Financial Reporting Choices and Labor Contract Negotiations: A Case Study in the University Sector
Bibliographic record
Abstract
Abstract A sizeable literature has developed that considers the interest of labor unions in employer financial statements and the effect that interest has on employer accounting decisions. Empirical results have been mixed, but there is at least some evidence that employers facing union pressure engage in earnings management and strategic disclosure decisions that help to win concessions from labor unions. We use a model of labor negotiations (Walton et al., 2000) to focus on a single employer–union relationship, the University of Manitoba (UM) and its academic faculty union, the University of Manitoba Faculty Association (UMFA). UMFA performed and published an analysis of the UM's financial statements in preparation for its 2010 round of collective bargaining, allowing us to identify accounting variables key to that analysis. We show that UM deducted internal restrictions and capital transfers from operating income to give the impression that its ability to pay was compromised. In assessing 16 subsequent disclosure events by UM, its disclosure strategy appears to reflect primarily, but not uniformly, a forcing bargaining posture. Our analysis indicates that UM enjoyed considerable latitude in its financial reporting, which it used to its advantage in negotiations and to hinder budgetary oversight.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".