Foreign Direct Investment across China: what should we learn from spatial dependences?
Bibliographic record
Abstract
The paper investigates the importance of spatial dependences on Foreign Direct Investment (FDI) localization across Chinese provinces over the 1992-2009. Based on exploratory spatial data analysis, spatial sigma-convergence and spatial Durbin specifications, we present a much clearer picture of FDI dispersion and spatial convergence across China by highlighting the spillover effects of FDI localization in Chinese provinces and regions. Our results are threefold. First, FDI convergence is more pronounced compared to the Central region, whereas the dispersion is greater when the Coastal and the Western regions are taken as reference points. Second, at the province level, FDI localization seems to present a substitutable configuration. Third, when controlling for the spatial distribution of FDI at the level of regions, it seems, conversely, that the FDI localization presents a complementary configuration. The finding resulting from the opposing configurations of the FDI localizations observed at the region and province levels seems to argue in favor of promoting FDI attractiveness policies based on regional complementarities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".