DOES RAIDING EXPLAIN THE NEGATIVE RETURNS TO FACULTY SENIORITY?
Bibliographic record
Abstract
We track faculty for 30 yr at five PhD‐granting departments of economics. Two‐thirds of faculty who take alternative employment move downward; less than one‐quarter moves upward. We find a substantial penalty for seniority, even after richly controlling for faculty productivity, and the penalty is little changed when we allow wages and returns to seniority to differ by mobility status. Faculty who end up moving to better or comparable positions were penalized as severely for seniority while they were in our sample as faculty who stay. These results are incompatible with the raiding hypothesis. Faculty from top 10 programs are also punished for seniority but to a lesser degree than other faculty, which could reflect reduced monopsony power against such faculty if they are more marketable. All results persist when we control for prospective publications and allow lower returns for older publications. Match‐quality bias has dissipated in the post‐internet period, which may be the consequence of greater availability of information. (JEL J62, J44, J42)
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".