The Impact of the Non-life Insurance Penetration on the Economic Growth of Developing Countries: Panel Data Analysis
Bibliographic record
Abstract
Purpose: This study explores the impact of non-life insurance industry performance on economic growth in 10 developing countries (Australia, Austria, Canada, Denmark, Finland, France, Italy, Luxembourg, Norway, and Portugal). Methodology: Insurance penetration is measured through proxy such as non-life insurance with the time-series statistics covering the period from 1990 to 2021. The ordinary least square regression was adopted for the testing of the hypotheses. The results are based on panel data models and panel cointegration. Findings: The outcomes of the study showed that non-life insurance penetration had a substantially positive effect on the economic growth in 10 developing countries during the period from 1990 to 2021. The results based on panel data models and panel cointegration suggests that Non-life insurance has a positive and significant effect on the economic growth of the chosen countries. Also, it shows that the application of the rule of law had a positive effect on the developing economies. On the other hand, the higher the level of education, the more people are aware of the application of non-life insurance, which positively affects economic growth. Furthermore, culture and population density have a positive impact on economic growth. Unique Contribution to Theory, Practice and Policy: The study recommends an improved modification in insurance products, especially in non-life businesses to availing clients the chance of choosing from a diversity of products. The study, therefore, recommends an increase in the awareness of non-life insurance services for its impact to be felt at all levels and to encourage participation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".