The Behavioralist Visits the Factory: Increasing Productivity Using Simple Framing Manipulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent discoveries in behavioral economics have led to important new insights concerning what can happen in markets. Such gains in knowledge have come primarily via laboratory experiments—a missing piece of the puzzle in many cases is parallel evidence drawn from naturally occurring field counterparts. We provide a small movement in this direction by taking advantage of a unique opportunity to work with a Chinese high-tech manufacturing facility. Our study revolves around using insights gained from one of the most influential lines of behavioral research—framing manipulations—in an attempt to increase worker productivity in the facility. Using a natural field experiment, we report several insights. For example, conditional incentives framed as both “losses” and “gains” increase productivity for both individuals and teams. In addition, teams more acutely respond to bonuses posed as losses than as comparable bonuses posed as gains. The magnitude of this framing effect is roughly 1%: that is, total team productivity is enhanced by 1% purely due to the framing manipulation. Importantly, we find that neither the framing nor the incentive effect lose their significance over time; rather, the effects are observed over the entire sample period. Moreover, we learn that repeated interaction with workers and conditionality of the bonus contract are substitutes for sustenance of incentive effects in the long run. This paper was accepted by Gérard P. Cachon, decision analysis.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it