Economic determinants of Ethereum transaction fees in the priority fee and proof of stake periods
Bibliographic record
Abstract
We analyse the economic determinants and dynamics of transaction fees in the Ethereum blockchain before and after two significant platform updates. The first is the August 2021 EIP-1559 ‘London’ upgrade, a switch from user-bid gas price (transaction fee per unit of complexity) to a fee model in which the gas price is the sum of an algorithmically determined base fee and an optional priority fee (tip) chosen by the user. The second update (‘the Merge’) is the switch from proof-of-work to proof-of-stake transactions validation in September 2022. We estimate the impact on Ethereum transaction fees of both demand factors (block utilization, transaction type, ETH price in USD) and algorithmic supply-side factors (the block gas limit and base fee). Using data from nearly 900 million blockchain transactions, we find that the gas price is statistically significantly positively associated with the block utilization rate. A larger share of contract call transactions or legacy (user-bid gas price) transactions is linked with higher gas prices on average. On the supply side, a higher block gas limit is statistically significantly associated with lower gas prices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".