Is there a link between economic growth and insurance and banking sector activities in the G‐20 countries?
Bibliographic record
Abstract
Abstract Rapid technological development over the last three decades has enabled different sectors of the economy to be seamlessly integrated. This has had an important spill‐over impact on the wealth of countries across the globe. In this paper we examine the inter‐linkages between the banking sector and the insurance industry on the economic growth of the G‐20 countries between 1980 and 2014. Using the vector auto‐regression model and the Granger causality test, the study shows that in the long run, developments in the banking sector and insurance industry have had a significant impact on the economic growth of the G‐20 countries. In the short term, the inter‐relationships between the three factors prove to be more complex in that they differ by countries in different stages of development. Based on the empirical findings, this paper discusses the policies and strategies policy makers and banks and insurance companies should have in place in order to create sustained economic growth in an increasingly inter‐connected world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".