Bibliographic record
Abstract
To systematically study the relationship between foreign direct investment inflow (FDI inflow) and capital flight, this chapter uses yearly-data in 1984-2012 in China, sets up two propositions and applies our model to explore the micro-relation and macro-relation between FDI inflow and capital flight.At proposition (4-1), we can find that the estimated values of long-term foreign direct investment inflow (FDI inflow) and long-term capital flight are significant correlation. Then, under Augmented Dickey-Fuller test (ADF test) or Philips-Perron test (PP test), the residuals reject the null hypothesis with statistical significant at 5% level, which implies that the result is stable. Causal test also shows that the relation between FDI inflow and capital flight is Granger causal. Besides, results of error correction model (ECM) show that FDI inflow and capital flight, they are not only have the long-term positive relation, but also have short-term dynamic relationship. So we can find the proposition (4-1) is true.At proposition (4-2), at first, we can find that the estimated values of long-term FDI inflow and long-term export are obviously correlated with statistical significant at 1% level, the R square value reaches highly level at 0.9, and they imply the model is good to fit. Next, results of error correction model show that FDI inflow and export, they are not only have the long-term positive relation, but also have short-term dynamic relationship. So we can find the first part of proposition (4-2) is true, which means that if FDI inflow increases, export will grows, too.Finally, for the remainder of proposition (4-2), at first, we can find that the estimated values of long-term capital flight and long-term export are obviously correlated. Then, under ADF test or PP test, we can find the remainder of proposition (4-2) is true, which means that if export grows up, capital flight will increase, too. In short, FDI inflow increase, export will grows apparently, capital flight will increase obviously which will attract more FDI inflow.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".