Performance Aggregation and Decentralized Contracting
Bibliographic record
Abstract
ABSTRACT We examine how accounting practices that aggregate or disaggregate the contributions of different economic agents influence the choice of organizational form. We consider a principal/multi-agent model where the principal either contracts with all parties directly or delegates part of the contracting authority to one of the agents. Delegated contracts improve risk sharing and generate implicit incentives for the agent entrusted with contracting authority. However, delegated contracts also entail a loss of control in motivating lower-level agents. In addition, when performance is aggregated, delegated contracts render agents' incentives more interdependent and create spillovers up and down the hierarchy. We demonstrate that accounting practices that aggregate the performance of multiple agents can complement organizational forms characterized by greater decentralization. In contrast, accounting practices that capture agents' performance contributions separately favor more centralized organizational forms. Our findings suggest that in settings where performance measurement systems are more aggregate, decentralization is more prevalent. JEL Classifications: L22; M12; M4.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".