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Record W1490670076

Water Supply in California: Economies of Scale, Efficiency, and Privatization

2003· preprint· en· W1490670076 on OpenAlexfundno aff
John Houtsma

Bibliographic record

VenueEconstor (Econstor) · 2003
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
FundersMount Allison UniversityGeorge Mason University
KeywordsSubsidyWater supplyPublic sectorPopulationPrivate sectorGovernment (linguistics)CommissionWater sectorEconomies of scaleScale (ratio)BusinessEconomicsPublic economicsEconomyFinanceMarket economyEconomic growthGeographyMarketingEngineering
DOInot available

Abstract

fetched live from OpenAlex

In a water supply industry that consists of private and public providers, questions of interest include the following: Are there significant economies of scale, and if so at which population levels' How, on average, do private and public sector rates compare? Has there, over time, been a systematic change in the gap between private and public sector rates' Can it be demonstrated that investor owned companes are more efficient than their public sector counterparts' Given major differences in taxes and subsidies, is the real water bill for government owned utilities higher than for investor owned companies' The first three questions will be answered by a statistical analysis of water charge data collected by Black and Veatch Corp. on a biannual basis for the period 1995-2001. The remaining two questions received affirmative answers in a 1996 study commissioned by the Reason Foundation, a conservative think tank located in Los Angeles. The current paper reexamines the last two questions in light of a broader literature on privatization, and by drawing on the more extensive and more representative data set referred to above. The findings and conclusions reached in this paper provide a new and quite different perspective on the fundemental questions as to who should supply the consumers water. In the past communities have opposed proposals for privatization because of concerns over whether the Public Utilities Commission would, or would be able to, keep water rates at acceptable levels. The present paper also sheds light on this question.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.202
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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