International Economic Integration: Comparing Exports and FDI Networks in the New Millennium
Bibliographic record
Abstract
Trade and foreign direct investments (FDI) represent the real and the capital side of international economic integration. While Network Analysis (NA) on world trade network (WTN) is wide, few analyses describe world investment networks (WIN), since FDI data suitable for comparison are very scarce and very complex to collect. In this paper, we exploit FDI Bilateral Statistics by UNCTAD (2014), to compare WTN and WIN in the first decade of the new millennium, before and after 2008 crisis. Results show that all countries are integrated since there are few isolated economies, and unique largest components emerge confirming the complexity of global value chain. 2008 economic crisis affected WTN, but not WIN. Geography, rather than economic similarity, is crucial in defining trading connections and cohesive subgroups. WIN and WTN links are mutual in all networks, confirming that once a link is established, it is easier to maintain all commercial relations. WIN and WTN key players are USA, Germany and China for Exports, while USA and Germany for FDI. There is a positive association between couplets of WTN and WIN links, conjecturing that FDI and Exports networks could be complements, rather than substitute.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".