Homophily and Long-Run Integration in Social Networks
Laval University - Département d'Économique
University of Leicester - Department of Economics; Ca Foscari University of Venice - Dipartimento di Economia
Matthew O. Jackson
Stanford University - Department of Economics; Santa Fe Institute; Canadian Institute for Advanced Research (CIFAR)
Dipartimento di Economia Politica, Università degli Studi di Siena
Brian W. Rogers
Northwestern University - Kellogg School of Management
We model network formation when heterogeneous nodes enter sequentially and form connections through both random meetings and network-based search, but with type-dependent biases. We show that there is "long-run integration," whereby the composition of types in sufficiently old nodes' neighborhoods approaches the global type distribution, provided that the network-based search is unbiased. However, younger nodes' connections still reflect the biased meetings process. We derive the type-based degree distributions and group-level homophily patterns when there are two types and location-based biases. Finally, we illustrate aspects of the model with an empirical application to data on citations in physics journals.
Number of Pages in PDF File: 39
Keywords: Networks, Social Networks, Network Formation, Homophily, Segregation, Integration, Citations
JEL Classification: D85, A14, Z13
Date posted: January 23, 2012 ; Last revised: May 1, 2012
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