Foreign competition threat and ethnic minority inclusion in the board
Bibliographic record
Abstract
The underrepresentation of ethnic minorities among directors of corporate boards has been widely recognized as a challenging issue for Corporate America. In this paper, we draw on a branch of social categorization theory and argue that competition threat from a foreign source enhances ethnic minority inclusion in the board by lowering existing intergroup bias. Foreign competition threat blurs the boundaries between ingroup (white directors) and outgroup (minority directors) and makes the common domestic identity and fate more salient. Leveraging the exogeneous shock that increased foreign competition threats — that is, China’s accession to the WTO in late 2001 and the predetermined industry variation in the importation costs — we devise a difference-in-differences study where we predict that the U.S. manufacturing firms facing greater competition threats from Chinese imports are more likely to have minority directors on their boards. The results support our theory, where firms exposed to high foreign competition threats are 15% more likely to include minority directors on their board than the unconditional probability. In line with the theory, our supplementary analysis shows that those exposed firms also express more “oneness” after the shock as they increase the usage of the collective pronoun “we” in their annual report. A series of robustness tests and supplementary analyses support our theory on ethnic minority inclusion and address potential alternative explanations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".