Contagious Treatments: The Policy Relevance of Social Networks

Authored By 
Molly Offer-Westort
Blog contributor 
Policy Fellow
November 29, 2016
The United States government has increasingly used insights from behavioral science to
inform government policy. However, policy interventions often ignore one of the most
valuable lessons from behavioral science: people change their attitudes and behavior in
response to those around them. We know that even the most targeted policy interventions
have impacts not just on treated individuals, but can spillover onto their friends, families,
neighbors, and their communities. These changes can spread over social networks, with
those closest to a policy intervention most directly affected. Ignoring these spillover effects,
we underestimate the effects of programs, and fail to accurately measure the ways policies
can benefit–and harm–communities.
When evaluating policy interventions, in general we would like to identify the causal effect
of the intervention–that is, the difference between people’s outcomes under the intervention
and in the absence of the intervention. If the program changes the outcomes of those directly
treated under the program and their communities, we need to consider spillover to get
an unbiased estimate of the total effects of the program. Additionally, we may think that
community-level effects are most significant when programs are targeted to certain types of
people, or that spillover effects become most important once a certain share of a person’s
peers have been treated. My research will advance methods to measure and compare
different kinds of spillover effects, to help answer some of these questions. Developing and
applying better tools to accurately measure how program effects spread across networks will
give us a more comprehensive picture of how programs affect communities, and will help
policy-makers target treatments most effectively.
We can think about two general kinds of spillover effects: spillover that happens at the group
level, and spillover that happens on the individual level. To measure spillover that happens
at the group level, some programs vary the proportion of treated individuals in a community.
This is most relevant when the share of the group treated affects an individual’s outcomes,
but interactions between treated and untreated individuals don’t matter. For example, in
of children receiving deworming medication affects the number of worms in the local
environment. Consequently, this treatment indirectly affects the probability for both treated
and untreated children of contracting worms in the future. Children can’t “catch” worms
from each other, so spillover is not person-to-person, but via an environmental factor. We
generally think about level of vaccination in a similar way, and often consider a group-level
immunity threshold, above which even unvaccinated individuals are extremely unlikely to
contract a disease.
For behavioral outcomes, however, we know that individuals are most affected by the
attitudes and behaviors of those closest to them. Using information about social ties,
network analysis can give us a more nuanced understanding of spillover effects, as we can
consider exposure not just at the community level in terms of the share of treated people,
but also at the individual level in terms of the number or characteristics of treated people
that a given person has contact with.
For example, suppose a school is implementing a smoking cessation program that targets
individual students, and the administrators of the program care about the effect the program
has on whether students smoke. The program may directly affect students who participate,
but these effects may also spillover from to the friends of students who went through the
program. We can imagine that a student who didn’t go through the program might be more
or less likely to change their smoking behavior based on how many of their close friends
went through the program, or based on characteristics of the treated students, such as how
popular or social they are.
We can also learn more about the conditions for spillover. Spillover effects flow through
two general channels: “outcome interference” and “treatment interference.” Outcome
interference occurs when a person’s outcomes are affected by the outcome of their peers. If
a student who went through the smoking cessation program stops smoking because of it,
their friends may also be more likely to stop smoking due to the social nature of the activity.
Treatment interference occurs when a person’s outcomes are affected by the treatment of
their peers, regardless of their peers’ outcomes. Even if a student who went through the
smoking cessation program doesn’t stop smoking, they may pass on information about the
program or discuss the topic with their friends, potentially resulting in behavior changes in
their friends.
These channels are of course not mutually exclusive. But refining methodological tools
for network analysis will help us understand the extent to which each of these channels
contributes to behavior change, and how this varies with different types of outcomes and
different types of programs.
Molly Offer-Westort is an ISPS Graduate Policy Fellow and a doctoral student in political science at Yale. Her research is on quantitative methodology for social science research.
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