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Record W4389006619 · doi:10.1093/rof/rfad039

The saliency of the CEO pay ratio

2023· article· en· W4389006619 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEuropean Finance Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
FundersPeking UniversityUniversity of Illinois at Urbana-ChampaignUniversity of TorontoGeorgia State UniversityUniversity of MiamiDrexel UniversityIowa State UniversityCity University of Hong KongVanderbilt UniversityHSBC Bank USA
KeywordsExecutive compensationSalientCorporate governanceCommissionBusinessCompensation (psychology)ProductivityMetric (unit)AccountingJob satisfactionLabour economicsEconomicsMarketingFinancePsychologySocial psychologyManagementLaw

Abstract

fetched live from OpenAlex

Abstract The US Securities and Exchange Commission’s mandated CEO pay ratio is a simple, but salient, metric that could resonate with employees given it focuses on their compensation. Reporting a relatively or surprisingly high ratio reduces employee perceptions of their pay, views of the CEO, and hampers productivity growth. Employee pay satisfaction drops after disclosing a high ratio even if their wages were previously disclosed and when the pay ratio disclosure adds little new information. Disclosures by firms with a high ratio contain more discretionary language to explain the ratio or portray employee relations positively and are more likely to be covered by the media. However, neither information source substantially alters the employee response to a salient ratio. Our work illustrates that requiring firms to disclose a salient metric can have unintended consequences on employees and suggests caution in requiring firms to report simplified Environmental, Social, and Governance (ESG) metrics that are inherently multifaceted.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.264
Teacher spread0.230 · 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