Framing Manipulations in Contests: A Natural Field Experiment
Bibliographic record
Abstract
Exploiting findings that losses loom larger than gains, studies have shown that framing manipulations can increase productivity of workers. Using a natural field experiment that exogenously manipulates wage bonuses within contests in a Chinese high-tech manufacturing facility, we show that how loss aversion affects worker behavior critically depends on the incentive scheme as well as the framing manipulation. Four sets of two identical teams competed against each other to win a bonus given to the team, within a set, with the higher average hourly productivity over the week. In each set, the bonus was framed as a reward or gain for one team and as a punishment or loss for the other. Average weekly productivity was slightly higher under the loss treatment, but this increase was statistically insignificant. However, the team under the loss treatment was at least 35% more likely to win the contest. As teams' payoffs are based on relative productivity under a contest, framing effect is much stronger in terms of relative productivity. Finally, workers seemingly responded to the bonus by increasing the quality of production as well as quantity-defect rate fell as productivity increased.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".