A Quantitative Framework for Analyzing the Distributional Effects of Incentive Schemes
Bibliographic record
Abstract
This paper develops the first quantitative framework for analyzing distributional effects of incentive schemes in public education. The analysis is built around a hump-shaped effort function, estimated semi-parametrically using exogenous incentive variation and rich administrative data. We identify key primitives that rationalize this effort function by estimating a flexible teacher effort-choice model. Both the model and parameter estimates are necessary components in our counterfactual framework for tracing the effects of alternative accountability systems on the entire test score distribution, with effort adjusting endogenously. We find widespread schemes that set a fixed target for all students give rise to a steep performanceinequality tradeoff. Further, counterfactual incentive policies can outperform existing schemes for the same cost -reducing the black-white test score gap by 7% (via student-specific bonuses), and lowering test-score inequality across students by 90% (via student-specific targets). Our quantitative approach opens up new possibilities for incentive design in practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".