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Xiaoting Sun receives SSHRC Insight Development Grant
Congratulations to assistant professor Xiaoting Sun who has been awarded the Social Sciences and Humanities Research Council (SSHRC) Insight Development Grant for her research project examining social interactions from an economics perspective.
Social interaction models study how an individual’s outcome (e.g. GPA) is influenced by peers’ outcomes and characteristics (e.g. socioeconomic status). This type of model originates from sociology and has received a wide range of attention in various social science disciplines such as economics and education. According to Sun, the overall goal of her project is to develop a methodologically empirical framework for identifying and estimating the social effects in social interaction models.
Sun is among the 18 researchers from Simon Fraser University (SFU) who successfully received a SSHRC Insight Development Grant for their projects. The grant provides funding to research in its initial stages and enables the development of new research questions, as well as experimentation with new methods, theoretical approaches and ideas.
Project title: Social Interactions
This paper studies a linear-in-means social interaction model with endogenous peer groups. We characterize group formation using a two-sided many-to-one matching model, where individuals choose among groups according to their preferences, and each group ranks individuals based on their qualifications and admits those with higher qualifications given the capacity. The groups formed in equilibrium are in general a function of the observed and unobserved characteristics of all individuals in the market, rendering it difficult to correct for the selection bias. Following the many-to-one matching literature, we show that equilibria in a finite market can be approximated by the equilibrium in a limiting market when the number of individuals approaches infinity, and in the limit the group that an individual joins only depends on her own characteristics.
Based on the limiting approximation, we derive the selection bias as a group-specific nonparametric function of the preference and qualification indices in group formation. We show that the parameters in social interactions can be identified once we control for the selection bias as in a sample selection model. The selection into groups also helps resolve the reflection problem. We propose a two-stage distribution-free estimation strategy, where we estimate the group formation parameters in the first stage and estimate the social interaction parameters in the second stage, both by semiparametric two-step estimators. We conduct a simulation study to examine the performance of the estimators.