UNVEILING THE DILEMMA: DO FINANCIAL DERIVATIVES IMPERIL OR PROPEL ECONOMIC PROSPERITY?
Bibliographic record
Abstract
The study delves into how regulated derivatives trading influences economic growth across 21 countries, spanning from 1995 to 2022, encompassing major economies like the USA, UK, India, Brazil, Canada, and Australia. While derivatives markets are often linked with the 2008 financial crisis, our research explores their potential to positively impact the economy through various pathways detailed in the paper. Employing a dynamic panel data model (GMM) mitigates potential endogeneity concerns. Our findings reveal notable insights: we identify a positive association between the expansion of regulated derivatives trading and economic growth, evident in both percentage change and per capita GDP. Additionally, inflation shows a negative correlation with economic growth, while trade openness and fertility rates exhibit positive correlations. Notably, our study uncovers unexpected outcomes, diverging from existing literature, particularly concerning the relationships involving foreign direct investments, gross domestic savings, government consumption expenditure, and economic growth.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".