Has FinTech reshaped global trade? New empirical evidence from structural gravity model
Bibliographic record
Abstract
Technological advancements in the financial sector are widely recognized as transformative for global trade and supply chains, significantly improving access to financial services while enhancing the security, efficiency, transparency, and flexibility of transactions between exporters and importers. In this context, the present study investigates the role of financial technology (fintech) in promoting international trade. Drawing on both theoretical and empirical frameworks that link trade and finance, the research explores how fintech innovations reduce trade costs and, in turn, enhance the gains from trade. By estimating a theory-consistent gravity model based on bilateral trade flows from 106 countries over the period 2014–2019, the study reveals that fintech innovations disproportionately stimulate international trade compared to domestic trade. These findings highlight the critical role of fintech in lowering trade barriers and suggest that policies promoting fintech development—such as those fostering innovation in blockchain, payment systems, and financial services—are essential to strengthening global trade competitiveness.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".