FinTech Investment and GDP Relationship: An Empirical Study for High Income Countries
Bibliographic record
Abstract
Technology, the internet, and demographic change have started a rapid transformation in the financial services sector. The widespread use of innovation and technology in financial services in social and economic areas made these services more effective and companies called Fintech have emerged important economic actors. The Fintech sector has generated changes in the traditional financial service understanding and the delivery of these services. In this area, Fintech companies are developing new financial business models with the help of the latest technological developments and offering innovative financial products and services such as payment services, asset management, and insurance services. This study investigates, the relationship between GDP and Fintech investment using panel causality methods from 2014Q1 to 2020Q4 for eight high-income countries: The United States, United Kingdom, Singapore, Australia, Canada, Germany, Israel, and France. The results indicate the existence of cross-sectional dependence among countries. According to Westerlund’s panel cointegration test results, a cointegration relationship between two variables has been found in the long run. In the short run, panel Granger causality variables have been found only in Germany. We find a positive effect of Fintech investment on GDP in seven countries, and we see a negative relationship in Singapore.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".