Top graduate programmes in economics: Historical evolution and recent evidence
Bibliographic record
Abstract
Abstract The first part of the paper provides a novel overview narrative of the historical evolution of PhD programmes in economics from 1880, drawing on multiple sources. The second part is empirical, and, also novel in terms of the data constructed. It attempts to bring the narrative up to date by looking at the cohorts of winners of the main young economist awards in economics in the US and Europe over the last twenty years or so and to chart at which universities they obtained their doctorates and undergraduate degrees. The total number of young (at the time of the award) economists so involved exceeds 350. The evolution of the American‐style PhD programme of today is traced to the emigration of European economists to the US from Nazi‐occupied Europe in the 1930s, and its subsequent spread first to Britain and then Continental Europe documented. What is new in the last twenty years or so is the emergence of many Top‐50 economics departments in Europe, with corresponding highly regarded PhD programmes. However, in relation to the recent elite young award‐winning economists the position in terms of PhD education of Harvard and MIT particularly remains largely unchallenged, in both the US and Europe.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".