Bibliographic record
Abstract
This paper documents the dramatic changes in volatility that occurred in the U.S. auto industry in the early 1980s.Namely, output volatility declined significantly, the covariance of inventory investment and sales became much more negative, and adjustments to output, which in earlier decades stemmed primarily from plants hiring and laying off workers, were more often accomplished with changes in average hours per worker after the mid 1980s.Building on the work of Blanchard (1983), we show how all of these changes could have stemmed from one underlying factor-a decline in the persistence of motor vehicle sales.We use both industry-level data as well as micro data on production schedules from 103 assembly plants in the United States and Canada to document the developments in the early 1980s.We then use the original Holt, Modigliani, Muth and Simon (1960) linear quadratic inventory model to show how a decline in the persistence of sales leads to all of the changes noted above, including the propensity to use intensive margins of adjustment over extensive labor margins, even in the absence of technological change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".