China's FDI and Non-FDI Economies and the Sustainability of Future High Chinese Growth
Bibliographic record
Abstract
This paper presents assesses of the contribution of inward FDI to China's recent rapid economic growth using a two stage growth accounting approach. Recent econometric literature focuses on testing whether Chinese growth depends on inward FDI rather than measuring the contribution. Foreign Invested Enterprises (FIEs), often (but not exclusively) are joint ventures between foreign companies and Chinese enterprises, and can be thought of as forming a distinctive subpart of the Chinese economy. These enterprises account for over 50% of China's exports and 60% of China's imports. Their share in Chinese GDP has been over 20% in the last two years, but they employ only 3% of the workforce, since their average labor productivity exceeds that of Non-FIEs by around 9:1. Their production is more heavily for export rather than the domestic market because FIEs provide access to both distribution systems abroad and product design for export markets. Our decomposition results indicate that China's FIEs may have contributed over 40% of China's economic growth in 2003 and 2004, and without this inward FDI, China's overall GDP growth rate could have been around 3.4 percentage points lower. We suggest that the sustainability of both China' export and overall economic growth may be questionable if inward FDI plateaus in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".