The Political Economy of Inward FDI: Opposition to Chinese Mergers and Acquisitions
Bibliographic record
Abstract
A great deal of political economy scholarship has focused on how countries can attract foreign direct investment (FDI), and the effects of FDI on growth and political stability. A related topic that has received almost no attention, however, is that of divergent political reactions to inflows of FDI in the countries receiving investments. This is an oversight, because inward FDI flows are not equally welcomed by the host country and, in fact, often encounter strong political opposition. We study this phenomenon by examining political opposition to attempts by Chinese companies at mergers and acquisitions (M&As) with US firms. This is especially important given rapidly expanding Chinese M&A activity. We hypothesise that although most legal barriers to foreign M&As are based on national security considerations, objections on these grounds are often vehicles through which to channel other grievances, and that economic distress and reciprocity are also key drivers of political opposition. To test this theory, we constructed an original dataset of 569 transactions that occurred between 1999 and 2014 involving Chinese acquirers and American targets. We find that there is more likely to be opposition to Chinese M&A attempts in security sensitive industries, economically distressed industries, and sectors in which US companies faced restrictions in China’s M&A markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".