Incentives and the Effects of Publication Lags on Life Cycle Research Productivity in Economics
Bibliographic record
Abstract
We investigate how increases in publication delays have affected the life-cycle of publications of recent Ph.D. graduates in economics.We construct a panel dataset of 14,271 individuals who were awarded Ph.D.s between 1986 and 2000 in US and Canadian economics departments.For this population of scholars, we amass complete records of publications in peer reviewed journals listed in the JEL (a total of 368,672 observations).We find evidence of significantly diminished productivity in recent relative to earlier cohorts when productivity of an individual is measured by the number of AER equivalent publications.Diminished productivity is less evident when number of AER equivalent pages is used instead.Our findings are consistent with earlier empirical findings of increasing editorial delays, decreasing acceptance rates at journals, and a trend toward longer manuscripts.This decline in productivity is evident in both graduates of top thirty and non-top thirty ranked economics departments and may have important implications for what should constitute a tenurable record.We also find that the research rankings of the faculty do not line up with the research quality of their students in many cases.
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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 | 0.023 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".