Would You Buy a Honda Made in the U.S.? The Impact of Production Location on Manufacturing Quality
Bibliographic record
Abstract
Are location-specific factors-such as the education and attitude of the local workforce, supplier networks, institutional infrastructure, and local "culture"-important for understanding persistent heterogeneities among firms?We address this question in the context of the automobile industry.Using a unique data set of over 565,000 used-car transactions at wholesale auctions, we test whether the long-run value and quality of otherwise identical cars depends on the country of assembly.We exploit the natural experiment provided by the establishment of assembly plants in the U.S. by Japanese auto manufacturers, and the fact that some of the most popular Japanese car models are assembled both in Japan and the U.S. We find evidence that the Japan-assembled cars on average sell for more than those built in the U.S., but the estimated difference is only $62.The average differences are driven almost entirely by older-model Toyotas, for which we find a more meaningful difference between the Japanese and U.S. built cars.For Hondas and more recent models of Toyotas, the Japan-built cars are no more valuable than those built in the U.S.These results suggest that Japanese automakers have been successful, though perhaps with some lag, at transferring their high-quality practices to their U.S. transplants.Our findings also suggest that there is not an inherent limitation to the U.S. manufacturing environment that prevents the production of high-quality cars in America.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".