Individual Preferences, Organization, and Competition in a Model of R&D Incentive Provision
Bibliographic record
Abstract
Understanding the organization of R&D activities requires the simultaneous consideration of scientific workers' talent and tastes, companies' organizational choices, and the characteristics of the relevant industry.We develop a model of the provision of incentives to corporate scientists, in an environment where (1) scientists engage in multiple activities when performing research; (2) knowledge is not perfectly appropriable; (3) scientists are responsive to both monetary and non-monetary incentives; and (4) firms compete on the product market.We show that both the degree of knowledge spillovers and of market competition affect the incentives given to scientists, and these effects interact.First, high knowledge spillovers lead firms to soften incentives when product market competition is high, and to strengthen incentives when competition is low.Second, the relationship between the intensity of competition and the power of incentives is U-shaped, with the exact shape depending on the degree of knowledge spillovers.We also show that the performance-contingent pay for both applied and basic research increases with the non-pecuniary benefits that scientists obtain from research.We relate our findings to the existing empirical research, and also discuss their implications for management and public policy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".