2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 357
with multiple equations embodying endogenous variables is to employ
instrumental variables (IVs) in place of the endogenous predictors.
There is an added benefit by doing so here: IV approaches also provide
a means of subduing bias from geographic selection on unobservables
(Galster, Marcotte et al. 2007). Three prototype efforts are promising.
Galster, Marcotte, et al. (2007) developed a model in which parental
housing tenure, expected length of stay, and neighborhood poverty
rate were endogenous over the first 18 years of a child’s lifetime and, in
turn, jointly affected their outcomes measured as young adults. They
used two-stage least squares to obtain IV estimates for shares of childhood
years spent in a home owned by parents, shares of childhood years
when there were no residential moves, and mean neighborhood poverty
rate experienced during childhood. These IV estimates were employed
in second-stage equations predicting education, fertility, and youngadulthood
income outcomes. The results indicated that being raised in
higher-poverty neighborhoods had a substantial negative effect on educational
attainments and indirectly on incomes. Though emphasizing
neighborhood effects, this holistic approach had the potential of also
providing insights about the mobility behavior of households, though
these IV results were not reported.
Sari (2012) developed a two-equation simultaneous model using 1999
data on Paris-region adult males in which residence or nonresidence in a
deprived neighborhood was one outcome, being employed or un employed
was the other, and the two were mutually causal. Sari employed a bivariate
probit maximum likelihood method with an IV to obtain an unbiased
estimate of the effect of deprived neighborhood residence on an
individual’s employment probability. He found that residence in a
deprived neighborhood was associated with substantially lower employment
probabilities. Unfortunately, Sari did not attempt to estimate the
deprived neighborhood residence equation with an IV for employment,
so a holistic empirical portrait is missing.
Most recently, we (Hedman and Galster 2013) estimated a structural
equation system in which neighborhood income mix and individual resident
income were specified as mutually causal. Statistical tests using data
from Stockholm verified the endogenous nature of these predictors. By
adopting a fixed-effects panel model with IV proxies of both endogenous
predictors, we addressed both sources of bias. We found that selection both
on unobservables and endogeneity were empirically important sources
of potential bias in studies of neighborhood effects and neighborhood