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Record W3123788439

Do heavily-unionized companies compensate their CEOs less in periods of financial distress? Evidence from Canadian companies during the financial crisis

2016· preprint· en· W3123788439 on OpenAlexaboutno aff
Muhammad Umar Boodoo

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2016
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial distressSalaryFinancial crisisExecutive compensationPensionBusinessEquity (law)FinanceLabour economicsStock (firearms)Corporate governanceEconomicsFinancial systemMarket economy
DOInot available

Abstract

fetched live from OpenAlex

This paper studies the strategic interaction between employee stakeholders, in particular labor unions, and top management, and evaluates the effect of the two parties’ inherent competitive rent-seeking behavior on CEO pay. Using a panel of firms listed on the S&P/TSX composite index, this paper finds that CEO compensation withstood the financial crisis despite lower and even negative corporate performance. Further, heavily-unionized companies were associated with higher CEO pay in terms of non-equity elements such as salary and pension allocations. The presence of unions had no observed effect in reducing bonuses, stock options, and restricted stock units. These findings have implications for the debate on income inequality, and the power of unions to bring about change.

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 score0.025
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.313
Teacher spread0.253 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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