Shareholder Value Effects of the Volkswagen Emissions Scandal on the Automotive Ecosystem
Bibliographic record
Abstract
This study provides empirical evidence on the effect of the September 2015 Volkswagen diesel emissions scandal on the stock prices of publicly traded firms in the global automotive ecosystem. We focus on both the supply chain partners of VW—tier‐1 suppliers; tier‐2 suppliers; and business customers—and three groups of firms that are not VW supply chain partners—other motor vehicle manufacturers; parts manufacturers not identified as VW suppliers; and wholesalers, retailers, and rental agencies not identified as VW customers. We find that tier‐1 suppliers of direct material to VW suffered a mean stock price reaction of ‒2.69% in the week following the scandal, but this effect varied by region. European suppliers were the most impacted with a mean stock price reaction of ‒5.52%. Suppliers with larger revenue dependence on VW experienced greater negative stock price reactions, as did suppliers of components for engines and/or emissions systems. Non‐VW parts manufacturers experienced a positive effect. We find a mean stock price reaction of ‒5.28% to VW’s European customers, but no significant effects for non‐VW customers. European motor vehicle manufacturers experienced a mean stock price reaction of ‒7.60%. Our results suggest that firms should not just focus on selecting and monitoring responsible suppliers but also apply some of the same principles to developing responsible customers. Our work also has implications for industry groups, regulators, and legal systems, entities that have the resources and capabilities to effectively monitor large firms to reduce illegal or irresponsible behavior such as the VW scandal.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".