Information and Communication Technology Adoption and Life Insurance Market Development: Evidence from Sub-Saharan Africa
Bibliographic record
Abstract
As part of the Fourth Industrial Revolution (4IR), blockchain, fintech (financial technology), and insurtech (insurance technology) are some innovations that have been rolled out in the financial landscape and have captured the imaginations of policymakers and scholars alike. The African continent lags in embracing technology and is still grappling with financial access and enhancing financial inclusion. As such, it is bewildering whether African insurance markets are at a stage where they can leverage the possibilities offered by the 4IR. Against this backdrop, the aim of the study was to investigate whether information communication technology (ICT) adoption influences the development of African life insurance markets. We utilised a sample of 31 sub-Saharan African countries for the period 2005–2020. Panel data techniques were employed, and the pooled ordinary least squares, fixed effects, and random effect estimators were used to test the relationship between life insurance density and the measures for ICT adoption (proxied by fixed telephones, internet use, mobile cellular telephones, and broadband) as well as financial freedom being the control variable. We found that the life insurance market development variable was positively related to three of the four ICT adoption variables, namely, fixed telephone, mobile cellular telephone, and broadband. Further, the life insurance market development variable is positively related to the financial freedom variable. These findings suggest that ICT adoption fosters the development of the life insurance market in Africa. The findings also lend credence to the view that the degrees of financial freedom of insurance companies (who are unencumbered by regulations) have a bearing on the levels of insurance sales and, hence, promote life insurance access in Africa. The policy imperatives that flow from this study are that African governments must ensure that they (1) institute ICT adoption-friendly policies and (2) regulate the life insurance sector optimally, in order to foster the development of their life insurance sectors.
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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 | 0.001 |
| 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".