The Effect of Government Subsidy Policy for Foreign Capital R&D from the Perspective of Global Supply Chains: An Empirical Analysis with Time Lag and Propensity Score Matching
Bibliographic record
Abstract
(1) Background: with the emergence and continuous development of more multinational corporations, capital and resources flow rapidly in the form of global supply chains around the world. Furthermore, government subsidies for R&D are one of the key factors that affect foreign-funded R&D activities and their innovation output and performance in global supply chains. (2) Methods: in this paper, firstly, based on two sets of time series and dynamic panel data, we propose a distribution time lag model to test the effect of R&D subsidy policies from the macro perspective. Secondly, we employ the propensity score matching method to test the micro effect of R&D subsidy policies. (3) Results: our empirical results show that there are significant differences in the impacts of R&D subsidy policies on foreign capital funded innovation and domestic innovation. The main effect of government subsidy on foreign capital R&D is to improve the innovation output. However, regarding domestic R&D, it is to promote innovation performance. (4) Conclusions: Government subsidy is the main cause of the individual differences among the foreign funded R&D institutions in terms of innovation output and innovation performance. From the perspective of global supply chains, our analysis and results provide managerial and policy insights on subsidizing foreign investment in R&D in China.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".