Designing Product Development Contracts in the Presence of Managerial Lobbying
Bibliographic record
Abstract
We examine a firm’s contract design problem in the context of allocating resources to new products with uncertain potential being developed by the managers vying for funds. The firm faces twin problems of identifying the right project(s) to prioritize them, and next also motivate the managers to exert effort in implementing the project to successful fruition. The firm’s contract design can incentivize the managers either using a resource-based incentive plan, which offers a reward based on the number of resources invested in the projects, or offering a resource-decoupled incentive plan, which provides a standard reward disregarding the differential amount of resources invested. We consider a set-up where the manager has private information about project quality. During the prioritizing first stage, we allow the manager to lobby for the project by manipulating the signal of the project quality. We show that a resource-decoupled incentive plan impacts the lobbying by encouraging the manager of the higher quality project to manipulate the quality signal more to stand out. Therefore, although a resource-based incentive plan is more efficient in inducing the manager in exerting implementation effort, a resource-decoupled incentive plan may be preferred as it leads to more accurate resource allocation. Thus, allowing lobbying that involves exaggeration may be beneficial by amplifying a firm’s ability to identify high caliber projects. We demonstrate the robustness of our results in models where the firm chooses the level of evaluation threshold, where there are multiple projects, and where the project evaluations involve a ranking procedure. This paper was accepted by Duncan Simester, marketing.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".