Geopolitical volatility and subsidiary investments
Bibliographic record
Abstract
Abstract Research Summary We examine how geopolitical volatility—the instability of bilateral political affinity between countries—affects foreign subsidiary investments. Building on prior work that shows that the level of political affinity between countries facilitates foreign investments, we argue that the volatility of political affinity impedes firms' ability to form expectations about stakeholder behavior and reduces subsequent investments in subsidiaries. We further argue that the effect of volatility of political affinity on foreign subsidiary investments is less pronounced when the level of political affinity between countries is high and when the firm has strong political connections at home. Our analyses examine 1054 US firms and their subsidiary investments in 106 countries from 2000 to 2015. Managerial Summary Geopolitical risk has emerged as an important factor in foreign investment decisions in recent years. The rise of geopolitical tensions worldwide and the fragmentation of relationships between countries have introduced new dimensions to foreign investment risks. We study the propensity for sudden and unpredictable shifts in the political relationship between countries—that is, volatility of political affinity in their bilateral political relations—and its effect on firms' foreign subsidiary investments. We show that volatility of political affinity negatively affects the number of subsidiaries, employees, and local sales in the host country because when bilateral relations change suddenly, it is more difficult for multinational firms to predict how stakeholder behavior will impact the performance of their investments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".