The Use of Key Performance Indicators for the FAS: Analysis Based on the Statistics of Adjudications
Bibliographic record
Abstract
Outcomes of the key performance indicators application for the assessment of the public authority performance are ambiguous. Theory of incentive contracts as well as international experience highlight difficulties and possible externalities of KPI setting for the public authority. The motivation system often distorts incentives of the public authorities, and the applied indicators do not correctly reflect the priorities of enforcement for the society. One specific example is the assessment of performance of the Russian competition authority. In the paper we analyze the peculiarities of formation of the ratio of infringement decisions that have come into legal force to all infringement decisions made by the competition authorities applied as one of the key performance indicators. Using the data of the database of judicial reviews of infringement decisions, we show that the assessment of FAS performance based on the share of infringement decisions that have come into legal force, distorts incentives of the authority substantially. It motivates the competition authority for making a large number of infringement decisions with a low probability to reverse but with a low positive impact on consumer surplus and total welfare.
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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 | 0.072 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".