Muffled price signals: Household water demand under increasing-block rates. FEEM Working Paper, 40/2002, Fondazione Eni Enrico Mattei
Bibliographic record
Abstract
In many areas of the world, including large parts of the United States, scarce water supplies are a serious resource and environmental concern. The possibility exists that water is being used at rates that exceed what would be dictated by efficiency criteria, particularly when externalities are taken into account. Because of this, much attention has been paid by policy makers and others to the use of demand management techniques, including requirements for the adoption of speciÞc technologies and restrictions on particular uses. A natural question for economists to ask is whether price would be a more cost-effective instrument to facilitate water demand management. As a Þrst step in such an investigation, this paper draws upon a newly-available set of detailed data to estimate econometrically the demand function for household use of urban water supplies. We analyze cross-sectional time-series data that track 1,082 single-family households served by 16 water utilities in 11 urban areas in the United States and Canada. Because of the diverse multiple-block pricing structures
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".