Sweeping it under the rug: Positioning and managing pollution‐intensive activities in organizational hierarchies
Bibliographic record
Abstract
Abstract Research Summary Many corporate groups have multiple layers with parent companies owning subsidiaries, which own other subsidiaries, and so forth, in a pyramid‐like ownership structure. We argue that corporate groups perform their pollution‐intensive activities at the lower levels of the corporate hierarchy to buffer the parent from pollution‐related regulatory risks. Our analysis of 7400 US‐based business establishments owned by the 67 largest US‐headquartered chemical manufacturing corporate groups supported this argument. We also found that they were even more likely to do so in states with greater environmental stringency, whether it be in the home state of the parent or the host state of the subsidiary. Our research calls into question the effectiveness of environmental regulations if companies have the opportunity to shift polluting activities lower in their corporate hierarchy. Managerial Summary Many commentators assert that firms offshore or outsource pollution‐intensive activities to avoid environmental regulations. In this research, we suggest a third approach in avoiding environmental regulations: locating pollution lower in the hierarchy of multilayered corporate groups, which are companies that own subsidiaries that own other subsidiaries and so on. By analyzing data on the 67 largest US‐headquartered chemical manufacturing corporate groups, we found support for this assertion. We also found that pollution is more likely to be located lower in multilayered corporate groups when they are subject to stringent environmental regulations. The multilayered corporate form allows parent companies to insulate themselves from the regulatory risks of pollution‐intensive activities of their subsidiaries through their limited liability status.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".