Financial Development and Life Insurance Demand in Sub-Sahara Africa

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.


Introduction
The insurance sector plays a critical role in financial and economic development. By introducing risk pooling and reducing the impact of large losses on firms and households, the sector reduces the amount of capital that would be needed to cover these losses individually, encouraging additional output, investment, innovation, and competition. By introducing risk-based pricing for insurance protection, the sector can change the behaviour of economic agents, contributing inter alia to the prevention of accidents, improved health outcomes, and efficiency gains. As financial intermediaries with long investment horizons, life insurance companies can contribute to the provision of long-term finance and more effective risk management. Finally, the sector can also improve the efficiency of other segments of the financial sector, such as banking and bond markets (e.g., by enhancing the value of collateral through property insurance, and reducing losses at default through credit guarantees and enhancements). ISSN 1923-4023 E-ISSN 1923 -to determine the effect of inflation on life insurance demand in Sub-Sahara Africa, and -to evaluate the significance of interest rate on life insurance demand in Sub-Sahara Africa.

Litreature Review and Framework
Financial development should have a positive effect on the life insurance sector. Also, the structure of the insurance market could have significant effects on the growth of the market. The presence of foreign insurers would be expected to contribute to market development through product innovation and marketing techniques. Outreville (1996) tested the impact of oligopolistic markets on market development, finding a negative and significant effect. Financial development is associated with the widespread securitization of cash flows, which enables households to secure future income through the ownership of financial assets. By offering similar benefits, life insurance is expected to generate higher sales in countries with a high level of financial development. Focusing on developing countries, Outreville (1996) documents a positive relationship between life insurance consumption and the complexity of the financial structure defined as the ratio of quasi-money (M2-M1) to broad money (M2).
Financial development as used in the study refers to the level of financial sector activities in an economy in in terms of breadth and depth. It is defined as the ratio of broad money supply to GDP. A country's level of financial development and the degree of competition in its insurance market appear to stimulate life insurance sales, whereas high inflation and real interest rates tend to decrease consumption.
Outreville (1996) focused on life insurance demand in 48 developing countries for 1986 and found that life insurance market size is related to the level of disposable income, the country's level of financial development, anticipated inflation and competitive markets. While this study employed one year data, Beck and Webb (2003) used panel data from 1961-2000 from 68 countries to determine factors driving insurance demand. They found that inflation, per capita income, banking sector development, religion and institutional development were predictors of demand. Surprisingly, education, life expectancy, dependency ratio and social security did not play a role in the demand for insurance.
There are established theories that provide some framework for this study. Two of them are discussed here. The implications are that there is the need for insurance in developing regions of the world especially Africa, and how life insurance demand can beneficially result into increase in output and economic activities of host economies.

Conventional Expected Utility Theory:
Under the simplest form, conventional expected utility theory assumes that a consumer's utility, U, is a function of disposable income, Y. Assuming a health insurance context, there is a probability, p, that the consumer will become ill and spend L on medical care. Alternatively, the consumer could purchase full insurance coverage for the actuarially fair premium of P = pL, for which the consumer would receive a payoff transfer, I, if ill. For simplicity, assume that I = L. Thus, expected utility without insurance is: With insurance, expected utility is: If marginal utility of income is diminishing, the consumer is better off paying P for insurance and avoiding the risk of loss, L. Thus, the expected-utility-maximizing consumer would purchase insurance coverage for these Because of the way that the theory is specified mathematically, it appears as if the choice is between certainty and uncertainty of actuarially equivalent losses. The choice to purchase insurance is associated with certainty and a higher level of expected utility, therefore, it appears as if insurance is demanded because of the certainty it provides (Nyman, 2001). ISSN 1923-4023 E-ISSN 1923 difference is that weighting is applied to the cumulative probability distribution function, as in rank-dependent expected utility theory but not applied to the probabilities of individual outcome.
Cumulative prospect theory assumes that investors display a risk seeking behavior on losses (e.g., payoffs below the reference point): investors are willing to take risk in order to avoid missing their investment goals for sure. This behavior has been documented in several experimental works. Recently, the risk attitude of fund managers has also been related to their contractual incentives. Dass, Massa, and Patgiri (2008) found that mutual fund managers with high contractual incentives to rank at the top (i.e., those with more ambitious investment goals) adopted riskier investment strategies.

Macroeconomic Determinants of Life Insurance Demand
Inflation: The negative effect of inflation on life insurance demand is well documented. Fortune (1973) explains that inflation erodes the value of life insurance, making it a less attractive product. Browne and Kim (1993) and Outreville (1996) provide empirical evidence that anticipated inflation has a negative effect on life insurance consumption.
Disposable Income: Income is a central variable in insurance demand models that positively affects life insurance consumption (see Fortune, 1973;Lewis, 1989). In addition to increasing the affordability of life insurance products, a large income results in a greater loss of expected utility for the dependents in the event of the income earner's death. This effect increases the value of life insurance coverage, and therefore contributes to the positive relationship with income. Working on household level data, Fitzgerald (1987) shows that insurance demand increases with the husband's future earnings (and decreases with the wife's future earnings). Most empirical works on cross-country data use nominal GDP per capita as a proxy for disposable income.

Real Interest Rates:
Real interest rate has not been systematically included in many studies. For example, Browne and Kim (1993) neglect the influence of this variable on life insurance demand. Outreville (1996) finds the correlation of real interest rates with life insurance demand to be almost not significant. One theoretical justification for this outcome is that high real interest rates may decrease the cost of insurance, thus stimulating its demand. On the other hand, they may cause consumers to reduce their number of purchases given the anticipation of higher returns. Beck and Webb (2003) appear to detect a positive relationship using average lending rates. However, it can be noted that lending rates contain a credit risk premium that varies from one country to another, depending on its credit default experience. In some cases, such as Iceland and Turkey, where bond markets are nonexistent, bond yields are replaced by money market rates. Beck and Webb, further argue that higher real interest rates would increase the investment return of providers which would be able to offer more attractive returns to consumers.

Data
Panel data with time series covering the period 1990 to 2011 and a cross section of fifteen (15) African countries from Sub-Sahara region are utilized for the analysis. The study involves the use of inferential techniques to estimate the empirical determinants of insurance demand.

Model Specification
The model specified in this study is an extension of the research works of Browne and Kim (1993), Li et al (2007), and Elango and Jones (2011). Since the prospects and utility theories that feed the model show decision making under uncertainty, the basic tenets from the framework show that insurance demand can be decomposed into two observable conceptsrisks (uncertainty) and preferences.
The uncertainties expressed in the models generally presents risk as a negative outcome that occurs with some given probability and implies a given loss with a money equivalent. This basic framework can be extended in various directions by considering some cases where correlated risks have to be considered simultaneously (e.g., an accident). More complex issues arise when utility is state dependent, since the risk then cannot be considered as purely monetary. For instance, the benefits derived from a life insurance contract depend on the current utility, for a person, of a future transfer to the offspring after the person's death. The underlying inter temporal rate of substitution/ altruistic motive may be hard to assess, let alone to distinguish from risk aversion. Hence, factors that generate risk for the policy holders are included in the model developed in this study. In particular, we draw the model from both the prospects and utility models as effectively combined by Einav (2013) -who devised that insurance demand evolves from a vector of consumer characteristics as well as tendency for market/public sector failure (or macroeconomic uncertainties). ISSN 1923-4023 E-ISSN 1923 The demand for insurance is therefore hypothesized to depend on both aggregate macroeconomic uncertainties (risks) and individual consumers (or demographic) factors in the economy. Thus, the general form of the model may be specified as: Where DINS = demand for insurance which may be measured as the number of insurance policy taken by individuals/households MAC = vector of macroeconomic factors (representing risks or prospects-based factors) Since, the price of a product is essential in the demand function, the price of insurance (PRICE) is included in the model. The use of the demand function in the model implies that estimates should report elasticities at the mean (Iyoha, 2004) by which the percentage changes in each of the explanatory variables can explain the percentage changes in insurance demand.
Equation 3.1 is therefore presented as a mathematical demand function as follows: Where α is the elasticity of insurance demand with respect to changes in macroeconomic factors, and ρ is the price elasticity of demand for insurance. The demand function above is a power function and reports how (after accounting for the price effect) demand for insurance will change when macroeconomic (policy induced) factors change.
To estimate equation 3.2, there is need to make it linear by taking logarithms of both sides and also include a stochastic term. Thus, equation (3.2) becomes where u a Gaussian whit noise error term.
In the general demand function quantity demanded and price of the product are endogenous (at the equilibrium level) and anyone can be used to measure the behavior of demand (see Iyoha, 2004). Indeed, a study like Phelps (1973) used insurance price to model insurance demand while Browne and Kim (1993) and Fitzgerald (1987) use quantity of insurance policy taken as representative of insurance demand. It should however be noted that using insurance quantity is often associated with micro-level studies while the macro-level studies, such as this current one, uses insurance price. Hence, in this study, the price of insurance (insurance premium) is used to represent the size of demand for insurance. MAC in equation ( Note that insurance price has been endogenized in the model and the effects of the exogenous variables on insurance demand are now captured by observing their impacts on the size of the amount of price paid for insurance cover. The relationship between price of insurance (premium) and insurance demand is rather straight forward as demonstrated in Spinnewijn (2012). A rise in insurance premium received by insurers due to the peculiarity of the African systems, indicates that the level of individual socio/economic development may play a major part in demand for insurance policy. Thus, the expanded demand for insurance model is presented as: Where DINS = Demand for insurance coverage (the insurance premium), the apriori relationships between each of the exogenous variables and the endogenous variable may be written as: f 1 , f 2 , f 3 > 0; f 4 < 0 where f i is the partial derivative of DINS with respect to each exogenous variable.
In order to obtain more robust results, we break down insurance demand to the extent of penetration within the economy (PEN) and the density of insurance cover (DEN). Penetration shows the level of development of insurance industry in the economy while density indicates the extent of individual embrace of the industry.

PEN = f (FIND, GDPPC, INFR, RIR) (3.5)
Where PEN = insurance penetration (measured as insurance demand/GDP); Where DEN = insurance density (measured as insurance demand/population) In equations (3.5) and (3.6), it is argued that the same factors that explain development of the insurance industry in terms of demand are also responsible for explaining the level of individual demand for insurance coverage.
Given the function generated in equation (3.3), the two main models specified in this study are presented in logarithmic forms as: Where i represents the country, t represents time, α represents the general intercept and Ui t is the general stochastic error term.
It should be noted that the model specified above (3.7) and (3.8) is a panel regression model that takes the cross sectional heterogeneity among the data into cognizance. The use of fifteen (15) countries in the sub Sahara Africa sub region would definitely generate within-sample bias when OLS technique is applied in the estimation. Hence, a model that can capture such biases and endogenise them is employed. The panel model also include the random effects (or cross sectional) term (δ) and the fixed effects (or period specific) term (γ). These coefficients account for the variations across countries and over time period (Greene, 2004).

Technique of Estimation:
In this study, the panel regression technique is applied. A variety of different models for panel data are used in studies where heterogeneous effects are noticed within time series across space. In the panel regression method, if z i contains only a constant term, then ordinary least squares method provides consistent and efficient estimates of the common α and the slope vector β. In this estimation, two effects are highlighted: (a) Fixed Effects: If z i is unobserved, but correlated with x it , then the least squares estimator of β is biased and inconsistent as a consequence of an omitted variable. However, in this instance, the model (where αi = z' i α,) embodies all the observable effects and specifies an estimable conditional mean. This fixed effects approach takes α i to be a group-specific constant term in the regression model. It should be noted that the term "fixed" as used here signifies the correlation of α i and x it , note that α i is non stochastic.

(b) Random Effects:
If the unobserved individual heterogeneity, however formulated, can be assumed to be uncorrelated with the included variables, then the model may be formulated as that is, as a linear regression model with a compound disturbance that may be consistently, albeit inefficiently, estimated by least squares. This random effects approach specifies that u i is a group-specific random element, similar to ε it except that for each group, there is but a single draw that enters the regression identically in each period.
The Hausman test of randomness is used to determine the best effects model to be used. The software package used in the analysis is the EVIEWS 8.0.

Method of Analysis
The study employs panel data for fifteen African countries for the period of twenty-two years; therefore the conditions for panel unit roots test of times series and cross-sectional observations greater than fifteen years and balanced panel data are met by the pooled observations of the study. In the study, the purposive sampling approach was used to select the fifteen (15)

Presentation and Analysis of Results
The following are the hypothesis as drawn from the study; The F-Statistic is 97.37 with its probability value of 0.0000. This shows that the overall model is highly significant at the 1% level. That is, all the explanatory variables are jointly significant in explaining the dependent variable (Insurance penetration). All the explanatory variables conform to their expected signs. Financial depth/development and Gross domestic product per capita were found to be positive. While, inflation and real interest rate were negative. The coefficient of financial development is 0.48. Its t-statistic is 2.68 with a probability value of 0.00. It is highly significant at 1% level of significance. This implies that 10% increase in financial development will result in about 4.8% increase in insurance penetration. Thus financial depth/development has a significant positive effect on insurance penetration in Sub-Sahara Africa.

Analysis of Results
Gross Domestic Product Per Capita (GPPC) has a coefficient of 2.15. Its t-statistic is 7.76 with a p-value of 0.00. The coefficient passes the individual test of statistical significance at 1% the level. This shows that 10% increase in gross domestic product per capita will lead to about 21.5% increase in insurance penetration. Thus, gross domestic product per capita has a significant positive effect on insurance penetration in Sub-Saharan Africa.
The coefficient of inflation is -0.49. It has at-statistic of -1.01 with a probability value of 0.3090. It is not significant at 10% level of significance. Thus inflation does not have a significant effect on insurance penetration in Sub-Saharan Africa.
Real interest rate has a coefficient of -0.01. Its t-statistic is -0.18. It is not significant at the 10% level. Thus real interest rate has no significant effect on insurance penetration in Sub-Saharan Africa.

Model II Interpretation
Hausman The F-Statistic is 58.33 with its probability value of 0.0000. This shows that the overall model is highly significant at the 1% level. Implying that all the variables are jointly significant in explaining the dependent variable (Insurance density). All the explanatory variables conform to their expected signs. Financial depth/development and Gross domestic product per capita were found to be positive. While Inflation and Real interest rate were negative. The coefficient of financial development is 0.55. Its t-statistic 3.13 with a probability value of 0.002. It is highly significant at 1% level. This implies that 100% increase in financial development will result in about 55% increase in Insurance density. Thus financial depth/ development has a significant positive effect on insurance density in Sub-Sahara Africa .  ISSN 1923-4023 E-ISSN 1923-4031 The coefficient of (GPPC) Gross domestic product per capita is 2.93. The t-statistic is 12.27 with a probability value of 0.0000. It is highly significant at 1% level. This implies that 10% increase in Gross domestic product per capita will result in about 29.3% increase in Insurance density. Thus Gross domestic product per capita has a significant positive effect on insurance density in Sub-Sahara Africa.
The coefficient of inflation is -0.076. And its t-statistic is -1.62 with a probability value of 0.1061. It is not significant at 1% level. Thus inflation has a significant negative effect on insurance density in Sub-Sahara Africa.
The coefficient of Real interest rate is-0.03. Its t-statistic -0.47 with a probability value of 0.68 It is not significant at 1% level. Thus real interest rate has a negative effect on insurance density in Sub-Sahara Africa.

Conclusion
It is obvious from the results that macroeconomic variables are largely responsible for the demand of life insurance in the African region. Beyond the macroeconomic factors that influence life insurance demand in Sub-Sahara African region, there is also is the financial indicator-(financial development).
This paper investigated the impact of financial development on life insurance demand in the Sub-Sahara region of Africa. In the analysis of financial development and major macroeconomic indicators, it was observed that apart from Gross Domestic Product per Capita other major macroeconomic indicators do not have any significant effect on life insurance demand. From the analysis, it was observed that financial development, the main variable under investigation has significant and positive effect on life insurance demand in Sub-Sahara Africa. This goes to show that for increased life insurance penetration and demand in this region of Africa, the level of involvement in the financial markets by individuals and corporations must deepen.