Insurance Penetration and Institutional Spillover on Economic Growth: A Dynamic Spatial Econometric Approach on the Asian and Europe Region
Bibliographic record
Abstract
The contemporary environment is interrelated, and interactions between markets, countries, and international actors at different levels exist in every corner of the globe. Amid this, the failures of the free-market system have paved the way for institutionalism, which proposes minimising transaction costs, substantial property rights, and enabling proper contract enforcement. Studies on institutions and insurance development spillover concerning growth relationships are rare and a critical area needing exploration. This study explores the behaviour of economic development in terms of potential spatial dependencies and spatial institutional and insurance development spillover on economic growth. To measure insurance development by the life insurance and non-life insurance penetration, economic growth by per capita gross domestic product (GDP), and indicators of good governance for institutions in the nations. The study explored the spatial impact between countries using panel data of 56 countries between 2002 and 2020 representing the Asian and European regions. We did this by using dynamic spatial econometric modelling (DSEM) on institutional and insurance development and seeing the spatial implications and the spatial institutional impact moderated by insurance development on growth. Results indicate that developing the life insurance and non-life insurance of surrounding countries creates a spillover impact on the local countries’ economies. In contrast, institutions have created a reverse spatial spillover impact on local countries. However, life insurance development, moderated through accountability and government effectiveness, has created a spatial spillover between countries. Both life and non-life penetration moderated by the control of corruption and overall institutions have shown a reverse spillover on countries’ economies. This suggests that global governance is a positive-sum game, and monitoring and governance structures have failed at the international level concerning separate countries. Therefore, it is seen that to prevent institutional failure at the state level, good governance and links with the global governance structure could disrupt or energise local institutions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".