2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 309
tended to stay within the racially segregated areas of their cities rather
than being exposed to suburban areas or ones with predominately white
populations. Finally, families assigned to the voucher group did not necessarily
move, and many families in the control group did not stay in
their initial unit. Moreover, movers often moved again after their initial
assignment, limiting their exposure to the lower-poverty condition.
Given the practical limitations on random assignment of households
to neighborhoods, the search continues for methodological improvements
that have the potential to address the problem of selection bias while
achieving broader population representation and accurately reflecting
the processes of residential mobility. Sharkey (2012) demonstrates how
longitudinal survey data can be used to model the effect of living in
disadvantaged neighborhoods by exploiting the change in concentrated
disadvantage that occurs over time in neighborhoods. He defines the
equivalent of the experimental treatment as living in a neighborhood
that decreased in concentrated disadvantage over a 10-year period. The
control condition is defined as living in a census tract that stayed the
same or went up in concentrated disadvantage. To control for selection
bias into neighborhoods, the experimental and control groups are rigorously
matched at the beginning of the 10-year window on neighborhood
disadvantage in the preceding 10-year period. The outcomes for the individuals
are observed during the 10 years following the treatment. Among
other things, this study demonstrates that it is possible in a national sample
to observe sizable neighborhood changes (e.g., those in the treatment
group saw their neighborhoods fall by more than one standard deviation
in concentrated disadvantage) and to model the effect of neighborhood
improvement on individual outcomes after neighborhood selection bias
has been taken into account.
As illustrated above, considerable scientific research effort has been
directed at trying innovative techniques to estimate the impact of neighborhood
disadvantage on individuals, net of their residential choices and
mobility constraints. However, residential mobility, neighborhood selection,
neighborhood effects, and neighborhood change are interrelated
processes that are of considerable importance because they not only affect
individuals and households, but at the aggregate level they are responsible
for the social transformation of neighborhoods and cities. In their
essay that follows this chapter, Galster and Hedman argue that rather
than narrowly focusing on making neighborhood selection ignorable,
which tends to require designs that limit the ability to generalize study