1. 1 Patterns of Residential Mobility Using Cluster Analysis to Identify Different Types of Movers, Stayers, and Newcomers in the Making Connections Sites

2. 2 High Rates of Family Mobility

3. 3 About Half the MC Households Moved

4. 4 Some Movers Stayed Nearby

5. 5 Spatial Patterns of Mobility Vary Des MoinesSan Antonio

6. 6 Implications for Early Childhood Initiatives?

7. 7 Mobility and Neighborhood Change

8. 8

9. 9 Less Engagement Among Newcomers

10. 10 Why Are Families Moving In and Out of the MC Neighborhoods: Cluster Analysis Hypotheses and Methods

11. 11 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)

12. 12 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

13. 13 Methods Step 1: Create cluster predictions Guided by theory, previous research, population in question, variation in data Making Connections cluster predictions (following slides)

14. 14 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

15. 15 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

16. 16 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. 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

18. 18 Hypothesized clusters of Stayers 1. Staying reflects attachment and satisfaction 2. Staying reflects dissatisfaction & lack of alternatives

19. 19 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

20. 20 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

21. 21 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

22. 22 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

23. 23 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

24. 24 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

25. 25 Why Are Families Moving In and Out of the MC Neighborhoods: Cluster Analysis Illustrative Findings

26. 26 Illustrative Results 4 Types of out-movers with kids Optimistic Homebuyers Changed Family Circumstances Reluctant Movers Unstable Families

27. 27 Illustrative Results (cont’d) Out-Mover Demographics

28. 28 Illustrative Results (cont’d) Out-Movers Differ By Sites

29. 29 Illustrative Results 3 Types of Stayers Subsidized Attached Trapped

30. 30 Illustrative Results (cont’d) Stayer Demographics

31. 31 Illustrative Results (cont’d) Stayers Differ by Sites

32. 32 Illustrative Results 3 Types of Newcomers Subsidized Attached Trapped

33. 33 Illustrative Results (cont’d) Newcomer Demographics

34. 34 Illustrative Results (cont’d) Newcomers Differ by Sites

35. 35 Analysis Next Steps

36. 36 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

37. 37 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.