An Empirical Guide to Hiring Assistant Professors in Economics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We study the research productivity of new graduates of top Ph.D. programs in economics. We find that class rank is as important as departmental rank as predictors of future research productivity. For example the best graduate from UIUC or Toronto in a given year will have roughly the same number of American Economic Review (AER) equivalent publications at year six after graduation as the number three graduate from Berkeley, U. Penn or Yale. We also find that research productivity of graduates drops off very quickly with class rank at all departments. For example, even at Harvard, the median graduate has only 0.04 AER paper at year six, an untenurable record at almost any department. These results provide guidance on how much weight to give to place of graduation relative to class standing when hiring new assistant professors. They also suggest that even the top departments are not doing a very good job of training students to be successful research economists for any not in the top of their class.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it