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dc.contributor.authorMele, Angeloen_US
dc.description.abstractIn this paper, I develop and estimate a dynamic model of strategic network formation with heterogeneous agents. While existing models have multiple equilibria, I prove the existence of a unique stationary equilibrium, which characterizes the likelihood of observing a specific network in the data. As a consequence, the structural parameters can be estimated using only one observation of the network at a single point in time. The estimation is challenging because the exact evaluation of the likelihood is computationally infeasible. To circumvent this problem, I propose a Bayesian Markov Chain Monte Carlo algorithm that avoids direct evaluation of the likelihood. This method drastically reduces the computational burden of estimating the posterior distribution and allows inference in high dimensional models. I present an application to the study of segregation in school friendship networks, using data from Add Health containing the actual social networks of students in a representative sample of US schools. My results suggest that for white students, the value of a same-race friend decreases with the fraction of whites in the school. The opposite is true for African American students. The model is used to study how different desegregation policies may affect the structure of the network in equilibrium. I find an inverted u-shaped relationship between the fraction of students belonging to a racial group and the expected equilibrium segregation levels. These results suggest that desegregation programs may decrease the degree of interracial interaction within schools.en_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP32/10en_US
dc.subject.keywordSocial Networksen_US
dc.subject.keywordBayesian Estimationen_US
dc.subject.keywordMarkov Chain Monte Carloen_US
dc.subject.stwSoziales Netzwerken_US
dc.subject.stwSoziale Gruppeen_US
dc.subject.stwEthnische Beziehungenen_US
dc.titleA structural model of segregation in social networksen_US
dc.typeWorking Paperen_US

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