FDI and Economic Growth in Western Region of China and Dynamic Mechanism: Based on Time-Series Data from 1986 to 2010
Bibliographic record
Abstract
In this paper, we analyze causal relationship between foreign direct investment (FDI) and economic growth in western region of China using time-series data from 1986-2010. The analysis is conducted by the means of time-series estimations through ADF unit root test, co-integration tests, error-correction analysis and Granger causality test. The purpose of the paper is to empirically investigate the impact of economic growth on FDI in western region and its dynamical mechanism. The results suggest that inward FDI flow does not lead to Granger-cause economic growth, and economic growth also does not exert significant impacts on FDI inflows, which means some studies have exaggerated the positive effect of FDI on economic growth, and eclipsed the influence of economic growth on FDI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 teacher head, 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".