Regulating inbound foreign direct investment in a world of hegemonic rivalry: the evolution and diffusion of US policy
Bibliographic record
Abstract
Abstract We employ insights from the international relations literature to offer a novel perspective on the regulation of inbound foreign direct investment (FDI). We argue that in a world of hegemonic rivalry, the incumbent, when it perceives a threat, tends to employ both internal and external policy mechanisms to maintain the balance of power. Specifically, in response to China’s rise, the US expanded its internal national security review regulations, moving from a primary focus on FDI by state-owned enterprises (SOEs) to including Chinese investments in a broad set of strategic industries, regardless of ownership. External mechanisms include the diffusion of those internal regulations to allied countries, and we focus on a specific Alliance, the Five Eye (FVEY) intelligence alliance. Empirically, we combine natural language processing of keywords with close reading of selected documents to analyze FDI regulations in the US and FVEY allies. The results suggest that the US is an early adopter of both SOE and broader FDI regulations targeting strategic sectors for national security considerations. While SOE regulations exhibit relatively limited evidence of convergence, we find a more significant and recent convergence between the US and its alliance partners on the national security reviews of FDI in strategic sectors.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".