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Record W3140072887 · doi:10.1093/rcfs/cfad008

Do Managers Do Good with Other People’s Money?

2023· article· en· W3140072887 on OpenAlexaff
Ing-Haw Cheng, Harrison Hong, Kelly Shue

Bibliographic record

VenueThe Review of Corporate Finance Studies · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsInsiderLiberian dollarCorporate governanceAgency (philosophy)Principal–agent problemBusinessAccountingDividendAgency costShareholderMonetary economicsCorporate social responsibilityThe InternetEconomicsFinancePublic relationsLaw

Abstract

fetched live from OpenAlex

Abstract There is mixed evidence on whether the marginal dollar spent on corporate social responsibility is due to agency problems. We propose an approach by modeling how the 2003 dividend tax cut, which increased after-tax insider ownership and better aligned managerial and shareholder interests, affected the marginal dollar spent on firm responsibility. We confirm key predictions of our agency model: following the tax cut, moderate insider-ownership firms experience larger declines in their responsibility ratings and increases in their valuations relative to other firms. We also confirm another implication regarding managerial misalignment using a regression-discontinuity design of close votes on shareholder-governance proposals. (JEL G30, G31, G35) Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.047
GPT teacher head0.261
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations119
Published2023
Admission routes1
Has abstractyes

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