Inferring comparative advantage via entropy maximization
Matteo Bruno, Dario Mazzilli, Aurelio Patelli, Tiziano Squartini,, Fabio Saracco

TL;DR
This paper revises the traditional method for inferring comparative advantage in economics, identifying biases in the existing approach, and proposes an entropy maximization framework with statistical validation to improve accuracy and robustness.
Contribution
It introduces an entropy-based method with hypothesis testing to accurately identify key products and correct biases in the traditional Balassa approach.
Findings
Countries' diversification is consistently observed.
Country rankings are stable across validation schemes.
Product complexity rankings vary significantly with validation.
Abstract
We revise the procedure proposed by Balassa to infer comparative advantage, which is a standard tool, in Economics, to analyze specialization (of countries, regions, etc.). Balassa's approach compares the export of a product for each country with what would be expected from a benchmark based on the total volumes of countries and products flows. Based on results in the literature, we show that the implementation of Balassa's idea generates a bias: the prescription of the maximum likelihood used to calculate the parameters of the benchmark model conflicts with the model's definition. Moreover, Balassa's approach does not implement any statistical validation. Hence, we propose an alternative procedure to overcome such a limitation, based upon the framework of entropy maximisation and implementing a proper test of hypothesis: the `key products' of a country are, now, the ones whose…
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Taxonomy
TopicsEconomic and Technological Innovation · Global Trade and Competitiveness
