Out of Africa? Locational determinants of South African cross-border mergers and acquisitions
Bibliographic record
Abstract
South Africa is the Africa’s biggest source of outward foreign direct investment. This study examines the principal locational motives of cross-border mergers and acquisitions CBMA by South African firms for the 1990–2014 period. The role of inter-country cultural and economic linkages is also studied. Firm-level data of South African merger and acquisition activities in 74 host countries are used to estimate a number of model specifications that control for host-country economic, geographical, cultural and institutional characteristics. Estimations are carried out using random-effects negative binomial panel model. Capturing the host-economy market and enhancing efficiency are found to be the two major motives driving South African corporations’ CBMA activities. Natural resources acquisition seems a less important motive, while strategic assets such as patents and technology do not appear to be attractive. The role of cultural and economic linkages between the home and the host country is found to be substantial. South African firms prefer investing in Africa, particularly in countries bordering South Africa. In light of the study’s findings, South African CBMA activities can be compared with those from other emerging economies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".