The economic costs of oligopoly in the markets of rare earth minerals

Giuseppe Di Vita et al.

Journal of Policy Modeling2026https://doi.org/10.1016/j.jpolmod.2025.11.004article
AJG 2ABDC A
Weight
0.50

Abstract

In this research, the costs of rare earth elements (REEs) due to the oligopolistic market structure are estimated. On the supply side, China exercises a dominant position in the international REEs market because it controls more than 40 percent of deposits and more than 60 percent of REEs traded. Other countries assume an ancillary role in international markets for these minerals, which will increasingly play a crucial role in economic growth in the coming decades, since REEs are considered the minerals of technology and the green transition. Using the fringe oligopoly model, the costs due to this market structure for economies less endowed with deposits of these minerals are estimated. To perform this analysis, the Herfindahl-Hirschman Index was calculated, and a new index of dependence on REEs, as a proxy for a country's economic vulnerability, was constructed. Our database covers fifty years and sixty-two countries. Most of the data used comes from the World Bank database. Using quantile econometric models, we can calculate the economic costs of countries with fewer REEs caused by the market power exerted by the leading country in this market and estimate the loss for each country involved in the international trade of these minerals. Finally, technological advancements play a pivotal role in aiding countries reliant on REEs to lower their costs. This can be achieved through policies aimed at minimizing the utilization of REEs during manufacturing, harnessing the recycling of minerals from used products, and discovering alternative materials to replace REEs.

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https://doi.org/https://doi.org/10.1016/j.jpolmod.2025.11.004

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@article{giuseppe2026,
  title        = {{The economic costs of oligopoly in the markets of rare earth minerals}},
  author       = {Giuseppe Di Vita et al.},
  journal      = {Journal of Policy Modeling},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jpolmod.2025.11.004},
}

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