Friendshoring: How Geopolitical Tensions Affect Foreign Supply Bases and Supply Network Structures
Bibliographic record
Abstract
Geopolitical tensions can lead to supply disruptions for firms with foreign supply bases. At the origin of these tensions lies disagreement in global affairs between nations that can lead to national animosity and the implementation of discriminatory practices toward foreign firms. In this study, we examine the influence of geopolitical tensions—operationalized as political divergence between governments—on global supply chain reconfiguration. Over the period 2003-2019, we investigate if political divergence affects foreign supply bases for 3,201 US firms sourcing from 108 countries, and how political divergence exposure impacts the supply network structure of 934 US firms. We find that while political divergence leads firms to reduce their supply base in hostile countries, it increases supply base complexity and sub-tier supplier sharing. Our study makes important theoretical and practical contributions to global sourcing and supply chain risk management. First, we highlight that geopolitical risk in global supply chains can originate from political divergence, which recognizes potential interventions from the governments of both the buyer and the supplier. It also captures the uncertainty associated with geopolitical disruptions before their occurrence. Second, we show that while using multi-sourcing as a buffering strategy can mitigate geopolitical disruptions, it also increases supply base complexity and thus expose the firm to other types of disruptions. Last, we uncover an unintended consequence of friendshoring. As firms reduce their supply base in hostile countries, their tier-1 suppliers end up sharing more tier-2 suppliers, amplifying the propagation risk in the focal firm’s supply network.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".