Vertical Relationships, Hostages, and Supplier Performance: Evidence from the Japanese Automotive Industry
Bibliographic record
Abstract
Drawing on the hostage model of Williamson (1983. “Credible Commitments: Using Hostages to Support Exchange.” The American Economic Review 73: 519–540) and recent studies identifying equity affiliation as a robust hostage in the Japanese automotive industry, we examine the relationship between automobile assemblers and their suppliers under different demand conditions. Specifically, we explore the extent to which assemblers buffer their equity-affiliated suppliers from demand fluctuations to a greater extent than is the case for unaffiliated suppliers. Our empirical analysis suggests that assemblers buffered their affiliated suppliers from the effects of a negative demand shock in 1992–95, apparently favoring affiliates over unaffiliated suppliers during this period, as predicted by the hostage model. However, affiliates in our sample also more frequently adjust production to accommodate short-run demand fluctuations faced by the auto assemblers. We discuss how our findings relate to alternative theoretical explanations, such as those featuring differential supplier capabilities, risk aversion, or supply assurance in the face of sticky price adjustments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".