A Quarter Century of Okun’s Law in Scholarly Literature
Bibliographic record
Abstract
Abstract In order to canvass the state of the art of research on Okun’s law, the paper surveys 84 articles published in Web of Science™ journals between 1995 and 2020 occupied with estimating the relationship between unemployment and output in the spirit of an approach proposed by Okun (1962). A bibliometric analysis is conducted to identify the most influential works and authors, to establish links between them, and to outline research fronts with main paths of knowledge diffusion. Under a content analysis, the articles included in the survey are further classified by their leitmotif and research agenda as well as by their geographical scope. The basal methodological choices of the articles are overviewed and their temporal patterns are studied. An emphasis is put on the stylized facts constituting the research agenda of 57 of the surveyed applications of Okun’s law (such as instability over time, asymmetries, or age and gender specificity). A majority of studies estimated Okun’s law on the basis of a regression equation that may suggest that it is unemployment that responds to fluctuations in output and adopted the difference version of Okun’s law. In estimating the gap version, the Hodrick-Prescott filter has continued to be a preferred choice despite its well-known flawed statistical properties. Lotka’s law indicates an above-average level of research productivity of authors in this field. The findings provide insights into the intellectual structure of the empirics of Okun’s law and act as guidance for future research on cyclical unemployment-output fluctuations.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".