Paper: arXiv 2602.17373

Authors: Rem Sadykhov, Geoff Goodell, Philip Treleaven

Abstract

In this paper, we analyse the impacts of exogenous and endogenous factors on wealth distribution in the Bitcoin token economy, where wealth distribution refers to the distribution of BTC between economic participants or groups of economic participants. The objective of the paper is to analyse the impact of economic policies on wealth distribution in the Bitcoin ecosystem. Different macroeconomic and microeconomic time series are used to eliminate noise in the wealth distribution time series, and the causality analysis is performed between Bitcoin Improvement Proposals (i.e., BIPs) and the cleaned wealth distribution data to reveal possible patterns in the impacts that the endogenous policies have on wealth distribution in token economies. Lastly, a structure for economic policy taxonomy in token economies is proposed where different the policy implementations are illustrated by existing BIPs. This approach highlights the actions available to the policy makers, as well as providing a technique for analysis of policy impacts in token economies and their categorization.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides an empirical methodology for filtering macroeconomic and microeconomic noise out of Bitcoin wealth distribution data to analyze the causal impact of Bitcoin Improvement Proposals (BIPs). The mathematics is basic (stationarity transformations, Granger causality/regression noise extraction), but the dataset combination across FRED, IADB, and CCData is empirically rigorous and introduces an interesting taxonomy for crypto economic policy.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Analyze Economic Policy Impacts on BTC Wealth Distribution"] --> B["Data Inputs:<br>Macro/Microeconomic Time Series & BIPs"]
    B --> C["Time Series Analysis:<br>Noise Elimination in Wealth Data"]
    C --> D["Causality Analysis:<br>Assess Endogenous BIP Impacts on Cleaned Wealth Data"]
    D --> E["Key Outcome 1:<br>Revealed Patterns of Policy Impacts on Wealth Distribution"]
    D --> F["Key Outcome 2:<br>Proposed Token Economy Policy Taxonomy"]

Cited References (13)

  1. DeTEcT: Dynamic and Probabilistic Parameters Extension — → 2405.16688
  2. Decentralized Token Economy Theory (DeTEcT) — → 2309.12330
  3. Array programming with NumPy (2020) — link
  4. Wikipedia (2014) — link
  5. Investigating causal relations by econometric models and cross-spectral methods (1969) — link
  6. Blockchain and Distributed Ledger Technologies - Vocabulary , (2023)
  7. Statsmodels: Econometric and Statistical Modeling with Python (2010) — link
  8. Iteratively filter out the insignificant lagged terms of X until only the terms that pass t-test at a given critical level remain
and 5 more
  1. Run the F-test between the nested and parent models to determine whether adding the independent variable X explains away some of the variance, given the specified critical level for the F-test
  2. CCData API
  3. DigiFi
  4. Extend the nested model to include autoregressive terms of X , where the order of X is selected by minimizing an information criterion
  5. Bitcoin Improvement Proposals

2 of 13 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.