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Characterizing Wealth Inequality in Cryptocurrencies
Open accessAbstract
Cryptocurrencies often tend to maintain a publically accessible ledger of all transactions. This open nature of the transactional ledger allows us to gain macroeconomic insight into the USD 1 Trillion crypto economy. In this paper, we explore the free market-based economy of eight major cryptocurrencies: Bitcoin, Ethereum, Bitcoin Cash, Dash, Litecoin, ZCash, Dogecoin, and Ethereum Classic. We specifically focus on the aspect of wealth distribution within these cryptocurrencies as understanding wealth concentration allows us to highlight potential information security implications associated with wealth concentration. We also draw a parallel between the crypto economies and real-world economies. To adequately address these two points, we devise a generic econometric analysis schema for cryptocurrencies. Through this schema, we report on two primary econometric measures: Gini value and Nakamoto Index which report on wealth inequality and 51% wealth concentration respectively. Our analysis reports that, despite the heavy emphasis on decentralization in cryptocurrencies, the wealth distribution remains in-line with the real-world economies, with the exception of Dash. We also report that 3 of the observed cryptocurrencies (Dogecoin, ZCash, and Ethereum Classic) violate the honest majority assumption with less than 100 participants controlling over 51% wealth in the ecosystem, potentially indicating a security threat. This suggests that the free-market fundamentalism doctrine may be inadequate in countering wealth inequality within a crypto-economic context: Algorithmically driven free-market implementation of these cryptocurrencies may eventually lead to wealth inequality similar to those observed in real-world economies.
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“Most of the existing academic work on wealth distribution in token economies is concerned with the centralization of wealth.”
From Decentralized token economy theory (DeTEcT): token pricing, stability and governance for token economies (2023) · cites this paper as Sai et al., 2021
In the citing paper
“Not all economies necessarily satisfy this assumption, but empirical works on wealth distribution in token economies (Sai et al., 2021; Ku ś mierz and Overko, 2022) show that some economic metrics (i.e., Gini coef fi cient, Shannon entropy, Nakamoto index) applied to the wealth distribution demonstrate initial volatility of the wealth distribution that stagnates over time.”
From Decentralized token economy theory (DeTEcT): token pricing, stability and governance for token economies (2023) · cites this paper as Sai et al., 2021
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Metadata
- Authors
- Sai, A. R., Buckley, J., & Le Gear, A.
- Affiliations
- University of Limerick (IE)
- Corresponding author
- Sai, Ashish Rajendra
- Venue
- Frontiers in Blockchain
- Publisher
- Frontiers Media SA
- Published
- 2021-12-20
- Volume
- 4
- ISSN
- 2624-7852 (ISSN-L)
- OpenAlex
- W4200500220
- Cited by
- 45
- References
- 64
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 7 · 2023: 9 · 2022: 3
- FWCI
- 4.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Blockchain (publishedVersion, cc-by)
- University of Limerick Institutional Repository (University of Limerick) (acceptedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by-nc-sa)
- University of Limerick Institutional Repository (University of Limerick) (submittedVersion, cc-by-nc-sa)
- University of Limerick Institutional Repository (University of Limerick)
- Funders
- Science Foundation Ireland
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Sociology and Political Science › Income, Poverty, and Inequality
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- wealth distribution, Gini coefficient, 51% attack, decentralization (distributed systems), Bitcoin, Ethereum, Dogecoin, Zcash, DASH, Litecoin, Bitcoin Cash, information security
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