Contracting Frame and Individual Behavior: Experimental Evidence
Bibliographic record
Abstract
ABSTRACT: This paper reports the results of an experiment examining the effect of the framing of incentive contracts on individual behavior. We examine two budget-based incentive contracts that, though economically equivalent, are framed differently. Previous research documents that individuals prefer bonus-framed to penalty-framed contracts (Luft 1994; Hannan et al. 2005). We explore whether these preferences affect effort expended on a task where increased effort results in increased performance. In addition, we test whether these preferences motivate effort differentially in the presence and absence of an effective financial incentive for performance. Consistent with prospect theory predictions, results indicate the penalty-framed contract motivates higher task performance than the bonus-framed contract for individuals whose performance falls within the bonus or penalty range (i.e., where financial incentives are effective in motivating performance). Performance did not differ according to contract frame for individuals whose performance enabled them to receive the maximum payment or for individuals whose performance resulted in them receiving the minimum payment (i.e., where financial incentives were not effective in motivating performance). Although prior research indicates contract framing affects contract preferences, our results indicate these preferences may not result in differences in individual performance unless effective financial incentives are also utilized.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.015 | 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".