Ethnic Chinese Networks and International Investment Evidence from Inward FDI in China
Bibliographic record
Abstract
This paper studies the role of business and social networks in international investment by examining the e#ects of ethnic Chinese networks on foreign direct investment (FDI) in China. After controlling for a variety of economic determinants of FDI, this study finds a significant positive role in inward FDI of ethnic Chinese networks proxied by the population share of ethnic Chinese in the investing country. 1. Introduction The role of business and social networks in promoting international trade has attracted increasing research interest in recent years. Rauch and Casella (1998), for example, provide a theoretical model in which information-sharing social or ethnic networks can improve the allocation of resources and increase the volume of trade in the international market under incomplete information. On the empirical front, Head, Ries and Wagner (1997) find that immigrants significantly increase trade between Canada and source countries; Rauch (1999) presents evidence that comm...
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".