Determinants of R&D activities of multinational firms abroad
Bibliographic record
Abstract
In recent years, firms have considerably decentralized their research and development (R&D) activities. Subsidiaries of foreign multinational enterprises (MNEs) are now among the top performers of R&D in many EU and non-EU countries. Specifically, MNE affiliates account for around 20% of total business R&D in France, Germany and Italy; between 30% and 50% in Canada, Portugal, the Slovak Republic, Sweden and the United Kingdom; and more than 50% in Austria, Belgium, the Czech Republic, Hungary and Ireland. Against that backdrop, the paper uses a novel and unique data base on R&D expenditure of foreign-owned firms for a set of OECD countries and identifies and analyzes factors that drive the scale of R&D expenditure across countries and sectors. The empirical analysis employs a gravity approach which demonstrates that geography plays a pivotal role as distance between host and home country of a foreign-owned firm, a common language spoken in both home and host countries, or common borders are key drivers of cross-border R&D investments. Moreover, results reveal that additional determinants such as larger host and home country markets or superior host country human capital bases are conducive to R&D expenditure of foreign-owned firms while stronger human capital bases in home countries deter R&D efforts of foreign-owned firms abroad. Keywords: internationalisation of research and development, multinational firms, gravity model JEL-codes: F23, O32, O33
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 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".