Abstract
Knowledge of beneficial owners of companies is key to monitoring and managing wealth inequality in any country. Here we propose a robust and scalable network-based algorithm to reveal hidden ultimate owners in public ownership data. Our approach is based on the idea of Katz centrality in complex networks and circumvents the problem of cyclic ownership used to obscure effective control
through closed chains of intermediaries. When applied to a country-scale directed ownership network with 6 million nodes, the algorithm identifies ultimate holders of every organisation in 2021’s Russia. The distribution of asset ownership in the country follows a power law, indicating strong wealth inequality with Gini index of 0.93. 51.7% of net assets of non-financial companies are ultimately held by the state and state-owned enterprises, 25.0% — by individuals (incl. 3.4% held by Forbes–200-listed individuals), and 11.3% are owned by foreign entities (incl. 5.7% in tax havens).
Keywords
- Data Mining: Networks
- Multidisciplinary Topics and Applications: Computational Sustainability
- Multidisciplinary Topics and Applications: Databases
- Multidisciplinary Topics and Applications: Economics
