Forecasting the countries’ gross domestic product growth: The case of Technological Fitness

Authors: Orazio Angelini (King’s College London), Andrea Gabrielli (CREF- Roma3) Andrea Tacchella (CREF)  Andrea Zaccaria (ISC-CNR, Roma3),   Luciano Pietronero (CREF, ISC-CNR), Tiziana Di Matteo (CREF, King’s College London, CSH Vienna)

in Chaos, Solitons and Franctals vol 184

https://doi.org/10.1016/j.chaos.2024.115006

The cornerstone of Economic Complexity (EC) studies is the assumption that most of the fundamental information about countries’ capabilities can be extracted from the products they export. This extreme dimensionality reduction is evident in the typical models used in EC for Gross Domestic Product Per Capita (GDPpc) forecasting, in which only two dimensions – Economic Fitness (EF) and GDPpc – are considered. In this work, we consider adding a third dimension, Technological Fitness (TF), computed from countries’ measured patenting activity. We find this improves the GDPpc forecast by disambiguating the different growth patterns of countries. The effect is clearer for those advanced-development countries that already export most of the products present in customs ontologies, saturating along the EF dimension. Importantly, we show that a higher dimensional model is not necessarily better for all countries and at all times. We illustrate a finding that exemplifies this: while adding TF information improves the GDPpc predictions for China, this is not true for India. This country, according to traditional metrics, is very similar. We suggest that future work introducing new information in EC should exercise care in tailoring the observable quantities employed to each examined country.