Financial Development and Life Insurance Demand in Sub-Sahara Africa
Bibliographic record
Abstract
This study examines the relationship between financial development and life insurance demand in Sub-Saharan Africa with a sample of fifteen countries. These countries are Nigeria, South Africa, Namibia, Cameroon, Ghana, Cote d’Ivoire, Sudan, Kenya, Uganda, Mozambique, Togo, Benin, Senegal, Cape Verde and Zambia. The specific objectives are to determine the relative effect of financial depth, as well as major macroeconomic factors, preferences and life insurance demand in the sampled countries. It is argued in this study that the traditional textbook and theoretical factors driving demand for life insurance may not be extensively dominant in the case of Sub-Sahara Africa where low formal financial patronage are rife. Using annual data covering the period 1990 – 2011 (22 years), the study applies the panel data estimation, which allows for endogenization of individual country characteristics in the analysis. The model adopted in this study categorises all the necessary macroeconomic factors in the study that seek to explain both insurance penetration and insurance density for the sampled countries. The results of the study show that financial development in African countries drives life insurance demand than major macroeconomic factors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".