Working Paper: CEPR ID: DP14141
Authors: Chihsheng Hsieh; Xu Lin; Eleonora Patacchini
Abstract: This paper is concerned with methods for analyzing social interaction effects. The attention is focused on how to estimate endogenous effects, where an individual's choice may depend on those of his/her contacts about the same activity. The analysis is guidedby the data structure that is available to measure social interactions, an intuitive aspect that allows empirical researchers to understand whether and how they could study social interaction effects in their own data. First, the case where the information on social interaction patterns is limited to membership to a given group is considered, then the discussion moves to the case where the data contain information on specific relationships among pairs of individuals within each group, and the availability of data on the co-evolution of social structures and outcomes. This paper also discusses some basic methods to deal with online social network data, and the novel literature estimating social interaction effects relying only on outcome data. For each data structure, the challenges and the main methods proposed in the literature to tackle them are reviewed.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| endogenous social effects (C21) | clustering of outcomes (C38) |
| interventions like tutoring programs (I24) | effects on non-targeted individuals (D62) |
| variations in group sizes (C92) | identification of social interaction effects (C31) |
| network data (D85) | complications in causal inference (C20) |
| longitudinal data (C23) | capturing dynamic aspects of social interactions (Z13) |
| temporal spillover effects (C41) | understanding social dynamics (Z13) |