EXAMPLES OF TABULATING DATA FROM THE PUBLIC USE MICRODATA SAMPLE OF
CENSUS 2000
CENSUS 2000
June 23, 2009
The following exercises are all based on using PDQ-Explore to access the Census 2000 micro
data in the IPACS08 data set. Unless otherwise noted, include the selection YEAR=2000 as part
of the Universe/Selection specification in the setup. Note that the default sample weight assigned
for this data set automatically selects only the first data set available for each year in the
concatenated 1850-2007 file of decennial and ACS data.
Setup entries in PDQ-Explore are not case-sensitive. Variable names in the following exercises
are in lower-case, but they may be entered in the setup windows in upper, lower, or mixed case.
Suggestion: Keep a window open to the IPUMS-USA documentation:
http://usa.ipums.org/usa
Click on “Variables” under “Documentation”
Example 1.
Question:
Do men and women hold similar jobs?
Title VII of the Civil Rights Act of 1964 prohibited gender
discrimination in the labor market. Consider persons aged 24 to
65 in 2000 who held a job. What detailed occupations were most
numerically dominated by women? By men?
Tabulation
Query Type:
Universe/Selection: year=2000 & age>24 & age<65 & empstat=1
occ1990
Row:
sex
Column:
(blank)
For:
Default
Weight:
Options:
Percent by row and then click on male or female
Example 2.
Question:
The Economic Status of Children by Race
Consider children under age 18 in 2000. How did the races differ
in the median household income of children?
Quantiles
Query Type:
Universe/Selection: year=2000 & age<18
race
Row:
(blank)
Column:
(blank)
For:
Default
Weight:
Quantile order &
expression
2
hhincome
or (hhincome+50)/100
Example 3.
Question:
Interracial Marriage
Consider married men age 20 and over classified by race in 2000.
What was the marital status distribution of their wives? Was there
a cohort pattern? That is, has there been an apparent increase
over time in interracial marriage?
Tabulation
Query Type:
Universe/Selection: year=2000 & sex=1 & age>24 & age<35
race
Row:
spouse(race)
Column:
age/5
For:
Default
Weight:
Options:
Percent by row (or percent by column)
Example 4.
Question:
Children in Interracial Marriages
How common was it for children under 18, classified by race, to be
living with a mother (or father) of the same or different race?
Tabulation
Query Type:
Universe/Selection: year=2000 & age<18
race
Row:
mother(race)
or father(race)
Column:
(blank)
For:
Default
Weight:
Options:
Percent by row (or percent by column)
Example 5.
Question:
The Relative Earnings of Husbands and Wives
How common was it for wives to earn more than their husbands?
Did this vary by wife’s age or by her race?
Tabulation
Query Type:
Universe/Selection: year=2000 & sex=2 & marst=1
incearn/15000
Row:
incearn > spouse(incearn)
Column:
For:
Default
Weight:
Options:
Percent by row
Example 6.
Question:
Federal Transfer Payments and the Prosperity of Local Areas
Which metropolitan areas in the United States in 1999 obtain the
largest amount of federal Social Security Payments? Assume that
only persons 62 and over collected Social Security?
Tabulation
Query Type:
Universe/Selection: year=2000 & age>=62 & incss>0 & incss<99999
metaread
Row:
Column:
For:
(datanum=1)*perwt*incss
Weight:
Example 7.
Does military service provide a long term financial advantage
to men, or women, who served in the nation’s Armed Forces?
Persons born between about 1940 and 1949 provided much of the
Question:
personnel that served in the Armed Forces during the war in the
former French-Indo China. In 2000, many of these people were 50
to 60 years old. Did those who served in the military have larger
or smaller incomes than those who did not serve? Was there a
difference by race or gender or by educational attainment?
Summary Statistics
Query Type:
Universe/Selection: year=2000 & age>=50 & age<60
vetvietn
Row:
sex
Column:
race
(or educ99)
For:
Default
Weight:
inctot
Describe
expression
Example 8.
Question:
The Benefits of Having a Major University in your town
Since the Russians launched Sputnik, higher education has been
among the most rapidly growing and financially rewarding
industrial sectors? Which metropolitan areas have the largest
payrolls from employers in the higher education industry?
Tabulation
Query Type:
Universe/Selection: year=2000 & empstat=1 & ind1990=850
metaread
Row:
sex>0
Column:
(blank)
For:
(datanum=1)*perwt*inctot
(or incearn)
Weight:
Option
Sort in ascending or descending order
Example 9.
Question:
Education and Regional Variations in Earnings
Suppose you had an advanced degree – a master’s degree or more.
You might think that there is a national market for people with this
level of human capital. Do the weekly earnings of such people vary
much from one state to another or from one state to another?
Are there big areal variations for women but not for men?
Summary statistics
Query Type:
Universe/Selection: year=2000 & educ99>14 & incearn>0 &
wkswork1>0
metaread
(or statefip)
Row:
sex
Column:
(blank)
For:
Default
Weight:
incearn/wkswork1
Describe
expression: