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Record W3121906629 · doi:10.1506/5ffq-qktq-102g-8d68

Executive Cash Compensation and Corporate Performance During Different Economic Cycles*

2000· article· en· W3121906629 on OpenAlexvenueno aff
Zoltan Matolcsy

Bibliographic record

VenueContemporary Accounting Research · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsExecutive compensationCashCompensation (psychology)Cash managementRecessionEquity (law)BusinessCash flowOperating cash flowMonetary economicsCash flow statementEconomicsFinanceCorporate governanceMacroeconomics

Abstract

fetched live from OpenAlex

Abstract Current practice of management cash compensation is based on financial targets. The financial targets for a year may be above, equal to, or below the previous year's publicly available performance measures based in part on the prevailing economic conditions. Accordingly, during economic downturn, a flat relation between changes in management cash compensation and simple changes in corporate performance, like annual profits or return on equity, is predicted, while during economic growth, a positive relation is predicted between changes of management cash compensation and corporate performance measures. The evidence in this study is based on the period 1987‐95. Pooled, cross‐sectional results are consistent with the propositions of no relation between changes in management cash compensation and changes in measures of corporate performance during periods of economic downturn and significant positive relation during economic growth. Further sensitivity analysis of these results with respect to market‐based performance measures, size, and industry classifications confirm the main results.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.266
Teacher spread0.202 · 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

Citations50
Published2000
Admission routes1
Has abstractyes

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