Chinese Outward Foreign Direct Investment in Developed and Developing Countries: Converging Characteristics?
Bibliographic record
Abstract
The spectacular surge in Chinese outward foreign direct investment (OFDI) has been reinforced by China's accession to the WTO (2001). The understanding of their determinants remains a key theoretical question, in particular whether they confirm the standard conceptual framework - 'ownership', 'location', 'internalisation' (OLI) and 'linkages' (augmenting competences by learning). The paper argues that the determinants of Chinese OFDI change over time and converge toward global strategies, via a comparison between Chinese OFDI in developed countries (based on an original database of 1800 investment operations in Europe from 2002 onwards) and in developing countries (Sub-Saharan Africa, Latin America). While their impacts indeed vary according to countries' contexts, Chinese OFDI in developed and developing countries converges toward complex and similar motives, become more mature through the combination of various modes of entry (greenfield and mergers-and-acquisitions), and exhibit more commonalities than differences. The comparison thus demonstrates that while the determinants of Chinese OFDI in developed countries were initially access to their markets, they now include efficiency-seeking motives (dispersing design, R&D and production) and assets-seeking (or augmenting assets) motives, the latter's prevalence in developed countries (e.g., patents, skills, brands) remaining a contrast with developing countries. Chinese OFDI in developing countries is mostly driven by resource-seeking motives (strategic inputs for China's growth), but also in resource-endowed developed countries (Australia, Canada). Large investments are driven by Chinese state-backed firms both in developed and developing countries. The growing number of Chinese small and medium private enterprises which invest in developing countries (e.g., Sub-Saharan Africa) shows that market access has increasingly become a determinant of OFDI, together with efficiency - and assets-seeking motives - rising labour costs in China being incentives for relocating abroad, in particular in labour-intensive sectors where competitiveness is driven by prices. Chinese firms often conduct these various strategies simultaneously.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".