Does"good government"draw foreign capital ? Explaining China's exceptional foreign direct investment inflow
Bibliographic record
Abstract
China is now the world's largest \n destination of foreign direct investment (FDI), despite \n assessments highlighting its institutional deficiencies. But \n this FDI inflow corresponds closely to predicted FDI flows \n into China from a model that predicts FDI inflow based on \n government quality indicators and controls and is estimated \n across a sample of other weak-institution countries. The \n only real discrepancy is that, if government quality is \n measured by constraints on executive power, China receives \n somewhat more FDI than the model predicts. This might \n reflect an underestimation of the strength of these \n constraints in China, a unique institutional setting for FDI \n operations, FDI based on expected future institutional \n improvements, or a unique Chinese model of development. The \n authors conclude that Ockham's razor disfavors the \n last. They also note that FDI may be elevated because \n Chinese institutions protect foreign firms better than \n domestic ones.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".