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Foreign competition threat and ethnic minority inclusion in the board

2021· article· en· W3207474806 on OpenAlexaff
Y. Lee, Heejung Jung, Jim Goldman

Bibliographic record

VenueAcademy of Management Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCompetition (biology)Ethnic groupInclusion (mineral)Social identity theoryForeign ownershipDemographic economicsBusinessPolitical scienceSocial psychologyPsychologyForeign direct investmentEconomicsSocial groupLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.326
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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