{"type":"ScholarlyArticle","title":"Ownership Concentration and Wealth Inequality in Russia","authors":[{"name":"Kirill Polovnikov","slug":"kirill-polovnikov","given":"Kirill","family":"Polovnikov","affiliation":"Skolkovo Institute of Science and Technology"},{"name":"Nikita Pospelov","slug":"nikita-pospelov","given":"Nikita","family":"Pospelov","affiliation":"Skolkovo Institute of Science and Technology"},{"name":"Dmitriy Skougarevskiy","slug":"dmitriy-skougarevskiy","given":"Dmitriy","family":"Skougarevskiy","affiliation":"European University at St. Petersburg"}],"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\r\nthrough 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).","doi":"10.24963/ijcai.2022/713","url":"https://ijcai.org/proceedings/2022/713","pdf":"https://ijcai.org/proceedings/2022/0713.pdf","markdown":"https://ijcai.org/proceedings/2022/713.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/713","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":713,"pages":{"first":5136,"last":5142},"keywords":["Multidisciplinary Topics and Applications: Databases","Data Mining: Networks","Multidisciplinary Topics and Applications: Computational Sustainability","Multidisciplinary Topics and Applications: Economics"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987140","length_sec":78,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985283","length_sec":718,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Alstadsæter et al., 2018] Annette Alstadsæter, Niels Johan- nesen, and Gabriel Zucman. 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