Determinants of Cross-Border Mergers and Acquisitions in Developed Markets: A Recent Empirical Analysis and it´s Practical Implications
Bibliographic record
Abstract
Cross-border mergers and acquisitions are contributing to economic growth, welfare, market expansions and competitiveness. Many countries are thriving to attract them and in political discussions it is one of the hot topics how to do so. For policy makers and corporate decision makers it is crucial to know what the real drivers of foreign corporate investments are. For this purpose, this study will analyze cross-border mergers and acquisitions in industrialized countries. The paper is focusing on two key questions: • What are the key determinants for cross-border mergers and acquisitions? • Does industry similarity between countries affect the deal frequency? The paper contains a theoretical and a literature review followed by an empirical investigation which was based on descriptive statistical methods and a regression analysis. The empirical findings revealed a concentration of transactions in certain countries, particularly the US, Canada, UK, Germany, and France. The healthcare and financial industries lead the way as the top sectors, followed by the industrial and technology sector. Some sectors, such as finance, healthcare, and high technology, had a high frequency of intra-industry cross-border transactions. The regression results showed that GDP size as a proxy for the attractiveness of the local market had the biggest impact. Cultural affinity and political stability followed in terms of impact, when the USA was excluded from the dataset. Including the USA in the estimation proved the strong significance of the GDP size again but also revealed the importance of lower corporate taxes, however, to a clearly lesser extent.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".