Modeling the International-Trade Network: A Gravity Approach

TL;DR

This paper evaluates the effectiveness of the gravity model in explaining the structure of the international trade network, finding it accurately predicts trade flows when the network's binary structure is fixed, but not when predicting link existence.

Contribution

It introduces an estimation strategy for the gravity model to predict the binary trade network and compares its performance to observed data.

Findings

GM replicates weighted-network structure when binary architecture is fixed.

GM performs poorly in predicting link existence and trade levels when binary structure is estimated.

The approach highlights limitations of GM in modeling the binary aspects of ITN.

Abstract

This paper investigates whether the gravity model (GM) can explain the statistical properties of the International Trade Network (ITN). We fit data on international-trade flows with a GM specification using alternative fitting techniques and we employ GM estimates to build a weighted predicted ITN, whose topological properties are compared to observed ones. Furthermore, we propose an estimation strategy to predict the binary ITN with a GM. We find that the GM successfully replicates the weighted-network structure of the ITN, only if one fixes its binary architecture equal to the observed one. Conversely, the GM performs very badly when asked to predict the presence of a link, or the level of the trade flow it carries, whenever the binary structure must be simultaneously estimated.