The Distribution of Decision Rights Within the Workplace: Evidence from Canadian
Bibliographic record
Abstract
The allocation of decision rights in an organization reflects a trade-off between the costs of transferring relevant information and the costs that occur when decision-making agents have different objectives than the principal. Motivated by a simple model in the tradition of Jensen and Meckling, we analyze the tradeoff between the costs of delaying a decision and delegating decision rights. Our discussion considers factors like incentive pay, union voice, and degree of competition. We use three broad, cross-sectional data sets to study the allocation of decision rights in countries with very different labor markets. We compare the decentralization of decision rights within hierarchies, and identify establishment characteristics and human resource practices related to the location of decision rights. The degree of competition, establishment age, a history of innovation, and presence in a high-tech industry are all closely associated with decentralized decision rights across surveys. Incentive pay, formal training and employee monitoring are also positively correlated with decision rights. The relationship with unions differs by country.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".