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Record W1985162420 · doi:10.1093/rfs/hhq093

Incentives, Targeting, and Firm Performance: An Analysis of Non-executive Stock Options

2010· article· en· W1985162420 on OpenAlexaff
Yael V. Hochberg, Laura Anne Lindsey

Bibliographic record

VenueReview of Financial Studies · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsIncentiveStock optionsPortfolioBusinessExecutive compensationAffect (linguistics)Stock (firearms)Instrumental variableNon-qualified stock optionMicroeconomicsEconomicsActuarial scienceRestricted stockFinanceEconometricsStock market

Abstract

fetched live from OpenAlex

We examine whether options granted to non-executive employees affect firm performance. Using new data on option programs, we explore the link between broad-based option programs, option portfolio implied incentives, and firm operating performance, utilizing an instrumental variables approach to identify causal effects. Firms whose employee option portfolios have higher implied incentives exhibit higher subsequent operating performance. Intuitively, the implied incentive-performance relation is concentrated in firms with fewer employees and in firms with higher growth opportunities. Additionally, the effect is concentrated in firms that grant options broadly to non-executive employees, consistent with theories of cooperation and mutual monitoring among co-workers. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.276
Teacher spread0.257 · 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

Citations251
Published2010
Admission routes1
Has abstractyes

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