2000115-Strengthening-Communities-with-Neighborhood-Data

Using Data for Neighborhood Improvement 171
due to life-cycle factors (e.g., a new baby meant they needed an
apartment with more room) than dissatisfaction with their old
house or neighborhood. They generally had positive views of their
neighborhood and new unit post-move.
• Churning movers (45 percent of movers). Also households with
very low incomes (median $14,000) who moved short distances
(median 1.7 miles). They generally viewed their neighborhoods as
unsafe and not good places to raise children. It seems likely that
many of them felt forced to move because of financial stress or
problems with their rental housing arrangements.
These findings significantly alter the way community developers should
regard mobility. First, there is no need to be disheartened by the idea that
a large share of the people who community initiatives are trying to help
will move away, as (and this is the major surprise) the majority of the
movers relocate within or near their original neighborhood. The nearby
attached and churning movers account for 70 percent of the Making
Connections moves. Second, recognizing that some moves are positive
for families, community developers can focus energy on reducing the
potentially problematic ones, the churning moves that represent residential
instability that can be very costly for children in particular (Kingsley,
Jordan, and Traynor 2012).
Another aspect of mobility needs to be taken into account when interpreting
data on trends in community well-being. Making Connections
researchers recognized that neighborhood economic indicators change
due to differentials in the incomes of in-movers and out-movers as well
as to changes in the incomes of the residents who do not move. For
example, a neighborhood’s poverty rate will go down if a large number
of poor residents move out, the average poverty rate of the in-movers is
the same as the initial neighborhood average, and the incomes of those
who stay do not change at all. The researchers calculated the implied
components of change in the poverty rates of Making Connections
neighborhoods between the wave 1 and wave 2 surveys. They found that
explanations varied across neighborhoods but, overall, “changes in poverty
occurred primarily through mobility, not because of changing circumstances
for stayers” (Coulton, Theodos, and Turner 2012, 75).
In addition to the results on mobility, Making Connections research
yielded new findings on another topic that also alters how community
improvement initiatives understand outcomes. Theodos, Coulton, and