Time-Varying Bidirectional Causal Relationships Between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network

ArXiv ID: 2501.05299 “View on arXiv”

Authors: Unknown

Abstract

The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite pro-cessing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally in-fluence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence.

Keywords: Gas Fees, Granger Causality, Blockchain Economics, Decentralized Finance (DeFi), Transaction Fees, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced econometric methods like time-varying Granger causality analysis, which involves complex statistical modeling. It is highly data-driven, analyzing real-world Ethereum blockchain data across multiple subsystems (e.g., DEXs, bridges, stablecoins) with detailed empirical findings on causal relationships and temporal heterogeneity.

  flowchart TD
    A["Research Goal<br>Dynamic causality between fees & subsystem activity"] --> B["Data Collection<br>Unique wallets, Tx volume per subsystem"]
    B --> C["Methodology<br>Time-Varying Granger Causality Analysis"]
    C --> D{"Computational Process<br>Temporal heterogeneity detection"}
    D --> E["Key Findings<br>Dynamic Outcomes"]
    E --> E1["Bidirectional: Bridge activity ↔ Fees<br>Diminishing over time"]
    E --> E2["Bidirectional: CEX activity ↔ Fees<br>Increasing competition"]
    E --> E3["Unidirectional: DEX Vol → Fees<br>Fees → User activity (Weakening)"]
    E --> E4["Intermittent: MEV Bots ↔ Fees"]
    E --> E5["Unidirectional: Fees → NFT Volume"]
    E --> E6["Strong & Growing: Fees → Stablecoin Activity"]