The impact of foreign direct investment on innovation at domestic firms: Evidence from the deregulation of foreign investment in China
Bibliographic record
Abstract
Abstract This paper studies the impact of foreign direct investment (FDI) on innovation by domestic firms in China. A difference‐in‐difference estimation strategy yields causal evidence by exploiting China's deregulation of FDI in 2002. Analysis of a matched firm–patent data set from 1998 to 2007 shows that both the quantity and quality of innovation by domestic firms benefited from the presence of FDI. Emphasizing the importance of knowledge spillover from FDI in similar technology domains, the authors examine the role of horizontal FDI and FDI in technologically close industries—those sharing similar technology domains. Findings show that the latter generates much more substantial positive spillover than the former. The paper also shows that knowledge spillover from FDI in similar technology domains is not driven by input–output linkages. In addition, the spillover effect is stronger in cities with higher human capital stock and firms with higher absorptive capacity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".