Relative Performance Evaluation and the Ratchet Effect
Bibliographic record
Abstract
ABSTRACT When targets depend on past performance, incentives are adversely affected by the ratchet effect. We provide theory and evidence that incorporating past peer performance into targets can alleviate this adverse incentive effect. In particular, we present an analytical model that characterizes optimal target revisions as a function of past own and past peer performance. We then test the predictions of our model using data on 2008–2010 performance targets from 354 units of a governmental agency responsible for reintegration of the long‐term unemployed into the labor market. As a unique feature of our data, we have information on peer group quality, defined as the extent to which peer performance is informative about common shocks. Consistent with our model, we find that higher peer group quality (a) increases sensitivity of target revisions to past peer performance, (b) reduces sensitivity of target revisions to past own performance, and (c) reduces the ratchet effect as reflected in managerial incentives to withhold end‐of‐year effort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".