A Model and Estimation of the Bitcoin Transaction Fee

TL;DR

This paper develops a structural model of Bitcoin fee formation, using high-frequency mempool data to analyze how congestion and transaction features influence fees and confirmation delays.

Contribution

It introduces a novel, data-driven approach to estimate fee dynamics by modeling the mempool as a market and characterizing it as a VCG mechanism.

Findings

Congestion is the primary factor affecting confirmation delays.

Fees are mainly driven by the marginal value of transaction priority.

Transaction strategies like RBF and CPFP significantly impact fees.

Abstract

Bitcoin transaction fees will become more important as the block subsidy declines, but fee formation is hard to study with blockchain data alone because the relevant queueing environment is unobserved. We develop and estimate a structural model of Bitcoin fee choice that treats the mempool as a market for scarce blockspace. We assemble a novel, high-frequency mempool panel, from a self-run Bitcoin node that records transaction arrivals, exits, block inclusion, fee-bumping events, and congestion snapshots. We characterize the fee market as a Vickery-Clarke-Groves mechanism and derive an equation to estimate fees. In the first-stage we estimate a monotone delay technology linking fee-rate priority and network state to expected confirmation delay. We then estimate how fees respond to that delay technology and to transaction characteristics. We find that congestion is the main determinant of delay; that the marginal value of priority is priced in fees, which is increasing in the gradient of confirmation time reduction per movement up in the fee queue; and that transactor choice of RBF, CPFP, and block conditions have economically important effects on fees.