2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 301
comprehensive approach to community improvement. Individuals who
participate in or qualify for the programs may be of interest in this case,
but the fact that the program is targeted within a place raises evaluation
questions and issues that are not of concern in the typical program
evaluation focused on individuals who are assumed to be independent
of one another. In fact, part of the evaluation may be to determine whether
the participants and the larger community benefit from the fact that a
threshold proportion of residents is now participating in a particular
program. Programs implemented in this way are sometimes referred to
as saturation models.
These complexities call for program theory to guide the evaluation
that takes into account direct effects on the participating or targeted
entities and also spillover effects on surrounding persons, residential
properties, or businesses. Similarly, the evaluation design cannot rely
on standard assumptions that prevail in individually focused randomized
trials, such as the assumption that the units, whether individuals
or neighborhoods, are independent from one another or that they are
exchangeable (Merlo et al. 2009; Oakes 2004). Among other things,
researchers need to be on the lookout for heterogeneous treatment effects
and correlated errors due to spatial proximity or social interaction patterns,
both within and between neighborhoods.
A number of practical considerations challenge the evaluator of neighborhood
interventions. Enrolling sufficient numbers of neighborhood
units to achieve adequate statistical power typically exceeds the costs of
enrolling a similar number of individuals. Moreover, although the principles
and methods of informed consent are well developed for individuals
who agree to be in randomized trials, the experience with enrolling
neighborhoods in experiments is quite limited. Indeed, given the many
persons and organizations that are stakeholders in the typical neighborhood,
it is not surprising that consensus about participation in research
is difficult to obtain.
As discussed in chapter 5, a high rate of residential mobility can also
complicate efforts to demonstrate the impact of a program or policy at
the neighborhood level (Coulton, Theodos, and Turner 2012). Households
move frequently even under normal circumstances, and persons
participating in a program may have a different probability of leaving
the neighborhood and reasons for moving than those who do not participate.
Residential mobility may also limit the length of households’
exposures to place-based interventions, or the move itself may exert an