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Career Concerns, Inaction and Market Inefficiency: Evidence From Utility Regulation

2012· article· en· W3121894813 on OpenAlex
Severin Borenstein, Meghan R. Busse, Ryan Kellogg

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Industrial Economics · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsInefficiencyIncentiveProcurementValue (mathematics)Outcome (game theory)EconomicsLead (geology)MicroeconomicsBusinessIndustrial organizationPublic economicsMarketing

Abstract

fetched live from OpenAlex

We study how incentive conflicts known as ‘career concerns’ can generate inefficiencies not only within firms but also in market outcomes. Career concerns may lead agents to avoid actions that, while value‐increasing in expectation, could potentially be associated with a bad outcome. We apply this theory to natural gas procurement by regulated public utilities and show that career concerns may lead to a reduction in surplus‐increasing market transactions during periods when the benefits of trade are likely to be greatest. We show that data from natural gas markets are consistent with this prediction and difficult to explain using alternative theories.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
GPT teacher head0.240
Teacher spread0.134 · 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