Aggregate Employment Fluctuations and Agricultural Share y
Bibliographic record
Abstract
Dierences in employment volatility and the correlation of employment with output across countries are often cited as examples of the limitation of standard real business cycle (RBC) theory to reproduce the observed labor market facts. These observations have lead researchers to argue for the necessity of Non-Walrasian features to reflect the labor institutions in European countries. In this paper, we show that the same labor market evidence is observed in regional economies with the same labor market institutions. We conjecture that dierences in agricultural activity can generate the observed dierences in labor market behavior. We show that a standard two-sector RBC model with agriculture and non-agriculture can account for the observed labor market facts. In particular, as the size of agricultural activity increases, aggregate employment volatility and the correlation between aggregate employment and output decrease. Moreover, contrary to the Non-Walrasian approach to business cycles, agricultural activity can account for the correlation between aggregate employment and output as reported by Danthine and Donaldson (1993) for Europe and the U.S.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".