Modeling Complex Network Patterns in International Trade

USITC Office of Economics Working Paper 2017–04–A

39 Pages Posted: 1 Jun 2020

See all articles by Peter Herman

Peter Herman

United States International Trade Commission, Office of Economics

Date Written: June 29, 2019

Abstract

When studying the formation of trade between two countries, traditional modeling has described it as being primarily dependent on individual and bilateral characteristics of the two trading partners. It is likely, however, that trade is dependent not only on the two countries involved but on the patterns by which all countries trade. Standard efforts to control for these complex network dependencies such as the inclusion of multilateral resistance terms in gravity models provide only a blunt reflection of these dependencies and overlook many of their details. This paper describes the explicit incorporation of complex network patterns in trade models. Two types of models are considered: gravity models that incorporate network covariates and exponential random graph models (ERGMs) that analyze trade from a network perspective. Estimates of both models provide evidence that network dependencies are influential in international trade. Comparisons of both models indicate that each approach outperforms the other at capturing and replicating certain types of network patterns. These results indicate that complex network patterns are an important determinant of trade, that gravity models can capture much of this dependency, and that other network models such as ERGMs can be valuable tools for capturing some types of network dependencies.

Keywords: trade, networks, gravity, ERGM

JEL Classification: F14, D83

Suggested Citation

Herman, Peter, Modeling Complex Network Patterns in International Trade (June 29, 2019). USITC Office of Economics Working Paper 2017–04–A, Available at SSRN: https://ssrn.com/abstract=3590317 or http://dx.doi.org/10.2139/ssrn.3590317

Peter Herman (Contact Author)

United States International Trade Commission, Office of Economics ( email )

500 E St SW
Washington, DC 20436
United States

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