DDI_KEN_1989_PHC_v01_M_v7.5_A_IPUMS 1989 Population and Housing census 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. 1989 Population and Housing census - IPUMS Subset PHC Kenya 1989 (IPUMS Harmonized Subset) KEN_1989_PHC_v01_M_v7.5_A_IPUMS Central Bureau of Statistics Ministry of Finance and Planning IPUMS (c) Copyright 1989, Central Bureau of Statistics Ministry of Finance and Planning and Minnesota Population Center MetadataEditor Central Bureau of Statistics Ministry of Finance and Planning Population and Housing Census [hh/popcen] IPUMS International DOI:10.18128/D020.V7.5 Version 7.5. The datasets contain selected variables from the original census microdata plus harmonized variables from the IPUMS-International database. Demographic Variables -- PERSON Geography: Global Variables -- HOUSEHOLD Fertility and Mortality Variables -- PERSON Nativity and Birthplace Variables -- PERSON Technical Household Variables -- HOUSEHOLD Geography: IPUMS-I, IPUMS-DHS Variables -- HOUSEHOLD Disability Variables -- PERSON Education Variables -- PERSON Constructed Family Interrelationship Variables -- PERSON Utilities Variables -- HOUSEHOLD Work Variables -- PERSON Dwelling Characteristics Variables -- HOUSEHOLD Geography: F-N Variables -- HOUSEHOLD Group Quarters Variables -- HOUSEHOLD Constructed Household Variables -- HOUSEHOLD Migration: F-N Variables -- PERSON Migration: Global Variables -- PERSON Household Economic Variables -- HOUSEHOLD Technical Person Variables -- PERSON Constructed Family Interrelationship Variables -- PERSON Education Variables -- PERSON Technical Person Variables -- PERSON Geography: F-N Variables -- HOUSEHOLD Technical Household Variables -- HOUSEHOLD Household Economic Variables -- HOUSEHOLD Dwelling Characteristics Variables -- HOUSEHOLD Utilities Variables -- HOUSEHOLD Demographic Variables -- PERSON Nativity and Birthplace Variables -- PERSON Migration: Global Variables -- PERSON Fertility and Mortality Variables -- PERSON Work Variables -- PERSON Work: Occupation Variables -- PERSON Kenya District Persons, households, and dwellings UNITS IDENTIFIED: - Dwellings: yes* - Vacant Units: - Households: yes - Individuals: yes - Group quarters: yes* UNIT DESCRIPTIONS: - Dwellings: A homestead is a structurally separate and independent place of abode. A structure is a building that is used for dwelling purposes. In rural areas most of the structures will be found within a homestead and may contain one or more dwelling units. A dwelling unit is the abode occupied by the respondents and constitutes one or more households. - Households: A person or a group of persons who live together in the same dwelling unit or homestead and eat together. They may or may not be related by blood or marriage - Group quarters: Group quarters consist of schools/colleges, barracks, prisons, hospitals and other institutions. All persons present in Kenya on the reference date. Persons who sleep outdoors and travelers in hotels, lodges, and boarding houses Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Kenya 1989 MICRODATA SOURCE: Central Bureau of Statistics Ministry of Finance and Planning SAMPLE SIZE (person records): 1074098. SAMPLE DESIGN: Systematic sample of every twentieth household. Persons who sleep outdoors and travelers in hotels, lodges, and boarding houses Face-to-face [f2f] A long form was used to enumerate individuals in private households and in institutions such as schools, colleges, barracks, prisons, and hospitals. The long form includes both individual and housing characteristics. A greatly abbreviated form was used for persons in transit or who slept outdoors, in hotels or boarding houses. de facto, CENSUS DAY: October 25, 1989 Self-weighting. Expansion factor = 20. IPUMS International distributes integrated microdata of individuals and households only by agreement of collaborating national statistical offices and under the strictest of confidence. Before data may be distributed to an individual researcher, an electronic license agreement must be signed and approved. To gain access to the data, a researcher must agree to the following: (1) Implement security measures to prevent unauthorized access to census microdata. Under IPUMS International agreements with collaborating agencies, redistribution of the data to third parties is prohibited. (2) Use the microdata for the exclusive purposes of scholarly research and education. Researchers must explicitly agree to not use microdata acquired for any commercial or income-generating venture. (3) Maintain the confidentiality of persons, households, and other entities. Any attempt to ascertain the identity of persons or households from the microdata is prohibited. Alleging that a person or household has been identified is also prohibited. (4) Report all publications based on these data to IPUMS International, which will in turn pass the information on to the relevant national statistical agencies. Once a project is approved, a password is issued and data may be acquired through the Internet. Penalties for violating the license include: revocation of the license, recall of all microdata acquired, filing of a motion of censure to the appropriate professional organizations, and civil prosecution under the relevant national or international statutes. These safeguards mirror the principles from the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. Employees of the Minnesota Population Center who work with the census microdata to produce the harmonized database also sign agreements to respect the confidentiality of the data. IPUMS International works with each country's statistical office to minimize the risk of disclosure of respondent information. The details of the confidentiality protections vary across countries, but in all cases, names and detailed geographic information are suppressed and top-codes are imposed on variables such as income that might identify specific persons. In addition, IPUMS International uses a variety of technical procedures to enhance confidentiality protection. These include the following: (1) Swapping an undisclosed fraction of records from one administrative district to another to make positive identification of individuals impossible. (2) Randomizing the placement of households within districts to disguise the order in which individuals were enumerated or the data processed. (3) Aggregating codes of sensitive characteristics (e.g., grouping together very small ethnic categories) (4) Top- and bottom-coding continuous variables to prevent identification of extreme cases. The safety record for public-use census microdata is apparently perfect. In almost four decades of use, there has not been a single verified breach of statistical confidentiality. The measures implemented by the IPUMS International are designed to extend this record. Central Bureau of Statistics Ministry of Finance and Planning 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: Kenya, Central Bureau of Statistics Ministry of Finance and Planning. 1989 Population and Housing census 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. KEN1989_PHC-H-H Household records 224 46 KEN1989_PHC-P-H Person records 1074098 95 Record type H Household P Person RECTYPE identifies the type of record for the case: household or person. NOTE: RECTYPE is an alphabetic (character string) variable with a value of 'H' for household records and 'P' for person records. RECTYPE will not appear as a variable in the default rectangular extracts produced by the data extract system. It is only available in hierarchical extracts, to distinguish between the two record types. IPUMS Country 032 Argentina 051 Armenia 040 Austria 050 Bangladesh 112 Belarus 204 Benin 068 Bolivia 072 Botswana 076 Brazil 854 Burkina Faso 116 Cambodia 120 Cameroon 124 Canada 152 Chile 156 China 170 Colombia 188 Costa Rica 192 Cuba 208 Denmark 214 Dominican Republic 218 Ecuador 818 Egypt 222 El Salvador 231 Ethiopia 242 Fiji 246 Finland 250 France 276 Germany 288 Ghana 300 Greece 320 Guatemala 324 Guinea 332 Haiti 340 Honduras 348 Hungary 352 Iceland 356 India 360 Indonesia 364 Iran 368 Iraq 372 Ireland 376 Israel 380 Italy 384 Ivory Coast 388 Jamaica 400 Jordan 404 Kenya 417 Kyrgyz Republic 418 Laos 426 Lesotho 430 Liberia 454 Malawi 458 Malaysia 466 Mali 480 Mauritius 484 Mexico 496 Mongolia 504 Morocco 508 Mozambique 104 Myanmar 524 Nepal 528 Netherlands 558 Nicaragua 566 Nigeria 578 Norway 586 Pakistan 275 Palestine 591 Panama 598 Papua New Guinea 600 Paraguay 604 Peru 608 Philippines 616 Poland 620 Portugal 630 Puerto Rico 642 Romania 643 Russia 646 Rwanda 662 Saint Lucia 686 Senegal 694 Sierra Leone 703 Slovak Republic 705 Slovenia 710 South Africa 728 South Sudan 724 Spain 729 Sudan 740 Suriname 752 Sweden 756 Switzerland 834 Tanzania 764 Thailand 768 Togo 780 Trinidad and Tobago 792 Turkey 800 Uganda 804 Ukraine 826 United Kingdom 840 United States 858 Uruguay 862 Venezuela 704 Vietnam 894 Zambia 716 Zimbabwe COUNTRY gives the country from which the sample was drawn. The codes assigned to each country are those used by the UN Statistics Division and the ISO (International Organization for Standardization). IPUMS Year 1703 1703 1729 1729 1787 1787 1801 1801 1819 1819 1845 1845 1848 1848 1850 1850 1851 1851 1852 1852 1860 1860 1861 1861 1865 1865 1868 1868 1870 1870 1871 1871 1875 1875 1880 1880 1881 1881 1885 1885 1890 1890 1891 1891 1900 1900 1901 1901 1910 1910 1911 1911 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1966 1966 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 YEAR gives the year in which the census or survey was taken. For samples that span years, the midpoint or first year of the interval is reported. IPUMS IPUMS sample identifier 032197001 Argentina 1970 032198001 Argentina 1980 032199101 Argentina 1991 032200101 Argentina 2001 032201001 Argentina 2010 051200101 Armenia 2001 051201101 Armenia 2011 040197101 Austria 1971 040198101 Austria 1981 040199101 Austria 1991 040200101 Austria 2001 040201101 Austria 2011 050199101 Bangladesh 1991 050200101 Bangladesh 2001 050201101 Bangladesh 2011 112199901 Belarus 1999 112200901 Belarus 2009 204197901 Benin 1979 204199201 Benin 1992 204200201 Benin 2002 204201301 Benin 2013 068197601 Bolivia 1976 068199201 Bolivia 1992 068200101 Bolivia 2001 068201201 Bolivia 2012 072198101 Botswana 1981 072199101 Botswana 1991 072200101 Botswana 2001 072201101 Botswana 2011 076196001 Brazil 1960 076197001 Brazil 1970 076198001 Brazil 1980 076199101 Brazil 1991 076200001 Brazil 2000 076201001 Brazil 2010 854198501 Burkina Faso 1985 854199601 Burkina Faso 1996 854200601 Burkina Faso 2006 116199801 Cambodia 1998 116200401 Cambodia 2004 116200801 Cambodia 2008 116201301 Cambodia 2013 116201901 Cambodia 2019 120197601 Cameroon 1976 120198701 Cameroon 1987 120200501 Cameroon 2005 124185201 Canada 1852 124187101 Canada 1871 124188101 Canada 1881 124189101 Canada 1891 124190101 Canada 1901 124191101 Canada 1911 124197101 Canada 1971 124198101 Canada 1981 124199101 Canada 1991 124200101 Canada 2001 124201101 Canada 2011 152196001 Chile 1960 152197001 Chile 1970 152198201 Chile 1982 152199201 Chile 1992 152200201 Chile 2002 152201701 Chile 2017 156198201 China 1982 156199001 China 1990 156200001 China 2000 170196401 Colombia 1964 170197301 Colombia 1973 170198501 Colombia 1985 170199301 Colombia 1993 170200501 Colombia 2005 188196301 Costa Rica 1963 188197301 Costa Rica 1973 188198401 Costa Rica 1984 188200001 Costa Rica 2000 188201101 Costa Rica 2011 192200201 Cuba 2002 192201201 Cuba 2012 208178701 Denmark 1787 208180101 Denmark 1801 208184501 Denmark 1845 208188001 Denmark 1880 208188501 Denmark 1885 214196001 Dominican Republic 1960 214197001 Dominican Republic 1970 214198101 Dominican Republic 1981 214200201 Dominican Republic 2002 214201001 Dominican Republic 2010 218196201 Ecuador 1962 218197401 Ecuador 1974 218198201 Ecuador 1982 218199001 Ecuador 1990 218200101 Ecuador 2001 218201001 Ecuador 2010 818184801 Egypt 1848 818186801 Egypt 1868 818198601 Egypt 1986 818199601 Egypt 1996 818200601 Egypt 2006 222199201 El Salvador 1992 222200701 El Salvador 2007 231198401 Ethiopia 1984 231199401 Ethiopia 1994 231200701 Ethiopia 2007 242196601 Fiji 1966 242197601 Fiji 1976 242198601 Fiji 1986 242199601 Fiji 1996 242200701 Fiji 2007 242201401 Fiji 2014 246201001 Finland 2010 250196201 France 1962 250196801 France 1968 250197501 France 1975 250198201 France 1982 250199001 France 1990 250199901 France 1999 250200601 France 2006 250201101 France 2011 276181901 Germany 1819 (Mecklenburg) 276197001 Germany 1970 (West) 276197101 Germany 1971 (East) 276198101 Germany 1981 (East) 276198701 Germany 1987 (West) 288198401 Ghana 1984 288200001 Ghana 2000 288201001 Ghana 2010 300197101 Greece 1971 300198101 Greece 1981 300199101 Greece 1991 300200101 Greece 2001 300201101 Greece 2011 320196401 Guatemala 1964 320197301 Guatemala 1973 320198101 Guatemala 1981 320199401 Guatemala 1994 320200201 Guatemala 2002 324198301 Guinea 1983 324199601 Guinea 1996 324201401 Guinea 2014 332197101 Haiti 1971 332198201 Haiti 1982 332200301 Haiti 2003 340196101 Honduras 1961 340197401 Honduras 1974 340198801 Honduras 1988 340200101 Honduras 2001 348197001 Hungary 1970 348198001 Hungary 1980 348199001 Hungary 1990 348200101 Hungary 2001 348201101 Hungary 2011 352170301 Iceland 1703 352172901 Iceland 1729 352180101 Iceland 1801 352190101 Iceland 1901 352191001 Iceland 1910 356198341 India 1983 356198741 India 1987 356199341 India 1993 356199941 India 1999 356200441 India 2004 356200941 India 2009 360197101 Indonesia 1971 360197601 Indonesia 1976 360198001 Indonesia 1980 360198501 Indonesia 1985 360199001 Indonesia 1990 360199501 Indonesia 1995 360200001 Indonesia 2000 360200501 Indonesia 2005 360201001 Indonesia 2010 364200601 Iran 2006 364201101 Iran 2011 368199701 Iraq 1997 372190101 Ireland 1901 372191101 Ireland 1911 372197101 Ireland 1971 372197901 Ireland 1979 372198101 Ireland 1981 372198601 Ireland 1986 372199101 Ireland 1991 372199601 Ireland 1996 372200201 Ireland 2002 372200601 Ireland 2006 372201101 Ireland 2011 372201601 Ireland 2016 376197201 Israel 1972 376198301 Israel 1983 376199501 Israel 1995 376200801 Israel 2008 380200101 Italy 2001 380201101 Italy 2011 380201121 Italy 2011 Q1 LFS 380201221 Italy 2012 Q1 LFS 380201321 Italy 2013 Q1 LFS 380201421 Italy 2014 Q1 LFS 380201521 Italy 2015 Q1 LFS 380201621 Italy 2016 Q1 LFS 380201721 Italy 2017 Q1 LFS 380201821 Italy 2018 Q1 LFS 380201921 Italy 2019 Q1 LFS 380202021 Italy 2020 Q1 LFS 384198801 Ivory Coast 1988 384199801 Ivory Coast 1998 388198201 Jamaica 1982 388199101 Jamaica 1991 388200101 Jamaica 2001 400200401 Jordan 2004 404196901 Kenya 1969 404197901 Kenya 1979 404198901 Kenya 1989 404199901 Kenya 1999 404200901 Kenya 2009 417199901 Kyrgyz Republic 1999 417200901 Kyrgyz Republic 2009 418199501 Laos 1995 418200501 Laos 2005 418201501 Laos 2015 426199601 Lesotho 1996 426200601 Lesotho 2006 430197401 Liberia 1974 430200801 Liberia 2008 454198701 Malawi 1987 454199801 Malawi 1998 454200801 Malawi 2008 458197001 Malaysia 1970 458198001 Malaysia 1980 458199101 Malaysia 1991 458200001 Malaysia 2000 466198701 Mali 1987 466199801 Mali 1998 466200901 Mali 2009 480199001 Mauritius 1990 480200001 Mauritius 2000 480201101 Mauritius 2011 484196001 Mexico 1960 484197001 Mexico 1970 484199001 Mexico 1990 484199501 Mexico 1995 484200001 Mexico 2000 484200501 Mexico 2005 484201001 Mexico 2010 484201501 Mexico 2015 484202001 Mexico 2020 484200521 Mexico 2005 Q1 LFS 484200522 Mexico 2005 Q2 LFS 484200523 Mexico 2005 Q3 LFS 484200524 Mexico 2005 Q4 LFS 484200621 Mexico 2006 Q1 LFS 484200622 Mexico 2006 Q2 LFS 484200623 Mexico 2006 Q3 LFS 484200624 Mexico 2006 Q4 LFS 484200721 Mexico 2007 Q1 LFS 484200722 Mexico 2007 Q2 LFS 484200723 Mexico 2007 Q3 LFS 484200724 Mexico 2007 Q4 LFS 484200821 Mexico 2008 Q1 LFS 484200822 Mexico 2008 Q2 LFS 484200823 Mexico 2008 Q3 LFS 484200824 Mexico 2008 Q4 LFS 484200921 Mexico 2009 Q1 LFS 484200922 Mexico 2009 Q2 LFS 484200923 Mexico 2009 Q3 LFS 484200924 Mexico 2009 Q4 LFS 484201021 Mexico 2010 Q1 LFS 484201022 Mexico 2010 Q2 LFS 484201023 Mexico 2010 Q3 LFS 484201024 Mexico 2010 Q4 LFS 484201121 Mexico 2011 Q1 LFS 484201122 Mexico 2011 Q2 LFS 484201123 Mexico 2011 Q3 LFS 484201124 Mexico 2011 Q4 LFS 484201221 Mexico 2012 Q1 LFS 484201222 Mexico 2012 Q2 LFS 484201223 Mexico 2012 Q3 LFS 484201224 Mexico 2012 Q4 LFS 484201321 Mexico 2013 Q1 LFS 484201322 Mexico 2013 Q2 LFS 484201323 Mexico 2013 Q3 LFS 484201324 Mexico 2013 Q4 LFS 484201421 Mexico 2014 Q1 LFS 484201422 Mexico 2014 Q2 LFS 484201423 Mexico 2014 Q3 LFS 484201424 Mexico 2014 Q4 LFS 484201521 Mexico 2015 Q1 LFS 484201522 Mexico 2015 Q2 LFS 484201523 Mexico 2015 Q3 LFS 484201524 Mexico 2015 Q4 LFS 484201621 Mexico 2016 Q1 LFS 484201622 Mexico 2016 Q2 LFS 484201623 Mexico 2016 Q3 LFS 484201624 Mexico 2016 Q4 LFS 484201721 Mexico 2017 Q1 LFS 484201722 Mexico 2017 Q2 LFS 484201723 Mexico 2017 Q3 LFS 484201724 Mexico 2017 Q4 LFS 484201821 Mexico 2018 Q1 LFS 484201822 Mexico 2018 Q2 LFS 484201823 Mexico 2018 Q3 LFS 484201824 Mexico 2018 Q4 LFS 484201921 Mexico 2019 Q1 LFS 484201922 Mexico 2019 Q2 LFS 484201923 Mexico 2019 Q3 LFS 484201924 Mexico 2019 Q4 LFS 484202021 Mexico 2020 Q1 LFS 484202023 Mexico 2020 Q3 LFS 496198901 Mongolia 1989 496200001 Mongolia 2000 504198201 Morocco 1982 504199401 Morocco 1994 504200401 Morocco 2004 504201401 Morocco 2014 508199701 Mozambique 1997 508200701 Mozambique 2007 104201401 Myanmar 2014 524200101 Nepal 2001 524201101 Nepal 2011 528196001 Netherlands 1960 528197101 Netherlands 1971 528200101 Netherlands 2001 528201101 Netherlands 2011 558197101 Nicaragua 1971 558199501 Nicaragua 1995 558200501 Nicaragua 2005 566200621 Nigeria 2006 566200721 Nigeria 2007 566200821 Nigeria 2008 566200921 Nigeria 2009 566201021 Nigeria 2010 578180101 Norway 1801 578186501 Norway 1865 578187501 Norway 1875 578190001 Norway 1900 578191001 Norway 1910 586197301 Pakistan 1973 586198101 Pakistan 1981 586199801 Pakistan 1998 275199701 Palestine 1997 275200701 Palestine 2007 275201701 Palestine 2017 591196001 Panama 1960 591197001 Panama 1970 591198001 Panama 1980 591199001 Panama 1990 591200001 Panama 2000 591201001 Panama 2010 598198001 Papua New Guinea 1980 598199001 Papua New Guinea 1990 598200001 Papua New Guinea 2000 600196201 Paraguay 1962 600197201 Paraguay 1972 600198201 Paraguay 1982 600199201 Paraguay 1992 600200201 Paraguay 2002 604199301 Peru 1993 604200701 Peru 2007 604201701 Peru 2017 608199001 Philippines 1990 608199501 Philippines 1995 608200001 Philippines 2000 608201001 Philippines 2010 616197801 Poland 1978 616198801 Poland 1988 616200201 Poland 2002 616201101 Poland 2011 620198101 Portugal 1981 620199101 Portugal 1991 620200101 Portugal 2001 620201101 Portugal 2011 630197001 Puerto Rico 1970 630198001 Puerto Rico 1980 630199001 Puerto Rico 1990 630200001 Puerto Rico 2000 630200501 Puerto Rico 2005 630201001 Puerto Rico 2010 630201501 Puerto Rico 2015 630202001 Puerto Rico 2020 642197701 Romania 1977 642199201 Romania 1992 642200201 Romania 2002 642201101 Romania 2011 643200201 Russia 2002 643201001 Russia 2010 646199101 Rwanda 1991 646200201 Rwanda 2002 646201201 Rwanda 2012 662198001 Saint Lucia 1980 662199101 Saint Lucia 1991 686198801 Senegal 1988 686200201 Senegal 2002 686201301 Senegal 2013 694200401 Sierra Leone 2004 694201501 Sierra Leone 2015 703199101 Slovak Republic 1991 703200101 Slovak Republic 2001 703201101 Slovak Republic 2011 705200201 Slovenia 2002 710199601 South Africa 1996 710200101 South Africa 2001 710200701 South Africa 2007 710201101 South Africa 2011 710201601 South Africa 2016 728200801 South Sudan 2008 724198101 Spain 1981 724199101 Spain 1991 724200101 Spain 2001 724201101 Spain 2011 724200521 Spain 2005 Q1 LFS 724200522 Spain 2005 Q2 LFS 724200523 Spain 2005 Q3 LFS 724200524 Spain 2005 Q4 LFS 724200621 Spain 2006 Q1 LFS 724200622 Spain 2006 Q2 LFS 724200623 Spain 2006 Q3 LFS 724200624 Spain 2006 Q4 LFS 724200721 Spain 2007 Q1 LFS 724200722 Spain 2007 Q2 LFS 724200723 Spain 2007 Q3 LFS 724200724 Spain 2007 Q4 LFS 724200821 Spain 2008 Q1 LFS 724200822 Spain 2008 Q2 LFS 724200823 Spain 2008 Q3 LFS 724200824 Spain 2008 Q4 LFS 724200921 Spain 2009 Q1 LFS 724200922 Spain 2009 Q2 LFS 724200923 Spain 2009 Q3 LFS 724200924 Spain 2009 Q4 LFS 724201021 Spain 2010 Q1 LFS 724201022 Spain 2010 Q2 LFS 724201023 Spain 2010 Q3 LFS 724201024 Spain 2010 Q4 LFS 724201121 Spain 2011 Q1 LFS 724201122 Spain 2011 Q2 LFS 724201123 Spain 2011 Q3 LFS 724201124 Spain 2011 Q4 LFS 724201221 Spain 2012 Q1 LFS 724201222 Spain 2012 Q2 LFS 724201223 Spain 2012 Q3 LFS 724201224 Spain 2012 Q4 LFS 724201321 Spain 2013 Q1 LFS 724201322 Spain 2013 Q2 LFS 724201323 Spain 2013 Q3 LFS 724201324 Spain 2013 Q4 LFS 724201421 Spain 2014 Q1 LFS 724201422 Spain 2014 Q2 LFS 724201423 Spain 2014 Q3 LFS 724201424 Spain 2014 Q4 LFS 724201521 Spain 2015 Q1 LFS 724201522 Spain 2015 Q2 LFS 724201523 Spain 2015 Q3 LFS 724201524 Spain 2015 Q4 LFS 724201621 Spain 2016 Q1 LFS 724201622 Spain 2016 Q2 LFS 724201623 Spain 2016 Q3 LFS 724201624 Spain 2016 Q4 LFS 724201721 Spain 2017 Q1 LFS 724201722 Spain 2017 Q2 LFS 724201723 Spain 2017 Q3 LFS 724201724 Spain 2017 Q4 LFS 724201821 Spain 2018 Q1 LFS 724201822 Spain 2018 Q2 LFS 724201823 Spain 2018 Q3 LFS 724201824 Spain 2018 Q4 LFS 724201921 Spain 2019 Q1 LFS 724201922 Spain 2019 Q2 LFS 724201923 Spain 2019 Q3 LFS 724201924 Spain 2019 Q4 LFS 724202021 Spain 2020 Q1 LFS 724202022 Spain 2020 Q2 LFS 724202023 Spain 2020 Q3 LFS 724202024 Spain 2020 Q4 LFS 729200801 Sudan 2008 740200401 Suriname 2004 740201201 Suriname 2012 752188001 Sweden 1880 752189001 Sweden 1890 752190001 Sweden 1900 752191001 Sweden 1910 756197001 Switzerland 1970 756198001 Switzerland 1980 756199001 Switzerland 1990 756200001 Switzerland 2000 756201101 Switzerland 2011 834198801 Tanzania 1988 834200201 Tanzania 2002 834201201 Tanzania 2012 764197001 Thailand 1970 764198001 Thailand 1980 764199001 Thailand 1990 764200001 Thailand 2000 768196001 Togo 1960 768197001 Togo 1970 768201001 Togo 2010 780197001 Trinidad and Tobago 1970 780198001 Trinidad and Tobago 1980 780199001 Trinidad and Tobago 1990 780200001 Trinidad and Tobago 2000 780201101 Trinidad and Tobago 2011 792198501 Turkey 1985 792199001 Turkey 1990 792200001 Turkey 2000 800199101 Uganda 1991 800200201 Uganda 2002 800201401 Uganda 2014 804200101 Ukraine 2001 826185101 United Kingdom 1851 (England and Wales) 826185102 United Kingdom 1851 (Scotland) 826185103 United Kingdom 1851 (2% sample) 826186101 United Kingdom 1861 (England and Wales) 826186102 United Kingdom 1861 (Scotland) 826187101 United Kingdom 1871 (Scotland) 826188101 United Kingdom 1881 (England and Wales) 826188102 United Kingdom 1881 (Scotland) 826189101 United Kingdom 1891 (England and Wales) 826189102 United Kingdom 1891 (Scotland) 826190101 United Kingdom 1901 (England and Wales) 826190102 United Kingdom 1901 (Scotland) 826191101 United Kingdom 1911 (England and Wales) 826196101 United Kingdom 1961 826197101 United Kingdom 1971 826199101 United Kingdom 1991 826200101 United Kingdom 2001 840185001 United States 1850 (100%) 840185002 United States 1850 (1%) 840186001 United States 1860 (1%) 840187001 United States 1870 (1%) 840188001 United States 1880 (100%) 840188002 United States 1880 (10%) 840190001 United States 1900 (5%) 840191001 United States 1910 (1%) 840196001 United States 1960 840197001 United States 1970 840198001 United States 1980 840199001 United States 1990 840200001 United States 2000 840200501 United States 2005 840201001 United States 2010 840201501 United States 2015 840202001 United States 2020 858196301 Uruguay 1963 858196302 Uruguay 1963 (full count) 858197501 Uruguay 1975 858197502 Uruguay 1975 (full count) 858198501 Uruguay 1985 858198502 Uruguay 1985 (full count) 858199601 Uruguay 1996 858199602 Uruguay 1996 (full count) 858200621 Uruguay 2006 858201101 Uruguay 2011 858201102 Uruguay 2011 (full count) 862197101 Venezuela 1971 862198101 Venezuela 1981 862199001 Venezuela 1990 862200101 Venezuela 2001 704198901 Vietnam 1989 704199901 Vietnam 1999 704200901 Vietnam 2009 704201901 Vietnam 2019 894199001 Zambia 1990 894200001 Zambia 2000 894201001 Zambia 2010 716201201 Zimbabwe 2012 SAMPLE identifies the IPUMS sample from which the case is drawn. Each sample receives a unique 9-digit code. The code is structured as follows: The first 3 digits are the ISO/UN codes used in COUNTRY The next 4 digits are the year of the census/survey The final 2 digits identify the sample within the year. For the last two digits, censuses or large census-like surveys have a value "0" (e.g, 01) in the second-to-last digit, household surveys have a value of "2" (e.g., 21), and employment surveys have a value of "4" (e.g., 41). IPUMS Household serial number SERIAL is an identifying number unique to each household in a given sample. All person records are assigned the same serial number as the household record that they follow. (Person records also have their own unique identifiers -- see PERNUM.) The combination of SAMPLE and SERIAL provides a unique identifier for every household in the IPUMS-International database; SAMPLE, SERIAL and PERNUM uniquely identify every person in the database. SERIAL can be used to identify dwellings in some samples. In these samples, the first 7 digits of SERIAL provide the dwelling number common to all households that were sampled from the same structure. The last three digits give the sequence of the household within the dwelling. The following is a list of samples in which dwellings can be inferred: Chile 1970, 1992, 2002Colombia 1993, 2005Costa Rica 1984, 2000Cuba 2002Dominican Republic 1981, 2002, 2010Ecuador 1990, 2001Germany 1971Hungary 1980, 1990, 2001Jamaica 1982, 1991, 2001Malaysia 1970, 1991, 2000Mexico 1995, 1990, 2000, 2005Nigeria 2006Panama 2000Peru 1993, 2007Portugal 1981, 1991, 2001Spain 1991Uruguay 2011Venezuela 1990, 2001Vietnam 1989In all other samples, the last 3 digits are always zeroes. SERIAL was constructed for IPUMS-International, and has no relation to the serial number in the original datasets. The U.S. 1900 sample and 1880 10% sample have multi-household dwellings that can be identified using the last 3 digits of SERIAL. SERIAL is a 10-digit numeric variable. The last 3 digits of SERIAL indicate household number within dwelling for selected samples noted in the variable description. In all other samples, the last 3 digits are always zeroes. IPUMS Number of person records in the household PERSONS indicates how many person records are included in the household (i.e., the number of person records associated with the household record in the sample). These person records will all have the same serial number (SERIAL) as the household record. The information contained in the household record will normally apply to all of these persons. PERSONS is a 4-digit numeric variable. IPUMS Household weight HHWT indicates the number of households in the population represented by the household in the sample. For the samples that are truly weighted (see the comparability discussion), HHWT must be used to yield accurate household-level statistics. NOTE: HHWT has 2 implied decimal places. That is, the last two digits of the eight-digit variable are decimal digits, but there is no actual decimal in the data. HHWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description. IPUMS Subsample number 00 1st 1% subsample 01 2nd 1% subsample 02 3rd 1% subsample 03 4th 1% subsample 04 5th 1% subsample 05 6th 1% subsample 06 7th 1% subsample 07 8th 1% subsample 08 9th 1% subsample 09 10th 1% subsample 10 11th 1% subsample 11 12th 1% subsample 12 13th 1% subsample 13 14th 1% subsample 14 15th 1% subsample 15 16th 1% subsample 16 17th 1% subsample 17 18th 1% subsample 18 19th 1% subsample 19 20th 1% subsample 20 21st 1% subsample 21 22nd 1% subsample 22 23rd 1% subsample 23 24th 1% subsample 24 25th 1% subsample 25 26th 1% subsample 26 27th 1% subsample 27 28th 1% subsample 28 29th 1% subsample 29 30th 1% subsample 30 31st 1% subsample 31 32nd 1% subsample 32 33rd 1% subsample 33 34th 1% subsample 34 35th 1% subsample 35 36th 1% subsample 36 37th 1% subsample 37 38th 1% subsample 38 39th 1% subsample 39 40th 1% subsample 40 41st 1% subsample 41 42nd 1% subsample 42 43rd 1% subsample 43 44th 1% subsample 44 45th 1% subsample 45 46th 1% subsample 46 47th 1% subsample 47 48th 1% subsample 48 49th 1% subsample 49 50th 1% subsample 50 51st 1% subsample 51 52nd 1% subsample 52 53rd 1% subsample 53 54th 1% subsample 54 55th 1% subsample 55 56th 1% subsample 56 57th 1% subsample 57 58th 1% subsample 58 59th 1% subsample 59 60th 1% subsample 60 61st 1% subsample 61 62nd 1% subsample 62 63rd 1% subsample 63 64th 1% subsample 64 65th 1% subsample 65 66th 1% subsample 66 67th 1% subsample 67 68th 1% subsample 68 69th 1% subsample 69 70th 1% subsample 70 71st 1% subsample 71 72nd 1% subsample 72 73rd 1% subsample 73 74th 1% subsample 74 75th 1% subsample 75 76th 1% subsample 76 77th 1% subsample 77 78th 1% subsample 78 79th 1% subsample 79 80th 1% subsample 80 81st 1% subsample 81 82nd 1% subsample 82 83rd 1% subsample 83 84th 1% subsample 84 85th 1% subsample 85 86th 1% subsample 86 87th 1% subsample 87 88th 1% subsample 88 89th 1% subsample 89 90th 1% subsample 90 91st 1% subsample 91 92nd 1% subsample 92 93rd 1% subsample 93 94th 1% subsample 94 95th 1% subsample 95 96th 1% subsample 96 97th 1% subsample 97 98th 1% subsample 98 99th 1% subsample 99 100th 1% subsample SUBSAMP allocates each case to one of 100 subsample replicates, randomly numbered from 0 to 99. Each subsample is nationally representative and preserves any stratification of the sample from which it is drawn. Users who need a representative subset of a sample can use SUBSAMP to select their cases. For example, to randomly extract 10% of the cases from a sample, select any 10 of the 100 subsamples. IPUMS Strata identifier This variable is the strata identifier for the sample. The STRATA variable provides information about the sample design that can be used to improve estimation. STRATA is a 12-digit numeric variable. IPUMS Group quarters (collective dwelling) status 00 Vacant 10 Households 20 Group quarters (collective), n.s. 21 Institutions 22 Other group quarters 29 1-person unit created by splitting large household 99 Unknown/group quarters not identified GQ identifies households as vacant dwellings, group quarters, or private households. Group quarters -- collective dwellings -- are generally institutions and other group living arrangements such as rooming houses and boarding schools. Institutions often retain persons under formal supervision or custody, such as correctional institutions, military barracks, asylums, or nursing homes. Educational and religious group dwellings (e.g., boarding schools, convents, monasteries, etc.) are also included in the institutional classification. Group quarter designations are often useful for understanding the universe of households that answered questions about household characteristics. Censuses will often exclude group quarters from such questions. IPUMS Number of unrelated persons 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9+ UNREL indicates the number of persons in the household who are unrelated to the head as defined in the variable RELATE. IPUMS Urban-rural status 1 Rural 2 Urban 9 Unknown URBAN indicates whether the household was located in a place designated as urban or as rural. IPUMS Continent and region of country 11 Eastern Africa 12 Middle Africa 13 Northern Africa 14 Southern Africa 15 Western Africa 21 Caribbean 22 Central America 23 North America 24 South America 31 Central Asia 32 Eastern Asia 33 Southern Asia 34 South-Eastern Asia 35 Western Asia 41 Eastern Europe 42 Northern Europe 43 Southern Europe 44 Western Europe 51 Australia and New Zealand 52 Melanesia 53 Micronesia 54 Polynesia REGIONW identifies the continent and region of each country. IPUMS 1st subnational geographic level, world [consistent boundaries over time] GEOLEV1 indicates the major administrative unit in which the household was enumerated. The variable incorporates the geographies for every country, to enable cross-national geographic analysis over time. First administrative units in GEOLEV1 have been spatiotemporally harmonized to provide spatially consistent boundaries across samples in each country. GEOLEV1 is a 6-digit numeric variable. GEOLEV1 codes and labels can be found here. Codes, labels, frequencies, and information about boundary changes for each country can be found in the country specific harmonized variable e.g. GEO1_BR. IPUMS 2nd subnational geographic level, world [consistent boundaries over time] GEOLEV2 indicates the second major administrative unit in which the household was enumerated. The variable incorporates the geographies for every country, to enable cross-national geographic analysis over time. Second administrative units in GEOLEV2 have been spatio-temporally harmonized to provide spatially consistent boundaries across samples in each country. GEOLEV2 is a 9-digit numeric variable. GEOLEV2 codes and labels can be found here. Codes, labels, frequencies, and information about boundary changes for each country can be found in the country specific harmonized variable e.g. GEO2_BR. IPUMS Population density of GEOLEV1 unit, in persons per square kilometer POPDENSGEO1 indicates the population density in persons per square kilometer of the major administrative unit in which the household was enumerated. The major administrative unit of the household is identified by the GEOLEV1 variable. The area of units in GEOLEV1 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. POPDENSGEO1 is an 8-digit string variable listing the population density in persons per square kilometer. IPUMS Population density of GEOLEV2 unit, in persons per square kilometer POPDENSGEO2 indicates the population density in persons per square kilometer of the second major administrative unit in which the household was enumerated. The second major administrative unit of the household is identified by the GEOLEV2 variable. The area of units in GEOLEV2 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. POPDENSGEO2 is a 12-digit string variable listing the population density in persons per square kilometer. IPUMS Area of GEOLEV1 unit in square kilometers AREAMOLLWGEO1 indicates the area in square kilometers of the major administrative unit in which the household was enumerated. The major administrative unit of the household is identified by the GEOLEV1 variable. The area of units in GEOLEV1 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. AREAMOLLWGEO1 is a 10-digit string variable listing the area in square kilometers. IPUMS Area of GEOLEV2 unit in square kilometers AREAMOLLWGEO2 indicates the area in square kilometers of the second major administrative unit in which the household was enumerated. The second major administrative unit of the household is identified by the GEOLEV2 variable. The area of units in GEOLEV2 is calculated using Mollweide's equal area projection. For a full set of geography variables refer to IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level refer to GEOLEV1 and GEOLEV2. More information on IPUMS-International geography can be found here. AREAMOLLWGEO2 is a 10-digit string variable listing the area in square kilometers. IPUMS Kenya, Province 1969 - 2009 [Level 1; consistent boundaries, GIS] 404001 Nairobi 404002 Central 404003 Coast 404004 Eastern 404005 Northeastern 404006 Nyanza 404007 Rift Valley 404008 Western GEO1_KE identifies the household's province or national capital within Kenya in all sample years. Provinces or national capital are the first level administrative units of the country. GEO1_KE is spatially harmonized to account for political boundary changes across census years. Some detail is lost in harmonization; see the comparability discussion. A GIS map (in shapefile format), corresponding to GEO1_KE can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Kenya 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 Kenya, Province 1989 [Level 1, GIS] 001 Nairobi 002 Central Province 003 Coast Province 004 Eastern Province 005 North-Eastern Province 006 Nyanza Province 007 South Rift Valley Province 008 North Rift Valley Province 009 Western Province GEO1_KE1989 identifies the household's province or national capital within Kenya in 1989. Provinces or national capital are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_KE1989 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Kenya 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 Kenya, District 1969 - 2009 [Level 2; consistent boundaries, GIS] 404001001 Nairobi East, Nairobi North, Nairobi West, Westlands 404002001 Gatanga, Gatundu, Githunguri, Kiambu (Kiambaa), Kiambu West, Kikuyu, Lari, Muranga North, Muranga South, Nyandarua North, Nyandarua South, Ruiru, Thika East, Thika West 404002002 Nyeri North, Nyeri South 404002003 Kirinyaga 404003001 Mombasa, Kilindini 404003002 Kinango, Kwale, Msambweni 404003003 Kaloleni, Kilifi, Malindi 404003004 Tana Delta, Tana River 404003005 Lamu 404003006 Taita, Taveta 404004001 Chalbi, Laisamis, Marsabit, Moyale 404004002 Garba Tulla, Igembe, Imenti Central, Imenti North, Imenti South, Isiolo, Maara, Meru South, Tharaka, Tigania 404004003 Embu, Kangundo, Kibwezi, Machakos, Makueni, Mbeere, Mbooni, Mwala, Nzaui, Yatta 404004004 Kitui North, Kitui South (Mutomo), Kyuso, Mwingi 404005001 Fafi, Garissa, Ijara, Lagdera 404005002 Wajir East, Wajir North, Wajir South, Wajir West 404005003 Mandera Central, Mandera East, Mandera West 404006001 Bondo, Rarieda, Siaya 404006002 Kisumu East, Kisumu West, Nyando 404006003 Homa Bay, Kuria East, Kuria West, Migori, Rachuonyo, Rongo, Suba 404006004 Borabu, Gucha, Gucha South, Kisii Central, Kisii South, Manga, Masaba, Nyamira 404007001 Turkana Central, Turkana North, Turkana South 404007002 Pokot Central, Pokot North, West Pokot 404007003 Samburu Central, Samburu East, Samburu North 404007004 Kwanza, Trans Nzoia East, Trans Nzoia West 404007005 Baringo, Baringo North, East Pokot, Koibatek, Laikipia East, Laikipia North, Laikipia West 404007006 Eldoret East, Eldoret West, Wareng 404007007 Keiyo, Marakwet 404007008 Nandi Central, Nandi East, Nandi North, Nandi South, Tinderet 404007009 Kaijiado Central, Kaijiado North, Loitoktok, Molo, Naivasha, Nakuru, Nakuru North 404007010 Narok North, Narok South, Trans Mara 404007011 Bomet, Buret, Kericho, Kipkelion, Sotik 404008001 Butere, Emuhaya, Hamisi, Kakamega Central, Kakamega East, Kakamega North, Kakamega South, Lugari, Mumias, Vihiga 404008002 Bungoma East, Bungoma North, Bungoma South, Bungoma West, Mt. Elgon 404008003 Bunyala, Busia, Samia, Teso North, Teso South GEO2_KE identifies the household's district within Kenya in all sample years. Districts are the second level administrative units of the country, after provinces. GEO2_KE is spatially harmonized to account for political boundary changes across census years. Some detail is lost in harmonization; see the comparability discussion. A GIS map (in shapefile format), corresponding to GEO2_KE can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Kenya 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 Kenya, District 1989 [Level 2, GIS] 001001 Nairobi 002001 Kiambu 002002 Kirinyaga 002003 Muranga 002004 Nyandaura 002005 Nyeri 003001 Kilifi 003002 Kwale 003003 Lamu 003004 Mombasa 003005 Taita Tave 003006 Tana River 004001 Embu 004002 Isiolo 004003 Kitui 004004 Machakos 004005 Marsabit 004006 Meru 005001 Garissa 005002 Mandera 005003 Wajir 006001 Kisii 006002 Kisumu 006003 Siaya 006004 South Nyanza 007001 Kajiado 007002 Kericho 007003 Laikipia 007004 Nakuru 007005 Nandi 007006 Narok 008001 Baringo 008002 Elgeyo-Marakwet 008003 Samburu 008004 Trans-Nzoia 008005 Turkana 008006 Uasin-Gishu 008007 West-Pokot 009001 Bugoma 009002 Busia 009003 Kakamega GEO2_KE1989 identifies the household's district within Kenya in 1989. Districts are the second level administrative units of the country, after provinces. A GIS map (in shapefile format), corresponding to GEO2_KE1989 can be downloaded from the GIS Boundary files page in the IPUMS International web site. The full set of geography variables for Kenya 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 DHS-IPUMS-I Kenya regions, 1969-2014 [consistent boundaries, GIS] 1 Nairobi 2 Central 3 Coast 4 Eastern 5 Nyanza 6 Rift Valley 7 Western 8 Northeastern DHS_IPUMSI_KE provides geographic codes for Kenya that match those in the DHS and IPUMS-International databases. This variable can be used to link contextual area data from IPUMS-DHS to IPUMS-International or vice versa. The codes in DHS_IPUMSI_KE indicate the major administrative unit in which the household was enumerated or surveyed. GIS shapefiles for Kenya can be downloaded here. IPUMS Ownership of dwelling [general version] NIU (not in universe) 1 Owned 2 Not owned 9 Unknown OWNERSHIP indicates whether a member of the household owned the housing unit. Households that acquired their unit with a mortgage or other lending arrangement were understood to "own" their unit even if they had not yet completed repayment. For those that did not own their housing unit, several options were possible: renting (from various types of owners), subletting, usufruct, and de facto occupation. IPUMS Ownership of dwelling [detailed version] 000 NIU (not in universe) 100 Owned 110 Owned, already paid 120 Owned, still paying 130 Owned, constructed 140 Owned, inherited 190 Owned, other 191 Owned, house 192 Owned, condominium 193 Apartment proprietor 194 Shared ownership 200 Not owned 210 Renting, not specified 211 Renting, government 212 Renting, local authority 213 Renting, parastatal 214 Renting, private 215 Renting, private company 216 Renting, individual 217 Renting, collective 218 Renting, joint state and individual 219 Renting, public subsidized 220 Renting, private subsidized 221 Renting, co-tenant 222 Renting, relative of tenant 223 Renting, cooperative 224 Renting, with a job or business 225 Renting, loan-backed habitation 226 Renting, mixed contract 227 Furnished dwelling 228 Sharecropping 230 Subletting 231 Rent to own 239 Renting, other 240 Occupied de facto/squatting 250 Free/usufruct (no cash rent) 251 Free, provided by employer 252 Free, without work or services 253 Free, provided by family or friend 254 Free, private 255 Free, public 256 Free, condemned 257 Free, other 260 Endowment, Waqf (Egypt historical) 290 Not owned, other 999 Unknown OWNERSHIP indicates whether a member of the household owned the housing unit. Households that acquired their unit with a mortgage or other lending arrangement were understood to "own" their unit even if they had not yet completed repayment. For those that did not own their housing unit, several options were possible: renting (from various types of owners), subletting, usufruct, and de facto occupation. IPUMS Electricity NIU (not in universe) 1 Yes 2 No 9 Unknown ELECTRIC indicates whether the household had access to electricity. IPUMS Water supply 00 NIU (not in universe) 10 Yes, piped water 11 Piped inside dwelling 12 Piped, exclusively to this household 13 Piped, shared with other households 14 Piped outside the dwelling 15 Piped outside dwelling, in building 16 Piped within the building or plot of land 17 Piped outside the building or lot 18 Have access to public piped water 20 No piped water 99 Unknown WATSUP describes the physical means by which the housing unit receives its water. The primary distinction is whether or not the household had piped (running) water. IPUMS Sewage 00 NIU (not in universe) 10 Connected to sewage system or septic tank 11 Sewage system (public sewage disposal) 12 Septic tank (private sewage disposal) 20 Not connected to sewage disposal system 99 Unknown SEWAGE indicates whether the household has access to a sewage system or septic tank. IPUMS Cooking fuel 00 NIU (not in universe) 10 None 20 Electricity 30 Petroleum gas, unspecified 31 Gas -- piped/utility 32 Gas -- tanked or bottled 33 Propane 34 Liquefied petroleum gas 35 Gas -- piped and bottled 40 Petroleum liquid 41 Oil, kerosene, and other liquid fuels 42 Kerosene/paraffin 43 Kerosene or oil 44 Kerosene or gasoline 45 Gasoline 46 Cocinol 47 Diesel 50 Wood, coal, and other solid fuels 51 Wood and other plant fuels 52 Non-wood plant materials 53 Coal or charcoal 54 Charcoal 55 Coal 56 Wood or charcoal 60 Multiple fuels 61 Bottled gas and wood 62 Propane and electricity 63 Propane, kerosene, and electricity 64 Propane and kerosene 65 Kerosene and electrictiy 66 Other combinations 70 Other 71 Alcohol 72 Biogas 73 Discarded or waste material 74 Dung/manure 75 Other combined organic waste materials 76 Solar energy 77 Candle 99 Unknown/missing FUELCOOK indicates the predominant type of fuel or energy used for cooking. IPUMS Toilet 00 NIU (not in universe) 10 No toilet 11 No flush toilet 20 Have toilet, type not specified 21 Flush toilet 22 Non-flush, latrine 23 Non-flush, other and unspecified 99 Unknown TOILET indicates whether the household had access to a toilet and, in most cases, whether it was a flush toilet or other type of installation. IPUMS Floor material 000 NIU (not in universe) 100 None/unfinished (earth) 110 Sand 120 Dung 200 Finished 201 Cement, tile, or brick 202 Cement 203 Concrete 204 Cement screed 205 Ceramic tile 206 Paving stone, cement tile 207 Stone 208 Brick 209 Brick or stone 210 Brick or cement 211 Block 212 Terrazzo 213 Wood 214 Palm, bamboo 215 Parquet 216 Parquet, tile, vinyl 217 Parquet, tile, marble 218 Ceramic, marble, granite 219 Ceramic, marble, tile, or vinyl 220 Marble 221 Mosaic 222 Tile 223 Tile, linoleum, ceramic, etc 224 Tile, cement 225 Tile, stone 226 Tile, stone, brick 227 Tile, stone, vinyl, brick 228 Tile, vinyl, brick 229 Tile, vinyl 230 Vinyl, linoleum, etc 231 Asphalt sheet, vinyl, etc 232 Synthetic, plastic 233 Cane 234 Carpet, rug 235 Scrap material 236 Other finished, n.e.c. 999 Unknown/missing FLOOR indicates the dwelling's predominant flooring material. IPUMS Wall or building material 000 NIU (not in universe) 100 No walls 200 Cardboard, scrap, and miscellaneous materials 201 Waste, scrap, or discarded material 202 Fabric or discarded material 203 Zinc, fabric, cardboard, tins, and waste material 204 Cardboard sheet 205 Plastic sheeting, cardboard 206 Makeshift, salvaged, or improvised materials 207 Reused materials 300 Wood 310 Rough wood 320 Wood, fibercement or plywood 330 Wood, formica, and other 340 Wood or bamboo 350 Wood or straw 400 Other plant-based materials 401 Plantain leaves and similar material 402 Bamboo or cane 403 Bamboo, sawali, cogon, nipa 404 Straw or bamboo 405 Grass, straw or reed 406 Reed, bamboo, or palm 407 Cane, palm leaves, logs 408 Palm leaves or palm planks 409 Bark, sticks, or cane 500 Masonry, stone, cement, adobe, metal, glass, and other fabricated materials (sometimes mixed with wood) 501 Brick, block, stone, or cement 502 Brick, stone, concrete 503 Brick, stone, or substitutes (dividing panels made of reinforced concrete) 504 Brick, stone, or substitutes (dividing panels made of wood) 505 Brick or tile 506 Brick or stone 507 Brick or cement block 508 Brick with plaster exterior 509 Brick without plaster exterior 510 Burnt or stabilized brick 511 Covered brick 512 Brick 513 Unburnt brick 514 Unburnt brick with cement 515 Unburnt brick with mud 516 Concrete 517 Landcrete, sandcrete 518 Cement blocks 519 Cement blocks or brick 520 Cement blocks or brick, unfinished 521 Cement and adobe bricks 522 Cement and stone block 523 Cement and tiles 524 Reinforced concrete, pre-cast concrete panels, or steel skeleton framed concrete 525 Concrete, reinforced concrete, blocks, panels 526 Fibercement 527 Adobe 528 Adobe walls with plaster exterior 529 Adobe walls without plaster exterior 530 Adobe with cement exterior 531 Wood and earth adobe 532 Wood and cement adobe 533 Mud or adobe 534 Pressed dirt 535 Clay 536 Coated clay/mud with sticks/cane 537 Clay or clay-covered sticks 538 Netted bamboo or cane with mud 539 Bundle of mud, straw, other materials 540 Mud with wood/wattle 541 Pole and mud 542 Mud with cement 543 Unfinished lathe and plaster, stucco, etc. 544 Stone 545 Hand-laid stone 546 Quarried stone 547 Cut stone and concrete 548 Cemented stone 549 Stone with clay 550 Blocks of light material 551 Prefabricated material 552 Asbestos 553 Metal or asbestos sheet 554 Metal or iron sheet 555 Metal or fibercement sheeting 556 Galvanized iron or aluminum 557 Tin 558 Glass 559 Cloth 560 Covintec panels 561 Mixed material 562 Mixed material: part wood; part concrete, brick, or stone 563 Wood plastered with clay, adobe, other materials; wood pressed panels; rolled mud bricks; etc. 564 Mixed material: wood or galvanized metal 570 Mainly permanent materials 600 Other material 601 Partition wall, lined with wood or steel 602 Partition wall, unlined 999 Unknown/missing This variable indicates the primary material used in the construction of the dwelling, particularly the dwelling's exterior walls. IPUMS Roof material 00 NIU (not in universe) 10 Masonry, concrete, clay tile, or tiles of unspecified type 11 Concrete or cement 12 Reinforced concrete (slab) 13 Cement or sheet metal 14 Tile, unspecified material 15 Clay tile 16 Tile or cement 17 Modern tiles, industrial 18 Traditional tiles, locally made 19 Tile or flat stone 20 Tile, unspecified or mixed materials 21 Fibercement or plastic 22 Fibercement or metal sheets 23 Asphalt or laminate cover 24 Tile, cement, asphalt 25 Asphalt tile 26 Slate or tile 27 Slate or asbestos 28 Asbestos 29 Adobe 30 Tiles or wood planks 31 Roofing shingles 32 Tar paper 33 Metal 34 Sheet metal 35 Zinc or tin 36 Tin 37 Sheet metal or other sheet material 38 Sheet metal, tile, slate 40 Wood and other plant materials 41 Wood 42 Wood, including bamboo 43 Bamboo 44 Cogon, nipa, anahaw 45 Thatch (straw, grass, leaves, palm, etc.) 46 Cane, wood, straw 47 Grass or straw 48 Papyrus 49 Banana leaves or fiber 50 Palm or makuti 51 Straw, bamboo, polythene 52 Wood with clay 53 Grass and mud 54 Rustic mat 60 Mud or earth 61 Clay 70 Cardboard, scrap, and miscellaneous materials 71 Discarded or scrap material 72 Cardboard 73 Plastic, tarpaulin 80 Other, unspecified 90 No roof 99 Unknown/missing This variable indicates the dwelling's predominant roofing material. IPUMS Household classification 00 Vacant household 01 One-person household 02 Married/cohab couple, no children 03 Married/cohab couple with children 04 Single-parent family 05 Polygamous family 06 Extended family, relatives only 07 Composite household, family and non-relatives 08 Non-family household 09 Unclassified subfamily 10 Other relative or non-relative household 11 Group quarters 99 Unclassifiable HHTYPE is a constructed variable that describes the composition of households. HHTYPE is constructed from information in RELATE (relationship to head), from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father), and from information on group quarters status, GQ. IPUMS Number of families in household Vacant household 1 1 family 2 2 families 3 3 families 4 4 families 5 5 families 6 6 families 7 7 families 8 8 families 9 9 or more families NFAMS is a constructed variable that indicates the number of families within each household. Family membership is defined by FAMUNIT. A "family" is any group of persons related by blood, adoption, or marriage. An unrelated individual within the household is considered a separate family. Thus, a household consisting of a widow and a domestic employee contains two families; a household consisting of a large, multi-generation extended family with no persons unrelated to the head counts as a single family. NFAMS is constructed from information in RELATE (relationship to head) and from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). See those variable descriptions for more detail. IPUMS Number of married couples in household No married couples in household 1 1 couple 2 2 couples 3 3 couples 4 4 couples 5 5 couples 6 6 couples 7 7 couples 8 8 couples 9 9 or more couples NCOUPLES is a constructed variable indicating the number of married/in-union couples within a household. NCOUPLES is constructed using the IPUMS-International pointer variable SPLOC (spouse's location in the household). IPUMS Number of mothers in household No mothers in household 1 1 mother 2 2 mothers 3 3 mothers 4 4 mothers 5 5 mothers 6 6 mothers 7 7 mothers 8 8 mothers 9 9 or more mothers in household NMOTHERS is a constructed variable indicating the number of mothers -- of persons of any age -- within a household. NMOTHERS is constructed using the IPUMS-International pointer variable MOMLOC (mother's location in the household). IPUMS Number of fathers in household No fathers in household 1 1 father 2 2 fathers 3 3 fathers 4 4 fathers 5 5 fathers 6 6 fathers 7 7 fathers 8 8 fathers 9 9 or more fathers in household NFATHERS is a constructed variable indicating the number of fathers -- of persons of any age -- within a household. NFATHERS is constructed using the IPUMS-International pointer variable POPLOC (father's location in the household). IPUMS Head's location in household HEADLOC gives the person number (PERNUM) of the head of household in samples in which persons are organized into households. HEADLOC is a 3-digit numeric variable. IPUMS Enumeration area type Kenya 1989: All records 1 Rural 2 Urban This variable indicates whether the type of the enumeration area is rural or urban. IPUMS Record type Identification</p> <p><svar a="all" v="KE89A001 KE89A002 KE89A004 KE89A005 KE89A006 KE89A008 KE89A009">____ Province<br />____ District<br />____ Location<br />____ Sub-location<br />____ E. A. [Enumeration Area] number<br />____ Household<br /></svar> Kenya 1989: All records 1 Household record 2 Household record: travellers and short questionnaires 3 Person record 4 Person record: short questionnaire 5 Person record: travellers This variable indicates the record type (household or person) and whether the record is for a traveler or a person receiving the short questionnaire. IPUMS Tenure <svar a="all" v="KE89A010">H10. Status of tenure of the main residential structure:<br /><br /><div class="i1">If owner-occupied, state whether</div><br /><div class="i2">[] 1 Purchased<br />[] 2 Constructed<br />[] 3 Inherited</div><br /><br /><div class="i1">If rented, state whether</div><br /><div class="i2">[] 4 Government<br />[] 5 Local authority<br />[] 6 Parastatal<br />[] 7 Private company<br />[] 8 Individual<br />[] 9 Other form of tenure</div><br /><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] 01 Owner-occupied: purchased 02 Owner-occupied: constructed by owner 03 Owner-occupied: inherited by owner 04 Rented: from government 05 Rented: from local authority 06 Rented: from a parastatal entity 07 Rented: from a private company 08 Rented: from an individual landlord 09 Other 98 Unknown 99 NIU (not in universe) This variable indicates the status of tenure of the main residential structure whether it is owned or rented. IPUMS Roof <svar a="all" v="KE89A011">H11. Roof<br /><div class="i1">State whether:<br /><br />[] 1 Iron sheets<br />[] 2 Tiles<br />[] 3 Concrete<br />[] 4 Asbestos sheets<br />[] 5 Grass/<span class="lang">makuti</span><br />[] 6 Other</div><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Iron sheets 2 Tiles 3 Concrete 4 Asbestos sheets 5 Grass/makuti 6 Other 8 Unknown 9 NIU (not in universe) This variable indicates the dwelling's predominant roofing material. IPUMS Wall <svar a="all" v="KE89A012">H.12. Wall<br /><div class="i1">State whether:<br /><br />[] 1 Stone<br />[] 2 Brick/block<br />[] 3 Mud/wood<br />[] 4 Mud/cement<br />[] 5 Wood only<br />[] 6 Iron sheets<br />[] 7 Grass/reeds<br />[] 8 Other</div><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] NIU (not in universe) 1 Stone 2 Brick/block 3 Mud/Wood 4 Cement / mud 5 Wood only 6 Iron sheets 7 Grass / reeds 8 Other 9 Unknown This variable indicates the primary material used in the construction of the dwelling's walls. IPUMS Floor <svar a="all" v="KE89A013">H13. Floor<br /><div class="i1">State whether:<br /><br />[] 1 Cement<br />[] 2 Earth<br />[] 3 Wood<br />[] 4 Tiles<br />[] 5 Other</div><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Cement 2 Earth 3 Wood 4 Tiles 5 Other 8 Unknown 9 NIU (not in universe) This variable indicates the dwelling's predominant flooring material. IPUMS Water source <svar a="all" v="KE89A014">H14. Main source of water<br /><div class="i1">State whether:<br /><br />[] 1 Pond<br />[] 2 Dam<br />[] 3 Lake<br />[] 4 Stream/river<br />[] 5 Well<br />[] 6 Borehole<br />[] 7 Piped<br />[] 8 Jabias<br />[] 9 Other</div><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] 01 Pond still 02 Dam-small 03 Lake 04 Stream / river 05 Well 06 Borehole 07 Piped 08 Jabias (tank, rainwater) 09 Other 98 Unknown 99 NIU (not in universe) This variable indicates the main source of water in the main housing structure. IPUMS Sewage <svar a="all" v="KE89A015">H15. Main type of sewage disposal<br /><div class="i1">State whether:<br /><br />[] 1 Main sewer<br />[] 2 Septic tank<br />[] 3 Pit latrine<br />[] 4 Bucket latrine<br />[] 5 Cesspool<br />[] 6 Bush</div><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Main sewer 2 Septic tank 3 Pit latrine 4 Bucket latrine 5 Cesspool 6 Bush 8 Unknown 9 NIU (not in universe) This variable indicates the main type of sewage disposal in the main housing structure. IPUMS Fuel <svar a="all" v="KE89A016">H16. Main cooking fuel<br /><div class="i1">State whether:<br /><br />[] 1 Electricity<br />[] 2 Paraffin<br />[] 3 Gas<br />[] 4 Firewood<br />[] 5 Charcoal<br />[] 6 Other</div><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Electricity 2 Paraffin 3 Gas 4 Firewood 5 Charcoal 6 Other 8 Unknown 9 NIU (not in universe) This variable indicates the main source of cooking fuel in the main housing structure. IPUMS Light <svar a="all" v="KE89A017">H17. Main type of lighting<br /><div class="i1">State whether:<br /><br />[] 1 Electricity<br />[] 2 Paraffin lamps<br />[] 3 Fuel wood<br />[] 4 Candle<br />[] 5 Solar<br />[] 6 Other</div><br /></svar> Kenya 1989: All households, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Electricity 2 Paraffin lamps 3 Fuel wood 4 Candle 5 Solar 6 Other 8 Unknown 9 NIU (not in universe) This variable indicates the main type of lighting in the main housing structure. IPUMS Strata Kenya 1989: 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 Mother's location in household MOMLOC is a constructed variable that indicates whether or not the person's mother lived in the same household and, if so, gives the person number of the mother (see PERNUM). MOMLOC makes it easy for researchers to link the characteristics of children and their (probable) mothers. The method by which probable child-mother links are identified is described in PARRULE. The general design of MOMLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly. For more details on the construction of MOMLOC, see the Comparability section of PARRULE and this paper on IPUMSI family linking methodology. Note: MOMLOC identifies social relationships (such as stepmother and adopted mother) as well as biological relationships. The variable STEPMOM is designed to identify some of these social relationships. To restrict MOMLOC to biological mothers, such as for own children fertility estimation, MOMLOC should be reset to zero when STEPMOM is greater than zero. MOMLOC is a 3-digit numeric variable. Codes0 = No mother of this person present in the household. 1 or higher = The person number of this person's mother IPUMS Father's location in household POPLOC is a constructed variable that indicates whether or not the person's father lived in the same household and, if so, gives the person number of the father (see PERNUM). POPLOC makes it easy for researchers to link the characteristics of children and their (probable) fathers. The method by which probable child-father links are identified is described in PARRULE. The general design of POPLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly. For more details on the construction of POPLOC, see the Comparability section of PARRULE and this paper on IPUMSI family linking methodology. Note: POPLOC identifies social relationships (such as stepfather and adopted father) as well as biological relationships. The variable STEPPOP is designed to identify some of these social relationships. To restrict POPLOC to biological mothers, such as for own children fertility estimation, POPLOC should be reset to zero when STEPPOP is greater than zero. POPLOC is a 3-digit numeric variable. Codes0 = No father of this person present in the household. 1 or higher = The person number of this person's father IPUMS Spouse's location in household SPLOC is a constructed variable that indicates whether or not the person's spouse lived in the same household and, if so, gives the person number (PERNUM) of the spouse. SPLOC makes it easy for researchers to link the characteristics of (probable) spouses. The method by which probable spouse-spouse links are identified is described in SPRULE. The general design of SPLOC and other constructed variables is modeled on the methods developed for IPUMS-USA "Family Interrelationships", but the details vary significantly. For more details on the construction of SPLOC, see the Comparability section of SPRULE and this paper on IPUMSI family linking methodology. SPLOC is a 3-digit numeric variable. Codes0 = No spouse of this person present in the household. 1 or higher = The person number of this person's spouse IPUMS Rule for linking parent 00 No parent of person in household 11 Link to head or spouse, unambiguous 12 Link to head or spouse, ambiguous 21 Child-Grandchild, within empirical child cap 22 Child-Grandchild, within constructed child cap 23 Child-Grandchild, exceeds child cap 31 Specified Other Relatives, within empirical child cap 32 Specified Other Relatives, within constructed child cap 33 Specified Other Relatives, exceeds child cap 41 Other Relatives, within empirical child cap 42 Other Relatives, within constructed child cap 51 Non-Relatives, within empirical child cap 52 Non-Relatives, within constructed child cap PARRULE describes the criteria by which the IPUMS International variables MOMLOC and POPLOC linked the person to a probable mother and/or father. IPUMS International establishes child-parent links according to five basic rules, and PARRULE gives the number of the rule that applied to the link in question. A link to any parent automatically generates a second link to that parent's spouse or partner, so only one rule is needed to describe both MOMLOC and POPLOC. The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology. IPUMS Rule for linking spouse 00 No spouse present 01 Rule 1: strong relationship pairing, couple adjacent 02 Rule 2: strong relationship pairing, couple not adjacent 03 Rule 3: weak relationship pairing, couple adjacent 04 Rule 4: weak relationship pairing, couple not adjacent 05 Rule 5: weak consensual union pairings 06 Rule 6: sample-specific rules (usually child-to-child) SPRULE explains the criteria by which the IPUMS-International variable SPLOC linked the person to his/her probable spouse. IPUMS International establishes spouse-spouse links according to five basic rules, and SPRULE gives the number of the rule that applied to the link in question. A sixth rule identifies sample-specific linking procedures only imposed in selected instances. The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology. IPUMS Probable stepmother Biological mother or no mother present 1 Mother has no children born or surviving 2 Child reports mother is deceased 3 Explicitly identified step relationship 4 Mother reports no children in the home 5 Age difference implausible 6 Child exceeds known fertility of mother STEPMOM indicates whether a person's mother, as identified by MOMLOC, was most probably not the person's biological mother. Non-zero values of STEPMOM explain why it is probable that the person's mother was a step- or adopted mother. A value of 0 indicates no likely stepmother because (1) the mother identified in MOMLOC was probably the biological mother or (2) there is no mother of this person present in the household. The codes for STEPMOM are as follows: 0 = Biological mother or no mother of this person present in household. 1 = Mother has no children born or surviving. 2 = Child reports mother is deceased. 3 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner, stepchild/child-in-law). 4 = Mother reports no children in the home. 5 = Age difference between mother and child was less than 12 or greater than 54 years. 6 = Child exceeds known fertility of mother. In cases where more than one criterion for a likely stepmother is met, STEPMOM will take the value of the criterion with the lowest code. See PARRULE for a description of the linking process. Users should note that there are many stepmothers and adopted mothers in the population that cannot be identified with information available in the censuses. Therefore, STEPMOM will always under-represent their actual number in the population. IPUMS Probable stepfather Biological father or no father present 1 Child reports father is deceased 2 Explicitly identified step relationship 3 Age difference implausible 4 Spouse of mother 5 Identified as adopted 6 Surname difference -- male child or never-married female STEPPOP indicates whether a person's father, as identified by POPLOC, was most probably not the person's biological father. Non-zero values of STEPPOP explain why it is probable that the person's father was a step- or adopted father. A value of 0 indicates no likely stepfather because (1) the father identified in POPLOC was probably the biological father or (2) there is no father of this person present in the household. The codes for STEPPOP are as follows: 0 = Biological father or no father of this person present in household. 1 = Child reports father is deceased. 2 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner; stepchild/child-in-law). 3 = Age difference between father and child was less than 12 or greater than 54 years. In cases where more than one criterion for a likely stepfather is met, STEPPOP will take the value of the criterion with the lowest code. See PARRULE for a description of the linking process. Users should note that there are many stepfathers and adopted fathers in the population that cannot be identified with information available in the censuses. Therefore, STEPPOP will always under-represent their actual number in the population. IPUMS Man with more than one wife linked No more than one wife linked via SPLOC 1 More than one wife linked via SPLOC POLYMAL indicates if a man had more than one wife linked to him in the constructed IPUMS variable SPLOC -- Spouse's Location in Household. The point of POLYMAL is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record. IPUMS Woman is second or higher order wife Person is not the 2nd or higher order wife linked via SPLOC 1 Person is the 2nd or higher order wife linked via SPLOC POLY2ND indicates if a woman was the second or higher order wife linked to a husband in the constructed IPUMS variable SPLOC -- Spouse's Location in Household. The variable does not suggest the actual marital order of wives, only their relative positions in the person order of the household as it was enumerated. The point of POLY2ND is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record. IPUMS Family unit membership FAMUNIT is a constructed variable indicating to which family within the household a person belongs. All persons related to the household head receive a 1 (see RELATE). Each secondary family or secondary individual receives a higher code. For purposes of FAMUNIT, secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). FAMUNIT is a 4-digit numeric variable. CodesIf there is only one group of related individuals within the household, all of them will be coded "1;" if there is a second, separate such group listed on the form, all of them will be coded "2," and so on. IPUMS Number of own family members in household 0001 1 family member present 0002 2 family members present 0003 3 family members present 0004 4 0005 5 0006 6 0007 7 0008 8 0009 9 0010 10 0011 11 0012 12 0013 13 0014 14 0015 15 0016 16 0017 17 0018 18 0019 19 0020 20 0021 21 0022 22 0023 23 0024 24 0025 25 0026 26 0027 27 0028 28 0029 29 0030 30 0031 31 0032 32 0033 33 0034 34 0035 35 0036 36 0037 37 0038 38 0039 39 0040 40 0041 41 0042 42 0043 43 0044 44 0045 45 0046 46 0047 47 0048 48 0049 49 0050 50 0051 51 0052 52 0053 53 0054 54 0055 55 0056 56 0057 57 0058 58 0059 59 0060 60 0061 61 0062 62 0063 63 0064 64 0065 65 0066 66 0067 67 0068 68 0069 69 0070 70 0071 71 0072 72 0073 73 0074 74 0075 75 0076 76 0077 77 0078 78 0079 79 0080 80 0081 81 0082 82 0083 83 0084 84 0085 85 0086 86 0087 87 0088 88 0089 89 0090 90 0091 91 0092 92 0093 93 0094 94 0095 95 0096 96 0097 97 0098 98 0099 99 or more persons FAMSIZE counts the number of the person's own family members living in the household with her/him, including the person her/himself. These include all persons related to the person by blood, adoption, or marriage as indicated by the census forms or inferred from them. FAMSIZE is calculated from the units identified in the IPUMS constructed variable FAMUNIT (family unit membership). The primary family is defined as all persons related to the head in the RELATE variable. Secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). IPUMS Number of own children in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 or more children in household NCHILD provides a count of the person's own children living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. IPUMS Number of own children under age 5 in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 or more own children under age 5 in household 98 One or more children have unknown age NCHLT5 provides a count of the person's own children under age five living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. IPUMS Age of eldest own child in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 or older 98 One or more children have unknown age 99 No own child in household ELDCH gives the age of the person's oldest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. ELDCH is top-coded at age 50 or older. IPUMS Age of youngest own child in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 or older 98 One or more children have unknown age 99 No own child in household YNGCH gives the age of the person's youngest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household. YNGCH is top-coded at age 50 or older. IPUMS Relationship to household head [general version] 1 Head 2 Spouse/partner 3 Child 4 Other relative 5 Non-relative 6 Other relative or non-relative 9 Unknown RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). IPUMS Relationship to household head [detailed version] 1000 Head 2000 Spouse/partner 2100 Spouse 2200 Unmarried partner 2210 Civil union 2300 Same-sex spouse/partner 3000 Child 3100 Biological child 3200 Adopted child 3300 Stepchild 3400 Child/child-in-law 3500 Child/child-in-law/grandchild 3600 Child of unmarried partner 4000 Other relative 4100 Grandchild 4110 Grandchild or great grandchild 4120 Great grandchild 4130 Great-great grandchild 4200 Parent/parent-in-law 4210 Parent 4211 Stepparent 4220 Parent-in-law 4300 Child-in-law 4301 Daughter-in-law 4302 Spouse/partner of child 4310 Unmarried partner of child 4400 Sibling/sibling-in-law 4410 Sibling 4420 Stepsibling 4430 Sibling-in-law 4431 Sibling of spouse/partner 4432 Spouse/partner of sibling 4500 Grandparent 4510 Great grandparent 4600 Parent/grandparent/ascendant 4700 Aunt/uncle 4800 Other specified relative 4810 Nephew/niece 4820 Cousin 4830 Sibling's sibling-in-law 4900 Other relative, not elsewhere classified 4910 Other relative with same family name 4920 Other relative with different family name 4930 Other relative, not specified (secondary family) 5000 Non-relative 5100 Friend/guest/visitor/partner 5110 Partner/friend 5111 Friend 5112 Partner/roommate 5113 Housemate/roommate 5120 Visitor 5130 Ex-spouse 5140 Godparent 5150 Godchild 5200 Employee 5210 Domestic employee 5220 Relative of employee, n.s. 5221 Spouse of servant 5222 Child of servant 5223 Other relative of servant 5300 Roomer/boarder/lodger/foster child 5310 Boarder 5311 Boarder or guest 5320 Lodger 5330 Foster child 5340 Tutored/foster child 5350 Tutored child 5400 Employee, boarder, or guest 5500 Other specified non-relative 5510 Agregado 5520 Temporary resident, guest 5600 Group quarters 5610 Group quarters, non-inmates 5620 Institutional inmates 5900 Non-relative, n.e.c. 6000 Other relative or non-relative 9999 Unknown RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). IPUMS Age 000 Less than 1 year 001 1 year 002 2 years 003 3 004 4 005 5 006 6 007 7 008 8 009 9 010 10 011 11 012 12 013 13 014 14 015 15 016 16 017 17 018 18 019 19 020 20 021 21 022 22 023 23 024 24 025 25 026 26 027 27 028 28 029 29 030 30 031 31 032 32 033 33 034 34 035 35 036 36 037 37 038 38 039 39 040 40 041 41 042 42 043 43 044 44 045 45 046 46 047 47 048 48 049 49 050 50 051 51 052 52 053 53 054 54 055 55 056 56 057 57 058 58 059 59 060 60 061 61 062 62 063 63 064 64 065 65 066 66 067 67 068 68 069 69 070 70 071 71 072 72 073 73 074 74 075 75 076 76 077 77 078 78 079 79 080 80 081 81 082 82 083 83 084 84 085 85 086 86 087 87 088 88 089 89 090 90 091 91 092 92 093 93 094 94 095 95 096 96 097 97 098 98 099 99 100 100+ 999 Not reported/missing AGE gives age in years as of the person's last birthday prior to or on the day of enumeration. IPUMS Age, grouped into intervals 01 0 to 4 02 5 to 9 03 10 to 14 04 15 to 19 05 0 to 5 06 6 to 10 07 10 to 15 08 11 to 14 09 15 to 17 10 16 to 19 11 18 to 24 12 20 to 24 13 25 to 29 14 30 to 34 15 35 to 39 16 40 to 44 17 45 to 49 18 50 to 54 19 55 to 59 20 60 to 64 21 65 to 69 22 70 to 74 23 75 to 79 24 80 to 84 25 85+ 98 Unknown AGE2 gives computed years of age grouped into intervals. IPUMS Sex 1 Male 2 Female 9 Unknown SEX reports the sex (gender) of the respondent. IPUMS Marital status [general version] NIU (not in universe) 1 Single/never married 2 Married/in union 3 Separated/divorced/spouse absent 4 Widowed 9 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS Marital status [detailed version] 000 NIU (not in universe) 100 Single/never married 110 Engaged 111 Never married and never cohabited 200 Married or consensual union 210 Married, formally 211 Married, civil 212 Married, religious 213 Married, civil and religious 214 Married, civil or religious 215 Married, traditional/customary 216 Married, monogamous 217 Married, polygamous 219 Married, spouse absent (historical samples) 220 Consensual union 300 Separated/divorced/spouse absent 310 Separated or divorced 320 Separated or annulled 330 Separated 331 Separated legally 332 Separated de facto 333 Separated from marriage 334 Separated from consensual union 335 Separated from consensual union or marriage 340 Annulled 350 Divorced 400 Widowed 410 Widowed or divorced 411 Widowed from consensual union or marriage 412 Widowed from marriage 413 Widowed from consensual union 420 Widowed, divorced, or separated 999 Unknown/missing MARST describes the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. Census instructions rarely explicitly limit marital status to strictly legal unions. Note regarding universe: The lowest age at which a person can be anything but "never married" varies among samples. IPUMS 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 female 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) CHBORNF indicates the number of female children ever born to a woman. Only live births are counted. IPUMS Number of male 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) CHBORNM indicates the number of male children ever born to a woman. Only live births are counted. IPUMS Number of female 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+ 98 Unknown 99 NIU (not in universe) CHSURVF indicates the number of female children ever born to a woman still living at the time of the census. IPUMS Number of male 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+ 98 Unknown 99 NIU (not in universe) CHSURVM indicates the number of male children ever born to a woman still living at the time of the census. IPUMS Month of last birth 01 January 02 February 03 March 04 April 05 May 06 June 07 July 08 August 09 September 10 October 11 November 12 December 98 Unknown 99 NIU (not in universe) LASTBMO indicates the month of birth of the last child born by the respondent. The data refer to live births. IPUMS Year of last birth 1900 1900 1901 1901 1902 1902 1903 1903 1904 1904 1905 1905 1906 1906 1907 1907 1908 1908 1909 1909 1910 1910 1911 1911 1912 1912 1913 1913 1914 1914 1915 1915 1916 1916 1917 1917 1918 1918 1919 1919 1920 1920 1921 1921 1922 1922 1923 1923 1924 1924 1925 1925 1926 1926 1927 1927 1928 1928 1929 1929 1930 1930 1931 1931 1932 1932 1933 1933 1934 1934 1935 1935 1936 1936 1937 1937 1938 1938 1939 1939 1940 1940 1941 1941 1942 1942 1943 1943 1944 1944 1945 1945 1946 1946 1947 1947 1948 1948 1949 1949 1950 1950 1951 1951 1952 1952 1953 1953 1954 1954 1955 1955 1956 1956 1957 1957 1958 1958 1959 1959 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1965 1965 1966 1966 1967 1967 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1988 1988 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 9998 Unknown 9999 NIU (not in universe) LASTBYR indicates the year of birth of the last child born by the respondent. The data refer to live births. IPUMS Sex of last birth NIU (not in universe) 1 Male 2 Female 3 Both sexes (multiple births) 9 Unknown LASTBSEX indicates the sex of a woman's most recent birth. IPUMS Number of children dead 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20+ 98 Unknown/missing 99 NIU (not in universe) CHDEAD reports how many of the children ever born to a woman were no longer living at the time of the census. Women were to consider all live births by all fathers; they were to exclude still births. IPUMS Number of female children dead 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20+ 98 Unknown 99 NIU (not in universe) CHDEADFEM indicates the number of female children ever born to a woman who are no longer living. Stillbirths are not counted. It is possible to calculate total child deaths for samples that have both the "Female children ever born" and "Female children surviving" variables. That is not done in CHDEADFEM, which includes only the samples that directly reported the information in the appropriate form. IPUMS Number of male children dead 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20+ 98 Unknown 99 NIU (not in universe) CHDEADMALE indicates the number of male children ever born to a woman who are no longer living. Stillbirths are not counted. It is possible to calculate total child deaths for samples that have both the "Male children ever born" and "Male children surviving" variables. That is not done in CHDEADMALE, which includes only the samples that directly reported the information in the appropriate form. IPUMS Mortality status of last birth NIU (not in universe) 1 Alive 2 Dead 9 Unknown LASTBMORT indicates the mortality status of the last child born to a woman. There is no constraint on how long ago the child may have been born. Only live births are considered. IPUMS Year of death of the last child born 0000 Last child not dead 1915 1915 1930 1930 1931 1931 1932 1932 1933 1933 1935 1935 1936 1936 1937 1937 1939 1939 1940 1940 1941 1941 1942 1942 1943 1943 1944 1944 1945 1945 1946 1946 1947 1947 1948 1948 1949 1949 1950 1950 1951 1951 1952 1952 1953 1953 1954 1954 1955 1955 1956 1956 1957 1957 1958 1958 1959 1959 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1965 1965 1966 1966 1967 1967 1968 1968 1969 1969 1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975 1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 1981 1982 1982 1983 1983 1984 1984 1985 1985 1986 1986 1987 1987 1988 1988 1989 1989 1990 1990 1991 1991 1992 1992 1993 1993 1994 1994 1995 1995 1996 1996 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 9998 Unknown/missing 9999 NIU (not in universe) CHDEADYR indicates whether the woman's last child had died and, if so, the year of the death. Respondents were to exclude still births from consideration. Also see CHDEADMO. IPUMS Month of death of the last child born 00 Last child not dead 01 January 02 February 03 March 04 April 05 May 06 June 07 July 08 August 09 September 10 October 11 November 12 December 98 Unknown/missing 99 NIU (not in universe) CHDEADMO indicates whether the woman's last child had died and, if so, the month of the death. Respondents were to exclude still births from consideration. Also see CHDEADYR. IPUMS Mortality status of mother 1 Alive 2 Dead 7 Does not know 8 Missing 9 NIU (not in universe) MORTMOT indicates whether the person's biological mother was still living at the time of the census. IPUMS Mortality status of father 1 Alive 2 Dead 7 Does not know 8 Missing 9 NIU (not in universe) MORTFAT indicates whether the person's biological father was still living. IPUMS Number of own children in household 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20+ 98 Unknown 99 NIU (not in universe) HOMECHILD indicates the number of surviving biological children living in the household with their mother (the respondent) at the time of the census. IPUMS Number of own female children in household 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 17 17 20 20+ 98 Unknown 99 NIU (not in universe) HOMEFEM indicates the number of female children born living in the household with their mother (the respondent). IPUMS Number of own male children in household 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20+ 98 Unknown 99 NIU (not in universe) HOMEMALE indicates the number of male children born living in the household with their mother (the respondent). IPUMS Number of own children living elsewhere 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 98 Unknown 99 NIU (not in universe) AWAYCHILD indicates the number of surviving biological children not living in the household with their mother (the respondent) at the time of the census. IPUMS Number of own female children living elsewhere 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20+ 98 Unknown 99 NIU (not in universe) AWAYFEM indicates the number of surviving biological female children not living in the household with their mother (the respondent). IPUMS Number of own male children living elsewhere 00 None 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 98 Unknown 99 NIU (not in universe) AWAYMALE indicates the number of surviving biological male children not living in the household with their mother (the respondent). IPUMS Nativity status NIU (not in universe) 1 Native-born 2 Foreign-born 9 Unknown/missing NATIVITY indicates whether the person was native-born or foreign-born. IPUMS Country of birth 00000 NIU (not in universe) 10000 Africa 11000 Eastern Africa 11005 British Indian Ocean Territory 11010 Burundi 11020 Comoros 11030 Djibouti 11040 Eritrea 11050 Ethiopia 11051 Ethiopia (including Eritrea) 11060 Kenya 11070 Madagascar 11080 Malawi 11090 Mauritius 11100 Mozambique 11110 Reunion 11120 Rwanda 11130 Seychelles 11140 Somalia 11150 South Sudan 11160 Uganda 11170 Tanzania 11180 Zambia 11190 Zimbabwe 11999 Eastern Africa, other or n.s. 12000 Middle Africa 12010 Angola 12020 Cameroon 12030 Central African Republic 12040 Chad 12050 Congo (Republic of) 12060 Democratic Republic of Congo 12070 Equatorial Guinea 12080 Gabon 12090 Sao Tome and Principe 12999 Middle Africa, other or n.s. 13000 Northern Africa 13010 Algeria 13011 Algeria/Tunisia 13020 Egypt 13021 Egypt/Sudan 13030 Libya 13040 Morocco 13050 Sudan 13060 Tunisia 13070 Western Sahara 13999 Northern Africa, other or n.s. 14000 Southern Africa 14010 Botswana 14020 Lesotho 14030 Namibia 14040 South Africa 14050 Swaziland 14999 Southern Africa, other or n.s. 15000 Western Africa 15010 Benin 15020 Burkina Faso 15021 Upper Volta 15030 Cape Verde 15040 Ivory Coast 15050 Gambia 15060 Ghana 15070 Guinea 15080 Guinea-Bissau 15081 Guinea-Bissau and Cape Verde 15090 Liberia 15100 Mali 15110 Mauritania 15120 Niger 15130 Nigeria 15140 St. Helena and Ascension 15150 Senegal 15160 Sierra Leone 15170 Togo 15180 Canary Islands 15999 West Africa, other or n.s. 19999 Africa, other or n.s. 20000 Americas 21000 Caribbean 21010 Anguilla 21020 Antigua-Barbuda 21030 Aruba 21040 Bahamas 21050 Barbados 21060 British Virgin Islands 21070 Cayman Isles 21080 Cuba 21090 Dominica 21100 Dominican Republic 21110 Grenada 21120 Guadeloupe 21130 Haiti 21140 Jamaica 21150 Martinique 21160 Montserrat 21170 Netherlands Antilles 21180 Puerto Rico 21190 St. Kitts-Nevis 21200 St. Croix 21210 St. John 21220 St. Lucia 21230 St Thomas 21240 St. Vincent 21250 Trinidad and Tobago 21260 Turks and Caicos 21270 U.S. Virgin Islands 21991 Caribbean commonwealth, n.s. 21999 Caribbean, other or n.s. 22000 Central America 22010 Belize/British Honduras 22020 Costa Rica 22030 El Salvador 22040 Guatemala 22050 Honduras 22060 Mexico 22070 Nicaragua 22080 Panama 22081 Panama Canal Zone 22999 Central America, other or n.s. 23000 South America 23010 Argentina 23020 Bolivia 23030 Brazil 23040 Chile 23050 Colombia 23060 Ecuador 23070 Falkland Islands 23080 French Guiana 23090 Guyana/British Guiana 23100 Paraguay 23110 Peru 23120 Suriname 23130 Uruguay 23140 Venezuela 23999 South America, other or n.s. 24000 North America 24010 Bermuda 24020 Canada 24030 Greenland 24040 United States 24999 North America, other or n.s. 29999 Americas, other or n.s. 30000 Asia 31000 Eastern Asia 31010 China 31011 Hong Kong 31012 Macau 31013 Taiwan 31020 Japan 31030 Korea 31031 Korea, DPR (North) 31032 Korea, RO (South) 31040 Mongolia 31999 Eastern Asia, other or n.s. 32000 South-Central Asia 32010 Afghanistan 32020 Bangladesh 32030 Bhutan 32040 India 32041 India/Pakistan 32042 India/Pakistan/Bangladesh/Sri Lanka 32050 Iran 32060 Kazakhstan 32070 Kyrgyzstan 32080 Maldives 32090 Nepal 32100 Pakistan 32101 Pakistan/Bangladesh 32110 Sri Lanka (Ceylon) 32120 Tajikistan 32130 Turkmenistan 32140 Uzbekistan 32999 South-Central Asia, other or n.s. 33000 South-Eastern Asia 33010 Brunei 33020 Cambodia (Kampuchea) 33030 East Timor 33040 Indonesia 33050 Laos 33060 Malaysia 33070 Myanmar (Burma) 33080 Philippines 33090 Singapore 33100 Thailand 33110 Vietnam 33999 South-Eastern Asia, other or n.s. 34000 Western Asia 34010 Armenia 34020 Azerbaijan 34030 Bahrain 34040 Cyprus 34050 Georgia 34051 Abkhazia 34052 South Ossetia 34060 Iraq 34070 Israel 34071 Israel/Palestine 34080 Jordan 34090 Kuwait 34100 Lebanon 34110 Palestine 34111 West Bank 34112 Gaza Strip 34120 Oman 34130 Qatar 34140 Saudi Arabia 34150 Syria 34151 Syria/Lebanon 34160 Turkey 34170 United Arab Emirates 34180 Yemen 34991 Middle East 34999 Western Asia, other or n.s. 39999 Asia, other or n.s. 40000 Europe 41000 Eastern Europe 41010 Belarus 41020 Bulgaria 41021 Bulgaria/Greece 41030 Czech Republic/Czechoslovakia 41040 Hungary 41050 Poland 41060 Moldova 41070 Romania 41080 Russia/USSR 41090 Slovakia 41100 Ukraine 41991 Albania, Bulgaria, Czech, Hungary, Romania, Yugoslavia 41992 Central-Eastern Europe 41999 Eastern Europe, other or n.s. 42000 Northern Europe 42010 Denmark 42020 Estonia 42030 Faroe Islands 42040 Finland 42050 Iceland 42060 Ireland 42070 Latvia 42080 Lithuania 42090 Norway 42100 Svalbard and Jan Mayen Islands 42110 Sweden 42120 United Kingdom 42999 Northern Europe, other or n.s. 43000 Southern Europe 43010 Albania 43020 Andorra 43030 Bosnia and Herzegovina 43040 Croatia 43050 Gibraltar 43060 Greece 43070 Italy 43071 Vatican City 43080 Malta 43090 Portugal 43100 San Marino 43110 Slovenia 43120 Spain 43121 Spain/Portugal 43130 Macedonia 43140 Yugoslavia 43141 Montenegro 43142 Serbia 43143 Kosovo 43144 Serbia and Montenegro 43991 Gibraltar/Malta 43992 Portugal/Greece 43993 Italy, Holy See, San Marino 43999 Southern Europe, other or n.s. 44000 Western Europe 44010 Austria 44020 Belgium 44021 Belgium/Luxemburg 44022 Belgium/Netherlands/Luxemburg 44030 France 44040 Germany 44042 West Germany 44043 Germany/Austria 44044 Mecklenburg-Schwerin 44050 Liechtenstein 44060 Luxembourg 44070 Monaco 44080 Netherlands 44090 Switzerland 44991 Belgium, Denmark, Luxembourg, Netherlands 44999 Western Europe, other or n.s. 49992 European Union 49993 European Union (original 15) 49994 Other European Union (not original 15) 49999 Europe, other or n.s. 50000 Oceania 51000 Australia and New Zealand 51010 Australia 51020 New Zealand 51030 Norfolk Islands 51999 Australia and New Zealand, n.s. 52000 Melanesia 52010 Fiji 52020 New Caledonia 52030 Papua New Guinea 52040 Solomon Islands 52050 Vanuatu (New Hebrides) 52999 Melanesia, n.s. 53000 Micronesia 53010 Kiribati 53020 Marshall Islands 53030 Nauru 53040 Northern Mariana Isls. 53050 Palau 53060 Federated States of Micronesia 53999 Micronesia, other or n.s. 54000 Polynesia 54010 Cook Islands 54020 French Polynesia 54030 Niue 54040 Pitcairn Island 54050 Western Samoa 54060 Eastern Samoa 54070 Tokelau 54080 Tonga 54090 Tuvalu 54100 Wallis and Futuna Isls. 54999 Polynesia, other or n.s. 55000 U.S. Pacific Possessions 55010 American Samoa 55020 Baker Island 55030 Guam 55040 Howland Island 55050 Johnston Atoll 55060 Kingman Reef 55070 Midway Islands 55080 Wake Island 55999 US Pacific, other or n.s. 59999 Oceania, other or n.s. 80000 AT SEA 90000 Other countries n.s. 99999 Unknown BPLCOUNTRY indicates the person's country of birth. IPUMS District of birth, Kenya 0101 Nairobi West 0102 Nairobi East 0103 Nairobi North 0104 Westlands 0110 Nairobi 0201 Nyandarua North 0202 Nyandarua South 0203 Nyeri North 0204 Nyeri South 0205 Kirinyaga 0206 Muranga North 0207 Muranga South 0208 Kiambu (Kiambaa) 0209 Kikuyu 0210 Kiambu West 0211 Lari 0212 Githunguri 0213 Thika East 0214 Thika West 0215 Ruiru 0216 Gatanga 0217 Gatundu 0220 Nyandaura 0221 Nyeri 0222 Muranga 0223 Thika 0224 Maragua 0225 Other Central Province 0301 Mombasa 0302 Kilindini 0303 Kwale 0304 Kinango 0305 Msambweni 0306 Kilifi 0307 Kaloleni 0308 Malindi 0309 Tana River 0310 Tana Delta 0311 Lamu 0312 Taita 0313 Taveta 0320 Taita Taveta 0321 Other Coast Province 0401 Marsabit 0402 Chalbi 0403 Laisamis 0404 Moyale 0405 Isiolo 0406 Garba Tulla 0407 Imenti Central 0408 Imenti North 0409 Imenti south 0410 Meru south 0411 Maara 0412 Igembe 0413 Tigania 0414 Tharaka 0415 Embu 0416 Mbeere 0417 Kitui North 0418 Kitui South (Mutomo) 0419 Mwingi 0420 Kyuso 0421 Machakos 0422 Mwala 0423 Yatta 0424 Kangundo 0425 Makueni 0426 Mbooni 0427 Kibwezi 0428 Nzaui 0430 Kitui 0431 Meru 0432 Meru North 0433 Meru Central 0434 Tharaka 0435 Other Eastern Province 0501 Garissa 0502 Lagdera 0503 Fafi 0504 Ijara 0505 Wajir South 0506 Wajir North 0507 Wajir East 0508 Wajir West 0509 Mandera Central 0510 Mandera East 0511 Mandera West 0520 Wajir 0521 Mandera 0522 Other North-Eastern Province 0601 Siaya 0602 Bondo 0603 Rarieda 0604 Kisumu East 0605 Kisumu West 0606 Nyando 0607 Homa Bay 0608 Suba 0609 Rachuonyo 0610 Migori 0611 Rongo 0612 Kuria West 0613 Kuria East 0614 Kisii Central 0615 Kisii South 0616 Masaba 0617 Gucha 0618 Gucha South 0619 Nyamira 0620 Manga 0621 Borabu 0630 Kisumu 0631 Kuria 0632 Kisii 0633 North Kisii 0634 South Nyanza 0635 Other Nyanza Province 0701 Turkana Central 0702 Turkana North 0703 Turkana South 0704 West Pokot 0705 Pokot North 0706 Pokot Central 0707 Samburu Central 0708 Samburu East 0709 Samburu North 0710 Trans Nzoia West 0711 Trans Nzoia East 0712 Kwanza 0713 Baringo 0714 Baringo North 0715 East Pokot 0716 Koibatek 0717 Eldoret West 0718 Eldoret East 0719 Wareng 0720 Marakwet 0721 Keiyo 0722 Nandi North 0723 Nandi Central 0724 Nandi East 0725 Nandi South 0726 Tinderet 0727 Laikipia North 0728 Laikipia East 0729 Laikipia West 0730 Nakuru 0731 Nakuru North 0732 Naivasha 0733 Molo 0734 Narok North 0735 Narok Wouth 0736 Trans Mara 0737 Kajiado Central 0738 Loitoktok 0739 Kericho 0740 Kipkelion 0741 Buret 0742 Sotik 0743 Bomet 0744 Kajiado North 0750 Turkana 0751 Samburu 0752 Trans Nzoia 0753 Nandi 0754 Laikipia 0755 Narok 0756 Kajiado 0757 Elgeyo Markwet 0758 Uasin Gishu 0759 Other Rift Valley Province 0801 Kakamega Central 0802 Kakamega South 0803 Kakamega North 0804 Kakamega East 0805 Lugari 0806 Vihiga 0807 Emuhaya 0808 Hamisi 0809 Mumias 0810 Butere 0811 Bungoma South 0812 Bungoma North 0813 Bungoma East 0814 Bungoma West 0815 Mt. Elgon 0816 Busia 0817 Teso North 0818 Samia 0819 Bunyala 0820 Teso South 0830 Kakamega 0831 Butere/Mumias 0832 Bungoma 0833 Teso 0834 Other Western Province 0900 Foreign-born 0999 Unknown/missing BPLKE indicates the person's district and province of birth within Kenya. IPUMS School attendance NIU (not in universe) 1 Yes 2 No, not specified 3 No, attended in the past 4 No, never attended 9 Unknown/missing SCHOOL indicates whether or not the person attended school at the time of the census or within some specified period of time prior to the census. IPUMS Literacy NIU (not in universe) 1 No, illiterate 2 Yes, literate 9 Unknown/missing LIT indicates whether or not the respondent could read and write in any language. A person is typically considered literate if he or she can both read and write. All other persons are illiterate, including those who can either read or write but cannot do both. IPUMS Educational attainment, international recode [general version] NIU (not in universe) 1 Less than primary completed 2 Primary completed 3 Secondary completed 4 University completed 9 Unknown EDATTAIN records the person's educational attainment in terms of the level of schooling completed (degree or other milestone). The emphasis on level completed is critical: a person attending the final year of secondary education receives the code for having completed lower secondary only -- and in some samples only primary. EDATTAIN does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. In addition to EDATTAIN, a country-specific education classification is provided which loses no information and reflects the particular educational system of that country (for example EDUCBR for Brazil, EDUCCL for Chile, and EDUCUS for the United States). As always, users can refer to the original education source variables for each sample, if they wish. Many samples also give single years of schooling completed, recorded in YRSCHOOL. Some samples provide educational information in a form that could not be incorporated into EDATTAIN. IPUMS Educational attainment, international recode [detailed version] 000 NIU (not in universe) 100 Less than primary completed (n.s.) 110 No schooling 120 Some primary completed 130 Primary (4 yrs) completed 211 Primary (5 yrs) completed 212 Primary (6 yrs) completed 221 Lower secondary general completed 222 Lower secondary technical completed 311 Secondary, general track completed 312 Some college completed 320 Secondary or post-secondary technical completed 321 Secondary, technical track completed 322 Post-secondary technical education 400 University completed 999 Unknown/missing EDATTAIN records the person's educational attainment in terms of the level of schooling completed (degree or other milestone). The emphasis on level completed is critical: a person attending the final year of secondary education receives the code for having completed lower secondary only -- and in some samples only primary. EDATTAIN does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. In addition to EDATTAIN, a country-specific education classification is provided which loses no information and reflects the particular educational system of that country (for example EDUCBR for Brazil, EDUCCL for Chile, and EDUCUS for the United States). As always, users can refer to the original education source variables for each sample, if they wish. Many samples also give single years of schooling completed, recorded in YRSCHOOL. Some samples provide educational information in a form that could not be incorporated into EDATTAIN. IPUMS Years of schooling 00 None or pre-school 01 1 year 02 2 years 03 3 years 04 4 years 05 5 years 06 6 years 07 7 years 08 8 years 09 9 years 10 10 years 11 11 years 12 12 years 13 13 years 14 14 years 15 15 years 16 16 years 17 17 years 18 18 years or more 90 Not specified 91 Some primary 92 Some technical after primary 93 Some secondary 94 Some tertiary 95 Adult literacy 96 Special education 98 Unknown/missing 99 NIU (not in universe) YRSCHOOL indicates the highest grade/level of schooling the person had completed, in years. Only formal schooling is counted. YRSCHOOL accounts for the number of years of study, regardless of the track or kind of study. Information on degree and/or technical track is available in EDATTAIN. Years of schooling for Israel, categorized into intervals, are given in YRSCHOOL2. Users should pay close attention to the top-codes in each sample, as discussed in the comparability section. IPUMS Educational attainment, Kenya 00 None 01 Pre-primary 02 Standard 1, incomplete 03 Standard 1 04 Standard 2 05 Standard 3 06 Standard 4 07 Standard 5 08 Standard 6 09 Standard 7 10 Standard 8 11 Form 1 12 Form 2 13 Form 3 14 Form 4 15 Form 5 16 Form 6 20 Literacy program 30 Youth polytechnic 40 University, completion unspecified 41 University, incomplete 42 University, complete 43 College (training schools, etc) 44 University, undergraduate 45 University, Masters or PhD 50 Madrassa 98 Unknown/missing 99 NIU (not in universe) EDUCKE indicates the person's educational attainment in Kenya 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 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 Country of residence 1 year ago 00000 NIU (not in universe) 10000 Africa 11000 Eastern Africa 11010 Burundi 11020 Comoros 11030 Djibouti 11040 Eritrea 11050 Ethiopia 11060 Kenya 11070 Madagascar 11080 Malawi 11090 Mauritius 11100 Mozambique 11110 Reunion 11120 Rwanda 11130 Seychelles 11140 Somalia 11150 South Sudan 11160 Uganda 11170 Tanzania 11180 Zambia 11190 Zimbabwe 11999 Eastern Africa, n.s. 12000 Middle Africa 12010 Angola 12020 Cameroon 12030 Central African Republic 12040 Chad 12050 Congo 12060 Democratic Republic of Congo 12070 Equatorial Guinea 12080 Gabon 12090 Sao Tome and Principe 12999 Middle Africa, n.s. 13000 Northern Africa 13010 Algeria 13020 Egypt/United Arab Rep. 13030 Libya 13040 Morocco 13050 Sudan 13060 Tunisia 13070 Western Sahara 13999 Northern Africa, n.s. 14000 Southern Africa 14010 Botswana 14020 Lesotho 14030 Namibia 14040 South Africa 14050 Swaziland 14999 Southern Africa, n.s. 15000 Western Africa 15010 Benin 15020 Burkina Faso 15030 Cape Verde 15040 Ivory Coast 15050 Gambia 15060 Ghana 15070 Guinea 15080 Guinea-Bissau 15090 Liberia 15100 Mali 15110 Mauritania 15120 Niger 15130 Nigeria 15140 St. Helena and Ascension 15150 Senegal 15160 Sierra Leone 15170 Togo 15199 Western African, n.s. 19990 Africa, n.s. 19991 Central and South Africa 20000 Americas 21000 Caribbean 21010 Anguilla 21020 Antigua-Barbuda 21030 Aruba 21040 Bahamas 21050 Barbados 21060 British Virgin Islands 21070 Cayman Isles 21080 Cuba 21090 Dominica 21100 Dominican Republic 21110 Grenada 21120 Guadeloupe 21130 Haiti 21140 Jamaica 21150 Martinique 21160 Montserrat 21170 Netherlands Antilles 21180 Puerto Rico 21190 St. Kitts-Nevis 21200 St. Croix 21210 St. Jon 21220 St. Lucia 21230 St. Thomas 21240 St. Vincent 21250 Trinidad and Tobago 21260 Turks and Caicos 21270 U.S. Virgin Islands 21999 Caribbean, n.s. 22000 Central America 22010 Belize/British Honduras 22020 Costa Rica 22030 El Salvador 22040 Guatemala 22050 Honduras 22060 Mexico 22070 Nicaragua 22080 Panama 22081 Panama Canal Zone 22999 Central America, n.s. 23000 South America 23010 Argentina 23020 Bolivia 23030 Brazil 23040 Chile 23050 Colombia 23060 Ecuador 23070 Falkland Islands 23080 French Guiana 23090 Guyana/British Guiana 23100 Paraguay 23110 Peru 23120 Suriname 23130 Uruguay 23140 Venezuela 23990 South America, n.s. 23991 Central and South America 24000 North America 24010 Bermuda 24020 Canada 24030 Greenland 24040 United States 24041 U.S. Outlying Areas and Territories 24999 North America, n.s. 29990 Americas, other and n.s. 30000 Asia 31000 Eastern Asia 31010 China 31011 Hong Kong 31012 Macau 31013 Taiwan 31020 Japan 31030 Korea 31031 Korea, DPR (North) 31032 Korea, RO (South) 31040 Mongolia 32000 South-Central Asia 32010 Afghanistan 32020 Bangladesh 32030 Bhutan 32040 India 32050 Iran 32060 Kazakhstan 32070 Kyrgyzstan 32080 Maldives 32090 Nepal 32100 Pakistan 32110 Sri Lanka (Ceylon) 32120 Tajikistan 32130 Turkmenistan 32140 Uzbekistan 33000 South-Eastern Asia 33010 Brunei 33020 Cambodia (Kampuchea) 33030 East Timor 33040 Indonesia 33050 Laos 33060 Malaysia 33070 Myanmar (Burma) 33080 Philippines 33090 Singapore 33100 Thailand 33110 Vietnam 33199 Other South-Eastern Asia 34000 Western Asia 34010 Armenia 34020 Azerbaijan 34030 Bahrain 34040 Cyprus 34050 Georgia 34051 Abkhazia 34052 South Ossetia 34060 Iraq 34070 Israel 34080 Jordan 34090 Kuwait 34100 Lebanon 34110 Palestine 34120 Oman 34130 Qatar 34140 Saudi Arabia 34150 Syria 34160 Turkey 34170 United Arab Emirates 34180 Yemen 34990 Western Asia, n.s. 34991 Middle East, n.e.c. 39999 Asia, n.s. 40000 Europe 41000 Eastern Europe 41010 Belarus 41020 Bulgaria 41030 Czech Republic 41040 Hungary 41050 Poland 41060 Moldova 41070 Romania 41080 Russia/USSR 41090 Slovakia 41100 Ukraine 41999 Eastern Europe, n.e.c. 42000 Northern Europe 42010 Denmark 42020 Estonia 42030 Faroe Islands 42040 Finland 42050 Iceland 42060 Ireland 42070 Latvia 42080 Lithuania 42090 Norway 42110 Sweden 42120 United Kingdom 42999 Northern Europe, n.s. 43000 Southern Europe 43010 Albania 43020 Andorra 43030 Bosnia and Herzegovina 43040 Croatia 43050 Gibraltar 43060 Greece 43070 Italy 43071 Vatican City 43080 Malta 43090 Portugal 43100 San Marino 43110 Slovenia 43120 Spain 43130 Macedonia 43140 Yugoslavia 43141 Montenegro 43142 Serbia 43144 Kosovo 43999 Southern Europe, n.s. 44000 Western Europe 44010 Austria 44020 Belgium 44030 France 44040 Germany 44050 Liechtenstein 44060 Luxembourg 44070 Monaco 44080 Netherlands 44090 Switzerland 44999 Western Europe, n.e.c. 49990 Europe, n.s. 49991 Central-Eastern Europe 49992 European Union 49993 European Union (original 15) 49994 Other European Union (not original 15) 50000 Oceania 51000 Australia and New Zealand 51010 Australia 51020 New Zealand 51030 Norfolk Islands 51999 Australia and New Zealand, n.s. 52000 Melanesia 52010 Fiji 52020 New Caledonia 52030 Papua New Guinea 52040 Solomon Islands 52050 Vanuatu (New Hebrides) 53000 Micronesia 53010 Kiribati 53020 Marshall Islands 53030 Nauru 53040 Northern Mariana Isls. 53050 Palau 53999 Micronesia, n.e.c. 54000 Polynesia 54010 Cook Islands 54020 French Polynesia 54030 Niue 54040 Pitcairn Island 54050 Samoa 54060 Tokelau 54070 Tonga 54080 Tuvalu 54090 Wallis and Futuna Isls. 59999 Oceania, n.s. 60000 Other 90000 Non-migrant (international) 99998 Response suppressed 99999 Unknown MIGCTRY1 indicates the country of residence 1 year ago for international migrants. Persons who did not live abroad 1 year prior are coded to the "non-migrant" category. IPUMS Province of residence 1 year ago, Kenya; consistent boundaries, GIS 404001 Nairobi 404002 Central 404003 Coast 404004 Eastern 404005 Northeastern 404006 Nyanza 404007 Rift Valley 404008 Western 404097 Abroad 404098 Unknown 404099 NIU (not in universe) MIG1_1_KE indicates the person's province of residence within Kenya 1 year ago. 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 District of residence 1 year ago, Kenya; consistent boundaries, GIS 404001001 Nairobi East, Nairobi North, Nairobi West, Westlands 404002001 Gatanga, Gatundu, Githunguri, Kiambu (Kiambaa), Kiambu West, Kikuyu, Lari, Muranga North, Muranga South, Nyandarua North, Nyandarua South, Ruiru, Thika East, Thika West 404002002 Nyeri North, Nyeri South 404002003 Kirinyaga 404002099 Central Province, unknown district 404003001 Mombasa, Kilindini 404003002 Kinango, Kwale, Msambweni 404003003 Kaloleni, Kilifi, Malindi 404003004 Tana Delta, Tana River 404003005 Lamu 404003006 Taita, Taveta 404003099 Coast Province, unknown district 404004001 Chalbi, Laisamis, Marsabit, Moyale 404004002 Garba Tulla, Igembe, Imenti Central, Imenti North, Imenti South, Isiolo, Maara, Meru South, Tharaka, Tigania 404004003 Embu, Kangundo, Kibwezi, Machakos, Makueni, Mbeere, Mbooni, Mwala, Nzaui, Yatta 404004004 Kitui North, Kitui South (Mutomo), Kyuso, Mwingi 404004099 Eastern Province, unknown district 404005001 Fafi, Garissa, Ijara, Lagdera 404005002 Wajir East, Wajir North, Wajir South, Wajir West 404005003 Mandera Central, Mandera East, Mandera West 404005099 North-Eastern province, unknown district 404006001 Bondo, Rarieda, Siaya 404006002 Kisumu East, Kisumu West, Nyando 404006003 Homa Bay, Kuria East, Kuria West, Migori, Rachuonyo, Rongo, Suba 404006004 Borabu, Gucha, Gucha South, Kisii Central, Kisii South, Manga, Masaba, Nyamira 404006099 Nyanza province, unknown district 404007001 Turkana Central, Turkana North, Turkana South 404007002 Pokot Central, Pokot North, West Pokot 404007003 Samburu Central, Samburu East, Samburu North 404007004 Kwanza, Trans Nzoia East, Trans Nzoia West 404007005 Baringo, Baringo North, East Pokot, Koibatek, Laikipia East, Laikipia North, Laikipia West 404007006 Eldoret East, Eldoret West, Wareng 404007007 Keiyo, Marakwet 404007008 Nandi Central, Nandi East, Nandi North, Nandi South, Tinderet 404007009 Kaijiado Central, Kaijiado North, Loitoktok, Molo, Naivasha, Nakuru, Nakuru North 404007010 Narok North, Narok South, Trans Mara 404007011 Bomet, Buret, Kericho, Kipkelion, Sotik 404007099 Rift Valley province, unknown district 404008001 Butere, Emuhaya, Hamisi, Kakamega Central, Kakamega East, Kakamega North, Kakamega South, Lugari, Mumias, Vihiga 404008002 Bungoma East, Bungoma North, Bungoma South, Bungoma West, Mt. Elgon 404008003 Bunyala, Busia, Samia, Teso North, Teso South 404008099 Western province, unknown district 404097097 Abroad 404098098 Unknown 404099099 NIU (not in universe) MIG2_1_KE indicates the person's district of residence in Kenya 1 year ago. Migration district codes from MIG2_1_KE are compatible with codes from GEO2_KE. 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 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 Relationship to head <svar a="all" v="KE89A401">P10. Relationship:<br /><br />What is the relationship of [the respondent] to the head of household?<br /><div class="i1">[] 1 Head<br />[] 2 Spouse<br />[] 3 Son<br />[] 4 Daughter<br />[] 5 Father<br />[] 6 Mother<br />[] 7 Other relative<br />[] 8 Non-relative</div><br /></svar> Kenya 1989: All persons, except travellers and those receiving the short questionnaire [discrepancies: none] NIU (not in universe) 1 Head 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Other relative 8 Non-relative 9 Unknown This variable indicates the relationship of the individual to the household head. IPUMS Sex <svar a="all" v="KE89A402">P11. Sex:<br /><div class="i1">[] Male<br />[] Female</div><br /></svar> Kenya 1989: All persons 1 Male 2 Female 9 Unknown This variable indicates the sex of the individual. IPUMS Age <svar a="all" v="KE89A403">P12. Age:<br /><br />How old is [the respondent]? Age in completed years _ _<br /><div class="i1">Use two digits in completing age; if under one year, write '00'</div><br /></svar> Kenya 1989: All persons 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97 97+ 99 Unknown This variable indicates the person's age in years. IPUMS Marital status <svar a="all" v="KE89A404">P13. Marital Status:<br /><br />What is [the respondent's] marital status?<br /><div class="i1">[] 1 Single<br />Married:<br />[] 2 Monogamous<br />[] 3 Polygamous<br />[] 4 Widowed<br />[] 5 Divorced<br />[] 6 Separated</div><br /></svar> Kenya 1989: All persons, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Single 2 Monogamous 3 Polygamous 4 Widowed 5 Divorced 6 Separated 8 Unknown 9 NIU (not in universe) This variable indicates the marital status of the individual. IPUMS Birthplace <svar a="all" v="KE89A406">P15. Birth place:<br /><br />Where was [the respondent] born? (State district if born in Kenya or country if born outside Kenya) ____ _ _<br /></svar> Kenya 1989: Persons except travellers and those receiving the short questionnaire [discrepancies: none] 01 Uganda 02 Tanzania 03 Ethiopia 04 Somalia 05 Sudan 06 Other Africa 09 Kenya, not specified 11 Nairobi 20 Central Province 21 Kiambu 22 Kirinyaga 23 Muranga 24 Nyandaura 25 Nyeri 30 Coast Province 31 Kilifi 32 Kwale 33 Lamu 34 Mombasa 35 Taita Tave 36 Tana River 40 Eastern Province 41 Embu 42 Isiolo 43 Kitui 44 Machakos 45 Marsabit 46 Meru 50 North-Eastern Province 51 Garissa 52 Mandera 53 Wajir 60 Nyanza Province 61 Kisii 62 Kisumu 63 Siaya 64 South Nyanza 70 Rift Valley Province 71 Kajiado 72 Kericho 73 Laikipia 74 Nakuru 75 Nandi 76 Narok 81 Baringo 82 Elgeyo-Marakwet 83 Samburu 84 Trans-Nzoia 85 Turkana 86 Uasin-Gishu 87 West-Pokot 90 Western Province 91 Bugoma 92 Busia 93 Kakamega 94 European countries 95 Asian countries 96 American countries 97 Other unspecified countries 98 Unknown 99 NIU (not in universe) This variable indicates the birth place of the individual (State district if born in Kenya or country if born outside Kenya). IPUMS Previous residence <svar a="all" v="KE89A407">P16. Previous residence:<br /><br />Where was [the respondent] living in August 1988? State district if in Kenya or country if outside Kenya (Code 00 if under 1years) ____ _ _<br /></svar> Kenya 1989: Persons age 1+, except travellers and those receiving the short questionnaire [discrepancies: none] 01 Uganda 02 Tanzania 03 Ethiopia 04 Somalia 05 Sudan 06 Other Africa 09 Kenya 11 Nairobi 20 Central Province 21 Kiambu 22 Kirinyaga 23 Muranga 24 Nyandarua 25 Nyeri 30 Coast Province 31 Kilifi 32 Kwale 33 Lamu 34 Mombasa 35 Taita Taveta 36 Tana River 40 Eastern Province 41 Embu 42 Isiolo 43 Kitui 44 Machakos 45 Marsabit 46 Meru 50 North Eastern Province 51 Garissa 52 Mandera 53 Wajir 60 Nyanza Province 61 Kisii 62 Kisumu 63 Siaya 64 South Nyanza 70 Rift Valley Province 71 Kajiado 72 Kericho 73 Laikipia 74 Nakuru 75 Nandi 76 Narok 81 Baringo 82 Elgeyo-Marakwet 83 Samburu 84 Trans-Nzoia 85 Turkan 86 Uasln-Gishu 87 West-Pokot 90 Western Province 91 Bungoma 92 Busia 93 Kakamega 94 European countries 95 Asian countries 96 American countries 97 Other unspecified countries 98 Unknown 99 NIU (not in universe) This variable indicates the previous residence in August 1988 (State district if in Kenya or Country if outside Kenya). IPUMS Father alive <svar a="all" v="KE89A408">P17. Is his/her father alive?<br /><div class="i1">[] 1 Yes<br />[] 2 No<br />[] 3 Not known</div><br /></svar> Kenya 1989: All persons, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Yes 2 No 3 Respondent did not know 8 Unknown 9 NIU (not in universe) This variable indicates whether the father of the individual is alive or not. IPUMS Mother alive <svar a="all" v="KE89A409">P18. Is his/her mother alive?<br /><div class="i1">[] 1 Yes<br />[] 2 No<br />[] 3 Not known</div><br /></svar> Kenya 1989: All persons, except travellers and those receiving the short questionnaire [discrepancies: none] 1 Yes 2 No 3 Respondent did not know 8 Unknown 9 NIU (not in universe) This variable indicates whether the mother of the individual is alive or not. IPUMS Literacy <svar v="KE89A410 KE89A411 KE89A412"><span class="h2">B. Persons aged 6 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A410">P19. Literacy:<br /><br />Does [the respondent] know how to read and write a simple statement in any language?<br /><div class="i1">[] 0 Not applicable<br />[] 1 Yes<br />[] 2 No</div><br /></svar> Kenya 1989: Persons age 6+, except travellers and those receiving the short questionnaires [discrepancies: type I 0.1%; type II none] 1 Yes 2 No 8 Unknown 9 NIU (not in universe) This variable indicates whether the individual knows how to read and write a simple statement in any language. IPUMS School attendance <svar v="KE89A410 KE89A411 KE89A412"><span class="h2">B. Persons aged 6 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A411">P20. Has [the respondent] ever attended school?<br /><div class="i1">[] 1 At school<br />[] 2 Left school<br />[] 3 Never went to school<br /><br />(Code 0 if age is 5 years or less)</div><br /></svar> Kenya 1989: Persons age 6+, except travellers and those receiving the short questionnaires [discrepancies: type I trace; type II none] 1 Attending school 2 Left school 3 Never attended school 8 Unknown 9 NIU (not in universe) This variable indicates whether the person has ever attended school. IPUMS Educational attainment <svar v="KE89A410 KE89A411 KE89A412"><span class="h2">B. Persons aged 6 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A412">P21. What is [the respondent's] highest level of education completed? (e.g., class, form, university) _ _<br /></svar> Kenya 1989: Persons age 6+ who had ever attended school, except travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II none] 00 Did not complete standard 1 01 Standard 1 02 Standard 2 03 Standard 3 04 Standard 4 05 Standard 5 06 Standard 6 07 Standard 7 08 Standard 8 11 Form 1 12 Form 2 13 Form 3 14 Form 4 15 Form 5 16 Form 6 17 University not completed 18 Completed university 98 Unknown 99 NIU (not in universe) This variable indicates the highest level of education completed. IPUMS Activity status <svar v="KE89A413 KE89A414 KE89A415 KE89A416"><span class="h2">C. Persons aged 10 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A413">P30. Activity:<br /><br />What was [the respondent] mainly doing during the last 7 days preceding the Census night?<br /><div class="i1">[] 01 Worked for pay or profit<br />[] 02 On leave/sick leave<br />[] 03 Working on family holding<br />[] 04 No work<br />[] 05 Seeking work<br />[] 06 Student<br />[] 07 Retired<br />[] 08 Disabled<br />[] 09 Home makers<br />[] 10 Other</div><br /></svar> Kenya 1989: Persons age 10+, except travellers and those receiving short questionnaires [discrepancies: type I trace; type II none] 01 Work for pay or profit 02 On leave, sick 03 Worked on family holding 04 No work 05 Seeking work 06 Student 07 Retired 08 Disabled 09 Homemakers 10 Other 98 Unknown 99 NIU (not in universe) This variable indicates economic activity during the last 7 days preceding the census night. IPUMS Occupation, 2 digits <svar v="KE89A413 KE89A414 KE89A415 KE89A416"><span class="h2">C. Persons aged 10 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A414 KE89A415">P31. Occupation:<br /><br />What was [the respondent's] main occupation? Write detailed description of type of work: e.g., clerical, motor mechanic, primary school teacher, etc. ____<br /></svar> Kenya 1989: Persons age 10+ who worked for pay, worked on family holding or were on leave 7 days before the census night [discrepancies: type I 0.6%; type II none] 11 Professional technical and related workers 12 Medical, paramedical and nursing workers 13 Physical and life scientists 14 Human related resource workers 15 Statisticians, mathematicians and economists 16 Teachers, lecturers, instructors 17 Jurists and legal practitioners 18 Cultural worker 19 Other professional and related workers 21 Public administrators 22 Private or Parastatal Managers Administrators 23 Accountant bookkeeper cashiers 24 Office clerks and office machine operators 25 Transport supervisors and clerks 26 Radio and telecom supervisors and clerks 29 Other admin managerial andclerical 30 Sales workers 41 Service workers 42 Lodging catering and cleaning service 43 Personal service workers 49 Other service workers 51 Agricultural workers 52 Forestry workers 53 Fishermen 54 Wildlife workers 59 Other agricultural, forestry and related 61 Production supervisor and controllers 62 Mine quarry and related workers 63 Chemical andmineral processing workers 64 Metal fabrication workers 66 Textile prepares 67 Textile andleather goods makers 68 Food beverage and tobacco processors 69 Wood workers and wood working machine operators 70 Smiths, welders and sheet metal workers 71 Metal work machine operators, machine fitters 72 Engine maintenance workers 73 Electrical and electronic workers 74 Printing and reproduction workers 75 Other manufacturing workers 76 Painters 77 Construction workers 78 Stationery operators 79 Other production and maintenance workers 81 Packer andpacking machine operators 82 Loading and storage workers 83 Transport workers 98 Unknown 99 NIU (not in universe) This variable indicates the main occupation (2 digits) during the last 7 days preceding the census night. IPUMS Occupation, 4 digits <svar v="KE89A413 KE89A414 KE89A415 KE89A416"><span class="h2">C. Persons aged 10 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A414 KE89A415">P31. Occupation:<br /><br />What was [the respondent's] main occupation? Write detailed description of type of work: e.g., clerical, motor mechanic, primary school teacher, etc. ____<br /></svar> Kenya 1989: Persons age 10+ who worked for pay, worked on family holding or were on leave 7 days before the census night [discrepancies: type I 0.6%; type II none] 0000 NIU (not in universe) 1111 Architect 1112 Town planner 1113 Architectural draughtsman 1114 Cartographer 1115 Surveyor (general) 1116 Cartographical draughtsman 1117 Civil engineer (general) 1118 Civil engineering technician/ draughtsman 1119 Electrical engineer 1120 Electrical technician/ draughtsman 1121 Electronics engineer (general) 1122 Electronics technician/ draughtsman 1123 Mechanical engineer (general) 1124 Mechanical technician/ draughtsman 1125 Heating ventilation and refrigeration engineer 1126 Heating, vent and refrigeration technician/draftsman 1127 Chemical engineer (general) 1128 Chemical technician/ laboratory assistant 1129 Mining engineer 1130 Mining technician 1131 Metallurgist 1132 Metallurgist technician 1133 Other engineer 1134 Other engineering technician/ draughtsman 1136 Turbine operator 1154 Undocumented 1211 Medical doctor 1212 Medical clinical assistant 1213 Medical laboratory technician 1214 Dentist 1215 Dental technician 1216 Veterinarian 1217 Veterinary assistant 1218 Pharmacist 1219 Pharmaceutical technologist assistant 1220 Nutritionist dietician 1221 Public health inspector 1222 X-ray and radiography technician 1223 Physiotherapist 1224 Nursing supervisor or matron 1225 Nurse 1226 Midwife 1227 Nursing aid 1228 First aid officer 1229 Other medical paramedical or nursing worker 1310 Chemist 1311 Physicist 1312 Geologist 1313 Non-identifiable occupations 1314 Agronomist 1315 Physical or life scientist/ technician 1316 Other physical or life scientist 1410 Psychologist 1411 Sociologist anthropologist 1412 Social worker (general) 1413 Other human relations and resource worker 1415 Undocumented 1416 Undocumented 1510 Statistician actuary 1511 Mathematician 1512 System analyst 1513 Computer programmer 1514 Computer technician or operator 1515 Economist (general) 1516 Statistical mathematical and related technician 1517 Other statistician mathematician, economist, or rel 1574 Undocumented 1610 University and higher education teacher/ lecturer 1611 Secondary education teacher/ instructor 1612 Primary education teacher/ instructor 1613 Special education teacher/instructor 1614 Job instructor 1615 Other teacher lecturing or instructor 1617 Undocumented 1618 Undocumented 1619 Undocumented 1622 Undocumented 1624 Undocumented 1672 Undocumented 1711 Judge magistrate 1712 Other judicial official 1713 Advocate 1714 Other jurist or legal practitioner 1716 Undocumented 1811 Author 1812 Journalist editor 1813 Public relations officer, information officer 1814 Other writers 1815 Librarian archivist curator 1816 Interpreter translator 1817 Photographer cameraman 1818 Industrial designer 1819 Other creative artists 1820 Performing artist, producer 1821 Other cultural worker 1824 Undocumented 1911 Minister of religion 1912 Other religion worker 1913 Professional, technical or related worker 1919 Undocumented 2190 Undocumented 2210 General manager 2211 Company secretary 2212 Financial manager controller 2213 Personnel manager 2214 Personnel officer 2215 Purchasing manager/ buyer 2216 Production operation works manager 2217 Marketing, sales manager 2218 Other private or parastatal manager administrator 2219 Undocumented 2311 Accountant, auditor (general) 2312 Accounts clerk 2313 Bookkeeper 2314 Bank teller 2315 Other account, book keeper or cashier 2318 Undocumented 2320 Undocumented 2410 Clerical service, data entry or control supervisor 2411 Records keeper stores clerk 2412 General office clerk 2413 Receptionist (general) 2414 Stenographer secretary 2415 Typist 2416 Telex operator 2417 Data control clerk 2418 Book keeping machine operator 2419 Other office machine operator 2511 Railway yard master/ station master 2512 Railway traffic controller 2513 Railway traffic controller operator/ cabin operator 2514 Locomotive/train inspector 2515 Road transport supervisor 2516 Road transport inspector 2517 Air transport controller 2518 Air transport inspector/supervisor 2519 Transport conductor 2520 Other transport supervisor or clerk 2611 Postmaster 2612 Postal service supervisor 2613 Postal counter teller 2614 Other postal clerk 2615 Telecommunications operator 2616 Radio communications operator 2617 Telephone switchboard operator 2618 Broadcasting station operator 2619 Other audio visual equipment operator 2620 Other post radio telecomms supervisor clerk 2990 Administrative managerial or clerical worker 3010 Shopkeepers 3011 Member of commercial or shop cooperative 3012 Sales supervisor 3013 Shop assistant 3014 Shop cashier till operator 3015 Marketer 3016 Street vendor, hawker 3017 Ticket seller 3018 Technical insurance real estate salesman 3019 Other sales workers 3070 Undocumented 3079 Undocumented 4110 Protective service supervisor 4111 Policeman 4112 Security guard 4113 Watchman 4114 Prison warder 4115 Fire fighter (general) 4116 Safety and accident prevention worker 4117 Other protective service workers 4119 Undocumented 4120 Undocumented 4130 Undocumented 4210 Working proprietor (hotel and restaurants) 4211 Hotel restaurant and catering managerial staff 4212 Housekeeping supervisor 4213 Building caretaker 4214 Sweeper cleaner 4215 Other housekeeping service workers 4216 Head cook chef 4217 Cook 4218 Waiter, barman, canteen assistant 4219 Other lodging catering or cleaning service workers 4311 Domestic servant 4312 Children nurse, ayah 4313 Domestic gardener 4314 Hairdresser, hair platter, barber 4315 Other personal service worker 4317 Undocumented 4318 Undocumented 4319 Undocumented 4324 Undocumented 4910 Aircraft cabin and group attendant, hostess 4911 Guide (general) 4912 Office messenger 4913 Service worker (n.e.c) 4914 Undocumented 5110 Non commercial farmer (general farmer) 5111 Commercial farmer 5112 Farm manager 5113 Farmfield assistant 5114 Farm supervisor 5115 Leading hand (farming) charge hand 5116 Crop picker, pruner, picker (field worker general) 5117 Livestock worker (general) 5118 Dairy worker 5119 Other farm laborer 5120 Farm machinery operator 5121 Plant nursery worker 5122 Parks and garden supervisor 5123 Parks and garden workers 5124 Other agricultural worker 5126 Undocumented 5174 Undocumented 5211 Forest manager 5212 Forest ranger 5213 Forest sew supervisor 5214 Feller, logger 5215 Other forestry machine operator 5216 Charcoal burner 5311 Fisherman 5312 Fisheries manager 5313 Fish scout fisheries supervisor 5410 Hunter, trapper 5411 Game warden, ranger 5412 Game scout, zookeeper 5413 Other wildlife worker 5990 Agricultural, forestry or related (n.e.c) 6111 Shift foreman supervisor (mining) 6112 Shift foreman supervisor (metal production) 6113 Shift foreman supervisor (chemical industry) 6114 Shift foreman supervisor (food and beverage industry) 6115 Shift foreman supervisor (textile industry) 6116 Shift foreman supervisor (mechanical industry) 6117 Shift foreman supervisor (construction) 6118 Shift foreman supervisor (other production) 6119 Section foreman supervisor (mining) 6120 Section foreman/ supervisor (textile industry) 6121 Quality controller (food and beverage industry) 6212 Miner 6311 Leading hand (chemical processing) 6312 Mixing machine operator 6313 Screening or floatation worker 6314 Miner 6315 Roaster 6316 Filtration operator (polythene) 6317 Metal smelting and refining worker 6318 Other chemical and mineral processing worker 6319 Undocumented 6411 Metal melter or heater 6412 Metal caster 6413 Metal molder 6414 Metal pipe and tube roller 6418 Other metal fabrication worker 6610 Leading hand (textile preparing) 6611 Undocumented 6612 Printing machine operator 6613 Spinning winding machine operator 6614 Weaving machine operator 6615 Knitting machine operator 6616 Bleaching range op. (bleaching) 6617 Textile printing (machine operator) 6618 Undocumented 6710 Leading hand (textile and leather goods making) 6711 Tanner (tanning deltress) 6712 Tailors dressmaker 6713 Upholsterer 6714 Shoemaker, shoe repairer 6715 Garment cutter 6716 Uppers or bottoms cutter 6717 Sewing machine operator 6718 Other garment or shoe maker 6719 Undocumented 6811 Leading hand, food and beverage 6812 Leading hand tobacco 6813 Grain miller 6814 Sugar grain presser 6815 Grain processor 6816 Slaughterer 6817 Butter 6818 Sausage maker 6819 Dairy products processor (pasteurizer) 6820 Baker (general) 6821 Other beverage maker 6822 Coffee blender 6823 Tobacco grader 6910 Leading hand (woodworking) 6911 Cabinet maker 6912 Wood working setter operator 6913 Model and pattern maker 7010 Leading hand 7011 Blacksmith 7012 Tool and die maker 7013 Welder 7014 Frame cutter 7015 Tin smith 7016 Panel beater 7017 Boiler maker 7018 Other sheet metal worker 7021 Undocumented 7110 Leading hand (mechanical industry) 7111 Metal working machine setter operator 7112 Lathe setter/operator turner 7113 Milling machine setter/operator 7114 Planing machine operator 7115 Undocumented 7116 Tool sharpener saw doctor 7117 Other machine setter operator 7118 Machine assembler (production) 7119 Machinery fitter (maintenance) 7120 Precission/optical instrument fitter 7121 Undocumented 7123 Undocumented 7126 Undocumented 7134 Undocumented 7210 Leading hand 7211 Automobile mechanic 7212 Diesel engine fitter 7213 Aircraft engine mechanic 7216 Undocumented 7310 Leading hand 7311 Electrical assembler 7312 Armature winder 7313 Electronic fitters/assemblers 7314 Radio and television repair 7315 Building electrician 7316 Electrical wireman 7317 Vehicle electrician 7318 Other electrician 7319 Electrical power lineman 7320 Telephone and telegraph lineman 7321 Telephone and telegraph installer 7324 Undocumented 7410 Leading hand 7411 Hand composition 7412 Keyboard operator 7413 Stereotyper 7414 Process camera operator 7415 Photographic darkroom worker 7416 Book binder 7521 Basket or brushmaker 7610 Leading hand painting 7611 Building and structural painter 7612 Spray painter 7613 Other painter 7616 Undocumented 7710 Leading hand construction 7711 Structural steel workers 7712 Track layer 7713 Pipe layer 7714 Rigger 7715 Concrete and finisher 7716 Brick layer mason 7717 Steel worker 7718 Carpenter 7719 Plumber, pipe fitter 7720 Glazier 7721 Other construction worker 7811 Boiler operator 7812 Stationery diesel engine operator 7813 Turbine operator 7815 Pumping machine operator 7816 Other stationary engine operator 7818 Undocumented 7819 Undocumented 7832 Undocumented 7990 Production maintenance worker (n.e.c) 8111 Supervisor foreman (packing) 8112 Leading hand (packing) 8113 Packer general 8119 Undocumented 8211 Supervisor/foreman loading and storage 8212 Loader hand any (industry) 8224 Undocumented 8311 Crane operator (stationary) 8312 Crane and hoist operator 8313 Forklift operator (truck) 8314 Bull dozer operator 8315 Excavating machine operator 8316 Road machine operator (roller) 8317 Dumper driver 8318 Lorry driver 8319 Bus driver 8320 Taxi driver 8321 Aircraft pilot 8322 Flight navigator 8323 Railway engine driver (fireman) 8324 Driver 9998 Unknown 9999 No occupation reported This variable indicates the main occupation (4 digits) during the last 7 days preceding the Census night. IPUMS Class of worker <svar v="KE89A413 KE89A414 KE89A415 KE89A416"><span class="h2">C. Persons aged 10 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A416">P32. Work status:<br /><br />What was [the respondent] working as?<br /><div class="i1">[] 1 Employer<br />[] 2 Self-employed<br />[] 3 Employee<br />[] 4 Family worker</div><br /></svar> Kenya 1989: Persons age 10+ who worked for pay, worked on family holding, or were on leave 7 days before the census night [discrepancies: type I 0.4%; type II none] 1 Employer 2 Self employed 3 Employee 4 Family worker 8 Unknown 9 NIU (not in universe) This variable indicates the person's employment status during the seven days before the census night. NOTE: These data are highly questionable. Most persons did not give a response, including the great majority of agricultural workers. IPUMS Male children living at home <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A417 KE89A418 KE89A429">How many children has [the respondent] born alive who are living in this household?<br /><div class="i1">P40. Boys ____<br />P41. Girls ____</div><br /></svar> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II 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 17 17 20 20 21 21 22 22 24 24 25 25 98 Unknown 99 NIU (not in universe) This variable indicates the number of male children born alive to the person who still live in the household. IPUMS Female children living at home <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A417 KE89A418 KE89A429">How many children has [the respondent] born alive who are living in this household?<br /><div class="i1">P40. Boys ____<br />P41. Girls ____</div><br /></svar> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: type I trace; type II 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 20 20 22 22 98 Unknown 99 NIU (not in universe) This variable indicated the number of female children born alive to the person who still live in the household. IPUMS Male children living away <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A419 KE89A420 KE89A429">How many children has [the respondent] born alive living elsewhere?<br /><div class="i1">P42. Boys ____<br />P43. Girls ____</div><br /></svar> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: type I trace; type II 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 16 16 20 20 22 22 23 23 25 25 26 26 98 Unknown 99 NIU (not in universe) This variable indicates the number of male children born alive to the person but do not live in the household. IPUMS Female children living away <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A419 KE89A420 KE89A429">How many children has [the respondent] born alive living elsewhere?<br /><div class="i1">P42. Boys ____<br />P43. Girls ____</div><br /></svar> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: type I trace; type II 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 17 17 20 20 21 21 26 26 98 Unknown 99 NIU (not in universe) This variable indicates the number of female children born alive to the person but do not live in the household. IPUMS Male children who have died <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A421 KE89A422 KE89A429">How many children has [the respondent] born alive who have died?<br /><div class="i1">P44. Boys ____<br />P45. Girls ____</div><br /></svar> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II none] 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 27 27 98 Unknown 99 NIU (not in universe) This variable indicates the number of male children born alive to the person who have died. IPUMS Female children who have died <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A421 KE89A422 KE89A429">How many children has [the respondent] born alive who have died?<br /><div class="i1">P44. Boys ____<br />P45. Girls ____</div><br /></svar> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II 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 17 17 18 18 19 19 20 20 22 22 27 27 28 28 29 29 98 Unknown 99 NIU (not in universe) This variable indicates the number of female children born alive to the person who have died. IPUMS Last birth, month <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><span class="h3">Particulars of her last live birth</span></p> <p>When was [the respondent's] last child born?</p> <p>P46. Month ____ Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II none] 01 January 02 February 03 March 04 April 05 May 06 June 07 July 08 August 09 September 10 October 11 November 12 December 98 Unknown 99 NIU (not in universe) This variable indicates the month of the last child's birth. IPUMS Last birth, year <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><span class="h3">Particulars of her last live birth</span></p> <p>When was [the respondent's] last child born?</p> <p>P47. Year ____ Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II none] 04 1904 05 1905 10 1910 11 1911 14 1914 15 1915 17 1917 18 1918 19 1919 20 1920 21 1921 22 1922 23 1923 24 1924 25 1925 26 1926 27 1927 28 1928 29 1929 30 1930 31 1931 32 1932 33 1933 34 1934 35 1935 36 1936 37 1937 38 1938 39 1939 40 1940 41 1941 42 1942 43 1943 44 1944 45 1945 46 1946 47 1947 48 1948 49 1949 50 1950 51 1951 52 1952 53 1953 54 1954 55 1955 56 1956 57 1957 58 1958 59 1959 60 1960 61 1961 62 1962 63 1963 64 1964 65 1965 66 1966 67 1967 68 1968 69 1969 70 1970 71 1971 72 1972 73 1973 74 1974 75 1975 76 1976 77 1977 78 1978 79 1979 80 1980 81 1981 82 1982 83 1983 84 1984 85 1985 86 1986 87 1987 88 1988 89 1989 98 Unknown 99 NIU (not in universe) This variable indicates the year of the last child's birth. IPUMS Sex of last birth <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><span class="h3">Particulars of her last live birth</span></p> <p>P48. Was it a boy or a girl?</p> <div class="i1">[] 1 Male<br />[] 2 Female<br />[] 3 Male twins<br />[] 4 Female twins<br />[] 5 Male-female twins<br />[] 6 Multiple births</div> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II none] 1 Male 2 Female 3 Male twins 4 Female twins 5 Male and female twins 6 Multiple births 8 Unknown 9 NIU (not in universe) This variable indicate the gender of the last child born. IPUMS Last birth alive <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><span class="h3">Particulars of her last live birth</span></p> <p>P49. Is this child still alive?</p> <div class="i1">[] 1 Yes<br />[] 2 No</div> Kenya 1989: Females age 12+ who had ever given birth, except the travellers and those receiving the short questionnaires [discrepancies: Type I trace; Type II none] 1 Alive 2 Dead 8 Unknown 9 NIU (not in universe) This variable indicates whether the last child born to the person is still alive or not. IPUMS Month of death of last birth <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><span class="h3">Particulars of her last live birth</span></p> <p>If no in column P49, then give date of death:</p> <p>P50. Month ____ Kenya 1989: Females age 12+ whose last child died, except travellers and those receiving the short questionnaire [discrepancies: Type I trace; Type II none] 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 98 Unknown 99 NIU (not in universe) This variable indicates the month of death of the last birth born alive. IPUMS Year of death of last birth <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><span class="h3">Particulars of her last live birth</span></p> <p>If no in column P49, then give date of death:</p> <p>P51. Year ____ Kenya 1989: Females age 12+ whose last child born died, except travellers and those receiving the short questionnaire [discrepancies: Type I trace; Type II none] 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 98 Unknown 99 NIU (not in universe) This variable indicates the year of death of last birth born alive. IPUMS Number of children ever born <svar v="KE89A417 KE89A418 KE89A419 KE89A420 KE89A421 KE89A422 KE89A423 KE89A424 KE89A425 KE89A426 KE89A427 KE89A428 KE89A429"><span class="h2">D. Females aged 12 years and over</span><br /></svar></p> <p><svar a="all" v="KE89A417 KE89A418 KE89A429">How many children has [the respondent] born alive who are living in this household?<br /><div class="i1">P40. Boys ____<br />P41. Girls ____</div><br /></svar></p> <p><svar a="all" v="KE89A419 KE89A420 KE89A429">How many children has [the respondent] born alive living elsewhere?<br /><div class="i1">P42. Boys ____<br />P43. Girls ____</div><br /></svar></p> <p><svar a="all" v="KE89A421 KE89A422 KE89A429">How many children has [the respondent] born alive who have died?<br /><div class="i1">P44. Boys ____<br />P45. Girls ____</div><br /></svar> Kenya 1989: Females age 12+, except travellers and those receiving the short questionnaire [discrepancies: none] 00 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 39 39 40 40 41 41 42 42 43 43 46 46 47 47 48 48 50 50 51 51 52 52 53 53 54 54 55 55 59 59 60 60 61 61 64 64 65 65 70 70 71 71 80 80 90 90 98 Unknown 99 NIU (not in universe) This variable indicates the number of children ever born. IPUMS