| Magazines |
Published online: 26 Aug 2019 |
| Author | Xiaoyi Han, Chih-Sheng HSIEH, Stanley I. M. KO |
| Content | This study primarily seeks to answer the following question: How do social networks evolve over time
and affect individual economic activity? To provide an adequate empirical tool to answer this question,
we propose a new modeling approach for longitudinal data of networks and activity outcomes. The key
features of our model are the inclusion of dynamic effects and the use of time-varying latent variables to
determine unobserved individual traits in network formation and activity interactions. The proposed model
combines two well-known models in the field: latent space model for dynamic network formation and
spatial dynamic panel data model for network interactions. This combination reflects real situations, where
network links and activity outcomes are interdependent and jointly influenced by unobserved individual
traits. Moreover, this combination enables us to (1) manage the endogenous selection issue inherited in
network interaction studies, and (2) investigate the effect of homophily and individual heterogeneity in
network formation. We develop a Bayesian Markov chain Monte Carlo sampling approach to estimate the
model. We also provide a Monte Carlo experiment to analyze the performance of our estimation method
and apply the model to a longitudinal student network data in Taiwan to study the friendship network
formation and peer effect on academic performance. Supplementary materials for this article are available
online. |
| JEL-Codes | |
| Keywords | Bayesian; Dynamic network formation; Latent variable; Peer effects; Spatial dynamic panel data model |
