FDI Spillovers at the National and Subnational Level: The Impact on Product Innovation by Chinese Firms
Bibliographic record
Abstract
Abstract We investigate the degree to which the presence of inward foreign direct investments (FDI) influences product innovation by emerging market firms. We begin with FDI spillover effects at the national level, the common approach in the literature. We further examine spillover effects at the subnational level because knowledge spillovers have been found to be localized. We study both intra-industry and inter-industry FDI spillovers in a subnational location, based on the distinction in the cluster literature between Marshall–Arrow–Romer specialization externalities and Jacobian diversification externalities. Using information from more than 346,000 Chinese manufacturing firms from 2000 to 2006, we find that Chinese firms improve product innovation when they are located in cities with concentrated foreign innovative activities in the same industry. These intra-industry spillover benefits decrease quickly, however, as foreign presence increases and, at high levels of foreign concentration, are dominated by the crowding-out effect. We also find evidence of inter-industry spillover benefits in a city; diversity of industries with a foreign presence contributes to product innovation by Chinese firms.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".