DDI_SDN_2008_PHC_v01_M_v02_A_IPUMS
Minnesota Population Center
2016-04-25
NADA
- v6.4 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)
5th Sudan Population and Housing Census 2008 - IPUMS Subset
PHC 2008 (IPUMS Harmonized Subset)
SDN_2008_PHC_v01_M_v02_A_IPUMS
Central Bureau of Statistics
Minnesota Population Center
(c) Copyright 2008, Central Bureau of Statistics and Minnesota Population Center
NADA
Central Bureau of Statistics
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.
Technical Household Variables -- HOUSEHOLD
Geography: Global Variables -- HOUSEHOLD
Technical Person Variables -- PERSON
Demographic Variables -- PERSON
Group Quarters Variables -- HOUSEHOLD
Geography: M-Z Variables -- HOUSEHOLD
Utilities Variables -- HOUSEHOLD
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
Dwelling Characteristics Variables -- HOUSEHOLD
Other Household Variables -- HOUSEHOLD
Nativity and Birthplace Variables -- PERSON
Constructed Family Interrelationship Variables -- PERSON
Fertility and Mortality Variables -- PERSON
Education Variables -- PERSON
Work Variables -- PERSON
Disability Variables -- PERSON
Migration Variables -- PERSON
Constructed Household Variables -- HOUSEHOLD
Household Economic Variables -- HOUSEHOLD
Household Imputation Flags Variables -- HOUSEHOLD
Work: Occupation Variables -- PERSON
Work: Industry Variables -- PERSON
Person Imputation Flags Variables -- PERSON
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.
Sudan
National coverage
State
Household
UNITS IDENTIFIED:
- Dwellings: No
- Vacant units: No
- Households: Yes
- Individuals: Yes
- Group quarters: No
- Special populations: Yes (Homeless, refugees, camps)
UNIT DESCRIPTIONS:
- Dwellings: A building is an independent free-standing structure irrespective of its construction material, composed of one or more rooms.
- Households: A household consists of a person or a group of persons who live together in the same housing unit or part of it and who consider themselves as one unit in terms of the provision of food and/or other essentials of living for the group. When most of the members of such a group are related by blood (i.e., biologically) the group shall be referred to as a Private Household for the purpose of the census. On the other hand when the group (i.e., household as defined earlier) consists of members who are not related by blood and they are more than 10, they will be considered as Non-Institutional Collective Household. Note that if the group consists of 10 or less members, it should be considered a private household.
- Group quarters: An institution is usually a set of premises used to house a large number of people who are not related by blood or marriage but bound together by a common objective or personal interest (e.g., universities, boarding houses, hospitals, army barracks, camps, prisons, hotels, etc.)
Residents of Sudan
Census/enumeration data [cen]
MICRODATA SOURCE: Central Bureau of Statistics
SAMPLE DESIGN: Long form questionnaire for sedentary households (selected enumeration areas) and a sample of nomad households.
SAMPLE UNIT: Household
SAMPLE FRACTION: 16.6%
SAMPLE SIZE (person records): 5,066,530
Face-to-face [f2f]
Two forms: Long Questionnaire (for a sample of areas) and Short Questionnaire (for the rest of the country). The information used here is based on the long form questionnaire.
De facto, CENSUS DAY: April 22nd, 2008, FIELD WORK PERIOD: April 22nd - May 6th, 2008
Direct enumeration
Computed by census agency and should be used for most types of analysis.
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:
Sudan, Central Bureau of Statistics, 5th Sudan Population and Housing Census
The licensing agreement for use of IPUMS-International data requires that users supply IPUMS-International with the title and full citation for any publications, research reports, or educational materials making use of the data or documentation.
Copies of such materials are also gratefully received at ipums@umn.edu.
Printed matter should be sent to:
IPUMS-International
Minnesota Population Center
University of Minnesota
50 Willey Hall
225 19th Avenue South
Minneapolis, MN 55455
An adapted version of the dataset, harmonized for international comparability, is available from IPUMS-International (https://international.ipums.org/international/) under the following conditions:
IPUMS-International distributes integrated microdata of individuals and households only by agreement of collaborating national statistical offices and under the strictest of confidence. Before data may be distributed to an individual researcher, an electronic license agreement must be signed and approved. To gain access to the data, a researcher must agree to the following:
(1) Implement security measures to prevent unauthorized access to census microdata. Under IPUMS-International agreements with collaborating agencies, redistribution of the data to third parties is prohibited.
(2) Use the microdata for the exclusive purposes of scholarly research and education. Researchers must explicitly agree to not use microdata acquired for any commercial or income-generating venture.
(3) Maintain the confidentiality of persons, households, and other entities. Any attempt to ascertain the identity of persons or households from the microdata is prohibited. Alleging that a person or household has been identified is also prohibited.
(4) Report all publications based on these data to IPUMS-International, which will in turn pass the information on to the relevant national statistical agencies.
Once a project is approved, a password is issued and data may be acquired through the Internet. Penalties for violating the license include: revocation of the license, recall of all microdata acquired, filing of a motion of censure to the appropriate professional organizations, and civil prosecution under the relevant national or international statutes.
These safeguards mirror the principles from the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. Employees of the Minnesota Population Center who work with the census microdata to produce the harmonized database also sign agreements to respect the confidentiality of the data.
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
SDN2008-H-H
Household records
0
151
SDN2008-P-H
Person records
0
177
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.
SERIAL is a 10-digit numeric variable.
The last 3 digits of SERIAL indicate household number within dwelling for selected samples noted in the variable description. In all other samples, the last 3 digits are always zeroes.
Technical Household Variables -- HOUSEHOLD
IPUMS
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.
RECTYPE is a 1-digit alphabetic (non-numeric) variable.
H = Household record
P = Person record
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
Urban-rural status
Urban-rural status
Urban-rural status
Urban-rural status
Urban-rural status
1
Rural
2
Urban
9
Unknown
URBAN indicates whether the household was located in a place designated as urban or as rural.
Geography: Global 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.
PERSONS is a 3-digit numeric variable.
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
Sudan, Region
Sudan, Region
Sudan, Region
Sudan, Region
Sudan, Region
1
Northern
2
Eastern
3
Khartoum
4
Central
5
Kordofan
6
Darfur
REGNSD indicates the regions in Sudan in which the household was enumerated. Regions are the largest-scale geographic identifier of the country. REGNSD is harmonized solely based on the names of the geographical unit. It does not take into account the changes that may have occurred in the political boundaries of the units.
The full set of geography variables for Sudan 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: M-Z Variables -- HOUSEHOLD
IPUMS
Telephone availability
Telephone availability
Telephone availability
Telephone availability
Telephone availability
NIU (not in universe)
1
No
2
Yes
9
Unknown/missing
PHONE indicates the availability of a telephone in the dwelling.
Utilities Variables -- HOUSEHOLD
IPUMS
Cellular phone availability
Cellular phone availability
Cellular phone availability
Cellular phone availability
Cellular phone availability
NIU (not in universe)
1
Yes
2
No
9
Unknown
CELL indicates the availability of a cellular phone in the household.
Utilities Variables -- HOUSEHOLD
IPUMS
Air conditioning
Air conditioning
Air conditioning
Air conditioning
Air conditioning
NIU (not in universe)
10
No air conditioning
20
Yes, air conditioning
21
1 unit or room
22
2 units or rooms
23
3 units or rooms
24
4 units or rooms
25
5 units or rooms
26
6 units or rooms
27
7 units or rooms
28
8 or more units or rooms
29
Central system
99
Unknown
This variable indicates whether the household had air conditioning.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Automobiles available
Automobiles available
Automobiles available
Automobiles available
Automobiles available
No autos
1
1 auto
2
2 autos
3
3 autos
4
4 autos
5
5 autos
6
6+ autos
7
Have auto, number unspecified
8
Unknown
9
NIU (not in universe)
AUTOS records whether a member of the household owned or had use of a vehicle and, in many samples, the number of such vehicles.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Television set
Television set
Television set
Television set
Television set
NIU (not in universe)
10
No
20
Yes, color or black-and-white not specified
21
1 television
22
2 televisions
23
3 televisions
24
4 televisions
25
5 televisions
26
6 televisions
27
7 televisions
28
8 televisions
29
9+ televisions
30
Yes, color only
31
1 color tv
32
2 color tvs
33
3+ color tvs
40
Yes, black-and-white only
41
1 black-white tv
42
2 black-white tvs
43
3+ black-white tvs
50
Yes, both color and black-and-white
52
2+ color and black-white tvs
53
3+ color and black-white tvs
54
4+ color and black-white tvs
99
Unknown/missing
TV indicates whether the household had a television.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Radio in household
Radio in household
Radio in household
Radio in household
Radio in household
NIU (not in universe)
1
No
2
Yes
9
Unknown/missing
RADIO indicates whether the household had a radio.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Refrigerator
Refrigerator
Refrigerator
Refrigerator
Refrigerator
NIU (not in universe)
1
No
2
Yes
9
Unknown/missing
REFRIG indicates whether the household had a refrigerator.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Toilet
Toilet
Toilet
Toilet
Toilet
NIU (not in universe)
10
No toilet
11
No flush toilet
20
Have toilet, type not specified
21
Flush toilet
22
Non-flush, latrine
23
Non-flush, other and unspecified
99
Unknown
TOILET indicates whether the household had access to a toilet and, in most cases, whether it was a flush toilet or other type of installation.
Dwelling Characteristics Variables -- HOUSEHOLD
IPUMS
Number of deaths in household last year
Number of deaths in household last year
Number of deaths in household last year
Number of deaths in household last year
Number of deaths in household last year
None
1
1 death
2
2 deaths
3
3 deaths
4
4 deaths
5
5 deaths
6
6 deaths
7
7 or more deaths
8
Unknown
9
NIU (not in universe)
MORTNUM indicates the number of deaths in the household in the past year.
Other Household Variables -- HOUSEHOLD
IPUMS
Any deaths in household last year
Any deaths in household last year
Any deaths in household last year
Any deaths in household last year
Any deaths in household last year
1
Yes
2
No
8
Unknown/missing
9
NIU (not in universe)
ANYMORT indicates whether there were any deaths in the household in the past year.
Other Household Variables -- HOUSEHOLD
IPUMS
Number of unrelated persons
Number of unrelated persons
Number of unrelated persons
Number of unrelated persons
Number of unrelated persons
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9+
UNREL indicates the number of persons in the household who are unrelated to the head.
Group Quarters 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.
This is a 3-digit numeric variable with 0 implied decimal places
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
Water supply
Water supply
Water supply
Water supply
Water supply
NIU (not in universe)
10
Yes, piped water
11
Piped inside dwelling
12
Piped, exclusively to this household
13
Piped, shared with other households
14
Piped outside the dwelling
15
Piped outside dwelling, in building
16
Piped within the building or plot of land
17
Piped outside the building or lot
18
Have access to public piped water
20
No piped water
99
Unknown
WATSUP describes the physical means by which the housing unit receives its water. The primary distinction is whether or not the household had piped (running) water.
Utilities 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 RÃos [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
RÃo 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
Potosà [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
Piauà [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
EspÃrito 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 AraucanÃa [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
BolÃvar 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, QuindÃo, 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 GuainÃa [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 RÃo [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
HolguÃn [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
ElÃas 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
MarÃa 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 MacorÃs [Province: Dominican Republic]
214024
Sánchez RamÃrez [Province: Dominican Republic]
214025
Santiago [Province: Dominican Republic]
214026
Santiago RodrÃguez [Province: Dominican Republic]
214027
Valverde [Province: Dominican Republic]
218001
Azuay [Province: Ecuador]
218002
BolÃvar [Province: Ecuador]
218004
Carchi [Province: Ecuador]
218005
Cotopaxi [Province: Ecuador]
218006
Chimborazo [Province: Ecuador]
218007
El Oro [Province: Ecuador]
218009
Cañar, Esmeraldas, Guayas, ManabÃ, Manga del Cura [Disputed canton], Pichincha, El Piedrero [Disputed canton], Los RÃos, 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, SucumbÃos [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
ÃŽle-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 Potosà [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
MadrÃz [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
RÃo 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, ChiriquÃ, 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 Paraguarà [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
ApurÃmac [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
JunÃn [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 MartÃn [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
PenÃnsula de Setúbal [Subregion: Portugal]
620185
LezÃria 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
PaÃs 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
AndalucÃa [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
RÃo 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
BolÃvar [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
Dwelling number
Dwelling number
Dwelling number
Dwelling number
Dwelling number
Dwelling number
All households
This variable indicates the dwelling number.
This is a 7-digit numeric variable with 0 implied decimal places
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
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
This variable indicates the number of persons in the 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 whether the dwelling was created by splitting apart a large dwelling or household.
Technical Household Variables -- HOUSEHOLD
IPUMS
Region
Region
Region
Region
Region
State _ _
County / Mahaliya _ _
Payam / Administrative unit _ _
Boma / Popular Administrative unit _ _ _
Enumeration Area number _ _ _
Town / village / nomad camp _ _
Household number _ _ _
All households
1
Northern
2
Eastern
3
Khartoum
4
Central
5
Kordofan
6
Darfur
This variable indicates the region the dwelling is located.
Geography: M-Z Variables -- HOUSEHOLD
IPUMS
State
State
State
State
State
State _ _
County / Mahaliya _ _
Payam / Administrative unit _ _
Boma / Popular Administrative unit _ _ _
Enumeration Area number _ _ _
Town / village / nomad camp _ _
Household number _ _ _
All households
11
Northern
12
Nahr El Nil
21
Red Sea
22
Kassala
23
Al Gedarif
31
Khartoum
41
Al Gezira
42
White Nile
43
Sinnar
44
Blue Nile
51
North Kordofan
52
South Kordofan
61
North Darfur
62
West Darfur
63
South Darfur
This variable indicates the state the dwelling is located.
Geography: M-Z Variables -- HOUSEHOLD
IPUMS
County
County
County
County
County
State _ _
County / Mahaliya _ _
Payam / Administrative unit _ _
Boma / Popular Administrative unit _ _ _
Enumeration Area number _ _ _
Town / village / nomad camp _ _
Household number _ _ _
All households
1101
Halfa
1102
Dalgo
1103
Alborgaig
1104
Dongola
1105
Algolid
1106
Aldaba
1107
Marwai
1201
Abu Hamed
1202
Berber
1203
Atbara
1204
El Damar
1205
Shendi
1206
El Matama
2101
Halayib
2102
Elgunab
2103
Port Sudan
2104
Sawakin
2105
Senkat
2106
Haya
2107
Toker
2108
Agieg
2201
North Aldalta
2202
Hamashkoreeb
2203
Talkook
2204
Aroma rural
2205
West Kassala
2206
Kassala city
2207
Kassala rural
2208
Halfa Al Gedida
2209
Nahr Atbara
2210
Setit
2211
Wad El Hilaiw
2301
El Botana
2302
El Fashaga
2303
Central Al Gedarif
2304
Al Gedarif city
2305
Al Fau
2306
El Rahad
2307
Qala'a El Nahal
2308
Al Galabat Al Garbia
2309
El Ghoreisha
2310
El Galabat Sharquia
3101
Karrari
3102
Om Bada
3103
Om Durman
3104
Bahri
3105
Sharg Alneel
3106
Khartoum
3107
Jabel Awliya
4101
El Gezira East
4102
El Kamlin
4103
El Hasaheisa
4104
Um Algora
4105
Wad Madni Alkobra
4106
South Aljazeera
4107
El Managil
4201
El Geteina
4202
Um Rimta
4203
Eldiwiem
4204
Rabak
4205
El Jebelein
4206
Kosti
4207
Al Salam
4208
Tandalti
4301
East Sinnar
4302
Sinnar
4303
Eldindir
4304
Alsoki
4305
Sinja
4306
Abu Hugar
4307
Eldali
4401
Elrosieris
4402
Eldamazin
4403
Al Tdamon
4404
Bau
4405
Giesan
4406
Elkurmuk
5101
Gabrat Elshiekh
5102
Sodari
5103
Bara
5104
Um Rwaba
5105
El Nohook
5106
Shekan
5107
Abu Zabad
5108
Wad Banda
5109
Gebieash
5201
Alrashad
5202
Abu Jibieha
5203
El Dalanj
5204
Kadogli
5205
El Salam
5206
Talody
5207
Lagawa
5208
Kielak
5209
Ab Yei
6101
El Malha
6102
Mellit
6104
Sarf Omra
6105
Alseraf
6106
Kebkabiya
6107
Kutum
6108
Alkoma
6109
El Fasher
6110
Um Kedada
6111
Kalmando
6112
Altewash/Alleet
6114
Alwaha
6201
Kulbus
6202
Sirba
6203
Kirienik
6204
El Geneina
6205
Bayda
6206
Habiela
6207
Azoom
6208
Zalingei
6209
Nertiti
6211
Wadi Salih
6212
Mukjar
6213
Um Dukhun
6301
Sheiria
6302
Nyala
6303
East Jabal Mara
6304
Kass
6305
Ed Al Fursan
6306
Alsalam
6307
Ed Da'ein
6308
Adiela
6309
Tullus
6310
Rihied El Birdi
6311
Buram
6312
Bahr Alarab
This variable indicates the county the dwelling is located.
Geography: M-Z Variables -- HOUSEHOLD
IPUMS
Urban/rural/nomad
Urban/rural/nomad
Urban/rural/nomad
Urban/rural/nomad
Urban/rural/nomad
Urban/rural/nomad
All households
1
Urban
2
Rural
3
Nomad
This variable indicates whether the dwelling is in an urban or rural area, or the household is nomadic.
Geography: M-Z Variables -- HOUSEHOLD
IPUMS
Type of household
Type of household
Type of household
Type of household
Type of household
Population group/type of household
[] 1 Private household
[] 2 Nomads
[] 3 Internally displaced
[] 4 Institutional household
[] 5 Homeless
[] 6 Refugees
[] 7 Cattle camp
[] 8 Overnight travelers
All households
1
Private household
2
Nomads
3
Internally displaced
6
Refugees
This variable indicates the type of household.
Group Quarters Variables -- HOUSEHOLD
IPUMS
Type of dwelling
Type of dwelling
Type of dwelling
Type of dwelling
Type of dwelling
33. What type of dwelling does this household live in?
[] 1 Tent
[] 2 Dwelling of straw mats
[] 3 Tukul / gottiya -- mud
[] 4 Tukul / gottiya -- sticks
[] 5 Flat or apartment
[] 6 Villa
[] 7 House of one floor -- mud
[] 8 House of one floor -- brick/concrete
[] 9 House constructed of wood
[] 10 Multi-storey house
[] 11 Incomplete
All households
1
Tent
2
Dwelling of straw mats
3
Tukul /gottiya of mud
4
Tukul /gottiya of sticks
5
Flat or apartment
6
Villa
7
House of one floor, mud
8
House of one floor, brick/concrete
9
House constructed of wood
10
Multi-story house
11
Incomplete
This variable indicates the physical type of dwelling used by the household.
Dwelling Characteristics Variables -- HOUSEHOLD
IPUMS
Number of sleeping rooms
Number of sleeping rooms
Number of sleeping rooms
Number of sleeping rooms
Number of sleeping rooms
34. How many rooms does this household use for sleeping indoors?
_ _
All households
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10+
This variable indicates the number of rooms used by the household for sleeping.
Dwelling Characteristics Variables -- HOUSEHOLD
IPUMS
Tenure status
Tenure status
Tenure status
Tenure status
Tenure status
35. What is the tenure status of this dwelling?
[] 1 Owned
[] 2 Rented
[] 3 Housing provided as part of work
[] 4 Free
All households
1
Owned
2
Rented
3
Housing provided by job
4
Free
This variable indicates the tenure status of the dwelling.
Household Economic Variables -- HOUSEHOLD
IPUMS
Source of drinking water
Source of drinking water
Source of drinking water
Source of drinking water
Source of drinking water
36. What is the main source of drinking water for this household?
[] 1 Water filtering stations with common network/stand pipe (koshk)
[] 2 Mechanical boreholes with common network stand pipe (koshk)
[] 3 Deep boreholes (donkey) with network
[] 4 Deep boreholes (donkey) without network
[] 5 Hand pumps
[] 6 Sand filters with common network stand pipe (koshk)
[] 7 Shallow wells (dug wells)
[] 8 Hafeer / dam without filter (still open water)
[] 9 Hafeer /dam with filter (still open water)
[] 10 Turda / fula / river (still open water)
[] 11 Running open water source (river, pond, tura'a)
[] 12 Water vendor (tanker-cart-bearer) from deep boreholes
[] 13 Water vendor - from shallow wells pond/river/spring
All households
1
Water filtering station with common network
2
Mechanical boreholes with common network
3
Deep borehole (donkey) with network
4
Deep borehole (donkey) without network
5
Hand pump
6
Sand filter with common network
7
Shallow well (dug well)
8
Hafeer/Dam without filter (still open water)
9
Hafeer/Dam with filter (still open water)
10
Seasonal river (fula)
11
River, stream (turaa)
12
Water vendor (from deep borehole)
13
Water vendor (from shallow well, pond, river, spring)
This variable indicates the main source of drinking water for the household.
Utilities Variables -- HOUSEHOLD
IPUMS
Source of lighting
Source of lighting
Source of lighting
Source of lighting
Source of lighting
37. What is the main source of lighting for this household?
[] 1 No lighting
[] 2 Public electricity
[] 3 Private electricity (generator)
[] 4 Gas
[] 5 Paraffin lantern
[] 6 Paraffin lamp
[] 7 Firewood
[] 8 Grass
[] 9 Candle wax
[] 10 Solar power
[] 11 Biogas
All households
1
No lighting
2
Public electricity
3
Private electricity (generator)
4
Gas
5
Paraffin lantern
6
Paraffin lamp
7
Firewood
8
Grass
9
Candle wax
10
Solar power
11
Biogas
This variable indicates the main source of lighting for the household.
Utilities Variables -- HOUSEHOLD
IPUMS
Cooking fuel
Cooking fuel
Cooking fuel
Cooking fuel
Cooking fuel
38. What is the main source of energy used for cooking in this household?
[] 1 Firewood
[] 2 Charcoal
[] 3 Gas
[] 4 Electricity
[] 5 Paraffin
[] 6 Cow dung
[] 7 Grass
[] 8 Biogas
[] 9 No cooking
All households
1
Firewood
2
Charcoal
3
Gas
4
Electricity
5
Paraffin
6
Cow dung
7
Grass
8
Biogas
9
No cooking
This variable indicates the main type of cooking fuel used in the household.
Utilities Variables -- HOUSEHOLD
IPUMS
Toilet facilities
Toilet facilities
Toilet facilities
Toilet facilities
Toilet facilities
39. What is the main type of toilet facility used by this household?
[] 1 Pit latrine private
[] 2 Shared pit latrine
[] 3 Private flush toilet
[] 4 Shared flush toilet
[] 5 Bucket toilet
[] 6 No toilet facility
All households
1
Pit latrine private
2
Pit latrine shared
3
Flush toilet private
4
Flush toilet shared
5
Bucket toilet
6
No toilet facility
This variable indicates the main type of toilet facility used by the household.
Dwelling Characteristics Variables -- HOUSEHOLD
IPUMS
Motor vehicle
Motor vehicle
Motor vehicle
Motor vehicle
Motor vehicle
40. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Motor vehicle
[] 2 Motor cycle / motor rickshaw
[] 3 Bicycle
[] 4 Canoe / boat
[] 5 Any type of animal used for transport
[] 6 Tractor
[] 7 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a motor vehicle.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Motorcycle
Motorcycle
Motorcycle
Motorcycle
Motorcycle
40. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Motor vehicle
[] 2 Motor cycle / motor rickshaw
[] 3 Bicycle
[] 4 Canoe / boat
[] 5 Any type of animal used for transport
[] 6 Tractor
[] 7 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a motorcycle.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Bicycle
Bicycle
Bicycle
Bicycle
Bicycle
40. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Motor vehicle
[] 2 Motor cycle / motor rickshaw
[] 3 Bicycle
[] 4 Canoe / boat
[] 5 Any type of animal used for transport
[] 6 Tractor
[] 7 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a bicycle.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Canoe/boat
Canoe/boat
Canoe/boat
Canoe/boat
Canoe/boat
40. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Motor vehicle
[] 2 Motor cycle / motor rickshaw
[] 3 Bicycle
[] 4 Canoe / boat
[] 5 Any type of animal used for transport
[] 6 Tractor
[] 7 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a canoe/boat.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Animal transport
Animal transport
Animal transport
Animal transport
Animal transport
40. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Motor vehicle
[] 2 Motor cycle / motor rickshaw
[] 3 Bicycle
[] 4 Canoe / boat
[] 5 Any type of animal used for transport
[] 6 Tractor
[] 7 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns an animal that is used for transport.
Other Household Variables -- HOUSEHOLD
IPUMS
Tractor
Tractor
Tractor
Tractor
Tractor
40. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Motor vehicle
[] 2 Motor cycle / motor rickshaw
[] 3 Bicycle
[] 4 Canoe / boat
[] 5 Any type of animal used for transport
[] 6 Tractor
[] 7 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a tractor.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
No transport
No transport
No transport
No transport
No transport
40. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Motor vehicle
[] 2 Motor cycle / motor rickshaw
[] 3 Bicycle
[] 4 Canoe / boat
[] 5 Any type of animal used for transport
[] 6 Tractor
[] 7 None
All households
1
Yes
2
No
This variable indicates if none of the household members own a form of transport, including animals.
Other Household Variables -- HOUSEHOLD
IPUMS
Television
Television
Television
Television
Television
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a television.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Radio
Radio
Radio
Radio
Radio
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a radio.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Mobile phone
Mobile phone
Mobile phone
Mobile phone
Mobile phone
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a mobile phone.
Utilities Variables -- HOUSEHOLD
IPUMS
Fixed phone
Fixed phone
Fixed phone
Fixed phone
Fixed phone
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a fixed phone.
Utilities Variables -- HOUSEHOLD
IPUMS
Computer
Computer
Computer
Computer
Computer
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a computer.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Refrigerator
Refrigerator
Refrigerator
Refrigerator
Refrigerator
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a refrigerator.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Satellite dish
Satellite dish
Satellite dish
Satellite dish
Satellite dish
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a satellite dish.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Fan
Fan
Fan
Fan
Fan
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns a fan.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
Air cooler/AC
Air cooler/AC
Air cooler/AC
Air cooler/AC
Air cooler/AC
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates whether any member of the household owns an air cooler or A/C unit.
Appliances, Mechanicals, Other Amenities Variables -- HOUSEHOLD
IPUMS
No household amenities
No household amenities
No household amenities
No household amenities
No household amenities
41. Does any member of this household own any of the following?
(Mark all that apply)
[] 1 Television
[] 2 Radio/transistor
[] 3 Mobile phone
[] 4 Fixed phone (landline)
[] 5 Computer
[] 6 Refrigerator
[] 7 Satellite dish
[] 8 Fan
[] 9 Air cooler / air-conditioner
[] 10 None
All households
1
Yes
2
No
This variable indicates if none of the household members own any of the mentioned household amenities.
Other Household Variables -- HOUSEHOLD
IPUMS
Source of livelihood
Source of livelihood
Source of livelihood
Source of livelihood
Source of livelihood
42. What is the household's main source of livelihood?
[] 1 Subsistence crop farming
[] 2 Subsistence animal husbandry
[] 3 Wages and salaries
[] 4 Owned business enterprise
[] 5 Property income
[] 6 Remittance
[] 7 Pension
[] 8 Humanitarian aid
All households
1
Subsistence crop farming
2
Subsistence animal husbandry
3
Wages and salaries
4
Owned business enterprise
5
Property income
6
Remittances
7
Pension
8
Humanitarian aid
This variable indicates the household's main source of livelihood.
Household Economic Variables -- HOUSEHOLD
IPUMS
Any cultivation/plantation
Any cultivation/plantation
Any cultivation/plantation
Any cultivation/plantation
Any cultivation/plantation
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
43. Does this household perform any cultivation/plantation activities?
[] 1 Yes
[] 2 No (skip to Q47)
All households
1
Yes
2
No
This variable indicates whether the household performs any cultivation/plantation activities.
Other Household Variables -- HOUSEHOLD
IPUMS
Area of cultivation/plantation
Area of cultivation/plantation
Area of cultivation/plantation
Area of cultivation/plantation
Area of cultivation/plantation
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
44. How large is the area being cultivated by this household? Code area in Feddans. If more than 999, mark "999".
[Q44 was asked of households that are engaged in cultivation or plantation activities, as per Q43.]
Area _ _ _
All households
No cultivation/plantation
1
1 feddan
2
2 feddans
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 to 34
35
35 to 39
40
40 to 44
45
45 to 49
50
50 to 54
55
55 to 59
60
60 to 64
65
65 to 69
70
70 to 74
75
75 to 79
80
80 to 84
85
85 to 89
90
90 to 94
95
95 to 99
100
100 to 109
110
110 to 119
120
120 to 129
130
130 to 139
140
140 to 149
150
150 to 159
160
160 to 169
170
170 to 179
180
180 to 189
190
190 to 199
200
200 to 219
220
220 to 239
240
240 to 259
260
260 to 279
280
280 to 299
300
300 to 319
320
320 to 339
340
340 to 359
360
360 to 379
380
380 to 399
400
400 to 419
420
420 to 439
440
440 to 459
460
460 to 479
480
480 to 499
500
500 to 519
520
520 to 539
540
540 to 559
560
560 to 579
580
580 to 599
600
600 to 619
620
620 to 639
640
640 to 659
660
660 to 679
680
680 to 699
700
700 to 719
720
720 to 739
740
740 to 759
760
760 to 779
780
780 to 799
800
800 to 819
820
820 to 839
840
840 to 859
860
860 to 879
880
880 to 899
900
900 to 919
920
920 to 939
940
940 to 959
960
960 to 979
980
980 to 998
999
999 or more feddans
This variable indicates the area, in feddans, used for cultivation/plantation by this household. Above 29 feddans, the areas are grouped into intervals.
Other Household Variables -- HOUSEHOLD
IPUMS
Cereals
Cereals
Cereals
Cereals
Cereals
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated cereals during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Vegetables and melons
Vegetables and melons
Vegetables and melons
Vegetables and melons
Vegetables and melons
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated vegetables and melons during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Fruits and nuts
Fruits and nuts
Fruits and nuts
Fruits and nuts
Fruits and nuts
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated fruits and nuts during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Oilseed crops
Oilseed crops
Oilseed crops
Oilseed crops
Oilseed crops
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated oilseed crops during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Root and tuber crops
Root and tuber crops
Root and tuber crops
Root and tuber crops
Root and tuber crops
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated root and tuber crops during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Beverage and spice crops
Beverage and spice crops
Beverage and spice crops
Beverage and spice crops
Beverage and spice crops
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated beverage and spice crops during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Leguminous crops
Leguminous crops
Leguminous crops
Leguminous crops
Leguminous crops
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether the household cultivated leguminous crops during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Sugar crops
Sugar crops
Sugar crops
Sugar crops
Sugar crops
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether the household cultivated sugar crops during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Cotton
Cotton
Cotton
Cotton
Cotton
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether the household cultivated cotton during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Coffee
Coffee
Coffee
Coffee
Coffee
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated coffee during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Tea
Tea
Tea
Tea
Tea
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated tea during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Other crop
Other crop
Other crop
Other crop
Other crop
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
45. What type of crops did this household cultivate during the previous year?
[Q45 was asked of households that are engaged in cultivation or plantation activities, per Q43]
(Mark all that apply)
[] 1 Cereals
[] 2 Vegetables
[] 3 Fruits and nuts
[] 4 Oil seed
[] 5 Root and tuber
[] 6 Beverage/spice
[] 7 Leguminous
[] 8 Sugar
[] 9 Cotton
[] 10 Coffee
[] 11 Tea
[] 12 Other
Households that cultivate land
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether this household cultivated other crops besides those mentioned above during the previous year.
Other Household Variables -- HOUSEHOLD
IPUMS
Tenure status of land
Tenure status of land
Tenure status of land
Tenure status of land
Tenure status of land
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
46. What is the tenure status of land under cultivation/plantation?
[Q46 was asked of households that are engaged in cultivation or plantation activities, as per Q43.]
[] 1 Owned
[] 2 Rented
[] 3 Partially owned
[] 4 Communal
Households that cultivate land
1
Owned
2
Rented
3
Partially owned
4
Communal
9
NIU (not in universe)
This variable indicates the tenure status of the land under cultivation/plantation.
Other Household Variables -- HOUSEHOLD
IPUMS
Fishery
Fishery
Fishery
Fishery
Fishery
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
47. Does this household perform any fishery activities?
[] 1 Yes
[] 2 No
All households
1
Yes
2
No
This variable indicates whether the household performs any fishery activities.
Other Household Variables -- HOUSEHOLD
IPUMS
Cattle
Cattle
Cattle
Cattle
Cattle
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of cattle the household owns.
SD08A077 is a 3-digit numeric variable.
999 = 999+
Other Household Variables -- HOUSEHOLD
IPUMS
Camels
Camels
Camels
Camels
Camels
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of camels the household owns.
SD08A078 is a 1-digit numeric variable.
9 = 9+
Other Household Variables -- HOUSEHOLD
IPUMS
Sheep
Sheep
Sheep
Sheep
Sheep
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of sheep the household owns.
SD08A079 is a 3-digit numeric variable.
999 = 999+
Other Household Variables -- HOUSEHOLD
IPUMS
Goats
Goats
Goats
Goats
Goats
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of goats the household owns.
SD08A080 is a 3-digit numeric variable.
999 = 999+
Other Household Variables -- HOUSEHOLD
IPUMS
Horses
Horses
Horses
Horses
Horses
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of horses the household owns.
SD08A081 is a 1-digit numeric variable.
3 = 3+
Other Household Variables -- HOUSEHOLD
IPUMS
Donkeys
Donkeys
Donkeys
Donkeys
Donkeys
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of donkeys the household owns.
SD08A082 is a 2-digit numeric variable. From 20 donkeys onwards, the variable is coded in intervals.
40 = 40+
Other Household Variables -- HOUSEHOLD
IPUMS
Pigs
Pigs
Pigs
Pigs
Pigs
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of pigs the household owns.
SD08A083 is a 2-digit numeric variable.
20 = 20+
Other Household Variables -- HOUSEHOLD
IPUMS
Poultry
Poultry
Poultry
Poultry
Poultry
For households engaged in cultivation/plantation, animal husbandry, fishery
[Questions 43 - 48]
48. Does this household own any of the following animals? Code the number for all that apply, if more than 999, code "999")
Cattle _ _ _
Camels _ _ _
Sheep _ _ _
Goats _ _ _
Horses _ _ _
Donkeys _ _ _
Pigs _ _ _
Poultry _ _ _
All households
This variable indicates the number of poultry the household owns.
SD08A084 is a 3-digit numeric variable.
100 = 100+
Other Household Variables -- HOUSEHOLD
IPUMS
Deaths in last 12 months
Deaths in last 12 months
Deaths in last 12 months
Deaths in last 12 months
Deaths in last 12 months
49. Was there any deaths among members of the household in the past 12 months?
[] 1 Yes (list names)
[] 2 No (end interview)
All households
1
Yes
2
No
9
Unknown
This variable indicates whether there were any deaths among household members in the last 12 months.
Other Household Variables -- HOUSEHOLD
IPUMS
Flag: type of household
Flag: type of household
Flag: type of household
Flag: type of household
Flag: type of household
Flag: type of household
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: type of dwelling
Flag: type of dwelling
Flag: type of dwelling
Flag: type of dwelling
Flag: type of dwelling
Flag: type of dwelling
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: number of rooms
Flag: number of rooms
Flag: number of rooms
Flag: number of rooms
Flag: number of rooms
Flag: number of rooms
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: tenure
Flag: tenure
Flag: tenure
Flag: tenure
Flag: tenure
Flag: tenure
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: source of drinking water
Flag: source of drinking water
Flag: source of drinking water
Flag: source of drinking water
Flag: source of drinking water
Flag: source of drinking water
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: source of lighting
Flag: source of lighting
Flag: source of lighting
Flag: source of lighting
Flag: source of lighting
Flag: source of lighting
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: cooking fuel
Flag: cooking fuel
Flag: cooking fuel
Flag: cooking fuel
Flag: cooking fuel
Flag: cooking fuel
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: toilet facilities
Flag: toilet facilities
Flag: toilet facilities
Flag: toilet facilities
Flag: toilet facilities
Flag: toilet facilities
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: motor vehicle
Flag: motor vehicle
Flag: motor vehicle
Flag: motor vehicle
Flag: motor vehicle
Flag: motor vehicle
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: motorcycle
Flag: motorcycle
Flag: motorcycle
Flag: motorcycle
Flag: motorcycle
Flag: motorcycle
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: bicycle
Flag: bicycle
Flag: bicycle
Flag: bicycle
Flag: bicycle
Flag: bicycle
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: canoe/boat
Flag: canoe/boat
Flag: canoe/boat
Flag: canoe/boat
Flag: canoe/boat
Flag: canoe/boat
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: animal transport
Flag: animal transport
Flag: animal transport
Flag: animal transport
Flag: animal transport
Flag: animal transport
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: tractor
Flag: tractor
Flag: tractor
Flag: tractor
Flag: tractor
Flag: tractor
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: no transport
Flag: no transport
Flag: no transport
Flag: no transport
Flag: no transport
Flag: no transport
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: television
Flag: television
Flag: television
Flag: television
Flag: television
Flag: television
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: radio
Flag: radio
Flag: radio
Flag: radio
Flag: radio
Flag: radio
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: mobile phone
Flag: mobile phone
Flag: mobile phone
Flag: mobile phone
Flag: mobile phone
Flag: mobile phone
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: fixed phone
Flag: fixed phone
Flag: fixed phone
Flag: fixed phone
Flag: fixed phone
Flag: fixed phone
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: computer
Flag: computer
Flag: computer
Flag: computer
Flag: computer
Flag: computer
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: refrigerator
Flag: refrigerator
Flag: refrigerator
Flag: refrigerator
Flag: refrigerator
Flag: refrigerator
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: satellite dish
Flag: satellite dish
Flag: satellite dish
Flag: satellite dish
Flag: satellite dish
Flag: satellite dish
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: fan
Flag: fan
Flag: fan
Flag: fan
Flag: fan
Flag: fan
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: air cooler/ac
Flag: air cooler/ac
Flag: air cooler/ac
Flag: air cooler/ac
Flag: air cooler/ac
Flag: air cooler/ac
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: no household amenities
Flag: no household amenities
Flag: no household amenities
Flag: no household amenities
Flag: no household amenities
Flag: no household amenities
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: source of livelihood
Flag: source of livelihood
Flag: source of livelihood
Flag: source of livelihood
Flag: source of livelihood
Flag: source of livelihood
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: any cultivation/plantation
Flag: any cultivation/plantation
Flag: any cultivation/plantation
Flag: any cultivation/plantation
Flag: any cultivation/plantation
Flag: any cultivation/plantation
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: area of cultivation/plantation
Flag: area of cultivation/plantation
Flag: area of cultivation/plantation
Flag: area of cultivation/plantation
Flag: area of cultivation/plantation
Flag: area of cultivation/plantation
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: cereals
Flag: cereals
Flag: cereals
Flag: cereals
Flag: cereals
Flag: cereals
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: vegetables
Flag: vegetables
Flag: vegetables
Flag: vegetables
Flag: vegetables
Flag: vegetables
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: fruits and nuts
Flag: fruits and nuts
Flag: fruits and nuts
Flag: fruits and nuts
Flag: fruits and nuts
Flag: fruits and nuts
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: oil seed
Flag: oil seed
Flag: oil seed
Flag: oil seed
Flag: oil seed
Flag: oil seed
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: root and tuber crops
Flag: root and tuber crops
Flag: root and tuber crops
Flag: root and tuber crops
Flag: root and tuber crops
Flag: root and tuber crops
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: beverage/spice
Flag: beverage/spice
Flag: beverage/spice
Flag: beverage/spice
Flag: beverage/spice
Flag: beverage/spice
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: leguminous crops
Flag: leguminous crops
Flag: leguminous crops
Flag: leguminous crops
Flag: leguminous crops
Flag: leguminous crops
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: sugar
Flag: sugar
Flag: sugar
Flag: sugar
Flag: sugar
Flag: sugar
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: cotton
Flag: cotton
Flag: cotton
Flag: cotton
Flag: cotton
Flag: cotton
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: coffee
Flag: coffee
Flag: coffee
Flag: coffee
Flag: coffee
Flag: coffee
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: tea
Flag: tea
Flag: tea
Flag: tea
Flag: tea
Flag: tea
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: other crop
Flag: other crop
Flag: other crop
Flag: other crop
Flag: other crop
Flag: other crop
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: tenure status of land
Flag: tenure status of land
Flag: tenure status of land
Flag: tenure status of land
Flag: tenure status of land
Flag: tenure status of land
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: fishery
Flag: fishery
Flag: fishery
Flag: fishery
Flag: fishery
Flag: fishery
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: cattle
Flag: cattle
Flag: cattle
Flag: cattle
Flag: cattle
Flag: cattle
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: camels
Flag: camels
Flag: camels
Flag: camels
Flag: camels
Flag: camels
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: sheep
Flag: sheep
Flag: sheep
Flag: sheep
Flag: sheep
Flag: sheep
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: goats
Flag: goats
Flag: goats
Flag: goats
Flag: goats
Flag: goats
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: horses
Flag: horses
Flag: horses
Flag: horses
Flag: horses
Flag: horses
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: donkeys
Flag: donkeys
Flag: donkeys
Flag: donkeys
Flag: donkeys
Flag: donkeys
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: pigs
Flag: pigs
Flag: pigs
Flag: pigs
Flag: pigs
Flag: pigs
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: poultry
Flag: poultry
Flag: poultry
Flag: poultry
Flag: poultry
Flag: poultry
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Flag: deaths in last 12 months
Flag: deaths in last 12 months
Flag: deaths in last 12 months
Flag: deaths in last 12 months
Flag: deaths in last 12 months
Flag: deaths in last 12 months
All households
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Household Imputation Flags Variables -- HOUSEHOLD
IPUMS
Housing weight
Housing weight
Housing weight
Housing weight
Housing weight
Housing weight
All households
This variable indicates the housing weight.
This is a 6-digit numeric variable with 4 implied decimal places
Technical Household Variables -- HOUSEHOLD
IPUMS
Number of death records
Number of death records
Number of death records
Number of death records
Number of death records
Number of death records
All households
1
1
2
2
3
3
4
4
5
5
6
6
7
7+
This variable indicates the number of death records.
Other Household 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.
HHWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description.
Technical Household Variables -- HOUSEHOLD
IPUMS
Sudan, State 2008 [Level 1; consistent boundaries, GIS]
Sudan, State 2008 [Level 1; consistent boundaries, GIS]
Sudan, State 2008 [Level 1; consistent boundaries, GIS]
Sudan, State 2008 [Level 1; consistent boundaries, GIS]
Sudan, State 2008 [Level 1; consistent boundaries, GIS]
729011
Northern
729012
Nahr El Nil
729021
Red Sea
729022
Kassala
729023
Al Gedarif
729031
Khartoum
729041
Al Gezira
729042
White Nile
729043
Sinnar
729044
Blue Nile
729051
North Kordofan
729052
South Kordofan
729061
North Darfur
729062
West Darfur
729063
South Darfur
GEO1_SD identifies the household's state within Sudan in 2008. States are the first level administrative units of the country. A GIS map (in shapefile format), corresponding to GEO1_SD can be downloaded from the GIS Boundary files page in the IPUMS International web site.
The full set of geography variables for Sudan 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.
At the present moment, IPUMS International is only releasing integrated geography for the first level of geography for Sudan. Year specific geography and maps along with variables that are spatially harmonized at the second level of geography will become available in the near future.
Geography: M-Z Variables -- HOUSEHOLD
IPUMS
Sudan, County 2008 [Level 2; inconsistent boundaries, harmonized by name]
Sudan, County 2008 [Level 2; inconsistent boundaries, harmonized by name]
Sudan, County 2008 [Level 2; inconsistent boundaries, harmonized by name]
Sudan, County 2008 [Level 2; inconsistent boundaries, harmonized by name]
Sudan, County 2008 [Level 2; inconsistent boundaries, harmonized by name]
1101
Halfa
1102
Dalgo
1103
Alborgaig
1104
Dongola
1105
Algolid
1106
Aldaba
1107
Marwai
1201
Abu Hamed
1202
Berber
1203
Atbara
1204
El Damar
1205
Shendi
1206
El Matama
2101
Halayib
2102
Elgunab
2103
Port Sudan
2104
Sawakin
2105
Senkat
2106
Haya
2107
Toker
2108
Agieg
2201
North Aldalta
2202
Hamashkoreeb
2203
Talkook
2204
Aroma rural
2205
West Kassala
2206
Kassala city
2207
Kassala rural
2208
Halfa Al Gedida
2209
Nahr Atbara
2210
Setit
2211
Wad El Hilaiw
2301
El Botana
2302
El Fashaga
2303
Central Al Gedarif
2304
Al Gedarif city
2305
Al Fau
2306
El Rahad
2307
Qala'a El Nahal
2308
Al Galabat Al Garbia
2309
El Ghoreisha
2310
El Galabat Sharquia
3101
Karrari
3102
Om Bada
3103
Om Durman
3104
Bahri
3105
Sharg Alneel
3106
Khartoum
3107
Jabel Awliya
4101
El Gezira East
4102
El Kamlin
4103
El Hasaheisa
4104
Um Algora
4105
Wad Madni Alkobra
4106
South Aljazeera
4107
El Managil
4201
El Geteina
4202
Um Rimta
4203
Eldiwiem
4204
Rabak
4205
El Jebelein
4206
Kosti
4207
Al Salam
4208
Tandalti
4301
East Sinnar
4302
Sinnar
4303
Eldindir
4304
Alsoki
4305
Sinja
4306
Abu Hugar
4307
Eldali
4401
Elrosieris
4402
Eldamazin
4403
Al Tdamon
4404
Bau
4405
Giesan
4406
Elkurmuk
5101
Gabrat Elshiekh
5102
Sodari
5103
Bara
5104
Um Rwaba
5105
El Nohook
5106
Shekan
5107
Abu Zabad
5108
Wad Banda
5109
Gebieash
5201
Alrashad
5202
Abu Jibieha
5203
El Dalanj
5204
Kadogli
5205
El Salam
5206
Talody
5207
Lagawa
5208
Kielak
5209
Ab Yei
6101
El Malha
6102
Mellit
6104
Sarf Omra
6105
Alseraf
6106
Kebkabiya
6107
Kutum
6108
Alkoma
6109
El Fasher
6110
Um Kedada
6111
Kalmando
6112
Altewash/Alleet
6114
Alwaha
6201
Kulbus
6202
Sirba
6203
Kirienik
6204
El Geneina
6205
Bayda
6206
Habiela
6207
Azoom
6208
Zalingei
6211
Wadi Salih
6212
Mukjar
6299
Counties in West Darfur State under 20,000
6301
Sheiria
6302
Nyala
6304
Kass
6305
Ed Al Fursan
6306
Alsalam
6307
Ed Da'ein
6309
Tullus
6310
Rihied El Birdi
6311
Buram
6312
Bahr Alarab
6399
Counties in South Darfur State under 20,000
GEO2_SDX identifies the household's county within Sudan in 2008. Counties are the second level administrative units of the country, after states. GEO2_SDX is harmonized by name and does not account for boundary changes over time.
The full set of geography variables for Sudan 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.
At the present moment, IPUMS International is only releasing integrated geography for the first level of geography for Sudan. Year specific geography and maps along with variables that are spatially harmonized at the second level of geography will become available in the near future.
Geography: M-Z 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
Electricity
Electricity
Electricity
Electricity
Electricity
NIU (not in universe)
1
Yes
2
No
9
Unknown
ELECTRIC indicates whether the household had access to electricity.
Utilities Variables -- HOUSEHOLD
IPUMS
Number of bedrooms
Number of bedrooms
Number of bedrooms
Number of bedrooms
Number of bedrooms
No bedrooms
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+
98
Unknown
99
NIU (not in universe)
BEDROOMS indicates the number of rooms available to members of the household for sleeping.
Dwelling Characteristics Variables -- HOUSEHOLD
IPUMS
Ownership of dwelling [general version]
Ownership of dwelling [general version]
Ownership of dwelling [general version]
Ownership of dwelling [general version]
Ownership of dwelling [general version]
NIU (not in universe)
1
Owned
2
Not owned
9
Unknown
OWNERSHIP indicates whether a member of the household owned the housing unit. Households that acquired their unit with a mortgage or other lending arrangement were understood to "own" their unit even if they had not yet completed repayment. For those that did not own their housing unit, several options were possible: renting (from various types of owners), subletting, usufruct, and de facto occupation.
Household Economic Variables -- HOUSEHOLD
IPUMS
Ownership of dwelling [detailed version]
Ownership of dwelling [detailed version]
Ownership of dwelling [detailed version]
Ownership of dwelling [detailed version]
Ownership of dwelling [detailed version]
NIU (not in universe)
100
Owned
110
Owned, already paid
120
Owned, still paying
130
Owned, constructed
140
Owned, inherited
190
Owned, other
191
Owned, house
192
Owned, condominium
193
Apartment proprietor
194
Shared ownership
200
Not owned
210
Renting, not specified
211
Renting, government
212
Renting, local authority
213
Renting, parastatal
214
Renting, private
215
Renting, private company
216
Renting, individual
217
Renting, collective
218
Renting, joint state and individual
219
Renting, public subsidized
220
Renting, private subsidized
221
Renting, co-tenant
222
Renting, relative of tenant
223
Renting, cooperative
224
Renting, with a job or business
225
Renting, loan-backed habitation
226
Renting, mixed contract
227
Furnished dwelling
228
Sharecropping
230
Subletting
231
Rent to own
239
Renting, other
240
Occupied de facto/squatting
250
Free/usufruct (no cash rent)
251
Free, provided by employer
252
Free, without work or services
253
Free, provided by family or friend
254
Free, private
255
Free, public
256
Free, condemned
257
Free, other
290
Not owned, other
999
Unknown
OWNERSHIP indicates whether a member of the household owned the housing unit. Households that acquired their unit with a mortgage or other lending arrangement were understood to "own" their unit even if they had not yet completed repayment. For those that did not own their housing unit, several options were possible: renting (from various types of owners), subletting, usufruct, and de facto occupation.
Household Economic Variables -- HOUSEHOLD
IPUMS
Main source of livelihood
Main source of livelihood
Main source of livelihood
Main source of livelihood
Main source of livelihood
NIU (not in universe)
10
Agricultural activities
11
Subsistence animal husbandry
12
Subsistence farming
13
Commercial farming
14
Fishing
20
Employment income
30
Business enterprise
31
Formal trading
32
Petty trading
40
Cottage industry
50
Property income
60
Family support/remittances
70
Humanitarian aid
80
Other
81
Rent or remittances
82
Religious work
83
Pension
99
Unknown
LIVEHOOD describes the main source of livelihood of the household. If there were multiple sources, one had to be chosen as the most important.
Household Economic Variables -- HOUSEHOLD
IPUMS
Cooking fuel
Cooking fuel
Cooking fuel
Cooking fuel
Cooking fuel
NIU (not in universe)
10
None
20
Electricity
30
Petroleum gas, unspecified
31
Gas -- piped/utility
32
Gas -- tanked or bottled
33
Propane
35
Liquefied petroleum gas
40
Petroleum liquid
41
Oil, kerosene, and other liquid fuels
42
Kerosene/paraffin
43
Kerosene or oil
44
Kerosene or gasoline
45
Gasoline
46
Cocinol
50
Wood, coal, and other solid fuels
51
Wood and other plant fuels
52
Non-wood plant materials
53
Coal or charcoal
54
Charcoal
55
Coal
56
Wood or charcoal
60
Multiple fuels
61
Bottled gas and wood
62
Propane and electricity
63
Propane, kerosene, and electricity
64
Propane and kerosene
65
Kerosene and electrictiy
66
Other combinations
70
Other
71
Alcohol
72
Biogas
73
Discarded or waste material
74
Dung/manure
75
Dung or grass
76
Solar energy
77
Candle
99
Unknown/missing
FUELCOOK indicates the predominant type of fuel or energy used for cooking.
Utilities Variables -- HOUSEHOLD
IPUMS
Computer
Computer
Computer
Computer
Computer
NIU (not in universe)
1
No
2
Yes
9
Unknown/missing
COMPUTER indicates whether the household had a personal computer.
Appliances, Mechanicals, Other Amenities 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.
PERNUM is a 3-digit numeric variable.
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
State of birth, Sudan
State of birth, Sudan
State of birth, Sudan
State of birth, Sudan
State of birth, Sudan
11
Northern
12
Nahr El Nil
21
Red Sea
22
Kassala
23
Al Gedarif
31
Khartoum
41
Al Gezira
42
White Nile
43
Sinnar
44
Blue Nile
51
North Kordofan
52
South Kordofan
61
North Darfur
62
West Darfur
63
South Darfur
71
Upper Nile
72
Jonglei
73
Unity
81
Warrap
82
Northern Bahr El Ghazal
83
Western Bahr El Ghazal
84
Lakes
91
Western Equatoria
92
Central Equatoria
93
Eastern Equatoria
99
Abroad
BPLSD indicates the person's state of birth in Sudan.
Sudan and South Sudan were enumerated as one country in 2008. States of birth with South Sudan are also identified in this variable.
Nativity and Birthplace Variables -- PERSON
IPUMS
Country of citizenship
Country of citizenship
Country of citizenship
Country of citizenship
Country of citizenship
NIU (not in universe)
10000
Africa
11000
Eastern Africa
11010
Burundi
11020
Comoros
11030
Djibouti
11040
Eritrea
11050
Ethiopia
11060
Kenya
11070
Madagascar
11080
Malawi
11090
Mauritius
11100
Mozambique
11110
Reunion
11120
Rwanda
11130
Seychelles
11140
Somalia
11150
Uganda
11160
Tanzania
11170
Zambia
11180
Zimbabwe
11999
Eastern Africa, n.s.
12000
Middle Africa
12010
Angola
12020
Cameroon
12030
Central African Republic
12040
Chad
12050
Congo (Republic of)
12060
Democratic Republic of Congo
12070
Equatorial Guinea
12080
Gabon
12090
Sao Tome and Principe
12999
Middle Africa, n.s.
13000
Northern Africa
13010
Algeria
13011
Algeria/Tunisia
13020
Egypt/United Arab Rep.
13021
Egypt/Sudan
13030
Libya
13040
Morocco
13050
Sudan
13060
Tunisia
13070
Western Sahara
13999
Northern Africa, n.s.
14000
Southern Africa
14010
Botswana
14020
Lesotho
14030
Namibia
14040
South Africa
14050
Swaziland
14999
Southern Africa, n.s.
15000
Western Africa
15010
Benin
15020
Burkina Faso
15030
Cape Verde
15040
Ivory Coast
15050
Gambia
15060
Ghana
15070
Guinea
15080
Guinea-Bissau
15090
Liberia
15100
Mali
15110
Mauritania
15120
Niger
15130
Nigeria
15140
St. Helena and Ascension
15150
Senegal
15160
Sierra Leone
15170
Togo
15991
Burkina-Faso, Niger, Ivory-Coast, Benin, Togo
15992
Liberia, Sierra-Leone, Nigeria, Ghana
15999
West Africa, n.s.
19990
Africa, n.s.
19991
Central and South Africa
19992
Arab African countries
19993
Non-Arab African countries
19994
Other Arab countries
19995
East, Central and South Africa
20000
Americas
21000
Caribbean
21010
Anguilla
21020
Antigua-Barbuda
21030
Aruba
21040
Bahamas
21050
Barbados
21060
British Virgin Islands
21070
Cayman Isles
21080
Cuba
21090
Dominica
21100
Dominican Republic
21110
Grenada
21120
Guadeloupe
21130
Haiti
21140
Jamaica
21150
Martinique
21160
Montserrat
21170
Netherlands Antilles
21180
Puerto Rico
21190
St. Kitts-Nevis
21200
St. Lucia
21210
St. Vincent
21220
Trinidad and Tobago
21230
Turks and Caicos
21999
Caribbean, n.s.
22000
Central America
22010
Belize/British Honduras
22020
Costa Rica
22030
El Salvador
22040
Guatemala
22050
Honduras
22060
Mexico
22070
Nicaragua
22080
Panama
22081
Panama Canal Zone
22999
Central America, n.s.
23000
South America
23010
Argentina
23020
Bolivia
23030
Brazil
23040
Chile
23050
Colombia
23060
Ecuador
23070
Falkland Islands
23080
French Guiana
23090
Guyana/British Guiana
23100
Paraguay
23110
Peru
23120
Suriname
23130
Uruguay
23140
Venezuela
23990
South America, n.s.
24000
North America
24010
Bermuda
24020
Canada
24030
Greenland
24040
United States
24041
U.S. Outlying Areas and Territories
24090
U.S. and Canada
24999
North America, n.s.
29900
Americas, n.s.
29901
America/Oceania
29902
Central/South America, n.s.
29903
Central-America and Carribean
29904
Central/South America and Caribbean
29905
North and Central America, n.s.
30000
Asia
31000
Eastern Asia
31010
China
31011
Hong Kong
31012
Macau
31013
Taiwan
31020
Japan
31030
Korea
31031
Korea, DPR (North)
31032
Korea, RO (South)
31040
Mongolia
31999
Eastern Asia, n.s.
32000
South-Central Asia
32010
Afghanistan
32020
Bangladesh
32030
Bhutan
32040
India
32041
India/Pakistan
32050
Iran
32060
Kazakhstan
32070
Kyrgyzstan
32080
Maldives
32090
Nepal
32100
Pakistan
32110
Sri Lanka (Ceylon)
32120
Tajikistan
32130
Turkmenistan
32140
Uzbekistan
32990
Burma, India, Pakistan, Ceylon
32999
South-Central Asia, n.s.
33000
South-Eastern Asia
33010
Brunei
33020
Cambodia (Kampuchea)
33030
East Timor
33040
Indonesia
33050
Laos
33060
Malaysia
33070
Myanmar (Burma)
33080
Philippines
33090
Singapore
33100
Thailand
33110
Vietnam
33991
Laos and Cambodia
33992
Malaysia and Singapore
33999
South-Eastern Asia, n.s.
34000
Western Asia
34010
Armenia
34020
Azerbaijan
34030
Bahrain
34040
Cyprus
34050
Georgia
34060
Iraq
34070
Israel
34080
Jordan
34090
Kuwait
34100
Lebanon
34110
Palestine
34120
Oman
34130
Qatar
34140
Saudi Arabia
34150
Syria
34151
Syria/Lebanon
34160
Turkey
34170
United Arab Emirates
34180
Yemen
34999
Western Asia, n.s.
39900
Asia, n.s.
39901
South-East/South Asia, n.s.
39902
West Central/Middle East Asia
39903
Arab Countries
39904
Non-Arab Asian countries
39905
Former Soviet Republics, Asia
40000
Europe
41000
Eastern Europe
41010
Belarus
41020
Bulgaria
41021
Bulgaria/Greece
41030
Czech Republic/Czechoslovakia
41040
Hungary
41050
Poland
41060
Moldova
41070
Romania
41080
Russia/USSR
41090
Slovakia
41100
Ukraine
41999
Eastern Europe, n.s.
42000
Northern Europe
42010
Denmark
42020
Estonia
42030
Faroe Islands
42040
Finland
42050
Iceland
42060
Ireland
42070
Latvia
42080
Lithuania
42090
Norway
42100
Svalbard and Jan Mayen Islands
42110
Sweden
42120
United Kingdom
42990
Nordic countries
42999
Northern Europe, n.s.
43000
Southern Europe
43010
Albania
43020
Andorra
43030
Bosnia and Herzegovina
43040
Croatia
43050
Gibraltar
43060
Greece
43070
Italy
43071
Vatican
43080
Malta
43090
Portugal
43100
San Marino
43110
Slovenia
43120
Spain
43130
Macedonia
43140
Yugoslavia
43141
Montenegro
43142
Serbia
43143
Kosovo
43144
Serbia and Montenegro
43999
Southern Europe, n.s.
44000
Western Europe
44010
Austria
44020
Belgium
44030
France
44040
Germany
44041
West Germany
44042
East Germany
44050
Liechtenstein
44060
Luxembourg
44070
Monaco
44080
Netherlands
44090
Switzerland
44999
Western Europe, n.s.
49990
Europe, n.s.
49991
Central-Eastern Europe
49992
European Union (Original 15)
49993
Other European Union
49994
Former Soviet Republics, Europe
49995
EEA, Switzerland, associated microstates
50000
Oceania
51000
Australia and New Zealand
51010
Australia
51020
New Zealand
51030
Norfolk Islands
51999
Australia and New Zealand, n.s.
52000
Melanesia
52010
Fiji
52020
New Caledonia
52030
Papua New Guinea
52040
Solomon Islands
52050
Vanuatu (New Hebrides)
52999
Melanesia, n.s.
53000
Micronesia
53010
Kiribati
53020
Marshall Islands
53030
Nauru
53040
Northern Mariana Isls.
53050
Palau
53999
Micronesia, n.e.c.
54000
Polynesia
54010
Cook Islands
54020
French Polynesia
54030
Niue
54040
Pitcairn Island
54050
Western Samoa
54060
Eastern Samoa
54070
Tokelau
54080
Tonga
54090
Tuvalu
54100
Wallis and Futuna Isls.
54999
Polynesia, n.s.
59999
Oceania, n.s.
60000
Asia n.s. or Oceania n.s.
70000
Other Countries
70010
Europe, Australia and New Zealand
70020
Australia, New Zealand, and stateless
99997
No citizenship/nationality
99998
Foreign, country unknown
99999
Unknown
NATION indicates the person's country of citizenship.
Nativity and Birthplace Variables -- PERSON
IPUMS
Citizenship
Citizenship
Citizenship
Citizenship
Citizenship
1
Citizen, not specified
2
Citizen by birth
3
Naturalized citizen
4
Not a citizen
5
Without citizenship, stateless
8
Unknown
9
NIU (not in universe)
CITIZEN indicates the person's citizenship status within the country in which they were enumerated.
Nativity and Birthplace 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).
FAMUNIT is a 2-digit numeric variable.
If there is only one group of related individuals within the household, all of them will be coded "1;" if there is a second, separate such group listed on the form, all of them will be coded "2," and so on.
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
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
Number of male children ever born
Number of male children ever born
Number of male children ever born
Number of male children ever born
Number of male children ever born
No children
1
1 child
2
2 children
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+
98
Unknown
99
NIU (not in universe)
CHBORNM indicates the number of male children ever born to a woman. Only live births are counted.
Fertility and Mortality Variables -- PERSON
IPUMS
Number of female children ever born
Number of female children ever born
Number of female children ever born
Number of female children ever born
Number of female children ever born
No children
1
1 child
2
2 children
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+
98
Unknown
99
NIU (not in universe)
CHBORNF indicates the number of female children ever born to a woman. Only live births are counted.
Fertility and Mortality Variables -- PERSON
IPUMS
Number of children dead
Number of children dead
Number of children dead
Number of children dead
Number of children dead
None
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+
98
Unknown/missing
99
NIU (not in universe)
CHDEAD reports how many of the children ever born to a woman were no longer living at the time of the census. Women were to consider all live births by all fathers; they were to exclude still births.
Fertility and Mortality Variables -- PERSON
IPUMS
Mortality status of father
Mortality status of father
Mortality status of father
Mortality status of father
Mortality status of father
1
Alive
2
Dead
7
Does not know
8
Missing
9
NIU (not in universe)
MORTFAT indicates whether the person's biological father was still living.
Fertility and Mortality Variables -- PERSON
IPUMS
Mortality status of mother
Mortality status of mother
Mortality status of mother
Mortality status of mother
Mortality status of mother
1
Alive
2
Dead
7
Does not know
8
Missing
9
NIU (not in universe)
MORTMOT indicates whether the person's biological mother was still living at the time of the census.
Fertility and Mortality Variables -- PERSON
IPUMS
Number of own female children in household
Number of own female children in household
Number of own female children in household
Number of own female children in household
Number of own female children in household
None
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
17
17
20
20
22
22
98
Unknown
99
NIU (not in universe)
HOMEFEM indicates the number of female children born living in the household with their mother (the respondent).
Fertility and Mortality Variables -- PERSON
IPUMS
Number of own female children living elsewhere
Number of own female children living elsewhere
Number of own female children living elsewhere
Number of own female children living elsewhere
Number of own female children living elsewhere
None
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
98
Unknown
99
NIU (not in universe)
AWAYFEM indicates the number of surviving biological female children not living in the household with their mother (the respondent).
Fertility and Mortality Variables -- PERSON
IPUMS
Children surviving
Children surviving
Children surviving
Children surviving
Children surviving
No children
1
1 child
2
2 children
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+
98
Unknown
99
NIU (not in universe)
CHSURV reports the number of children born to a woman who were still living at the time of the census.
Fertility and Mortality Variables -- PERSON
IPUMS
Literacy
Literacy
Literacy
Literacy
Literacy
NIU (not in universe)
1
No, illiterate
2
Yes, literate
9
Unknown/missing
LIT indicates whether or not the respondent could read and write in any language. A person is typically considered literate if he or she can both read and write. All other persons are illiterate, including those who can either read or write but cannot do both.
Education Variables -- PERSON
IPUMS
School attendance
School attendance
School attendance
School attendance
School attendance
NIU (not in universe)
1
Yes
2
No, not specified
3
No, attended in the past
4
No, never attended
9
Unknown/missing
SCHOOL indicates whether or not the person attended school at the time of the census or within some specified period of time prior to the census.
Education Variables -- PERSON
IPUMS
Status in employment (class of worker) [general version]
Status in employment (class of worker) [general version]
Status in employment (class of worker) [general version]
Status in employment (class of worker) [general version]
Status in employment (class of worker) [general version]
NIU (not in universe)
1
Self-employed
2
Wage/salary worker
3
Unpaid worker
4
Other
9
Unknown/missing
CLASSWK refers to the status of an economically active person with respect to his or her employment -- that is, the type of explicit or implicit contract of employment with other persons or organizations that the person has in his/her job. In general, the variable indicates whether a person was self-employed, or worked for someone else, either for pay or as an unpaid family worker. CLASSWK is related to EMPSTAT, which is used to define the universe in many samples.
Class of worker is often referred to as "status in employment" in other sources.
Work Variables -- PERSON
IPUMS
Status in employment (class of worker) [detailed version]
Status in employment (class of worker) [detailed version]
Status in employment (class of worker) [detailed version]
Status in employment (class of worker) [detailed version]
Status in employment (class of worker) [detailed version]
NIU (not in universe)
100
Self-employed
101
Self-employed, unincorporated
102
Self-employed, incorporated
110
Employer
111
Sharecropper, employer
120
Working on own account
121
Own account, agriculture
122
Domestic worker, self-employed
123
Subsistence worker, own consumption
124
Own account, other
125
Own account, without temporary/unpaid help
126
Own account, with temporary/unpaid help
130
Member of cooperative
140
Sharecropper
141
Sharecropper, self-employed
142
Sharecropper, employee
150
Kibbutz member
200
Wage/salary worker
201
Management
202
Non-management
203
White collar (non-manual)
204
Blue collar (manual)
205
White and blue collar
206
Day laborer
207
Employee, with a permanent job
208
Employee, occasional, temporary, contract
209
Employee without legal contract
210
Wage/salary worker, private employer
211
Apprentice
212
Religious worker
213
Wage/salary worker, non-profit, NGO
214
White collar, private
215
Blue collar, private
216
Paid family worker
217
Cooperative employee
220
Wage/salary worker, government
221
Federal, government employee
222
State government employee
223
Local government employee
224
White collar, public
225
Blue collar, public
226
Public companies
227
Civil servants, local collectives
230
Domestic worker (work for private household)
240
Seasonal migrant
241
Seasonal migrant, no broker
242
Seasonal migrant, uses broker
250
Other wage and salary
251
Canal zone/commission employee
252
Government employment/training program
253
Mixed state/private enterprise/parastatal
254
Government public work program
300
Unpaid worker
310
Unpaid family worker
320
Apprentice, unpaid or unspecified
330
Trainee
340
Apprentice or trainee
350
Works for others without wage
400
Other
999
Unknown/missing
CLASSWK refers to the status of an economically active person with respect to his or her employment -- that is, the type of explicit or implicit contract of employment with other persons or organizations that the person has in his/her job. In general, the variable indicates whether a person was self-employed, or worked for someone else, either for pay or as an unpaid family worker. CLASSWK is related to EMPSTAT, which is used to define the universe in many samples.
Class of worker is often referred to as "status in employment" in other sources.
Work 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.
POPLOC is a 3-digit numeric variable.
0 = No father of this person present in the household.
1 or higher = The person number of this person's father
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.
SPLOC is a 3-digit numeric variable.
0 = No spouse of this person present in the household.
1 or higher = The person number of this person's spouse
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
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.
MOMLOC is a 3-digit numeric variable.
0 = No mother of this person present in the household.
1 or higher = The person number of this person's mother
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
Number of female children surviving
Number of female children surviving
Number of female children surviving
Number of female children surviving
Number of female children surviving
No children
1
1 child
2
2 children
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+
98
Unknown
99
NIU (not in universe)
CHSURVF indicates the number of female children ever born to a woman who were still living at the time of the census.
Fertility and Mortality Variables -- PERSON
IPUMS
Number of male children surviving
Number of male children surviving
Number of male children surviving
Number of male children surviving
Number of male children surviving
No children
1
1 child
2
2 children
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+
98
Unknown
99
NIU (not in universe)
CHSURVM indicates the number of male children ever born to a woman who were still living at the time of the census.
Fertility and Mortality Variables -- PERSON
IPUMS
Children ever born
Children ever born
Children ever born
Children ever born
Children ever born
No children
1
1 child
2
2 children
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+
98
Unknown
99
NIU (not in universe)
CHBORN reports the number of children ever born to each woman of whom the question was asked. In most samples, women were to report all live births by all fathers, whether or not the child was still living.
Fertility and Mortality Variables -- PERSON
IPUMS
Activity status (employment status) [general version]
Activity status (employment status) [general version]
Activity status (employment status) [general version]
Activity status (employment status) [general version]
Activity status (employment status) [general version]
NIU (not in universe)
1
Employed
2
Unemployed
3
Inactive
9
Unknown/missing
EMPSTAT indicates whether or not the respondent was part of the labor force -- working or seeking work -- over a specified period of time. Depending on the sample, EMPSTAT can also convey further information.
The first digit of EMPSTAT is fully comparable, and classifies the population into three groups: employed, unemployed, and inactive. The combination of employed and unemployed yields the total labor force. The second and third digits of EMPSTAT preserve additional information available for some countries and census years but not for others.
Employment status is sometimes referred to in other sources as "activity status".
Work Variables -- PERSON
IPUMS
Activity status (employment status) [detailed version]
Activity status (employment status) [detailed version]
Activity status (employment status) [detailed version]
Activity status (employment status) [detailed version]
Activity status (employment status) [detailed version]
NIU (not in universe)
100
Employed, not specified
110
At work
111
At work, and 'student'
112
At work, and 'housework'
113
At work, and 'seeking work'
114
At work, and 'retired'
115
At work, and 'no work'
116
At work, and other situation
117
At work, family holding, not specified
118
At work, family holding, not agricultural
119
At work, family holding, agricultural
120
Have job, not at work in reference period
130
Armed forces
131
Armed forces, at work
132
Armed forces, not at work in reference period
133
Military trainee
140
Marginally employed
200
Unemployed, not specified
201
Unemployed 6 or more months
202
Worked fewer than 6 months, permanent job
203
Worked fewer than 6 months, temporary job
210
Unemployed, experienced worker
220
Unemployed, new worker
230
No work available
240
Inactive unemployed
300
Inactive (not in labor force)
310
Housework
320
Unable to work/disabled
321
Permanent disability
322
Temporary illness
323
Disabled or imprisoned
330
In school
340
Retirees and living on rent
341
Living on rents
342
Living on rents or pension
343
Retirees/pensioners
344
Retired
345
Pensioner
346
Non-retirement pension
347
Disability pension
348
Retired without benefits
350
Elderly
351
Elderly or disabled
360
Institutionalized
361
Prisoner
370
Intermittent worker
371
Not working, seasonal worker
372
Not working, occasional worker
380
Other income recipient
390
Inactive, other reasons
391
Too young to work
392
Dependent
999
Unknown/missing
EMPSTAT indicates whether or not the respondent was part of the labor force -- working or seeking work -- over a specified period of time. Depending on the sample, EMPSTAT can also convey further information.
The first digit of EMPSTAT is fully comparable, and classifies the population into three groups: employed, unemployed, and inactive. The combination of employed and unemployed yields the total labor force. The second and third digits of EMPSTAT preserve additional information available for some countries and census years but not for others.
Employment status is sometimes referred to in other sources as "activity status".
Work Variables -- PERSON
IPUMS
Disability affecting upper extremities
Disability affecting upper extremities
Disability affecting upper extremities
Disability affecting upper extremities
Disability affecting upper extremities
NIU (not in universe)
1
Yes
2
No
9
Unknown
DISUPPR indicates whether the person lacked full use of at least one arm or hand.
Disability Variables -- PERSON
IPUMS
Disability affecting lower extremities
Disability affecting lower extremities
Disability affecting lower extremities
Disability affecting lower extremities
Disability affecting lower extremities
NIU (not in universe)
1
Yes
2
No
9
Unknown
DISLOWR indicates whether the person lacked use of one or both legs.
Disability Variables -- PERSON
IPUMS
Mute or speech impaired
Mute or speech impaired
Mute or speech impaired
Mute or speech impaired
Mute or speech impaired
NIU (not in universe)
1
Yes
2
No
9
Unknown
DISMUTE indicates if the person could not speak or had a significant speech impediment.
Disability Variables -- PERSON
IPUMS
Deaf or hearing-impaired
Deaf or hearing-impaired
Deaf or hearing-impaired
Deaf or hearing-impaired
Deaf or hearing-impaired
NIU (not in universe)
1
Yes
2
No
9
Unknown
DISDEAF indicates whether the person was deaf or had limited hearing.
Disability Variables -- PERSON
IPUMS
Blind or vision-impaired
Blind or vision-impaired
Blind or vision-impaired
Blind or vision-impaired
Blind or vision-impaired
NIU (not in universe)
1
Yes
2
No
9
Unknown
DISBLND indicates whether the person was blind or had limited vision.
Disability Variables -- PERSON
IPUMS
Employment disability
Employment disability
Employment disability
Employment disability
Employment disability
1
Disabled
2
Not disabled
8
Unknown
9
NIU (not in universe)
DISEMP indicates if the respondent was economically inactive because of disabilities.
Disability Variables -- PERSON
IPUMS
Industry, general recode
Industry, general recode
Industry, general recode
Industry, general recode
Industry, general recode
NIU (not in universe)
10
Agriculture, fishing, and forestry
20
Mining
30
Manufacturing
40
Electricity, gas and water
50
Construction
60
Wholesale and retail trade
70
Hotels and restaurants
80
Transportation and communications
90
Financial services and insurance
100
Public administration and defense
110
Services, not specified
111
Real estate and business services
112
Education
113
Health and social work
114
Other services
120
Private household services
130
Other industry, n.e.c.
998
Response suppressed
999
Unknown
INDGEN recodes the industrial classifications of the various samples into twelve groups that can be fairly consistently identified across all available samples. The groupings roughly conform to the International Standard Industrial Classification (ISIC). The third digit of INDGEN retains important detail among the service industries that could not be consistently distinguished in all samples.
"Industry" refers to the activity or product of the establishment or sector in which a person worked.
Work Variables -- PERSON
IPUMS
Occupation, unrecoded
Occupation, unrecoded
Occupation, unrecoded
Occupation, unrecoded
Occupation, unrecoded
OCC records the person's primary occupation, classified according to the system used by the respective national census office at the time. For someone with more than one job, the primary occupation is usually the one in which the person spent the most time or earned the most money, although this may not have been explicit in the instructions for a specific census.
To ensure confidentiality, very small occupations are recoded to a residual category indicating the persons had an occupation, but the job title is not identified. The number of cases recoded should be too small to affect analyses.
OCC is a 4-digit numeric variable.
Some samples use fewer than 4 digits. In those cases, the data are right-justified, and the extra leading digits are padded with zeroes.
Please see the codes for: SD2008A_0436
Work Variables -- PERSON
IPUMS
State of residence 1 year ago, Sudan
State of residence 1 year ago, Sudan
State of residence 1 year ago, Sudan
State of residence 1 year ago, Sudan
State of residence 1 year ago, Sudan
11
Northern
12
Nahr El Nil
21
Red Sea
22
Kassala
23
Al Gedarif
31
Khartoum
41
Al Gezira
42
While Nile
43
Sinnar
44
Blue Nile
51
North Kordofan
52
South Kordofan
61
North Darfur
62
West Darfur
63
South Darfur
71
Upper Nile
72
Jonglei
73
Unity
81
Warrap
82
Northern Bahr El Ghazal
83
Western Bahr El Ghazal
84
Lakes
91
Western Equatoria
92
Central Equatoria
93
Eastern Equatoria
98
Abroad
MIGSD indicates the person's state of residence one year ago within Sudan.
Presumably, infants are recorded where one of their parents resided one year ago.
Sudan and South Sudan were enumerated as one country in 2008. Enumerated states of residence 1 year ago within South Sudan are also included in this variable.
Migration Variables -- PERSON
IPUMS
Mental disability
Mental disability
Mental disability
Mental disability
Mental disability
NIU (not in universe)
1
Yes
2
No
9
Unknown
DISMNTL indicates whether the person suffered a mental disability in the form of diminished capacity.
Disability Variables -- PERSON
IPUMS
Age at first marriage or union
Age at first marriage or union
Age at first marriage or union
Age at first marriage or union
Age at first marriage or union
NIU (not in universe)
10
10 or younger
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
Unknown
AGEMARR indicates the person's age at first marriage or consensual union.
Demographic Variables -- PERSON
IPUMS
Educational attainment, Sudan
Educational attainment, Sudan
Educational attainment, Sudan
Educational attainment, Sudan
Educational attainment, Sudan
NIU (not in universe)
10
None
11
Preschool
23
Primary less than grade 4
24
Primary grade 4
26
Primary grade 6
28
Primary grade 8
31
Junior secondary, 3
32
Junior secondary, 4
33
Secondary, grade 3
34
Secondary, grade 4
35
Secondary, grade 5
36
Secondary, grade 6
41
Post-secondary diploma
42
University first degree
51
Post-graduate diploma
52
Master's degree
53
Doctorate degree
99
Unknown
EDUCSD indicates the person's educational attainment in terms of the level of schooling and grade completed.
Education Variables -- PERSON
IPUMS
Industry, unrecoded
Industry, unrecoded
Industry, unrecoded
Industry, unrecoded
Industry, unrecoded
"Industry" refers to the activity or product of the establishment or sector in which the person worked. IND is classified according to the system used by the respective national census office at the time, and is not recoded by IPUMS-International.
IND is a 5-digit numeric variable.
Some samples use fewer than 5 digits. In those cases, the data are right-justified, and the extra leading digits are padded with zeroes.
Please see the codes for: SD2008A_0437
Work Variables -- PERSON
IPUMS
Occupation, ISCO general
Occupation, ISCO general
Occupation, ISCO general
Occupation, ISCO general
Occupation, ISCO general
1
Legislators, senior officials and managers
2
Professionals
3
Technicians and associate professionals
4
Clerks
5
Service workers and shop and market sales
6
Skilled agricultural and fishery workers
7
Crafts and related trades workers
8
Plant and machine operators and assemblers
9
Elementary occupations
10
Armed forces
11
Other occupations, unspecified or n.e.c.
97
Response suppressed
98
Unknown
99
NIU (not in universe)
OCCISCO records the person's primary occupation, coded according to the major categories in the International Standard Classification of Occupations (ISCO) scheme for 1988. For someone with more than one job, the primary occupation is typically the one in which the person had spent the most time or earned the most money.
Work 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
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
This variable indicates the person number (within household).
Technical Person Variables -- PERSON
IPUMS
Relationship
Relationship
Relationship
Relationship
Relationship
2. What is the relationship of [the respondent] to the head of household?
[] 1 Head
[] 2 Spouse
[] 3 Son / daughter
[] 4 Wife of son / husband of daughter
[] 5 Grandchild
[] 6 Niece / nephew
[] 7 Parent / parent-in-law
[] 8 Sister / brother-in-laws
[] 9 Other relative
[] 10 Non relative
All persons
1
Head
2
Spouse
3
Daughter/son
4
Spouse of son/daughter
5
Grandchild
6
Niece/nephew
7
Parent/parent in-law
8
Sibling/sibling-in-law
9
Other relative
10
Non-relative
This variable indicates the person's relationship to the head of household.
Demographic Variables -- PERSON
IPUMS
Sex
Sex
Sex
Sex
Sex
3. Is [the respondent] male or female?
[] 1 Male
[] 2 Female
All persons
1
Male
2
Female
This variable indicates the person's sex.
Demographic Variables -- PERSON
IPUMS
Age
Age
Age
Age
Age
4. What is [the respondent's] age in completed years?
If less than one year, code "0", if over 95, code "95".
_ _
All persons
Less than 1 years old
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
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 years and above
This variable indicates the person's age in completed years.
Demographic Variables -- PERSON
IPUMS
Nationality
Nationality
Nationality
Nationality
Nationality
5. What is [the respondent's] nationality?
Use the nationality codes on the separate sheet
_ _
All persons
1
Sudanese
2
Egyptian
3
Libyan
4
Eritrean
5
Ethiopian
6
Ugandan
7
Kenyan
8
Congolese - DRC
9
Chadian
10
Central African
11
Nigerian
12
Malian
13
Chinese
14
Indian
15
Other countries
This variable indicates the person's nationality.
Nativity and Birthplace Variables -- PERSON
IPUMS
Regional group
Regional group
Regional group
Regional group
Regional group
6. To what regional group does [the respondent] belong?
[] 1 Northern Sudanese
[] 2 Southern Sudanese
[] 3 Non Sudanese
[] 4 No response
All persons
1
Northern Sudanese
2
Southern Sudanese
3
Non-Sudanese
This variable indicates the person's regional group (Northern or Southern).
Nativity and Birthplace Variables -- PERSON
IPUMS
Region of origin
Region of origin
Region of origin
Region of origin
Region of origin
7. What is [the respondent's] region of origin?
[] 1 Northern
[] 2 Eastern
[] 3 Khartoum
[] 4 Central
[] 5 Kordofan
[] 6 Darfur
[] 7 Upper Nile
[] 8 Bahr El Ghazal
[] 9 Equatoria
[] 10 Not Sudan
[] 11 No response
All persons
1
Northern
2
Eastern
3
Khartoum
4
Central
5
Kordofan
6
Darfur
7
Upper Nile
8
Bahr El Ghazal
9
Equatoria
10
Not Sudan
11
Unknown
This variable indicates the person's region of origin.
Nativity and Birthplace Variables -- PERSON
IPUMS
State of birth
State of birth
State of birth
State of birth
State of birth
8. In what state was [the respondent] born?
If not born in Sudan, code "99". Use the state codes on the separate sheet
_ _
All persons
11
Northern
12
Nahr El Nil
21
Red Sea
22
Kassala
23
Al Gedarif
31
Khartoum
41
Al Gezira
42
White Nile
43
Sinnar
44
Blue Nile
51
North Kordofan
52
South Kordofan
61
North Darfur
62
West Darfur
63
South Darfur
71
Upper Nile
72
Jonglei
73
Unity
81
Warrap
82
Northern Bahr El Ghazal
83
Western Bahr El Ghazal
84
Lakes
91
Western Equatoria
92
Central Equatoria
93
Eastern Equatoria
99
Abroad
This variable indicates the person's state of birth.
Nativity and Birthplace Variables -- PERSON
IPUMS
State of usual residence
State of usual residence
State of usual residence
State of usual residence
State of usual residence
9. What is [the respondent's] current usual state of usual residence? Where [the respondent] lived or intends to live for 6 months or more.
If foreigner less than 1 year old, code "99". Use the state codes on the separate sheet.
_ _
All persons
11
Northern
12
Nahr El Nil
21
Red Sea
22
Kassala
23
Al Gedarif
31
Khartoum
41
Al Gezira
42
White Nile
43
Sinnar
44
Blue Nile
51
North Kordofan
52
South Kordofan
61
North Darfur
62
West Darfur
63
South Darfur
71
Upper Nile
72
Jonglei
73
Unity
81
Warrap
82
Northern Bahr El Ghazal
83
Western Bahr El Ghazal
84
Lakes
91
Western Equatoria
92
Central Equatoria
93
Eastern Equatoria
99
Abroad
This variable indicates the person's state of usual residence.
Migration Variables -- PERSON
IPUMS
Duration of residence
Duration of residence
Duration of residence
Duration of residence
Duration of residence
10. How many years has [the respondent] lived continuously in the state of usual residence?
If less than one year, code "0". If foreigner less than one year, code "99"
_ _
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
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 years and beyond
99
Unknown
This variable indicates the number of years the person has lived continuously in the state of usual residence.
Migration Variables -- PERSON
IPUMS
Previous residence 1 year ago
Previous residence 1 year ago
Previous residence 1 year ago
Previous residence 1 year ago
Previous residence 1 year ago
11. In what state did [the respondent] usually reside this time last year?
If foreigner less than 1 year, code "99". Use the state codes on the separate sheet.
_ _
All persons
11
Northern
12
Nahr El Nil
21
Red Sea
22
Kassala
23
Al Gedarif
31
Khartoum
41
Al Gezira
42
White Nile
43
Sinnar
44
Blue Nile
51
North Kordofan
52
South Kordofan
61
North Darfur
62
West Darfur
63
South Darfur
71
Upper Nile
72
Jonglei
73
Unity
81
Warrap
82
Northern Bahr El Ghazal
83
Western Bahr El Ghazal
84
Lakes
91
Western Equatoria
92
Central Equatoria
93
Eastern Equatoria
98
Abroad
This variable indicates the person's previous state of residence last year.
Migration Variables -- PERSON
IPUMS
Mother's survival
Mother's survival
Mother's survival
Mother's survival
Mother's survival
12. Is [the respondent's] biological mother alive?
[] 1 Yes
[] 2 No
[] 3 Don't know
All persons
1
Mother alive
2
Mother dead
This variable indicates whether the person's mother is still alive.
Fertility and Mortality Variables -- PERSON
IPUMS
Father's survival
Father's survival
Father's survival
Father's survival
Father's survival
13. Is [the respondent's] biological father alive?
[] 1 Yes
[] 2 No
[] 3 Don't know
All persons
1
Father alive
2
Father dead
This variable indicates whether the person's father is still alive.
Fertility and Mortality Variables -- PERSON
IPUMS
Limited use of leg(s)
Limited use of leg(s)
Limited use of leg(s)
Limited use of leg(s)
Limited use of leg(s)
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has limited use of leg(s).
Disability Variables -- PERSON
IPUMS
Loss of leg(s)
Loss of leg(s)
Loss of leg(s)
Loss of leg(s)
Loss of leg(s)
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has loss of leg(s).
Disability Variables -- PERSON
IPUMS
Limited use of arm(s)
Limited use of arm(s)
Limited use of arm(s)
Limited use of arm(s)
Limited use of arm(s)
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has limited use of arm(s).
Disability Variables -- PERSON
IPUMS
Loss of arm(s)
Loss of arm(s)
Loss of arm(s)
Loss of arm(s)
Loss of arm(s)
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has loss of arm(s).
Disability Variables -- PERSON
IPUMS
Difficulty in hearing
Difficulty in hearing
Difficulty in hearing
Difficulty in hearing
Difficulty in hearing
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has difficulty in hearing.
Disability Variables -- PERSON
IPUMS
Deaf
Deaf
Deaf
Deaf
Deaf
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person is deaf.
Disability Variables -- PERSON
IPUMS
Difficulty seeing
Difficulty seeing
Difficulty seeing
Difficulty seeing
Difficulty seeing
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has difficulty in seeing.
Disability Variables -- PERSON
IPUMS
Blind
Blind
Blind
Blind
Blind
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person is blind.
Disability Variables -- PERSON
IPUMS
Difficulty in speaking
Difficulty in speaking
Difficulty in speaking
Difficulty in speaking
Difficulty in speaking
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has difficulty in speaking.
Disability Variables -- PERSON
IPUMS
Mute
Mute
Mute
Mute
Mute
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person is mute.
Disability Variables -- PERSON
IPUMS
Mental difficulty
Mental difficulty
Mental difficulty
Mental difficulty
Mental difficulty
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
Yes
2
No
This variable indicates whether the person has mental difficulty.
Disability Variables -- PERSON
IPUMS
Have disability
Have disability
Have disability
Have disability
Have disability
14. Does [the respondent] have any difficulty in moving, seeing, hearing, speaking or learning?
(Mark all that apply)
[] 1 Limited use of legs
[] 2 Loss of leg(s)
[] 3 Limited use of arms
[] 4 Loss of arm(s)
[] 5 Difficulty in hearing
[] 6 Deaf
[] 7 Difficulty in seeing
[] 8 Blind
[] 9 Difficulty in speaking
[] 10 Mute
[] 11 Mental disability
[] 12 No disability
[] 13 Don't know
All persons
1
No disability
2
Have disability
This variable indicates whether the person has no disability(ies).
Disability Variables -- PERSON
IPUMS
Literacy
Literacy
Literacy
Literacy
Literacy
Education status
[Questions 15 - 18: Persons age 6 or older]
15. Can [the respondent] read and write with understanding a simple sentence in any language?
[] 1 Yes
[] 2 No
Persons age 6+
1
Yes
2
No
9
NIU (not in universe)
This variable indicates whether the person is literate.
Education Variables -- PERSON
IPUMS
School attendence
School attendence
School attendence
School attendence
School attendence
Education status
[Questions 15 - 18: Persons age 6 or older]
16. Has [the respondent] ever attended, or is currently attending school this year?
[] 1 Currently attending
[] 2 Previously attended (skip to Q18)
[] 3 Never attended (skip to Q19)
Persons age 6+
1
Currently attending
2
Previously attended
3
Never attended
9
NIU (not in universe)
This variable indicates whether the person has ever attended school.
Education Variables -- PERSON
IPUMS
School level attending
School level attending
School level attending
School level attending
School level attending
Education status
[Questions 15 - 18: Persons age 6 or older]
17. For those currently attending: what is the grade and level that [the respondent] is attending?
[Q17 is asked of persons age 6 or older who are currently attending school, as per Q16.]
[] 1 P1
[] 2 P2
[] 3 P3
[] 4 P4
[] 5 P5
[] 6 P6
[] 7 P7
[] 8 P8
[] 9 S1
[] 10 S2
[] 11 S3
[] 12 S4
[] 13 S5
[] 14 S6
[] 15 Post secondary diploma program
[] 16 College
[] 17 University
[] 18 Master degree
[] 19 Ph.D. degree
Persons age 6+ who are currently attending school
1
P1
2
P2
3
P3
4
P4
5
P5
6
P6
7
P7
8
P8
9
S1
10
S2
11
S3
12
S4
13
S5
14
S6
15
Post secondary/diploma program
16
College
17
University
18
Masters program
19
PhD program
98
Unknown
99
NIU (not in universe)
This variable indicates the school level the person is attending.
Education Variables -- PERSON
IPUMS
Education attainment
Education attainment
Education attainment
Education attainment
Education attainment
Education status
[Questions 15 - 18: Persons age 6 or older]
18. What is the highest level [the respondent] completed?
[Q18 is asked of persons age 6 or older who are currently attending school or have attended school, as per Q16.]
[] 1 No qualification (previously)
[] 2 Incomplete primary (currently)
[] 3 Primary 4
[] 4 Primary 6
[] 5 Primary 8
[] 6 Junior 3
[] 7 Junior 4
[] 8 Secondary 3
[] 9 Secondary 4
[] 10 Secondary 5
[] 11 Secondary 6
[] 12 Postsecondary diploma
[] 13 University first degree
[] 14 Postgraduate diploma
[] 15 Master's degree
[] 16 Ph.D. degree
[] 17 Khawa
Persons who ever attended school
1
No qualification
2
Incomplete primary
3
Primary 4
4
Primary 6
5
Primary 8
6
Junior 3
7
Junior 4
8
Secondary 3
9
Secondary 4
10
Secondary 5
11
Secondary 6
12
Post-secondary diploma
13
University first degree
14
Post-graduate diploma
15
Masters degree
16
PhD degree
17
Khalwa
98
Unknown
99
NIU (not in universe)
This variable indicates the person's highest level of educational attainment.
Education Variables -- PERSON
IPUMS
Activity status
Activity status
Activity status
Activity status
Activity status
Economic activity
[Questions 19 - 23: Persons age 10 or older]
19. During the week before census night, did [the respondent] work at least one hour for pay (or without pay), profit, in kind, or for family business?
[] 1 Worked (skip to Q21)
[] 2 Did not work but have a job to go back to (skip to Q21)
[] 3 Did not work but worked before and seeking work and available for work (skip to Q21)
[] 4 Did not work, seeking work for the first time and available for work (skip to Q24)
[] 5 Did not work and not seeking work
Persons age 10+
1
Worked last week
2
Temporarily away from job
3
Worked before and seeking work
4
First time job seeker
5
Not working or seeking work
9
NIU (not in universe)
This variable indicates the person's employment status.
Work Variables -- PERSON
IPUMS
Reason for economic inactivity
Reason for economic inactivity
Reason for economic inactivity
Reason for economic inactivity
Reason for economic inactivity
Economic activity
[Questions 19 - 23: Persons age 10 or older]
20. For those who did no work and were not seeking work, why did [the respondent] not seek work?
[Q20 is asked of persons age 10 or older who did not work and are not seeking work, as per Q19.]
[] 1 No hope of finding job
[] 2 Full-time student
[] 3 Income recipient
[] 4 Too old
[] 5 Disabled / too sick
[] 6 Full-time homemaker / housewife
[] 7 Pensioner / retired
Persons age 10+ who did not work and were not seeking work last week
1
No hope to find job
2
Full-time student
3
Income recipient
4
Too old
5
Disabled/too sick
6
Full-time homemaker
7
Pensioner/retired
8
Unknown
9
NIU (not in universe)
This variable indicates the reason for economic inactivity.
Work Variables -- PERSON
IPUMS
Occupation
Occupation
Occupation
Occupation
Occupation
Economic activity
[Questions 19 - 23: Persons age 10 or older]
21. For those who worked or have worked before (Q19, codes 1-3), state in detail, the main job that [the respondent] was engaged in during the week before census night?
[Q21 is asked of persons age 10 or older who worked, did not work but have a job to go back to, or did not work but last week but have work and seeking work and available for work, as per Q19.]
Occupation ________
Persons age 10+ who worked, or were temporary away from job, or were seeking work not for the first time
1
Commissioned armed forces officers
2
Non-commissioned armed forces officers
3
Armed forces occupations, other ranks
11
Chief executives senior officials and legislators
12
Administrative and commercial managers
13
Production and specialized services managers
14
Hospitality, retail and other services managers
21
Science and engineering professionals
22
Health professionals
23
Teaching professionals
24
Business and administration professionals
25
Information and communications technology (ICT) professionals
26
Legal, social and cultural professionals
31
Science and engineering associate professionals
32
Health associate professionals
33
Business and administration associate professionals
34
Legal, social, cultural and related associate professionals
35
Information and communications technicians (ICT)
41
General and keyboard clerks
42
Customer services clerks
43
Numerical and material recording clerks
44
Other clerical support workers
51
Personal service workers
52
Sales workers
54
Protective services workers
61
Market-oriented skilled agricultural workers
62
Market-oriented skilled forestry, fishery and hunting workers
63
Subsistence farmers, fishers, hunters and gatherers
71
Building and related trades workers, excluding electricians
72
Metal, machinery and related trades workers
73
Handicraft and printing workers
74
Electrical and electronic trades workers
75
Food processing, wood working, garment and other craft and related trades workers
81
Stationary plant and machine operators
83
Drivers and mobile plant operators
90
Other occupation, response suppressed
91
Cleaners and helpers
92
Agricultural, forestry and fishery labourers
93
Labourers in mining, construction, manufacturing and transport
94
Food preparation assistants
95
Street and related sales and service workers
96
Refuse workers and other elementary workers
98
Unknown
99
NIU (not in universe)
This variable indicates the person's occupation.
Work: Occupation Variables -- PERSON
IPUMS
Industry
Industry
Industry
Industry
Industry
Economic activity
[Questions 19 - 23: Persons age 10 or older]
22. For those who worked or have worked before (Q19, codes 1-3), what was [the respondent's] place of work or main activity of [the respondent's] place of work during the week before census night?
[Q21 is asked of persons age 10 or older who worked, did not work but have a job to go back to, or did not work but last week but have work and seeking work and available for work, as per Q19.]
Industry ________ _ _
Persons age 10+ who worked, or were temporary away from job, or were seeking work not for the first time
1
Crop and animal production, hunting and related service activities
2
Forestry and logging
3
Fishing and aquaculture
6
Extraction of crude petroleum and natural gas
7
Mining of metal ores
8
Other mining and quarrying
10
Manufacture of food products
11
Manufacture of beverages
12
Manufacture of tobacco products
13
Manufacture of textiles
14
Manufacture of wearing apparel
15
Manufacture of leather and related products
16
Manufacture of wood and of products of wood and cork, except furniture
18
Printing and reproduction of recorded media
19
Manufacture of coke and refined petroleum products
20
Manufacture of chemicals and chemical products
21
Manufacture of pharmaceuticals, medicinal chemical and botanical products
22
Manufacture of rubber and plastics products
23
Manufacture of other non-metallic mineral products
24
Manufacture of basic metals
25
Manufacture of fabricated metal products, except machinery and equipment
29
Manufacture of motor vehicles, trailers and semi-trailers
31
Manufacture of furniture
32
Other manufacturing
33
Repair and installation of machinery and equipment
35
Electricity, gas, steam and air conditioning supply
36
Water collection, treatment and supply
37
Sewerage
38
Waste collection, treatment and disposal activities
41
Construction of buildings
42
Civil engineering
43
Specialized construction activities
45
Wholesale and retail trade and repair of motor vehicles and motorcycles
46
Wholesale trade, except of motor vehicles and motorcycles
47
Retail trade, except of motor vehicles and motorcycles
49
Land transport and transport via pipelines
50
Water transport
51
Air transport
52
Warehousing and support activities for transportation
53
Postal and courier activities
55
Accommodation
56
Food and beverage service activities
58
Publishing activities
59
Motion picture, video and television programme production, sound recording and music publishing activities
60
Programming and broadcasting activities
61
Telecommunications
62
Computer programming, consultancy and related activities
64
Financial service activities, except insurance and pension funding
65
Insurance, reinsurance and pension funding, except compulsory social security
66
Activities auxiliary to financial service and insurance activities
68
Real estate activities
69
Legal and accounting activities
71
Architectural and engineering activities
72
Scientific research and development
74
Other professional, scientific and technical activities
75
Veterinary activities
77
Rental and leasing activities
79
Travel agency, tour operator, reservation service and related activities
80
Security and investigation activities
81
Services to buildings and landscape activities
82
Office administrative, office support and other business support activities
84
Public administration and defence
85
Education
86
Human health activities
90
Creative, arts and entertainment activities
91
Libraries, archives, museums and other cultural activities
92
Gambling and betting activities
93
Sports activities and amusement and recreation activities
94
Activities of membership organizations
95
Repair of computers and personal and household goods
96
Other personal service activities
97
Activities of households as employers of domestic personnel
98
Undifferentiated goods- and services-producing activities of private households for own use
99
Activities of extraterritorial organizations and bodies
100
Other industry, response suppressed
998
Unknown
999
NIU (not in universe)
This variable indicates the industry the person worked in.
Work: Industry Variables -- PERSON
IPUMS
Class of worker
Class of worker
Class of worker
Class of worker
Class of worker
Economic activity
[Questions 19 - 23: Persons age 10 or older]
23. For those who worked or have worked before, what was [the respondent's] employment status?
[Q21 is asked of persons age 10 or older who worked, did not work but have a job to go back to, or did not work but last week but have work and seeking work and available for work, as per Q19.]
[] 1 Paid employee
[] 2 Employer
[] 3 Own account worker
[] 4 Unpaid family worker
[] 5 Unpaid working for others
Persons age 10+ who worked, or were temporary away from job, or were seeking work not for the first time
1
Paid employee
2
Employer
3
Own account worker
4
Unpaid family worker
5
Unpaid working for others
9
NIU (not in universe)
This variable indicates the person's class of worker.
Work Variables -- PERSON
IPUMS
Marital status
Marital status
Marital status
Marital status
Marital status
Marital status
[Questions 24 - 25: Persons age 12 or older]
24. What is [the respondent's] marital status?
[] 1 Never married
[] 2 Married
[] 3 Widowed
[] 4 Divorced
Persons age 12+
1
Never married
2
Married
3
Widowed
4
Divorced
9
NIU (not in universe)
This variable indicates the person's marital status.
Demographic Variables -- PERSON
IPUMS
Age at first marriage
Age at first marriage
Age at first marriage
Age at first marriage
Age at first marriage
Marital status
[Questions 24 - 25: Persons age 12 or older]
25. For all ever-married persons: what was [the respondent's] age at first marriage?
Code age in completed years
_ _
Ever-married persons age 12+
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
98
Unknown
99
NIU (not in universe)
This variable indicates the person's age, in completed years, at first marriage.
Demographic Variables -- PERSON
IPUMS
Males ever born
Males ever born
Males ever born
Males ever born
Males ever born
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
26. What is the total number of children [the respondent] has ever born alive? (even if the child was only alive for a short time and died soon after birth)
Male _ _
Female _ _
Ever-married females age 12 to 54
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+
99
NIU (not in universe)
This variable indicates the number of male children ever born to the person.
Fertility and Mortality Variables -- PERSON
IPUMS
Females ever born
Females ever born
Females ever born
Females ever born
Females ever born
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
26. What is the total number of children [the respondent] has ever born alive? (even if the child was only alive for a short time and died soon after birth)
Male _ _
Female _ _
Ever-married females age 12 to 54
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+
99
NIU (not in universe)
This variable indicates the number of female children ever born to the person.
Fertility and Mortality Variables -- PERSON
IPUMS
Male children at home
Male children at home
Male children at home
Male children at home
Male children at home
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
27. How many of these children are living with [the respondent] in this household?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12+
99
NIU (not in universe)
This variable indicates the number of male children ever born to the person that are living with the person in the household.
Fertility and Mortality Variables -- PERSON
IPUMS
Female children at home
Female children at home
Female children at home
Female children at home
Female children at home
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
27. How many of these children are living with [the respondent] in this household?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10+
99
NIU (not in universe)
This variable indicates the number of female children ever born to the person that are living with the person in the household.
Fertility and Mortality Variables -- PERSON
IPUMS
Male children living elsewhere
Male children living elsewhere
Male children living elsewhere
Male children living elsewhere
Male children living elsewhere
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
28. How many of those children are living elsewhere (not in this household)?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10+
99
NIU (not in universe)
This variable indicates the number of male children ever born to the person that are living elsewhere.
Fertility and Mortality Variables -- PERSON
IPUMS
Female children living elsewhere
Female children living elsewhere
Female children living elsewhere
Female children living elsewhere
Female children living elsewhere
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
28. How many of those children are living elsewhere (not in this household)?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10+
99
NIU (not in universe)
This variable indicates the number of female children ever born to the person that are living elsewhere.
Fertility and Mortality Variables -- PERSON
IPUMS
Male children deceased
Male children deceased
Male children deceased
Male children deceased
Male children deceased
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
29. How many of those children are no longer alive?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10+
99
NIU (not in universe)
This variable indicates the number of male children ever born to the person that are deceased.
Fertility and Mortality Variables -- PERSON
IPUMS
Female children deceased
Female children deceased
Female children deceased
Female children deceased
Female children deceased
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
29. How many of those children are no longer alive?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10+
99
NIU (not in universe)
This variable indicates the number of female children ever born to the person that are deceased.
Fertility and Mortality Variables -- PERSON
IPUMS
Any births last 12 months
Any births last 12 months
Any births last 12 months
Any births last 12 months
Any births last 12 months
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
30. Did [the respondent] give birth during the last 12 months?
If no birth in last 12 months, go to question 33.
Male _ _
Female _ _
Ever-married females age 12 to 54
1
Yes
2
No
8
Unknown
9
NIU (not in universe)
This variable indicates whether the person had any births in the last 12 months.
Fertility and Mortality Variables -- PERSON
IPUMS
Males born last 12 months
Males born last 12 months
Males born last 12 months
Males born last 12 months
Males born last 12 months
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
31. How many children has [the respondent] given birth to in the last 12 months?
[Q31 was asked of females who have given birth during the last 12 months, as per Q30.]
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
8
Unknown
9
NIU (not in universe)
This variable indicates the number of male children born in the last 12 months.
Fertility and Mortality Variables -- PERSON
IPUMS
Females born last 12 months
Females born last 12 months
Females born last 12 months
Females born last 12 months
Females born last 12 months
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
31. How many children has [the respondent] given birth to in the last 12 months?
[Q31 was asked of females who have given birth during the last 12 months, as per Q30.]
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
8
Unknown
9
NIU (not in universe)
This variable indicates the number of female children born in the last 12 months.
Fertility and Mortality Variables -- PERSON
IPUMS
Males born last 12 months surviving
Males born last 12 months surviving
Males born last 12 months surviving
Males born last 12 months surviving
Males born last 12 months surviving
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
32. How many of these children born in the last 12 months are still alive?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
8
Unknown
9
NIU (not in universe)
This variable indicates the number of male children born in the last 12 months who are still alive.
Fertility and Mortality Variables -- PERSON
IPUMS
Females born last 12 months surviving
Females born last 12 months surviving
Females born last 12 months surviving
Females born last 12 months surviving
Females born last 12 months surviving
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
32. How many of these children born in the last 12 months are still alive?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
8
Unknown
9
NIU (not in universe)
This variable indicates the number of female children born in the last 12 months who are still alive.
Fertility and Mortality Variables -- PERSON
IPUMS
Flag: Age
Flag: Age
Flag: Age
Flag: Age
Flag: Age
Flag: Age
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Nationality
Flag: Nationality
Flag: Nationality
Flag: Nationality
Flag: Nationality
Flag: Nationality
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Regional group
Flag: Regional group
Flag: Regional group
Flag: Regional group
Flag: Regional group
Flag: Regional group
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Region of origin
Flag: Region of origin
Flag: Region of origin
Flag: Region of origin
Flag: Region of origin
Flag: Region of origin
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: State of birth
Flag: State of birth
Flag: State of birth
Flag: State of birth
Flag: State of birth
Flag: State of birth
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: State of usual residence
Flag: State of usual residence
Flag: State of usual residence
Flag: State of usual residence
Flag: State of usual residence
Flag: State of usual residence
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Duration of residence
Flag: Duration of residence
Flag: Duration of residence
Flag: Duration of residence
Flag: Duration of residence
Flag: Duration of residence
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: State of previous residence
Flag: State of previous residence
Flag: State of previous residence
Flag: State of previous residence
Flag: State of previous residence
Flag: State of previous residence
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Mother's survival
Flag: Mother's survival
Flag: Mother's survival
Flag: Mother's survival
Flag: Mother's survival
Flag: Mother's survival
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Father's survival
Flag: Father's survival
Flag: Father's survival
Flag: Father's survival
Flag: Father's survival
Flag: Father's survival
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Loss of leg(s)
Flag: Loss of leg(s)
Flag: Loss of leg(s)
Flag: Loss of leg(s)
Flag: Loss of leg(s)
Flag: Loss of leg(s)
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
Flag: Loss of leg(s).
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Loss of arm(s)
Flag: Loss of arm(s)
Flag: Loss of arm(s)
Flag: Loss of arm(s)
Flag: Loss of arm(s)
Flag: Loss of arm(s)
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Deaf
Flag: Deaf
Flag: Deaf
Flag: Deaf
Flag: Deaf
Flag: Deaf
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Blind
Flag: Blind
Flag: Blind
Flag: Blind
Flag: Blind
Flag: Blind
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Mute
Flag: Mute
Flag: Mute
Flag: Mute
Flag: Mute
Flag: Mute
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Literacy
Flag: Literacy
Flag: Literacy
Flag: Literacy
Flag: Literacy
Flag: Literacy
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: School attendence
Flag: School attendence
Flag: School attendence
Flag: School attendence
Flag: School attendence
Flag: School attendence
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: School level currently attending
Flag: School level currently attending
Flag: School level currently attending
Flag: School level currently attending
Flag: School level currently attending
Flag: School level currently attending
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Education attainment
Flag: Education attainment
Flag: Education attainment
Flag: Education attainment
Flag: Education attainment
Flag: Education attainment
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Activity status
Flag: Activity status
Flag: Activity status
Flag: Activity status
Flag: Activity status
Flag: Activity status
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Reason for economic inactivity
Flag: Reason for economic inactivity
Flag: Reason for economic inactivity
Flag: Reason for economic inactivity
Flag: Reason for economic inactivity
Flag: Reason for economic inactivity
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Occupation
Flag: Occupation
Flag: Occupation
Flag: Occupation
Flag: Occupation
Flag: Occupation
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Economic sector
Flag: Economic sector
Flag: Economic sector
Flag: Economic sector
Flag: Economic sector
Flag: Economic sector
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Employment status
Flag: Employment status
Flag: Employment status
Flag: Employment status
Flag: Employment status
Flag: Employment status
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Marital status
Flag: Marital status
Flag: Marital status
Flag: Marital status
Flag: Marital status
Flag: Marital status
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
Flag: Marital status.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Age at first marriage
Flag: Age at first marriage
Flag: Age at first marriage
Flag: Age at first marriage
Flag: Age at first marriage
Flag: Age at first marriage
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Males ever born
Flag: Males ever born
Flag: Males ever born
Flag: Males ever born
Flag: Males ever born
Flag: Males ever born
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Females ever born
Flag: Females ever born
Flag: Females ever born
Flag: Females ever born
Flag: Females ever born
Flag: Females ever born
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Male children at home
Flag: Male children at home
Flag: Male children at home
Flag: Male children at home
Flag: Male children at home
Flag: Male children at home
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Female children at home
Flag: Female children at home
Flag: Female children at home
Flag: Female children at home
Flag: Female children at home
Flag: Female children at home
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Male children living elsewhere
Flag: Male children living elsewhere
Flag: Male children living elsewhere
Flag: Male children living elsewhere
Flag: Male children living elsewhere
Flag: Male children living elsewhere
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Female children living elsewhere
Flag: Female children living elsewhere
Flag: Female children living elsewhere
Flag: Female children living elsewhere
Flag: Female children living elsewhere
Flag: Female children living elsewhere
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Male children deceased
Flag: Male children deceased
Flag: Male children deceased
Flag: Male children deceased
Flag: Male children deceased
Flag: Male children deceased
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Female children deceased
Flag: Female children deceased
Flag: Female children deceased
Flag: Female children deceased
Flag: Female children deceased
Flag: Female children deceased
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Any births last 12 months
Flag: Any births last 12 months
Flag: Any births last 12 months
Flag: Any births last 12 months
Flag: Any births last 12 months
Flag: Any births last 12 months
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Males born last 12 months
Flag: Males born last 12 months
Flag: Males born last 12 months
Flag: Males born last 12 months
Flag: Males born last 12 months
Flag: Males born last 12 months
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Females born last 12 months
Flag: Females born last 12 months
Flag: Females born last 12 months
Flag: Females born last 12 months
Flag: Females born last 12 months
Flag: Females born last 12 months
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Males born last 12 months (alive)
Flag: Males born last 12 months (alive)
Flag: Males born last 12 months (alive)
Flag: Males born last 12 months (alive)
Flag: Males born last 12 months (alive)
Flag: Males born last 12 months (alive)
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Flag: Females born last 12 months (alive)
Flag: Females born last 12 months (alive)
Flag: Females born last 12 months (alive)
Flag: Females born last 12 months (alive)
Flag: Females born last 12 months (alive)
Flag: Females born last 12 months (alive)
All persons
No imputation
1
Logical from blank
2
Logical from valid non-blank
3
Logical from invalid non-blank
4
Imputation from blank
5
Imputation from valid non-blank
6
Imputation from invalid non-blank
This variable means that the indicated variable was edited during processing.
Person Imputation Flags Variables -- PERSON
IPUMS
Person weight
Person weight
Person weight
Person weight
Person weight
Person weight
All persons
This variable indicates the person weight.
This is a 6-digit numeric variable with 4 implied decimal places
Technical Person Variables -- PERSON
IPUMS
Children living in household
Children living in household
Children living in household
Children living in household
Children living in household
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
27. How many of these children are living with [the respondent] in this household?
Male _ _
Female _ _
Ever-married females age 12 to 54
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+
99
NIU (not in universe)
This variable indicates the number of own children living in the household.
Fertility and Mortality Variables -- PERSON
IPUMS
Children living elsewhere
Children living elsewhere
Children living elsewhere
Children living elsewhere
Children living elsewhere
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
28. How many of those children are living elsewhere (not in this household)?
Male _ _
Female _ _
Ever-married females age 12 to 54
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+
99
NIU (not in universe)
This variable indicates the number of own children living elsewhere.
Fertility and Mortality Variables -- PERSON
IPUMS
Children who have died
Children who have died
Children who have died
Children who have died
Children who have died
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
29. How many of those children are no longer alive?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12+
99
NIU (not in universe)
This variable indicates the number of children who have died.
Fertility and Mortality Variables -- PERSON
IPUMS
Children ever born
Children ever born
Children ever born
Children ever born
Children ever born
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
26. What is the total number of children [the respondent] has ever born alive? (even if the child was only alive for a short time and died soon after birth)
Male _ _
Female _ _
Ever-married females age 12 to 54
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
99
NIU (not in universe)
This variable indicates the number of children ever born to the person.
Fertility and Mortality Variables -- PERSON
IPUMS
Children surviving
Children surviving
Children surviving
Children surviving
Children surviving
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
27. How many of these children are living with [the respondent] in this household?
Male _ _
Female _ _
28. How many of those children are living elsewhere (not in this household)?
Male _ _
Female _ _
Ever-married females age 12 to 54
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+
99
NIU (not in universe)
This variable indicates the number of own children who are still surviving.
Fertility and Mortality Variables -- PERSON
IPUMS
Male children surviving
Male children surviving
Male children surviving
Male children surviving
Male children surviving
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
27. How many of these children are living with [the respondent] in this household?
Male _ _
Female _ _
28. How many of those children are living elsewhere (not in this household)?
Male _ _
Female _ _
Ever-married females age 12 to 54
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+
99
NIU (not in universe)
This variable indicates the number of male children ever born to the person who are still alive.
Fertility and Mortality Variables -- PERSON
IPUMS
Female children surviving
Female children surviving
Female children surviving
Female children surviving
Female children surviving
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
27. How many of these children are living with [the respondent] in this household?
Male _ _
Female _ _
28. How many of those children are living elsewhere (not in this household)?
Male _ _
Female _ _
Ever-married females age 12 to 54
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+
99
NIU (not in universe)
This variable indicates the number of female children ever born to the person who are still alive.
Fertility and Mortality Variables -- PERSON
IPUMS
Children born last year
Children born last year
Children born last year
Children born last year
Children born last year
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
31. How many children has [the respondent] given birth to in the last 12 months?
[Q31 was asked of females who have given birth during the last 12 months, as per Q30.]
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4
8
Unknown
9
NIU (not in universe)
This variable indicates the number of children born to the person last year.
Fertility and Mortality Variables -- PERSON
IPUMS
Children born last year surviving
Children born last year surviving
Children born last year surviving
Children born last year surviving
Children born last year surviving
Number of children ever born alive
[Questions 26 - 32: Women aged 12 to 54 years (if in North Sudan only complete this section for women who are or have ever been married)]
If no children, write "00"
32. How many of these children born in the last 12 months are still alive?
Male _ _
Female _ _
Ever-married females age 12 to 54
1
1
2
2
3
3
4
4+
8
Unknown
9
NIU (not in universe)
This variable indicates the number of children born to the person last year who are still surviving.
Fertility and Mortality Variables -- PERSON
IPUMS
Parents mortality status
Parents mortality status
Parents mortality status
Parents mortality status
Parents mortality status
12. Is [the respondent's] biological mother alive?
[] 1 Yes
[] 2 No
[] 3 Don't know
13. Is [the respondent's] biological father alive?
[] 1 Yes
[] 2 No
[] 3 Don't know
All persons
1
Father alive, mother alive
2
Father alive, mother dead
3
Father dead, mother alive
4
Father dead, mother dead
This variable indicates the parent's mortality status.
Fertility and Mortality Variables -- PERSON
IPUMS
Polygamous union
Polygamous union
Polygamous union
Polygamous union
Polygamous union
NIU (not in universe)
1
No, in monogamous union
2
Yes, in polygamous union
3
Man in polygamous union
4
Polygamous man, 2 wives
5
Polygamous man, 3 or more wives
6
Woman in polygamous union
7
Polygamous marriage, 2 wives
8
Polygamous marriage, 3 or more wives
9
First wife
10
Second wife
11
Third or higher order wife
99
Unknown/missing
POLYGAM indicates whether the respondent was in a polygamous union and, in some samples, the number of wives or the rank order of the wife.
Demographic 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.
PERWT is an 8-digit numeric variable with 2 implied decimal places. See the variable description.
Technical Person Variables -- PERSON
IPUMS
Number of own male children living elsewhere
Number of own male children living elsewhere
Number of own male children living elsewhere
Number of own male children living elsewhere
Number of own male children living elsewhere
None
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
98
Unknown
99
NIU (not in universe)
AWAYMALE indicates the number of surviving biological male children not living in the household with their mother (the respondent).
Fertility and Mortality Variables -- PERSON
IPUMS
Disability status
Disability status
Disability status
Disability status
Disability status
NIU (not in universe)
1
Yes, disabled
2
No, not disabled
9
Unknown
DISABLED indicates whether the person reported a disability of any kind.
Disability Variables -- PERSON
IPUMS
Years residing in current locality
Years residing in current locality
Years residing in current locality
Years residing in current locality
Years residing in current locality
Less than 1 year
1
1 year (or 1 year or less)
2
2 years
3
3 years
4
4 years
5
5 years
6
6 years
7
7 years
8
8 years
9
9 years
10
10 years
11
11 years
12
12 years
13
13 years
14
14 years
15
15 years
16
16 years
17
17 years
18
18 years
19
19 years
20
20 years
21
21 years
22
22 years
23
23 years
24
24 years
25
25 years
26
26 years
27
27 years
28
28 years
29
29 years
30
30 years
31
31 years
32
32 years
33
33 years
34
34 years
35
35 years
36
36 years
37
37 years
38
38 years
39
39 years
40
40 years
41
41 years
42
42 years
43
43 years
44
44 years
45
45 years
46
46 years
47
47 years
48
48 years
49
49 years
50
50 years
51
51 years
52
52 years
53
53 years
54
54 years
55
55 years
56
56 years
57
57 years
58
58 years
59
59 years
60
60 years
61
61 years
62
62 years
63
63 years
64
64 years
65
65 years
66
66 years
67
67 years
68
68 years
69
69 years
70
70 years
71
71 years
72
72 years
73
73 years
74
74 years
75
75 years
76
76 years
77
77 years
78
78 years
79
79 years
80
80 years
81
81 years
82
82 years
83
83 years
84
84 years
85
85 years
86
86 years
87
87 years
88
88 years
89
89 years
90
90 years
91
91 years
92
92 years
93
93 years
94
94 years
95
95+
96
Less than 5 years
97
More than 5 years
98
Unknown
99
NIU (not in universe)
MIGYRS1 indicates how many years the person has resided in their current locality of residence.
Migration Variables -- PERSON
IPUMS
Number of own male children in household
Number of own male children in household
Number of own male children in household
Number of own male children in household
Number of own male children in household
None
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
24
24
25
25
98
Unknown
99
NIU (not in universe)
HOMEMALE indicates the number of male children born living in the household with their mother (the respondent).
Fertility and Mortality Variables -- PERSON
IPUMS
Migration status, 1 year
Migration status, 1 year
Migration status, 1 year
Migration status, 1 year
Migration status, 1 year
NIU (not in universe)
10
Same major administrative unit
11
Same major, same minor administrative unit
12
Same major, different minor administrative unit
20
Different major administrative unit
30
Abroad
99
Unknown/missing
MIGRATE1 indicates the person's place of residence 1 year ago. The first digit records movement across major administrative divisions and countries; the second digit reports movement across minor administrative divisions.
Migration Variables -- PERSON
IPUMS
Educational attainment, international recode [general version]
Educational attainment, international recode [general version]
Educational attainment, international recode [general version]
Educational attainment, international recode [general version]
Educational attainment, international recode [general version]
NIU (not in universe)
1
Less than primary completed
2
Primary completed
3
Secondary completed
4
University completed
9
Unknown
EDATTAIN records the person's educational attainment in terms of the level of schooling completed (degree or other milestone). The emphasis on level completed is critical: a person attending the final year of secondary education receives the code for having completed lower secondary only -- and in some samples only primary.
EDATTAIN does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. In addition to EDATTAIN, a country-specific education classification is provided which loses no information and reflects the particular educational system of that country (for example EDUCBR for Brazil, EDUCCL for Chile, and EDUCUS for the United States). As always, users can refer to the original education source variables for each sample, if they wish.
Many samples also give single years of schooling completed, recorded in YRSCHOOL. Some samples provide educational information in a form that could not be incorporated into EDATTAIN.
Education Variables -- PERSON
IPUMS
Educational attainment, international recode [detailed version]
Educational attainment, international recode [detailed version]
Educational attainment, international recode [detailed version]
Educational attainment, international recode [detailed version]
Educational attainment, international recode [detailed version]
NIU (not in universe)
100
Less than primary completed (n.s.)
110
No schooling
120
Some primary completed
130
Primary (4 yrs) completed
211
Primary (5 yrs) completed
212
Primary (6 yrs) completed
221
Lower secondary general completed
222
Lower secondary technical completed
311
Secondary, general track completed
312
Some college completed
320
Secondary or post-secondary technical completed
321
Secondary, technical track completed
322
Post-secondary technical education
400
University completed
999
Unknown/missing
EDATTAIN records the person's educational attainment in terms of the level of schooling completed (degree or other milestone). The emphasis on level completed is critical: a person attending the final year of secondary education receives the code for having completed lower secondary only -- and in some samples only primary.
EDATTAIN does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. In addition to EDATTAIN, a country-specific education classification is provided which loses no information and reflects the particular educational system of that country (for example EDUCBR for Brazil, EDUCCL for Chile, and EDUCUS for the United States). As always, users can refer to the original education source variables for each sample, if they wish.
Many samples also give single years of schooling completed, recorded in YRSCHOOL. Some samples provide educational information in a form that could not be incorporated into EDATTAIN.
Education Variables -- PERSON
IPUMS
Number of births last year
Number of births last year
Number of births last year
Number of births last year
Number of births last year
None
1
1 (1 or more)
2
2
3
3
4
4+
8
Unknown
9
NIU (not in universe)
BIRTHSLYR indicates whether any -- and in most cases how many -- children were born to a woman in the past twelve months.
Fertility and Mortality Variables -- PERSON
IPUMS
Children surviving from births last year
Children surviving from births last year
Children surviving from births last year
Children surviving from births last year
Children surviving from births last year
1
1 (1 or more)
2
2
3
3
4
4+
8
Unknown
9
NIU (not in universe)
BIRTHSURV indicates the number of children born in the past twelve months who were still living at the time of the census.
Fertility and Mortality 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
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20+
98
Unknown
99
NIU (not in universe)
HOMECHILD indicates the number of surviving biological children living in the household with their mother (the respondent) at the time of the census.
Fertility and Mortality Variables -- PERSON
IPUMS
Number of own children living elsewhere
Number of own children living elsewhere
Number of own children living elsewhere
Number of own children living elsewhere
Number of own children living elsewhere
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
98
Unknown
99
NIU (not in universe)
AWAYCHILD indicates the number of surviving biological children not living in the household with their mother (the respondent) at the time of the census.
Fertility and Mortality Variables -- PERSON
IPUMS
Nativity status
Nativity status
Nativity status
Nativity status
Nativity status
NIU (not universe)
1
Native-born
2
Foreign-born
9
Unknown/missing
NATIVITY indicates whether the person was native- or foreign-born.
Nativity and Birthplace Variables -- PERSON
IPUMS
Number of female children dead
Number of female children dead
Number of female children dead
Number of female children dead
Number of female children dead
None
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+
98
Unknown
99
NIU (not in universe)
CHDEADFEM indicates the number of female children ever born to a woman who are no longer living. Stillbirths are not counted.
It is possible to calculate total child deaths for samples that have both the "Female children ever born" and "Female children surviving" variables. That is not done in CHDEADFEM, which includes only the samples that directly reported the information in the appropriate form.
Fertility and Mortality Variables -- PERSON
IPUMS
Number of male children dead
Number of male children dead
Number of male children dead
Number of male children dead
Number of male children dead
None
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+
98
Unknown
99
NIU (not in universe)
CHDEADMALE indicates the number of male children ever born to a woman who are no longer living. Stillbirths are not counted.
It is possible to calculate total child deaths for samples that have both the "Male children ever born" and "Male children surviving" variables. That is not done in CHDEADMALE, which includes only the samples that directly reported the information in the appropriate form.
Fertility and Mortality 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.]
This is a 4-digit numeric variable with 0 implied decimal places
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.]
This is a 9-digit numeric variable with 0 implied decimal places
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.]
This is a 10-digit numeric variable with 0 implied decimal places
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.]
This is a 3-digit numeric variable with 0 implied decimal places
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.]
This is a 1-digit numeric variable with 0 implied decimal places
Technical Person Variables -- PERSON
IPUMS