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Work‐related perks, agency problems, and optimal incentive contracts

2008· article· en· W1981712407 on OpenAlex
Anthony M. Marino, Ján Zábojník

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.

Bibliographic record

VenueThe RAND Journal of Economics · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsIncentiveCorporate governanceBusinessAutonomyAgency (philosophy)Consumption (sociology)Work (physics)Offset (computer science)Agency costProduction (economics)MicroeconomicsPublic economicsEconomicsFinance

Abstract

fetched live from OpenAlex

Work‐related perks, such as corporate jets, nice offices, and so forth, improve the tradeoff between incentives and insurance that determines the optimal incentive contract. We show that (i) such perks may be offered even if their direct consumption benefits are offset by their costs; (ii) they will be offered for free; (iii) agents in more uncertain production environments will receive more perks; (iv) senior executives should receive more perks; and (v) better corporate governance can lead to more perk consumption by CEOs. Our analysis also offers insights into firms' decisions about how much autonomy they should grant to their employees.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.193
Teacher spread0.157 · 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