Dynamic Evolution of the Location of FDI:An Empirical Study Based on City-level Data
Bibliographic record
Abstract
With the development of China’s domestic economy,the operating environment of multinational enterprises is changing as well;therefore,the determinants of FDI are not static all the time.Based on the cities data of Jing-jin-ji Region,the Yangtze River Delta and the Pearl River Delta,this paper tries to explore the dynamic evolution of FDI’s location.The results show that,at the early stages,preferential policies,low labor costs and the size of the market are crucial for all the three economic regions to attract FDI.However,preferential policies lost their decisive roles in attracting FDI in recent years,agglomeration effectand infrastructure,especially postal and telecommunications facilities are foremost for FDI location.However,high costs in land using inhibit FDI inflow for the three economic regions in different degrees.Moreover,the results also reveal that FDI in Jingjin-ji Region and the Yangtze River Delta is changing from a labor-intensive feature to a knowledge-intensive and technology-intensive transformation gradually,while FDI is still labor-intensive in Pearl River Delta region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".