Using the AHS to Explore Job
Change, Commuting, and Salary
–
An Investigative Extension
The Dean’s Research Seminar Series
March 16, 2007
General Discussion Outline
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Purpose
Literature, Research
The American Housing Survey
Current Effort (Exploratory Extension)
Analysis, Results
Comments, Limitations
Conclusions
The Initial Article
• Impact of Moving and Job Changes on
Commuting Time
– Econometrica, Inc. (Eggers & Moumen),
January 2005
• “Published” at http://www.huduser.org/
– Contracted by HUD to study relationship
between moving, job changes, and
commuting time
– Question of what the AHS can be used for
when specific questions are not asked
The Initial Article
• Had to distinguish between different
household members and relationships
– “Members commute, households do not”
• Problems
– AHS is concerned with the housing unit; the
people are secondary
– Data is not collected on location of
employment
Current Research Purpose
• Exploratory Effort
– Intention is to inform current research on the
AHS national dataset
– Examine how salary is related to job change,
distance, and time commuted
– Use regression analysis to look at SES
and other factors
– Evaluate if the Econometrica article had merit
(specifically, the proxy value)
Current Research Purpose
• Continues past and current work with the
AHS
– Chapman, et al (2003). Presentation at 2003
Urban Affairs Association Conference
(Washington, DC).
– Chapman& Lombard (2006). Determinants of
Neighborhood Satisfaction in Fee-Based
Gated and Nongated Communities. Urban
Affairs Review, 41(6), 769-799.
– Chapman, et al. Dissertation-in-progress
(Defended and Defensible Neighborhoods).
Selected Literature
• White (1986) - Men tended to have longer commute
lengths. Women had a significant positive result with the
presence of young children in the house. Male
homeowners tended to have longer commute lengths
than renters, but there was no significant difference
between female owners and renters. For both genders,
blacks had a larger commuting length.
• Shen (2000) indicated that commute time is generally
longer for central city low-income, lower educated
minorities, primarily due to reliance on public
transportation. Shen also found a gender factor, where
females have a shorter commute.
• Dubin (1991) - commuting time is more important to
workers than commuting distance, in relation to their
decisions on employment decentralization
Selected Literature
• Clark, Huang, and Withers (2003) - households with
large distances between employment location and home
tend to make changes to decrease the time and
distance. Males are less likely to minimize commuting
after a move and females commute shorter distances.
• Ory, et al, (2004) were not persuaded that the traditional
view of commuting was true for every individual, as
some economic models may suggest. Two commuters
who travel the same amount each week might view the
amount differently. Some commuters may appreciate the
private time in their vehicle, enjoy traveling, and/or find
the drive somehow productive.
…thus, we have a mixed bag.
The Working Dataset
Let’s take a look at what the American
Housing Survey is…
American Housing Survey
A Brief Introduction
• National and Metropolitan version
• Collected biannually by U. S. Census
Bureau for the U.S. Department of
Housing and Urban Development
• Housing Unit is the basis for all cases
• Longitudinal components
• More than 55,000 housing units surveyed
(many different types of housing units)
• More than 100,000 individuals in
those housing units
American Housing Survey
A Brief Introduction
• Relational dataset (eight tables)
• Over 800 variables (columns)
• SAS data – can be converted to SPSS,
Stata, etc.
• Contains “weight” variables for descriptive
analysis
• Non-weighted dataset is “close” and
recommended by HUD for actual usage
• Many different housing situations to
research
American Housing Survey
A Brief Introduction
American Housing Survey
A Brief Introduction
• How to make
sense of it?
– Newhouse file
• Housing unit
• Respondent
Case 1
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Case 2
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Case n
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– File flattening
programs (SAS)
• Flattens relational
structure
• Individuals 1-16
• Downsides:
– No inherent or
assumed order of
individuals
– Huge dataset
Control # a
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Control # b
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Control # xyzm
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Control # a
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Control # b
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Control # xyzm
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Control # a
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Control # b
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Control # xyzm
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Control # a
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Control # b
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Control # xyzm
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The Suggested Proxy
• How?
– Econometrica used a
change in the
reported distance
(miles) to work as a
proxy for job change
– Linked two years of
data (1999 and
2001) by Control
variable for housing
unit
+X
-X
X
The Proxy
• Defined job change when:
IN01_DISTJ < IN99_DISTJ – X
or
IN99_DISTJ + X < IN01_DISTJ
IN99 and IN01 are the distance (miles) to work in
each year
• X is a distance buffer used to filter
out varying responses between
surveys, as well as the proxied
move component
-X
+X
X
The Proxy
• Tacit assumption
– Any job (distance) located in the donut is considered the
same job
– Anything inside or outside “the donut” is a job change
• Econometrica explored various values
– e.g., 1, 2, 5, 10 miles
• Settled on X = 3 miles
– Covers an area of 377 sq miles, given a
radius of 10 miles
– Other values appeared to over or
under estimate the apparent job change
-X
+X
X
Conclusions of Econometrica Study
• Type of household appears related to decision to
move, change jobs, and commuting changes
• “Movers” have shortest current commuting
distance
• “Stayers” who change jobs have the longest
commute distances
• Relationship between job change and
commuting time seems to depend on type of
household
• Suggest that HUD and Census add a simple
question about job change since last survey
Econometrica Study
• Weaknesses:
– Discussed statistical significance for changes in
commuting time between 1999 and 2001 for “job
changers” for X=3, but not the type of test (most likely
paired t-tests)
– Significance level & # of cases
• Suggestions by authors:
– Suggest a study of 2001 and 2003 to see if the
patterns repeat
– Look at how salary is related to job change,
distance, and time commuted
– Use regression analysis to look at SES
and other factors
-X
+X
X
Extension of the Econometrica
Study Using the AHS
• AHS (National)
– 2005, 2003
• Look at housing units across the more current period
• Merge data, order vertically/horizontally, and examine lagged
values
• Restrictions
– Narrow the range of housing
unit types
– Commuting time of day
– Age Range (25-65 in 2005)
– Salary Range ($20K-100K
in 2003 and 2005)
-X
+X
X
Extension of the Econometrica
Study Using the AHS
• Restrictions
– Households who worked for a salary and
commuted (distance > 0, time > 0)
– Eliminate “super” commuters
(distance > 100 miles or time > 120 minutes )
– Owners/renters
– People who live in
“standard” housing
-X
+X
X
Note: cannot look at tenure and
housing value simultaneously
Extension of the Econometrica
Study Using the AHS
• Used suggested proxy of original study
– Created nominal categories for year
groupings
– Looked at two groups
• Single salary
• Multi-salary (preliminary)
– Higher salary as primary
• Checked for inconsistencies
-X
+X
– Year checks, cross checks
– Anomalies, others…
X
Descriptives – One Salary Household
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n = 2453 (housing units)
Salary (2005): $45K (median)
Salary delta: $2,300 (median)
Distance (2005): 14.6 miles
(mean); SD 12.7
Distance delta: 0.34 miles
(mean); SD 10.9
Time (2005): 23.2 (mean); SD
16.3
Time delta: 0.23 minutes
(mean); SD 14.3
Job Changers: 42.3%
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Descriptives from 2005 dataset based on stated restrictions
Note: “delta” is the difference between 2005 and 2003 data
Age: 45.8 (mean)
Male: 59.4%
White: 78.1%
Hispanic: 8.6%
Owners: 81.8%
Central City: 28.4%
Married: 42.8%
1 or 2 adults: 93.2%
0 children: 63.4%
Let’s Look at Two Models
• Job Changer (y/n), using one survey year
• Job Changer (y/n) with commuting
distance & time and salary deltas (20052003)
The Simple Case
s = α + β1d + β2t + β3[job changer 0/1] +
βses1…sesn [SES factors] +
βloc1…locn[Locational factors] + ε
where:
s = salary in 2005
d = distance to work in 2005
t = time to work in 2005
The Simple Case
Results – One Salary Household
• OLS
– α < .01
• Significant results
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Female (-)
Black or Hispanic (-)
Renter (-)
Not married (-)
Education (+)
2+ adults (-)
Outside of MSA (urban
or rural) (-)
• Not significant
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Job Change Construct
Commute Distance
Commute time
Others (age, presence
of children)
• Adjusted R2 = .253
The Delta Model
Δs = α + β1Δ d + β1Δ t +
β1 [job changer 0/1] +
βses1…sesn [SES factors] +
βloc1…locn [Locational factors] + ε
where:
Δ s = difference in salary (2005-2003)
Δ d = difference in distance (2005-2003)
Δ t = difference in time (2005-2003)
The Delta Model
Results – One Salary Household
• OLS
– α < .01
• Significant results
– Age (-)
– Female (-)
– Education (grad
degree) (+)
– 3+ adults (-)
• Not significant
– Job Change Construct
– Commute Time delta
– Commute Distance
delta
– Others (ethnicity,
home ownership,
marital status, area)
• Adjusted R2 = .023 (!)
Multi-Salary Households
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Performed preliminary investigations
Problems with outliers and residuals
Many combinations of household types
No great improvements or hint of anything
there
Comments
• “So What?”
– The significant results in the one-person case barely
look like a generalized study on salary and not much
else
– The multi-person cases do not appear to be any
better one-person cases
– The proxy value did not appear to add value (of any
sort)
– Undoubtedly, other IVs could be explored
• May be a problem of cross-year
comparisons within the AHS
• AHS may be “the wrong wine
for the meal”
Limitations
• Original article was asking questions not directly
answered by the AHS survey
– Data derivation is daunting
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Original study was basically descriptive
Tremendous combinations of housing unit types
Censored cases
No clear order in the dataset of “importance” in the
household
• Many additional exclusions and data checking are possible
– Clearly violates any sense of “Occam's razor”
• Uncovered inconsistencies in the AHS
– Some were already known to us
– May have discovered additional survey bias (e.g.,
non-sampled housing units between survey years
could be SES driven)
Next Steps
• Investigate finer levels of the construct of
job change (0, 1, 2, ...)
• Investigate further survey bias along SES
dimensions
• Investigate commute distance and time
deltas
Conclusions
• AHS has tremendous width/breadth, but…
– The suggestion of a proxy distance to suggest job
change may be a stretch
– Indirect answers are difficult and laborious to cull
– The problem may be compounded when looking
across survey years
– The information may be in the dataset, but may
require too many resources (or other tools) to dig out
• Use the right tool for the job