1979 Population Census DDI_KEN_1979_PHC_v01_M_v7.6_A_IPUMS IPUMS

Integrated Public Use Microdata Series (IPUMS) International

IPUMS April 1, 2025 IPUMS, 50 Willey Hall, 225 - 19th Avenue South, Minneapolis, MN 55455 Organization for Economic Co-operation and Development, Development Co-operation Directorate JADE#:60525;MEHLB(2010)12 IPUMS Version 7.6 October 2025 : NEW FEATURES. --NO "new features" listed in Revision History NEW SAMPLES. --Six new census samples for Honduras (2013), Kenya (2019), Malawi (2018), Mongolia (2010, 2020), and Mozambique (2017) were added to the data series. All census samples extend pre-existing series for those countries. --91 quarterly labor force surveys from the Philippines (1997 - 2019) were added to IPUMS. SUPPLEMENTAL DATA. --No "supplemental data" listed in revision history NEW VARIABLES. --New spatially harmonized birthplace and previous-residence variables are available for samples in this data release. More information is available here (https://international.ipums.org/international/geo_mig.shtml). --Users should note that many older migration and birthplace variables are available by different names. Refer to this table for a crosswalk of old and corresponding new migration variables. For birthplace variables refer to this table (https://international.ipums.org/international/resources/misc_docs/migCrosswalk_names.pdf). EDITED SAMPLES. --For the Zambia 2000 sample, an error in the household breaks was corrected, resulting in the creation of 1,988 new households (1% increase) that were previously combined with other households. The person records included in the sample did not change. Due to an inconsistency in the original file, no household-level information other than geographic location is available for these newly identified households, necessitating the addition of "unknown" values for this sample to the following variables: BEDROOMS, ELECTRIC, FLOOR, FUELCOOK, FUELHEAT, OWNERSHIP, PHONE, RADIO, REFRIG, ROOMS, SEWAGE, TRASH, WATSRC, TV, TOILET, GQ, ROOF, WATSUP, BIKE, MOTORCYCLE, KITCHEN, GQTYPE, AUTOS, and WALL. EDITED VARIABLES. --For the 1998 and 2008 Malawi samples, the family interrelationship pointer variables MOMLOC and POPLOC were modified to allow a "Spouse/partner" of the household head to be linked as a parent to an "Other relative", because the enumeration instructions specify that adopted and stepchildren were categorized as "Other relative". These samples are now consistent with the links made in the newly released 2018 Malawi sample, which had the same enumeration instructions for adopted and stepchildren. --In the samples for Côte d'Ivoire 1988 and 1998, Rwanda 1991 and 2002, Togo 1960 and 2010, and South Africa 2001, for the harmonized variable POLYGAM, persons in consensual unions were previously coded as "No, in monogamous union". Because there was no response option in these samples for polygamous consensual unions, it is more appropriate to treat these cases as not-in-universe, so they have been recoded to "NIU (not in universe)". --MARST has been edited for Honduras 1974 to reclassify the source variable responses "married, wife lives separately" and "consensual union, companion lives separately" as separations. The documentation suggests that "separately" actually indicates a relationship separation and not an absent spouse or companion. Other minor edits were implemented for MARST for Mozambique 1997 and 2007. --In the Mozambique 1997 sample, an error was corrected that recoded persons with a relationship of "Unknown" in the source data to "Other relative or non-relative" (6000) in the harmonized variable RELATE. These persons are now coded as "Not Stated/Unknown" (9999). --In the Malawi 1987, 1998, and 2008 samples, for variable WATSUP, a programming error was corrected such that any households who reported having piped water in either the wet or the dry season are classified as having access to piped water. This programming was also applied to the newly released 2018 sample. --The NATIVITY variable has been edited in the Chile 2017 sample to correct a programming error that mistakenly classified as foreign-born about 20 thousand person records that were actually native-born. --The MIGRATE5 variable has been edited in the Chile 2017 sample, given a programming error that classified most migrants as having changed their major geographic unit. The MIGRATE5 variable for the Chile 1982 and 1992 samples has been edited to use spatially harmonized geographic units to calculate migration status. --In the 1989, 1999, and 2009 Kenya samples, households who indicated that their lighting type or fuel was "Solar" were recoded from "No" to "Yes" in ELECTRIC, based on secondary sources documenting the spread of home solar energy systems in Kenya beginning in the mid-1980s. In the 1989 and 1999 Kenya samples, programming was removed that previously recoded households that reported using electricity as their main cooking fuel to "Yes" in the access to electricity variable ELECTRIC, making it more consistent across samples. Other minor edits were implemented for ELECTRIC in Botswana 2011, Ethiopia 1984 and 1994, Mongolia 1989, Mozambique 2007. --Some samples in DISCARE classified responses indicating "some" difficulty into "yes". These cases were revised to consistently include in "yes" only responses indicating "a lot of difficulty" or "cannot do at all". --Some codes were improperly labeled for municipalities in Honduras 1961 and 1974, which affect variables on place of residence, birthplace, and previous residence. 1979 Population Census - IPUMS Subset PHC ke1979a (IPUMS Harmonized Subset) KEN_1979_PHC_v01_M_v7.6_A_IPUMS Central Bureau of Statistics Ministry of Finance and Planning IPUMS (c) Copyright 1979, Central Bureau of Statistics Ministry of Finance and Planning and Minnesota Population Center Central Bureau of Statistics Ministry of Finance and Planning Population and Housing Census [hh/popcen] IPUMS International DOI:10.18128/D020.V7.6 Version 7.6. The datasets contain selected variables from the original 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 Education Variables -- PERSON Geography: F-N Variables -- HOUSEHOLD Migration: Global Variables -- PERSON Group Quarters Variables -- HOUSEHOLD Migration: F-N Variables -- PERSON Technical Person Variables -- PERSON August 24, 1979 Kenya District Persons UNITS IDENTIFIED: - Dwellings: no - Vacant Units: - Households: no - Individuals: yes - Group quarters: no UNIT DESCRIPTIONS: - Dwellings: No - Households: A group of persons who normally live and eat together, whether or not they are related by blood or marriage. - Group quarters: Group quarters consist of schools/colleges, barracks, prisons, hospitals and other institutions. All persons who were in Kenya at midnight on Census Night. Population and Housing Census [hh/popcen] Additional notes on a sample that is part of this study: Kenya 1979 Note: Persons not organized into households MICRODATA SOURCE: Central Bureau of Statistics Ministry of Finance and Planning SAMPLE SIZE (person records): 1033769. SAMPLE DESIGN: Unknown sample design. Original weights were adjusted by IPUMS to properly reflect the population distribution by age, sex, and province. NOTE: The sample is a sample of individuals. Face-to-face [f2f] Single enumeration form that requested information on individuals. de facto, CENSUS DAY: August 24, 1979 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, and Nate Merrill. Integrated Public Use Microdata Series, International: Version 7.6 [dataset]. Minneapolis, MN: IPUMS, 2025. https://doi.org/10.18128/D020.V7.6 Researchers should also acknowledge the statistical agency that originally produced the data: Kenya, Central Bureau of Statistics Ministry of Finance and Planning. 1979 Population 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. User-provided description: DOI:10.18128/D020.V7.6 Extract for ke1979a, 2025 KEN1979_PHC-H-H.dat Household records n.a. ascii Minnesota Population Center Version 7.5, IPUMS sample KEN1979_PHC-P-H.dat Person records 1033769 ascii Minnesota Population Center Version 7.5, IPUMS sample Record type 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. H Household P Person Technical Household Variables -- HOUSEHOLD Country 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). 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 Côte d'Ivoire 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 Technical Household Variables -- HOUSEHOLD Year 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. 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 Technical Household Variables -- HOUSEHOLD IPUMS sample identifier 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). 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 340201301 Honduras 2013 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 Côte d'Ivoire 1988 384199801 Côte d'Ivoire 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 404201901 Kenya 2019 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 454201801 Malawi 2018 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 496201001 Mongolia 2010 496202001 Mongolia 2020 504198201 Morocco 1982 504199401 Morocco 1994 504200401 Morocco 2004 504201401 Morocco 2014 508199701 Mozambique 1997 508200701 Mozambique 2007 508201701 Mozambique 2017 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 608199721 Philippines 1997 Q1 LFS 608199722 Philippines 1997 Q2 LFS 608199723 Philippines 1997 Q3 LFS 608199724 Philippines 1997 Q4 LFS 608199821 Philippines 1998 Q1 LFS 608199822 Philippines 1998 Q2 LFS 608199823 Philippines 1998 Q3 LFS 608199824 Philippines 1998 Q4 LFS 608199921 Philippines 1999 Q1 LFS 608199922 Philippines 1999 Q2 LFS 608199923 Philippines 1999 Q3 LFS 608199924 Philippines 1999 Q4 LFS 608200021 Philippines 2000 Q1 LFS 608200022 Philippines 2000 Q2 LFS 608200023 Philippines 2000 Q3 LFS 608200024 Philippines 2000 Q4 LFS 608200121 Philippines 2001 Q1 LFS 608200122 Philippines 2001 Q2 LFS 608200123 Philippines 2001 Q3 LFS 608200124 Philippines 2001 Q4 LFS 608200221 Philippines 2002 Q1 LFS 608200222 Philippines 2002 Q2 LFS 608200223 Philippines 2002 Q3 LFS 608200224 Philippines 2002 Q4 LFS 608200321 Philippines 2003 Q1 LFS 608200322 Philippines 2003 Q2 LFS 608200323 Philippines 2003 Q3 LFS 608200324 Philippines 2003 Q4 LFS 608200421 Philippines 2004 Q1 LFS 608200422 Philippines 2004 Q2 LFS 608200423 Philippines 2004 Q3 LFS 608200424 Philippines 2004 Q4 LFS 608200521 Philippines 2005 Q1 LFS 608200522 Philippines 2005 Q2 LFS 608200523 Philippines 2005 Q3 LFS 608200524 Philippines 2005 Q4 LFS 608200621 Philippines 2006 Q1 LFS 608200622 Philippines 2006 Q2 LFS 608200623 Philippines 2006 Q3 LFS 608200624 Philippines 2006 Q4 LFS 608200721 Philippines 2007 Q1 LFS 608200722 Philippines 2007 Q2 LFS 608200723 Philippines 2007 Q3 LFS 608200724 Philippines 2007 Q4 LFS 608200821 Philippines 2008 Q1 LFS 608200822 Philippines 2008 Q2 LFS 608200823 Philippines 2008 Q3 LFS 608200824 Philippines 2008 Q4 LFS 608200921 Philippines 2009 Q1 LFS 608200922 Philippines 2009 Q2 LFS 608200923 Philippines 2009 Q3 LFS 608200924 Philippines 2009 Q4 LFS 608201021 Philippines 2010 Q1 LFS 608201022 Philippines 2010 Q2 LFS 608201023 Philippines 2010 Q3 LFS 608201024 Philippines 2010 Q4 LFS 608201121 Philippines 2011 Q1 LFS 608201122 Philippines 2011 Q2 LFS 608201123 Philippines 2011 Q3 LFS 608201124 Philippines 2011 Q4 LFS 608201221 Philippines 2012 Q1 LFS 608201222 Philippines 2012 Q2 LFS 608201223 Philippines 2012 Q3 LFS 608201224 Philippines 2012 Q4 LFS 608201321 Philippines 2013 Q1 LFS 608201322 Philippines 2013 Q2 LFS 608201323 Philippines 2013 Q3 LFS 608201324 Philippines 2013 Q4 LFS 608201421 Philippines 2014 Q1 LFS 608201422 Philippines 2014 Q2 LFS 608201423 Philippines 2014 Q3 LFS 608201424 Philippines 2014 Q4 LFS 608201521 Philippines 2015 Q1 LFS 608201522 Philippines 2015 Q2 LFS 608201523 Philippines 2015 Q3 LFS 608201524 Philippines 2015 Q4 LFS 608201621 Philippines 2016 Q1 LFS 608201622 Philippines 2016 Q2 LFS 608201623 Philippines 2016 Q3 LFS 608201624 Philippines 2016 Q4 LFS 608201721 Philippines 2017 Q1 LFS 608201722 Philippines 2017 Q2 LFS 608201723 Philippines 2017 Q3 LFS 608201724 Philippines 2017 Q4 LFS 608201821 Philippines 2018 Q1 LFS 608201822 Philippines 2018 Q2 LFS 608201823 Philippines 2018 Q3 LFS 608201824 Philippines 2018 Q4 LFS 608201921 Philippines 2019 Q1 LFS 608201922 Philippines 2019 Q2 LFS 608201923 Philippines 2019 Q3 LFS 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 Technical Household Variables -- HOUSEHOLD 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. Technical Household Variables -- HOUSEHOLD 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. Technical Household Variables -- HOUSEHOLD 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. Technical Household Variables -- HOUSEHOLD Subsample number 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. 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 Technical Household Variables -- HOUSEHOLD 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. Technical Household Variables -- HOUSEHOLD Group quarters (collective dwelling) status 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. 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 Group Quarters Variables -- HOUSEHOLD Urban-rural status URBAN indicates whether the household was located in a place designated as urban or as rural. 1 Rural 2 Urban 9 Unknown Geography: Global Variables -- HOUSEHOLD Continent and region of country REGIONW identifies the continent and region of each 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 Geography: Global Variables -- HOUSEHOLD 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. Geography: Global Variables -- HOUSEHOLD 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. Geography: Global Variables -- HOUSEHOLD 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 numeric variable listing the population density in persons per square kilometer. Codes0 = Unknown. Geography: Global Variables -- HOUSEHOLD 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 numeric variable listing the population density in persons per square kilometer. Codes0 = Unknown. Geography: Global Variables -- HOUSEHOLD 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 numeric variable listing the area in square kilometers. Codes0 = Unknown. Geography: Global Variables -- HOUSEHOLD 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 numeric variable listing the area in square kilometers. Codes0 = Unknown. Geography: Global Variables -- HOUSEHOLD Kenya, Province 1969 - 2019 [Level 1; consistent boundaries, GIS] 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. 404001 Nairobi 404002 Central 404003 Coast 404004 Eastern 404005 Northeastern 404006 Nyanza 404007 Rift Valley 404008 Western Geography: F-N Variables -- HOUSEHOLD Kenya, Province 1979 [Level 1, GIS] GEO1_KE1979 identifies the household's province or national capital within Kenya in 1979. Provinces or national capital are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_KE1979 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. 001 Nairobi 002 Central 003 Coast 004 Eastern 005 North-Eastern 006 Nyanza 007 Rift Valley South 008 Rift Valley North 009 Western Geography: F-N Variables -- HOUSEHOLD Kenya, District 1969 - 2019 [Level 2; consistent boundaries, GIS] GEO2_KE identifies the household's county within Kenya in all sample years. Counties are the second level administrative units of the country. 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 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. 404001047 Nairobi City 404002018 Nyandarua 404002019 Nyeri 404002020 Kirinyaga 404002022 Kiambu, Murang'a 404003001 Mombasa 404003002 Kwale 404003003 Kilifi 404003004 Tana River 404003005 Lamu 404003006 Taita-Taveta 404004010 Marsabit 404004011 Isiolo 404004012 Meru, Tharaka-Nithi 404004015 Kitui 404004016 Machakos, Makueni, Embu 404005007 Garissa 404005008 Wajir 404005009 Mandera 404006041 Siaya 404006042 Kisumu 404006043 Homa Bay, Migori 404006045 Kisii, Nyamira 404007023 Turkana 404007024 West Pokot 404007025 Samburu 404007026 Trans Nzoia 404007027 Uasin Gishu 404007028 Elgeyo-Marakwet 404007029 Nandi 404007030 Baringo, Laikipia 404007032 Nakuru 404007033 Narok 404007034 Kajiado 404007035 Kericho, Bomet 404008037 Kakamega, Vihiga 404008039 Bungoma 404008040 Busia Geography: F-N Variables -- HOUSEHOLD Kenya, District 1979 [Level 2, GIS] GEO2_KE1979 identifies the household's district within Kenya in 1979. Districts are the second level administrative units of the country, after provinces. A GIS map (in shapefile format), corresponding to GEO2_KE1979 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. 001000 Nairobi 002001 Kiambu 002002 Kirinyaga 002003 Murang'a 002004 Nyandarua 002005 Nyeri 003001 Kilifi 003002 Kwale 003003 Lamu 003004 Mombasa 003005 Taita Taveta 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 Markwet 008003 Samburu 008004 Trans Nzoia 008005 Turkana 008006 Uasin Gishu 008007 West Pokot 009001 Bungoma 009002 Busia 009003 Kakamega Geography: F-N Variables -- HOUSEHOLD DHS-IPUMS-I Kenya regions, 1969-2019 [consistent boundaries, GIS] 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. 1 Nairobi 2 Central 3 Coast 4 Eastern 5 Nyanza 6 Rift Valley 7 Western 8 Northeastern Geography: IPUMS-I, IPUMS-DHS Variables -- HOUSEHOLD Dwelling number Kenya 1979: All records This variable indicates dwelling number. This is a 7-digit numeric variable with 0 implied decimal places Technical Household Variables -- HOUSEHOLD Number of persons in household Kenya 1979: All records This variable indicates the number of persons in household. 1 1 (all households have 1 person) Technical Household Variables -- HOUSEHOLD Urban-rural Kenya 1979: All households This variable indicates whether the household is located in an urban or rural area. 1 Rural 2 Urban Geography: F-N Variables -- HOUSEHOLD Strata Kenya 1979: All households This variable is the strata identifier for the sample. Strata is a constructed variable that captures implicit geographic stratification resulting from the sample design. It is created by assigning a unique identifier to groups of between 10 and 19 adjacent households. Additional documentation is available on the Variance Estimation page. This is a 6-digit numeric variable with 0 implied decimal places Geography: F-N Variables -- HOUSEHOLD 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. Technical Person Variables -- PERSON 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. Technical Person Variables -- PERSON Relationship to household head [general version] RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). 1 Head 2 Spouse/partner 3 Child 4 Other relative 5 Non-relative 6 Other relative or non-relative 9 Unknown Demographic Variables -- PERSON Relationship to household head [detailed version] RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person). 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 Demographic Variables -- PERSON Age AGE gives age in years as of the person's last birthday prior to or on the day of enumeration. 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 Demographic Variables -- PERSON Age, grouped into intervals AGE2 gives computed years of 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 Demographic Variables -- PERSON Sex SEX reports the sex (gender) of the respondent. 1 Male 2 Female 9 Unknown Demographic Variables -- PERSON Marital status [general version] 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. 0 NIU (not in universe) 1 Single/never married 2 Married/in union 3 Separated/divorced/spouse absent 4 Widowed 9 Unknown/missing Demographic Variables -- PERSON Marital status [detailed version] 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. 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 Demographic Variables -- PERSON Children ever born 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. 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) Fertility and Mortality Variables -- PERSON Children surviving CHSURV reports the number of children born to a woman who were still living at the time of the census. 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) Fertility and Mortality Variables -- PERSON Month of last birth LASTBMO indicates the month of birth of the last child born by the respondent. The data refer to live births. 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) Fertility and Mortality Variables -- PERSON Sex of last birth LASTBSEX indicates the sex of a woman's most recent birth. 0 NIU (not in universe) 1 Male 2 Female 3 Both sexes (multiple births) 9 Unknown Fertility and Mortality Variables -- PERSON Number of children dead 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. 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) Fertility and Mortality Variables -- PERSON Number of female children dead 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. 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) Fertility and Mortality Variables -- PERSON Number of male children dead 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. 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) Fertility and Mortality Variables -- PERSON Mortality status of mother MORTMOT indicates whether the person's biological mother was still living at the time of the census. 1 Alive 2 Dead 7 Does not know 8 Missing 9 NIU (not in universe) Fertility and Mortality Variables -- PERSON Mortality status of father MORTFAT indicates whether the person's biological father was still living. 1 Alive 2 Dead 7 Does not know 8 Missing 9 NIU (not in universe) Fertility and Mortality Variables -- PERSON Number of own children in household HOMECHILD indicates the number of surviving biological children living in the household with their mother (the respondent) at the time of the census. 00 0 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) Fertility and Mortality Variables -- PERSON Number of own female children in household HOMEFEM indicates the number of female children born living in the household with their mother (the respondent). 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) Fertility and Mortality Variables -- PERSON Number of own male children in household HOMEMALE indicates the number of male children born living in the household with their mother (the respondent). 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) Fertility and Mortality Variables -- PERSON Number of own children living elsewhere AWAYCHILD indicates the number of surviving biological children not living in the household with their mother (the respondent) at the time of the census. 00 0 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) Fertility and Mortality Variables -- PERSON Number of own female children living elsewhere AWAYFEM indicates the number of surviving biological female children not living in the household with their mother (the respondent). 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) Fertility and Mortality Variables -- PERSON Number of own male children living elsewhere AWAYMALE indicates the number of surviving biological male children not living in the household with their mother (the respondent). 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) Fertility and Mortality Variables -- PERSON Nativity status NATIVITY indicates whether the person was native-born or foreign-born. 0 NIU (not in universe) 1 Native-born 2 Foreign-born 9 Unknown/missing Nativity and Birthplace Variables -- PERSON Country of birth BPLCOUNTRY indicates the person's 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 Nativity and Birthplace Variables -- PERSON Citizenship CITIZEN indicates the person's citizenship status within the country in which they were enumerated. 1 Citizen, not specified 2 Citizen by birth 3 Naturalized citizen 4 Not a citizen 5 Without citizenship, stateless 8 Unknown 9 NIU (not in universe) Nativity and Birthplace Variables -- PERSON Country of citizenship NATION indicates the person's country of citizenship. 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, 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/United Arab Rep. 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 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 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 21220 St. Lucia 21240 St. Vincent 21250 Trinidad and Tobago 21260 Turks and Caicos 21270 U.S. Virgin Islands 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 24021 Canada, First Nations 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 32050 Iran 32060 Kazakhstan 32070 Kyrgyzstan 32080 Maldives 32090 Nepal 32100 Pakistan 32110 Sri Lanka (Ceylon) 32120 Tajikistan 32130 Turkmenistan 32140 Uzbekistan 32990 Burma, India, Pakistan, Ceylon 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 33991 Laos and Cambodia 33992 Malaysia and Singapore 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 34080 Jordan 34090 Kuwait 34100 Lebanon 34110 Palestine 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 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 42121 Britain 42122 Scotland 42123 Wales 42990 Nordic countries 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 43130 Macedonia 43140 Yugoslavia 43141 Montenegro 43142 Serbia 43143 Kosovo 43144 Serbia and Montenegro 43999 Southern Europe, other or n.s. 44000 Western Europe 44010 Austria 44011 Austro-Hungarian 44020 Belgium 44022 Belgium/Netherlands/Luxemburg 44030 France 44040 Germany 44041 East Germany 44042 West Germany 44050 Liechtenstein 44060 Luxembourg 44070 Monaco 44080 Netherlands 44090 Switzerland 44999 Western Europe, other or n.s. 49992 European Union 49993 European Union (Original 15) 49994 Other European Union 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 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. 90000 Other countries n.s. 99998 No citizenship/nationality 99999 Unknown Nativity and Birthplace Variables -- PERSON Province of birth, Kenya; [Level1: consistent boundaries, GIS] BPL1_KE indicates the person's province of birth within Kenya. Birthplace province codes from BPL1_KE are compatible with codes from GEO1_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. 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) Nativity and Birthplace Variables -- PERSON District of birth, Kenya; [Level 2; consistent boundaries, GIS] BPL2_KE indicates the person's district of birth within Kenya. Birthplace district codes from BPL2_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. 404001047 Nairobi City 404002018 Nyandarua 404002019 Nyeri 404002020 Kirinyaga 404002022 Kiambu, Murang'a 404002099 Central Province, unknown district 404003001 Mombasa 404003002 Kwale 404003003 Kilifi 404003004 Tana River 404003005 Lamu 404003006 Taita-Taveta 404003099 Coast Province, unknown district 404004010 Marsabit 404004011 Isiolo 404004012 Meru, Tharaka-Nithi 404004015 Kitui 404004016 Machakos, Makueni, Embu 404004099 Eastern Province, unknown district 404005007 Garissa 404005008 Wajir 404005009 Mandera 404005099 Northeastern Province, unknown district 404006041 Siaya 404006042 Kisumu 404006043 Homa Bay, Migori 404006045 Kisii, Nyamira 404006099 Nyanza Province, unknown district 404007023 Turkana 404007024 West Pokot 404007025 Samburu 404007026 Trans Nzoia 404007027 Uasin Gishu 404007028 Elgeyo-Marakwet 404007029 Nandi 404007030 Baringo, Laikipia 404007032 Nakuru 404007033 Narok 404007034 Kajiado 404007035 Kericho, Bomet 404007099 Rift Valley Province, unknown district 404008037 Kakamega, Vihiga 404008039 Bungoma 404008040 Busia 404008099 Western Province, unknown district 404097097 Abroad 404098098 Unknown 404099099 NIU (not in universe) Nativity and Birthplace Variables -- PERSON School attendance 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. 0 NIU (not in universe) 1 Yes 2 No, not specified 3 No, attended in the past 4 No, never attended 9 Unknown/missing Education Variables -- PERSON Educational attainment, international recode [general version] 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. 0 NIU (not in universe) 1 Less than primary completed 2 Primary completed 3 Secondary completed 4 University completed 9 Unknown Education Variables -- PERSON Educational attainment, international recode [detailed version] 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. 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 Education Variables -- PERSON Years of schooling 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. 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) Education Variables -- PERSON Educational attainment, Kenya EDUCKE indicates the person's educational attainment in Kenya in terms of the level of schooling completed. 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 21 Basic literacy 22 Adult basic education 23 Adult secondary education 30 Youth polytechnic 40 University, completion unspecified 41 University, incomplete 42 University, complete 43 College (training schools) 44 University, undergraduate 45 University, Masters or PhD 50 Madrassa 98 Unknown 99 NIU (not in universe) Education Variables -- PERSON Migration status, 1 year 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. 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 Migration: Global Variables -- PERSON Country of residence 1 year ago 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. 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 Migration: Global Variables -- PERSON 1st subnational geographic level of residence 1 years prior to survey, world [consistent boundaries over time] GEOMIG1_1 indicates the major administrative unit in which the person resided one year prior to the survey. Only intra-national migrations are recorded; however, the variable incorporates geographies for every country that lists place of residence one year ago, to enable comparative analysis of subnational migration. Foreign migrants are coded 097 or 997. Codes for GEOMIG1_1 match the geographic codes in GEOLEV1 (current place of residence). For similar information for different time intervals since migration, see variables GEOMIG1_P, GEOMIG1_5, and GEOMIG1_10. More on migration and geography can be found here. 040011 Burgenland [State: Austria] 040012 Niederösterreich [State: Austria] 040013 Wien [State: Austria] 040021 Kärnten [State: Austria] 040022 Steiermark [State: Austria] 040031 Oberösterreich [State: Austria] 040032 Salzburg [State: Austria] 040033 Tirol [State: Austria] 040034 Vorarlberg [State: Austria] 040097 Foreign country [State: Austria] 040099 NIU [State: Austria] 854001 Boucle du Mouhoun [Region: Burkina Faso] 854002 Cascades [Region: Burkina Faso] 854003 Centre [Region: Burkina Faso] 854004 Centre-Est [Region: Burkina Faso] 854005 Centre-Nord [Region: Burkina Faso] 854006 Centre-Ouest [Region: Burkina Faso] 854007 Centre-Sud [Region: Burkina Faso] 854008 Est [Region: Burkina Faso] 854009 Hauts-Bassins [Region: Burkina Faso] 854010 Nord [Region: Burkina Faso] 854011 Plateau Central [Region: Burkina Faso] 854012 Sahel [Region: Burkina Faso] 854013 Sud-Ouest [Region: Burkina Faso] 854997 Abroad [Region: Burkina Faso] 854998 Unknown [Region: Burkina Faso] 854999 NIU [Region: Burkina Faso] 072001 Gaborone [District: Botswana] 072002 Francistown [District: Botswana] 072003 Lobatse [District: Botswana] 072004 Selebi Phikwe [District: Botswana] 072007 Central Tutume, Sowa [District: Botswana] 072010 Ngwaketse, Ngwaketse West, Ngwaketse Southern, Southern, Jwaneng [District: Botswana] 072011 Borolong [District: Botswana] 072020 South East [District: Botswana] 072030 Kweneng, Kweneng South, Kweneng North [District: Botswana] 072040 Kgatleng [District: Botswana] 072050 Central Serowe/Palapye [District: Botswana] 072051 Central Mahalapye [District: Botswana] 072052 Central Bobonong [District: Botswana] 072053 Central Boteti, Orapa [District: Botswana] 072060 North East [District: Botswana] 072070 Ngamiland East [District: Botswana] 072071 Ngamiland West, Delta [District: Botswana] 072072 Chobe [District: Botswana] 072080 Ghanzi, Central Kgalagadi Game Reserve (CKGR) [District: Botswana] 072090 Tshabong (Kgalagadi South) [District: Botswana] 072091 Hukunsti (Kgalagadi North) [District: Botswana] 072092 Botswana, district unknown [District: Botswana] 072097 Abroad [District: Botswana] 072098 Unknown [District: Botswana] 072099 NIU (not in universe) [District: Botswana] 124010 Newfoundland and Labrador [Province: Canada] 124011 Prince Edward Island, Yukon Territory, Northwest Territories, Nunavut [Province: Canada] 124012 Nova Scotia [Province: Canada] 124013 New Brunswick [Province: Canada] 124024 Quebec [Province: Canada] 124035 Ontario [Province: Canada] 124046 Manitoba [Province: Canada] 124047 Saskatchewan [Province: Canada] 124048 Alberta [Province: Canada] 124059 British Columbia [Province: Canada] 124098 Unknown [Province: Canada] 124099 NIU (not in universe) [Province: Canada] 384001 Lagunes, Sud Comoé, Sud Bandama, Agneby [Region: Cote D'Ivoire] 384002 Haut Sassandra, Marahoué, Fromager [Region: Cote D'Ivoire] 384003 Savanes [Region: Cote D'Ivoire] 384004 Vallée du Bandam, Lacs, N'Zi Comoé [Region: Cote D'Ivoire] 384005 Moyen Comoé [Region: Cote D'Ivoire] 384006 Montagnes, Moyen Cavally [Region: Cote D'Ivoire] 384008 Zanzan [Region: Cote D'Ivoire] 384009 Bas Sassandra [Region: Cote D'Ivoire] 384010 Denguele, Worodougou, Bafing [Region: Cote D'Ivoire] 384097 Abroad [Region: Cote D'Ivoire] 384098 Unknown [Region: Cote D'Ivoire] 384099 NIU [Region: Cote D'Ivoire] 300001 Etolia and Akarnania [Department: Greece] 300003 Viotia [Department: Greece] 300004 Evia [Department: Greece] 300005 Evrytania [Department: Greece] 300006 Fthiotida [Department: Greece] 300007 Fokida [Department: Greece] 300011 Argolida [Department: Greece] 300012 Arkadia [Department: Greece] 300013 Achaia [Department: Greece] 300014 Ilia [Department: Greece] 300015 Korinthia [Department: Greece] 300016 Lakonia [Department: Greece] 300017 Messinia [Department: Greece] 300021 Zakynthos [Department: Greece] 300022 Kerkyra [Department: Greece] 300023 Kefallinia [Department: Greece] 300024 Lefkada [Department: Greece] 300031 Arta [Department: Greece] 300032 Thesprotia [Department: Greece] 300033 Ioannina [Department: Greece] 300034 Preveza [Department: Greece] 300041 Karditsa [Department: Greece] 300042 Larissa [Department: Greece] 300043 Magnissia [Department: Greece] 300044 Trikala [Department: Greece] 300051 Grevena [Department: Greece] 300052 Drama [Department: Greece] 300053 Imathia [Department: Greece] 300054 Thessaloniki [Department: Greece] 300055 Kavala [Department: Greece] 300056 Kastoria [Department: Greece] 300057 Kilkis [Department: Greece] 300058 Kozani [Department: Greece] 300059 Pella [Department: Greece] 300061 Pieria [Department: Greece] 300062 Serres [Department: Greece] 300063 Florina [Department: Greece] 300064 Chalkidiki and Agion Oros [Department: Greece] 300071 Evros [Department: Greece] 300072 Xanthi [Department: Greece] 300073 Rodopi [Department: Greece] 300081 Dodekanissos [Department: Greece] 300082 Kyklades [Department: Greece] 300083 Lesvos [Department: Greece] 300084 Samos [Department: Greece] 300085 Chios [Department: Greece] 300091 Iraklio [Department: Greece] 300092 Lassithi [Department: Greece] 300093 Rethymno [Department: Greece] 300094 Chania [Department: Greece] 300101 Prefecture of Athens [Department: Greece] 300102 Prefecture of East Attiki [Department: Greece] 300103 Prefecture of West Attiki [Department: Greece] 300104 Prefecture of Pireas [Department: Greece] 300996 Foreign country [Department: Greece] 300997 Response suppressed [Department: Greece] 300998 Unknown [Department: Greece] 300999 NIU (not in universe) [Department: Greece] 372001 Dublin [Region: Ireland] 372002 Midlands [Region: Ireland] 372003 Mid-East, Border [Region: Ireland] 372004 Mid-West, South-East [Region: Ireland] 372006 West [Region: Ireland] 372008 South West [Region: Ireland] 372096 Ireland, region not specified [Region: Ireland] 372097 Abroad [Region: Ireland] 372098 Unknown [Region: Ireland] 372099 NIU (not in universe) [Region: Ireland] 380001 Piemonte, Valle d'Aosta [Region: Italy] 380003 Lombardia [Region: Italy] 380004 Trentino Alto Adige [Region: Italy] 380005 Veneto [Region: Italy] 380006 Friuli Venezia Giulia [Region: Italy] 380007 Liguria [Region: Italy] 380008 Emilia Romagna, Marche [Region: Italy] 380009 Toscana [Region: Italy] 380010 Umbria [Region: Italy] 380012 Lazio [Region: Italy] 380013 Abruzzo [Region: Italy] 380014 Molise [Region: Italy] 380015 Campania [Region: Italy] 380016 Puglia [Region: Italy] 380017 Basilicata [Region: Italy] 380018 Calabria [Region: Italy] 380019 Sicilia [Region: Italy] 380020 Sardegna [Region: Italy] 380097 Foregin Country [Region: Italy] 380099 NIU [Region: Italy] 404001 Nairobi [Province: Kenya] 404002 Central [Province: Kenya] 404003 Coast [Province: Kenya] 404004 Eastern [Province: Kenya] 404005 Northeastern [Province: Kenya] 404006 Nyanza [Province: Kenya] 404007 Rift Valley [Province: Kenya] 404008 Western [Province: Kenya] 404097 Abroad [Province: Kenya] 404098 Unknown [Province: Kenya] 404099 NIU (not in universe) [Province: Kenya] 454101 Chitipa [District: Malawi] 454102 Karonga [District: Malawi] 454103 Nkhata Bay, Likoma [District: Malawi] 454104 Rumphi [District: Malawi] 454105 Mzimba, Mzuzu City [District: Malawi] 454201 Kasungu [District: Malawi] 454202 Nkhotakota [District: Malawi] 454203 Ntchisi [District: Malawi] 454204 Dowa [District: Malawi] 454205 Salima [District: Malawi] 454206 Lilongwe, Lilongwe City [District: Malawi] 454207 Mchinji [District: Malawi] 454208 Dedza [District: Malawi] 454209 Ntcheu [District: Malawi] 454301 Mangochi [District: Malawi] 454302 Machinga [District: Malawi] 454303 Zomba, Zomba City [District: Malawi] 454304 Chiradzulu [District: Malawi] 454305 Blantyre, Blantyre City [District: Malawi] 454306 Mwanza, Neno [District: Malawi] 454307 Thyolo [District: Malawi] 454308 Mulanje [District: Malawi] 454309 Phalombe [District: Malawi] 454310 Chikwawa [District: Malawi] 454311 Nsanje [District: Malawi] 454312 Balaka [District: Malawi] 454997 Abroad [District: Malawi] 454998 Unknown [District: Malawi] 454999 NIU (not in universe) [District: Malawi] 508001 Niassa [Province: Mozambique] 508002 Cabo Delgado [Province: Mozambique] 508003 Nampula [Province: Mozambique] 508004 Zambézia [Province: Mozambique] 508005 Tete [Province: Mozambique] 508006 Manica [Province: Mozambique] 508007 Sofala [Province: Mozambique] 508008 Inhambane [Province: Mozambique] 508009 Gaza [Province: Mozambique] 508010 Maputo [Province: Mozambique] 508011 Cidade de Maputo [Province: Mozambique] 508097 Foreign Country [Province: Mozambique] 508098 Unknown [Province: Mozambique] 508099 NIU (not in universe) [Province: Mozambique] 598001 Western [Province: Papua New Guinea] 598002 Gulf [Province: Papua New Guinea] 598003 Central [Province: Papua New Guinea] 598004 National Capital District [Province: Papua New Guinea] 598005 Milne Bay [Province: Papua New Guinea] 598006 Northern [Province: Papua New Guinea] 598007 Southern Highlands, Hela [Province: Papua New Guinea] 598008 Enga [Province: Papua New Guinea] 598009 Western Highlands, Jiwaka [Province: Papua New Guinea] 598010 Chimbu [Province: Papua New Guinea] 598011 Eastern Highlands [Province: Papua New Guinea] 598012 Morobe [Province: Papua New Guinea] 598013 Madang [Province: Papua New Guinea] 598014 East Sepik [Province: Papua New Guinea] 598015 West Sepik [Province: Papua New Guinea] 598016 Manus [Province: Papua New Guinea] 598017 New Ireland [Province: Papua New Guinea] 598018 East New Britain [Province: Papua New Guinea] 598019 West New Britain [Province: Papua New Guinea] 598020 Autonomous Region of Bougainville [Province: Papua New Guinea] 598097 Foreign country [Province: Papua New Guinea] 598098 Unknown [Province: Papua New Guinea] 598099 NIU (not in universe) [Province: Papua New Guinea] 616002 Dolnośląskie [Vovoidship (Province): Poland] 616004 Kujawsko pomorskie [Vovoidship (Province): Poland] 616006 Lubelskie [Vovoidship (Province): Poland] 616008 Lubuskie [Vovoidship (Province): Poland] 616010 Łódzkie [Vovoidship (Province): Poland] 616012 Małopolskie [Vovoidship (Province): Poland] 616014 Mazowieckie [Vovoidship (Province): Poland] 616016 Opolskie [Vovoidship (Province): Poland] 616018 Podkarpackie [Vovoidship (Province): Poland] 616020 Podlaskie [Vovoidship (Province): Poland] 616022 Pomorskie [Vovoidship (Province): Poland] 616024 Śląskie [Vovoidship (Province): Poland] 616026 Świętokrzyskie [Vovoidship (Province): Poland] 616028 Warmińsko mazurskie [Vovoidship (Province): Poland] 616030 Wielkopolskie [Vovoidship (Province): Poland] 616032 Zachodniopomorskie [Vovoidship (Province): Poland] 616098 Unknown [Vovoidship (Province): Poland] 616099 NIU (not in universe) [Vovoidship (Province): Poland] 620111 Minho-Lima [Subregion: Portugal] 620112 Cávado [Subregion: Portugal] 620113 Ave [Subregion: Portugal] 620114 Grande Porto [Subregion: Portugal] 620115 Tâmega [Subregion: Portugal] 620116 Entre Douro e Vouga [Subregion: Portugal] 620117 Douro [Subregion: Portugal] 620118 Alto Trás-os-Montes [Subregion: Portugal] 620150 Algarve [Subregion: Portugal] 620161 Baixo Vouga [Subregion: Portugal] 620162 Baixo Mondego [Subregion: Portugal] 620163 Pinhal Litoral [Subregion: Portugal] 620165 Dão-Lafões [Subregion: Portugal] 620166 Oeste [Subregion: Portugal] 620167 Médio Tejo [Subregion: Portugal] 620169 Other Center [Subregion: Portugal] 620171 Grande Lisboa [Subregion: Portugal] 620172 Península de Setúbal [Subregion: Portugal] 620185 Lezíria do Tejo [Subregion: Portugal] 620189 Other Alentejo [Subregion: Portugal] 620200 Região Autónoma dos Açores [Subregion: Portugal] 620300 Região Autónoma da Madeira [Subregion: Portugal] 620998 Foreign country [Subregion: Portugal] 620999 NIU (not in universe) [Subregion: Portugal] 643001 Altai Krai [Region: Russia] 643003 Krasnodar Krai [Region: Russia] 643004 Krasnoyarsk Krai [Region: Russia] 643005 Primorsky Krai [Region: Russia] 643007 Stavropol Krai [Region: Russia] 643008 Khabarovsk Krai [Region: Russia] 643010 Amur Oblast [Region: Russia] 643011 Arkhangelsk Oblast [Region: Russia] 643012 Astrakhan Oblast [Region: Russia] 643013 Nenets Autonomous District 643014 Belgorod Oblast [Region: Russia] 643015 Bryansk Oblast [Region: Russia] 643017 Vladimir Oblast [Region: Russia] 643018 Volgograd Oblast [Region: Russia] 643019 Vologda Oblast [Region: Russia] 643020 Voronezh Oblast [Region: Russia] 643022 Nizhny Novgorod Oblast [Region: Russia] 643024 Ivanovo Oblast [Region: Russia] 643025 Irkutsk Oblast [Region: Russia] 643026 Republic of Ingushetia [Region: Russia] 643027 Kaliningrad Oblast [Region: Russia] 643028 Tver Oblast [Region: Russia] 643029 Kaluga Oblast [Region: Russia] 643030 Kamchatka Krai [Region: Russia] 643032 Kemerovo Oblast [Region: Russia] 643033 Kirov Oblast [Region: Russia] 643034 Kostroma Oblast [Region: Russia] 643036 Samara Oblast [Region: Russia] 643037 Kurgan Oblast [Region: Russia] 643038 Kursk Oblast [Region: Russia] 643040 City of Federal Importance St. Petersburg [Region: Russia] 643041 Leningrad Oblast [Region: Russia] 643042 Lipetsk Oblast [Region: Russia] 643044 Magadan Oblast [Region: Russia] 643045 City of Federal Importance Moscow [Region: Russia] 643046 Moscow Oblast [Region: Russia] 643047 Murmansk Oblast [Region: Russia] 643049 Novgorod Oblast [Region: Russia] 643050 Novosibirsk Oblast [Region: Russia] 643052 Omsk Oblast [Region: Russia] 643053 Orenburg Oblast [Region: Russia] 643054 Oryol Oblast [Region: Russia] 643056 Penza Oblast [Region: Russia] 643057 Perm Krai [Region: Russia] 643058 Pskov Oblast [Region: Russia] 643060 Rostov Oblast [Region: Russia] 643061 Ryazan Oblast [Region: Russia] 643063 Saratov Oblast [Region: Russia] 643064 Sakhalin Oblast [Region: Russia] 643065 Sverdlovsk Oblast [Region: Russia] 643066 Smolensk Oblast [Region: Russia] 643068 Tambov Oblast [Region: Russia] 643069 Tomsk Oblast [Region: Russia] 643070 Tula Oblast [Region: Russia] 643071 Tyumen Oblast [Region: Russia] 643072 Khanty-Mansi Autonomous District - Yugra [Region: Russia] 643073 Ulyanovsk Oblast [Region: Russia] 643074 Yamalo-Nenets Autonomous District [Region: Russia] 643075 Chelyabinsk Oblast [Region: Russia] 643076 Zabaykalsky Krai, Aginsky Buryat District [Region: Russia] 643077 Chukotka Autonomous Region [Region: Russia] 643078 Yaroslavskaya Oblast [Region: Russia] 643079 Republic of Adygea [Region: Russia] 643080 Republic of Bashkortostan [Region: Russia] 643081 Republic of Buryatia [Region: Russia] 643082 Republic of Dagestan [Region: Russia] 643083 Kabardino-Balkaria Republic [Region: Russia] 643084 Altai Republic [Region: Russia] 643085 Republic of Kalmykia [Region: Russia] 643086 Republic of Karelia [Region: Russia] 643087 Komi Republic [Region: Russia] 643088 Mari El Republic [Region: Russia] 643089 Republic of Mordovia [Region: Russia] 643090 Republic of North Ossetia-Alania [Region: Russia] 643091 Karachay-Cherkess Republic [Region: Russia] 643092 Republic of Tatarstan [Region: Russia] 643093 Tyva Republic [Region: Russia] 643094 Udmurtia Republic [Region: Russia] 643095 Republic of Khakassia [Region: Russia] 643096 Chechen Republic [Region: Russia] 643097 Chuvash Republic [Region: Russia] 643098 Republic of Sakha (Yakutia) [Region: Russia] 643099 Jewish Autonomous Region [Region: Russia] 643997 Foreign Country [Region: Russia] 643998 Unknown [Region: Russia] 643999 NIU [Region: Russia] 686001 Dakar [Region: Senegal] 686002 Ziguinchor [Region: Senegal] 686003 Diourbel [Region: Senegal] 686004 Saint Louis, Louga, Matam [Region: Senegal] 686005 Tambacounda, Kedougou [Region: Senegal] 686006 Kaolack, Fatick, Kaffrine [Region: Senegal] 686007 Thiès [Region: Senegal] 686010 Kolda, Sedhiou [Region: Senegal] 686097 Abroad [Region: Senegal] 686099 NIU (not in universe) [Region: Senegal] 724011 Galicia [Communities & autonomous city: Spain] 724012 Principado de Asturias [Communities & autonomous city: Spain] 724013 Cantabria [Communities & autonomous city: Spain] 724021 País Vasco [Communities & autonomous city: Spain] 724022 Comunidad Foral de Navarra [Communities & autonomous city: Spain] 724023 La Rioja [Communities & autonomous city: Spain] 724024 Aragón [Communities & autonomous city: Spain] 724030 Comunidad de Madrid [Communities & autonomous city: Spain] 724041 Castilla y León [Communities & autonomous city: Spain] 724042 Castilla-La Mancha [Communities & autonomous city: Spain] 724043 Extremadura [Communities & autonomous city: Spain] 724051 Cataluña [Communities & autonomous city: Spain] 724052 Comunidad Valenciana [Communities & autonomous city: Spain] 724053 Islas Baleares [Communities & autonomous city: Spain] 724061 Andalucía [Communities & autonomous city: Spain] 724062 Región de Murcia [Communities & autonomous city: Spain] 724063 Ciudad Autónoma de Ceuta [Communities & autonomous city: Spain] 724064 Ciudad Autónoma de Melilla [Communities & autonomous city: Spain] 724070 Canarias [Communities & autonomous city: Spain] 724097 Foreign country [Communities & autonomous city: Spain] 724999 NIU (not in universe) [Communities & autonomous city: Spain] 728011 Northern [State:South Sudan] 728012 Nahr El Nil [State:South Sudan] 728021 Red Sea [State:South Sudan] 728022 Kassala [State:South Sudan] 728023 Al Gedarif [State:South Sudan] 728031 Khartoum [State:South Sudan] 728041 Al Gezira [State:South Sudan] 728042 White Nile [State:South Sudan] 728043 Sinnar [State:South Sudan] 728044 Blue Nile [State:South Sudan] 728051 North Kordofan [State:South Sudan] 728052 South Kordofan [State:South Sudan] 728061 North Darfur [State:South Sudan] 728062 West Darfur [State:South Sudan] 728063 South Darfur [State:South Sudan] 728071 Upper Nile [State: South Sudan] 728072 Jonglei [State: South Sudan] 728073 Unity [State: South Sudan] 728081 Warrap [State: South Sudan] 728082 Northern Bahr El Ghazal [State: South Sudan] 728083 Western Bahr El Ghazal [State: South Sudan] 728084 Lakes [State: South Sudan] 728091 Western Equatoria [State: South Sudan] 728092 Central Equatoria [State: South Sudan] 728093 Eastern Equatoria [State: South Sudan] 728098 Abroad [State: South Sudan] 728099 NIU (Not in universe) [State: South Sudan] 729011 Northern [State: Sudan] 729012 Nahr El Nil [State: Sudan] 729021 Red Sea [State: Sudan] 729022 Kassala [State: Sudan] 729023 Al Gedarif [State: Sudan] 729031 Khartoum [State: Sudan] 729041 Al Gezira [State: Sudan] 729042 White Nile [State: Sudan] 729043 Sinnar [State: Sudan] 729044 Blue Nile [State: Sudan] 729051 North Kordofan [State: Sudan] 729052 South Kordofan [State: Sudan] 729061 North Darfur [State: Sudan] 729062 West Darfur [State: Sudan] 729063 South Darfur [State: Sudan] 729071 Upper Nile [State: Sudan] 729072 Jonglei [State: Sudan] 729073 Unity [State: Sudan] 729081 Warrap [State: Sudan] 729082 Northern Bahr El Ghazal [State: Sudan] 729083 Western Bahr El Ghazal [State: Sudan] 729084 Lakes [State: Sudan] 729091 Western Equatoria [State: Sudan] 729092 Central Equatoria [State: Sudan] 729093 Eastern Equatoria [State: Sudan] 729098 Abroad [State: Sudan] 780010 Port of Spain [Region: Trinidad and Tobago] 780020 San Fernando [Region: Trinidad and Tobago] 780080 Diego Martin, San Juan/Laventille, Tunapuna/Piarco, Chaguanas, Sangre Grande, Couva/Tabaquite /Talparo, Rio Claro/Mayaro, Siparia, Penal/Debe, Princess Town, Port Fontin, Caroni, St. Andrew/St. David, Victoria, St. Patrick, Arima [Region: Trinidad and Tobago] Diego Martin, San Juan/Laventille, Tunapuna/Piarco, Chaguanas, Sangre Grande, Couva/Tabaquite /Talparo, Rio Claro/Mayaro, Siparia, Penal/Debe, Princess Town, Port Fontin, Caroni, St. Andrew/St. David, Victoria, St. Patrick, Arima [Region: Trinidad and Tobago] 780094 St. Paul, St. Mary, St. David, St. George, St. Patrick, St. Andrew, St. John, Tobago [Region: Trinidad and Tobago] 780098 Unknown [Region: Trinidad and Tobago] 780099 NIU (not in universe) [Region: Trinidad and Tobago] 826101 North East [Region: United Kingdom] 826102 North West [Region: United Kingdom] 826103 Yorkshire and the Humber [Region: United Kingdom] 826104 East Midlands [Region: United Kingdom] 826105 West Midlands [Region: United Kingdom] 826106 East of England [Region: United Kingdom] 826107 South East [Region: United Kingdom] 826108 South West [Region: United Kingdom] 826110 Outer London, Inner London [Region: United Kingdom] 826111 Scotland [Region: United Kingdom] 826112 Wales [Region: United Kingdom] 826113 Northern Ireland [Region: United Kingdom] 826197 Other country [Region: United Kingdom] 826198 Unknown [Region: United Kingdom] 826199 NIU (not in universe) [Region: United Kingdom] 834001 Dodoma [Region: Tanzania] 834002 Arusha, Manyara [Region: Tanzania] 834003 Kilimanjaro [Region: Tanzania] 834004 Tanga [Region: Tanzania] 834005 Morogoro [Region: Tanzania] 834006 Pwani [Region: Tanzania] 834007 Dar es Salaam [Region: Tanzania] 834008 Lindi [Region: Tanzania] 834009 Mtwara [Region: Tanzania] 834010 Ruvuma [Region: Tanzania] 834011 Iringa, Njombe [Region: Tanzania] 834012 Mbeya [Region: Tanzania] 834013 Singida [Region: Tanzania] 834014 Tabora [Region: Tanzania] 834015 Katavi, Rukwa [Region: Tanzania] 834016 Kigoma [Region: Tanzania] 834019 Geita, Kagera, Mwanza, Shinyanga, Simiyu [Region: Tanzania] 834020 Mara [Region: Tanzania] 834051 Zanzibar North [Region: Tanzania] 834052 Zanzibar South [Region: Tanzania] 834053 Zanzibar Town/West [Region: Tanzania] 834054 Pemba North [Region: Tanzania] 834055 Pemba South [Region: Tanzania] 834097 Abroad [Region: Tanzania] 834098 Unknown [Region: Tanzania] 834099 NIU (not in universe) [Region: Tanzania] 840001 Alabama [State: United States] 840002 Alaska [State: United States] 840004 Arizona [State: United States] 840005 Arkansas [State: United States] 840006 California [State: United States] 840008 Colorado [State: United States] 840009 Connecticut [State: United States] 840010 Delaware [State: United States] 840011 District of Columbia [State: United States] 840012 Florida [State: United States] 840013 Georgia [State: United States] 840015 Hawaii [State: United States] 840016 Idaho [State: United States] 840017 Illinois [State: United States] 840018 Indiana [State: United States] 840019 Iowa [State: United States] 840020 Kansas [State: United States] 840021 Kentucky [State: United States] 840022 Louisiana [State: United States] 840023 Maine [State: United States] 840024 Maryland [State: United States] 840025 Massachusetts [State: United States] 840026 Michigan [State: United States] 840027 Minnesota [State: United States] 840028 Mississippi [State: United States] 840029 Missouri [State: United States] 840030 Montana [State: United States] 840031 Nebraska [State: United States] 840032 Nevada [State: United States] 840033 New Hampshire [State: United States] 840034 New Jersey [State: United States] 840035 New Mexico [State: United States] 840036 New York [State: United States] 840037 North Carolina [State: United States] 840038 North Dakota [State: United States] 840039 Ohio [State: United States] 840040 Oklahoma [State: United States] 840041 Oregon [State: United States] 840042 Pennsylvania [State: United States] 840044 Rhode Island [State: United States] 840045 South Carolina [State: United States] 840046 South Dakota [State: United States] 840047 Tennessee [State: United States] 840048 Texas [State: United States] 840049 Utah [State: United States] 840050 Vermont [State: United States] 840051 Virginia [State: United States] 840053 Washington [State: United States] 840054 West Virginia [State: United States] 840055 Wisconsin [State: United States] 840056 Wyoming [State: United States] 840097 Abroad [State: United States] 840999 NIU (not in universe) [State: United States] 894001 Central [Province: Zambia] 894002 Copperbelt [Province: Zambia] 894003 Eastern, Muchinga, Northern [Province: Zambia] 894004 Luapula [Province: Zambia] 894005 Lusaka [Province: Zambia] 894008 North Western [Province: Zambia] 894009 Southern [Province: Zambia] 894010 Western [Province: Zambia] 894097 Foreign country [Province: Zambia] 894098 Unknown [Province: Zambia] 894099 NIU (not in universe) [Province: Zambia] Migration: Global Variables -- PERSON Province of residence 1 year ago, Kenya; consistent boundaries, GIS 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. 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) Migration: F-N Variables -- PERSON District of residence 1 year ago, Kenya; consistent boundaries, GIS 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. 404001047 Nairobi City 404002018 Nyandarua 404002019 Nyeri 404002020 Kirinyaga 404002022 Kiambu, Murang'a 404002099 Central Province, unknown district 404003001 Mombasa 404003002 Kwale 404003003 Kilifi 404003004 Tana River 404003005 Lamu 404003006 Taita-Taveta 404003099 Coast Province, unknown district 404004010 Marsabit 404004011 Isiolo 404004012 Meru, Tharaka-Nithi 404004015 Kitui 404004016 Machakos, Makueni, Embu 404004099 Eastern Province, unknown district 404005007 Garissa 404005008 Wajir 404005009 Mandera 404005099 Northeastern Province, unknown district 404006041 Siaya 404006042 Kisumu 404006043 Homa Bay, Migori 404006045 Kisii, Nyamira 404006099 Nyanza Province, unknown district 404007023 Turkana 404007024 West Pokot 404007025 Samburu 404007026 Trans Nzoia 404007027 Uasin Gishu 404007028 Elgeyo-Marakwet 404007029 Nandi 404007030 Baringo, Laikipia 404007032 Nakuru 404007033 Narok 404007034 Kajiado 404007035 Kericho, Bomet 404007099 Rift Valley Province, unknown district 404008037 Kakamega, Vihiga 404008039 Bungoma 404008040 Busia 404008099 Western Province, unknown district 404097097 Abroad 404098098 Unknown 404099099 NIU (not in universe) Migration: F-N Variables -- PERSON Person number (within household) Kenya 1979: All records This variable indicates person number (within household). 0 Household record 1 1 (all persons in separate households) Technical Person Variables -- PERSON Relationship to head <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A400" a="all">b. Relationship: ____ <div class="i1">What is the relationship of this person to the head or other members of the household?</div> </svar> <svar v="KE79A400" a="all">73. Write 'head' against the name of the head of the household, if he or she was present on Census Night. If the usual head of the household was neither present nor on night duty, write 'head' against the name of the person who was in charge of the household on Census Night. Then write the relationship of each person to the head or to his or her parents if they were present, or show the relationship husband/wife. 74. Sometimes a person is related to more than one person in the household. In such cases concentrate first on relating parents and their own children, then on relating husbands and wives, and then on relating persons to the head of the household or other members of it. 75. Since every person will be entered against the line number in column (b) his or her relationship may easily be written as 'wife of 1 ', '2nd wife of 3', 'daughter of 1 and 2', ' son of 4', 'servant of 1', and so on. 76. Relationships must be described accurately. Be careful to distinguish between children born of the parents and adopted or step children. 77. Where a man and woman live together although not married, treat them as man and wife if they so regard themselves. The census is not concerned with the form of marriage. 78. The following relationships will cover all cases with which the census is concerned: head, wife, husband, son, daughter, mother, father, brother, sister, adopted son, grandson, granddaughter, grandfather, grandmother, adopted daughter. 79. For all other relationships, write 'other relative'. For members of the household who are not related to the head and do not have parents present, write 'servant', 'visitor', or as is appropriate. If such people have their own children present, you will, of course, relate the children to their parents. 80. Where several persons with no family relationship share the responsibility of running the household name one as head and describe the rest as 'partners'. 81. In an institution such as a hospital, or boarding school or prison where there is no head of household, describe the persons as 'patient', 'student', 'prisoner', or as the case may be. 82. Make sure you understand the relationship before you make an entry. </svar> Kenya 1979: All persons This variable indicates the individual's relationship to head of the household. 1 Head 2 Wife of head 3 Child of head or wife 4 Other relative 5 Not related or not stated Demographic Variables -- PERSON Sex <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A401" a="all">c. Sex: _ <div class="i1">Write M for males or F for females.</div> </svar> <svar v="KE79A401" a="all">83. Record the person's sex by writing 'M' for males and 'F' for females. Check that the sex is compatible with relationship - don't write 'M' for persons shown as wives or daughters, nor 'F' for persons shown as sons or husbands. 84. Take particular care to record the sex of very young children correctly. Often you will not know whether a baby carried on its mother's back is a boy or a girl, in such cases you must ask -- don't guess. </svar> Kenya 1979: All persons This variable indicates the individual's sex. 1 Male 2 Female Demographic Variables -- PERSON Age <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A402" a="all">d. Age: ____ <div class="i1">How old was this person at his or her last birthday?</div> </svar> <svar v="KE79A402" a="all">Column (d): Age 85. 'How old is this person?' 86. Write the person's age in completed years -- that is, the person's age at his or her last birthday. For babies under one year of age, write the age in months, for example '7 months' or '7/12'. 87. Be careful not to round ages up to the next birthday. A child who is aged four years and eleven months should, for example, be entered as '4' and not '5'. 88. Many people do not know their ages. If a person's age is not known you must make the best estimate possible. Avoid the use of 'NK' in this column. 89. There are various ways in which you can estimate a person's age. Sometimes people have documents, such as baptismal certificates, which show the year of birth, in which case it is easy to calculate age. pg 20 90. Generally it is not so easy. Concentrate first on establishing the ages of one or two people in the household. One reliable age may help in working out the ages of others if it is known whether they are older or younger and by how many years. 91. It is sometimes possible to estimate a person's age by relating his or her birth to some notable event. With these instructions is a calendar of events which lists the dates of events in the history of each district. If the person can remember how old he or she was at the time, you can work out the person's age. 92. Some tribes have systems of 'age grades' or 'age sets' from which a person's age can be worked out. A person's age grade may only give a rough idea of his or her age since the same grade may have in it people of widely different ages, but it is better than nothing. Some tribes have age grades for men but not for women, but you can often obtain an idea of a woman's age by asking which age grade of men she associated with, or which her brothers belonged to and whether they are older/younger. Some age grades are listed in the Event Calendars, you can enquire about others from chiefs and elders. 93. If all else fails, you will have to make the best estimate you can, judging by such things as the person's appearance and position in the household and using your common sense knowledge that parents are seldom younger than 15 or 16 years of age when their first child is born, that women do not normally bear children below the age of twelve or over the age of fifty and so on. 94. When you have arrived at the best estimate you can make of a person's age, check that it is compatible with his or her relationship to others in the household. Obviously children cannot be older than their parents; women seldom marry before they are 12 and men before they are 18 and so on. Check these points. If necessary adjust your estimates of age. 95. Any estimate of age, however rough, is better than 'NK' in this column. Do the best you can to report ages accurately. </svar> Kenya 1979: All persons This variable indicates the individual's age. 00 Less than one year old 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+ 99 Unknown Demographic Variables -- PERSON Birthplace <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A404" a="all">f. Birthplace: ____ <div class="i1">Where was this person born? State district if born in Kenya or country if born outside Kenya.</div> </svar> <svar v="KE79A404" a="all">Column (f): Birthplace 101. 'Where was this person born?' 102. For persons born in Kenya, write the name of the district where they were born. Do not write the name of the location or town. 103. Relate the person's birthplace to the present districts as far as possible. District boundaries have been changed over the years and we want to relate a person's place of birth to the district as they are now. 104. If the district of birth, or the district where the person was living in August 1978, is not known, write the province. 105. For persons born outside Kenya, write the country of birth. For example, 'Tanzania', 'Uganda', 'Somalia', 'Ethiopia', 'Holland', 'UK', or as the case may be. </svar> Kenya 1979: All persons This variable indicates the individual's birth place. 10 Nairobi 21 Kiambu 22 Kirinyaga 23 Muranga 24 Nyandarua 25 Nyeri 26 Central Province not specified 31 Kilifi 32 Kwale 33 Lamu 34 Mombasa 35 Taita Taveta 36 Tana River 37 Coast Province not specified 41 Embu 42 Isiolo 43 Kitui 44 Machakos 45 Marsabit 46 Meru 47 Eastern Province not specified 51 Garissa 52 Mandera 53 Wajir 54 N.E Province not specified 61 Kisii 62 Kisumu 63 Siaya 64 South Nyanza 65 Nyanza Province not specified 71 Kajiado 72 Kericho 73 Laikipia 74 Nakuru 75 Nandi 76 Narok 81 Baringo 82 Elgeyo Markwet 83 Samburu 84 Trans Nzoia 85 Turkana 86 Uasin Gishu 87 West Pokot 88 R.Valley Province not specified 91 Bungoma 92 Busia 93 Kakamega 94 Western Province not specified 95 Kenya not specified 96 Tanzania 97 Uganda 98 Born Elsewhere 99 Unknown Nativity and Birthplace Variables -- PERSON District or country of residence in August 1978 <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A405" a="all">g. Previous residence: ____ <div class="i1">Where was this person living in August 1978? State district if in Kenya or country if outside Kenya.</div> </svar> <svar v="KE79A405" a="all">Column (g): Previous residence 106. 'Where was this person living in August 1978?' 107. For persons who were living in Kenya, write the name of the district. For persons who were living outside Kenya, write the name of the country. 108. If the person is under one year of age, write 'N/A' in this column. 109. A person who may have been absent from home temporarily for some reason such as visiting relatives or being in hospital, or who may have been overseas on a visit of less than six months, should be shown where they normally lived in August 1978. 110. It is necessary to make separate enquiry for each member of the household because a man does not always take his wife and children with him when he goes away to work, or he may only have some of his family with him and others may have been elsewhere at the time. </svar> Kenya 1979: Persons age 1+ [discrepancies: type I 0.4%; type II trace] This variable indicates the individual's district or country of residence in August 1978. 010 Nairobi 021 Kiambu 022 Kirinyaga 023 Murang'a 024 Nyandarua 025 Nyeri 026 Central Province not specified 031 Kilifi 032 Kwale 033 Lamu 034 Mombasa 035 Taita Taveta 036 Tana River 037 Coast Province not specified 041 Embu 042 Isiolo 043 Kitui 044 Machakos 045 Marsabit 046 Meru 047 Eastern Province not specified 051 Garissa 052 Mandera 053 Wajir 054 N.E Province not specified 061 Kisii 062 Kisumu 063 Siaya 064 South Nyanza 065 Nyanza Province not specified 071 Kajiado 072 Kericho 073 Laikipia 074 Nakuru 075 Nandi 076 Narok 081 Baringo 082 Elgeyo Markwet 083 Samburu 084 Trans Nzoia 085 Turkana 086 Uasin Gishu 087 West Pokot 088 R.Valley Province not specified 091 Bungoma 092 Busia 093 Kakamega 094 Western Province not specified 095 Kenya not specified 096 Tanzania 097 Uganda 098 Resident outside Kenya 998 Unknown 999 NIU (not in universe) Migration: Global Variables -- PERSON School attendance <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A406" a="all">h. Education: School attendance ____ <div class="i1">State whether currently attending school, ever attended school or never attended school</div> </svar> Column (h) and (i): Education 111. 'Has this person ever been to school?' 112. State, in column (h), whether the person is attending school this year -- write 'at'; or whether the person has been to school and has left school -- write 'left' ; or whether the person has never been to school -- write 'never'. Kenya 1979: All persons This variable indicates the individual's school attendance. 1 At School 2 Left School 3 Never been to school or under 5 years old 9 Unknown Education Variables -- PERSON Educational attainment <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A407" a="all">[If the person has been to school or is at school] i. Education: ____ <div class="i1">What was/is the highest class or form reached?</div> </svar> <svar v="KE79A407" a="all">113. If the person has been to school or is at school, ask, 'What was or is the highest class or form he or she has reached?' 114. Enter, in column (h), the highest class or form the person has reached in the formal primary and secondary school system. 115. You may shorten 'standard' and 'form' by writing 'St, 4', 'F3', or as the case may be. If the person has attended University, or is doing so, write 'univ'. The census is not concerned with other post-secondary or vocational training, nor is it concerned with educational qualifications. </svar> Kenya 1979: Persons age 5+ who attended school [discrepancies: none] This variable indicates the individual's educational attainment. 01 Standard 1 02 Standard 2 03 Standard 3 04 Standard 4 05 Standard 5 06 Standard 6 07 Standard 7/8 11 Form 1 12 Form 2 13 Form 3 14 Form 4 15 Form 5 16 Form 6 17 University 98 Unknown 99 NIU (not in universe) Education Variables -- PERSON Father alive <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A408" a="all">j. Is his or her father alive? ____ <div class="i1">Write yes or no</div> </svar> <svar v="KE79A408 KE79A409" a="all"> Column (j): Is father alive? Column (k): Is mother alive? 116. 'Is this person's father/mother alive?' 117. Write 'yes or 'no' in respect of the person's real, natural or biological father and mother, Foster parents or other relatives who may have adopted the person should not be considered as the father or mother in these columns. </svar> Kenya 1979: All persons This variable indicates whether the individual's father is alive. 1 Yes 2 No 9 Unknown Fertility and Mortality Variables -- PERSON Mother alive <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A409" a="all">k. Is his or her mother alive? ____ <div class="i1">Write yes or no</div> </svar> <svar v="KE79A408 KE79A409" a="all"> Column (j): Is father alive? Column (k): Is mother alive? 116. 'Is this person's father/mother alive?' 117. Write 'yes or 'no' in respect of the person's real, natural or biological father and mother, Foster parents or other relatives who may have adopted the person should not be considered as the father or mother in these columns. </svar> Kenya 1979: All persons This variable indicates whether the individual's mother is alive. 1 Yes 2 No 9 Unknown Fertility and Mortality Variables -- PERSON Marital status <svar v="KE79A400 KE79A401 KE79A402 KE79A403 KE79A404 KE79A405 KE79A406 KE79A407 KE79A408 KE79A409 KE79A410">For all persons </svar> <svar v="KE79A410" a="all">l. Marital status: ____ <div class="i1">State whether single, married, widowed, divorced, or separated.</div> </svar> <svar v="KE79A410" a="all">Column (l): Marital status 118. 'Is this person single, married, widowed, divorced, or separated?' 119. Persons who have never been married and children under 12 years of age should be shown as 'single'. 120. People living together as man and wife, and who so regard themselves should be shown as 'married' whether or not they have been through any civil, religious or customary ceremonies. The census is not trying to find out who is legally married and who is not. Accept the answer as it is given you. 121. If a person is widowed at the time of the census, he or she should be shown as 'widowed'. If a person has been widowed but has since re-married and is now married, he or she should be shown as 'married'. 122. If people think of themselves as divorced or separated, show them as such. It does not matter whether they have been to court or gone through other formalities. Accept the answer as it is given you. 123. Accept what people say about their marital status. Do not embarrass yourself or the person by enquiring into the nature of marriage and divorce. </svar> Kenya 1979: All persons This variable indicates the individual's marital status. 1 Never Married/Single 2 Married 3 Divorced/Separated 4 Widowed 9 Unknown Demographic Variables -- PERSON Male children at home <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> <svar v="KE79A411 KE79A412 KE79A420" a="all">How many children has this woman given birth to, who were born alive and are now living in this household? <div class="i1">m. Boys ____ n. Girls ____</div> </svar> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A411 KE79A412" a="all">130. If the woman has borne children alive, ask, 'Of the children she has borne alive, how many are living in this household?' 131. Write the number of boys who are living in the household in column (m) and the number of girls in column (n). If none of the boys or girls she has borne is living in the household, write '0' in the appropriate column. 132. Check that the number agrees with the number of children you have shown as her children in the relationship column (b). If the numbers differ, enquire as to the reason and correct the answers as may be necessary. </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of male children (born alive by the individual) who are living at home. 00 0 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Female children at home <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> <svar v="KE79A411 KE79A412 KE79A420" a="all">How many children has this woman given birth to, who were born alive and are now living in this household? <div class="i1">m. Boys ____ n. Girls ____</div> </svar> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A411 KE79A412" a="all">130. If the woman has borne children alive, ask, 'Of the children she has borne alive, how many are living in this household?' 131. Write the number of boys who are living in the household in column (m) and the number of girls in column (n). If none of the boys or girls she has borne is living in the household, write '0' in the appropriate column. 132. Check that the number agrees with the number of children you have shown as her children in the relationship column (b). If the numbers differ, enquire as to the reason and correct the answers as may be necessary. </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of female children (born alive by the individual) who are living at home. 00 0 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Male children away <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> <svar v="KE79A413 KE79A414 KE79A420" a="all">How many children has this woman given birth to, who were born alive and are now living elsewhere? <div class="i1">o. Boys____ p. Girls ____</div> </svar> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A413 KE79A414" a="all">133. Next, ask 'Of the children she has borne alive, how many are living elsewhere?' 134. Write the number of boys who are living elsewhere in column (o) and the number of girls in column (p). If none of the boys or girls she has borne is living elsewhere, write '0' in the appropriate column. 135. Include in these columns all the children she has borne who are living somewhere else. It may be that they have grown up and married, pg 22 or have gone off to work, or be living with relatives, or be in a boarding school, etc. Make sure that none of the children she has borne are missed and ask further questions to probe the matter fully -- 'are any of your children away?', 'at work?', 'married?', 'with relatives?' </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of male children (born alive by the individual) who are living elsewhere. 00 0 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Female children away <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> <svar v="KE79A413 KE79A414 KE79A420" a="all">How many children has this woman given birth to, who were born alive and are now living elsewhere? <div class="i1">o. Boys____ p. Girls ____</div> </svar> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A413 KE79A414" a="all">133. Next, ask 'Of the children she has borne alive, how many are living elsewhere?' 134. Write the number of boys who are living elsewhere in column (o) and the number of girls in column (p). If none of the boys or girls she has borne is living elsewhere, write '0' in the appropriate column. 135. Include in these columns all the children she has borne who are living somewhere else. It may be that they have grown up and married, pg 22 or have gone off to work, or be living with relatives, or be in a boarding school, etc. Make sure that none of the children she has borne are missed and ask further questions to probe the matter fully -- 'are any of your children away?', 'at work?', 'married?', 'with relatives?' </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of female children (born by the individual) who are living elsewhere. 00 0 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Male children died <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> <svar v="KE79A415 KE79A416" a="all">How many children has this woman given birth to, who were born alive and who have died? <div class="i1">q. Boys ____ r. Girls ____</div> </svar> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A415 KE79A416" a="all">136. Then ask, 'Of the children she has borne alive, how many have died?' 137. Many people do not talk of the dead and many others find it painful to talk about their dead children. It is best to ask this question in a matter of fact way and without embarrassment. 138. Write the number of boys who have died in column (q) and the number of girls in column (r). If none of the boys and girls she has borne has died, write '0' in the appropriate column. 139. If, in spite of your best efforts, you cannot obtain this information about the children who have died, write 'refuses' in columns (q) and (r). Do not leave these, or any other columns, blank. 140. You have recorded in columns (m) to (r) details of all the children the woman has borne alive. You are next required to record particulars of her most recent birth, that is the particulars relating to the birth of her last borne child. </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of male children (born alive by the individual) who have died. 00 0 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Female children died <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> <svar v="KE79A415 KE79A416" a="all">How many children has this woman given birth to, who were born alive and who have died? <div class="i1">q. Boys ____ r. Girls ____</div> </svar> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A415 KE79A416" a="all">136. Then ask, 'Of the children she has borne alive, how many have died?' 137. Many people do not talk of the dead and many others find it painful to talk about their dead children. It is best to ask this question in a matter of fact way and without embarrassment. 138. Write the number of boys who have died in column (q) and the number of girls in column (r). If none of the boys and girls she has borne has died, write '0' in the appropriate column. 139. If, in spite of your best efforts, you cannot obtain this information about the children who have died, write 'refuses' in columns (q) and (r). Do not leave these, or any other columns, blank. 140. You have recorded in columns (m) to (r) details of all the children the woman has borne alive. You are next required to record particulars of her most recent birth, that is the particulars relating to the birth of her last borne child. </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of female children (born alive by the individual) who have died. 00 0 01 1 02 2 03 3 04 4 05 5 06 6 07 7 08 8 09 9 10 10 11 11 12 12 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Month of last birth <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> Particulars of her last birth: <div class="i1">When was her last child borne?</div><div class="i2">s. Year ____ t. Month ____</div> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A417 KE79A418" a="all">141. Ask, 'In what year was her last child borne?' 142. Record the year of birth in column (s). For the years 1960 to 1979 state the year, but if the child was borne before 1960 and the exact year is not known you may write '1959'. 143. If the child was born in 1975, 1976, 1977, 1978 or 1979, ask, 'In what month of the year was it borne?' 144. Record the month in column (t). If the child was born in 1974 or before you need not record the month of birth. 145. Then ask, 'Was it a boy or a girl?' 146. Record the sex of the last borne child in column (u). Write 'M' for males and 'F' for females. If they were male twins, write '2M'; if female twins, write '2F'; if twins with one of each sex, write 'MF' and so on. 147. If the last borne child is alive and is living with the mother in the household, check that the year of birth agrees with the age of the child which you have recorded in column (d). If the dates do not agree, find out what has gone wrong and make any correction that is necessary. </svar> Kenya 1979: Females age 15+ who ever gave birth [type I trace, type II 7.9%] This variable indicates the month of the individual's 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) Fertility and Mortality Variables -- PERSON Sex of last born child <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> Particulars of her last birth: <div class="i1">When was her last child borne?</div><div class="i2">s. Year ____ t. Month ____</div> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A417 KE79A418" a="all">141. Ask, 'In what year was her last child borne?' 142. Record the year of birth in column (s). For the years 1960 to 1979 state the year, but if the child was borne before 1960 and the exact year is not known you may write '1959'. 143. If the child was born in 1975, 1976, 1977, 1978 or 1979, ask, 'In what month of the year was it borne?' 144. Record the month in column (t). If the child was born in 1974 or before you need not record the month of birth. 145. Then ask, 'Was it a boy or a girl?' 146. Record the sex of the last borne child in column (u). Write 'M' for males and 'F' for females. If they were male twins, write '2M'; if female twins, write '2F'; if twins with one of each sex, write 'MF' and so on. 147. If the last borne child is alive and is living with the mother in the household, check that the year of birth agrees with the age of the child which you have recorded in column (d). If the dates do not agree, find out what has gone wrong and make any correction that is necessary. </svar> Kenya 1979: Females age 15+ who ever gave birth [type I trace, type II 7.9%] This variable indicates the sex of last child born by the individual. 1 Male 2 Female 8 Unknown 9 NIU (not in universe) Fertility and Mortality Variables -- PERSON Children ever born <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> <svar v="KE79A419" a="all">127. Ask of all females aged 12 years and over, "Has this woman borne any children?' 128. If the woman has never borne any children alive, write '0' in each of columns (m) to (r). 129. A child born alive is one who cries after being born. The census is concerned only with born children alive. Do not include still-births -- that is, children who were born dead and did not cry. </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of children ever born by the individual. 00 0 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 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Children surviving <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A420"> For all females aged 12 years and older </svar> <svar v="KE79A411 KE79A412 KE79A420" a="all">How many children has this woman given birth to, who were born alive and are now living in this household? <div class="i1">m. Boys ____ n. Girls ____</div> </svar> <svar v="KE79A413 KE79A414 KE79A420" a="all">How many children has this woman given birth to, who were born alive and are now living elsewhere? <div class="i1">o. Boys____ p. Girls ____</div> </svar> <svar v="KE79A411 KE79A412 KE79A413 KE79A414 KE79A415 KE79A416 KE79A417 KE79A418 KE79A419 KE79A420">The questions in columns (m) to (u) apply to all women and girls aged twelve years and over. 124. Answers are required for all women in this category. It does not matter whether or not they are married, single, divorced or separated; whether or not they are still attending school; whether or not you think they have or have not borne children; or what their relationship to the head of the household -- you must ask the questions of all women and girls aged twelve and over. 125. For males and for girls under twelve years of age, write 'n/a' in column (m) and leave the rest blank. 126. Many women do not like answering questions about their children. There are various reasons for this, but it is your job to obtain the answers. It will require firmness and tact. </svar> Kenya 1979: Females age 15+ [type I none, type II 15.8%] This variable indicates the number of surviving children of the individual. 00 0 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 or more 98 Unknown (no fertility form) 99 NIU (not in universe) Fertility and Mortality Variables -- PERSON Person weight [IPUMS constructed] Kenya 1979: All persons This variable indicates person weight (IPUMS constructed). This is a 6-digit numeric variable with 4 implied decimal places Technical Person Variables -- PERSON