Incentivizing Effort Allocation Through Resource Allocation: Evidence from Scientists’ Response to Changes in Funding Policy
Bibliographic record
Abstract
Prior research in management and economics has predominantly focused on how managers or policymakers can shape workers’ allocation of effort using output-based or effort-based incentives. In many settings, however, managers may seek to influence workers’ effort choices through resource allocation—that is, changing the cost of securing resources for different projects or activities. In this paper, we develop a formal model to investigate how a worker changes the allocation of a fixed amount of effort across different projects in response to changes in the cost of securing resources for each project. Our model shows how cutting resources available to one project, under certain circumstances, can inadvertently reduce the share of effort allocated to other projects and vice versa. We use the insights from the model to explore the effectiveness of funding strategies designed to influence the research direction of academic scientists. We specifically examine how U.S. scientists working in stem cell research responded to a 2001 policy change that restricted access to federal funding for research in the human embryonic stem cell (hESC) area. In line with our model’s predictions, we find that cutting resources for hESC research inadvertently reduced U.S. scientists’ output in non-hESC areas of stem cell research—an effect that is strongest among the highest-ability scientists. Our findings highlight the complexities of incentivizing effort allocation using resource-based incentives. In particular, we show how altering resource-based incentives in one area can have unforeseen spillover effects on effort allocation in other areas. Funding: Financial support from the London Business School is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1565 .
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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 | 0.029 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".