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Record W4413744458 · doi:10.1257/aeri.20240332

A Theory of Fair CEO Pay

2025· article· en· W4413744458 on OpenAlex

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

VenueAmerican Economic Review Insights · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsBusinessEconomics

Abstract

fetched live from OpenAlex

This paper studies executive pay with fairness concerns: If the CEO's wage falls below a perceived fair share of output, he suffers disutility that is increasing in the discrepancy. Fairness concerns do not always lead to fair wages; instead, the firm threatens the CEO with unfair wages for low output to induce effort. The contract sometimes involves performance-vesting equity: The CEO is paid a constant share of output if it is sufficiently high and zero otherwise. Even without moral hazard, the contract features pay-for-performance to address fairness concerns and ensure participation. This rationalizes pay-for-performance even if effort incentives are unnecessary. (JEL D63, D82, D86, G34, M12, M52)

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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