Modeling the Macroeconomic and Demographic Determinants of Life Insurance Demand in Ghana Using the Elastic Net Algorithm

Phyllis Asorh Oteng, Victor Curtis Lartey and Amos Kwasi Amofa

SAGE Open, 2023, vol. 13, issue 3, 21582440231196658

Abstract: The government of Ghana and the National Insurance Commission have shown concern over the low insurance patronage in Ghana. In order to take the necessary steps to increase insurance patronage, there is the need to, among other things, find the macroeconomic determinants of insurance demand in Ghana. The purpose of this study is to model the macroeconomic and demographic determinants of life insurance demand in Ghana. Data covering the period 1994 through 2020 are used for the study. Even though many studies have been done on determinants of insurance demand elsewhere (not in Ghana), almost all these studies use ordinary least square regression, stepwise regression, or similar regression methods. However, these methods are not robust enough to handle problems of multicollinearity, over-fitting, and inability to do out-of-sample prediction. This current study uses a regularization method known as elastic net regression algorithm which is more robust for handling the aforementioned problems, and more. The results of the study show that the dominating predictors (those with non-zero coefficients) of life insurance demand include old aged dependency ratio, life expectancy, urbanization, and financial development. The first three have positive relation with life insurance demand, while the last one has negative relation with life insurance demand. Insurance regulators and insurance companies are advised to design more innovative and attractive insurance policies for the aged and the old aged dependents as they have the highest tendency to affect insurance demand in Ghana.

Keywords: life insurance; insurance demand; insurance penetration; insurance density; elastic net regression (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231196658

DOI: 10.1177/21582440231196658

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