Performance effects of insulating and non‐insulating cost allocations in stable and unstable production environments
Bibliographic record
Abstract
Abstract Firms allocate significant amounts of common costs, and these allocations have implications for performance evaluation and remuneration. Non‐insulating cost allocations distribute costs based on same‐period relative performance, creating a contemporaneous interdependence between managers that in turn adds uncertainty to the link between effort and performance. In contrast, insulating cost allocations are independent of relative performance during the period and can thus be determined with greater certainty ex ante. In an experiment, we predict and find that managers' effortful performance in a stable production environment—where the pre‐allocation return for effort is constant—is higher when costs are allocated via an insulating allocation compared to when costs are allocated via a non‐insulating allocation. We further find that in an unstable production environment—where the pre‐allocation return for effort can vary from period to period—there are no differences in performance between the allocation methods when managers face a lower return for effort. Conversely, when managers face a higher return for effort in this environment, performance is greater when costs are allocated via an insulating allocation. Taken together, overall performance in the unstable production environment is greater when managers work under insulating cost allocations, suggesting the net effects of cost allocation methods are similar in each type of production environment. As such, our study identifies an important cost—lower effortful performance—of using non‐insulating methods to allocate common costs.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".