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Record W2736017287 · doi:10.1111/ecin.12879

THE EXTENT OF THE MARKET AND INTEGRATION THROUGH FACTOR MARKETS: EVIDENCE FROM WHOLESALE ELECTRICITY

2020· article· en· W2736017287 on OpenAlexaff
R. Andrew Butters, Daniel F. Spulber

Bibliographic record

VenueEconomic Inquiry · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsKellogg's (Canada)
FundersNorthwestern University
KeywordsAutarkyEconomicsFactor marketElectricityElectricity marketMarket integrationMicroeconomicsInternational economicsMonetary economicsMarket economy

Abstract

fetched live from OpenAlex

We document the influence of factor markets in determining the extent of the market, appealing to the Mundell hypothesis that trade in goods and factor markets are substitutes. We confirm this influence using the U.S. wholesale market for electric power. Although the Eastern, Western, and Texas regions cannot trade electricity, inputs such as natural gas move freely across these regions. Through a set of price transmission ratios, and a supply model for natural gas, we find regional electricity shocks do propagate across regions. We conclude output markets institutionally in autarky achieve modest degrees of economic integration through factor markets. (JEL C32, L94, Q41)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.126
GPT teacher head0.257
Teacher spread0.131 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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