2000115-Strengthening-Communities-with-Neighborhood-Data

372 Strengthening Communities with Neighborhood Data
experiment, which was designed to test, among other things, if public housing
residents in concentrated poverty neighborhoods would improve
employment outcomes as a result of moving to lower-poverty neighborhoods.
Although the Moving to Opportunity interim evaluation
(Goering and Feins 2003) did not find any significant impacts in this
regard, Clampet-Lundquist and Massey (2008) reanalyzed the data and
found significant positive impacts after controlling for the time residents
actually spent in lower-poverty neighborhoods after moving frequently.
The problem of modifiable zone boundaries is also related to the question
of how constant these boundaries remain over time and how much
the composition of residents changes. For instance, if programs in target
zones are designed to improve individual outcomes (e.g., through job
training, education, or health services), what is the extent of in- and outmigration
of the target area, and how does this migration affect program
theory, design, and outcomes? Although place-based work is often based
in areas of concentrated poverty to reach residents affected by it, what
proportion of residents who are employed by businesses or using services
in the target area actually lives within (or outside of) the target area? Relevant
research includes the assessment by Coulton, Theodos, and Turner
(2009) of the impact of mobility on neighborhood change in the context
of evaluating community initiatives (Kubisch et al. 2010, chapter 7).
Research on neighborhood change has traditionally focused on fixed
neighborhood boundaries with changing socioeconomic indicators. In
an example of recent spatial analysis that seeks to address MAUP and
resident mobility, Rey et al. (2011) identify neighborhood change not
only based on such socioeconomic indicators but also based on neighborhood
boundaries. The authors develop a new neighborhood change
index that is based on a spatially constrained cluster algorithm that generates
new neighborhood areas based on value and area similarity. In
contrast to the assumption that neighborhood boundaries remain relatively
constant over time, they find that change in the spatial extent of
neighborhoods “appears to be the rule rather than the exception because
two thirds of the census tracts [were] found to experience some reconfiguration
in what constitute[d] their neighborhood set between 1990
and 2000” (Rey et al. 2011, 61). Denser inner-city areas (which are more
likely to host place-based programs) experienced more of these neighborhood
changes than outlying areas during this period. The notion that
neighborhoods are not fixed entities is congruent with findings of RW
Ventures’s Dynamic Neighborhood Taxonomy project that 70 percent of