2000115-Strengthening-Communities-with-Neighborhood-Data

356 Strengthening Communities with Neighborhood Data
characteristics of households. In any statistical study of residential
mobility there likely will be a set of unobserved individual or household
characteristics that influences both selection into and out of neighborhoods
and observed individual behaviors. In these circumstances, partial
correlations between neighborhood characteristics and individual
characteristics will not provide unbiased estimates of the true magnitude
of the causal influence of one on the other, regardless of which direction
of causation is posited. An illustration of an unobserved characteristic
is the salience given to visible symbols of prestige; those placing
great weight on such symbols will work harder to evince higher incomes
and will try to live in prestigious neighborhoods. In a model testing the
effect of individuals’ incomes on the prestige of their neighborhoods,
how much causal impact can be rightfully attributed to the former with
“prestige salience” uncontrolled?
Challenge 2: Endogeneity
The endogeneity problem refers to the mutual causality of individual
(household) characteristics and associated neighborhood characteristics.
10 As is apparent from the italicized terms in equations 1 to 7, neighborhood
changes, neighborhood effects on residents, and household
mobility patterns both affect and are affected by each other. In statistical
terms, this means that error terms are correlated among the various
equations, which produces biased estimates of the coefficients of
the endogenous variables. We emphasize that this likely source of bias
plagues both the neighborhood effects and the residential mobility literatures,
though it has almost never been recognized (let alone confronted
statistically) in either (Hedman 2011; Hedman and Galster 2013).
Meeting the Dual Challenges of Geographic Selection
and Endogeneity Biases
Although the neighborhood effects literature has devoted substantial
effort to reducing geographic selection bias through econometric techniques,
natural experiments, and (in one case) a random assignment
experiment, neither it nor the residential mobility literature has taken
the threat of endogeneity bias seriously. We think that the cutting edge
of both fields lies in the holistic econometric modeling of relationships
such as those sketched in equations 1 to 7. The classic method for dealing