Drivers of Globalization of R&D Investment by U.S. Multinational Enterprises: Evidence from Industry-Level Data
Bibliographic record
Abstract
Existing country-level and firm-level studies have shed light on the mechanisms driving the globalization of R&D investment by multinational enterprises. However, there is a lack of industry-level evidence on this issue, which is much needed for the robustness of the theoretical and conceptual framework developed from country- and firm-level studies. Therefore, this study examines the determinants of overseas R&D investment by multinational enterprises from a single country, the United States, using an industry-level panel dataset. This study covers U.S. multinational enterprises in seven two-digit-level North American Industry Classification System (NAICS) manufacturing industries in twenty-three countries over the period 1999-2008. The empirical findings suggest that technology-seeking motive, technology-adaptation motive, and access to an abundant pool of researchers exert positive impact on the R&D intensity of U.S.-based multinational enterprises in a host country. The roles of investment position, institutional quality and distance are not found to be robust. These findings are largely consistent with the current theoretical understanding on R&D globalization by multinational enterprises. The findings point to the need for policies that strengthen domestic R&D stock, enhance human capital endowment and support a domestic market that is open to the world in order to attract overseas R&D investment by multinational enterprises.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".