Crypto Microeconomics: The Distribution of Bitcoin Wealth Among Diverse Economic Agents

Jul 16, 2026

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Bitcoin (BTC) wealth distribution is examined through micro-level analysis rather than purely macro indicators such as wallet balances, prices, network activity, fees, and hashrate. A “Crypto-Microeconomic Observability Framework” evaluates disparities across five labeled agent classes: Service, Abuse, Malware, Individuals, and Benign. Descriptive, inequality, and longitudinal concentration metrics reveal strong cross-class concentration aligned with a persistent “Whale-Effect”. Service entities hold 75.15% of observed BTC, while Abuse controls 24.26% with only 3.53% of entities.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Introduction

Macro vs. microeconomic perspectives

Wealth inequality metrics used

Identified gaps and motivations

Study contribution and purpose

Crypto-Microeconomics: The Distribution of Bitcoin Wealth Among Diverse Economic Agents Syed Azhar Hussain Munster Technological University, Ireland Kashif Ahmad Munster Technological University, Ireland Mubashir Husain Rehmani Munster Technological University, Ireland arXiv :2607 .03646v 1 [ cs .CE] 3 Jul 2026 Abstract—Bitcoin (BTC) wealth distribution is often studied with macro indicators like wallet balances, prices, network activity, fees, and hashrate. This letter proposes a “CryptoMicroeconomic Observability Framework” to examine microlevel Bitcoin wealth disparities across five labeled agent classes: Service, Abuse, Malware, Individuals, and Benign. Using descriptive, inequality, and longitudinal concentration metrics, we show that Bitcoin wealth is highly concentrated across major classes, consistent with a persistent “Whale-Effect”. Service entities hold the largest share of observed BTC (75.15%), while Abuse controls a disproportionately large share relative to its entity count (24.26% of BTC vs. 3.53% of entities). Individuals, Abuse, and Service show near-maximal within-class inequality (e.g., Gini = 0 .9993 for Individuals), and time-series analysis indicates these patterns persist. Overall, Bitcoin wealth among labeled economic agents remains structurally uneven and concentrated in a small subset of entities. Index Terms—Bitcoin wealth distribution, Cryptomicroeconomics, Wealth inequality metrics, Whale-effect in cryptocurrency, Labeled economic agents I. INTRODUCTION Macroeconomic evaluations employ aggregate data to track wealth distribution patterns [1], while microeconomic models examines how the behavior and market participation of individual economic agents influence income and pay distribution [2] as well as Bitcoin’s supply, demand, and value [3], thereby potentially contributing to wealth inequality. In both conventional and crypto-currency economies, analyses at both macro- and microlevels identify the effects of capital accumulation and disparities in wealth. In Bitcoin, understanding the wealth distribution is essential for assessing claims about decentralization. In addition, it contributes to strengthening security by uncovering vulnerabilities associated with wealth aggregation and offers deeper economic insights into the behavior and stability of the evolving crypto-economy [4] . Initially promoted as an instrument to democratize finance and address economic disparity, Bitcoin’s wealth allocation frequently mirrors or worsens conventional economic inequalities [1], and is widely regarded as highly volatile and speculative asset [5], with limited adoption as a medium of exchange, even where recognized as legal tender [6] . In the crypto-economy, complex networks connected withdeanonymized Bitcoin holders, wallets, and addresses [4] have presented challenges to the investigation of wealth distribution. Traditionally, the examination of the wealth distribution within the Bitcoin ecosystem has utilized “macroeconomic indicators”, including wallet and account balances, market prices, mining pools attributes, network activity, among others, analyzed with widely recognized wealth inequality metrics. Also recent literature about crypto-microeconomics by [3] examines the supply, demand, market value, and competition of cryptocurrencies, focusing on the incentives of miners, users, exchanges, and rival networks. Moreover, these empirical investigations rely on established wealth inequality metrices such as Lorentz and Lame curves, the HerfindahlHirschman index, Pareto, Zipf’s laws, Top-k wealth shares, Shannon entropy, Gini coefficient, and Nakamoto coefficient [7, 8, 4, 9] . Specifically, the Gini coefficient measures wealth inequality, while the Nakamoto index evaluates wealth concentration ata threshold 51%, particularly relevant in Proof-of-Work cryptocurrencies [1] . The macroeconomic assessment of Bitcoin revealed that its Gini coefficient decreased from an extreme 0.997 i

FAQs

What framework is proposed to study Bitcoin wealth at the micro level?

The study proposes a “Crypto-Microeconomic Observability Framework” to examine microlevel Bitcoin wealth disparities across five labeled agent classes: Service, Abuse, Malware, Individuals, and Benign.

How concentrated is Bitcoin wealth across the labeled agent classes?

Bitcoin wealth is highly concentrated across major classes, consistent with a persistent “Whale-Effect”. Service holds the largest share of observed BTC (75.15%), while Abuse holds 24.26% relative to 3.53% of entities.

Which kinds of metrics are used to quantify inequality and concentration?

The work uses descriptive, inequality, and longitudinal concentration metrics, including Gini coefficient and concentration-related measures such as the Nakamoto index and other established inequality/concentration tools mentioned in the text.

Jul 16, 2026

Upload by Aria