The Electric Gini: Income Redistribution through Energy Prices
Bibliographic record
Abstract
Efficient electricity pricing involves two-part tariffs: a volumetric price equal to the marginal cost of producing an additional kilowatt hour (kWh) and a fixed fee to cover any remaining fixed costs.In this paper we explore how US electricity regulators depart from this simple two-part tariff to address concerns about income inequality.We first show that in theory, price setters concerned about inequality will charge lower fixed monthly fees and higher per-kWh prices, and increasing block prices to target higher users with even higher prices.Then we use a new dataset of 1,300 utilities across the US to show that these theoretical predictions are borne out in practice.Utilities whose ratepayers have more unequal incomes levy more redistributive tariffs, charging less to low users and more to high users.To quantify these comparisons, we develop a new measure of the redistributive extent of utility tariffs that we call the "electric Gini."Utilities with higher electric Ginis (more redistributive tariffs) shift costs from households that use relatively little electricity to households that use more.But because electricity use is only loosely correlated with income, that redistribution does not meaningfully shift costs from households with low incomes to those with high incomes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".