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Researchers have long recognized that the non-random sorting of individuals into groups
generates correlation between individual and group attributes that is likely to bias
naïve estimates of both individual and group effects. This paper proposes a non-parametric
strategy for identifying these effects in a model that allows for both individual
and group unobservables, applying this strategy to the estimation of neighborhood
effects on labor market outcomes. The first part of this strategy is guided by a robust
feature of the equilibrium in vertical sorting models - a monotonic relationship between
neighborhood housing prices and neighborhood quality. This implies that under certain
conditions a non-parametric function of neighborhood housing prices serves as a suitable
control function for the neighborhood unobservable in the labor market outcome regression.
This control function transforms the problem to a model with one unobservable so that
traditional instrumental variables solutions may be applied. In our application, we
instrument for each individuals observed neighborhood attributes with the average
neighborhood attributes of a set of observationally identical individuals. The neighborhood
effects model is estimated using confidential microdata from the 1990 Decennial Census
for the Boston MSA. The results imply that the direct effects of geographic proximity
to jobs, neighborhood poverty rates, and average neighborhood education are substantially
larger than the conditional correlations identified using OLS, although the net effect
of neighborhood quality on labor market outcomes remains small. These findings are
robust across a wide variety of specifications and robustness checks.
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