Inventory Dynamics and Business Cycles: What Has Changed?
Bibliographic record
Abstract
Despite the recent patch of sluggish growth, the U.S. economy has experienced a period of remarkable stability since the mid-1980s. One popular explanation attributes the diminished variability of economic activity to information-technology-led improvements in inventory management. Our results, however, indicate that the changes in inventory dynamics since the mid-1980s played a reinforcing--rather than a leading--role in the volatility reduction. Movements in the volatility of manufacturing output over the past three decades almost entirely reflect changes in the variability of the growth contribution of sales. Although the volatility of total inventory investment has fallen, the decline occurred well before the mid-1980s and was driven by the reduced variability of materials and supplies. Our analysis does show that since the mid-1980s, inventory dynamics have played a role in stabilizing manufacturing production: Inventory "imbalances" tend to correct more rapidly, and the quicker response of inventories to monetary policy and commodity price shocks buffers production from fluctuations in sales to a greater extent. But more extensive production smoothing and faster dissolution of inventory imbalances appear to be a consequence of changes in the way industry-level sales and aggregate economic activity respond to shocks, rather than a cause of changes in macroeconomic behavior.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".