The Effect of Technology Choice on Automobile Assembly Plant Productivity
Bibliographic record
Abstract
Abstract: Productivity growth is usually represented by a continuous shift of the production or cost function. In the automobile industry, there is evidence of a more discrete change in the technology. I estimate a structural model of production and technology choice, using a panel of US automobile assembly plants from 1963 to 1996. New decomposition results suggest that plant-level changes, as opposed to compositional effects, are the most important determinant of aggregate productivity growth. I UNDERSTANDING PRODUCTIVITY IS IMPORTANT In recent decades, the US automobile industry has seen several importantpolicy interventions. Voluntary export restraints were in effect for most of the 1980s, restricting Japanese imports; many states awarded significant subsidies to attract greenfield investments of both domestic and foreign firms; and a joint venture between General Motors and Toyota, with the exchange of technologies as an explicit goal, was given antitrust exemption in 1984. 65 * jovb@chass.utoronto.ca. I wish to thank Frank Wolak for many helpful discussions and suggestions. I benefited from comments from Lanier Benkard, Peter Reiss, and seminar participants at Stanford University. I am also grateful to Mike Clune at the CCRDC for help with
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".