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Labor Unions and Management’s Incentive to Signal a Negative Outlook*

2012· article· en· W2328183673 on OpenAlexaffvenue
Francesco Bova

Bibliographic record

VenueContemporary Accounting Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEarningsNegotiationIncentivePosition (finance)WageProfitability indexLabour economicsOrder (exchange)EconomicsEarnings managementAction (physics)BusinessFinanceMicroeconomicsPolitical science

Abstract

fetched live from OpenAlex

Evidence suggests that the negotiated wage for a unionized employee group is an increasing function of the firm’s prior profitability. As a result, managers may have an incentive to strategically signal a negative outlook to their unionized workers in order to improve the firm’s bargaining position. I assess the strategy of missing mean consensus analysts’ earnings estimates as a way for managers to signal a negative outlook to their unionized employees. I find that unionized firms are more likely to miss estimates than their nonunionized counterparts. Additionally, this propensity to miss estimates is increasing in both the firm’s percentage of unionized employees and multiunionism, but is unaffected by the timing of the signal relative to contract renewal. Finally, the increased propensity to miss estimates appears to be driven by both differences in expectations management and earnings management across the two groups. Specifically, managers of unionized firms take less action than their nonunionized counterparts to guide forecasts downward when estimates are too high, and they take more action to deflate earnings when expectations are too low. Taken together, the findings suggest that managers do seek to project a negative outlook to their unions, and that this tendency is increasing in the union’s negotiation strength.

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 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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.042
GPT teacher head0.302
Teacher spread0.259 · 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 teacher head, not a consensus.

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

Citations154
Published2012
Admission routes2
Has abstractyes

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