Location matters: Valuing firm‐specific nonmarket risk in the global mining industry
Bibliographic record
Abstract
Abstract Research summary Using collective action and social movement theory, we investigate the potential incentives and ability of stakeholders to engage in collective action that can increase firm‐specific nonmarket risk of mining companies. We argue that proximity to the nearest environmentally sensitive water source increases the probability that local stakeholders will take collective actions that impose material costs on the focal mine. We hypothesize that stock markets recognize this nonmarket risk and apply a discount on announcements related to mines located near such areas, and that these risks are moderated by the type of mineral, the nature of the water source, and the strength of host country institutions. Using a unique data set and an event study method, we find support for most of our arguments. Managerial summary We argue that mines located near environmentally sensitive water sources are subject to nonmarket risks arising from the potential collective actions of local stakeholders and their allies. Stakeholder mobilization can impose material costs on a mine in the form of delays, regulatory hurdles, and closure. We find that stock markets recognize these nonmarket risks and apply a discount on announcements by mining companies whose mines are located near environmentally sensitive water sources, particularly rivers. However, we also find that investor reaction is stronger in countries with strong institutions that support collective action. Thus, nonmarket risk management is important even in countries that are typically characterized by low political and institutional risks. We discuss the degree to which these results can be generalized beyond mining.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".