Price Elasticity of Gas Demand on L1 and L2: Evidence from Ethereum and Arbitrum

2026-06-11

AI Summary

This study provides the first empirical identification of the price elasticity of gas demand on Ethereum Mainnet (Layer 1) and Arbitrum One (Layer 2), offering micro-level insights for transaction fee mechanism design and resource pricing. Leveraging on-chain data from 2025–2026, the analysis employs a two-way fixed-effects panel regression combined with an instrumental variables approach to address endogeneity, alongside behavioral clustering and resource-type decomposition. The results reveal an overall elasticity of −0.006 on Layer 1 and −0.036 on Layer 2, with refundable resources on Layer 2 exhibiting a notably higher elasticity of −0.27. Moreover, highly active user clusters demonstrate elasticities up to six times the overall average, underscoring substantial heterogeneity across both resource types and user segments.

Abstract

We estimate the causal price elasticity of gas demand on Ethereum mainnet (L1) and Arbitrum One (L2), a quantity necessary for calibrating fee mechanism simulations, evaluating resource pricing reforms, and explaining observed usage patterns. A two-way fixed effects panel regression instrumented by each wallet's own lagged base fee removes the congestion-driven endogeneity that causes naive regressions to substantially underestimate demand sensitivity. On Ethereum mainnet (full year 2025), the pooled IV elasticity is -0.006***, near-inelastic: a 10% fee increase reduces total gas demand by approximately 0.06%. On Arbitrum One (October 2025--April 2026), the pooled IV elasticity is -0.036**. Both chains are inelastic in the aggregate, with L2 measurably more responsive than L1. A per-resource decomposition of L2 demand reveals elasticities ranging from modestly elastic computation (-0.027*) to -0.27*** for refunds, with storage growth (-0.15***) and calldata (-0.06*) in between. Behavioral clustering identifies always-on protocol wallets as near-inelastic and high-volume operators as substantially more responsive, with cluster-level elasticities up to roughly 6x the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.

Problem

• price elasticity
• gas demand
• Ethereum
• Arbitrum
• fee mechanism

Innovation

• price elasticity
• instrumental variables
• gas demand
• fee mechanism
• behavioral clustering