Shareholder Value Effects of the Volkswagen Emissions Scandal on the Automotive Ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it