Navigating global uncertainty: Do foreign national directors protect <scp>US</scp> firms from supply chain disruptions?
Bibliographic record
Abstract
Abstract We examine whether foreign national directors (FNDs) on US corporate boards help their firms mitigate the adverse effects of economic policy uncertainty (EPU) shocks originating from the directors' home countries. Using a comprehensive data set of US manufacturing firms' international supply chain relationships from 2003 to 2019, we find that EPU spikes in supplier countries lead to significant declines in aggregate US imports as well as in buyer firms' inventory purchases, sales, and market valuation. However, firms with FNDs from the affected countries are better able to mitigate these negative impacts. Cross‐sectional analyses reveal that the beneficial role of FNDs is more pronounced in firms with limited operational slack, greater difficulty accessing information about supplier countries, and higher financial constraints. Robust to a battery of sensitivity tests, our findings underscore the importance of FNDs on corporate boards during times of increased global uncertainty, especially for firms heavily reliant on foreign suppliers, and inform the debate on board diversity and supply chain resilience amid economic policy‐driven uncertainties.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".