Performance Targets and Ex Post Incentive Plan Adjustments†
Bibliographic record
Abstract
ABSTRACT Performance evaluations are typically based on a formula that specifies in advance all performance measures, their relative incentive weights, and targets to be met. However, beginning‐of‐year performance targets can become outdated due to unforeseen events that call for ex post adjustments to formula‐based incentive plans to restore incentives. We discuss three types of ex post incentive plan adjustments—end‐of‐year subjective performance evaluation, changes in next‐year relative incentive weights, and changes in next‐year performance targets—and empirically examine the extent to which they are used to discourage failure to meet a target by a wide margin. Specifically, we use 2004–2015 data on formula‐based bonus plans, subjective performance evaluations, and performance in Korean state‐owned enterprises. Consistent with our predictions, we find that very low performance relative to target is associated with (i) low subjective evaluations and (ii) an increase in next‐year incentive weights, conditions that render areas with poor performance more important in future evaluations. These findings are more pronounced on performance dimensions of high importance and less pronounced when very low performance is due to an adverse uncontrollable shock. Finally, we find evidence that ex post incentive plan adjustments are associated with future performance improvements. Combined, our findings suggest that ex post incentive plan adjustments can be used to strengthen incentives when performance targets get outdated.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".