Multidimensional heterogeneity and the economic importance of risk and matching: evidence from contractual data and field experiments
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
We measure the cost of risk and the benefits of matching heterogeneous workers to risk levels within a firm that pays its workers piece rates. The workers of this firm are heterogeneous in two dimensions: risk preferences and ability. Our results suggest that workers’ willingness to pay to avoid risk is heterogeneous. It can attain 40% of their expected net earnings but averages to only 1%. Moreover, the benefits to the firm of matching are relatively small: profits are predicted to increase by only 2.3%, 4% if we restrict attention to cases where matching is possible. Although labor‐market sorting contributes to this result (the workers in this firm are relatively risk tolerant), it is not the primary cause. More important is the relative homogeneity of risk conditions in this firm that give rise to limited opportunities for matching.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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