2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 359
lation of these interconnections is not controversial, its implication for
empirical studies perhaps is more so. We show that statistical studies in
both the fields of neighborhood effects and residential mobility potentially
suffer from biasing forces related to geographic selection on un observed
individual characteristics and endogeneity. Though the former field has
devoted a good deal of effort to develop methods to overcome selection
bias, the latter field has almost universally overlooked it. Neither field has
made much effort to address the challenge of endogeneity.
We believe that prototype efforts to model neighborhood effects and
mobility holistically through the use of IVs represent the cutting edge of
the next generation of studies in both fields. We have offered our own
work as an example of the important empirical difference that such an
approach produces, demonstrating that our concerns over bias are not
just theoretical or hypothetical. We recognize that the IV approach is
rife with challenges of its own. Paramount is the difficulty in identifying
valid and powerful variables to serve as instruments. Nevertheless, we
believe that the potential payoffs from further explorations in this realm
should prove worthwhile.
Notes
1. The need for a more holistic and dynamic approach to neighborhoods has
previously been emphasized by scholars interested in neighborhood effects (Galster
1987, 2003; Tienda 1991; Galster, Marcotte, et al. 2007). Ioannides and Zabel (2008), van
Ham and Manley (2010), Hedman and van Ham (2011), and Hedman, van Ham, and
Manley (2012) have argued that one must uncover neighborhood selection processes
to accurately assess neighborhood effects. They do not, however, holistically extend the
argument conceptually or empirically to consider how neighborhood effects may alter
neighborhood selection.
2. We recognize that perceptions of alternative neighborhoods are based on
imperfect information and that there are spatial biases to this information. A fuller explanation
of the residential search process is beyond the scope of this essay.
3. Despite its vaunted reputation, the Moving to Opportunity demonstration
research has major flaws that render any conclusions regarding neighborhood effects
based on its data highly suspect. For a thorough review see Galster (2011).
4. We here refer both to neighborhoods with high shares of ethnic minorities and
neighborhoods inhabited by an ethnically homogenous majority (native) population.
5. See Bråmå (2006) for an attempt to apply the white flight and white avoidance
theories to a Swedish context.
6. The framework is more problematic for long-distance migration (or moves
within very large metropolitan areas) as other factors come into play; see Roseman (1983).
7. This is referred to as chain migration or migration network theory.