design and use of the ipums-international data series matt sobek minnesota population center...
TRANSCRIPT
![Page 1: Design and Use of the IPUMS-International Data Series Matt Sobek Minnesota Population Center sobek@pop.umn.edu](https://reader035.vdocuments.site/reader035/viewer/2022062408/56649e5d5503460f94b5698e/html5/thumbnails/1.jpg)
Design and Use of the IPUMS-International Data Series
http://international.ipums.org
Matt SobekMinnesota Population Center
![Page 2: Design and Use of the IPUMS-International Data Series Matt Sobek Minnesota Population Center sobek@pop.umn.edu](https://reader035.vdocuments.site/reader035/viewer/2022062408/56649e5d5503460f94b5698e/html5/thumbnails/2.jpg)
Overview
Processing
Dissemination system
Strengths and limitations
Users
IPUMS-International
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END
https://international.ipums.orghttps://international.ipums.org
Matt SobekMinnesota Population Center
![Page 4: Design and Use of the IPUMS-International Data Series Matt Sobek Minnesota Population Center sobek@pop.umn.edu](https://reader035.vdocuments.site/reader035/viewer/2022062408/56649e5d5503460f94b5698e/html5/thumbnails/4.jpg)
What is IPUMS-International?
Census data – 1960 to present
Samples – 1 to 10%, nationally representative
Microdata – individual-level
Extract system – select variables – pooled data
Downloadable – anonymized
Integrated – consistent codes across time and place
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Map of IPUMS Partners
Dark green = disseminating dataLight green = partners, not yet disseminating
83 countries
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Current Countries in IPUMS
44 countries130 samples279 million persons
EgyptGhanaGuineaKenyaRwandaSouth AfricaUganda
ArmeniaCambodiaChinaIndiaIraqIsraelJordanKyrgyz Rep.MalaysiaMongoliaPalestinePhilippinesVietnam
ArgentinaBoliviaBrazilCanadaChileColombiaCosta RicaEcuadorMexicoPanamaUnited StatesVenezuela
AustriaBelarusFranceGreeceHungaryItalyNetherlandsPortugalRomaniaSloveniaSpainUnited Kingdom
Africa Asia Americas Europe
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Countries in IPUMS Archive
BangladeshBotswanaCubaCzech RepublicDominican Rep.El SalvadorEthiopiaFijiGermanyGuatemalaHaiti
Honduras
IndonesiaLiberiaMadagascarMalawiMaliMauritiusNepalNicaraguaPakistanParaguayPeru
Puerto RicoSenegalSaint LuciaSierra LeoneSudanSwitzerlandTanzaniaThailandTurkmenistanUruguayZambia
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IPUMS MicrodataRelation to head
Marital status Literacy Occupation
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Relationship to head 130 Religion 54
Age 130 Language 33
Sex 130 Ethnicity 41
Marital status 129 Race 20
Age at first marriage 16 School attendance 105
Children ever born 91 Literacy 91
Children surviving 59 Education attainment 119
Mother's mortality status 16 Years of schooling 72
Country of birth 81 Employment status 119
Place of birth 90 Class of worker 120
Citizenship 67 Occupation 116
Year of immigration 22 Industry 116
Migration, international 53 Hours worked weekly 38
Migration, internal 101 Total income 24
Disability 32 Earned income 26
Availability of Selected Person Variables
(Number of samples)
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Urban-rural status 89 Electricity 81
Geography, 1st level 120 Water 95
Geography, 2nd level 86 Sewage 76
Home ownership 107 Toilet 86
Number of rooms 102 Cooking fuel 39
Floor material 46 Telephone 57
Wall material 40 Television 45
Roof material 27 Computer 16
Living Area 20 Automobiles 42
Availability of Selected Household Variables
(Number of samples)
536 Integrated variables
10,600 Unharmonized variables
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User Access
Application
• Scholarly and educational purposes
• Key: it must not be redistributed
Once approved, access to all data
Free
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Making the IPUMS
Pre-processing
Integration
Dissemination
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Making the IPUMS
Pre-processing
Integration
• Language translation • Reformatting• Error correction• Sampling• Confidentiality
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Making the IPUMS
Pre-processing
Integration
• Language translation • Reformatting• Error correction• Sampling• Confidentiality
• Metadata • Data harmonization• Constructed variables
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Census Questionnaire (Mexico 2000)
Water
Access
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5. Number of Rooms
How many rooms are used for sleeping without counting hallways? _____ Write the number
Without counting the hallways or bathrooms how many total rooms are in this dwelling? Count the kitchen
_____Write the number
6. Access to water
Read all of the options until you get an affirmative answer. Circle only one answer
1 Running water inside the dwelling 2 Running water outside the dwelling but on the land 3 Running water from a public faucet or hydrant 4 Running water that is carried from another dwelling 5 Tanked in by truck 6 Water from a well, river, lake, stream or other
Answers 3, 4, 5, 6 continue with number 8
7. Water supply
How many days of the week is water available? Circle only one answer
1 Daily 2 Every third day 3 Twice a week 4 Once a week 5 Occasionally
Text of Census Questionnaire (Mexico 2000)
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Water access
XML-Tagged Census Questionnaire (Mexico 2000)
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Data Integration – Marital Status
MARST Marital Status
code label CN82A403 CO73A411 KN89A413 MX70A402 US90A425
100 SINGLE/NEVER MARRIED 1=never married 4=single 1=single 9=single 6=never married
200 MARRIED/IN UNION
210 Married (not specified) 2=married 2=married 3=monogamous 1=married
211 Civil 3=only civil
212 Religious 4=only religious
213 Civil and religious 2=civil and religious
214 Polygamous 3=polygamous
220 Consensual union 1=free union 5=free union
300 SEPARATED/DIVORCED 3=sep. or divorced
310 Separated 6=separated 8=separated 3=separated
321 Legally separated
322 De facto separated
330 Divorced 4=divorced 5=divorced 7=divorced 4=divorced
400 WIDOWED 3=widowed 5=widowed 4=widowed 6=widowed 5=widowed
999 UNKNOWN/MISSING 0=missing 6=unknown B=blank 1=unknown
China1982
Colombia1973
Kenya1989
Mexico1970
U.S.A.1990
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Pernum Relate Age Sex Marst Chborn
1 head 46 male married n/a
2 spouse 44 female married 3
3 aunt 77 female widow 7
4 child 15 female single 0
5 child 13 female single n/a
6 child 11 male single n/a
Pernum Relate Age Sex Marst Chborn
1 head 46 male married n/a
2 spouse 44 female married 3
3 aunt 77 female widow 7
4 child 15 female single 0
5 child 13 female single n/a
6 child 11 male single n/a
Spouse’s
Mother’s Father’s
Family Interrelationship Variables
Location
2
1
0
0
0
0
Location
Location
0
0
0 0
0
0
2 1
1
1
2
2
(Simple household)
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Pernum Relationship Age Sex Marst Chborn
1 head 53 female separated 6
2 child 28 male single n/a
3 child 22 male single n/a
4 child 21 male single n/a
5 child 25 female married 2
6 child-in-law 28 male married n/a
7 grandchild 3 male single n/a
8 grandchild 1 male single n/a
9 non-relative 32 female separated 2
10 non-relative 10 male single n/a
11 non-relative 5 female single n/a
Location
Location
Location
0
0
0
0
0
6
5
0
0
0
0
0
0
1
1
1
1
0
5
5
0
9
9
0
0
0
6
6
0
0
0
0
0
Spouse’s Father’sMother’s
IPUMS “Pointer” Variables(Complex household)
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Family Interrelationship Pointers
13 censuses include data on location of parent or spouse
Agree Disagree
Spouse 99.5 0.5
Mother 98.7 1.3
Father 99.4 0.6
Mother 97.5 2.5
Father 98.7 1.3Under age 18
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IPUMS Home Page
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Variables Page
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Variables Page
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Variables Page
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Sample Filtering
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Variables Page
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Unharmonized Variables
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Variable Description(Marital status)
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Comparability Discussion(Marital status)
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Enumeration Text(Marital status)
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Enumeration Text(Marital status, Cambodia)
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Variable Codes(Marital status)
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Variable Codes(Marital status)
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Variable Codes(Marital status)
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IPUMS Home Page
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Extract Step 1 – Login
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Extract Step 2 – Select Samples
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Extract Step 3 – Select Variables
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Extract Step 4 – Variable Options
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Extract Step 4 – Select Cases
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Age of spouse
Employment status of father
Occupation of father
Extract Step 4 – Attach Characteristics
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Extract Step 5 – Customize Sample Sizes
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Extract Step 5 – Customize Sample Sizes
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Extract Step 5 – Customize Sample Sizes
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Extract Step 6 – Submit
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Download or Revise Extract
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Key Strengths of the Census Samples
• Internationally comparable
Pool data across countries – integrated variables
Enable study of relatively small populations
• Large
• Temporal depth
Provide historical perspective
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Key Strengths of the Census Samples
• Microdata
All of a person’s characteristics – multivariate analysis
• Hierarchical
Characteristics of everyone a person resided with
Cohabitation and family interrelationships
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Limitations Due to Confidentiality
• Geography
20,000 population or larger
• Sensitive variables, very small categories
• Samples
Too small to answer some questions
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Other Issues and Limitations
• Cross-sectional dataNot longitudinal
• User burdenInformation overload; culturally specific knowledge
Variable labels are insufficient
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Academic field (%)
47 Economics
21 Demography
10 Sociology
22 Other
IPUMS Users
54% Graduate students
2200 registered users
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67% multiple samples
45% multiple countries
Samples Extracted
17% 5 or more countries
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Decade of Extracted Sample
1960s 11
1970s 14
1980s 16
1990s 30
2000s 29
Decade Percent
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Most Frequently Extracted Countries
1. Mexico
2. Brazil
3. United States
4. Colombia
5. France
6. Chile
7. Ecuador
8. Vietnam
9. Kenya
10. Argentina
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Most Frequently Extracted Variables
Relation to headAgeSexMarital statusEducational attainmentYears of schoolingSchool attendanceLiteracyEmployment statusClass of workerOccupation recodeIndustry recodeOccupationIndustryUrban-rural status
Country of birthNativity statusMigration status, 5 yearsChildren ever bornChildren survivingReligionOwnership of dwellingWaterElectricitySewageNumber of roomsToiletEarned incomeTotal incomeSpouse’s location in household
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Median Age by CountryItaly 42 Chile 29 Kyrgyz Republic 22
Greece 39 Argentina 27 Mongolia 21
Austria 38 Israel 27 Philippines 21
Hungary 38 Brazil 25 Bolivia 20
Portugal 38 China 25 Egypt 20
Canada 37 Colombia 25 Jordan 20
France 37 Costa Rica 24 Ghana 19
Netherlands 37 Mexico 24 Cambodia 17
Slovenia 37 Panama 24 Guinea 17
Spain 37 South Africa 24 Iraq 17
United Kingdom 37 Ecuador 23 Kenya 17
Belarus 36 Malaysia 23 Palestine 17
United States 36 Venezuela 23 Rwanda 17
Romania 35 Vietnam 23 Uganda 15
Armenia 31 India 22
(Calculated from the most recent sample from each country.)
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10 8 6 4 2 0 2 4 6 8 10
10 8 6 4 2 0 2 4 6 8 10
10 8 6 4 2 0 2 4 6 8 10
Population Pyramids
Palestine
IraqEgypt
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10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10
Population Pyramids
Young(Uganda 2002)
Medium(Philippines 2000)
Old(USA 2005)
10 8 6 4 2 0 2 4 6 8 10
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10 8 6 4 2 0 2 4 6 8 1010 8 6 4 2 0 2 4 6 8 10
Belarus1998
Cambodia1998
China1990
Population Pyramids
10 8 6 4 2 0 2 4 6 8 10
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10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10
Population Pyramids
Mexico
1960 1990 2005
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0
5
10
15
20
25
30
35
40
45
50
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Pe
rce
nt
in L
ab
or
Fo
rce
MexicoCosta Rica
Ecuador
Chile
Venezuela
Colombia
Brazil
Married Female Labor Force Participation in Latin America(age 18 to 65)
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0
10
20
30
40
50
60
70
1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Pe
rce
nt
in L
ab
or
Fo
rce
Latin America
United States
Married Female Labor Force Participation:Latin America and U.S. (age 18 to 65)
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0
10
20
30
40
50
60
70
1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Pe
rce
nt
in L
ab
or
Fo
rce
United States
MexicoCosta Rica
EcuadorChile
Venezuela
Colombia
Brazil
Married Female Labor Force Participation:Latin America and U.S. (age 18 to 65)
Compare Latin America to U.S. 40 years earlier
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Married Female Labor Force Participation:Mexican-born Women, 1970-2000
0
10
20
30
40
50
60
70
1970 1975 1980 1985 1990 1995 2000
Pe
rce
nt
in L
ab
or
Fo
rce
Mexican-born Women in United States
Women in Mexico
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Working-Age Population in the Labor Force, by Sex
0
10
20
30
40
50
60
70
80
90
100B
razi
l 19
60
Bra
zil 1
97
0B
razi
l 19
80
Bra
zil 1
99
1B
razi
l 20
00
Ch
ile 1
96
0C
hile
19
70
Ch
ile 1
98
2C
hile
19
92
Ch
ile 2
00
2
Co
lom
bia
19
64
Co
lom
bia
19
73
Co
lom
bia
19
85
Co
lom
bia
19
93
Co
sta
Ric
a 1
96
3C
ost
a R
ica
19
73
Co
sta
Ric
a 1
98
4C
ost
a R
ica
20
00
Ecu
ad
or
19
62
Ecu
ad
or
19
74
Ecu
ad
or
19
82
Ecu
ad
or
19
90
Ecu
ad
or
20
01
Me
xico
19
70
Me
xico
19
90
Me
xico
20
00
Ve
ne
zue
la 1
97
1V
en
ezu
ela
19
81
Ve
ne
zue
la 1
99
0
Ch
ina
19
82
Vie
tna
m 1
98
9V
ietn
am
19
99
Ke
nya
19
89
Ke
nya
19
99
So
uth
Afr
ica
19
96
So
uth
Afr
ica
20
01
Fra
nce
19
62
Fra
nce
19
68
Fra
nce
19
75
Fra
nce
19
82
Fra
nce
19
90
Un
ited
Sta
tes
19
60
Un
ited
Sta
tes
19
70
Un
ited
Sta
tes
19
80
Un
ited
Sta
tes
19
90
Un
ited
Sta
tes
20
00
Pe
rce
nt
of
Wo
rkin
g-A
ge
Po
pu
lati
on
Males Females Persons age 16 to 65.
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Population Residing with an Elderly Person
0
5
10
15
20
25
30
1960
1970
1980
1991
2000
1973
1985
1993
1970
1990
2000
1989
1999
1996
2001
1982
1989
1999
1962
1968
1975
1982
1990
1960
1970
1980
1990
2000
Per
cen
t o
f to
tal
po
pu
lati
on
Elderly persons (age 65+) Non-elderly residing with an elderly person
Brazil Mexico KenyaColombia VietnamChinaS Africa France United States
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Percent of elders in elder-head intergenerational families
0
10
20
30
40
50
1970 1975 1980 1985 1990 1995 2000
Per
cent
Argentina
Brazil
Chile
Colombia
Costa Rica
Ecuador
Kenya
Mexico
Philippines
Romania
Rwanda
Vietnam
South Africa
Uganda
Venezuela
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Percent of elders in younger-head families
0
10
20
30
40
50
1970 1975 1980 1985 1990 1995 2000
Per
cent
Argentina
Brazil
Chile
Colombia
Costa Rica
Ecuador
Kenya
Mexico
Philippines
Romania
Rwanda
Vietnam
South Africa
Uganda
Venezuela
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Trends in Intergenerational Families
Intergenerational families headed by the older generation are becoming more common in most countries, with exceptions mainly in Africa.
Intergenerational families headed by the younger generation—the configuration that suggests old-age support—are much rarer, and they are on the decline in most countries.
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Persons with Completed Secondary Education:National Populations Versus Migrants to the United States
0
10
20
30
40
50
60
70
80
90
100
Brazil Chile Costa Rica Ecuador Mexico Vietnam Kenya South Africa
Pe
rce
nt
In home country, ca. 2000 Migrants to U.S. 1995-2000