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Record W3125599707

Cost Constrained Industry Inefficiency

2014· preprint· en· W3125599707 on OpenAlexaboutno aff
Antonio Peyrache

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typepreprint
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
Fundersnot available
KeywordsInefficiencyNonparametric statisticsParametric statisticsMathematical optimizationComputer scienceGeneralizationComputationEconometricsNoise (video)MathematicsAlgorithmEconomicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

In this paper a definition of industry inefficiency in cost constrained production environments is introduced. This definition uses the indirect directional distance function and quantifies the inefficiency of the industry in terms of the overall output loss, given the industry cost budget. The industry inefficiency indicator is then decomposed into sources components: reallocation inefficiency arising from sub-optimal configuration of the industry; firm inefficiency arising from a failure to select optimal input quantities (given the prevalent inputs prices); firm inefficiency due to lack of best practices. The method is illustrated using data on Ontario electricity distributors. These data show that lack of best practices is only a minor component of the overall inefficiency of the industry (less than 10 percent), with reallocation inefficiency accounting for more than 75 percent of the overall inefficiency of the system. An analysis based on counter-factual input prices is conducted in order to illustrate how the model can be used to estimate the effects of a change in the regulation regime.

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)0.028
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.002
Scholarly communication0.0010.000
Open science0.0050.003
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.144
GPT teacher head0.433
Teacher spread0.289 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

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