DDI_IRL_1979_PHC_v01_M_v02_A_IPUMS
Minnesota Population Center
2016-04-25
NADA
Version 6.4 (April 2016): Documentation of census data and harmonized variables as found in IPUMS-International. The International Household Survey Network (IHSN) contracted IPUMS International for generating DDI and Dublin Core-compliant metadata related to population and housing census datasets from developing countries. The objective was to provide countries with detailed metadata in a format compatible with the DDI standard used by most of these countries, with a view to guarantee the preservation of the data and metadata, and the publishing of metadata.
The intellectual rights (including copyright) for the data and metadata in IPUMS are retained by the countries under a Memorandum of Understanding with the contributing countries. IPUMS-International has distribution rights to the metadata and data. The XML documents generated by this process are viewed as a distribution of the metadata.
Fields edited by the World Bank are: DDI ID and study ID to match World Bank study naming convention, as well as DDI Document Version and Version Description to reflect changes included in version 6.4.
Previous version documented in the World Bank Microdata Library:
- v6.3 (August 2014)
Census of Population of Ireland 1979 - IPUMS Subset
PHC 1979 (IPUMS Harmonized Subset)
IRL_1979_PHC_v01_M_v02_A_IPUMS
Central Statistics Office
Minnesota Population Center
Minnesota Population Center
(c) Copyright 1979, Central Statistics Office and Minnesota Population Center
NADA
Central Statistics Office
Population and Housing Census [hh/popcen]
Version 6.4. The datasets contain selected variables from the original census microdata plus harmonized variables from the IPUMS-International database.
In v6.4, the research team continued to carry out improvements to geography, providing harmonized geographic units for the second administrative level for roughly half the countries. More information about IPUMS geography variables is available <a href='https://international.ipums.org/international/geography_variables.shtml'>here</a>. Also, approximately 100 integrated variables were renamed. Affected variables with their current and previous names are listed <a href='https://international.ipums.org/international/resources/misc_docs/renamed_variables_sept2015.pdf'>here</a>. Geography variable also underwent wholesale renaming.
In this update, IPUMS added 19 new samples for Armenia, Austria, Costa Rica, Ethiopia, France, Ghana, Mozambique, Paraguay, Portugal, Puerto Rico, South Africa, and Spain. Ethiopia, Mozambique, and Paraguay were newly added countries to IPUMS. Samples for other countries extend pre-existing series for those countries.
In this version, geographic variables are significantly revised. IPUMS has developed subnational geographies for each country that are consistent over time and have associated GIS shape files. To distinguish the harmonized and unharmonized geographic variables, which will ultimately be available at the first and second administrative levels for most countries, a new, more systematic variable-naming convention have been imposed. The available geographic variables and their old and new names are described <a href='https://international.ipums.org/international/geography_variables.shtml'>here</a>. Further explanation of the new geographic variables and the GIS boundary files is available <a href='https://international.ipums.org/international/geography_gis.shtml'>here</a>.
Technical Household Variables -- HOUSEHOLD
Geography: Global Variables -- HOUSEHOLD
Technical Person Variables -- PERSON
Demographic Variables -- PERSON
Group Quarters Variables -- HOUSEHOLD
Constructed Family Interrelationship Variables -- PERSON
Constructed Household Variables -- HOUSEHOLD
Migration Variables -- PERSON
Geography: A-L Variables -- HOUSEHOLD
IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.
The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.
Ireland
National coverage
Region
Household
UNITS IDENTIFIED:
- Dwellings: No
- Vacant units: No
- Households: Yes
- Individuals: Yes
- Group quarters: Yes
UNIT DESCRIPTIONS:
- Group quarters: A non-private household is a boarding house, hotel, guest house, barrack, hospital, nursing home, boarding schools, religious institution, welfare institution, prison, or ship, etc. However, proprietors and manager of hotels, principals of boarding schools, persons in charge of various other types of institutions and members of staff who, with their families, occupy flats on the premises are considered as private households.
All persons present in Ireland at the time of census, including visitors and those in residence. Usual residents temporarily absent from the State and members of the Defence Forces, who on Census night, were serving abroad with the United Nations were excluded.
Census/enumeration data [cen]
UNITS IDENTIFIED:
- Dwellings: No
- Vacant units: No
- Households: Yes
- Individuals: Yes
- Group quarters: Yes
UNIT DESCRIPTIONS:
- Group quarters: A non-private household is a boarding house, hotel, guest house, barrack, hospital, nursing home, boarding schools, religious institution, welfare institution, prison, or ship, etc. However, proprietors and manager of hotels, principals of boarding schools, persons in charge of various other types of institutions and members of staff who, with their families, occupy flats on the premises are considered as private households.
MICRODATA SOURCE: Central Statistics Office
SAMPLE DESIGN: A 10% random sample of the recoded household records from each county was selected. The records within each county were sorted randomly before output to the sample file.
SAMPLE UNIT: Household
SAMPLE FRACTION: 10%
SAMPLE SIZE (person records): 337,686
Face-to-face [f2f]
The information is based on Form A - Household Schedule.
De facto, CENSUS DAY: April 1, 1979
Direct and self-enumeration
Self-weighting (expansion factor=10)
IPUMS-International distributes integrated microdata of individuals and households only by agreement of collaborating national statistical offices and under the strictest of confidence. Before data may be distributed to an individual researcher, an electronic license agreement must be signed and approved.
To gain access to the data, a researcher must agree to the following:
(1) Implement security measures to prevent unauthorized access to census microdata. Under IPUMS-International agreements with collaborating agencies, redistribution of the data to third parties is prohibited.
(2) Use the microdata for the exclusive purposes of scholarly research and education. Researchers must explicitly agree to not use microdata acquired for any commercial or income-generating venture.
(3) Maintain the confidentiality of persons, households, and other entities. Any attempt to ascertain the identity of persons or households from the microdata is prohibited. Alleging that a person or household has been identified is also prohibited.
(4) Report all publications based on these data to IPUMS-International, which will in turn pass the information on to the relevant national statistical agencies.
Once a project is approved, a password is issued and data may be acquired through the Internet. Penalties for violating the license include: revocation of the license, recall of all microdata acquired, filing of a motion of censure to the appropriate professional organizations, and civil prosecution under the relevant national or international statutes.
These safeguards mirror the principles from the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. Employees of the Minnesota Population Center who work with the census microdata to produce the harmonized database also sign agreements to respect the confidentiality of the data.
IPUMS-International works with each country's statistical office to minimize the risk of disclosure of respondent information. The details of the confidentiality protections vary across countries, but in all cases, names and detailed geographic information are suppressed and top-codes are imposed on variables such as income that might identify specific persons. In addition, IPUMS-International uses a variety of technical procedures to enhance confidentiality protection. These include the following:
(1) Swapping an undisclosed fraction of records from one administrative district to another to make positive identification of individuals impossible.
(2) Randomizing the placement of households within districts to disguise the order in which individuals were enumerated or the data processed.
(3) Aggregating codes of sensitive characteristics (e.g., grouping together very small ethnic categories)
(4) Top- and bottom-coding continuous variables to prevent identification of extreme cases.
The safety record for public-use census microdata is apparently perfect. In almost four decades of use, there has not been a single verified breach of statistical confidentiality. The measures implemented by the IPUMS-International are designed to extend this record.
IPUMS International
Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 6.4 [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D020.V6.4.
Researchers should also acknowledge the statistical agency that originally produced the data:
Ireland, Central Statistics Office, Census of Population of Ireland, 1979
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.
IRL1979-H-H
Household records
0
29
IRL1979-P-H
Person records
0
39
Record type
Record type
Record type
Record type
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.
Technical Household Variables -- HOUSEHOLD
IPUMS
Household serial number
Household serial number
Household serial number
Household serial number
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, 2002
Colombia 1993, 2005
Costa Rica 1984, 2000
Cuba 2002
Dominican Republic 1981, 2002, 2010
Ecuador 1990, 2001
Germany 1971
Hungary 1980, 1990, 2001
Jamaica 1982, 1991, 2001
Malaysia 1970, 1991, 2000
Mexico 1995, 1990, 2000, 2005
Nigeria 2006
Panama 2000
Peru 1993, 2007
Portugal 1981, 1991, 2001
Spain 1991
Uruguay 2011
Venezuela 1990, 2001
Vietnam 1989
In 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.
Technical Household Variables -- HOUSEHOLD
IPUMS
Year
Year
Year
Year
Year
1960
1960
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
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
YEAR gives the year in which the census was taken.
Technical Household Variables -- HOUSEHOLD
IPUMS
IPUMS sample identifier
IPUMS sample identifier
IPUMS sample identifier
IPUMS sample identifier
IPUMS sample identifier
32197001
Argentina 1970
32199101
Argentina 1991
32200101
Argentina 2001
32201001
Argentina 2010
32219801
Argentina 1980
40197101
Austria 1971
40198101
Austria 1981
40199101
Austria 1991
40200101
Austria 2001
40201101
Austria 2011
50199101
Bangladesh 1991
50200101
Bangladesh 2001
50201101
Bangladesh 2011
51200101
Armenia 2001
51201101
Armenia 2011
68197601
Bolivia 1976
68199201
Bolivia 1992
68200101
Bolivia 2001
76196001
Brazil 1960
76197001
Brazil 1970
76198001
Brazil 1980
76199101
Brazil 1991
76200001
Brazil 2000
76201001
Brazil 2010
112199901
Belarus 1999
116199801
Cambodia 1998
116200801
Cambodia 2008
120197601
Cameroon 1976
120198701
Cameroon 1987
120200501
Cameroon 2005
124197101
Canada 1971
124198101
Canada 1981
124199101
Canada 1991
124200101
Canada 2001
152196001
Chile 1960
152197001
Chile 1970
152198201
Chile 1982
152199201
Chile 1992
152200201
Chile 2002
156198201
China 1982
156199001
China 1990
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
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
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
250196201
France 1962
250196801
France 1968
250197501
France 1975
250198201
France 1982
250199001
France 1990
250199901
France 1999
250200601
France 2006
250201101
France 2011
275199701
Palestine 1997
275200701
Palestine 2007
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
324198301
Guinea 1983
324199601
Guinea 1996
332197101
Haiti 1971
332198201
Haiti 1982
332200301
Haiti 2003
348197001
Hungary 1970
348198001
Hungary 1980
348199001
Hungary 1990
348200101
Hungary 2001
356198341
India 1983
356198741
India 1987
356199341
India 1993
356199941
India 1999
356200441
India 2004
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
368199701
Iraq 1997
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
376197201
Israel 1972
376198301
Israel 1983
376199501
Israel 1995
380200101
Italy 2001
388198201
Jamaica 1982
388199101
Jamaica 1991
388200101
Jamaica 2001
400200401
Jordan 2004
404196901
Kenya 1969
404197901
Kenya 1979
404198901
Kenya 1989
404199901
Kenya 1999
404200901
Kenya 2009
417199901
Kyrgyz Republic 1999
417200901
Kyrgyz Republic 2009
430197401
Liberia 1974
430200801
Liberia 2008
454198701
Malawi 1987
454199801
Malawi 1998
454200801
Malawi 2008
458197001
Malaysia 1970
458198001
Malaysia 1980
458199101
Malaysia 1991
458200001
Malaysia 2000
466198701
Mali 1987
466199801
Mali 1998
466200901
Mali 2009
484196001
Mexico 1960
484197001
Mexico 1970
484199001
Mexico 1990
484199501
Mexico 1995
484200001
Mexico 2000
484200501
Mexico 2005
484201001
Mexico 2010
496198901
Mongolia 1989
496200001
Mongolia 2000
504198201
Morocco 1982
504199401
Morocco 1994
504200401
Morocco 2004
508199701
Mozambique 1997
508200701
Mozambique 2007
524200101
Nepal 2001
528196001
Netherlands 1960
528197101
Netherlands 1971
528200101
Netherlands 2001
558197101
Nicaragua 1971
558199501
Nicaragua 1995
558200501
Nicaragua 2005
566200621
Nigeria 2006
566200721
Nigeria 2007
566200821
Nigeria 2008
566200921
Nigeria 2009
566201021
Nigeria 2010
586197301
Pakistan 1973
586198101
Pakistan 1981
586199801
Pakistan 1998
591196001
Panama 1960
591197001
Panama 1970
591198001
Panama 1980
591199001
Panama 1990
591200001
Panama 2000
591201001
Panama 2010
600196201
Paraguay 1962
600197201
Paraguay 1972
600198201
Paraguay 1982
600199201
Paraguay 1992
600200201
Paraguay 2002
604199301
Peru 1993
604200701
Peru 2007
608199001
Philippines 1990
608199501
Philippines 1995
608200001
Philippines 2000
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
642197701
Romania 1977
642199201
Romania 1992
642200201
Romania 2002
646199101
Rwanda 1991
646200201
Rwanda 2002
662198001
Saint Lucia 1980
662199101
Saint Lucia 1991
686198801
Senegal 1988
686200201
Senegal 2002
694200401
Sierra Leone 2004
704198901
Vietnam 1989
704199901
Vietnam 1999
704200901
Vietnam 2009
705200201
Slovenia 2002
710199601
South Africa 1996
710200101
South Africa 2001
710200701
South Africa 2007
710201101
South Africa 2011
724198101
Spain 1981
724199101
Spain 1991
724200101
Spain 2001
724201101
Spain 2011
728200801
South Sudan 2008
729200801
Sudan 2008
756197001
Switzerland 1970
756198001
Switzerland 1980
756199001
Switzerland 1990
756200001
Switzerland 2000
764197001
Thailand 1970
764198001
Thailand 1980
764199001
Thailand 1990
764200001
Thailand 2000
792198501
Turkey 1985
792199001
Turkey 1990
792200001
Turkey 2000
800199101
Uganda 1991
800200201
Uganda 2002
804200101
Ukraine 2001
818199601
Egypt 1996
818200601
Egypt 2006
826199101
United Kingdom 1991
826200101
United Kingdom 2001
834198801
Tanzania 1988
834200201
Tanzania 2002
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
854198501
Burkina Faso 1985
854199601
Burkina Faso 1996
854200601
Burkina Faso 2006
858196301
Uruguay 1963
858197501
Uruguay 1975
858198501
Uruguay 1985
858199601
Uruguay 1996
858200621
Uruguay 2006
858201101
Uruguay 2011
862197101
Venezuela 1971
862198101
Venezuela 1981
862199001
Venezuela 1990
862200101
Venezuela 2001
894199001
Zambia 1990
894200001
Zambia 2000
894201001
Zambia 2010
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).
Technical Household Variables -- HOUSEHOLD
IPUMS
Continent and region of country
Continent and region of country
Continent and region of country
Continent and region of country
Continent and region of country
11
Eastern Africa
12
Middle Africa
13
Northern Africa
14
Southern Africa
15
Western Africa
21
Caribbean
22
Central America
23
North America
24
South America
31
Central Asia
32
Eastern Asia
33
Southern Asia
34
South-Eastern Asia
35
Western Asia
41
Eastern Europe
42
Northern Europe
43
Southern Europe
44
Western Europe
51
Australia and New Zealand
52
Melanesia
53
Micronesia
54
Polynesia
REGIONW identifies the continent and region of each country.
Geography: Global Variables -- HOUSEHOLD
IPUMS
Number of person records in the household
Number of person records in the household
Number of person records in the household
Number of person records in the 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.
Technical Household Variables -- HOUSEHOLD
IPUMS
Subsample number
Subsample number
Subsample number
Subsample number
Subsample number
1st 1% subsample
1
2nd 1% subsample
2
3rd 1% subsample
3
4th 1% subsample
4
5th 1% subsample
5
6th 1% subsample
6
7th 1% subsample
7
8th 1% subsample
8
9th 1% subsample
9
10th 1% subsample
10
11th 1% subsample
11
12th 1% subsample
12
13th 1% subsample
13
14th 1% subsample
14
15th 1% subsample
15
16th 1% subsample
16
17th 1% subsample
17
18th 1% subsample
18
19th 1% subsample
19
20th 1% subsample
20
21st 1% subsample
21
22nd 1% subsample
22
23rd 1% subsample
23
24th 1% subsample
24
25th 1% subsample
25
26th 1% subsample
26
27th 1% subsample
27
28th 1% subsample
28
29th 1% subsample
29
30th 1% subsample
30
31st 1% subsample
31
32nd 1% subsample
32
33rd 1% subsample
33
34th 1% subsample
34
35th 1% subsample
35
36th 1% subsample
36
37th 1% subsample
37
38th 1% subsample
38
39th 1% subsample
39
40th 1% subsample
40
41st 1% subsample
41
42nd 1% subsample
42
43rd 1% subsample
43
44th 1% subsample
44
45th 1% subsample
45
46th 1% subsample
46
47th 1% subsample
47
48th 1% subsample
48
49th 1% subsample
49
50th 1% subsample
50
51st 1% subsample
51
52nd 1% subsample
52
53rd 1% subsample
53
54th 1% subsample
54
55th 1% subsample
55
56th 1% subsample
56
57th 1% subsample
57
58th 1% subsample
58
59th 1% subsample
59
60th 1% subsample
60
61st 1% subsample
61
62nd 1% subsample
62
63rd 1% subsample
63
64th 1% subsample
64
65th 1% subsample
65
66th 1% subsample
66
67th 1% subsample
67
68th 1% subsample
68
69th 1% subsample
69
70th 1% subsample
70
71st 1% subsample
71
72nd 1% subsample
72
73rd 1% subsample
73
74th 1% subsample
74
75th 1% subsample
75
76th 1% subsample
76
77th 1% subsample
77
78th 1% subsample
78
79th 1% subsample
79
80th 1% subsample
80
81st 1% subsample
81
82nd 1% subsample
82
83rd 1% subsample
83
84th 1% subsample
84
85th 1% subsample
85
86th 1% subsample
86
87th 1% subsample
87
88th 1% subsample
88
89th 1% subsample
89
90th 1% subsample
90
91st 1% subsample
91
92nd 1% subsample
92
93rd 1% subsample
93
94th 1% subsample
94
95th 1% subsample
95
96th 1% subsample
96
97th 1% subsample
97
98th 1% subsample
98
99th 1% subsample
99
100th 1% subsample
SUBSAMP allocates each case to one of 100 subsample replicates, randomly numbered from 0 to 99. Each subsample is nationally representative and preserves any stratification of the sample from which it is drawn. Users who need a representative subset of a sample can use SUBSAMP to select their cases. For example, to randomly extract 10% of the cases from a sample, select any 10 of the 100 subsamples.
Technical Household Variables -- HOUSEHOLD
IPUMS
Group quarters (collective dwelling) status
Group quarters (collective dwelling) status
Group quarters (collective dwelling) status
Group quarters (collective dwelling) status
Group quarters (collective dwelling) status
Vacant
10
Households
20
Group quarters, n.s.
21
Institutions
22
Other group quarters
29
1-person unit created by splitting large household
99
Unknown/group quarters not identified
GQ identifies households as vacant dwellings, group quarters, or private households. Group quarters -- collective dwellings -- are generally institutions and other group living arrangements such as rooming houses and boarding schools.
Institutions often retain persons under formal supervision or custody, such as correctional institutions, military barracks, asylums, or nursing homes. Educational and religious group dwellings (e.g., boarding schools, convents, monasteries, etc.) are also included in the institutional classification.
Group quarter designations are often useful for understanding the universe of households that answered questions about household characteristics. Censuses will often exclude group quarters from such questions.
Group Quarters Variables -- HOUSEHOLD
IPUMS
NUTS1 Region, Europe
NUTS1 Region, Europe
NUTS1 Region, Europe
NUTS1 Region, Europe
NUTS1 Region, Europe
101
AT1 / Ostösterreich
102
AT2 / Südösterreich
103
AT3 / Westösterreich
601
DE1 / Baden-Württemberg
602
DE2 / Bayern
603
DE3 / Berlin
604
DE4 / Brandenburg
605
DE5 / Bremen
606
DE6 / Hamburg
607
DE7 / Hessen
608
DE8 / Mecklenburg-Vorpommern
609
DE9 / Niedersachsen
610
DEA / Nordrhein-Westfalen
611
DEB / Rheinland-Pfalz
612
DEC / Saarland
613
DED / Sachsen
614
DEE / Sachsen-Anhalt
615
DEF / Schleswig-Holstein
616
DEG / Thüringen
901
ES1 / Noroeste
902
ES2 / Noreste
903
ES3 / Comunidad de Madrid
904
ES4 / Centro (E)
905
ES5 / Este
906
ES6 / Sur
907
ES7 / Canarias
909
ES / Unknown
1101
FR1 / Île de France
1102
FR2 / Bassin Parisien
1103
FR3 / Nord - Pas-de-Calais
1104
FR4 / Est
1105
FR5 / Ouest
1106
FR6 / Sud-Ouest
1107
FR7 / Centre-Est
1108
FR8 / Méditerranée
1109
FR9 / Département d’Outre-Mer
1199
FR99 / Unknown
1201
GR1 / Voreia Ellada
1202
GR2 / Kentriki Ellada
1203
GR3 / Attiki
1204
GR4 / Nisia Aigaiou, Kriti
1400
IE0 / Republic of Ireland
1501
ITC / Nord-Ovest
1502
ITD / Nord-Est
1503
ITE / Centro
1504
ITF / Sud
1505
ITG / Isole
2201
PT1 / Continente
2202
PT2 / Região Autónoma dos Açores
2203
PT3 / Região Autónoma da Madeira
2301
RO1 / Macroregiunea Unu
2302
RO2 / Macroregiunea Doi
2303
RO3 / Macroregiunea Trei
2304
RO4 / Macroregiunea Patru
2501
SI0 / Slovenia
2701
UKC / North East (England)
2702
UKD / North West (England)
2703
UKE / Yorkshire and the Humber (England)
2704
UKF / East Midlands (England)
2705
UKG / West Midlands (England)
2706
UKH / East of England (England)
2707
UKI / LONDON (England)
2708
UKJ / South East (England)
2709
UKK / South West (England)
2710
UKL / WALES
2711
UKM / SCOTLAND
2712
UKN / NORTHERN IRELAND
2713
UKC / North East (England) + UKD / North West (England)
2714
UKH / East of England (England) + UKJ / South East (England)
3401
CH0/Switzerland
9999
UNKNOWN
ENUTS1 identifies the Nomenclature of Territorial Units for Statistics (NUTS) within Europe in which the household was enumerated. NUTS1 is the first level territorial units within countries. NUTS is a standard administrative division of the European Union, and was developed by the EU. The European Free Trade Association extends the NUTS system to several additional countries outside of the EU, and they are also incorporated into this variable.
The code labels include the standard code for the NUTS1 system and the name of the NUTS1 region, separated by a slash.
The full set of geography variables for the countries 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.
Geography: Global Variables -- HOUSEHOLD
IPUMS
NUTS2 Region, Europe
NUTS2 Region, Europe
NUTS2 Region, Europe
NUTS2 Region, Europe
NUTS2 Region, Europe
111
AT11 / Burgenland
112
AT12 / Niederösterreich
113
AT13 / Wien
121
AT21 / Kärnten
122
AT22 / Steiermark
131
AT31 / Oberösterreich
132
AT32 / Salzburg
133
AT33 / Tirol
134
AT34 / Vorarlberg
911
ES11 / Galicia
912
ES12 / Principado de Asturias
913
ES13 / Cantabria
921
ES21 / País Vasco
922
ES22 / Comunidad Foral de Navarra
923
ES23 / La Rioja
924
ES24 / Aragón
930
ES30 / Comunidad de Madrid
941
ES41 / Castilla y León
942
ES42 / Castilla-La Mancha
943
ES43 / Extremadura
951
ES51 / Cataluña
952
ES52 / Comunidad Valenciana
953
ES53 / Illes Balears
961
ES61 / Andalucía
962
ES62 / Región de Murcia
963
ES63 / Ciudad Autónoma de Ceuta
964
ES64 / Ciudad Autónoma de Melilla
970
ES70 / Canarias
999
ES / Unknown
1110
FR10 / Region d'Île de France
1121
FR21 / Champagne-Ardenne
1122
FR22 / Picardie
1123
FR23 / Haute-Normandie
1124
FR24 / Centre
1125
FR25 / Basse-Normandie
1126
FR26 / Bourgogne
1130
FR30 / Nord-Pas-de-Calais
1141
FR41 / Lorraine
1142
FR42 / Alsace
1143
FR43 / Franche-Comté
1151
FR51 / Pays de la Loire
1152
FR52 / Bretagne
1153
FR53 / Poitou-Charentes
1161
FR61 / Aquitaine
1162
FR62 / Midi-Pyrénées
1163
FR63 / Limousin
1171
FR71 / Rhône-Alpes
1172
FR72 / Auvergne
1181
FR81 / Lanquedoc-Roussillon
1182
FR82 / Provence-Alpes-Côte d'Azur
1183
FR83 / Corse
1191
FR91 / Guadeloupe
1192
FR92 / Martinique
1193
FR93 / Guyane
1194
FR94 / Réunion
1199
FR99 / Unknown
1211
GR11 / Anatoliki Makedonia, Thraki
1212
GR12 / Kentriki Makedonia
1213
GR13 / Dytiki Makedonia
1214
GR14 / Thessalia
1221
GR21 / Ipeiros
1222
GR22 / Ionia Nisia
1223
GR23 / Dytiki Ellada
1224
GR24 / Sterea Ellada
1225
GR25 / Peloponnisos
1230
GR30 / Attiki
1241
GR41 / Voreio Aigaio
1242
GR42 / Notio Aigaio
1243
GR43 / Kriti
1401
IE01 / Border, Midland and Western
1402
IE02 / Southern and Eastern
1511
ITC1 / Piemonte + ITC2 / Valle d'Aosta
1513
ITC3 / Liguria
1514
ITC4 /Lombardia
1521
ITD1 / Bolzano-Bozen + ITD2 / Trento
1523
ITD3 / Veneto
1524
ITD4 / Friuli-Venezia Giulia
1525
ITD5 / Emilia-Romagna
1531
ITE1 / Toscana
1532
ITE2 / Umbria
1533
ITE3 / Marche
1534
ITE4 / Lazio
1541
ITF1 / Abruzzo
1542
ITF2 / Molise
1543
ITF3 / Campania
1544
ITF4 / Puglia
1545
ITF5 / Basilicata
1546
ITF6 / Calabria
1551
ITG1 / Sicilia
1552
ITG2 / Sardegna
2211
PT11 / Norte
2215
PT15 / Algarve
2216
PT16 / Centro (P)
2217
PT17 / Lisboa
2218
PT18 / Alentejo
2220
PT20 / Região Autónoma dos Açores
2230
PT30 / Região Autónoma da Madeira
2311
RO11 / Nord-Vest
2312
RO12 / Centru
2321
RO21 / Nord-Est
2322
RO22 / Sud-Est
2331
RO31 / Sud - Muntenia
2332
RO32 / Bucuresti - Ilfov
2341
RO41 / Sud-Vest Oltenia
2342
RO42 / Vest
2501
SI01 / Vzhodna Slovenija
2502
SI02 / Zahodna Slovenija
2599
SI / Unknown
3401
CH01/Lake Geneva region (Région lémanique)
3402
CH02/Espace Mittelland
3403
CH03/Northwestern Switzerland (Nordwestschweiz)
3404
CH04/Zurich
3405
CH05/Eastern Switzerland (Ostschweiz)
3406
CH06/Central Switzerland (Zentralschweiz)
3407
CH07/Ticino
ENUTS2 identifies the Nomenclature of Territorial Units for Statistics (NUTS) within Europe in which the household was enumerated. NUTS2 is the second level territorial units within countries. NUTS is a standard administrative division of the European Union, and was developed by the EU. The European Free Trade Association extends the NUTS system to several additional countries outside of the EU, and they are also incorporated into this variable.
The code labels include the standard code for the NUTS2 system and the name of the NUTS2 region, separated by a slash.
The full set of geography variables for the countries 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.
Geography: Global Variables -- HOUSEHOLD
IPUMS
NUTS3 Region, Europe
NUTS3 Region, Europe
NUTS3 Region, Europe
NUTS3 Region, Europe
NUTS3 Region, Europe
1111
AT111 / Mittelburgenland + AT113 / Südburgenland
1112
AT112 / Nordburgenland
1121
AT121 / Mostviertel-Eisenwurzen
1122
AT122 / Niederösterreich-Süd
1123
AT123 / Sankt Pölten
1124
AT124 / Waldviertel
1125
AT125 / Weinviertel
1126
AT126 / Wiener Umland/Nordteil
1127
AT127 / Wiener Umland/Südteil
1130
AT130 / Wien
1211
AT211 / Klagenfurt-Villach
1212
AT212 / Oberkärnten
1213
AT213 / Unterkärnten
1221
AT221 / Graz
1222
AT222 / Liezen
1223
AT223 / Östliche Obersteiermark
1224
AT224 / Oststeiermark
1225
AT225 / West- und Südsteiermark
1226
AT226 / Westliche Obersteiermark
1311
AT311 / Innviertel
1312
AT312 / Linz-Wels
1313
AT313 / Mühlviertel
1314
AT314 / Steyr-Kirchdorf
1315
AT315 / Traunviertel
1321
AT321 / Lungau + AT322 / Pinzgau-Pongau
1323
AT323 / Salzburg und Umgebung
1331
AT331 / Außerfern + AT334 / Oberland
1332
AT332 / Innsbruck
1333
AT333 / Osttirol + AT335 / Tiroler Unterland
1341
AT341 / Bludenz-Bregenzer Wald
1342
AT342 / Rheintal-Bodenseegebiet
9111
ES111 / Coruña (A)
9112
ES112 / Lugo
9113
ES113 / Ourense
9114
ES114 / Pontevedra
9120
ES120 / Asturias
9130
ES130 / Cantabria
9211
ES211 / Álava
9212
ES212 / Guipúzcoa
9213
ES213 / Vizcaya
9220
ES220 / Navarra
9230
ES230 / Rioja (La)
9241
ES241 / Huesca
9242
ES242 / Teruel
9243
ES243 / Zaragoza
9300
ES300 / Madrid
9411
ES411 / Ávila
9412
ES412 / Burgos
9413
ES413 / León
9414
ES414 / Palencia
9415
ES415 / Salamanca
9416
ES416 / Segovia
9417
ES417 / Soria
9418
ES418 / Valladolid
9419
ES419 / Zamora
9421
ES421 / Albacete
9422
ES422 / Ciudad Real
9423
ES423 / Cuenca
9424
ES424 / Guadalajara
9425
ES425 / Toledo
9431
ES431 / Badajoz
9432
ES432 / Cáceres
9511
ES511 / Barcelona
9512
ES512 / Girona
9513
ES513 / Lleida
9514
ES514 / Tarragona
9521
ES521 / Alicante/Alacant
9522
ES522 / Castellón/Castelló
9523
ES523 / Valencia/València
9530
ES530 / Balears (Illes)
9611
ES611 / Almería
9612
ES612 / Cádiz
9613
ES613 / Córdoba
9614
ES614 / Granada
9615
ES615 / Huelva
9616
ES616 / Jaén
9617
ES617 / Málaga
9618
ES618 / Sevilla
9620
ES620 / Murcia
9630
ES630 / Ceuta
9640
ES640 / Melilla
9701
ES701 / Palmas (Las)
9702
ES702 / Santa Cruz de Tenerife
9999
ES / Unknown
12111
GR111 / Evros
12112
GR112 / Xanthi
12113
GR113 / Rodopi
12114
GR114 / Drama
12115
GR115 / Kavala
12121
GR121 / Imathia
12122
GR122 / Thessaloniki
12123
GR123 / Kilkis
12124
GR124 / Pella
12125
GR125 / Pieria
12126
GR126 / Serres
12127
GR127 / Chalkidiki and Aghion Oros
12131
GR131 / Grevena
12132
GR132 / Kastoria
12133
GR133 / Kozani
12134
GR134 / Florina
12141
GR141 / Karditsa
12142
GR142 / Larissa
12143
GR143 / Magnissia
12144
GR144 / Trikala
12211
GR211 / Arta
12212
GR212 / Thesprotia
12213
GR213 / Ioannina
12214
GR214 / Preveza
12221
GR221 / Zakynthos
12222
GR222 / Kerkyra
12223
GR223 / Kefallinia
12224
GR224 / Lefkada
12231
GR231 / Etolia and Akarnania
12232
GR232 / Achaia
12233
GR233 / Ilia
12241
GR241 / Viotia
12242
GR242 / Evia
12243
GR243 / Evrytania
12244
GR244 / Fthiotida
12245
GR245 / Fokida
12251
GR251 / Argolida
12252
GR252 / Arkadia
12253
GR253 / Korinthia
12254
GR254 / Lakonia
12255
GR255 / Messinia
12300
GR300 / Attiki
12411
GR411 / Lesvos
12412
GR412 / Samos
12413
GR413 / Chios
12421
GR421 / Dodekanissos
12422
GR422 / Kyklades
12431
GR431 / Iraklio
12432
GR432 / Lassithi
12433
GR433 / Rethymno
12434
GR434 / Chania
14011
IE011 / Border
14012
IE012 / Midlands
14013
IE013 / West
14021
IE021 / Dublin
14022
IE022 / Mid-East
14023
IE023 / Mid-West
14024
IE024 / South-East
14025
IE025 / South-West
22111
PT111 / Minho-Lima
22112
PT112 / Cávado
22113
PT113 / Ave
22114
PT114 / Grande Porto
22115
PT115 / Tâmega
22116
PT116 / Entre Douro e Vouga
22117
PT117 / Douro
22118
PT118 / Alto Trás-os-Montes
22150
PT150 / Algarve
22161
PT161 / Baixo Vouga
22162
PT162 / Baixo Mondego
22163
PT163 / Pinhal Litoral
22165
PT165 / Dão-Lafões
22166
PT16B / Oeste
22167
PT16C / Médio Tejo
22169
PT164, PT166, PT167, PT168, PT169, PT16A
22171
PT171 / Grande Lisboa
22172
PT172 / Península de Setúbal
22185
PT185 / Lezíria do Tejo
22189
PT181, PT182, PT183, PT184
22200
PT200 / Região Autónoma dos Açores
22300
PT300 / Região Autónoma da Madeira
23111
RO111 / Bihor
23112
RO112 / Bistrita Nasaud
23113
RO113 / Cluj
23114
RO114 / Maramures
23115
RO115 / Satu Mare
23116
RO116 / Salaj
23121
RO121 / Alba
23122
RO122 / Brasov
23123
RO123 / Covasna
23124
RO124 / Harghita
23125
RO125 / Mures
23126
RO126 / Sibiu
23211
RO211 / Bacau
23212
RO212 / Botosani
23213
RO213 / Iasi
23214
RO214 / Neamt
23215
RO215 / Suceava
23216
RO216 / Vaslui
23221
RO221 / Braila
23222
RO222 / Buzau
23223
RO223 / Constanta
23224
RO224 / Galati
23225
RO225 / Tulcea
23226
RO226 / Vrancea
23311
RO311 / Arges
23312
RO312 / Calarasi
23313
RO313 / Dimbovita
23314
RO314 / Giurgiu
23315
RO315 / Ialomita
23316
RO316 / Prahova
23317
RO317 / Teleorman
23321
RO321 / Bucuresti
23322
RO322 / Ilfov
23411
RO411 / Dolj
23412
RO412 / Gorj
23413
RO413 / Mehedinti
23414
RO414 / Olt
23415
RO415 / Valcea
23421
RO421 / Arad
23422
RO422 / Caras Severin
23423
RO423 / Hunedoara
23424
RO424 / Timis
25011
SI011 / Pomurska
25012
SI012 / Podravska
25013
SI013 / Koroka
25014
SI014 / Savinjska
25015
SI015 / Zasavska
25016
SI016 / Spodnjeposavska
25017
SI017 / Jugovzhodna Slovenija
25018
SI018 / Notranjsko-kraka
25021
SI021 / Osrednjeslovenska
25022
SI022 / Gorenjska
25023
SI023 / Gorika
25024
SI024 / Obalno-kraka
25999
SI / Unknown
34011
CH011 / Vaud
34012
CH012 / Valais
34013
CH013 / Geneva
34021
CH021 / Bern
34022
CH022 / Freiburg
34023
CH023 / Solothurn
34024
CH024 / Neuchatel
34025
CH025 / Jura
34031
CH031 / Basel-Stadt
34032
CH032 / Basel-Landschaft
34033
CH033 / Aargau
34040
CH040 / Zurich
34051
CH051 / Glarus
34052
CH052 / Schaffhausen
34053
CH053 / Appenzell Ausserrhoden + CH054 / Appenzell Innerrhoden
34055
CH055 / St. Gallen
34056
CH056 / Graubundun
34057
CH057 / Thurgau
34061
CH061 / Luzern
34062
CH062 / Uri
34063
CH063 / Schwyz
34064
CH064 / Obwalden
34065
CH065 / Nidwalden
34066
CH066 / Zug
34070
CH070 / Ticino
ENUTS3 identifies the Nomenclature of Territorial Units for Statistics (NUTS) within Europe in which the household was enumerated. NUTS3 is the third level territorial units within countries. NUTS is a standard administrative division of the European Union, and was developed by the EU. The European Free Trade Association extends the NUTS system to several additional countries outside of the EU, and they are also incorporated into this variable.
The code labels include the standard code for the NUTS3 system and the name of the NUTS3 region, separated by a slash.
The full set of geography variables for the countries 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.
Geography: Global Variables -- HOUSEHOLD
IPUMS
Head's location in household
Head's location in household
Head's location in household
Head's location in household
Head's location in household
HEADLOC gives the person number of the head of household in samples in which persons are organized into households.
Constructed Household Variables -- HOUSEHOLD
IPUMS
Household classification
Household classification
Household classification
Household classification
Household classification
Vacant household
1
One-person household
2
Married/cohab couple, no children
3
Married/cohab couple with children
4
Single-parent family
5
Polygamous family
6
Extended family, relatives only
7
Composite household, family and non-relatives
8
Non-family household
9
Unclassified subfamily
10
Other relative or non-relative household
11
Group quarters
99
Unclassifiable
HHTYPE is a constructed variable that describes the composition of households.
HHTYPE is constructed from information in RELATE (relationship to head), from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father), and from information on group quarters status, GQ.
Constructed Household Variables -- HOUSEHOLD
IPUMS
Number of families in household
Number of families in household
Number of families in household
Number of families in household
Number of families in household
Vacant household
1
1 family
2
2 families
3
3 families
4
4 families
5
5 families
6
6 families
7
7 families
8
8 families
9
9 or more families
NFAMS is a constructed variable that indicates the number of families within each household. A "family" is any group of persons related by blood, adoption, or marriage. An unrelated individual within the household is considered a separate family. Thus, a household consisting of a widow and her servant contains two families; a household consisting of a large, multiple-generation extended family with no lodgers or servants would count as a single family.
NFAMS is constructed from information in RELATE (relationship to head) and from the constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father). See those variable descriptions for more detail.
Constructed Household Variables -- HOUSEHOLD
IPUMS
1st subnational geographic level, world [consistent boundaries over time]
1st subnational geographic level, world [consistent boundaries over time]
1st subnational geographic level, world [consistent boundaries over time]
1st subnational geographic level, world [consistent boundaries over time]
1st subnational geographic level, world [consistent boundaries over time]
32002
City of Buenos Aires [Province: Argentina]
32006
Buenos Aires province [Province: Argentina]
32010
Catamarca [Province: Argentina]
32014
Córdoba [Province: Argentina]
32018
Corrientes [Province: Argentina]
32022
Chaco [Province: Argentina]
32026
Chubut [Province: Argentina]
32030
Entre Ríos [Province: Argentina]
32034
Formosa [Province: Argentina]
32038
Jujuy [Province: Argentina]
32042
La Pampa [Province: Argentina]
32046
La Rioja [Province: Argentina]
32050
Mendoza [Province: Argentina]
32054
Misiones [Province: Argentina]
32058
Neuquén [Province: Argentina]
32062
Río Negro [Province: Argentina]
32066
Salta [Province: Argentina]
32070
San Juan [Province: Argentina]
32074
San Luis [Province: Argentina]
32078
Santa Cruz [Province: Argentina]
32082
Santa Fe [Province: Argentina]
32086
Santiago del Estero [Province: Argentina]
32090
Tucumán [Province: Argentina]
32094
Tierra del Fuego [Province: Argentina]
32099
Unknown [Province: Argentina]
40011
Burgenland [State: Austria]
40012
Niederösterreich [State: Austria]
40013
Wien [State: Austria]
40021
Kärnten [State: Austria]
40022
Steiermark [State: Austria]
40031
Oberösterreich [State: Austria]
40032
Salzburg [State: Austria]
40033
Tirol [State: Austria]
40034
Vorarlberg [State: Austria]
50010
Barisal [Division, Bangladesh]
50020
Chittagong [Division, Bangladesh]
50030
Dhaka [Division, Bangladesh]
50040
Khulna [Division, Bangladesh]
50050
Rajshahi, Rangpur [Division, Bangladesh]
50060
Sylhet [Division, Bangladesh]
51901
Yerevan [Province: Armenia]
51902
Aragatsotn [Province: Armenia]
51903
Ararat [Province: Armenia]
51904
Armavir [Province: Armenia]
51905
Gegharkunik [Province: Armenia]
51906
Lori [Province: Armenia]
51907
Kotayk [Province: Armenia]
51908
Shirak [Province: Armenia]
51909
Syunik [Province: Armenia]
51910
Vayots Dzor [Province: Armenia]
51911
Tavush [Province: Armenia]
68001
Chuquisaca [Department: Bolivia]
68002
La Paz [Department: Bolivia]
68003
Cochabamba [Department: Bolivia]
68004
Oruro [Department: Bolivia]
68005
Potosí [Department: Bolivia]
68006
Tarija [Department: Bolivia]
68007
Santa Cruz [Department: Bolivia]
68008
Beni [Department: Bolivia]
68009
Pando [Department: Bolivia]
76011
Rondonia [State: Brazil]
76012
Acre [State: Brazil]
76013
Amazonas [State: Brazil]
76014
Roraima [State: Brazil]
76015
Pará [State: Brazil]
76016
Amapa [State: Brazil]
76021
Maranhao [State: Brazil]
76022
Piauí [State: Brazil]
76023
Ceará [State: Brazil]
76024
Rio Grande do Norte [State: Brazil]
76025
Paraiba [State: Brazil]
76026
Pernambuco [State: Brazil]
76027
Alagoas [State: Brazil]
76028
Sergipe [State: Brazil]
76029
Bahia [State: Brazil]
76031
Minas Gerais [State: Brazil]
76032
Espírito Santo [State: Brazil]
76033
Rio de Janeiro [State: Brazil]
76035
São Paulo [State: Brazil]
76041
Parana [State: Brazil]
76042
Santa Catarina [State: Brazil]
76043
Rio Grande do Sul [State: Brazil]
76051
Mato Grosso, Mato Grosso do Sul [State: Brazil]
76052
Goiás and Tocantins [State: Brazil]
76053
Distrito Federal [State: Brazil]
112001
Brest [Region: Belarus]
112002
Vitebsk [Region: Belarus]
112003
Gomel [Region: Belarus]
112004
Grodno [Region: Belarus]
112006
Minsk [Region: Belarus]
112007
Mogilev [Region: Belarus]
116001
Banteay Meanchey [Province: Cambodia]
116002
Battambang [Province: Cambodia]
116003
Kampong Cham [Province: Cambodia]
116004
Kampong Chhnang [Province: Cambodia]
116005
Kampong Speu [Province: Cambodia]
116006
Kampong Thom [Province: Cambodia]
116007
Kampot [Province: Cambodia]
116008
Kandal [Province: Cambodia]
116009
Koh Kong [Province: Cambodia]
116010
Kratie [Province: Cambodia]
116011
Mondul Kiri [Province: Cambodia]
116012
Phnom Penh [Province: Cambodia]
116013
Preah Vihear [Province: Cambodia]
116014
Prey Veng [Province: Cambodia]
116015
Pursat [Province: Cambodia]
116016
Rotanak Kiri [Province: Cambodia]
116017
Siem Reap and Otdar Meanchey [Province: Cambodia]
116018
Preah Sihanouk [Province: Cambodia]
116019
Stung Treng [Province: Cambodia]
116020
Svay Rieng [Province: Cambodia]
116021
Takeo [Province: Cambodia]
116023
Kep [Province: Cambodia]
116024
Pailin [Province: Cambodia]
120002
Centre, Sud [Province: Cameroon]
120003
Est [Province: Cameroon]
120004
Nord, Adamoua , Extrème Nord [Province: Cameroon]
120005
Littoral [Province: Cameroon]
120007
Nord Ouest [Province: Cameroon]
120008
Ouest [Province: Cameroon]
120010
Sud Ouest [Province: Cameroon]
124010
Newfoundland and Labrador [Province: Canada]
124011
Prince Edward Island, Yukon, Northwest Territories, and 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]
152002
Antofagasta and Tarapacá [Region: Chile]
152004
Atacama and Coquimbo [Region: Chile]
152007
Del Maule [Region: Chile]
152008
Del Biobio [Region: Chile]
152009
La Araucanía [Region: Chile]
152010
Aysen del Gral Carlos Ibáñez del Campo and Los Lagos [Region: Chile]
152012
Magallanes and La Antártica Chilena [Region: Chile]
152013
Libertador General Bernardo O"Higgins, Metropolitana de Santiago, and Valparaiso [Region: Chile]
152099
Unknown [Region: Chile]
156011
Beijing (municipality) [Province: China]
156012
Tianjin (municipality) [Province: China]
156013
Hebei [Province: China]
156014
Shanxi [Province: China]
156015
Inner Mongolia [Province: China]
156021
Liaoning [Province: China]
156022
Jilin [Province: China]
156023
Heilongjiang [Province: China]
156031
Shanghai (municipality) [Province: China]
156032
Jiangsu [Province: China]
156033
Zhejiang [Province: China]
156034
Anhui [Province: China]
156035
Fujian [Province: China]
156036
Jiangxi [Province: China]
156037
Shangdong [Province: China]
156041
Henan [Province: China]
156042
Hubei [Province: China]
156043
Hunan [Province: China]
156044
Guangdong and Hainan [Province: China]
156045
Guangxi [Province: China]
156051
Sichuan [Province: China]
156052
Guizhou [Province: China]
156053
Yunnan [Province: China]
156054
Tibet [Province: China]
156061
Shaanxi [Province: China]
156062
Gansu [Province: China]
156063
Qinghai [Province: China]
156064
Ningxia [Province: China]
156065
Xinjiang [Province: China]
170005
Antioquia [Department: Colombia]
170008
Atlántico [Department: Colombia]
170011
Bogotá [Department: Colombia]
170013
Bolívar and Sucre [Department: Colombia]
170015
Boyacá and Casanare [Department: Colombia]
170018
Caquetá [Department: Colombia]
170019
Cauca [Department: Colombia]
170023
Córdoba [Department: Colombia]
170025
Cundinamarca [Department: Colombia]
170027
Chocó [Department: Colombia]
170041
Huila [Department: Colombia]
170044
La Guajira [Department: Colombia]
170047
Cesar and Magdalena [Department: Colombia]
170050
Meta and Vichada [Department: Colombia]
170052
Nariño [Department: Colombia]
170054
Norte de Santander [Department: Colombia]
170066
Caldas, Quindío, and Risaralda [Department: Colombia]
170068
Santander [Department: Colombia]
170073
Tolima [Department: Colombia]
170076
Valle [Department: Colombia]
170081
Arauca [Department: Colombia]
170086
Putumayo [Department: Colombia]
170088
San Andrés [Department: Colombia]
170091
Amazonas [Department: Colombia]
170095
Guaviare, Vaupés, and Guainía [Department: Colombia]
188001
San José [Province: Costa Rica]
188002
Alajuela [Province: Costa Rica]
188003
Cartago [Province: Costa Rica]
188004
Heredia [Province: Costa Rica]
188005
Guanacaste [Province: Costa Rica]
188006
Puntarenas [Province: Costa Rica]
188007
Limón [Province: Costa Rica]
192001
Pinar del Río [Province: Cuba]
192002
La Habana [Province: Cuba]
192003
Ciudad de la Habana [Province: Cuba]
192004
Matanzas [Province: Cuba]
192005
Villa Clara [Province: Cuba]
192006
Cienfuegos [Province: Cuba]
192007
Sancti Spiritus [Province: Cuba]
192008
Ciego de Avila [Province: Cuba]
192009
Camagüey [Province: Cuba]
192010
Las Tunas [Province: Cuba]
192011
Holguín [Province: Cuba]
192012
Granma [Province: Cuba]
192013
Santiago de Cuba [Province: Cuba]
192014
Guantánamo [Province: Cuba]
192099
Isla de la Juventud [Province: Cuba]
214001
Federal district and Santo Domingo [Province: Dominican Republic]
214002
Azua [Province: Dominican Republic]
214003
Baoruco [Province: Dominican Republic]
214004
Barahona [Province: Dominican Republic]
214005
Dajabón [Province: Dominican Republic]
214006
Duarte [Province: Dominican Republic]
214007
Elías Piña [Province: Dominican Republic]
214008
El Seibo and Hato Mayor [Province: Dominican Republic]
214009
Espaillat [Province: Dominican Republic]
214010
Independencia [Province: Dominican Republic]
214011
La Altagracia and La Romana [Province: Dominican Republic]
214013
La Vega and Monseñor Nouel [Province: Dominican Republic]
214014
María Trinidad Sánchez and Samaná [Province: Dominican Republic]
214015
Monte Cristi [Province: Dominican Republic]
214016
Pedernales [Province: Dominican Republic]
214017
Peravia and San José de Ocoa [Province: Dominican Republic]
214018
Puerto Plata [Province: Dominican Republic]
214019
Hermanas Mirabal [Province: Dominican Republic]
214021
San Cristóbal and Monte Plata [Province: Dominican Republic]
214022
San Juan [Province: Dominican Republic]
214023
San Pedro de Macorís [Province: Dominican Republic]
214024
Sánchez Ramírez [Province: Dominican Republic]
214025
Santiago [Province: Dominican Republic]
214026
Santiago Rodríguez [Province: Dominican Republic]
214027
Valverde [Province: Dominican Republic]
218001
Azuay [Province: Ecuador]
218002
Bolívar [Province: Ecuador]
218004
Carchi [Province: Ecuador]
218005
Cotopaxi [Province: Ecuador]
218006
Chimborazo [Province: Ecuador]
218007
El Oro [Province: Ecuador]
218009
Cañar, Esmeraldas, Guayas, Manabí, Manga del Cura [Disputed canton], Pichincha, El Piedrero [Disputed canton], Los Ríos, Santa Elena, Santo Domingo de las Tsáchilas, Galápagos [Disputed canton], Pichincha, El Piedrero
218010
Imbabura, Las Golondrinas [Disputed canton] [Disputed canton]
218011
Loja [Province: Ecuador]
218014
Morona Santiago [Province: Ecuador]
218016
Pastaza [Province: Ecuador]
218018
Tungurahua [Province: Ecuador]
218019
Zamora Chinchipe [Province: Ecuador]
218021
Napo, Orellana, Sucumbíos [Province: Ecuador]
218099
Unknown [Province: Ecuador]
222001
Ahuachapán [Department: El Salvador]
222002
Santa Ana [Department: El Salvador]
222003
Sonsonate [Department: El Salvador]
222004
Chalatenango [Department: El Salvador]
222005
La Libertad [Department: El Salvador]
222006
San Salvador [Department: El Salvador]
222007
Cuscatlán [Department: El Salvador]
222008
La Paz [Department: El Salvador]
222009
Cabañas [Department: El Salvador]
222010
San Vicente [Department: El Salvador]
222011
Usulután [Department: El Salvador]
222012
San Miguel [Department: El Salvador]
222013
Morazán [Department: El Salvador]
222014
La Unión [Department: El Salvador]
231001
Tigray [Region: Ethiopia]
231002
Affar [Region: Ethiopia]
231003
Amhara [Region: Ethiopia]
231004
Oromiya [Region: Ethiopia]
231005
Somali [Region: Ethiopia]
231006
Benishangul-Gumz [Region: Ethiopia]
231007
Southern Nations, Nationalities, and People (SNPP) [Region: Ethiopia]
231012
Gambela [Region: Ethiopia]
231013
Harari [Region: Ethiopia]
231014
Addis Ababa [Region: Ethiopia]
231015
Dire Dawa [Region: Ethiopia]
231017
Special region [Region: Ethiopia]
238094
Falkland Islands [Province: Argentina]
239094
South Georgia and South Sandwich Islands [Province: Argentina]
242001
Ba [Province: Fiji]
242003
Bua, Cakaudrove [Province: Fiji]
242006
Kadavu, Lau, Lomaiviti, Rotuma [Province: Fiji]
242007
Macuata [Province: Fiji]
242008
Nadroha [Province: Fiji]
242009
Naitasiri, Rewa [Province: Fiji]
242011
Ra [Province: Fiji]
242014
Serua, Namosi [Province: Fiji]
242015
Tailevu [Province: Fiji]
242099
Ships, unknown [Province: Fiji]
250001
Guadeloupe [Oversea Department, France]
250002
Martinique [Oversea Department, France]
250003
French Guyana [Oversea Department, France]
250004
Réunion Island [Oversea Department, France]
250011
Île-de-France [Region: France]
250021
Champagne-Ardenne [Region: France]
250022
Picardy [Region: France]
250023
Upper Normandy [Region: France]
250024
Centre [Region: France]
250025
Lower Normandy [Region: France]
250026
Burgundy [Region: France]
250031
North Pas-de-Calais [Region: France]
250041
Lorraine [Region: France]
250042
Alsace [Region: France]
250043
Franche-Comté [Region: France]
250052
Loire Valley [Region: France]
250053
Brittany [Region: France]
250054
Poitou-Charentes [Region: France]
250072
Aquitaine [Region: France]
250073
Midi-Pyrénées [Region: France]
250074
Limousin [Region: France]
250082
Rhône-Alpes [Region: France]
250083
Auvergne [Region: France]
250091
Languedoc-Roussillon [Region: France]
250093
Provence-Alpes-Riviera [Region: France]
250094
Corsica [Region: France]
250999
Unknown [Region: France]
275001
Jenin [Governorate: Palestine]
275005
Tubas [Governorate: Palestine]
275010
Tulkarm [Governorate: Palestine]
275015
Nablus [Governorate: Palestine]
275020
Qalqiliya [Governorate: Palestine]
275025
Salfit [Governorate: Palestine]
275030
Ramallah and Al-Bireh [Governorate: Palestine]
275035
Jericho [Governorate: Palestine]
275040
Jerusalem [Governorate: Palestine]
275045
Bethlehem [Governorate: Palestine]
275050
Hebron [Governorate: Palestine]
275055
North Gaza [Governorate: Palestine]
275060
Gaza [Governorate: Palestine]
275065
Deir Al-Balah [Governorate: Palestine]
275070
Khan Yunis [Governorate: Palestine]
275075
Rafah [Governorate: Palestine]
276001
Schleswig-Holstein [State: Germany]
276002
Hamburg [State: Germany]
276003
Niedersachsen [State: Germany]
276004
Bremen [State: Germany]
276005
Nordrhein-Westfalen [State: Germany]
276006
Hessen [State: Germany]
276007
Rheinland-Pfalz [State: Germany]
276008
Baden-Württemberg [State: Germany]
276009
Bayern [State: Germany]
276010
Saarland [State: Germany]
276012
Brandenburg [State: Germany]
276013
Mecklenburg-West Pomerania [State: Germany]
276014
Saxony [State: Germany]
276015
Saxony-Anhalt [State: Germany]
276016
Thuringia [State: Germany]
276017
East Berlin [State: Germany]
276018
West Berlin [State: Germany]
276099
NIU (Not in universe) [State: Germany]
288001
Western [Region: Ghana]
288002
Central [Region: Ghana]
288003
Greater Accra [Region: Ghana]
288004
Volta [Region: Ghana]
288005
Eastern [Region: Ghana]
288006
Ashanti [Region: Ghana]
288007
Brong Ahafo [Region: Ghana]
288008
Northern [Region: Ghana]
288009
Upper East [Region: Ghana]
288010
Upper West [Region: Ghana]
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 Aghion 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]
324001
Boké [Region: Guinea]
324002
Faranah [Region: Guinea]
324003
Kankan [Region: Guinea]
324004
Kindia, Labe, Mamou [Region: Guinea]
324007
N'zerekore [Region: Guinea]
324008
Conakry [Region: Guinea]
332003
Nord (North) and Nord'est (North East) [Department: Haiti]
332006
Centre (Central), L'Artibonite, Ouest (West), Sud'Est (South East) [Department: Haiti]
332007
Grand'Anse, Nippes, Sud (South) [Department: Haiti]
332009
Nord'Ouest (North West) [Department: Haiti]
356001
Jammu and Kashmir [State: India]
356002
Himachal Pradesh [State: India]
356003
Punjab [State: India]
356004
Chandigarh [State: India]
356006
Haryana [State: India]
356007
Delhi [State: India]
356008
Rajasthan [State: India]
356009
Uttar Pradesh and Uttaranchal [State: India]
356010
Bihar and Jharkhand [State: India]
356011
Sikkim [State: India]
356012
Arunachal Pradesh [State: India]
356013
Nagaland [State: India]
356014
Manipur [State: India]
356015
Mizoram [State: India]
356016
Tripura [State: India]
356017
Meghalaya [State: India]
356018
Assam [State: India]
356019
West Bengal [State: India]
356021
Orissa [State: India]
356023
Chhattisgarh and Madhya Pradesh [State: India]
356024
Gujarat [State: India]
356026
Dadra and Nagar Haveli [State: India]
356027
Maharashtra [State: India]
356028
Andhra Pradesh [State: India]
356029
Karnataka [State: India]
356030
Daman and Diu and Goa [State: India]
356031
Lakshadweep [State: India]
356032
Kerala [State: India]
356033
Tamil Nadu [State: India]
356034
Pondicherry [State: India]
356035
Andaman and Nicobar Islands [State: India]
360011
Nanggroe Aceh Darussalam [Province: Indonesia]
360012
Sumatera Utara [Province: Indonesia]
360013
Sumatera Barat [Province: Indonesia]
360014
Riau and Kepulauan Riau [Province: Indonesia]
360015
Jambi [Province: Indonesia]
360016
Sumatera Selatan and Bangka Belitung [Province: Indonesia]
360017
Bengkulu [Province: Indonesia]
360018
Lampung [Province: Indonesia]
360031
DKI Jakarta [Province: Indonesia]
360032
West Java and Banten [Province: Indonesia]
360033
Jawa Tengah [Province: Indonesia]
360034
DI Yogyakarta [Province: Indonesia]
360035
Jawa Timur [Province: Indonesia]
360051
Bali [Province: Indonesia]
360052
Nusa Tenggara Barat [Province: Indonesia]
360053
East Nusa Tenggara [Province: Indonesia]
360061
Kalimantan Barat [Province: Indonesia]
360062
Kalimantan Tengah [Province: Indonesia]
360063
Kalimantan Selatan [Province: Indonesia]
360064
Kalimantan Timur [Province: Indonesia]
360071
Sulawesi Utara and Gorontalo [Province: Indonesia]
360072
Sulawesi Tengah [Province: Indonesia]
360073
Sulawesi Selatan, Sulawesi Tenggara and Sulawesi Barat [Province: Indonesia]
360081
Maluku and Maluku Utara [Province: Indonesia]
360094
Papua and Papua Barat [Province: Indonesia]
364000
Markazi [Province: Iran]
364001
Gilan [Province: Iran]
364002
Mazandaran [Province: Iran]
364003
East Azarbayejan [Province: Iran]
364004
West Azarbayejan [Province: Iran]
364005
Kermanshah [Province: Iran]
364006
Khuzestan [Province: Iran]
364007
Fars [Province: Iran]
364008
Kerman [Province: Iran]
364009
Khorasan-e- Razavi [Province: Iran]
364010
Esfahan [Province: Iran]
364011
Sistan and Baluchestan [Province: Iran]
364012
Kordestan [Province: Iran]
364013
Hamedan [Province: Iran]
364014
Chaharmahal and Bakhtiyari [Province: Iran]
364015
Lorestan [Province: Iran]
364016
Ilam [Province: Iran]
364017
Kohgiluyeh and Boyerahmad [Province: Iran]
364018
Bushehr [Province: Iran]
364019
Zanjan [Province: Iran]
364020
Semnan [Province: Iran]
364021
Yazd [Province: Iran]
364022
Hormozgan [Province: Iran]
364023
Tehran [Province: Iran]
364024
Ardebil [Province: Iran]
364025
Qom [Province: Iran]
364026
Qazvin [Province: Iran]
364027
Golestan [Province: Iran]
364028
North Khorasan [Province: Iran]
364029
South Khorasan [Province: Iran]
368011
Dhok [Governorate: Iraq]
368012
Nineveh [Governorate: Iraq]
368013
Al-Sulaimaniya [Governorate: Iraq]
368014
Al-Tameem [Governorate: Iraq]
368015
Arbil [Governorate: Iraq]
368021
Diala [Governorate: Iraq]
368022
Al-Anbar [Governorate: Iraq]
368023
Baghdad [Governorate: Iraq]
368024
Babylon [Governorate: Iraq]
368025
Kerbela [Governorate: Iraq]
368026
Wasit [Governorate: Iraq]
368027
Salah Al-Deen [Governorate: Iraq]
368028
Al-Najaf [Governorate: Iraq]
368031
Al-Qadisiya [Governorate: Iraq]
368032
Al-Muthanna [Governorate: Iraq]
368033
Thi-Qar [Governorate: Iraq]
368034
Maysan [Governorate: Iraq]
368035
Al-Basrah [Governorate: Iraq]
372001
Border [Region: Ireland]
372002
Dublin [Region: Ireland]
372003
Mid-East [Region: Ireland]
372004
Midlands [Region: Ireland]
372005
Mid-West [Region: Ireland]
372006
South-East [Region: Ireland]
372007
South-West [Region: Ireland]
372008
West [Region: Ireland]
376001
Jerusalem [District: Israel]
376002
Northern [District: Israel]
376003
Haifa [District: Israel]
376004
Central [District: Israel]
376005
Tel-Aviv [District: Israel]
376006
Southern [District: Israel]
376009
Judea, Samaria, and Gaza areas [District: Israel]
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 [Region: Italy]
380009
Toscana [Region: Italy]
380010
Umbria [Region: Italy]
380011
Marche [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]
388001
Kingston [Parish: Jamaica]
388002
Saint Andrew [Parish: Jamaica]
388003
Saint Thomas [Parish: Jamaica]
388004
Portland [Parish: Jamaica]
388005
Saint Mary [Parish: Jamaica]
388006
Saint Ann [Parish: Jamaica]
388007
Trelawny [Parish: Jamaica]
388008
Saint James [Parish: Jamaica]
388009
Hanover [Parish: Jamaica]
388010
Westmoreland [Parish: Jamaica]
388011
Saint Elizabeth [Parish: Jamaica]
388012
Manchester [Parish: Jamaica]
388013
Clarendon [Parish: Jamaica]
388014
Saint Catherine [Parish: Jamaica]
400011
Amman [Governorate: Jordan]
400012
Balqa [Governorate: Jordan]
400013
Zarqa [Governorate: Jordan]
400014
Madaba [Governorate: Jordan]
400021
Irbid [Governorate: Jordan]
400022
Mafraq [Governorate: Jordan]
400023
Jarash [Governorate: Jordan]
400024
Ajlun [Governorate: Jordan]
400031
Karak [Governorate: Jordan]
400032
Tafilah [Governorate: Jordan]
400033
Ma'an [Governorate: Jordan]
400034
Aqaba [Governorate: Jordan]
404001
Nairobi [Province: Kenya]
404002
Central Province [Province: Kenya]
404003
Coast Province [Province: Kenya]
404004
Eastern Province [Province: Kenya]
404005
North-Eastern Province [Province: Kenya]
404006
Nyanza Province [Province: Kenya]
404007
Rift Valley Province [Province: Kenya]
404008
Western Province [Province: Kenya]
417001
Gorkenesh Bishkek [Region: Kyrgyz Republic]
417002
Issyk-Kul [Region: Kyrgyz Republic]
417003
Dzhalal-Abad [Region: Kyrgyz Republic]
417004
Naryn [Region: Kyrgyz Republic]
417005
Batken [Region: Kyrgyz Republic]
417006
Oshskaya [Region: Kyrgyz Republic]
417007
Talasskaya [Region: Kyrgyz Republic]
417008
Chuya [Region: Kyrgyz Republic]
430006
Bong [County: Liberia]
430009
Grand Bassa and Rivercess [County: Liberia]
430012
Grand Cape Mount [County: Liberia]
430015
Grand Gedeh and River Gee [County: Liberia]
430021
Lofa and Gbarpolu [County: Liberia]
430027
Maryland and Grand Kru [County: Liberia]
430030
Montserrado, Bomi, and Margibi [County: Liberia]
430033
Nimba [County: Liberia]
430039
Sinoe [County: Liberia]
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
Nkhota Kota [District: Malawi]
454203
Ntchisi [District: Malawi]
454204
Dowa [District: Malawi]
454205
Salima [District: Malawi]
454206
Lilongwe [District: Malawi]
454207
Mchinji [District: Malawi]
454208
Dedza [District: Malawi]
454209
Ntcheu [District: Malawi]
454301
Mangochi [District: Malawi]
454302
Machinga [District: Malawi]
454303
Zomba [District: Malawi]
454304
Chiradzulu [District: Malawi]
454305
Blantyre [District: Malawi]
454307
Thyolo [District: Malawi]
454308
Mulanje [District: Malawi]
454310
Chikwawa [District: Malawi]
454311
Nsanje [District: Malawi]
454313
Mwanza, Neno [District: Malawi]
458001
Johor [State: Malaysia]
458002
Kedah [State: Malaysia]
458003
Kelantan [State: Malaysia]
458004
Melaka [State: Malaysia]
458005
Negeri Sembilan [State: Malaysia]
458006
Pahang [State: Malaysia]
458007
Pulau Pinang [State: Malaysia]
458008
Perak [State: Malaysia]
458009
Perlis [State: Malaysia]
458010
Selangor and Kuala Lumpur Federal Territory [State: Malaysia]
458011
Terengganu [State: Malaysia]
458012
Sabah and Labuan Federal Territory [State: Malaysia]
458013
Sarawak [State: Malaysia]
466001
Kayes [Region: Mali]
466002
Koulikoro [Region: Mali]
466003
Sikasso [Region: Mali]
466004
Ségou [Region: Mali]
466005
Mopti [Region: Mali]
466006
Tombouctou [Region: Mali]
466007
Gao and Kidal [Region: Mali]
466009
Bamako [Region: Mali]
466099
Unknown [Region: Mali]
484001
Aguascalientes [State: Meico]
484002
Baja California [State: Meico]
484003
Baja California Sur [State: Meico]
484004
Campeche [State: Meico]
484005
Coahuila [State: Meico]
484006
Colima [State: Meico]
484007
Chiapas [State: Meico]
484008
Chihuahua [State: Meico]
484009
Distrito Federal [State: Meico]
484010
Durango [State: Meico]
484011
Guanajuato [State: Meico]
484012
Guerrero [State: Meico]
484013
Hidalgo [State: Meico]
484014
Jalisco [State: Meico]
484015
México [State: Meico]
484016
Michoacán [State: Meico]
484017
Morelos [State: Meico]
484018
Nayarit [State: Meico]
484019
Nuevo León [State: Meico]
484020
Oaxaca [State: Meico]
484021
Puebla [State: Meico]
484022
Querétaro [State: Meico]
484023
Quintana Roo [State: Meico]
484024
San Luis Potosí [State: Meico]
484025
Sinaloa [State: Meico]
484026
Sonora [State: Meico]
484027
Tabasco [State: Meico]
484028
Tamaulipas [State: Meico]
484029
Tlaxcala [State: Meico]
484030
Veracruz [State: Meico]
484031
Yucatán [State: Meico]
484032
Zacatecas [State: Meico]
496001
Arkhangai [Province: Mongolia]
496002
Bayan-Ölgii [Province: Mongolia]
496003
Bayankhongor [Province: Mongolia]
496004
Bulgan [Province: Mongolia]
496005
Govi-Altai [Province: Mongolia]
496006
Dornogovi [Province: Mongolia]
496007
Dornod [Province: Mongolia]
496008
Dundgovi and Govisumber [Province: Mongolia]
496009
Zavkhan [Province: Mongolia]
496010
Övörkhangai [Province: Mongolia]
496011
Ömnögovi [Province: Mongolia]
496012
Sükhbaatar [Province: Mongolia]
496013
Selenge [Province: Mongolia]
496014
Töv [Province: Mongolia]
496015
Uvs [Province: Mongolia]
496016
Khovd [Province: Mongolia]
496017
Khövsgöl [Province: Mongolia]
496018
Khentii [Province: Mongolia]
496019
Darkhan-Uul [Province: Mongolia]
496020
Ulaanbaatar [Province: Mongolia]
496021
Orkhon [Province: Mongolia]
504001
Oued-Ed-Dahab-Lagouira [Region: Morocco]
504002
Laâyoune-Boujdour-Sakia El Hamra [Region: Morocco]
504003
Guelmin-Es-Samara [Region: Morocco]
504004
Souss-Massa-Draâ [Region: Morocco]
504005
Charb-Chrarda-Béni Hssen [Region: Morocco]
504006
Chaouia-Ouardigha [Region: Morocco]
504007
Marrakech-Tensift-Al Haouz [Region: Morocco]
504008
Oriental [Region: Morocco]
504009
Grand-Casablanca [Region: Morocco]
504010
Rabat-Salé-Zemmour-Zaer [Region: Morocco]
504011
Doukala Abda [Region: Morocco]
504012
Tadla Azilal [Region: Morocco]
504013
Meknès-Tafilalet [Region: Morocco]
504014
Fès-Boulemane [Region: Morocco]
504015
Taza-Al Heiceima-Taounate [Region: Morocco]
504016
Tanger-Tétouan [Region: Morocco]
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 [Province: Mozambique]
508011
Maputo city [Province: Mozambique]
524001
Mechi [Administrative zone: Nepal]
524002
Koshi [Administrative zone: Nepal]
524003
Sagarmatha [Administrative zone: Nepal]
524004
Janakpur [Administrative zone: Nepal]
524005
Bagmati [Administrative zone: Nepal]
524006
Narayani [Administrative zone: Nepal]
524007
Gandaki [Administrative zone: Nepal]
524008
Dhawalagiri [Administrative zone: Nepal]
524009
Lumbini [Administrative zone: Nepal]
524010
Rapti [Administrative zone: Nepal]
524011
Bheri [Administrative zone: Nepal]
524012
Karnali [Administrative zone: Nepal]
524013
Seti [Administrative zone: Nepal]
524014
Mahakali [Administrative zone: Nepal]
558005
Nueva Segovia [Department: Nicaragua]
558010
Jinotega [Department: Nicaragua]
558020
Madríz [Department: Nicaragua]
558030
Chinandega [Department: Nicaragua]
558035
Leon and Esteli [Department: Nicaragua]
558040
Matagalpa [Department: Nicaragua]
558050
Boaco [Department: Nicaragua]
558055
Managua [Department: Nicaragua]
558060
Masaya [Department: Nicaragua]
558065
Chontales [Department: Nicaragua]
558070
Granada [Department: Nicaragua]
558075
Carazo [Department: Nicaragua]
558080
Rivas [Department: Nicaragua]
558085
Río San Juan [Department: Nicaragua]
558093
Atlántico Norte and Atlántico Sur [Department: Nicaragua]
558099
Unknown [Department: Nicaragua]
566001
Abia [State: Nigeria]
566002
Adamawa [State: Nigeria]
566003
Akwa Ibom [State: Nigeria]
566004
Anambra [State: Nigeria]
566005
Bauchi [State: Nigeria]
566006
Bayelsa [State: Nigeria]
566007
Benue [State: Nigeria]
566008
Borno [State: Nigeria]
566009
Cross River [State: Nigeria]
566010
Delta [State: Nigeria]
566011
Ebonyi [State: Nigeria]
566012
Edo [State: Nigeria]
566013
Ekiti [State: Nigeria]
566014
Enugu [State: Nigeria]
566015
Gombe [State: Nigeria]
566016
Imo [State: Nigeria]
566017
Jigawa [State: Nigeria]
566018
Kaduna [State: Nigeria]
566019
Kano [State: Nigeria]
566020
Katsina [State: Nigeria]
566021
Kebbi [State: Nigeria]
566022
Kogi [State: Nigeria]
566023
Kwara [State: Nigeria]
566024
Lagos [State: Nigeria]
566025
Nasarawa [State: Nigeria]
566026
Niger [State: Nigeria]
566027
Ogun [State: Nigeria]
566028
Ondo [State: Nigeria]
566029
Osun [State: Nigeria]
566030
Oyo [State: Nigeria]
566031
Plateau [State: Nigeria]
566032
Rivers [State: Nigeria]
566033
Sokoto [State: Nigeria]
566034
Taraba [State: Nigeria]
566035
Yobe [State: Nigeria]
566036
Zamfara [State: Nigeria]
566037
Federal Capital Territory Abuja [State: Nigeria]
566099
Unknown [State: Nigeria]
586001
North-West Frontier Province [Province: Pakistan]
586002
Fata [Province: Pakistan]
586003
Punjab, Islamabad [Province: Pakistan]
586004
Sind [Province: Pakistan]
586005
Baluchistan [Province: Pakistan]
586007
Northern areas [Province: Pakistan]
586008
Kashmir [Province: Pakistan]
591002
Coclé [Province: Panama]
591003
Colón, Comarca Kuna Yala (San Blas) [Province: Panama]
591004
Bocas de Toro, Chiriquí, Comarca Ngäbe Buglé, Veraguas [Province: Panama]
591005
Comarca Emberá, Darién [Province: Panama]
591006
Herrera [Province: Panama]
591007
Los Santos [Province: Panama]
591008
Panamá [Province: Panama]
600000
Asunción [Department: Paraguay]
600001
Concepción [Department: Paraguay]
600002
San Pedro [Department: Paraguay]
600007
Itapúa [Department: Paraguay]
600008
Misiones and Ñeembucú [Department: Paraguay]
600009
Guairá, Caazapá, and Paraguarí [Department: Paraguay]
600010
Cordillera, Caaguazú, Alto Paraná, and Canindeyú [Department: Paraguay]
600011
Central [Department: Paraguay]
600013
Amambay [Department: Paraguay]
600015
Presidente Hayes, Boqueron, and Alto Paraguay [Department: Paraguay]
600099
Unknown [Department: Paraguay]
604001
Amazonas [Region: Peru]
604002
Ancash [Region: Peru]
604003
Apurímac [Region: Peru]
604004
Arequipa [Region: Peru]
604005
Ayacucho [Region: Peru]
604006
Cajamarca [Region: Peru]
604007
Callao [Region: Peru]
604008
Cusco [Region: Peru]
604009
Huancavelica [Region: Peru]
604010
Huánuco [Region: Peru]
604011
Ica [Region: Peru]
604012
Junín [Region: Peru]
604013
La Libertad [Region: Peru]
604014
Lambayeque [Region: Peru]
604015
Lima [Region: Peru]
604016
Loreto [Region: Peru]
604017
Madre de Dios [Region: Peru]
604018
Moquegua [Region: Peru]
604019
Pasco [Region: Peru]
604020
Piura [Region: Peru]
604021
Puno [Region: Peru]
604022
San Martín [Region: Peru]
604023
Tacna [Region: Peru]
604024
Tumbes [Region: Peru]
604025
Ucayali [Region: Peru]
608001
Ilocos [Region: Philippines]
608002
Cagayan Valley [Region: Philippines]
608003
Central Luzon [Region: Philippines]
608004
Southern Tagalog [Region: Philippines]
608005
Bicol [Region: Philippines]
608006
Western Visayas [Region: Philippines]
608007
Central Visayas [Region: Philippines]
608008
Eastern Visayas [Region: Philippines]
608009
Western Mindanao [Region: Philippines]
608011
Northern Mindanao, Southern Mindanao, and Caraga [Region: Philippines]
608012
Central Mindanao and Autonomous Region of Muslim Mindanao [Region: Philippines]
608013
National Capital Region [Region: Philippines]
608014
Cordillera Administrative Region [Region: Philippines]
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]
630101
G7201001 [PUMA: Puerto Rico]
630104
G7201002, G7201003, G7201004 [PUMA: Puerto Rico]
630110
G7201100 [PUMA: Puerto Rico]
630180
G7201800 [PUMA: Puerto Rico]
630200
G7200100, G7200200, G7200300, G7200400, G7200500, G72000700, G7201200, G7201300, G7201400, G7201500, G7201600, G7201700, G7201900, G7202000, G7202100, G7202200, G7202300, G7202400, G7202600, G7200600, G7200801, G7200802, G7200900 [PUMA: Puerto Rico]
630250
G7202500 [PUMA: Puerto Rico]
642001
Alba [County: Romania]
642002
Arad [County: Romania]
642003
Arges [County: Romania]
642004
Bacau [County: Romania]
642005
Bihor [County: Romania]
642006
Bistrita Nasaud [County: Romania]
642007
Botosani [County: Romania]
642008
Brasov [County: Romania]
642009
Braila [County: Romania]
642010
Buzau [County: Romania]
642011
Caras Severin [County: Romania]
642012
Cluj [County: Romania]
642013
Constanta [County: Romania]
642014
Covasna [County: Romania]
642015
Dimbovita [County: Romania]
642016
Dolj [County: Romania]
642017
Galati [County: Romania]
642018
Gorj [County: Romania]
642019
Harghita [County: Romania]
642020
Hunedoara [County: Romania]
642022
Iasi [County: Romania]
642024
Maramures [County: Romania]
642025
Mehedinti [County: Romania]
642026
Mures [County: Romania]
642027
Neamt [County: Romania]
642028
Olt [County: Romania]
642029
Prahova [County: Romania]
642030
Satu Mare [County: Romania]
642031
Salaj [County: Romania]
642032
Sibiu [County: Romania]
642033
Suceava [County: Romania]
642034
Teleorman [County: Romania]
642035
Timis [County: Romania]
642036
Tulcea [County: Romania]
642037
Vaslui [County: Romania]
642038
Valcea [County: Romania]
642039
Vrancea [County: Romania]
642043
Bucharest Sector 1 to 6 [County: Romania]
642051
Calarasi, Giurgiu, Ialomita, Ilfov [County: Romania]
646001
Kigali City [Province: Rwanda]
646002
Kigali Ngali [Province: Rwanda]
646004
Gitarama [Province: Rwanda]
646005
Butare [Province: Rwanda]
646006
Gikongoro [Province: Rwanda]
646007
Cyangugu [Province: Rwanda]
646008
Kibuye [Province: Rwanda]
646009
Gisenyi [Province: Rwanda]
646010
Ruhengeri [Province: Rwanda]
646012
Byumba, Kibungo and Umutara [Province: Rwanda]
686001
Dakar [Region: Senegal]
686002
Diourbel [Region: Senegal]
686003
Fatick [Region: Senegal]
686004
Kaolack [Region: Senegal]
686005
Kolda [Region: Senegal]
686008
Louga, Saint Louis, Matam [Region: Senegal]
686009
Tambacounda [Region: Senegal]
686010
Thiès [Region: Senegal]
686011
Ziguinchor [Region: Senegal]
694011
Kailahun [District: Sierra Leone]
694012
Kenema [District: Sierra Leone]
694013
Kono [District: Sierra Leone]
694021
Bombali [District: Sierra Leone]
694022
Kambia [District: Sierra Leone]
694023
Koinadugu [District: Sierra Leone]
694024
Port Loko [District: Sierra Leone]
694025
Tonkolili [District: Sierra Leone]
694031
Bo [District: Sierra Leone]
694032
Bonthe [District: Sierra Leone]
694033
Moyamba [District: Sierra Leone]
694034
Pujehun [District: Sierra Leone]
694041
Western - rural [District: Sierra Leone]
694042
Western - urban [District: Sierra Leone]
704001
Ninh Binh, Hoa Binh, Ha Noi, Phu Tho, Vinh Phuc, Ha Nam, and Nam Dinh [Province: Vietnam]
704002
Ha Giang and Tuyen Quang [Province: Vietnam]
704004
Cao Bang [Province: Vietnam]
704014
Son La [Province: Vietnam]
704015
Lai Chau, Dien Bien, Lao Cai, and Yen Bai [Province: Vietnam]
704019
Bac Kan and Thai Nguyen [Province: Vietnam]
704020
Lang Son [Province: Vietnam]
704022
Quang Ninh [Province: Vietnam]
704024
Bac Giang, and Bac Ninh [Province: Vietnam]
704030
Hai Duong and Hung Yen [Province: Vietnam]
704031
Hai Phong [Province: Vietnam]
704034
Thai Binh [Province: Vietnam]
704038
Thanh Hoa [Province: Vietnam]
704040
Nghe An and Ha Tinh [Province: Vietnam]
704046
Quang Binh, Quang Tri, and Thua Thien - Hue [Province: Vietnam]
704049
Da Nang and Quang Nam [Province: Vietnam]
704051
Binh Dinh and Quang Ngai [Province: Vietnam]
704054
Phu Yen and Khanh Hoa [Province: Vietnam]
704060
Thuan Hai, Ninh Thuan, and Binh Thuan [Province: Vietnam]
704062
Gia Lai and Kon Tum [Province: Vietnam]
704066
Dak Lak and Dak Nong [Province: Vietnam]
704068
Lam Dong [Province: Vietnam]
704072
Tay Ninh [Province: Vietnam]
704074
Binh Duong and Binh Phuoc [Province: Vietnam]
704075
Dong Nai and Ba Ria - Vung Tau [Province: Vietnam]
704079
Ho Chi Minh City [Province: Vietnam]
704080
Long An [Province: Vietnam]
704082
Tien Giang [Province: Vietnam]
704083
Ben Tre [Province: Vietnam]
704086
Vinh Long and Tra Vinh [Province: Vietnam]
704087
Dong Thap [Province: Vietnam]
704089
An Giang [Province: Vietnam]
704091
Kien Giang [Province: Vietnam]
704094
Hau Giang, Can Tho City, and Soc Trang [Province: Vietnam]
704096
Bac Lieu and Ca Mau [Province: Vietnam]
705001
Pomurska [Region: Slovenia]
705002
Podravska [Region: Slovenia]
705003
Koroška [Region: Slovenia]
705004
Savinjska [Region: Slovenia]
705005
Zasavska [Region: Slovenia]
705006
Spodnjeposavska [Region: Slovenia]
705007
Jugovzhodna Slovenija [Region: Slovenia]
705008
Osrednjeslovenska [Region: Slovenia]
705009
Gorenjska [Region: Slovenia]
705010
Notranjsko-kraška [Region: Slovenia]
705011
Goriška [Region: Slovenia]
705012
Obalno-kraška [Region: Slovenia]
705099
Unknown [Region: Slovenia]
710001
Western Cape [Province: South Africa]
710004
Free State [Province: South Africa]
710005
Eastern Cape, KwaZulu-Natal [Province: South Africa]
710007
Gauteng, Limpopo, Mpumalanga, North West, Northern Cape [Province: South Africa]
710999
Unknown [Province: South Africa]
724011
Galicia [Communities and Autonomous Cities: Spain]
724012
Principado de Asturias [Communities and Autonomous Cities: Spain]
724013
Cantabria [Communities and Autonomous Cities: Spain]
724021
País Vasco [Communities and Autonomous Cities: Spain]
724022
Comunidad Foral de Navarra [Communities and Autonomous Cities: Spain]
724023
La Rioja [Communities and Autonomous Cities: Spain]
724024
Aragón [Communities and Autonomous Cities: Spain]
724030
Comunidad de Madrid [Communities and Autonomous Cities: Spain]
724041
Castilla y León [Communities and Autonomous Cities: Spain]
724042
Castilla-La Mancha [Communities and Autonomous Cities: Spain]
724043
Extremadura [Communities and Autonomous Cities: Spain]
724051
Cataluña [Communities and Autonomous Cities: Spain]
724052
Comunidad Valenciana [Communities and Autonomous Cities: Spain]
724053
Illes Balears [Communities and Autonomous Cities: Spain]
724061
Andalucía [Communities and Autonomous Cities: Spain]
724062
Región de Murcia [Communities and Autonomous Cities: Spain]
724063
Ciudad Autónoma de Ceuta [Communities and Autonomous Cities: Spain]
724064
Ciudad Autónoma de Melilla [Communities and Autonomous Cities: Spain]
724070
Canarias [Communities and Autonomous Cities: Spain]
724099
Unknown [Communities and Autonomous Cities: Spain]
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]
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]
756001
Zurich [Canton: Switzerland]
756002
Bern [Canton: Switzerland]
756003
Luzern (Lucerne) [Canton: Switzerland]
756004
Uri [Canton: Switzerland]
756005
Schwyz [Canton: Switzerland]
756006
Obwalden (Obwald) [Canton: Switzerland]
756007
Nidwalden (Nidwald) [Canton: Switzerland]
756008
Glarus [Canton: Switzerland]
756009
Zug [Canton: Switzerland]
756010
Fribourg [Canton: Switzerland]
756011
Solothurn [Canton: Switzerland]
756012
Basel-Stadt (Basel-City) [Canton: Switzerland]
756013
Basel-Landschaft (Basel-Country) [Canton: Switzerland]
756014
Schaffhausen [Canton: Switzerland]
756015
Outer and Inner Rhodes [Canton: Switzerland]
756017
St. Gallen (St. Gall) [Canton: Switzerland]
756018
Graubundun (Grisons) [Canton: Switzerland]
756019
Aargau (Argovia) [Canton: Switzerland]
756020
Thurgau (Thurgovia) [Canton: Switzerland]
756021
Ticino [Canton: Switzerland]
756022
Vaud [Canton: Switzerland]
756023
Valais [Canton: Switzerland]
756024
Neuchatel [Canton: Switzerland]
756025
Geneva [Canton: Switzerland]
756026
Jura [Canton: Switzerland]
764010
Bangkok [Province: Thailand]
764011
Samut Prakan [Province: Thailand]
764012
Nonthaburi [Province: Thailand]
764013
Pathum Thani [Province: Thailand]
764014
Phra Nakhon si Ayutthaya [Province: Thailand]
764015
Ang Thong [Province: Thailand]
764016
Lop Buri [Province: Thailand]
764017
Sing Buri [Province: Thailand]
764018
Chai Nat [Province: Thailand]
764019
Prachin Buri and Sa Kaeo [Province: Thailand]
764020
Chon Buri [Province: Thailand]
764021
Rayong [Province: Thailand]
764022
Chanthaburi [Province: Thailand]
764023
Trat [Province: Thailand]
764024
Chachoengsao [Province: Thailand]
764026
Nakhon Nayok [Province: Thailand]
764027
Saraburi [Province: Thailand]
764030
Nakhon Ratchasima [Province: Thailand]
764031
Buri Ram [Province: Thailand]
764032
Surin [Province: Thailand]
764033
Si Sa Ket [Province: Thailand]
764034
Ubon Ratchathani, Yasothon and Amnat Charoen [Province: Thailand]
764036
Chaiyaphum [Province: Thailand]
764040
Khon Kaen [Province: Thailand]
764041
Udon Thani and Nong Bua Lam Phu [Province: Thailand]
764042
Loei [Province: Thailand]
764043
Nong Khai [Province: Thailand]
764044
Maha Sarakham [Province: Thailand]
764045
Roi Et [Province: Thailand]
764046
Kalasin [Province: Thailand]
764047
Sakon Nakhon [Province: Thailand]
764048
Nakhon Phanom and Mukdahan [Province: Thailand]
764050
Chiang Mai [Province: Thailand]
764051
Lamphun [Province: Thailand]
764052
Lampang [Province: Thailand]
764053
Uttaradit [Province: Thailand]
764054
Phrae [Province: Thailand]
764055
Nan [Province: Thailand]
764057
Chiang Rai and Phayao [Province: Thailand]
764058
Mae Hong Son [Province: Thailand]
764060
Nakhon Sawan [Province: Thailand]
764061
Uthai Thani [Province: Thailand]
764062
Kamphaeng Phet [Province: Thailand]
764063
Tak [Province: Thailand]
764064
Sukhothai [Province: Thailand]
764065
Phitsanulok [Province: Thailand]
764066
Phichit [Province: Thailand]
764067
Phetchabun [Province: Thailand]
764070
Ratchaburi [Province: Thailand]
764071
Kanchanaburi [Province: Thailand]
764072
Suphanburi [Province: Thailand]
764073
Nakhon Pathom [Province: Thailand]
764074
Samut Sakhon [Province: Thailand]
764075
Samut Songkhram [Province: Thailand]
764076
Phetchaburi [Province: Thailand]
764077
Prachuap Khiri Khan [Province: Thailand]
764080
Nakhon Si Thammarat [Province: Thailand]
764081
Krabi [Province: Thailand]
764082
Phangnga [Province: Thailand]
764083
Phuket [Province: Thailand]
764084
Surat Thani [Province: Thailand]
764085
Ranong [Province: Thailand]
764086
Chumphon [Province: Thailand]
764090
Songkhla [Province: Thailand]
764091
Satun [Province: Thailand]
764092
Trang [Province: Thailand]
764093
Phatthalung [Province: Thailand]
764094
Pattani [Province: Thailand]
764095
Yala [Province: Thailand]
764096
Narathiwat [Province: Thailand]
792001
Adana, Gaziantep, Osmaniye and Kilis [Province: Turkey]
792002
Adiyaman [Province: Turkey]
792003
Afyon [Province: Turkey]
792004
Agri [Province: Turkey]
792005
Amasya [Province: Turkey]
792006
Ankara and Kirikkale [Province: Turkey]
792007
Antalya [Province: Turkey]
792008
Artvin [Province: Turkey]
792009
Aydin [Province: Turkey]
792010
Balikesir [Province: Turkey]
792011
Bilecik [Province: Turkey]
792012
Bingöl [Province: Turkey]
792013
Bitlis [Province: Turkey]
792014
Bolu and Düzce [Province: Turkey]
792015
Burdur [Province: Turkey]
792017
Çanakkale [Province: Turkey]
792019
Çorum [Province: Turkey]
792020
Denizli [Province: Turkey]
792021
Diyarbakir [Province: Turkey]
792022
Edirne [Province: Turkey]
792023
Elazig [Province: Turkey]
792024
Erzincan [Province: Turkey]
792025
Erzurum [Province: Turkey]
792026
Eskisehir [Province: Turkey]
792028
Giresun [Province: Turkey]
792029
Gümüshane and Bayburt [Province: Turkey]
792031
Hatay [Province: Turkey]
792032
Isparta [Province: Turkey]
792033
Mersin (içel) [Province: Turkey]
792034
Istanbul, Bursa, Kocaeli and Yalova [Province: Turkey]
792035
Izmir [Province: Turkey]
792036
Kars, Ardahan and Igdir [Province: Turkey]
792037
Kastamonu [Province: Turkey]
792038
Kayseri [Province: Turkey]
792039
Kirklareli [Province: Turkey]
792040
Kirsehir [Province: Turkey]
792042
Konya and Karaman [Province: Turkey]
792043
Kütahya [Province: Turkey]
792044
Malatya [Province: Turkey]
792045
Manisa [Province: Turkey]
792046
Kahramanmaras [Province: Turkey]
792047
Mardin, Hakkari, Siirt, Batman and Sirnak [Province: Turkey]
792048
Mugla [Province: Turkey]
792049
Mus [Province: Turkey]
792050
Nevsehir [Province: Turkey]
792051
Nigde and Aksaray [Province: Turkey]
792052
Ordu [Province: Turkey]
792053
Rize [Province: Turkey]
792054
Sakarya [Province: Turkey]
792055
Samsun [Province: Turkey]
792057
Sinop [Province: Turkey]
792058
Sivas [Province: Turkey]
792059
Tekirdag [Province: Turkey]
792060
Tokat [Province: Turkey]
792061
Trabzon [Province: Turkey]
792062
Tunceli [Province: Turkey]
792063
Sanliurfa [Province: Turkey]
792064
Usak [Province: Turkey]
792065
Van [Province: Turkey]
792066
Yozgat [Province: Turkey]
792067
Zonguldak, Çankiri, Karabuk and Bartin [Province: Turkey]
800101
Kalangala [District: Uganda]
800102
Kampala [District: Uganda]
800103
Kiboga [District: Uganda]
800104
Luwero and Nakasongola [District: Uganda]
800105
Masaka and Sembabule [District: Uganda]
800107
Mubende [District: Uganda]
800108
Mukono and Kayunga [District: Uganda]
800110
Rakai [District: Uganda]
800113
Mpigi and Wakiso [District: Uganda]
800203
Iganga, Buguri, and Mayuge [District: Uganda]
800204
Jinja [District: Uganda]
800205
Kamuli [District: Uganda]
800206
Kapchorwa [District: Uganda]
800208
Kumi [District: Uganda]
800209
Mbale and Sironko [District: Uganda]
800210
Pallisa [District: Uganda]
800211
Soroti, Katakwi, and Kaberamaido [District: Uganda]
800212
Busia and Tororo [District: Uganda]
800301
Moyo and Adjumani [District: Uganda]
800302
Apac [District: Uganda]
800303
Arua and Yumbe [District: Uganda]
800304
Gulu [District: Uganda]
800306
Kotido [District: Uganda]
800307
Lira [District: Uganda]
800308
Moroto and Nakapiripirit [District: Uganda]
800310
Nebbi [District: Uganda]
800312
Kitgum and Pader [District: Uganda]
800401
Bundibugyo [District: Uganda]
800403
Hoima [District: Uganda]
800404
Kabale [District: Uganda]
800405
Kabarole, Kamwenge, and Kyenjojo [District: Uganda]
800406
Kasese [District: Uganda]
800407
Kibaale [District: Uganda]
800408
Kisoro [District: Uganda]
800409
Masindi [District: Uganda]
800410
Bushenyi, Mbarara, and Ntungamo [District: Uganda]
800412
Rukungiri and Kanungu [District: Uganda]
800999
Unknown [District: Uganda]
804001
The Autonomous Republic of Crimea [Region: Ukraine]
804005
Vinnytska oblast [Region: Ukraine]
804007
Volynska oblast [Region: Ukraine]
804012
Dnipropetrovska oblast [Region: Ukraine]
804014
Donetska oblast [Region: Ukraine]
804018
Zhytomyrska oblast [Region: Ukraine]
804021
Zakarpatska oblast [Region: Ukraine]
804023
Zaporizka oblast [Region: Ukraine]
804026
Ivano-Frankivska oblast [Region: Ukraine]
804032
Kyivska oblast [Region: Ukraine]
804035
Kirovohradska oblast [Region: Ukraine]
804044
Luhanska oblast [Region: Ukraine]
804046
Lvivska oblast [Region: Ukraine]
804048
Mykolaivska oblast [Region: Ukraine]
804051
Odeska oblast [Region: Ukraine]
804053
Poltavska oblast [Region: Ukraine]
804056
Rivnenska oblast [Region: Ukraine]
804059
Sumska oblast [Region: Ukraine]
804061
Ternopilska oblast [Region: Ukraine]
804063
Kharkivska oblast [Region: Ukraine]
804065
Khersonska oblast [Region: Ukraine]
804068
Khmelnytska oblast [Region: Ukraine]
804071
Cherkaska oblast [Region: Ukraine]
804073
Chernivetska oblast [Region: Ukraine]
804074
Chernihivska oblast [Region: Ukraine]
804080
Kyiv [Region: Ukraine]
804085
Sevastopol [Region: Ukraine]
818001
Cairo [Governorate: Egypt]
818002
Alexandria [Governorate: Egypt]
818003
Port Said [Governorate: Egypt]
818004
Suez [Governorate: Egypt]
818011
Damietta [Governorate: Egypt]
818012
Dakahlia [Governorate: Egypt]
818013
Sharkia [Governorate: Egypt]
818014
Kaliobia [Governorate: Egypt]
818015
Kafr Sheikh [Governorate: Egypt]
818016
Gharbia [Governorate: Egypt]
818017
Menoufia [Governorate: Egypt]
818018
Behera [Governorate: Egypt]
818019
Ismailia [Governorate: Egypt]
818021
Giza [Governorate: Egypt]
818022
Bani Swif [Governorate: Egypt]
818023
Fayoum [Governorate: Egypt]
818024
Menia [Governorate: Egypt]
818025
Asiut [Governorate: Egypt]
818026
Sohag [Governorate: Egypt]
818027
Qena [Governorate: Egypt]
818028
Aswan [Governorate: Egypt]
818029
Luxor [Governorate: Egypt]
818031
Red Sea [Governorate: Egypt]
818032
New Valley [Governorate: Egypt]
818033
Marsa Matroh [Governorate: Egypt]
818034
North Sinai [Governorate: Egypt]
818035
South Sinai [Governorate: Egypt]
826011
North East [Region: United Kingdom]
826013
North West [Region: United Kingdom]
826014
Yorkshire and the Humber [Region: United Kingdom]
826021
East Midlands [Region: United Kingdom]
826022
West Midlands [Region: United Kingdom]
826031
East of England [Region: United Kingdom]
826032
South East and London [Region: United Kingdom]
826040
South West [Region: United Kingdom]
826060
Scotland [Region: United Kingdom]
826070
Wales [Region: United Kingdom]
826080
Northern Ireland [Region: United Kingdom]
834001
Dodoma [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
Ruvumba [Region: Tanzania]
834011
Iringa [Region: Tanzania]
834012
Mbeya [Region: Tanzania]
834013
Singida [Region: Tanzania]
834014
Tabora [Region: Tanzania]
834015
Rukwa [Region: Tanzania]
834016
Kigoma [Region: Tanzania]
834017
Shinyanga [Region: Tanzania]
834018
Kagera [Region: Tanzania]
834019
Mwanza [Region: Tanzania]
834020
Mara [Region: Tanzania]
834021
Arusha and Manyara [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]
840001
Alabama [State: U.S.]
840002
Alaska [State: U.S.]
840004
Arizona [State: U.S.]
840005
Arkansas [State: U.S.]
840006
California [State: U.S.]
840008
Colorado [State: U.S.]
840009
Connecticut [State: U.S.]
840010
Delaware [State: U.S.]
840011
District of Columbia [State: U.S.]
840012
Florida [State: U.S.]
840013
Georgia [State: U.S.]
840015
Hawaii [State: U.S.]
840016
Idaho [State: U.S.]
840017
Illinois [State: U.S.]
840018
Indiana [State: U.S.]
840019
Iowa [State: U.S.]
840020
Kansas [State: U.S.]
840021
Kentucky [State: U.S.]
840022
Louisiana [State: U.S.]
840023
Maine [State: U.S.]
840024
Maryland [State: U.S.]
840025
Massachusetts [State: U.S.]
840026
Michigan [State: U.S.]
840027
Minnesota [State: U.S.]
840028
Mississippi [State: U.S.]
840029
Missouri [State: U.S.]
840030
Montana [State: U.S.]
840031
Nebraska [State: U.S.]
840032
Nevada [State: U.S.]
840033
New Hampshire [State: U.S.]
840034
New Jersey [State: U.S.]
840035
New Mexico [State: U.S.]
840036
New York [State: U.S.]
840037
North Carolina [State: U.S.]
840038
North Dakota [State: U.S.]
840039
Ohio [State: U.S.]
840040
Oklahoma [State: U.S.]
840041
Oregon [State: U.S.]
840042
Pennsylvania [State: U.S.]
840044
Rhode Island [State: U.S.]
840045
South Carolina [State: U.S.]
840046
South Dakota [State: U.S.]
840047
Tennessee [State: U.S.]
840048
Texas [State: U.S.]
840049
Utah [State: U.S.]
840050
Vermont [State: U.S.]
840051
Virginia [State: U.S.]
840053
Washington [State: U.S.]
840054
West Virginia [State: U.S.]
840055
Wisconsin [State: U.S.]
840056
Wyoming [State: U.S.]
840099
State not identified [State: U.S.]
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]
858001
Montevideo [Department: Uruguay]
858002
Artigas [Department: Uruguay]
858003
Canelones [Department: Uruguay]
858004
Cerro Largo [Department: Uruguay]
858005
Colonia [Department: Uruguay]
858006
Durazno [Department: Uruguay]
858007
Flores [Department: Uruguay]
858008
Florida [Department: Uruguay]
858009
Lavalleja [Department: Uruguay]
858010
Maldonado [Department: Uruguay]
858011
Paysandú [Department: Uruguay]
858012
Río Negro [Department: Uruguay]
858013
Rivera [Department: Uruguay]
858014
Rocha [Department: Uruguay]
858015
Salto [Department: Uruguay]
858016
San Jose [Department: Uruguay]
858017
Soriano [Department: Uruguay]
858018
Tacuarembó [Department: Uruguay]
858019
Treinta Y Tres [Department: Uruguay]
862001
Federal District, Vargas [State: Venezuela]
862002
Amazonas Federal Territory [State: Venezuela]
862003
Anzoátegui [State: Venezuela]
862004
Apure [State: Venezuela]
862005
Aragua [State: Venezuela]
862007
Bolívar [State: Venezuela]
862008
Carabobo [State: Venezuela]
862009
Cojedes [State: Venezuela]
862010
Amacuros Delta Federal Territory [State: Venezuela]
862011
Falcón [State: Venezuela]
862012
Guárico [State: Venezuela]
862013
Lara [State: Venezuela]
862014
Barinas, Mérida [State: Venezuela]
862015
Miranda [State: Venezuela]
862016
Monagas [State: Venezuela]
862017
Nueva Esparta, Federal Dependencies [State: Venezuela]
862018
Portuguesa [State: Venezuela]
862019
Sucre [State: Venezuela]
862020
Táchira [State: Venezuela]
862021
Trujillo [State: Venezuela]
862022
Yaracuy [State: Venezuela]
862023
Zulia [State: Venezuela]
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]
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.
Geography: Global Variables -- HOUSEHOLD
IPUMS
Record type
Record type
Record type
Record type
Record type
Record type
All records
1
Household
2
Person
This variable indicates the record type.
Technical Household Variables -- HOUSEHOLD
IPUMS
Dwelling number
Dwelling number
Dwelling number
Dwelling number
Dwelling number
Dwelling number
All households
This variable indicates the dwelling number.
Technical Household Variables -- HOUSEHOLD
IPUMS
Household number (within dwelling)
Household number (within dwelling)
Household number (within dwelling)
Household number (within dwelling)
Household number (within dwelling)
Household number (within dwelling)
All households
1
1
This variable indicates the household number (within dwelling).
Technical Household Variables -- HOUSEHOLD
IPUMS
Number of persons in household
Number of persons in household
Number of persons in household
Number of persons in household
Number of persons in household
Number of persons in household
All households
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
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
This variable indicates the number of persons in household.
Technical Household Variables -- HOUSEHOLD
IPUMS
Dwelling created by splitting apart a large dwelling or household
Dwelling created by splitting apart a large dwelling or household
Dwelling created by splitting apart a large dwelling or household
Dwelling created by splitting apart a large dwelling or household
Dwelling created by splitting apart a large dwelling or household
Dwelling created by splitting apart a large dwelling or household
All households
No problem
1
Yes: households within a large dwelling were split apart into separate dwellings
2
Yes: persons within a large household were split apart into separate dwellings
This variable indicates if the dwelling was created by splitting apart a large dwelling or household.
Technical Household Variables -- HOUSEHOLD
IPUMS
Household type
Household type
Household type
Household type
Household type
B.
[] 1 Private household in house or flat
[] 2 Private household in caravan, mobile home etc.
[] 3 Non-private household
All households
1
Private household in house or flat
2
Private household in caravan/mobile home
3
Non-private household
This variable indicates the household type.
Group Quarters Variables -- HOUSEHOLD
IPUMS
Communal dwelling
Communal dwelling
Communal dwelling
Communal dwelling
Communal dwelling
Communal dwelling
All households
1
Private household
2
Communal establishment
This variable indicates communal dwelling.
Group Quarters Variables -- HOUSEHOLD
IPUMS
Household weight
Household weight
Household weight
Household weight
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.
Technical Household Variables -- HOUSEHOLD
IPUMS
Ireland, Region 1971 - 2011 [Level 1; consistent boundaries, GIS]
Ireland, Region 1971 - 2011 [Level 1; consistent boundaries, GIS]
Ireland, Region 1971 - 2011 [Level 1; consistent boundaries, GIS]
Ireland, Region 1971 - 2011 [Level 1; consistent boundaries, GIS]
Ireland, Region 1971 - 2011 [Level 1; consistent boundaries, GIS]
372001
Border
372002
Dublin
372003
Mid-East
372004
Midlands
372005
Mid-West
372006
South-East
372007
South-West
372008
West
GEO1A_IE identifies the household's regional authority within Ireland in all sample years. Regional authorities are the first level administrative units of the country. GEO1A_IE is spatially harmonized to account for political boundary changes across census years. Some detail is lost in harmonization. A GIS map (in shapefile format), corresponding to GEO1A_IE can be downloaded from the GIS Boundary files page in the IPUMS International web site.
The full set of geography variables for Ireland 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.
Geography: A-L Variables -- HOUSEHOLD
IPUMS
Ireland, Region 1979 [Level 1; GIS]
Ireland, Region 1979 [Level 1; GIS]
Ireland, Region 1979 [Level 1; GIS]
Ireland, Region 1979 [Level 1; GIS]
Ireland, Region 1979 [Level 1; GIS]
1
Border
2
Dublin
3
Mid-East
4
Midlands
5
Mid-West
6
South-East
7
South-West
8
West
GEO1_ IE1979 identifies the household's region within Ireland in 1979. Regions are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_ IE1979 can be downloaded from the GIS Boundary files page in the IPUMS International web site.
The full set of geography variables for Ireland can be found in the IPUMS International Geography variables list. For cross-national geographic analysis on the first and second major administrative level of any country refer to GEOLEV1, and GEOLEV2. More information on IPUMS-International geography can be found here.
Geography: A-L Variables -- HOUSEHOLD
IPUMS
Number of married couples in household
Number of married couples in household
Number of married couples in household
Number of married couples in household
Number of married couples in household
No married couples in household
1
1 couple
2
2 couples
3
3 couples
4
4 couples
5
5 couples
6
6 couples
7
7 couples
8
8 couples
9
9 or more couples
NCOUPLES is a constructed variable indicating the number of married/in-union couples within a household.
NCOUPLES is constructed using the IPUMS-International pointer variable SPLOC (spouse's location in the household).
Constructed Household Variables -- HOUSEHOLD
IPUMS
Number of mothers in household
Number of mothers in household
Number of mothers in household
Number of mothers in household
Number of mothers in household
No mothers in household
1
1 mother
2
2 mothers
3
3 mothers
4
4 mothers
5
5 mothers
6
6 mothers
7
7 mothers
8
8 mothers
9
9 or more mothers in household
NMOTHERS is a constructed variable indicating the number of mothers -- of persons of any age -- within a household.
NMOTHERS is constructed using the IPUMS-International pointer variable MOMLOC (mother's location in the household).
Constructed Household Variables -- HOUSEHOLD
IPUMS
Number of fathers in household
Number of fathers in household
Number of fathers in household
Number of fathers in household
Number of fathers in household
No fathers in household
1
1 father
2
2 fathers
3
3 fathers
4
4 fathers
5
5 fathers
6
6 fathers
7
7 fathers
8
8 fathers
9
9 or more fathers in household
NFATHERS is a constructed variable indicating the number of fathers -- of persons of any age -- within a household.
NFATHERS is constructed using the IPUMS-International pointer variable POPLOC (father's location in the household).
Constructed Household Variables -- HOUSEHOLD
IPUMS
Country
Country
Country
Country
Country
32
Argentina
40
Austria
50
Bangladesh
51
Armenia
68
Bolivia
76
Brazil
112
Belarus
116
Cambodia
120
Cameroon
124
Canada
152
Chile
156
China
170
Colombia
188
Costa Rica
192
Cuba
214
Dominican Republic
218
Ecuador
222
El Salvador
231
Ethiopia
242
Fiji
250
France
275
Palestine
276
Germany
288
Ghana
300
Greece
324
Guinea
332
Haiti
348
Hungary
356
India
360
Indonesia
364
Iran
368
Iraq
372
Ireland
376
Israel
380
Italy
388
Jamaica
400
Jordan
404
Kenya
417
Kyrgyz Republic
430
Liberia
454
Malawi
458
Malaysia
466
Mali
484
Mexico
496
Mongolia
504
Morocco
508
Mozambique
524
Nepal
528
Netherlands
558
Nicaragua
566
Nigeria
586
Pakistan
591
Panama
600
Paraguay
604
Peru
608
Philippines
620
Portugal
630
Puerto Rico
642
Romania
646
Rwanda
662
Saint Lucia
686
Senegal
694
Sierra Leone
704
Vietnam
705
Slovenia
710
South Africa
724
Spain
728
South Sudan
729
Sudan
756
Switzerland
764
Thailand
792
Turkey
800
Uganda
804
Ukraine
818
Egypt
826
United Kingdom
834
Tanzania
840
United States
854
Burkina Faso
858
Uruguay
862
Venezuela
894
Zambia
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).
Technical Household Variables -- HOUSEHOLD
IPUMS
Person number
Person number
Person number
Person number
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.
Technical Person Variables -- PERSON
IPUMS
Age
Age
Age
Age
Age
Less than 1 year
1
1 year
2
2 years
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20
21
21
22
22
23
23
24
24
25
25
26
26
27
27
28
28
29
29
30
30
31
31
32
32
33
33
34
34
35
35
36
36
37
37
38
38
39
39
40
40
41
41
42
42
43
43
44
44
45
45
46
46
47
47
48
48
49
49
50
50
51
51
52
52
53
53
54
54
55
55
56
56
57
57
58
58
59
59
60
60
61
61
62
62
63
63
64
64
65
65
66
66
67
67
68
68
69
69
70
70
71
71
72
72
73
73
74
74
75
75
76
76
77
77
78
78
79
79
80
80
81
81
82
82
83
83
84
84
85
85
86
86
87
87
88
88
89
89
90
90
91
91
92
92
93
93
94
94
95
95
96
96
97
97
98
98
99
99
100
100+
999
Not reported/missing
AGE gives age in years as of the person's last birthday prior to or on the day of enumeration.
Demographic Variables -- PERSON
IPUMS
Sex
Sex
Sex
Sex
Sex
1
Male
2
Female
9
Unknown
SEX reports the sex (gender) of the respondent.
Demographic Variables -- PERSON
IPUMS
Probable stepfather
Probable stepfather
Probable stepfather
Probable stepfather
Probable stepfather
Biological father or no father present
1
Child reports father is deceased
2
Explicitly identified step relationship
3
Age difference implausible
STEPPOP indicates whether a person's father, as identified by POPLOC , was most probably not the person's biological father. Non-zero values of STEPPOP explain why it is probable that the person's father was a step- or adopted father. A value of 0 indicates no likely stepfather because (1) the father identified in POPLOC was probably the biological father or (2) there is no father of this person present in the household.
The codes for STEPPOP are as follows:
0 = Biological father or no father of this person present in household.
1 = Child reports father is deceased.
2 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner; stepchild/child-in-law).
3 = Age difference between father and child was less than 12 or greater than 54 years.
See PARRULE for a description of the linking process.
Users should note that there are many stepfathers and adopted fathers in the population that cannot be identified with information available in the censuses. Therefore, STEPPOP will always under-represent their actual number in the population.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Probable stepmother
Probable stepmother
Probable stepmother
Probable stepmother
Probable stepmother
Biological mother or no mother present
1
Mother has no children borne or surviving
2
Child reports mother is deceased
3
Explicitly identified step relationship
4
Mother reports no children in the home
5
Age difference implausible
6
Child exceeds known fertility of mother
STEPMOM indicates whether a person's mother, as identified by MOMLOC, was most probably not the person's biological mother. Non-zero values of STEPMOM explain why it is probable that the person's mother was a step- or adopted mother. A value of 0 indicates no likely stepmother because (1) the mother identified in MOMLOC was probably the biological mother or (2) there is no mother of this person present in the household.
The codes for STEPMOM are as follows:
0 = Biological mother or no mother of this person present in household.
1 = Mother has no children borne or surviving.
2 = Child reports mother is deceased.
3 = Explicitly identified relationship (stepchild, adopted child, child of unmarried partner, stepchild/child-in-law).
4 = Mother reports no children in the home.
5 = Age difference between mother and child was less than 12 or greater than 54 years.
6 = Child exceeds known fertility of mother.
See PARRULE for a description of the linking process.
Users should note that there are many stepmothers and adopted mothers in the population that cannot be identified with information available in the censuses. Therefore, STEPMOM will always under-represent their actual number in the population.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Woman is second or higher order wife
Woman is second or higher order wife
Woman is second or higher order wife
Woman is second or higher order wife
Woman is second or higher order wife
Person is not the 2nd or higher order wife linked via SPLOC
1
Person is the 2nd or higher order wife linked via SPLOC
POLY2ND indicates if a woman was the second or higher order wife linked to a husband in the constructed IPUMS variable SPLOC -- Spouse's Location in Household. The variable does not suggest the actual marital order of wives, only their relative positions in the person order of the household as it was enumerated.
The point of POLY2ND is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Family unit membership
Family unit membership
Family unit membership
Family unit membership
Family unit membership
FAMUNIT is a constructed variable indicating to which family within the household a person belongs.
All persons related to the household head receive a 1 (see RELATE). Each secondary family or secondary individual receives a higher code. For purposes of FAMUNIT, secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father).
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Number of own family members in household
Number of own family members in household
Number of own family members in household
Number of own family members in household
Number of own family members in household
1
1 family member present
2
2 family members present
3
3 family members present
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20
21
21
22
22
23
23
24
24
25
25
26
26
27
27
28
28
29
29
30
30
31
31
32
32
33
33
34
34
35
35
36
36
37
37
38
38
39
39
40
40
41
41
42
42
43
43
44
44
45
45
46
46
47
47
48
48
49
49
50
50
51
51
52
52
53
53
54
54
55
55
56
56
57
57
58
58
59
59
60
60
61
61
62
62
63
63
64
64
65
65
66
66
67
67
68
68
69
69
70
70
71
71
72
72
73
73
74
74
75
75
76
76
77
77
78
78
79
79
80
80
81
81
82
82
83
83
84
84
85
85
86
86
87
87
88
88
89
89
90
90
91
91
92
92
93
93
94
94
95
95
96
96
97
97
98
98
99
99 or more persons
FAMSIZE counts the number of the person's own family members living in the household with her/him, including the person her/himself. These include all persons related to the person by blood, adoption, or marriage as indicated by the census forms or inferred from them.
FAMSIZE is calculated from the units identified in the IPUMS constructed variable FAMUNIT (family unit membebership). The primary family is defined as all persons related to the head in the RELATE variable. Secondary families are individuals or groups of persons linked together by the IPUMS constructed pointer variables SPLOC, MOMLOC, and POPLOC (location of spouse, mother, and father).
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Number of own children in household
Number of own children in household
Number of own children in household
Number of own children in household
Number of own children in household
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9 or more children in household
NCHILD provides a count of the person's own children living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Number of own children under age 5 in household
Number of own children under age 5 in household
Number of own children under age 5 in household
Number of own children under age 5 in household
Number of own children under age 5 in household
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9 or more own children under age 5 in household
NCHLT5 provides a count of the person's own children under age five living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Age of eldest own child in household
Age of eldest own child in household
Age of eldest own child in household
Age of eldest own child in household
Age of eldest own child in household
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20
21
21
22
22
23
23
24
24
25
25
26
26
27
27
28
28
29
29
30
30
31
31
32
32
33
33
34
34
35
35
36
36
37
37
38
38
39
39
40
40
41
41
42
42
43
43
44
44
45
45
46
46
47
47
48
48
49
49
50
50 or older
99
No own child in household
ELDCH gives the age of the person's oldest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household.
ELDCH is top-coded at age 50 or older.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Age of youngest own child in household
Age of youngest own child in household
Age of youngest own child in household
Age of youngest own child in household
Age of youngest own child in household
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20
21
21
22
22
23
23
24
24
25
25
26
26
27
27
28
28
29
29
30
30
31
31
32
32
33
33
34
34
35
35
36
36
37
37
38
38
39
39
40
40
41
41
42
42
43
43
44
44
45
45
46
46
47
47
48
48
49
49
50
50 or older
99
No own child in household
YNGCH gives the age of the person's youngest own child living in the household with her or him. These include all children linked to the person via the constructed IPUMS pointer variables MOMLOC or POPLOC -- mother's and father's location in the household.
YNGCH is top-coded at age 50 or older.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Relationship to head of subfamily
Relationship to head of subfamily
Relationship to head of subfamily
Relationship to head of subfamily
Relationship to head of subfamily
1000
Head
2000
Spouse/partner
2100
Spouse
2200
Unmarried partner
3000
Child
3100
Biological child
3200
Adopted or step child
4000
Other relative
4100
Grandchild
4200
Parent/parent-in-law
4210
Parent
4220
Parent-in-law
4300
Child-in-law
4400
Sibling/sibling-in-law
4410
Sibling
4430
Sibling-in-law
4500
Grandparent
4600
Parent/grandparent
4810
Nephew/niece
4900
Other relative, n.e.c.
5000
Non-relative
5120
Visitor
5210
Domestic employee
5220
Relative of employee, n.s.
5300
Roomer/boarder/lodger/foster child
5310
Boarder
5311
Boarder or guest
5400
Employee, boarder or guest
5510
Agregado
5600
Group quarters
6000
Other relative or non-relative
9999
Unknown
SUBFREL describes the relationship of the individual to the head of the subfamily (in most cases, conjugal unit). It is distinct from RELATE, which identifies a person's relationship to the head of the household. There can be multiple subfamilies within households. The particular subfamily to which a person belongs is recorded in SUBFNUM.
Persons living alone without other family are identified as "heads" of family.
Demographic Variables -- PERSON
IPUMS
Subfamily membership number
Subfamily membership number
Subfamily membership number
Subfamily membership number
Subfamily membership number
Non-family
1
1st subfamily
2
2nd subfamily
3
3rd subfamily
4
4th subfamily
5
5th subfamily
6
6th subfamily
7
7th subfamily
8
8th subfamily
9
9th subfamily
10
10th subfamily
11
11th subfamily
12
12th subfamily
13
13th subfamily
SUBFNUM gives the number of the subfamily to which the person belongs within the household (1 = first subfamily, 2 = second subfamily, etc.). SUBFNUM records the identification of subfamilies in the original dataset, which generally correspond to conjugal units and their offspring.
Demographic Variables -- PERSON
IPUMS
Age, grouped into intervals
Age, grouped into intervals
Age, grouped into intervals
Age, grouped into intervals
Age, grouped into intervals
1
0 to 4
2
5 to 9
3
10 to 14
4
15 to 19
5
15 to 17
6
18 to 19
7
18 to 24
8
20 to 24
9
25 to 29
10
30 to 34
11
35 to 39
12
40 to 44
13
45 to 49
14
50 to 54
15
55 to 59
16
60 to 64
17
65 to 69
18
70 to 74
19
75 to 79
20
80+
98
Unknown
AGE2 gives computed years of age grouped into intervals.
Demographic Variables -- PERSON
IPUMS
Father's location in household
Father's location in household
Father's location in household
Father's location in household
Father's location in household
POPLOC is a constructed variable that indicates whether or not the person's father lived in the same household and, if so, gives the person number of the father (see PERNUM). POPLOC makes it easy for researchers to link the characteristics of children and their (probable) fathers.
The method by which probable child-father links are identified is described in PARRULE.
The general design of POPLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly.
Note: POPLOC identifies social relationships (such as stepfather and adopted father) as well as biological relationships. The variable STEPPOP is designed to identify some of these social relationships.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Rule for linking spouse
Rule for linking spouse
Rule for linking spouse
Rule for linking spouse
Rule for linking spouse
No spouse present
1
Rule 1: strong relationship pairing, couple adjacent
2
Rule 2: strong relationship pairing, couple not adjacent
3
Rule 3: weak relationship pairing, couple adjacent
4
Rule 4: weak relationship pairing, couple not adjacent
5
Rule 5: weak consensual union pairings
6
Rule 6: sample-specific rules (usually child-to-child)
SPRULE explains the criteria by which the IPUMS-International variable SPLOC linked the person to his/her probable spouse.
IPUMS-International establishes spouse-spouse links according to five basic rules, and SPRULE gives the number of the rule that applied to the link in question. A sixth rule identifies sample-specific linking procedures only imposed in selected instances.
The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Spouse's location in household
Spouse's location in household
Spouse's location in household
Spouse's location in household
Spouse's location in household
SPLOC is a constructed variable that indicates whether or not the person's spouse lived in the same household and, if so, gives the person number (PERNUM) of the spouse. SPLOC makes it easy for researchers to link the characteristics of (probable) spouses.
The method by which probable spouse-spouse links are identified is described in SPRULE.
The general design of SPLOC and other constructed variables is modeled on the methods developed for IPUMS-USA "Family Interrelationships", but the details vary significantly.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Mother's location in household
Mother's location in household
Mother's location in household
Mother's location in household
Mother's location in household
MOMLOC is a constructed variable that indicates whether or not the person's mother lived in the same household and, if so, gives the person number of the mother (see PERNUM). MOMLOC makes it easy for researchers to link the characteristics of children and their (probable) mothers.
The method by which probable child-mother links are identified is described in PARRULE.
The general design of MOMLOC and other constructed variables follows the methods developed for IPUMS-USA "Family Interrelationships," but the details vary significantly.
Note: MOMLOC identifies social relationships (such as stepmother and adopted mother) as well as biological relationships. The variable STEPMOM is designed to identify some of these social relationships.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Man with more than one wife linked
Man with more than one wife linked
Man with more than one wife linked
Man with more than one wife linked
Man with more than one wife linked
No more than one wife linked via SPLOC
1
More than one wife linked via SPLOC
POLYMAL indicates if a man had more than one wife linked to him in the constructed IPUMS variable SPLOC -- Spouse's Location in Household.
The point of POLYMAL is to facilitate using SPLOC in samples that identify polygamy. Some statistical matching procedures expect to find only one matching record for each subject record.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Rule for linking parent
Rule for linking parent
Rule for linking parent
Rule for linking parent
Rule for linking parent
No parent of person in household
11
Link to head or spouse, unambiguous
12
Link to head or spouse, ambiguous
21
Child-Grandchild, within empirical child cap
22
Child-Grandchild, within constructed child cap
23
Child-Grandchild, exceeds child cap
31
Specified Other Relatives, within empirical child cap
32
Specified Other Relatives, within constructed child cap
33
Specified Other Relatives, exceeds child cap
41
Other Relatives, within empirical child cap
42
Other Relatives, within constructed child cap
51
Non-Relatives, within empirical child cap
52
Non-Relatives, within constructed child cap
PARRULE describes the criteria by which the IPUMS-International variables MOMLOC and POPLOC linked the person to a probable mother and/or father.
IPUMS-International establishes child-parent links according to five basic rules, and PARRULE gives the number of the rule that applied to the link in question. A link to any parent automatically generates a second link to that parent's spouse or partner, so only one rule is needed to describe both MOMLOC and POPLOC.
The design of the interrelationship variables is described in this paper on IPUMSI family linking methodology.
Constructed Family Interrelationship Variables -- PERSON
IPUMS
Relationship to household head [general version]
Relationship to household head [general version]
Relationship to household head [general version]
Relationship to household head [general version]
Relationship to household head [general version]
1
Head
2
Spouse/partner
3
Child
4
Other relative
5
Non-relative
6
Other relative or non-relative
9
Unknown
RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person).
Demographic Variables -- PERSON
IPUMS
Relationship to household head [detailed version]
Relationship to household head [detailed version]
Relationship to household head [detailed version]
Relationship to household head [detailed version]
Relationship to household head [detailed version]
1000
Head
2000
Spouse/partner
2100
Spouse
2200
Unmarried partner
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 of sibling-in-law
4900
Other relative, not elsewhere classified
4910
Other relative with same family name
4920
Other relative with different family name
4930
Other relative, not specified (secondary family)
5000
Non-relative
5100
Friend/guest/visitor/partner
5110
Partner/friend
5111
Friend
5112
Partner/roommate
5113
Housemate/roommate
5120
Visitor
5130
Ex-spouse
5140
Godparent
5150
Godchild
5200
Employee
5210
Domestic employee
5220
Relative of employee, n.s.
5221
Spouse of servant
5222
Child of servant
5223
Other relative of servant
5300
Roomer/boarder/lodger/foster child
5310
Boarder
5311
Boarder or guest
5320
Lodger
5330
Foster child
5340
Tutored/foster child
5350
Tutored child
5400
Employee, boarder or guest
5500
Other specified non-relative
5510
Agregado
5520
Temporary resident, guest
5600
Group quarters
5610
Group quarters, non-inmates
5620
Institutional inmates
5900
Non-relative, n.e.c.
6000
Other relative or non-relative
9999
Unknown
RELATE describes the relationship of the individual to the head of household (sometimes called the householder or reference person).
Demographic Variables -- PERSON
IPUMS
Marital status [general version]
Marital status [general version]
Marital status [general version]
Marital status [general version]
Marital status [general version]
NIU (not in universe)
1
Single/never married
2
Married/in union
3
Separated/divorced/spouse absent
4
Widowed
9
Unknown/missing
[program universe for et,mz samples.
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.
Demographic Variables -- PERSON
IPUMS
Marital status [detailed version]
Marital status [detailed version]
Marital status [detailed version]
Marital status [detailed version]
Marital status [detailed version]
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
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
360
Married, spouse absent
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
[program universe for et,mz samples.
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.
Demographic Variables -- PERSON
IPUMS
Relationship to head, Europe
Relationship to head, Europe
Relationship to head, Europe
Relationship to head, Europe
Relationship to head, Europe
10
Reference person / Head
20
Spouse or partner
21
Husband or wife
22
Partner in consensual union
30
Child/child-in-law of head or of spouse/partner
31
Spouse or partner of child of head
40
Parent of head, of spouse, or of partner
50
Other relative of head, spouse, or partner
60
Non-relative of head
61
Foster child
62
Boarder
63
Domestic servant
64
Other
99
Not stated / unknown
ERELATE describes for the European samples the relationship of the individual to the head of household -- sometimes called the householder or reference person.
ERELATE has been classified according to the recommendations of the Conference of European Statisticians for the 2010 Population and Housing Censuses.
Demographic Variables -- PERSON
IPUMS
Marital status, Europe
Marital status, Europe
Marital status, Europe
Marital status, Europe
Marital status, Europe
NIU (not in universe)
1
Never married
2
Married
3
Widowed and not remarried
4
Divorced/separated and not remarried
5
Widowed or divorced
9
Unknown / missing
EMARST describes for the European samples the person's current marital status according to law or custom. Individuals who remarried should report the status relevant to their most recent marriage. European census instructions generally limit marital status to legal unions, but there are exceptions.
EMARST has been classified according to the recommendations given by the Conference of European Statisticians for the 2010 Population and Housing Censuses.
Demographic Variables -- PERSON
IPUMS
Person number (within household)
Person number (within household)
Person number (within household)
Person number (within household)
Person number (within household)
Person number (within household)
All persons
Household record
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
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
This variable indicates the person number (within household).
Technical Person Variables -- PERSON
IPUMS
Relationship to family head
Relationship to family head
Relationship to family head
Relationship to family head
Relationship to family head
3. Relationship to head of household _______
Write "Head", "Wife", "Son", "Daughter", "Visitor", "Patient", "Employee", etc. as appropriate.
Anyone in a private household whose usual residence is elsewhere should be described as "Visitor" whether related to the head of the household or not.
All persons
1
Head
2
Spouse
3
Child
4
Visitor, other relative, or nonrelative
9
Unknown
This variable indicates the relationship of the person to the head of household.
Demographic Variables -- PERSON
IPUMS
Sex
Sex
Sex
Sex
Sex
2. Sex
[] 1 Male
[] 2 Female
All persons
1
Males
2
Females
This variable indicates the person's sex.
Demographic Variables -- PERSON
IPUMS
Age
Age
Age
Age
Age
4. Date of birth
Use numbers: e.g., 14/2/1936
Day___
Month___
Year___
All persons
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20-24
25
25-29
30
30-34
35
35-39
40
40-44
45
45-49
50
50-54
55
55-59
60
60-64
65
65-69
70
70-74
75
75-79
80
80-84
85
85+
This variable indicates the person's age.
Demographic Variables -- PERSON
IPUMS
Marital status
Marital status
Marital status
Marital status
Marital status
5. Marital status
The marital status indicated should relate to the person's present legal status.
If under 15 years of age (i.e., born after 1 April, 1964), please check box 1.
[] 1 Child
[] 2 Single
[] 3 Married
[] 4 Widowed
[] 5 Other status
Persons age 15 years and older
NIU (not in universe)
2
Single
3
Married
4
Widowed
This variable indicates the person's marital status.
Demographic Variables -- PERSON
IPUMS
Changed residence from outside state
Changed residence from outside state
Changed residence from outside state
Changed residence from outside state
Changed residence from outside state
6. Change of residence from outside the state
Did the person change [his/her] permanent residence to Ireland (Republic) from outside the country during the 12 months before 31 March, 1979?
[] 1 Yes
[] 2 No
Persons who are not permanent residents
NIU (Not in universe)
1
Yes
2
No
This variable indicates if the person changed their residence from abroad to Ireland.
Migration Variables -- PERSON
IPUMS
Person weight
Person weight
Person weight
Person weight
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.
Technical Person Variables -- PERSON
IPUMS
Year [person version]
Year [person version]
Year [person version]
Year [person version]
Year [person version]
[This file is just a placeholder. See the household version of the variable.]
Technical Person Variables -- PERSON
IPUMS
IPUMS sample identifier [person version]
IPUMS sample identifier [person version]
IPUMS sample identifier [person version]
IPUMS sample identifier [person version]
IPUMS sample identifier [person version]
[This file is just a placeholder. See the household version of the variable.]
Technical Person Variables -- PERSON
IPUMS
Household serial number [person version]
Household serial number [person version]
Household serial number [person version]
Household serial number [person version]
Household serial number [person version]
[This file is just a placeholder. See the household version of the variable.]
Technical Person Variables -- PERSON
IPUMS
Country [person version]
Country [person version]
Country [person version]
Country [person version]
Country [person version]
[This file is just a placeholder. See the household version of the variable.]
Technical Person Variables -- PERSON
IPUMS
Record type [person version]
Record type [person version]
Record type [person version]
Record type [person version]
Record type [person version]
[This file is just a placeholder. See the household version of the variable.]
Technical Person Variables -- PERSON
IPUMS