Paper: Impacts of Economic Policies on Wealth Distribution in Token Economies
Authors: R. Sadykhov, Dr. G. Goodell, Prof. P. Treleaven
Date: January 2026
Estimated Reading Time: 34 minutes
This paper examines how economic policies affect wealth distribution within a token economy using the Bitcoin ecosystem as the empirical case. The authors distinguish between exogenous policies, such as macroeconomic interest rates and taxes imposed outside the system, and endogenous policies, represented by Bitcoin Improvement Proposals (BIPs) that modify protocol rules. Wealth distribution is measured through buckets of Bitcoin address balances, allowing the researchers to observe redistribution patterns across different wealth tiers. A statistical process removes noise from macroeconomic variables through transformations and regression models so that the residual data reflects primarily endogenous dynamics. Using this cleaned dataset, the authors apply Granger causality tests to determine whether sets of BIPs statistically precede shifts in wealth distribution. The results suggest that different classes of policy changes influence different wealth segments, with smaller holders reacting to large structural updates and larger holders responding to broader sets of policy changes. The paper concludes by proposing a taxonomy of economic policies in token economies, separating those analogous to traditional fiscal and monetary policies from those unique to blockchain systems.
Core insights
- Endogenous vs. Exogenous Policy Distinction: The paper separates economic influences into exogenous policies that originate outside the token economy and endogenous policies embedded in the protocol itself. Exogenous policies include interest rates, tax levels, and bond yields, while endogenous policies correspond to BIPs that alter the functioning of the Bitcoin network.
- Wealth Distribution as a Policy Impact Metric: Wealth distribution is measured through buckets of Bitcoin addresses holding different ranges of BTC. Changes in these buckets over time are interpreted as indicators of economic activity and redistribution across participants.
- Noise Removal from Macroeconomic Factors: The authors apply transformations such as first-order differencing and log change transformations to make economic time series stationary. Linear regression models are then used to identify and remove variance attributable to external macroeconomic factors.
- Causal Analysis of BIPs and Redistribution: Granger causality tests evaluate whether changes in wealth distribution follow specific sets of BIPs. The study constructs several BIP subsets including all BIPs, economy related BIPs, and major economy related BIPs to examine different policy signals.
- Initial Taxonomy for Token Economy Policies: The paper proposes a structured taxonomy of token economy policies based on fiscal-like, monetary-like, and token specific mechanisms. This framework links blockchain protocol rules to conventional economic policy categories.
The analysis begins by constructing a framework that separates external economic influences from protocol level policy actions. The authors treat macroeconomic indicators such as the Federal Funds Rate, money supply measures, and bond yields as potential drivers of wealth redistribution in the Bitcoin ecosystem. These variables are transformed into stationary time series and tested through regression models against ten Bitcoin wealth distribution buckets. The results show that only a small subset of external factors significantly explains variation in the data, specifically the Federal Funds Rate affecting the smallest wealth bucket and M2 money supply influencing the 10 to 100 BTC bucket. This filtering process allows the residual time series to be interpreted as the component of redistribution not explained by external macroeconomic conditions. With this cleaned dataset, the study evaluates whether endogenous protocol policies influence wealth redistribution. BIPs are converted into a monthly signal time series where a value of 1 indicates a proposal introduced in that month. Several subsets of BIPs are constructed to represent different policy groupings, including all BIPs, economy related BIPs, major economy related BIPs, and economy related BIPs excluding major ones. The causal relationship between these signals and wealth distribution buckets is tested using two implementations of the Granger causality test. These procedures compare autoregressive models of the wealth distribution series with models that include lagged BIP signals to determine whether policy events explain additional variance.
Results indicate that endogenous policies do correlate with redistribution patterns after removing macroeconomic noise. Large protocol changes such as SegWit and Taproot appear to influence lower wealth buckets, while broader sets of policy changes correlate with movements among higher wealth buckets. This finding leads the authors to propose that participants with greater holdings may monitor protocol changes more closely or anticipate them earlier. If large holders track proposals during development, they might adjust positions before implementation, while smaller participants react only after major policy events become widely visible.
A key methodological assumption is that Bitcoin addresses represent economic agents. In reality, a single participant can control multiple addresses or multiple participants may share one address. The authors acknowledge this limitation and treat address level data as a proxy for individuals. This raises an important question about interpretation of redistribution signals: are observed movements caused by different actors responding to policy, or simply by internal restructuring of holdings by a small number of entities? Another question concerns the role of expectations. If policy changes are anticipated before formal release, does the measured lag between policy and redistribution reflect the true economic response or only the moment of public implementation?
The paper also estimates that policy effects occur within a relatively short window. Sensitivity tests varying the maximum lag length suggest that the most significant policy impact occurs within roughly six months after a BIP event. This suggests that protocol changes propagate through economic behavior over a medium term horizon rather than producing immediate shifts in wealth distribution.
The final section proposes a policy taxonomy for token economies. The framework maps protocol mechanisms to categories analogous to fiscal and monetary policy in traditional economies. Fiscal like policies include transaction fee structures and incentive distributions that allocate tokens to network participants. Monetary like policies include rules governing supply limits, token issuance, and transaction validation mechanisms. Additional categories capture blockchain specific mechanisms such as fork activation rules, wallet architectures, and governance mechanisms for protocol updates. The authors highlight that redistribution effects appear to arise from specific atomic policies rather than from broad policy labels, suggesting that individual protocol rules are the relevant analytical unit.
Overall, the study proposes a methodology for measuring policy impact in decentralized systems by combining macroeconomic filtering, statistical causality testing, and protocol analysis. It also provides an initial structure for classifying token economy policies and linking them to observable redistribution dynamics.
