Modeling complex network patterns in international trade

This paper examines the role of complex network patterns in determining international trade. The author proposes two empirical approaches to better identify the influences that the full structure of the trade network has on individual bilateral flows. The first uses gravity models that incorporate novel network covariates. The second uses exponential random graph models (ERGMs) that analyze trade from a network perspective. Estimates from both types of models provide strong evidence that network dependencies are influential determinants at both the intensive and extensive margin. Direct comparisons of the two approaches indicate that each can outperform the other at capturing and replicating certain types of network features. These results indicate that complex network patterns are an important determinant of trade, that gravity models can capture much of this dependency even if not explicitly controlled for, and that empirical network models such as ERGMs can be valuable tools for better capturing certain network patterns.