1. Matt Sobek Minnesota Population Center sobek@pop.umn.edu
Integrated Public Use Microdata Series
IPUMS
Matt Sobek Minnesota Population Center

2. IPUMS Overview 1. What is the IPUMS 1. What is the IPUMS
2. Harmonization
3. Additional Data Enhancements
4. Access
5. Strengths and Limitations
6. Research examples

3. Brief History IPUMS-USA 1991 -- Steve Ruggles
All existing samples of US census
Data extraction system 1998
IPUMS-International 2001
2004 IPUMS-Latin America
2005 IPUMS-Europe
2005 NSF Expansion
World’s largest collection census data
30 samples per year for the next 3 years
Bob McCaa

4. Datasets in IPUMS

5. IPUMS Census Sample Holdings and Release Dates

6. IPUMS Global Coverage Dark green = disseminating
Medium green = data held by IPUMS
Light green = negotiating
Yellow = not negotiating

7. Selected Variable Availability -- PERSON

8. Selected Variable Availability -- HOUSEHOLD

9. What Are Microdata? Individual-level data
• every record represents a separate person
• all of their individual characteristics are recorded
• users must manipulate the data themselves
Different from aggregate/summary/tabular data
• a count of persons by municipality
• an employment status table by sex from a
published census volume

10. Kenya 1999 Census Questionnaire
Now, I want to back up for a minute. Before we get too into examples of how microdata can be used, I want to talk to you briefly about how microdatasets are constructed. And what it is that a user actually gets when he requests one of our datasets.
This is the source of it all. I’m sure all of you have seen these records. They’re organized by household. [go through (a) geography, (b) a family] [Emphasize how variables are in COLUMNS].
What we do is type all this in and convert every possible variable to a number. So when a user requests a dataset from us they don’t get a table, or an immediate answer to their question. Rather, they get mostly raw numeric data. That they have to manipulate.

11. Raw Census Microdata from IPUMS
So, here’s a typical slice of a census microdataset.
Note the “H”s and “P”s at the beginning of each line
H=household record. Info common to the whole HH
P=person record. This is the people from the census form. With all of their info turned in to #s.
Without going in to too much detail I want to show you a little more about the structure of a census microdata set.

12. IPUMS Data Structure Age Birthplace Sex Mother’s birthplace
Relationship
Race
Occupation
IPUMS Data
Structure
Household record
(shaded) followed
by a person record
for each member
of the household
Just like the original census form, the data is organized by columns.
Each column represents a variable.
[go through some variables], like especially relationship
For each type of
record, columns
correspond to
specific variables

13. The Advantages of Microdata
 Combination of all of a person’s characteristics
 Characteristics of everyone with whom a person lived
 Freedom to make any table you need
 Freedom to make models examining multivariate
relationships
 Basically, you are only limited by the questions asked in the particular census
Why would you want to work with data like this? It’s certainly more of a hassle to use…
--combination of all of a person’s chars: so you don’t just know, say, how many southern migrants in the north there were. You know their age, marital status, occupation, income, how long they’ve been there, etc.
--characteristics of everyone with whom a person lived. A friend of mine studying junk peddlers wanted to know whether their children’s rates of school attendence was comparable to that of other children. You would never see a census table on this. With IPUMS, it’s a cinch.
--freedom to make any table you need
--freedom to model using multivariate relationships. EXAMPLE?

14. IPUMS Overview 1. What is the IPUMS 2. Harmonization
3. Additional Data Enhancements
4. Access
5. Strengths and Limitations
6. Research examples

15. Translation Table – Marital Status (IPUMS-International)
China
1982
Colombia
1973
Kenya
1989
Mexico
1970
U.S.A.
1990

16. Translation Table – Marital Status
General Codes

17. Variable Description: Literacy (International)

18. IPUMS Overview 1. What is the IPUMS 2. Harmonization
3. Additional Data Enhancements
4. Access
5. Strengths and Limitations
6. Research examples

19. IPUMS “Pointer” Variables
(Simple household)
Spouse’s
Pernum
Relate
Age
Sex
Marst
Chborn 1
head 46
male
married
n/a 2
spouse 44
female 3
aunt 77
widow 7 4
child 15
single 5 13 6 11
Location 2 1
Mother’s
Father’s
Pernum
Relate
Age
Sex
Marst
Chborn 1
head 46
male
married
n/a 2
spouse 44
female 3
aunt 77
widow 7 4
child 15
single 5 13 6 11
Location
Location 2 1 2 1 2 1

20. IPUMS “Pointer” Variables
(Complex household)
Spouse’s
Mother’s
Father’s
Pernum
Relationship
Age
Sex
Marst
Chborn 1
head 53
female
separated 6 2
child 28
male
single
n/a 3 22 4 21 5 25
married
child-in-law 7
grandchild 8 9
non-relative 32 10 11
Location
Location
Location 1 6 5 5 6 5 6 9 9

21. IPUMS Overview 1. What is the IPUMS 2. Harmonization
3. Additional Data Enhancements
4. Access
5. Strengths and Limitations
6. Dissemination

22. IPUMS Access Restricted access Scholarly and educational purposes
Conditions of use: key is not to redistribute
Serious vetting

23. IPUMS Overview 1. What is the IPUMS 2. Harmonization
3. Additional Data Enhancements
4. Access
5. Strengths and Limitations
6. Research examples

24. Census Microdata Samples
4 Key Strengths of the
Census Microdata Samples
More cases than any comparable datasets
Enable study of relatively small populations
Large
National in scope
Results not subject to local peculiarities
Provide context for local studies
Temporal depth
Provide historical perspective
To summarize…..
Microdata
Can make your own tabulations
Apply multivariate techniques

25. Limitations of the Microdata Samples
Confidentiality
Samples
Too small to answer some questions
Geography
20,000 population or larger
Sensitive variables, swapping, etc

26. Other Issues and Limitations
Not annual
Any historical analysis will have gaps
Cross-sectional data
Not longitudinal
Very large extracts
Need knowledge of a statistical package
User burden
Information overload; culturally specific knowledge

27. IPUMS Overview 1. What is the IPUMS 2. Harmonization
3. Additional Data Enhancements
4. Users and Access
5. Strengths and Limitations
6. Research examples

28. IPUMS-International Research Topics
Child labor outside the household in Mexico and Colombia
Effect of NAFTA on educational attainment and school
enrollment by region within Mexico
Concentration of mortality within families in Kenya
Life course patterns of co-residence among Mexicans in
Mexico, Mexicans in the U.S., and Mexican Americans
Brain drain from developing countries
How language diversity is affected by migration and economic
factors

29. Married Female Labor Force Participation in Latin America
(age 18 to 65) 50
Brazil 45 40 35
Chile 30
Ecuador
Colombia
Venezuela
Mexico
Percent in Labor Force 25
Costa Rica 20 15 10 5
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005

30. Married Female Labor Force Participation:10 20 30 40 50 60 70
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
Percent in Labor Force
Latin America and U.S. (age 18 to 65)
United States
Latin America

31. Compare Latin America to U.S. 40 years ago
Married Female Labor Force Participation:
Latin America and U.S. (age 18 to 65) 70
United States 60 50
Brazil 40
Compare Latin America to U.S. 40 years ago
Percent in Labor Force
Colombia
Chile 30
Ecuador
Venezuela
Mexico
Costa Rica 20 10
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010

32. Mexican-born Women in United States
Married Female Labor Force Participation:
Mexican-born Women, 70 60
Mexican-born Women in United States 50 40
Percent in Labor Force 30
Women in Mexico 20 10
1970
1975
1980
1985
1990
1995
2000

36. End