Greenfield versus merger and acquisition FDI: Same wine, different bottles?
Bibliographic record
Abstract
Abstract Relying on a large foreign direct investment (FDI) transaction level dataset, unique both in terms of disaggregation and time and country coverage, this paper examines patterns in greenfield (GF) versus merger and acquisition (M&A) investment. Although both are found to seek out large markets with low international barriers, important differences emerge. M&A is more affected by geographic and cultural barriers and exhibits opportunistic behaviours as it is more sensitive to temporary shocks such as a currency crisis. Further, M&A is more affected by destination factors such as financial development and institutional quality. GF, on the other hand, is relatively more driven by factors such as origin comparative advantage and destination taxes. These empirical facts are consistent with the conceptual distinction made between these two modes, i.e., M&A involves transfer of ownership for integration or arbitrage reasons while GF relies on firms’ own capacities, which are linked to origin country attributes. They also suggest that GF and M&A are likely to respond differently to policies intended to attract FDI.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".