Cross-Border Mergers and Acquisitions: Key Influencing Factors in the U.S.-China Context
Bibliographic record
Abstract
This paper examines the critical role of national regulatory frameworks in shaping cross-border mergers and acquisitions (M&A) between the United States and China, with a particular focus on the core factors of antitrust review, foreign investment review, national security review, and tax policy. Through case studies, statistical data and literature review, the study finds that while these regulatory measures aim to protect national interests, they also significantly increase transaction costs, time delays and uncertainty. Data show that between 2015 and 2020, about 11% of Chinese M&A deals with the US were terminated due to regulatory hurdles, and the failure rate in the high-tech sector even exceeded 45%. It is worth noting that the impact of these factors changed significantly before and after the New Crown outbreak. The epidemic exposed the vulnerability of global supply chains, prompting countries to pay more attention to investment security and industrial localization in key areas; at the same time, countries increased regulation of multinational firms in tax policy to cover fiscal deficits. This paper suggests that firms should adopt more flexible strategies to cope with the more complex regulatory environment in the aftermath of the epidemic and calls on policymakers to find a better balance between safeguarding national security and promoting global economic recovery.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".