The disparate economic outcomes of stigma: Evidence from the arms industry
Bibliographic record
Abstract
Abstract Research Summary Organizational stigma has been commonly associated with a number of negative economic externalities in prior literature, but the mechanism by which this occurs and the extent of the associated consequences have received little attention. We address these gaps by theorizing that stigmatizing labels damage the legitimacy of the target by highlighting a deviation from the expectations of relevant audiences. We also argue that the content and focus of stigmatizing labels, as well as the features of the stigmatizer audience deploying them, will affect the magnitude of the negative economic consequences of stigma. Through an analysis linking the condemnation of arms producers in the media between 1998 and 2016 to cumulative abnormal returns (CAR) in the stock market, we find broad support for our arguments. Managerial Summary This paper examines the economic impact of stakeholder criticism on firms, using data from the global arms industry to develop a typology of criticism based on its content, focus, and origin. We find that criticism can negatively affect a firm's stock market returns, particularly when those criticizing have the authority to condemn specific behaviors. Civil society entities like nonprofits or the media, politicians, and economic players such as investors each have unique authority to criticize harmful behavior, illegal behavior, and unethical affiliations, respectively. Understanding the different types of criticism and their potential economic consequences can help firms better manage stakeholder relationships and mitigate negative impacts on their financial performance.
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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 | 0.004 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".