THE EFFECT OF DEMOGRAPHIC FACTORS ON DEMAND FOR LIFE INSURANCE IN ETHIOPIA

Mr. Bereket Sorsa 1 and Dr. Durga Rao P. V 2 . 1. Lecturer in the Department of Accounting & Finance, College of Business & Economics, Wolaita Sodo University, Wolaita Sodo, Ethiopia. 2. Associate Professor in the Department of Accounting & Finance, College of Business & Economics, Wolaita Sodo University, Wolaita Sodo, Ethiopia. ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History


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In many countries in the world, Insurance companies are playing significant role in the service-based economy and its services are being integrated into the wider financial sector. Insurance companies both private and public companies consisting of life, fire, accident, causality and many other forms of insurance. (Hailu, 2007).
According to Beck and Webb (2002) in many countries around the world life insurance products are important which encourage long-term savings of the country that could be channeled to investment in both private and public sector projects of the country. Because life insurance products offer a means of disciplined contractual savings, these become effective instruments for encouraging substantial amounts of savings, competing with other forms of saving such as bank deposits, financial securities, and other contractual savings.

Statement of the Problem:-
In Ethiopia, the insurance market segment is under developed, uncompetitive and there exist paucity of information on the kind of life insurance that is currently present. The current practice of bulk of insurance coverage and business in Ethiopia is target the corporate market and focuses mainly on general insurance with a very limited coverage of life insurance. In Ethiopia the total life insurance premium generated is minimal compared to other African countries; like South Africa, Kenya and Egypt. The percentage contribution of life insurance business to the Gross Domestic Product (GDP) in the above mentioned countries is 15.3, 2.5 and 0.6 in the year 2006-2007 respectively while Ethiopia is one of the countries which has very low level insurance penetration equating is 0.2 percent (Chamberlin, 2010).
Based on the last fifteen years data of Central Statistical Authority of Ethiopia there has been a continuous increase in life insurance premium amount. This indicates that the Ethiopia insurance industry in general, and its life insurance in specific, both have a bright prospect and a high potential role to play in contributing to the national development.
Many studies examining life insurance demand have focused mainly on the Asian market. They have tried to develop and test the different socio-economic determinants and institutional factors as possible determinants of life insurance consumption across different countries. However a study in the continent of Africa especially in Ethiopia is apparently scares. Some of the studies have been conducted concerning general insurance business in Ethiopia.
Ayalew (2007) tried to understand the structure, trends, and performance of life insurance business with reference to the Ethiopian Insurance Corporation (EIC), Zeleke, (2007) tried to analyze the historical development of insurance business in Ethiopia and its future challenges. The studies clearly stated that the total life insurance market contribution of African countries to the world is very minimal.
Increasing penetration of insurance demand and insurance sector is one of the answers for the country development. So now what to do to increase this penetration is our problem of the statement. Analysis and understanding of prospective insurance demand from one of the important section of people such as government employees according their demographic factors thus becomes important.
Understanding and analyzing the government sector employees of Wolaita sodo town demand intention towards insurance according to their demographic factors such as age, gender, marital status, level of income, level of education, family size etc., can play an important role in predicting demand intention for insurance and their by increasing penetration of insurance through enabling insurers in preparing their business strategies and designing products as per the requirements of the people.
Life Insurance in Ethiopia:-Modern insurance activities taken place in Ethiopia since 1905, where the Bank of Abysinia was operating as an agent to a foreign insurance company and began underwriting fire and marine insurance policy. In 1923 the first insurance company was established in Addis Ababa by a company called La.Balois. It was followed by other foreign insurance companies that placed their agents in Addis Ababa. During the Italian evasion the activity was undertaken by their companies, and immediately after the end of the invasion insurance companies from Europe continued to run the business.
In 1970, a proclamation was promulgated in order to license and regulate insurance businesses in Ethiopia. It was empowered the Ministry of Commerce and Industry to regulate the activities of insurance sector. While 15 local 1384 insurance companies were licensed and two of them discontinued their business and at the eve of the 1974 Ethiopian revolution, finally there were 13 such companies. Consequently, proclamation No.26/197 nationalized the existing 13 insurance companies and a provisional insurance board was formed to supervise the all insurance companies. Later on the nationalized companies were merged together and formed as the Ethiopian Insurance Corporation. (EIC Annual bulletin 4th edition).
The life insurance market in Ethiopia is very small as the number of life insurance companies has remained relatively stable. In 2012 the life insurance company is only one i.e. Ethio-Life became composite and is now known as Ethio-Life and general insurance. Also in 2012, a non-life insurance company, Oromia insurance company obtained a life license and became a composite insurer. There are now (NBA 2018) a total of seventeen insurance companies (Africa, Awash, Global, Lion, NIB, Nile, Nyala, The United, Ethiopian, Abay, Berhan, National, Oromia, Ethio-life, Tsehay, Lucy and Bunna) licensed to underwrite life insurance; all of them are composite underwriters.
Review of Literature:-Min Li (2008) concludes that as person age increase demand for insurance decrease and it reduces to near about zero when age of person is 56 in case of term insurance and 58 for other types of traditional products. People at young age prefer more term insurance as income is low and higher age peoples prefer more variable insurance. As education is concerned when education level raises demand for life insurance also increases. Other demographic factors like Income or number of family member"s increases insurance demand also increases. People without any life insurance were relatively young, less educated, unemployed, not married, and renters; they expected to die in their 70s, had low income, and were preferred not to take risks.
Vinod Kumar Bishnoi and Bharti (2009) study concluded that whatever is the leading brand in all the products, that remains leading irrespective of any demographic variables be it income, education, age or gender. But with the increasing income and education level, the consumers were found using other sophisticated brands in that product category. The younger rural consumers have been found more variety seeking whereas the old aged consumers are stick to two or three brands.
Huihui Wang (2010) stated that increased levels of income, higher education levels, and demographic factors such as family structure and the number of dependent children were important factors in determining life insurance demand in China.
Dr. Dipin Mathur, Mr. Ashish Tripathi (2014) found that demographic variables play a very important role in understanding customer"s demand. Thus they assessed the impact of demographic profiles. As per their study there is no significant impact of sex and education on the factors influencing customer"s choice for the organisation.
Syed Shahid Mazhar, Anisur Rehman, Shahab Ud Din (2015) study highlights the influence of demographic variables on insurance sector in India. It is found that there is a significant impact of demographic factors such as sex, educational level, income and profession on the areas like saving, investment, risk and protection tax benefit and children education and marriage to purchase e-insurance products.
According to some empirical studies, a demographic factor such as Income is the most influential determinant ( According to the life-cycle hypothesis of Ando&Modigliani (1963), individuals plan their saving behaviour over the long-term. Since income varies over the individual"s life cycle, saving differs depending on the individual"s age. The same is true for contractual savings through life insurance. Age class of 18 to 30 is very heterogeneous.
Some of the individuals get employed after age 18 while some continue education. Part of them gets married. Consequently, insurance demand of the individuals belonging to this age class varies remarkably. During age between 30 years and mid 40 years individuals spend most of the income on dependent members of family and durable goods.

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Thus, less income is available for life insurance comparing to those in age class of mid 40 years to mid 50 years. Shortly, in the middle age of lifetime individuals have higher level of income and less pressure on consumption. The same is true for the following age period until retirement when income decreases. Truett & Truett (1990) and Showers & Shotick (1994) find positive relationship between age and life insurance demand while Hammond et al. (1967) show insignificance of the relationship. Following the results of Gandolfi & Miners (1996) there is no influence of age on life insurance demand by wives, while husbands" age negatively affects life insurance consumption. Gandolfi & Miners (1996) investigate influence of gender on life insurance consumption. Namely, demand for insurance could vary among men and women based on difference in lifetime. Following the assumption that the insurance demand is increasing with probability of death and the fact that men live shorter than women, they will demand insurance more.
According to Outreville (1996) individuals with higher level of education are more aware of risk and the importance of risk management. Thus, education increases risk aversion and encourages people to demand insurance. Additionally, individuals with higher education have higher income and can expect that the income will continue to increase at faster rate and in long term compared to those of lower level of education. Consequently, more insurance will be purchased by more educated individuals.
Moreover, according to Browne & Kim (1993) higher education implies that individuals are dependent on family income earner. Thus, education could serve as additional proxy for dependence on the family breadwinner.

Objectives of the Study:-General Objective:-
The major objective of the study is to investigate the effect of demographic factors on demand for life insurance in Ethiopia, with reference to Wolaita Sodo town government employees.

Significance of the Study:-
Assessing the effect of demographic factors on life insurance penetration and density is important and this indicates some problems in the way it is being sold in our country. Selling insurance to few people in the society is not going to be the panacea for all the insurers. They need to realize that every insurable individual has to be insured and then only the motive of insurance can be fulfilled in the right sense. Analysis and understanding the insurance according to their demographic characteristics in specific geographical regions thus becomes important. This will enable the insurers to better prepare their business strategies as per the requirements of the people in the region.

Research Methodology:-
The study is Quantitative & descriptive in nature. The researcher adopted this research design to gather information from the respondents to identify major demographic factors affecting the demand of the respondents.
The study is conducted with 150 samples from Wolaita Sodo town particularly from government employees working in the organisations such as Banks, Hospitals, Revenue & Tax authority and Finance & economic development departments.
The sampling method adopted for the study is non-probability convenience sampling where the researchers can select the sample elements based on the ease of researcher. Structured questionnaire is prepared to survey the 1386 respondents. The primary data has been collected through a structured questionnaire. The secondary data has been collected from the books, journals, magazines, online databases and websites.

Data Analysis and Presentation:-
The collected data was examined and coded personally by the researchers and the data were analyzed using different statistical techniques and tools depending on the nature of data collected from the respondents and the objectives of the study. Descriptive statistics like mean, percentages analysis and frequency distribution and inferential statistics such as Logistic Regression and chi-square test analysis were applied. The data collected was processed using SPSS (version 20.0).

Data Presentation and Discussion:-Inferential Analysis:-Binary Logistic Regression:-(Sex, Age, Marital status and Religion):-
A binary logistic regression analysis was conducted to predict the purchasing of the insurance for 142 sample size of government employees in the study area, Wolaita Sodo town, using gender, age, marital status, religion, organisation, income level, level of education, and family size as predictors. Therefore, the regression model includes a total of eight independent variables. A test of the full model against a constant only model was statistically significant, indicating that the predictors as a set reliably distinguished between purchases or not purchase. Nagelkerke"s R2 of .195 indicates a moderately strong relationship between prediction and grouping. Prediction success overall was 90.1% (0% for not purchase and 100% for purchase).
The Wald criterion demonstrate that age 26 -35 years category and age 36 -45 years category made a significant contribution to prediction (p = .011 and p = .031 respectively). These variables are found to be significantly influence the purchase intention of the insurance of the respondents. EXP (B) value for age 26 -35 years category indicates that when the age 26 -35 years category was raised by one person the odds ratio was 13.93 times as large and therefore government employees were 13.93 more times likely decided to purchase of insurance. EXP (B) value for age 36 -45 years indicates that when the age 36 -45 is raised by one person the odds ratio was 21.6 times as large and therefore government employees were 21.6 more times likely decide to purchase of insurance. Therefore, the researchers rejected the null hypothesis that there is no significant impact of age category 26 -35 as well as age category 36 -45 on purchasing intention of the insurance of government employees in the study area. Hence researchers accepted the alternative hypothesis that there are significant impacts of age category 26 -35 as well as age category 36 -45 on purchase intention of insurance of the government employees in the study area.
However, sex, marital status and religion as predictors were not a significant predictor. Therefore, according to the logistic regression results the demographic variables such as sex, marital status and religion had not showing a significant effect on purchase intention of the government employees in the study area as it shows significance level of 0.412, 0.549 and 0.814, respectively, which more than the standard significance level 0.05. Therefore, the researchers accepted the null hypothesis that there are no significant impact of sex, marital status and religion on purchase intention of life insurance of the government employees in the study area.

Binary Logistic Regression: (Organisation, Monthly income, level of education and family size):-
A binary logistic regression analysis was conducted to predict the purchasing intention of the insurance for 142 sample size of government employees in the study area, Wolaita Sodo town, using gender, age, marital status, religion, organisation, income level, level of education, and family size as predictors. Therefore, the regression model includes a total of eight independent variables. A test of the full model against a constant only model was statistically significant, indicating that the predictors as a set reliably distinguished between purchases or not purchase. Nagelkerke"s R2 of .161 indicates a moderately strong relationship between prediction and grouping. Prediction success overall was 89.4% (0% for not purchase and 100% for purchase).
However, Organisation, level of monthly income, level of education and family size as predictors were not a significant predictor. Therefore, according to the logistic regression results the demographic variables such as Organisation, level of monthly income, level of education and family size had not showing a significant effect on purchase intention of the insurance of the government employees in the study area as it shows significance level of 0.144, 0.492, 0.739 and 0.678 respectively, which more than the standard significance level 0.05. Therefore, the researchers accepted the null hypothesis that there are no significant impact of Organisation, level of monthly income, level of education and family size on purchase intention of insurance of the government employees in the study area.  Descriptive Analysis:-Response Rate:-Total 150 questionnaires were distributed to the respondents, who were working in government sectors in Wolaita Sodo town during the period of February to March, 2018. Out of which only 145 questionnaires were received back from the respondents. After analyzing the questionnaires, 3 were found incomplete and not properly filled. Finally, total 142 questionnaires were used for analyzing purpose. The response variable considered in this study was the purchase intention or demand for insurance of the government employees.
Respondents Profile:-It can be observed from the below Table that the total sample, 42.3% were female government employees and 57.7% were male government employees. The age distribution indicates that 16.2% of the respondents were in the age category below 25 years, 66.2% in 26 -35 years, 13.4% in 36 -45 and the reaming 4.2% respondents were 46 -55 years. In case of marital status 57.7% of the respondents were married, 40.1% were unmarried, divorced and 1389 widowed were 0.7% and 1.4% respectively. Religion of the respondents were 88% were Christians, 7.7% of the respondents were Muslim and 4.2% were belongs to other category.
Regarding Organisation, 45.8% of the respondents were from Banking sector, 21.8% of the respondents were from Hospital sector and 31.7% of the respondents from Revenue & Tax sector. In the case of Monthly income of the respondents 12.7% were earning below Br.3k, 70.4% of the respondents earning between 3k to 8k birr, and 14.8% of the respondents were between 8k to 10k Birr, 2.1% respondents were earning above 10,000 birr. Educational level of the respondents" showed that 7.0% of the respondents were diploma, 82.4% of the respondents completed first degree, and 10.6% of the respondents completed Master"s degree. Considering the family size of the respondents 26.8% of the respondents had only one person, 17.6% of the respondents were two, 21.8% of the respondents had 3, 16.9% of the respondents were having 4 and 16.9% of the respondents were having above five members family size.  From the above table it can be observed that among the eight demographic factors in the study family size is having high mean, respondents age and level of monthly income showing same mean such as 2.06, educational level of the respondents shows that 2.04, organisation mean is 1.86, marital status of the respondents shows 1.46 mean and religion of the respondents shows least mean that is 1.16.

Conclusion and Recommendations:-
In this case study the government employees in Wolaita Sodo town; we analyzed demographic impact on life insurance demand in Ethiopia. The empirical research is based on the survey data collected on the sample of 142 respondents. According to the results age category shows statistically significant impact on the life insurance demand of government employees in Wolaita sodo town, Ethiopia. Other demographic factorssex, marital status, religion, organization, monthly income, educational level and family size have no influence on the life insurance demand.
The results of the research have implications on decisions makers on both macroeconomic level and specific insurance companies. The results are in line with Marijana Curak et.al (2013) that age, educational employment impact on life insurance demand of household in Croatia. To encourage life insurance demand decision makers should provide policies based on the age category of the respondents. The findings of the research should be taken into consideration by insurance companies especially in planning their policies. Since age is the relevant factor of insurance demand.
For the future research work there is suggestion to broaden the scope of the study the set of social, economical and other demographic factors should be considered.