Latent Dirichlet Allocation Models for World Trade Analysis

TL;DR

This paper applies Latent Dirichlet Allocation, a technique from NLP, to analyze international trade data, revealing latent product classifications and insights into countries' trade specialization over time.

Contribution

It introduces the use of LDA models for analyzing international trade data, uncovering latent product groupings and their distribution across countries.

Findings

Higher level classifications of goods identified

Insights into countries' trade specialization patterns

Temporal evolution of trade classifications analyzed

Abstract

The international trade is one of the classic areas of study in economics. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. The present paper shows the application of the Latent Dirichlet Allocation Models, a well known technique from the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries' exports of goods from 1962 to 2016. The findings show the possibility to generate higher level classifications of goods based on the empirical evidence, and also allow to study the distribution of those classifications within countries. The latter show interesting insights about countries' trade specialisation.