Studying the Social Networks of Hard-to-Reach Populations

Across the social and behavioral sciences, there is interest in how relationships shape individual welfare. However, in many contexts, collecting data on social networks is difficult because the relevant population is inaccessible with conventional sampling methodologies. This talk will focus on two methods for studying the social networks of these “hidden” or “hard-to-reach” populations. First, we will discuss respondent-driven sampling (RDS), which is widely used to study marginalized or stigmatized populations by incentivizing study participants to recruit their social connections. The success and efficiency of RDS can depend critically on the nature of the incentives, including their number, value, call to action, etc.

Standard RDS uses an incentive structure that is set a priori and held fixed throughout the study. Thus, it does not make use of accumulating information on which incentives are effective and for whom. We propose a reinforcement learning (RL) based adaptive RDS study design in which the incentives are tailored over time to maximize cumulative utility during the study. We show that these designs are more efficient, cost-effective, and can generate new insights into the social structure of hidden populations. Second, we will address hard-to-reach populations in community social network surveys.

In many settings, only specific members of a household, such as the household head, can be accessed and queried about their social connections. This makes a complete census impossible and results in an incomplete sampling frame for the social network. To remedy this issue, we explore how questions that address household behavior, such as the exchange of household goods, can be leveraged to infer missing information about community members who cannot be sampled.