The Impact of Culture on the Demand for Non-life Insurance Penetration in Developing Countries: Panel Data Analysis
Bibliographic record
Abstract
Purpose: The impact of insurance market activity within financial development is gaining more attention in academia, as the sector experiences growth within emerging markets. The paper aims to understand which macro-economic and social variables impact the growth or decline of the non-life insurance sector broadly across European countries with a view to provide recommendations to drive increased penetration across the region. Methodology: Using Fixed Effects Panel Data Regression and annual data from 1990 to 2021 on 10 countries, the study examines the explanatory factors of non-life insurance demand in European countries (Australia, France, Austria, Italy, Canada, Luxemburg, Denmark, Norway, Finland and Portugal). Findings: The study found that GDP, and urbanization and education rates have a significant negative impact on non-life insurance penetration and density; urbanization, religion, education level and rule of law can explain positively variation in non-life insurance density and penetration across countries. Countries with higher urbanization levels, higher education level, Christian or Buddhist beliefs and more effective rule of law spend more on non- life insurance than other countries. The control of corruption and government effectiveness explain negatively variance in non-life insurance. Unique Contribution to Theory, Practice and Policy: Notably, governments can develop the non-life insurance sector through policies that support urbanization.Similarly, ensuring an environment that promotes economic freedom (such as low tariff, high personal choice, low government spending and high security of property rights) could be an effective way of promoting non-life insurance demand. In contrast, policies that help to reduce the rate of urbanization may yield a double dividend: less population and congestion in cities and better opportunities for the development of non-life insurance markets. Also, countries with high level of education, can develop the development of non- life insurance demand. Among many socio-economic factors such as income, urbanization and education level, our analysis suggests that cultural dimensions such as beliefs and rule of law play a role.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".