Sorting, Incentives and Risk Preferences: Evidence from a Field Experiment
Bibliographic record
Abstract
The, often observed, positive correlation between incentive intensity and risk has been explained in two ways: the presence of transaction costs as determinants of contracts and the sorting of risk-tolerant individuals into firms using high-intensity incentive contracts. The empirical importance of sorting is perhaps best evaluated by directly measuring the risk tolerance of workers who have selected into incentive contracts under risky environments. We use experiments, conducted within a real firm, to measure the risk preferences of a sample of workers who are paid incentive contracts and face substantial daily income risk. Our experimental results indicate the presence of sorting; Workers in our sample are risk-tolerant. Moreover, their level of tolerance is considerably higher than levels observed for samples of individuals representing broader populations. Interestingly, the high level of risk tolerance suggests that both sorting and transaction costs are important determinants of contract choices when workers have heterogeneous preferences.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".