Multinational enterprises' R&D commitments in Chinese provinces: A configurational approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multinational enterprises (MNEs) are increasingly off-shoring some of their R&D to emerging markets, including China. Much of the extant literature on MNEs' investments in R&D facilities abroad analyses technological and institutional factors at the national level, typically using regressions to examine how host-country institutions influence foreign MNEs' outlays. It, therefore, tends to downplay the importance of sub-national and non-technology-related institutions, and how configurations of home- and host-country institutions interact to influence R&D commitments abroad. Drawing on the global factory model and the Varieties of Capitalism approach, we identify five causal conditions that may influence MNEs' R&D commitments abroad. Conducting an abductive fuzzy-set qualitative comparative analysis, we find four combinations of causal conditions are sufficient to explain substantial R&D commitments in different Chinese provinces. The combination of local corruption and provincial R&D intensity is important, as are the MNE's home-country stock-market capitalization to GDP ratio and minority investor protection. We contribute to the literature on MNEs' investments abroad by extending the importance of sub-national institutions to include those not directly related to technology. We also reveal how combinations of institutions (rather than individual ones acting independently) from the MNE's home and host contexts explain MNEs' R&D commitments in Chinese provinces.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it