Financial Incentives and Professionals’ Work Tasks: The Moderating Effects of Jurisdictional Dominance and Prominence
Bibliographic record
Abstract
This research addresses the important question of how organizations can use financial incentives to influence the work tasks of their professional workforce—a constituency that is notoriously difficult to manage because of their specialized knowledge, considerable autonomy, strong socialization, and powerful professional norms. In particular, I explore how a baseline incentive effect is moderated by two features of professionals’ tasks and jurisdictions: jurisdictional dominance (i.e., how much the profession controls the provision of the task relative to other professions) and jurisdictional prominence (i.e., how commonly provided the task is within a profession relative to other tasks). Using data on thousands of physician tasks from Ontario, Canada, and a difference-in-differences empirical design, I find that professionals’ incentive responses are smaller when a profession has higher jurisdictional dominance over a task, but are larger when the task has higher jurisdictional prominence within the profession. This research contributes to the literature on professions and professionals in multiple ways. First, I introduce the concepts of jurisdictional dominance and jurisdictional prominence, distinguishing them from each other and from existing conceptions of professional control. Second, this study shows that financial incentives can be an effective tool for influencing professionals, but highlights that their efficacy is shaped by a task’s jurisdictional dominance and jurisdictional prominence. Finally, I show that these new conceptions of jurisdictional control influence professionals’ behaviors in meaningful ways and should therefore be considered in future studies of professions.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".