1 patterns of residential mobility using cluster analysis to identify different types of movers,...
TRANSCRIPT
1
Patterns of Residential Mobility
Using Cluster Analysis to Identify Different Types of Movers,Stayers, and Newcomers
in the Making Connections Sites
2
High Rates of Family Mobility
3
About Half the MC Households Moved
Percent of Wave 1 Households that Moved
61.8
55.2
69.9
53.5 53.452.9
45.6
49.4
27.5
42.5
0
10
20
30
40
50
60
70
80
Denver Des Moines Indianapolis San Antonio White Center
Families w/Kids
Childless HHs
4
Some Movers Stayed NearbyPercent of Family Movers Remaining within Two Miles
39.3
35.2
33.4
39.7
29.9
0
5
10
15
20
25
30
35
40
45
Denver Des Moines Indianapolis San Antonio White Center
5
Spatial Patterns of Mobility Vary
Des Moines San Antonio
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Implications for Early Childhood Initiatives?
Share of 5 & 6 Year-Olds Who Are Newcomers
37.5 36.936.0
32.1
40.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Denver Des Moines Indianapolis San Antonio White Center
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Mobility and Neighborhood Change
Demographic Characteristics of Families with Kids -- Des Moines
48%
24%
45%
30%
42%
22%
44%
32%30%
34%
38%
26%
0%
10%
20%
30%
40%
50%
60%
Two parents Foreign born White Black
Stayers
Movers
Newcomers
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Mobility and Neighborhood Change
Demographic Characteristics of Families with Kids -- White Center
65%
35%
49%
8%
42%
50%
33%
16%
30%
68%
27%
18%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Two parents Foreign born White Black
Stayers
Movers
Newcomers
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Less Engagement Among Newcomers
Percent of Families Who Have Gotten Together with Neighbors to Do Something
40.3
28.3
39.2
28.5
37.9
18.720.4
15.1
18.4
15.9
0
5
10
15
20
25
30
35
40
45
Denver Des Moines Indianapolis San Antonio White Center
Stayers
Newcomers
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Why Are Families Moving In and Out of the MC Neighborhoods:Cluster Analysis Hypotheses and Methods
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Why are families moving?
Few direct survey questions re reasons for moving Milwaukee and Louisville Wave 2 survey
Lots of information about possible push and pull factors Literature inventory of relevant factors
Three illustrative survey questions Volunteer in neighborhood (attachment) Trouble w/housing expenses (instability) Housing tenure (home purchase)
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Intro to Cluster Analysis
Analytic technique to classify observations into groups based on variables of interest
Measure distance between individual observations and the centroids of groups of observations
Can use dichotomous and continuous variables
No independent confirmation of cluster groupings
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Methods
Step 1: Create cluster predictions Guided by theory, previous research, population
in question, variation in data Making Connections cluster predictions
(following slides)
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4 Separate Cluster Analysis Models
1. Out-movers with children – Wave 1 and 2
2. Childless out-movers – Wave 1
3. Stayers – Wave 1 and 2
4. Newcomers – Wave 2
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Lots of variation among out-movers with children (5 site pooled data)
Household change 11% got married; 13% separated 33% added a child; 13% have fewer kids
Employment change 12% became employed; 13% lost their jobs
Tenure change 18% became homeowners; 11% shifted to rental
Perception of neighborhood 63% think new neighborhood is safer 24% think it’s a better place to raise kids
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Hypothesized clusters of Out-movers with children
1. Moves reflect a step up to better housing and neighborhood circumstances
2. Moves reflect a change in household composition (& housing needs)
3. Moves reflect instability & insecurity
17
Some variation among stayers
Neighborhood engagement 38% attend neighborhood events; 29% volunteer in the
neighborhood; 31% work with neighbors for change
Perception of neighborhood 46% score safety high 55% think it’s getting better; 12% think it’s getting worse
Satisfaction with services 86% highly satisfied with kid’s school; 6% dissatisfied 74% highly satisfied with banking services; 3% dissatisfied 90% highly satisfied with parks; 7% dissatisfied
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Hypothesized clusters of Stayers
1. Staying reflects attachment and satisfaction
2. Staying reflects dissatisfaction & lack of alternatives
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Lots of variation among newcomers Employment
26% have no employed adults; 37% have a stable job Income
6% have incomes > 300% poverty; 66% have incomes below poverty
Housing 22% are homebuyers; 26% live in subsidized housing; 40%
report difficulty paying housing costs Perception of neighborhood
65% think it’s a good place to raise kids; 47% think it’s likely to get better
Engagement 29% attend neighborhood events; 18% volunteer in the
neighborhood; 15% work with neighbors to solve problems
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Hypothesized clusters of Newcomers
1. Affluent newcomers investing in expectation of neighborhood change (gentrifiers)
2. Newcomers similar to current residents & optimistic about neighborhood quality
3. Newcomers whose moves reflect instability & insecurity
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Methods (cont’d)
Step 1: Create cluster predictions Step 2: Identify variables of interest for each model
Different variables selected for the four models based on theory and data availability
Individual factors Demographic/family composition,
employment/income, hardship, homeownership, neighborhood services and perceptions, neighborhood attachment
Neighborhood factors Housing market, poverty, racial composition
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Methods (cont’d)
Step 3: Test for correlations among variables that reflect push & pull factors Correlation Matrices
Step 4: Principle components analysis to identify possible composite factors Collapse data where appropriate
Step 5: Look at the data Scatter diagrams, tree graph
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Methods (cont’d)
Step 6: Cluster Procedures Standardize coefficients
Jaccard coefficient is a reliable and simple method Hierarchical or Non-hierarchical (k-means) cluster
analyses SPSS, SAS, and STATA have established commands
Specify number of clusters Run cluster procedure multiple times with different
numbers of clusters specified
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Methods (cont’d)
Step 6: Cluster Procedures (cont’d) Review generated clusters
Investigate clusters, interpret, profile groups A heuristic: Local maximum of pseudo F statistic, with local
minimum of R-squared Step 7: Robustness tests
Run multiple cluster tests Compare with different variable specifications Split sample, cluster again
Step 8: Use the findings! Compare groups along key measures
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Why Are Families Moving In and Out of the MC Neighborhoods: Cluster Analysis Illustrative Findings
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Illustrative Results
4 Types of out-movers with kids
Optimistic Homebuyers
Changed Family Circumstances
Reluctant Movers
Unstable Families
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Illustrative Results (cont’d)Demographic Characteristics of Out-Mover Types
0
0.1
0.2
0.3
0.4
0.5
0.6
%non-Hisp white %non-Hisp black %Hispanic %Asian %foreign born
optimistic homebuyers
changed circumstances
reluctant movers
unstable families
Out-Mover Demographics
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Illustrative Results (cont’d)Out-Movers Differ By Sites
Types of Family Out-Movers
32.3
42.636.5
41.9
63.2
15.2
13.1
5.7
12.9
6.1
9.4
16.6
12.6
12.9
3.1
43.1
27.7
45.2
32.327.6
0
10
20
30
40
50
60
70
80
90
100
Denver Des Moines Indianapolis San Antonio White Center
pe
rce
nt
of
mo
ve
rs
unstable families
reluctant movers
changed family circumstances
optimistic homebuyers
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Illustrative Results
3 Types of Stayers
Subsidized
Attached
Trapped
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Illustrative Results (cont’d)Stayer Demographics
Demographic Characteristics of Stayer Types
0%
10%
20%
30%
40%
50%
60%
%non-Hisp white %non-Hisp black %Hispanic %Asian %foreign born
subsidized
attached
trapped
31
Illustrative Results (cont’d)Stayers Differ by Sites
Types of Family Stayers
25.3
8.9 10.06.8 7.4
40.2
52.0 50.351.9
64.7
34.539.1 39.7 41.4
27.9
0
10
20
30
40
50
60
70
80
90
100
Denver Des Moines Indianapolis San Antonio White Center
pe
rce
nt
of
sta
ye
rs Trapped
Attached
Subsidized
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Illustrative Results
3 Types of Newcomers
Subsidized
Attached
Trapped
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Illustrative Results (cont’d)Newcomer Demographics
Demographic Characteristics of Newcomer Types
0%
10%
20%
30%
40%
50%
60%
%non-Hisp white %non-Hisp black %Hispanic %Asian %foreign born
subsidized
unstable
better-off
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Illustrative Results (cont’d)Newcomers Differ by Sites
Types of Family Newcomers
27.2
13.118.2
30.5
13.9
35.9
24.9
34.8
27.7
21.3
36.9
62.0
47.041.8
64.8
0
10
20
30
40
50
60
70
80
90
100
Denver Des Moines Indianapolis San Antonio White Center
pe
rce
nt
of
ne
wc
om
ers
better-off
unstable
subsidized
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Analysis Next Steps
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Cluster Analysis Next Steps
Apply cluster analysis to 9 site pooled data Conduct robustness tests Analyze clusters to find:
Distribution of households across clusters by site Service utilization, demographic characteristics, and key
outcomes of cluster groups Use clusters to characterize MC neighborhoods
Incubators, launch pads, traps, gentrifying
Map locations for different types of out-movers with kids, childless movers, stayers, and newcomers
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Cluster Analysis References
Afifi, Abdelmonem, Virginia Clark, and Susanne May. 2003. Computer-Aided Multivariate Analysis. Chapman and Hall.
Finch, Holmes. 2005. Comparison of Distance Measures in Cluster Analysis with Dichotomous Data. Journal of Data Science, 3.