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        <identifier>oai:drops-oai.dagstuhl.de:27889</identifier>
        <datestamp>2026-10-02T17:40:23Z</datestamp>
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          <dc:title>Price Elasticity of Gas Demand on L1 and L2: Evidence from Ethereum and Arbitrum</dc:title>
          <dc:creator>Anchuri, Pranay</dc:creator>
          <dc:creator>Mamageishvili, Akaki</dc:creator>
          <dc:subject>gas pricing</dc:subject>
          <dc:subject>demand elasticity</dc:subject>
          <dc:subject>instrumental variables</dc:subject>
          <dc:subject>panel data</dc:subject>
          <dc:subject>fixed effects</dc:subject>
          <dc:subject>Arbitrum</dc:subject>
          <dc:subject>Ethereum</dc:subject>
          <dc:subject>transaction fee mechanism</dc:subject>
          <dc:description>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 6× the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Pranay Anchuri and Akaki Mamageishvili</dc:contributor>
          <dc:date>2026</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 395, 8th Conference on Advances in Financial Technologies (AFT 2026)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.AFT.2026.35</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-278895</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2026.35</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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