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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".