Augmented Gravity Model of Trade with Social Network Analysis

Abstract

The 2030 Agenda for Sustainable Development recognizes international trade as an engine for inclusive economic growth and poverty reduction, and an important means to achieve the sustainable development goals (SDGs). Given this role, international trade and global trade networks have received preeminent attention in recent times due to the sharp growth in trade volumes and their contributions to poverty reduction and sustainable development over the years. Despite the growth in global trade volumes and the associated benefits, there are worrying concerns about the unprecedented changes in international trade patterns and growing imbalances in trade in recent times. Whereas China’s trade volumes have more than tripled over recent decades and have maintained the largest trade surplus spot ahead of Germany, other major trade surplus countries such as Japan, the USA, etc., have become trade deficit countries with their trade volumes increasing at a much slower pace. In view of the growing changes in international trade patterns and the need to understand the dynamics of the widening global trade imbalances among major trading partners, this paper investigates the factors that determine the export performance of countries. More specifically, the purpose of this paper is to highlight the forces that determine the export performance of countries using an augmented gravity model that includes social networks. The paper provides useful insights into the underlying currents responsible for the changes in international trade patterns. Using data from 51 countries for 41 years, our results indicate that the GDP of both the home country and the partner country affects the home country’s exports positively. Similarly, the real effective exchange rate REER, trade openness OPEN, and dummy variables BORDER, ENGLISH, and EU have produced positive and statistically significant coefficient estimates, and these are in line with our theoretical expectations.