2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 347
ture, Galster (2002) finds consistent threshold effects on various resident
behaviors when the neighborhood poverty rates exceed 15 to 20 percent.
Galster et al. (2010) and Hedman and Galster (2013) find substantial negative
effects on individual Stockholm residents’ incomes if they reside in
neighborhoods with over 40 percent low-income neighbors.
Often embedded in neighborhood effect theory is the implicit assumption
that neighborhoods are static. Neighborhoods are often discussed as
separate entities providing a relatively constant context that consistently
affects individuals who are exposed to it for an extended period (e.g.,
childhood upbringing). The neighborhood’s status in the urban hierarchy
is typically regarded as fixed over time, as are the features that make up
its opportunity structures. For example, an area once defined as a poverty
area is implicitly assumed to remain a poverty area, with essentially
unaltered or only slowly changing physical and population characteristics.
Of course, neighborhoods typically are dynamic; they often change
their aggregate demographic and socioeconomic profiles with frequently
selective moves in and out by households. The selectivity of who stays,
who moves in, and who moves out can maintain, improve, or impair a
neighborhood’s status position in the urban hierarchy and its internal
social dynamics. But, more fundamentally, the dynamism of the neighborhood
context can directly shape the magnitude of any measured
neighborhood effect by shaping the duration of exposure (Galster 2012).
The temporal dimension of neighborhood effects has been made
more explicit in limited empirical work, with a few studies paying attention
to how variations in the timing and duration of exposure modified
the observed relationship. They paint a consistent portrait, however, that
neighborhood effects seem to be stronger if the exposure is cumulative,
and sometimes effects appear only after a lag. Aaronson (1998) examined
how neighborhood poverty rates affected teens’ school dropout rates
and found that the average (cumulative) neighborhood conditions experienced
between ages 10 and 18 were much stronger predictors than contemporaneous
conditions. Wheaton and Clarke (2003) investigated the
temporal dimension of neighborhood disadvantage effects on the mental
health of young adults. They found that current neighborhood had
no effect, but earlier neighborhood disadvantage experienced as a child
had a lagged effect that grew stronger as cumulative exposure intensified.
Turley (2003) found that white (though not black) children’s school
test scores and several behavioral indicators grew more efficacious the
greater the mean income of their neighborhoods. These relationships