Bank Negara Malaysia (BNM) took over the supervision of the insurance industry in 1988. The primary reason for the move was to enable an integrated approach in the regulation and supervision of major financial institutions, in view of the growing convergence of crossholdings and integration of interests between banks and insurance companies. The economic environment may have a profound effect on the growth of the insurance industry.
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In Malaysia, the performance of the insurance industry in 1998 was affected by an economic downturn. The total and non-life premium income declined by 2.1% and 9.7% respectively whereas the life premium income experienced a lower positive growth rate of 4.6% in 1998 (1997: 13.5%) (BNM, 1999-2000). In line with the sustained economic recovery, the life insurance industry rebounded strongly to register an impressive double-digit premium growth in 1999, soaring well above pre-crisis levels.
The performance of the insurance industry showed an improvement in 1999 following the recovery of the Malaysian economy. The combined premium income of the insurance industry recorded a growth of 8.5% (1998: -2.1%) to reach RM11,829.9 million (1998: RM10,902.9 million). The life sector has been the major contributor accounting for RM7,152.7 million (1998: RM6,217.2 million) or 60.5% of the premium income, while the remaining balance of RM4,677.2 million (1998: RM4,685.7 million) represented premium income generated from the general sector. Premium income of the industry as a proportion of nominal gross national product (GNP) increased to 4.2% in 1999, compared with 4.1% in 1998.
BACKGROUND OF RESEARCH
In Malaysia, BNM Annual Report that been issued yearly regularly indicate policies and development of insurance market in Malaysia. to implement policies and measures to prepare the industry for the challenges posed by the new requirements of the new economy and the increasingly more liberalised market environment. Several measures were initiated directed at improving market penetration through the promotion of new life insurance products and in order to do that the process of formulating and identifying strategies need to be done to enhance the marketing channel for life insurance business so that it can achieve the desired penetration level and raking in all the advantages given by positive economic environment. In order to do that, first step that need to be taken is to identify which macroeconomic variables that really positively significant to the demand of life and health insurance market and from there on best formulation and strategies can be initiated to create accurate result on the demand of life and health insurance market. Efforts were also made by Bank Negara Malaysia to enhance the discipline and standards of conduct amongst life insurer in Malaysia.
Statistical data from BNM has shown that due to economic downturn in 1998, the performance of insurance industry in Malaysia reportedly experienced negative growth of -2.1%. Generally, it shows that economic environment may possibly have direct influence on the performance of insurance industry in Malaysia as a whole. The combined premium income of the insurance industry recorded a growth of 8.5% in 1999 following the economic recovery situation.
The life and health sector has been the major contributor accounting for RM7,152.7 million or 60.5% of the premium income, while the remaining balance of RM4,677.2 million represented premium income generated from the general sector. However, despite the vast potential for growth given the relatively low market penetration in Malaysia, domestic premium income to GNP was comparatively lower than that observed in more saturated markets.
This research need to done so that it can specifically identified which macroeconomic variables that really effect the growth of life and health sector in Malaysia in order to ensure that it can contribute clearly to developing pricing strategies to achieve a specific sales target for life and health business. Macroeconomics is the study of the behavior of the overall economy and economic models normally consist of variables such as real GDP, inflation, price and population density. This study attempts to examine the relationship between macroeconomic variable to performance and demand of life and health insurance industry in Malaysia by using the LS analysis to prove that certain key macroeconomic environment may have a profound and significant effect on the growth of the life and health insurance market.
As in the context of Malaysia, few studies has been carried out to seek evidence of the relationship between macroeconomic variables and performance of life insurance industry from Malaysia perspective but various studies comes out with various results as they are using different period of data and did not include health insurance data like this research and also holds different and various macroeconomic variable. Study by Lim and Haberman (2002) indicate major findings of this study that the savings deposits rate and price change in insurance are two important macroeconomic variables associated with the demand for life insurance in Malaysia. Study conducted by Rubayah and Zaidi (2000) indicate that income has a positive relationship with life insurance demand. Life insurance becomes more affordable when income increases. They examine two types of income variable in their study, namely GDP and income per capita. Income per capita is defined as the GDP divided by the size of the population but on the other hand, their finding also show an insignificant positive relationship between inflation rates and the performance of life insurance.
Economists use these type of data and variables to measure the performance of an economy and the focus on macroeconomic variables in this paper are, price of the life and health insurance product, inflation rates, income per capita and population density. This study is to further examined the direct linkage between these economic environments and whether each one of key variables (price of the life and health insurance product, inflation rates, income per capita and population density) has direct influence on the performance of life and health insurance in Malaysia.
The purpose of this study is to examine the impact of various macroeconomic variables towards performance of life and health insurance market in Malaysia.. The specific aims of this study are:
To determine which various macroeconomic variables that might have influence on the performance of life and health insurance market in Malaysia
To examine the relationship of each macroeconomic variables ie price of the product, income per capita, inflation rates and population density with the performance of life and health insurance market in Malaysia
To identify which macroeconomic variables that influence the performance of life and health insurance in Malaysia the most.
To suggest the most suitable and appropriate strategies that can be used to improve the performance of life and health insurance market in Malaysia by using all the advantages given by positive economic environment
How to determine which macroeconomic variables that influence the performance of life and health insurance in Malaysia?
Is there any relationship between each macroeconomic variables ie price of the product, income per capita, inflation rates and population density with the performance of life and health insurance market in Malaysia
Which macroeconomic variables that influence the performance of life and health insurance in Malaysia the most?
What are the most suitable strategies that can be suggested to improve the performance of life and health insurance market in Malaysia by using all the advantages given by positive economic environment?
Significance of Research/ Contribution to the body of knowledge
There is no unique and integrated theory for life insurance demand. Research on the impact of macroeconomic variables towards performance of life and health insurance industry in Malaysia very scanty at best. Very little (if at all) is understood about the. urgent need for research focusing on the Malaysian industry and the Malaysian economic environment, which is unfamiliar to most readers. Hence, important impetuses for this research are established.
1. The Government
This research is important for the government to formulate policies, acts and regulations for the improvement on the best strategies available in a suitable economic environment in order to develop and guide healthy demand on the insurance industry as a whole.
2. The University/ Academician
This study will be used for reference and information for the students and academician who learn on insurance area, risk management or other related fields. Students and lecturers can have an extra knowledge on information provided by the researcher.
3. Management team of Life insurer in Malaysia
This research is important for the management team Life Office especially if changes or corrective actions are required due to the changes in various economic environments occur in Malaysia or globally. Hopefully, this research can help the management team of Life insurer in Malaysia able to implement and generate new strategies with regard to the suitable current economic environment.
4. General Public
Public must know the factors that influence their purchase decision of life and health policy offered in the market. Besides, they also need to be alert and aware on the coverage offered by Life insurer in Malaysia. This research will help them to really identify the needs to buy life and health product and there is also a growing awareness among Malaysians of individual responsibility in financial planning hence it directly will affect the demand of the said industry.
5. The Researcher
By completing this research, the researcher has experienced and being exposed to view the economics side on the insurance industry as a whole and specifically on life and health sector which the researcher have never attempt before. It is a researcher attempt to view as a macroeconomists attempt in order to explain the economic side of this sector and to devise policies to improve its performance as economists use different models to examine different issues. Thus, other researcher might need the information to make their research in the future.
CHAPTER 2: LITERATURE REVIEW
The performance for insurance is influenced by many factors and economic factors might be one of them. For example, inflation rate, income per capita and price of the product may affect the performance for insurance in a country. A number of studies have examined the effects of macroeconomic factors on the performance for life and health insurance. Among them are the studies conducted by Cargill and Troxel (1979), Babbel (1985), Browne and Kim (1993), Outreville (1996) and Rubayah and Zaidi (2000). The macroeconomic factors investigated in these studies are highlighted and discussed in brief below.
The findings of Outreville (1996) indicate that the level of financial development directly affects the development of life insurance sector. However, the findings are not statistically significant. Two different proxies have been used as a measurement for financial development. The first one is the ratio of quasi-money (M2-M1) to broad money (M2). This is an indicator for the complexity of financial structure. The second one is the broad definition of money (M2). It is an average value over four years. M2 is regarded as an adequate measure for the financial development in developing countries because banking is the predominant sector in the financial market of developing countries.
Lewis (1989), Hakansson (1969), Fischer (1973), Fortune (1973), and Campbell (1980) have shown that the demand for life insurance is positively correlated with income. As income increases, life insurance becomes more affordable. In addition, the need for life insurance increases with income as it protects dependents against the loss of expected future income due to premature death of the wage earner.
According to prior research (Beenstock, Dickinson, and Khajuria (1986), Browne and Kim (1993), Outreville (1996) the ability to pay insurance premium has been argued to be related to the level of income. This is because, when there is an increase of income levels, there follows a need for a financial instrument to absorb the individualâ€™s surplus funds and to enable them to accumulate wealth. This shows the income level significantly affects the demand for life insurance.
Two different measures have been used for disposable personal income in the study of Babbel (1985). The single-year income is used as a proxy for human capital and the three-year moving average income is used as a proxy for permanent income. The income variables are the real amounts of aggregate disposable personal income. The nominal income values are deflated by the yearly average indices of personal consumption expenditure deflator to render the nominal values in constant dollar terms.
The conclusion from Cargill and Troxel (1979), Babbel (1985), Browne and Kim (1993), Outreville (1996) and Rubayah and Zaidi (2000) verified that life insurance demand has a positive relationship with income. It shows when income increase, it can create more opportunity the life insurance becomes more affordable for people.
In the study of Browne and Kim (1993), disposable personal income refers to the national income. It is defined as when the depreciation (capital consumption) and indirect business taxes have been taken away from GNP. National income is a more accurate measurement of disposable personal income for a country than GNP or GDP because national income is the income earned by the various production factors; it is refer to Browne and Kim (1993). Meanwhile, Outreville (1996) relates the income variable in his study as the real disposable income per capita. GDP is used as the basis for the disposable personal income. The income variable is expressed in linear form and in logarithmic form.
On the other hand, Rubayah and Zaidi (2000) identified GDP and income per capita have been the two types of income variable in their study. Income per capita is defined as the GDP divided by the size of the population. In the initial stage, both the GDP and income per capita are found to have a positive relationship with the demand for life insurance but are not significant. It is only when stepwise regression analysis is applied in the later stage that GDP appears to have a significant positive relationship with the demand for life insurance but income per capita has been aborted. This is because income per capita contains the element of GDP and therefore multicollinearity exists because the two income variables are highly correlated.
If income has a positive relationship with demand for life insurance, it is different when Browne and Kim (1993) and Outreville (1996) did their research to find relationship for inflation. From their research, it shows that inflation has a significant negative relationship with life insurance demand. Inflation gives a diminishing effect on the amount of insurance purchased in a country. Consequently, it makes the value of life insurance eroded. As the result, it leads to the situation where insurance become less desirable good. High inflation tends to cause the purchasing of life insurance to be less attractive because of the rising cost of living.
Meanwhile, Cargill and Troxel (1979) and Rubayah and Zaidi (2000) have revealed different result. Their findings are not in line with the findings of Browne and Kim (1993) and Outreville (1996). Measured up to between these two research, it has found Cargill and Troxel (1979) comparatively defined savings model (i.e. the model that takes into account the changes in policy loans besides the changes in life insurance reserves/savings and dividend accumulations) produce a significant result with the expected negative sign for this variable. It shows a week relationship between life insurance savings and price expectation. Meanwhile different with the findings of Rubayah and Zaidi (2000) it shows between inflation rates and the demand for life insurance has a significant positive relationship
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An average inflation rate for the last eight years, Browne and Kim (1993) has used an average inflation to represent the expected inflation rate. Meanwhile, Outreville (1996) uses a weighted average of realised price changes over the last five years as a measure of anticipated price change. Therefore, in Cargill and Troxel (1979) the price expectation in the study refers to the percentage changes in the Consumer Price Index (CPI) over a period of 14 months. Moreover, Rubayah and Zaidi (2000) used in the same way apply the CPI as a basis for the anticipated rate of inflation in their study.
A part from the research, in Cargill and Troxel (1979) the price expectation in the study refers to the percentage changes in the Consumer Price Index (CPI) over a period of 14 months based on the data contained in the Livingston Survey that have been revised by Carlson. Similarly, Rubayah and Zaidi (2000) use the CPI as a basis for the anticipated rate of inflation in their study.
The findings on the relationship between interest rates and the demand for life insurance are questionable.
Cargill and Troxel (1979) examine two kinds of interest rates in their study: the competing yield on other savings products and the return earned by life insurers. The findings on the competing yield are inconsistent. However, the competing yield tends to be negatively related to life insurance savings. A higher interest rate on alternative savings products tends to cause insurance products to become less attractive as a savings instrument. The yield on newly issued AAA utility bonds is used to represent all the competing rates of return on alternative savings products. Cargill and Troxel (1979) include the current and twelve-quarter distributed lag variables of competing yields in their study. The lag variables are included to reflect the delayed reactions of savers towards new information regarding interest rates on savings because changes in interest rates are assumed to produce a lagged response. Likewise, the findings on the return earned by life insurers are mixed. However, the return earned by life insurers is frequently positively related to life insurance savings. Life insurers earning a higher rate of return tend to attract individuals to purchase insurance from them. The yield on industrial bonds placed privately with a representative group of life insurance companies is used as a proxy for the return earned by life insurers. It is the new money rate of return earned by the life insurers, not the average rate of return on the invested funds. Similar to the competing yield, the current and twelve-quarter distributed lags of the return earned by life insurers are included in the models to investigate the immediate and lagged responses of changes in interest rates on life insurance demand.
Outreville (1996) has shown that the demand for life insurance has not determined by the interest rate such as the real interest rate and the lending rate. The real interest rate is obtained by subtracting the anticipated inflation from the current bank discount rate. For the meantime, there are three types of interest rated, which are the personal savings rate, short-term interest and current interest rate has been identified by Rubayah and Zaidi (2000)
The personal savings rate and short-term interest rate are found to influence significantly and negatively the demand for life insurance, despite the fact that the current interest rate is found to have no significant effect on life insurance demand. The personal savings rate refers to the interest rate offered by banks on normal savings, the short-term interest rate refers to the interest rate on three-month Treasury Bills, and the current interest rate refers to the base lending rate on bank borrowings.
Price of Insurance.
From Babble (1985) and Browne and Kim (1993), the findings reported with respect to the effect of price on the demand for life insurance are consistent in the both studies. The price of insurance is significantly and inversely related to the demand for life insurance. A high insurance cost tends to discourage the purchasing of life insurance.
The various insurance price indices in the study of Babbel (1985) are the net present cost per 1000 present-valued unit of insurance expected to be in force over any arbitrary time horizon selected based on the published policy values for a male of age 35. Specifically, the price index refers to the ratio of the present value of expected premium cost, net of dividends and accumulations of cash values, per 1000 present-valued unit of indemnification benefits expected to be received, in excess of the actuarially fair cost. Two different discount rates, namely the yields of 10-year prime grade municipal bonds and double-A-rated corporate bonds, are used to discount the expected future cash flows from the policies.
Browne and Kim (1993) use the policy loading charge as the price measure. It is the ratio of the life insurance premiums to the amount of insurance in force. In fact, it is the cost per dollar of life insurance coverage.
INDEPENDENT VIARABLES (IV)
Income per Capita
Performance of Life and Health Insurance in Malaysia
Price of the Product DEPENDENT VARIABLE (DV)
Figure 1.6.1: Theoretical Framework
Sources: Adapted from Shimp, T.A (2003); Pitta, et. Al. (2006); Rowley, (1998); Ndubisi, N.O., and Chew, (2006)
Ho : Income per Capita is not significantly related with the performance of life and health insurance in Malaysia.
H1 : Income per Capita is significantly related with the performance of life and health insurance in Malaysia.
Ho : Price of the Product is not significantly related with the performance of life and health insurance in Malaysia.
H1 : Price of the Product is significantly related with the performance of life and health insurance in Malaysia.
Ho : Inflation rates is not significantly related with the performance of life and health insurance in Malaysia.
H1 : Inflation rates is significantly related with the performance of life and health insurance in Malaysia.
Ho : Population density is not significantly related with the performance of life and health insurance in Malaysia.
H1 : Population density is significantly related with the performance of life and health insurance in Malaysia.
All data in this study are secondary in nature. Secondary data is used in finding the resources for this study. Secondary data are statistic not gathered for the immediate study at hand, but for some other purpose. The data related to the demand for life insurance are obtained from the following annual reports: the Annual Report of the Insurance Commissioner and the Annual Report of the Director General of Insurance.
The researcher has gathered the external information from various types of annual
reports: Monthly Statistical Bulletin, Economic Report, Annual Insurance Report of the Bank Negara Malaysia. Materials obtained online are gathered from the official websites of BNM and Kuala Lumpur Stock Exchange (KLSE). Besides, the sources like books, newspapers, journals and internet that were relevant to the research topic were used. All the sources have been referred throughout the findings and analysis of the research. Researcher will analyze the data gathered to proof the evidence that various macroeconomic factors influenced the growth and performance of life and health insurance in Malaysia.
Scope of the study
Basically the scope of this study focuses on the macroeconomic variables ie income per capita, inflation rates, price of the product and population density that effect the performance of life and health insurance in Malaysia. There are lots of other macroeconomic variables that can be contributed to the economic growth of Malaysia but this research shall only involved four key factor as for the remaining balance of variables can be included in the future studies as an extension from this research.The research area for this study is from Malaysia perspective only and the time frame shall be from 1998 to 2008 only.
E Views version 6.0 applications were used by the researcher to analyze the data that have been gathered throughout the research process. The data need to be analyzed in order to obtain accurate answer for the question. The Multiple Regression Model will be used to predict the relationships in the construct. The Regression assumptions with respect to autocorrelation (independent of residual), normality (residual is normally distributed), homoscedasticity of error terms, multicollinearity of independent variables will be verified before making any interpretation of the statistical results.