Life Insurance and Economic Growth Nexus: Evidence from The MENA Region
Bibliographic record
Abstract
This paper conducts an empirical analysis of the relationship between life insurance market development and economic growth in the Middle East and North Africa (MENA) region. The study examines data from 15 countries over the period from 1999 to 2023. This is accomplished by employing panel unit root tests, panel cointegration inquiries and pooled mean group (PMG) estimation to uncover potential causal relationships. The results pertinently demonstrate a substantial and positive long-term relationship between the life insurance sector and economic growth in the MENA region. They reveal that there is evidence in support of supply-leading hypothesis rather than the demand-following hypothesis. This long-term connection suggests that advancements and expansions within the life insurance industry are significantly associated with, and potentially contribute to, overall economic growth in the region. Enhancing the life insurance sector may be a proactive strategy to promote economic development, rather than a reaction to economic growth. Therefore, policymakers should promote insurance literacy, establish a supportive regulatory framework and provide tax incentives so as to enhance the uptake of life insurance. Furthermore, promoting financial inclusion, deploying digital platforms, and encouraging public-private partnerships can enhance the growth of life insurance, thereby contributing to broader economic development.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".