Bibliographic record
Abstract
Using a new data set, we examine the characteristics and dynamics of cross-border mergers and acquisitions during emerging-market financial crises, that is, so-called "fire-sale FDI".Our findings shed fresh light on whether the transactions undertaken during crisis periods differ in fundamental ways from those undertaken during more tranquil periods.The increase in foreign acquisitions during crises is mainly driven by non-financial acquirers targeting firms in the same industry rather than foreign financial firms.This increase in acquisition activity in a given industry is unrelated to the industry's dependence on external finance.There is also no evidence of an increase in the size of stakes bought during crises.In terms of the effect of crises on emerging-market mergers and acquisitions, we find little evidence that foreign acquisitions are resold, or "flipped", more frequently than domestic acquisitions.Moreover, flipping rates are uncorrelated with the industry's dependence on external finance.Finally, the probability of being flipped to a domestic buyer does not differ across crisis and non-crisis periods.All of these results are robust to alternative empirical specifications, different definitions of crises, and the inclusion of macroeconomic controls.Contrary to conventional wisdom, fire-sale FDI and asset flipping by foreign firms appear to have been "business as usual".
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".