Within and across department variability in individual productivity : the case of economics
Bibliographic record
Abstract
University departments (or research institutes) are the governance units in any scientific field \nwhere the demand for and the supply of researchers interact. As a first step towards a formal \nmodel of this process, this paper investigates the characteristics of productivity distributions \nof a population of 2,530 individuals with at least one publication who were working in 81 \nworld top Economics departments in 2007. Individual productivity is measured in two ways: \nas the number of publications until 2007, and as a quality index that weights differently the \narticles published in four journal equivalent classes. The academic age of individuals, \nmeasured as the number of years since obtaining the PhD until 2007, is used to measure \nproductivity per year. Independently of the two productivity measures, and both before and \nafter age normalization, the main findings of the paper are the following five. Firstly, \nindividuals within each department have very different productivities. Secondly, there is not \na single pattern of productivity inequality and skewness at the department level. On the \ncontrary, productivity distributions are very different across departments. Thirdly, the effect \non overall productivity inequality of differences in productivity distributions across \ndepartments is greater than the analogous effect in other contexts. Fourthly, to a large \nextent, this effect on overall productivity inequality is accounted for by scale factors well \ncaptured by departments’ mean productivities. Fifthly, this high degree of departmental \nheterogeneity is found to be compatible with greater homogeneity across the members of a \npartition of the sample into seven countries and a residual category.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".