A New Approach to an Age-Old Problem: Solving Externalities by Incenting Workers Directly
Bibliographic record
Abstract
Understanding motivations in the workplace remains of utmost import as economies around the world rely on increases in labor productivity to foster sustainable economic growth. This study makes use of a unique opportunity to "look under the hood" of an organization that critically relies on worker effort and performance. By partnering with Virgin Atlantic Airways on a field experiment that includes over 40,000 unique flights covering an eight-month period, we explore how information and incentives affect captains' performance. Making use of more than 110,000 captain-level observations, we find that our set of treatments-which include performance information, personal targets, and prosocial incentives-induces captains to improve efficiency in all three key flight areas: pre-flight, in-flight, and post-flight. We estimate that our treatments saved between 266,000-704,000 kg of fuel for the airline over the eight-month experimental period. These savings led to between 838,000-2.22 million kg of CO2 abated at a marginal abatement cost of negative $250 per ton of CO2 (i.e. a $250 savings per ton abated) over the eight-month experimental period. Methodologically, our approach highlights the potential usefulness of moving beyond an experimental design that focuses on short-run substitution effects, and it also suggests a new way to combat firm-level externalities: target workers rather than the firm as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".