Abstract
Microinsurance is a risk management tool for low-income households. However, its adoption is low in Ghana. This study examines the determinants of microinsurance adoption in Ghana, analysing primary data from 1453 households across six key markets and three regions. We also gathered secondary data from 14 microinsurance firms and 47 microinsurance products between 2017 and 2021. We estimate the critical factors influencing microinsurance uptake using robust probit, fixed-effects and panel-corrected standard error models. Our findings indicate that income levels, trust in financial institutions and participation in community risk management groups and the national health insurance scheme are the key determinants affecting microinsurance adoption. Firm- and product-specific factors such as affordability, outstanding claims, risk premiums and benefits paid to microinsurance participants also influence adoption. This study also highlights the crucial role of structural, social and economic factors in predicting demand for microinsurance, utilising Outreville's four-factor insurance demand framework.
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Data availability
Data is available upon reasonable request.
Notes
The conceptual framework consists of four categories that categorise the factors affecting insurance demand. The first is economic, which typically includes the price of insurance and the influence of wealth and income. The second category comprises social and cultural factors, which are concerned with the utility function of insurance. The third category is structural factors, which refer to the underlying market conditions that affect demand for insurance, including discount rates. The fourth category is personal and demographic factors, which represent the various types of loss exposures individuals may face.
Factor analysis was employed for the categorisation. The results are presented in “Appendix”.
Assets represent ownership of a television set, microwave, gas cooker, car, home theatre, freezer, mobile phone, computer or laptop, radio set, DVD player, bicycle, motorbike or house.
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Emmanuel Owusu Oppong: Data collection, modeling, analysis of data and writing of the paper. Baorong Yu: Modelling and guidance on paper writing. Bruvine Orchidée Mazonga Mfoutou: Data curation, writing and reviewing. All authors read and approved the final manuscript.
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Appendix: Factor analysis construct
Appendix: Factor analysis construct
Construct | Variable | Factor | Uniqueness | Eigenvalue |
|---|---|---|---|---|
Structural | Has NHIS | 0.5797 | 0.6639 | 0.67221 |
Alternative | 0.5797 | 0.6639 | 0.24843 | |
Demographic | Age | 0.2775 | 0.9230 | 0.15995 |
Gender | 0.0706 | 0.9950 | − 0.00425 | |
Marriage | 0.2792 | 0.9221 | − 0.11734 | |
Economic | Income | 0.1123 | 0.9564 | 0.61644 |
Assets | 0.5616 | 0.6841 | 0.03473 | |
Saving | 0.5364 | 0.7083 | − 0.26283 | |
Social–cultural | Years of schooling | 0.5221 | 0.7264 | 0.58875 |
Years of schooling * have NHIS | 0.4252 | 0.8172 | 0.05219 | |
Risk | − 0.0233 | 0.9645 | 0.04508 | |
Trust | 0.0125 | 0.9760 | 0.03947 | |
Willingness | 0.0355 | 0.9739 | 0.06789 | |
Area | 0.3651 | 0.8560 | − 0.24089 |
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Oppong, E.O., Baorong, Y. & Mazonga Mfoutou, B.O. Microinsurance in Ghana: investigating the impact of Outreville's four-factor framework and firm and product characteristics on adoption. Geneva Pap Risk Insur Issues Pract (2024). https://doi.org/10.1057/s41288-024-00324-1
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DOI: https://doi.org/10.1057/s41288-024-00324-1