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Record W2801722650 · doi:10.5430/afr.v7n2p248

An Empirical Examination of the Compensation-Dividend Relation to Compare Conflict Resolution Strategies at Public versus Private Firms

2018· article· en· W2801722650 on OpenAlexvenueno aff
Amy J. N. Yurko

Bibliographic record

VenueAccounting and Finance Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersTemple UniversityCarnegie Mellon UniversityUniversity of Pittsburgh
KeywordsAgency (philosophy)DividendPrincipal–agent problemBusinessAgency costCompensation (psychology)Executive compensationControl (management)Sample (material)Dividend policyAccountingEconomicsFinanceCorporate governanceShareholder

Abstract

fetched live from OpenAlex

While agency theory predicts that the unification of ownership and control of private family firms reduces agency concerns, some prior studies suggest that the complex family relationships of private, family firms increases agency conflicts. To investigate these conflicting predictions, this study empirically examines with regression analysis how executive total compensation levels relate to dividends at public versus private firms to compare the conflict resolution strategies of public versus private firms. For public firms, this study finds a positive compensation-dividend relation, indicating that public firms increase total compensation levels to reward executives for supporting firms’ dividend policies and realign the interests of owners and managers from the conflict created by dividends. Drawing from special access to Forms 1120, this study examines a large sample of privately held U.S. firms. For private firms, this study finds a negative compensation-dividend relation, indicating that private, family firms do not use compensation to realign the interests of owners and managers and overcome the conflict created by dividends. This new evidence suggests that the ownership structure of private, family firms systematically mitigates agency concerns to some degree. On a practical level, this study indicates that firms can provide compensation arrangements that support firms’ dividend policies, and that regulatory agencies should continue to focus on public firms where the great dispersion of ownership systematically increases agency concerns.

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 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)0.000
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 score0.076
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.160
GPT teacher head0.361
Teacher spread0.201 · 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 teacher head, 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

Citations2
Published2018
Admission routes1
Has abstractyes

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