaged 111 Never married and never cohabited 200 Married or consensual union 210 Married, formally 211 Married, civil 212 Married, religious 213 Married, civil and religious 214 Married, civil or religious 215 Married, traditional/customary 216 Married, monogamous 217 Married, polygamous 219 Married, spouse absent (historical samples) 220 Consensual union 300 Separated/divorced/spouse absent 310 Separated or divorced 320 Separated or annulled 330 Separated 331 Separated legally 332 Separated de facto 333 Separated from marriage 334 Separated from consensual union 335 Separated from consensual union or marriage 340 Annulled 350 Divorced 400 Widowed 410 Widowed or divorced 411 Widowed from consensual union or marriage 412 Widowed from marriage 413 Widowed from consensual union 420 Widowed, divorced, or separated 999 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS Polygamous union 00 NIU (not in universe) 01 No, in monogamous union 02 Yes, in polygamous union 03 Man in polygamous union 04 Polygamous man, 2 wives 05 Polygamous man, 3 or more wives 06 Woman in polygamous union 07 Polygamous marriage, 2 wives 08 Polygamous marriage, 3 or more wives 09 First wife 10 Second wife 11 Third or higher order wife 99 Unknown/missing POLYGAM indicates whether the respondent was in a polygamous union and, in some samples, the number of wives or the rank order of the wife. IPUMS Children ever born 00 No children 01 1 child 02 2 children 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30+ 98 Unknown 99 NIU (not in universe) CHBORN reports the number of children ever born to each woman of whom the question was asked. In most samples, women were to report all live births by all fathers, whether or not the child was still living. IPUMS Children surviving 00 No children 01 1 child 02 2 children 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30+ 98 Unknown 99 NIU (not in universe) CHSURV reports the number of children born to a woman who were still living at the time of the census. IPUMS Number of births last year None 1 1 (1 or more) 2 2 3 3 4 4+ 8 Unknown 9 NIU (not in universe) BIRTHSLYR indicates whether any -- and in most cases how many -- children were born to a woman in the past twelve months. IPUMS Province of birth, Burkina Faso 01 Bam 02 Bazega 03 Bougouriba 04 Boulgou 05 Boulkiemde 06 Comoe 07 Ganzourgou 08 Gnagna 09 Gourma 10 Houet 11 Kadiogo 12 Kenedougou 13 Kossi 14 Kouritenga 15 Mouhoun 16 Nahouri 17 Namentenga 18 Oubritenga 19 Oudalan 20 Passore 21 Poni 22 Sanguie 23 Sanmatenga 24 Seno 25 Sissili 26 Soum 27 Sourou 28 Tapoa 29 Yatenga 30 Zoundweogo 31 Bale 32 Banwa 33 Ioba 34 Komandjoari 35 Kompienga 36 Koulpelogo 37 Kourweogo 38 Leraba 39 Loroum 40 Nayala 41 Noumbiel 42 Tuy 43 Yagha 44 Ziro 45 Zondoma 98 Foreign country 99 Unknown BPLBF indicates the province in Burkina Faso in which the person was born. 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, Burkina Faso 000 NIU (not in universe) 100 No education 200 Primary 201 Primary, year 1 202 Primary, year 2 203 Primary, year 3 204 Primary, year 4 205 Primary, year 5 206 Primary, year 6 207 Primary, year unspecified 300 Secondary 310 Secondary, 1st cycle 311 Secondary, 1st cycle, year 1 312 Secondary, 1st cycle, year 2 313 Secondary, 1st cycle, year 3 314 Secondary, 1st cycle, year 4 315 Secondary, 1st cycle, attending 316 Secondary, 1st cycle, completed 320 Secondary, 2nd cycle 321 Secondary, 2nd cycle, year 5 322 Secondary, 2nd cycle, year 6 323 Secondary, 2nd cycle, year 7 324 Secondary, 2nd cycle, attending 325 Secondary, 2nd cycle, completed 330 Secondary, year unspecified 400 Professional training 410 Post-primary 411 Post-primary professional training, year 1 412 Post-primary professional training, year 2 413 Post-primary professional training, year 3 414 Post-primary professional training, year 4 415 Post-primary professional training, year unspecified 420 Post-BEPC 421 Post-BEPC professional training, year 1 422 Post-BEPC professional training, year 2 423 Post-BEPC professional training, year 3 424 Post-BEPC professional training, year unspecified 500 Higher education 501 Higher education, year 1 502 Higher education, year 2 503 Higher education, year 3 504 Higher education, year 4 505 Higher education, year 5 506 Higher education, year 6 507 Higher education, year 7+ 508 Higher education, year unspecified 509 Higher education, attending 510 Higher education, completed 600 Other 610 Arabic school 611 Arabic school, year 1 612 Arabic school, year 2 613 Arabic school, year 3 614 Arabic school, year 4 615 Arabic school, year 5 616 Arabic school, year 6 617 Arabic school, year unspecified 620 Koranic school 998 Unknown EDUCBF indicates the person's educational attainment in Burkina Faso in terms of the level of schooling completed. IPUMS Activity status (employment status) [general version] NIU (not in universe) 1 Employed 2 Unemployed 3 Inactive 9 Unknown/missing EMPSTAT indicates whether or not the respondent was part of the labor force -- working or seeking work -- over a specified period of time. Depending on the sample, EMPSTAT can also convey further information. The first digit of EMPSTAT is fully comparable, and classifies the population into three groups: employed, unemployed, and inactive. The combination of employed and unemployed yields the total labor force. The second and third digits of EMPSTAT preserve additional information available for some countries and census years but not for others. Employment status is sometimes referred to in other sources as "activity status". IPUMS Activity status (employment status) [detailed version] 000 NIU (not in universe) 100 Employed, not specified 110 At work 111 At work, and 'student' 112 At work, and 'housework' 113 At work, and 'seeking work' 114 At work, and 'retired' 115 At work, and 'no work' 116 At work, and other situation 117 At work, family holding, not specified 118 At work, family holding, not agricultural 119 At work, family holding, agricultural 120 Have job, not at work in reference period 130 Armed forces 131 Armed forces, at work 132 Armed forces, not at work in reference period 133 Military trainee 140 Marginally employed 200 Unemployed, not specified 201 Unemployed 6 or more months 202 Worked fewer than 6 months, permanent job 203 Worked fewer than 6 months, temporary job 210 Unemployed, experienced worker 220 Unemployed, new worker 230 No work available 240 Inactive unemployed 300 Inactive (not in labor force) 310 Housework 320 Unable to work, disabled or health reasons 321 Permanent disability 322 Temporary illness 323 Disabled or imprisoned 330 In school 340 Retirees and living on rent 341 Living on rents 342 Living on rents or pension 343 Retirees/pensioners 344 Retired 345 Pensioner 346 Non-retirement pension 347 Disability pension 348 Retired without benefits 350 Elderly 351 Elderly or disabled 360 Institutionalized 361 Prisoner 370 Intermittent worker 371 Not working, seasonal worker 372 Not working, occasional worker 380 Other income recipient 390 Inactive, other reasons 391 Too young to work 392 Dependent 999 Unknown/missing EMPSTAT indicates whether or not the respondent was part of the labor force -- working or seeking work -- over a specified period of time. Depending on the sample, EMPSTAT can also convey further information. The first digit of EMPSTAT is fully comparable, and classifies the population into three groups: employed, unemployed, and inactive. The combination of employed and unemployed yields the total labor force. The second and third digits of EMPSTAT preserve additional information available for some countries and census years but not for others. Employment status is sometimes referred to in other sources as "activity status". IPUMS Labor force participation 1 No, not in the labor force 2 Yes, in the labor force 8 Unknown 9 NIU (not in universe) LABFORCE is a dichotomous variable identifying whether a person participated in the labor force. Labor force participation generally means working or seeking work within a specified reference period. For most samples LABFORCE is a recode of EMPSTAT (employment status). A consistent lower age universe of 15 or older has been applied to increase comparability across samples. Full detail is retained in EMPSTAT, which should be used for any study of child labor. IPUMS Occupation, ISCO general 01 Legislators, senior officials and managers 02 Professionals 03 Technicians and associate professionals 04 Clerks 05 Service workers and shop and market sales 06 Skilled agricultural and fishery workers 07 Crafts and related trades workers 08 Plant and machine operators and assemblers 09 Elementary occupations 10 Armed forces 11 Other occupations, unspecified or n.e.c. 97 Response suppressed 98 Unknown 99 NIU (not in universe) OCCISCO records the person's primary occupation, coded according to the major categories in the International Standard Classification of Occupations (ISCO) scheme for 1988. For someone with more than one job, the primary occupation is typically the one in which the person had spent the most time or earned the most money. IPUMS Occupation, unrecoded OCC records the person's primary occupation, classified according to the system used by the respective national census office at the time. For someone with more than one job, the primary occupation is usually the one in which the person spent the most time or earned the most money, although this may not have been explicit in the instructions for a specific census. To ensure confidentiality, very small occupations are recoded to a residual category indicating the persons had an occupation, but the job title is not identified. The number of cases recoded should be too small to affect analyses. OCC is a 4-digit numeric variable. Some samples use fewer than 4 digits. In those cases, the data are right-justified, and the extra leading digits are padded with zeroes. CodesArgentina 1970 - Spanish Argentina 1980 - Spanish Argentina 1991 - Spanish Argentina 2001 - Spanish Armenia 2011 Austria 1971-2001 - German Belarus 1999 - Russian Belarus 2009 Benin 1979 Benin 1992 Benin 2002 Benin 2013 Bolivia 1976 Bolivia 1992 Bolivia 2001 Bolivia 2012 Botswana 1981 Botswana 1991 Botswana 2001 Botswana 2011 Brazil 1960 - Portuguese Brazil 1970 - Portuguese Brazil 1980 - Portuguese Brazil 1991 - Portuguese Brazil 2000 - Portuguese Brazil 2010 Burkina Faso 1985 Burkina Faso 1996 Cambodia 1998 Cambodia 2004 Cambodia 2008 Cambodia 2013 Cambodia 2019 Cameroon 1976 Cameroon 2005 Canada 1971 Canada 1981-1991 Canada 2001 Canada 2011 Chile 1960 Chile 1970 Chile 1982 Chile 1992 Chile 2002 China 1982 China 1990 China 2000 Colombia 1964 Colombia 1973 - Spanish Costa Rica 1973 Costa Rica 1984 Costa Rica 2000 Costa Rica 2011 Cote d'Ivoire 1988 Cote d'Ivoire 1998 Cuba 2002 Cuba 2012 Denmark 1845 Denmark 1880 Denmark 1885 Dominican Republic 1960 Dominican Republic 1970 Dominican Republic 1981 Dominican Republic 2002 Dominican Republic 2010 Ecuador 1962 Ecuador 1974 Ecuador 1982 Ecuador 1990 Ecuador 2001 Ecuador 2010 Egypt 1986 Egypt 2006 El Salvador 1992 El Salvador 2007 Ethiopia 1984 Ethiopia 1994 Fiji 1976 Fiji 1986 Fiji 1996 Fiji 2007 Fiji 2014 Finland 2010 France 1962-1990 - French France 1999 France 2006 France 2011 Germany 1970 Germany 1981 Germany 1987 Ghana 1984 Ghana 2000 Ghana 2010 Greece 1971-1991 - Greek Greece 2001 - Greek Greece 2011 Guatemala 1964 Guatemala 1973 Guatemala 1981 Guatemala 1994 Guatemala 2002 Guinea 1983 Guinea 1996 Guinea 2014 Haiti 1982 Haiti 2003 Honduras 1961 Honduras 1974 Honduras 1988 Honduras 2001 Hungary 1970-1990 Hungary 2001 Hungary 2011 India 1983-2004 India 2009 Indonesia 1971 Indonesia 1976 Indonesia 1980 Indonesia 1985 Indonesia 1990 Indonesia 1995 Indonesia 2005 Iran 2006 Iran 2011 Iraq 1997 Ireland 1901 Ireland 1911 Ireland 1971 Ireland 1981 Ireland 1986 Ireland 1991 Ireland 1996 Ireland 2002 Ireland 2006 Ireland 2011 Ireland 2016 Israel 1972 Israel 1983 Israel 1995 Israel 2008 Italy 2001 Italy 2011 Italy Surveys 2011-2020 Jamaica 1982 Jamaica 1991 Jamaica 2001 Jordan 2004 Kenya 1989 Kyrgyz Republic 1999 Laos 1995 Lesotho 1996 Lesotho 2006 Liberia 1974 Liberia 2008 Malawi 1987 Malawi 1998 Malawi 2008 Malaysia 1970 Malaysia 1980-1991 Malaysia 2000 Mali 1987 Mali 1998 Mali 2009 Mauritius 1990 Mauritius 2000 Mauritius 2011 Mexico 1960 - Spanish Mexico 1970 - Spanish Mexico 1990 - Spanish Mexico 1995 - Spanish Mexico 2000 - Spanish Mexico 2010 Mexico 2015 Mexico 2020 Mexico Surveys 2005-2020 Mongolia 2000 Morocco 1982 Morocco 1994 Morocco 2004 Morocco 2014 Mozambique 1997 Mozambique 2007 Myanmar 2014 Nepal 2001 Nepal 2011 Netherlands 1960 Netherlands 1971 Netherlands 2001 Netherlands 2011 Nicaragua 1971 Nicaragua 1995 Nicaragua 2005 Nigeria 2008 Nigeria 2009 Nigeria 2010 Pakistan 1973 Palestine 1997 Palestine 2007 Palestine 2017 Panama 1960 - Spanish Panama 1970 - Spanish Panama 1980 - Spanish Panama 1990 - Spanish Panama 2000 - Spanish Panama 2010 Papua New Guinea 1980 Papua New Guinea 1990 Papua New Guinea 2000 Paraguay 1962 Paraguay 1972 Paraguay 1982 Paraguay 1992 Paraguay 2002 Peru 1993 Peru 2007 Peru 2017 Philippines 1990 Philippines 2000 Philippines 2010 Poland 1978 Poland 1988 Poland 2002 Portugal 1981 - Portuguese Portugal 1991 - Portuguese Portugal 2001 - Portuguese Portugal 2011 Puerto Rico 1970 Puerto Rico 1980 Puerto Rico 1990 Puerto Rico 2000-2005 Puerto Rico 2010 Puerto Rico 2015 Puerto Rico 2020 Romania 1977 Romania 1992 Romania 2002 Romania 2011 Rwanda 2002 - French Rwanda 2012 Saint Lucia 1991 Senegal 1988 Senegal 2002 Senegal 2013 Slovak Republic 1991 Slovak Republic 2001 Slovak Republic 2011 Sierra Leone 2004 Sierra Leone 2015 Slovenia 2002 South Africa 1996 South Africa 2001 South Africa 2007 South Sudan 2008 Spain 1981 - Spanish Spain 1991 - Spanish Spain 2001 - Spanish Spain 2011 Spain Surveys 2005-2020 Sudan 2008 Suriname 2004 Suriname 2012 Switzerland 1970 Switzerland 1980 Switzerland 1990 Switzerland 2000 Switzerland 2011 Tanzania 1988 Tanzania 2002 Tanzania 2012 Thailand 1970 Thailand 1980 Thailand 1990 Thailand 2000 Togo 1960 Togo 1970 Togo 2010 Trinidad and Tobago 1990 Trinidad and Tobago 2000 Trinidad and Tobago 2011 Turkey 1985 Turkey 1990 Turkey 2000 Uganda 1991 Uganda 2002 Uganda 2014 United Kingdom 1961 United Kingdom 1971 United Kingdom 1991 United Kingdom 2001 United States 1960 United States 1970 United States 1980 United States 1990 United States 2000-2005 United States 2010 United States 2015 United States 2020 Uruguay 1963 Uruguay 1975 Uruguay 1996 Uruguay 2006 Venezuela 1981 Venezuela 1990 Venezuela 2001 - Spanish Vietnam 1989 Vietnam 1999 Vietnam 2009 Vietnam 2019 Zambia 1990 Zambia 2000 Zambia 2010 Zimbabwe 2012 IPUMS Status in employment (class of worker) [general version] NIU (not in universe) 1 Self-employed 2 Wage/salary worker 3 Unpaid worker 4 Other 9 Unknown/missing CLASSWK refers to the status of an economically active person with respect to his or her employment -- that is, the type of explicit or implicit contract of employment with other persons or organizations that the person has in his/her job. In general, the variable indicates whether a person was self-employed, or worked for someone else, either for pay or as an unpaid family worker. CLASSWK is related to EMPSTAT, which is used to define the universe in many samples. Class of worker is often referred to as "status in employment" in other sources. IPUMS Status in employment (class of worker) [detailed version] 000 NIU (not in universe) 100 Self-employed 101 Self-employed, unincorporated 102 Self-employed, incorporated 110 Employer 111 Sharecropper, employer 120 Working on own account 121 Own account, agriculture 122 Domestic worker, self-employed 123 Subsistence worker, own consumption 124 Own account, other 125 Own account, without temporary/unpaid help 126 Own account, with temporary/unpaid help 130 Member of cooperative 140 Sharecropper 141 Sharecropper, self-employed 142 Sharecropper, employee 150 Kibbutz member 199 Self-employed, not specified 200 Wage/salary worker 201 Management 202 Non-management 203 White collar (non-manual) 204 Blue collar (manual) 205 White or blue collar 206 Day laborer 207 Employee, with a permanent job 208 Employee, occasional, temporary, contract 209 Employee without legal contract 210 Wage/salary worker, private employer 211 Apprentice 212 Religious worker 213 Wage/salary worker, non-profit, NGO 214 White collar, private 215 Blue collar, private 216 Paid family worker 217 Cooperative employee 220 Wage/salary worker, government 221 Federal, government employee 222 State government employee 223 Local government employee 224 White collar, public 225 Blue collar, public 226 Public companies 227 Civil servants, local collectives 230 Domestic worker (work for private household) 240 Seasonal migrant 241 Seasonal migrant, no broker 242 Seasonal migrant, uses broker 250 Other wage and salary 251 Canal zone/commission employee 252 Government employment/training program 253 Mixed state/private enterprise/parastatal 254 Government public work program 255 State enterprise employee 256 Coordinated and continuous collaboration job 300 Unpaid worker 310 Unpaid family worker 320 Apprentice, unpaid or unspecified 330 Trainee 340 Apprentice or trainee 350 Works for others without wage 400 Other 999 Unknown/missing CLASSWK refers to the status of an economically active person with respect to his or her employment -- that is, the type of explicit or implicit contract of employment with other persons or organizations that the person has in his/her job. In general, the variable indicates whether a person was self-employed, or worked for someone else, either for pay or as an unpaid family worker. CLASSWK is related to EMPSTAT, which is used to define the universe in many samples. Class of worker is often referred to as "status in employment" in other sources. IPUMS Has a secondary or additional jobs 1 No, only 1 job 2 Yes, has 2+ jobs 8 Unknown 9 NIU (not in universe) WRKADD indicates whether the respondent has more than one job. IPUMS Migration status, 1 year 00 NIU (not in universe) 10 Same major administrative unit 11 Same major, same minor administrative unit 12 Same major, different minor administrative unit 20 Different major administrative unit 30 Abroad 99 Unknown/missing MIGRATE1 indicates the person's place of residence 1 year ago. The first digit records movement across major administrative divisions and countries; the second digit reports movement across minor administrative divisions. IPUMS Region of residence 1 year ago, Burkina Faso 1985; GIS 01 Centre-Nord 03 Sud-Ouest 04 Centre-Est 05 Centre-Ouest 08 Est 10 Hauts-Bassins 11 Centre 13 Volta-Naire 19 Sahel 20 Nord 97 Abroad 98 Unknown 99 NIU (not in universe) MIG1ALT_1_BF indicates the person's region of residence in Burkina Faso 1 year ago in 1985. Migration region codes from MIG1ALT_1_BF are compatible with codes from GEO1_BF1985. Click on the Source Variables tab for information on place of residence for each sample year. Source variables may contain more geographic unit detail but are not suitable for cross-temporal comparison. IPUMS Province of residence 1 year ago, Burkina Faso 1985; GIS 000001001 Bam 000001016 Namentenga 000001023 Sanmatenga 000003003 Bougouriba 000003021 Poni 000004004 Boulgou 000004014 Kouritenga 000005005 Boulkiemdé 000005020 Passoré 000005022 Sanguié 000005025 Sissili 000008008 Gnagna 000008009 Gourma 000008028 Tapoa 000010006 Comoé 000010010 Houet 000010012 Kénédougou 000011002 Bazèga 000011007 Ganzourgou 000011011 Kadiogo 000011017 Nahouri 000011018 Oubritenga 000011030 Zoundwéogo 000013013 Kossi 000013015 Mouhoun 000013027 Sourou 000019019 Oudalan 000019024 Séno 000019026 Soum 000020029 Yatenga 000097997 Abroad 000098998 Unknown 000099999 NIU (not in universe) MIG2ALT_1_BF indicates the person's province of residence in Burkina Faso 1 year ago in 1985. Migration province codes from MIG2ALT_1_BF are compatible with codes from GEO2_BF1985. Click on the Source Variables tab for information on place of residence for each sample year. Source variables may contain more geographic unit detail but are not suitable for cross-temporal comparison. IPUMS Disability status NIU (not in universe) 1 Yes, disabled 2 No, not disabled 9 Unknown DISABLED indicates whether the person reported a disability of any kind. IPUMS Employment disability 1 Disabled 2 Not disabled 8 Unknown 9 NIU (not in universe) DISEMP indicates if the respondent was economically inactive because of disabilities or, in some instances, other health-related reasons. IPUMS Blind or vision-impaired NIU (not in universe) 1 Yes 2 No 9 Unknown DISBLND indicates whether the person was blind or had limited vision. IPUMS Deaf or hearing-impaired NIU (not in universe) 1 Yes 2 No 9 Unknown DISDEAF indicates whether the person was deaf or had limited hearing. IPUMS Mute or speech impaired NIU (not in universe) 1 Yes 2 No 9 Unknown DISMUTE indicates if the person could not speak or had a significant speech impediment. IPUMS Disability affecting upper extremities NIU (not in universe) 1 Yes 2 No 9 Unknown DISUPPR indicates whether the person lacked full use of at least one arm or hand. IPUMS Sex <svar v="BF85A400 BF85A401 BF85A402 BF85A403 BF85A404 405"><span class="h3">All persons</span><br />[Questions 1-8 were asked for all persons.]<br /></svar></p> <p><svar a="all" v="BF85A401">3. Sex _<br /></svar> Burkina Faso 1985: All persons 1 Male 2 Female This variable indicates the person's sex. IPUMS Residential status <svar v="BF85A400 BF85A401 BF85A402 BF85A403 BF85A404 405"><span class="h3">All persons</span><br />[Questions 1-8 were asked for all persons.]<br /></svar></p> <p><svar a="all" v="BF85A402">5. Residential status ____<br /></svar> Burkina Faso 1985: All persons 1 Present resident 2 Absent resident 3 Visitor 4 Emigrant This variable indicates the person's residential status in the household. IPUMS Age <svar v="BF85A400 BF85A401 BF85A402 BF85A403 BF85A404 405"><span class="h3">All persons</span><br />[Questions 1-8 were asked for all persons.]<br /></svar></p> <p><svar a="all" v="BF85A403">6. Age or date of birth<br /><div class="i1">____ Age in months or years<br />____ Date of birth</div><br /></svar> Burkina Faso 1985: All persons 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+ 999 Unknown This variable indicates the person's age. IPUMS Age in months Burkina Faso 1985: All persons 100 0 months 101 1 month 102 2 months 103 3 months 104 4 month 105 5 months 106 6 months 107 7 months 108 8 months 109 9 months 110 10 months 111 11 months 199 Less than 1 year, unknown number of months 200 1 year or older 999 Unknown This variable indicates the person's age in months. IPUMS Province or country of birth <svar v="BF85A400 BF85A401 BF85A402 BF85A403 BF85A404 405"><span class="h3">All persons</span><br />[Questions 1-8 were asked for all persons.]<br /></svar></p> <p><svar a="all" v="BF85A404">7. Place of birth ____<br /></svar> Burkina Faso 1985: All persons 01 Bam 02 Bazega 03 Bougouriba 04 Boulgou 05 Boulkiemde 06 Comoe 07 Ganzourgou 08 Gnagna 09 Gourma 10 Houet 11 Kadiogo 12 Kenedougou 13 Kossi 14 Kouritenga 15 Mouhoun 16 Namentenga 17 Naouri 18 Oubritenga 19 Oudalan 20 Passore 21 Poni 22 Sanguie 23 Sanmatenga 24 Seno 25 Sissili 26 Soum 27 Sourou 28 Tapoa 29 Yatenga 30 Zoundweogo 97 Other countries This variable indicates the person's place of birth. IPUMS Province or country of residence in 1984 <svar v="BF85A400 BF85A401 BF85A402 BF85A403 BF85A404 405"><span class="h3">All persons</span><br />[Questions 1-8 were asked for all persons.]<br /></svar></p> <p><svar a="all" v="BF85A405">8. Place of residence one year ago ____<br /></svar> Burkina Faso 1985: Persons age 1+ [discrepancies: type 1 none; type 2 0.1%] 01 Bam 02 Bazega 03 Bougouriba 04 Boulgou 05 Boulkiemde 06 Comoe 07 Ganzourgou 08 Gnagna 09 Gourma 10 Houet 11 Kadiogo 12 Kenedougou 13 Kossi 14 Kouritenga 15 Mouhoun 16 Namentenga 17 Naouri 18 Oubritenga 19 Oudalan 20 Passore 21 Poni 22 Sanguie 23 Sanmatenga 24 Seno 25 Sissili 26 Soum 27 Sourou 28 Tapoa 29 Yatenga 30 Zoundweogo 97 Other countries 98 Unknown 99 NIU (not in universe) This variable indicates the person's place of residence in 1984. IPUMS Literacy (reading and writing) <svar v="BF85A406 BF85A407 BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="h3">Residents 10 years of age and older</span><br />[Questions 9-15 were asked for residents age 10 and older.]<br /></svar></p> <p><svar a="all" v="BF85A406">9. Knows how to read and write (in which language) ____<br /></svar> Burkina Faso 1985: Residents age 10+ [discrepancies: type 1 none; type 2 0.2%] 00 None 01 French 02 Arabic 03 English 04 Moore 05 Dioula 06 Fulfulde 07 Other national language 08 Other foreign language 98 Unknown 99 NIU (not in universe) This variable indicates which language the person is literate in (reading and writing). IPUMS Level of schooling <svar v="BF85A406 BF85A407 BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="h3">Residents 10 years of age and older</span><br />[Questions 9-15 were asked for residents age 10 and older.]<br /></svar></p> <p><svar a="all" v="BF85A407">10. Level of schooling ____<br /></svar> Burkina Faso 1985: Residents age 10+ [discrepancies: type 1 none; type 2 0.3%] 00 None 11 Primary, year 1 (CP1) 12 Primary, year 2 (CP2) 13 Primary, year 3 (CE1) 14 Primary, year 4 (CE2) 15 Primary, year 5 (CM1) 16 Primary, year 6 (CM2) 19 Primary, not otherwise specified 21 Secondary, year 1 22 Secondary, year 2 23 Secondary, year 3 24 Secondary, year 4 25 Secondary, year 5 26 Secondary, year 6 27 Secondary, year 7 (terminale) 29 Secondary, not otherwise specified 31 Higher, year 1 32 Higher, year 2 33 Higher, year 3 34 Higher, year 4 35 Higher, year 5 36 Higher, year 6 37 Higher, year 7 and above 39 Higher not specified 41 Post primary professional training, year 1 42 Post primary professional training, year 2 43 Post primary professional training, year 3 44 Post primary professional training, year 4 49 Post primary professional training, not otherwise specified 51 Post BEPC professional training, year 1 52 Post BEPC professional training, year 2 53 Post BEPC professional training, year 3 59 Post BEPC professional training, not otherwise specified 61 Arabic school, year 1 62 Arabic school, year 2 63 Arabic school, year 3 64 Arabic school, year 4 65 Arabic school, year 5 66 Arabic school, year 6 69 Arabic school, not otherwise specified 70 Koranic school 98 Unknown 99 NIU (not in universe) This variable indicates the person's level of schooling. IPUMS Employment status <svar v="BF85A406 BF85A407 BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="h3">Residents 10 years of age and older</span><br />[Questions 9-15 were asked for residents age 10 and older.]<br /></svar></p> <p><svar v="BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="em">Economic activity</span><br /></svar></p> <p><svar a="all" v="BF85A408">11. Employment status ____<br /></svar> Burkina Faso 1985: Residents age 10+ [discrepancies: type 1 none; type 2 0.3%] NIU (not in universe) 1 Employed 2 Unemployed 3 Looking for first job 4 Homemaker 5 Student 6 Retired/pensioner 7 Invalid 8 Other 9 Unknown This variable indicates the person's employment status. IPUMS Principal occupation <svar v="BF85A406 BF85A407 BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="h3">Residents 10 years of age and older</span><br />[Questions 9-15 were asked for residents age 10 and older.]<br /></svar></p> <p><svar v="BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="em">Economic activity</span><br /></svar></p> <p><svar a="all" v="BF85A409">12. Principal occupation ____<br /></svar> Burkina Faso 1985: Residents age 10+ who are employed or unemployed [discrepancies: type 1 none; type 2 0.1%] 001 Farmer 002 Greengrocer 003 Gardener/farmer 004 Orchard or tree nursery operator 005 Lumberjack 006 Livestock farmer (cattle) 007 Poultry farmer 008 Shepherd 009 Fisherman 010 Hunter 011 Other occupations in this group, not otherwise classified 109 Other in senior level of land administration, work administration or social security 110 Teachers at universities 111 Attache of university stewardship 112 Sociologist, anthropologist, psychologist, ethnologist 113 Researcher (Research Associate, Fellow, Senior Research Fellow) 114 Secondary school teacher (1st cycle) 115 Secondary school teacher (2nd cycle) 116 Headmaster 117 Critic (?) 118 Inspector of education (primary or secondary) 120 Medical doctor 121 Physician specialist: gynecologist, pediatrician, dentist, opthamologist 122 Pharmacist, pharmacist helper 123 Hospital adminstrator 124 Senior level lab technician 125 Sanitary engineer 126 Senior orthopedist 127 Senior level in social affairs 130 Pilot and engineer of air navigation 131 Architect or planner 132 Public works inspector 133 Upper level meterology 134 Upper level roads and bridges surveyor 135 Rural engineer, hydraulic engineer 136 Executive of P and T, P and T enginner, director of P and T 137 Senior technician and rural equipment 138 Senior level telecommunication, not otherwise specified 140 Senior economic affairs 141 Banker 142 Senior executives of finance, treasurey, tax, customs 143 Director of tax services, financial services 144 Planner 145 Statistical engineer 146 Senior statistician, economist, demographer 147 IT 150 Senior level agriculture 151 Senior level water and forestry 152 Senior level livestock 154 Engineer and consultant FJA 155 Veterinarian 156 Senior level geology and mines 157 Geology and mines technician 170 Administrator of cultural affairs 171 Senior level tourism and hospitality services 172 Senior level documentation 175 Teacher of youth and sports (?) 177 Other senior level 200 Executive secretary 201 Administrative secretary 202 Office manager (?) 210 Schoolmaster 211 Assistant to schoolmaster 212 Assistant FJA 213 Superintendent of schools and colleges 214 Sports instructor 220 State nurse specialist 221 State midwife 222 Health assistant 223 Sanitation assistant or technician 225 Laboratory assistant 226 Hospital manager 227 Walfare or social educator 228 Nursery worker, pediatric nurse 229 Other midlevel 230 TP driver 231 Chief engineer or site manager 232 Technical assistant TP 233 Controller , receiver of P and T 234 Assistant meteorologist and navigation 235 Rural engineering technician 236 Overseer 240 Midlevel officer in budget or financial resources 241 Accountant, collector 242 Assistant in economic affairs 243 Technical assistant in statistics 250 Conducting agricultural work 251 Technical officer of specialized agriculture 252 Comptroller of water affairs and forestry 253 Assistant and livestock specialist 254 Geology technician 260 Clerk, agent of justice 261 Secretary of foreign affairs 262 Supervisor of information 263 Reporter, radio or TV host 300 Framer of CRPA 301 Instructor and trainer FJA 302 Employee and agent of waters and forests 303 Technical officer of agriculture and livestock 304 Veterinary nurse 305 Minor 306 Well driller 310 Bookkeeper 311 Cashier 312 Junior officer of budget and financial resources 313 Office agent, recovery, tax services, typist 314 Tax collector orderly, liaison, administrative clerk 315 Receptionist 316 Bookbinder, imager 317 Junior officer of economic affiars, economic affairs officer 318 Deputy administration, customs officer, economic control 319 Agent or technical assistant of statistics 320 Machine operator of TP 321 Designer 322 Operator, topographer 323 Railwayman 324 Operator of rural engineering 325 Supervisor of geology and mines 326 Junior officer of meterological transmissions 327 Weather forecasting assistant 328 Junior staff of P and T 329 Telecommunications monitoring 330 Boy or girl of the room 331 Mobile health officer 332 Tablet dispenser 333 Nurse, other medical caregiver 334 Midwife, village midwife, auxiliary midwife 335 Lab assistant 336 Sanitation/hygiene agent 337 Other health personnel 340 Operator 341 Laborer 342 Storekeeper 343 Auditor 400 Trader 401 Shopkeeper 402 Seller of fabric and thrift 403 Fruit vendor 404 Seller of donuts, cakes, candy etc. 405 Seller of vegetables, peanuts and other condiments 406 Help to a seller 407 Seller 408 Commercial counsellor 409 Bookseller 410 Traditional beer seller 411 Butcher 412 Miller 413 Other commerce trades 500 Weaver or knitter 501 Smith 502 Carpenter 503 Dyer 504 Potter 505 Basket-maker 506 Cobbler/tanner 507 Mason 508 Beer-maker (?) 509 Cotton spinner 510 Jeweler 511 Sculptor 512 Painter 520 Engineer 521 Mechanic 522 Electrician 523 Watch and clock repair 524 Radio and TV repair 525 Plumber 526 Photographer 527 Tailor 528 Sheet or scrap metal worker 529 Upholsterer, matress maker 600 Boy - cook, housekeeper 601 Server in restaurant or hotel 602 Server in bar 603 Night guard 604 Bartender 605 Taxi driver, bus driver, other dirver 606 Bleaching and ironing 607 Hairdresser 608 Shoe shiner 609 Cleaner 610 Hotel manager 611 Contractor 612 Well-digger 613 Baker 800 Christian religious staff: pastor, biship, nun/monk, catechist 801 Muslim religious staff: imam, marabout, Koranic master 802 Religious staff of other faiths 803 Traditional healer 998 Unknown 999 NIU (not in universe) This variable indicates the person's principal occupation. IPUMS Situation of principal occupation <svar v="BF85A406 BF85A407 BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="h3">Residents 10 years of age and older</span><br />[Questions 9-15 were asked for residents age 10 and older.]<br /></svar></p> <p><svar v="BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="em">Economic activity</span><br /></svar></p> <p><svar a="all" v="BF85A410">13. Employment situation in principal occupation ____<br /></svar> Burkina Faso 1985: Residents age 10+ who are employed or unemployed [discrepancies: type 1 none; type 2 0.1%] 1 Employer 2 Independent (self-employed) 3 Salaried 4 Apprentice 5 Family help 6 Other 8 Unknown 9 NIU (not in universe) This variable indicates the person's employment situation at their principal occupation. IPUMS Secondary occupation <svar v="BF85A406 BF85A407 BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="h3">Residents 10 years of age and older</span><br />[Questions 9-15 were asked for residents age 10 and older.]<br /></svar></p> <p><svar v="BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="em">Economic activity</span><br /></svar></p> <p><svar a="all" v="BF85A411">14. Secondary occupation ____<br /></svar> Burkina Faso 1985: Residents age 10+ who are employed or unemployed, who have a second occupation [unverifiable] 001 Farmer 002 Greengrocer 003 Gardener/farmer 004 Orchard or tree nursery operator 005 lumberjack 006 Livestock farmer (cattle) 007 Poultry farmer 008 Shepherd 009 Fisherman 010 Hunter 011 Other occupations in this group, not otherwise classified 109 Other in senior level of land administration, work administration or social security 110 Teachers at universities 111 Attache of university stewardship 112 Sociologist, anthropologist, psychologist, ethnologist 113 Researcher (Research Associate, Fellow, Senior Research Fellow) 124 Senior level lab technician 132 Public works inspector 178 [no label] 200 Executive secretary 201 Administrative secretary 202 Office manager (?) 210 Schoolmaster 220 State nurse specialist 221 State midwife 222 Health assistant 227 Walfare or social educator 229 Other midlevel 230 TP driver 231 Chief engineer or site manager 241 Accountant, collector 250 Conducting agricultural work 263 Reporter, radio or TV host 300 Framer of CRPA 301 Instructor and trainer FJA 302 Employee and agent of waters and forests 303 Technical officer of agriculture and livestock 304 Veterinary nurse 305 Minor 306 Well driller 310 Bookkeeper 311 Cashier 313 Office agent, recovery, tax services, typist 314 Tax collector orderly, liaison, administrative clerk 315 Receptionist 316 Bookbinder, imager 319 Agent or technical assistant of statistics 320 Machine operator of TP 321 Designer 323 Railwayman 325 Supervisor of geology and mines 328 Junior staff of P and T 330 Boy or girl of the room 331 Mobile health officer 332 Tablet dispenser 333 Nurse, other medical caregiver 334 Midwife, village midwife, auxiliary midwife 337 Other health personnel 340 Operator 341 Laborer 342 Storekeeper 343 Auditor 400 Trader 401 Shopkeeper 402 Seller of fabric and thrift 403 Fruit vendor 404 Seller of donuts, cakes, candy etc. 405 Seller of vegetables, peanuts and other condiments 406 Help to a seller 407 Seller 408 Commercial counsellor 409 Bookseller 410 Traditional beer seller 411 Butcher 412 Miller 413 Other commerce trades 414 Commerce not otherwise specified 500 Weaver or knitter 501 Smith 502 Carpenter 503 Dyer 504 Potter 505 Basket-maker 506 Cobbler/tanner 507 Mason 508 Beer-maker (?) 509 Cotton spinner 510 Jeweler 511 Sculptor 512 Painter 520 Engineer 521 Mechanic 522 Electrician 523 Watch and clock repair 524 Radio and TV repair 525 Plumber 526 Photographer 527 Tailor 528 Sheet or scrap metal worker 529 Upholsterer, matress maker 600 Boy - cook, housekeeper 601 Server in restaurant or hotel 602 Server in bar 603 Night guard 604 Bartender 605 Taxi driver, bus driver, other dirver 606 Bleaching and ironing 607 Hairdresser 608 Shoe shiner 609 Cleaner 610 Hotel manager 612 Well-digger 613 Baker 615 Domestic services not otherwise specified 800 Christian religious staff: pastor, biship, nun/monk, catechist 801 Muslim religious staff: imam, marabout, Koranic master 802 Religious staff of other faiths 803 Traditional healer 998 Unknown 999 NIU (not in universe) This variable indicates the person's secondary occupation. IPUMS Situation of secondary occupation <svar v="BF85A406 BF85A407 BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="h3">Residents 10 years of age and older</span><br />[Questions 9-15 were asked for residents age 10 and older.]<br /></svar></p> <p><svar v="BF85A408 BF85A409 BF85A410 BF85A411 BF85A412"><span class="em">Economic activity</span><br /></svar></p> <p><svar a="all" v="BF85A412">15. Employment situation in secondary occupation ____<br /></svar> Burkina Faso 1985: Residents age 10+ who are employed or unemployed and have a secondary occupation [unverifiable] 1 Employer 2 Independent (self-employed) 3 Salaried 4 Apprentice 5 Family help 6 Other 9 NIU (not in universe) This variable indicates the person's employment situation in their secondary occupation. IPUMS Marital status <svar v="BF85A413 BF85A414 BF85A415 BF85A416 BF85A417"><span class="h3">Residents 12 years of age and older</span><br /> [Questions 16-20 were asked for residents age 12 and older.]<br /></svar></p> <p><svar a="all" v="BF85A413">16. Marital status ____<br /></svar> Burkina Faso 1985: Residents age 12+ [discrepancies: type 1 none; type 2 0.3%] 00 Single 01 Married to one person (for males) 02 Married to 2 people (for males) 03 Married to 3 people (for males) 04 Married to 4+ people (for males) 05 In monogamous marriage (for females) 06 In polygamous marriage (for females) 07 Widow(er) 08 Divorced or separated 98 Unknown 99 NIU (not in universe) This variable indicates the person's marital status. IPUMS Number of children born alive <svar v="BF85A413 BF85A414 BF85A415 BF85A416 BF85A417"><span class="h3">Residents 12 years of age and older</span><br /> [Questions 16-20 were asked for residents age 12 and older.]<br /></svar></p> <p><svar v="BF85A414 BF85A415 BF85A416 BF85A417"><span class="em">Women only</span><br /> [Questions 17-20 were asked for females age 12 and older.]<br /></svar></p> <p><svar a="all" v="BF85A414">17. Total number of children born alive ____<br /></svar> Burkina Faso 1985: Females age 12+ [discrepancies: none] 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 99 NIU (not in universe) This variable indicates the number of children born alive to a woman. IPUMS Number of children still alive <svar v="BF85A413 BF85A414 BF85A415 BF85A416 BF85A417"><span class="h3">Residents 12 years of age and older</span><br /> [Questions 16-20 were asked for residents age 12 and older.]<br /></svar></p> <p><svar v="BF85A414 BF85A415 BF85A416 BF85A417"><span class="em">Women only</span><br /> [Questions 17-20 were asked for females age 12 and older.]<br /></svar></p> <p><svar a="all" v="BF85A415">18. Total number of children still alive ____<br /></svar> Burkina Faso 1985: Females age 12+ [discrepancies: none] 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 99 NIU (not in universe) This variable indicates the number of children born alive to a woman that are still alive. IPUMS Number of male births in last 12 months <svar v="BF85A413 BF85A414 BF85A415 BF85A416 BF85A417"><span class="h3">Residents 12 years of age and older</span><br /> [Questions 16-20 were asked for residents age 12 and older.]<br /></svar></p> <p><svar v="BF85A414 BF85A415 BF85A416 BF85A417"><span class="em">Women only</span><br /> [Questions 17-20 were asked for females age 12 and older.]<br /></svar></p> <p><svar a="all" v="BF85A416 BF85A417">Births in the last 12 months<br /><div class="i1">19. Male _ _<br />20. Female _ _</div><br /></svar> Burkina Faso 1985: Females age 12+ [discrepancie: type 1 none; type 2 trace] 1 1 2 2 3 3 4 4 8 Unknown 9 NIU (not in universe) This variable indicates the number of male births a woman has had in the last 12 months. IPUMS Number of female births in last 12 months <svar v="BF85A413 BF85A414 BF85A415 BF85A416 BF85A417"><span class="h3">Residents 12 years of age and older</span><br /> [Questions 16-20 were asked for residents age 12 and older.]<br /></svar></p> <p><svar v="BF85A414 BF85A415 BF85A416 BF85A417"><span class="em">Women only</span><br /> [Questions 17-20 were asked for females age 12 and older.]<br /></svar></p> <p><svar a="all" v="BF85A416 BF85A417">Births in the last 12 months<br /><div class="i1">19. Male _ _<br />20. Female _ _</div><br /></svar> Burkina Faso 1985: Females age 12+ [discrepancie: type 1 trace; type 2 trace] 1 1 2 2 3 3 4 4 8 Unknown 9 NIU (not in universe) This variable indicates the number of female births a woman has had in the last 12 months. IPUMS Handicap <svar a="all" v="BF85A418"><span class="em">Handicap</span><br />[Question 21 was asked of all persons.]<br />21. Type of handicap ____<br /></svar> Burkina Faso 1985: Residents of the household [dicrepancies: type 1 none; type 2 0.1%] 00 None 01 Blind 02 Deaf-mute, deaf, mute 03 Paralyzed 04 Insane, mentally ill, mentally disabled 05 One-armed 06 Amputee 07 Leper 08 Other 98 Unknown 99 NIU (not in universe) This variable indicates the person's type of handicap. IPUMS