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Record W3124120816 · doi:10.1111/jbfa.12741

Restricting CEO pay backfires: Evidence from China

2023· article· en· W3124120816 on OpenAlex
Kee‐Hong Bae, Zhaoran Gong, Wilson H.S. Tong

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsYork University
FundersSungkyunkwan UniversityHong Kong Polytechnic UniversityMassey UniversityKorea UniversitySeoul National UniversityYork University
KeywordsIncentiveLimitingChinaExecutive compensationBusinessPay for performanceUnintended consequencesPerformance-related payConsumption (sociology)Labour economicsEconomicsAccountingMonetary economicsMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Using the pay restriction imposed on CEOs of centrally administered state‐owned enterprises (CSOEs) in China in 2009, we study the effects of limiting CEO pay. Compared with CEOs of firms not subject to the restriction, the CEOs of CSOEs experienced a significant pay cut. In response to the pay cut, CEOs increased the consumption of perks and siphoned off firm resources for their own benefit. Pay‐performance sensitivity for these firms also significantly decreases. The performance of these firms dropped following the pay restriction. Our findings suggest that restricting CEO pay distorts CEO incentives and brings unintended consequences. Our findings caution against limiting CEO pay.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0010.008
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.243
Teacher spread0.205 · 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