Academic salaries and public evaluation of university research: Evidence from the UK Research Excellence Framework
Bibliographic record
Abstract
SUMMARY We study the effects of public evaluation of university research on the pay structures of academic departments. A simple equilibrium model of university pay determination shows how the pay–performance relationship can be explained by the incentives inherent in the research evaluation process. We then analyse the pay–performance relationship using data on the salary of all UK university full professors, matched to the performance of their departments from the 2014 UK government evaluation of research, the Research Excellence Framework (REF). A cross sectional empirical analysis shows that both average pay level and pay inequality in a department are positively related to performance. It also shows that the pay–performance relationship is driven by a feature of the research evaluation that allows academics to transfer the affiliation of published research across universities. To assess the effect of the REF on pay structure, we take advantage of the time dimension of our data and of inherent uncertainty in the evaluation of the performance of academic departments generated by the rules of the exercise. Our results indicate that higher achieving departments benefit from increased subsequent hiring and higher professorial salaries with the salary benefits of REF performance concentrated among the highest paid professors.
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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 | 0.027 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".