The Role of Agriculture in Aggregate Business Cycle Fluctuations
Bibliographic record
Abstract
The agricultural sector has certain distinctive features over the business cycle: it is more volatile than and not positively correlated with the rest of the economy and its employment is counter-cyclical. Because of these features and even though the agricultural sector represents less than 2% of the U.S. economy, we show that agriculture plays an essential role in understanding aggregate business cycles. The inclusion of agriculture into standard business cycle analysis resolves the longstanding problems of the standard theory in matching the observed volatility of aggregate labor and the correlation of aggregate labor and productivity (the so called Dunlop-Tharshis observation). In addition, the role of agriculture in the economy can account for the substantial differences observed in business cycle patterns across countries. This novel implication of the model is consistent with the systematic relationship observed between business cycle patterns and the share of agriculture across countries. Our theory has two important implications. First, the model implies that as the size of the agricultural sector falls, business cycle properties across countries should converge. Second, the role of agriculture provides a simple, measurable, and contrastable explanation for the historical properties of aggregate business cycles documented by Backus and Kehoe (1992).
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".