Discretionary Sanctions and Rewards in the Repeated Inspection Game
Bibliographic record
Abstract
We experimentally investigate a repeated “inspection game” where, in the stage game, an employee can either work or shirk and an employer simultaneously chooses to inspect or not inspect. The unique equilibrium of the stage game is in mixed strategies with positive probabilities of shirking/inspecting while combined payoffs are maximized when the employee works and the employer does not inspect. We examine the effects of allowing the employer discretion to sanction or reward the employee after observing stage game payoffs. When employers have limited discretion, and can only apply sanctions and/or rewards following an inspection, we find that both instruments are equally effective in reducing shirking and increasing joint earnings. When employers have discretion to reward and/or sanction independently of whether they inspect, we find that rewards are more effective than sanctions. In treatments where employers can combine sanctions and rewards, employers rely mainly on rewards, and outcomes closely resemble those of treatments where only rewards are possible. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2124 . This paper was accepted by John List, behavioral economics.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".