Engel's law in the commodity composition of exports

TL;DR

This paper investigates how the composition of exports across different commodity sectors correlates with economic growth, providing large-scale quantitative evidence for Engel's law using international trade data from 1962 to 2000.

Contribution

It identifies specific commodity categories with export shares significantly correlated to GDP and models these relationships with power-law elasticity, revealing patterns of economic development.

Findings

Machinery export shares positively correlate with GDP.

Food and crude materials export shares negatively correlate with GDP.

Countries with significant GDP growth tend to shift towards machinery-heavy export portfolios.

Abstract

Different shares of distinct commodity sectors in production, trade, and consumption illustrate how resources and capital are allocated and invested. Economic progress has been claimed to change the share distribution in a universal manner as exemplified by the Engel's law for the household expenditure and the shift from primary to manufacturing and service sector in the three sector model. Searching for large-scale quantitative evidence of such correlation, we analyze the gross-domestic product (GDP) and international trade data based on the standard international trade classification (SITC) in the period 1962 to 2000. Three categories, among ten in the SITC, are found to have their export shares significantly correlated with the GDP over countries and time; The machinery category has positive and food and crude materials have negative correlations. The export shares of commodity categories of a country are related to its GDP by a power-law with the exponents characterizing the GDP-elasticity of their export shares. The distance between two countries in terms of their export portfolios is measured to identify several clusters of countries sharing similar portfolios in 1962 and 2000. We show that the countries whose GDP is increased significantly in the period are likely to transit to the clusters displaying large share of the machinery category.