PRODUCTIVITY OF LIFE INSURANCE COMPANIES IN INDIA

Transcription

1 CHAPTER V PRODUCTIVITY OF LIFE INSURANCE COMPANIES IN INDIA 5.1 INTRODUCTION An economic measured of output per unit of input. Inputs include labor and capital, while output is typically measured in revenues and other GDP components such as business inventories. Productivity measures may be examined collectively (across the whole economy) or viewed industry by industry to examine trends in labor growth, wage levels and technological improvement. Productivity is a measure of the efficiency of production. Productivity is a ratio of production output to what is required to produce it (inputs). The measure of productivity is defined as a total output per one unit of a total input. These definitions are short but too general and insufficient to make the phenomenon productivity understandable. A more detailed theory of productivity is needed, which explains the phenomenon productivity and makes it comprehensible. In order to obtain a measurable form of productivity, operational zing of the concept is necessary. In explaining the operational zing a set of production models are used. A production model is a numerical expression of the production process that is based on production data, i.e. measured data in the form of prices and quantities of inputs and outputs. It is advisable to examine any phenomenon whatsoever only after defining the entity the phenomenon under review forms. Hence, productivity cannot be examined as a phenomenon independently but it is necessary to identify the entity it belongs to. Such an entity is defined as production process. The benefits of high productivity are manifold. At the national level, productivity growth raises living standards because more real income improves people's ability to purchase goods and services, enjoy leisure, improve housing and education and contribute to social and environmental programs. Productivity growth is important to the firm because more real income means that the firm can meet its (perhaps growing) obligations to customers, suppliers, workers, shareholders, and governments (taxes and regulation), and still remain competitive or even improve its competitiveness in the market place. In view of the changing scenario of competition in the life insurance sector, the section of the study compares 12 life insurance companies for the financial years in respect of productivity efficiency and changes in partial factor productivity. 122

2 5.2 PARTIAL PRODUCTIVITY Partial productivity is the ratio of output to one class of input. For example, Labour productivity (the ratio of output to labour input), capital productivity (the ratio of output to capital input), Branches productivity (the ratio of output to branches input) and insurance policies productivity (the ratio of output to policies input) are examples of partial productivity. In this chapter, the concept of partial factor productivity has been used to analyze productivity. Increase in any of the partial productivity like Labour of agencies, Capital, Branches and Policies will mean that there is saving in the use of that particular factor Measurement of Partial Factor Productivity Productivity means the output per unit of input and hence, theoretically there are as many indices of productivity as there are inputs. However, by adopting gross value added as output, only four factors of input viz., Labour, Capital Branches and Policies are considered in this study. The Partial factors productivity of labour, capital, Branches and Policies are estimated as: The Agent productivity, Capital productivity, Branches productivity, Policies productivity and Capital intensity are found on the basis of applying below formulas. Labour (agent) Productivity = Real value Added (Total premium) / Real Agencies of Life insurance Capital Productivity = Real value Added (Total premium) / Real Gross Fixed capital Branches Productivity = Real value Added (Total premium) / Real Number of branches Policies Productivity = Real value Added (Total premium) / Real Number of Policies Agents Productivity Agents are motivated and trained to pursue more and more people to purchase life insurance policies. Their performance is highly linked with the premium they collect as 123

3 they are paid commission on the basis of the premium they have collected in a particular year. So the productivity of agents can be calculated by dividing Premium Income in a particular year by the Number of active agents in that year Branch Productivity Premium is the consideration paid by the insured to the insurer for the risk undertaken by the insurer. So the productivity of branches can be measured in terms of Premium Income received by all the branches during a particular year. It is calculated by dividing the total Annual Premium Income in a particular year by the total number of branches in that year Policies Productivity The growth of insurance business to a large extent is dependent on the skills and the ability of well trained agents to attract the public to its fold. The productivity of agents can be measured by calculating the average business done by each agent in terms of number of policies i.e. number of policies sold by each agent in a particular period Capital Productivity Capital productivity is the relationship between the gross premiums earned from the capital contributed. How much of the gross premium earned from the capital contributed measured by calculating the average business Capital intensity The linking factor of labour and capital in a production function relationship would be what is called Capital intensity, i.e. proxying for technical changes. The possible role this technical change might have performed in productivity increases could be assessed from the changes in Capital-Labour ratio. It is, therefore, considered desirable to measure the extent of increase in capital intensity in the selected life insurance companies. The capital intensity is the ratio which indicates capital requirement per unit of output. The capital intensity can be estimated as C / L C = Capital L = Labour of Agencies. Table 5.1 explains the gross premium productivity of agent of life insurance companies in India from the study period to

4 TABLE: 5.1 GROSS PREMIUM PRODUCTIVITY OF AGENT OF LIFE INSURANCE COMPANIES IN INDIA ( in Rs.lakhs) Year LIC BIRLA ICICI ING HDFC MAX NEW Reliance TATA MET SBI Bajaj Kotak Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs Mean SD CV(%) CGR Source: Compiled from IRDA Annual Reports of Various Years 125

5 The data presented in the above Table 5.1 shows that the agent (Labour) productivity. The average productivity of a single agent of LIC was Rs lakhs in and with yearly fluctuation it stood at the average annum growth of intermediary production in terms of gross premium collection of this public sector insurance companies was noted to be 2.32 per cent during the study period with a co-variation of per cent. This is far lesser than the other private life insurance companies agent productivity. This implies the impact of private sector entry into Indian insurance business and growth of competitive pressure from domestic private insurance companies. The average productivity of a single agent of BIRLA life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of 1.85 per cent. An average premium value of Rs.5.13 lakhs collected per year per agent with a co-variation of per cent. The average productivity of a single agent of ICICI life insurance was Rs lakhs in and increased to Rs lakhs in with fluctuation it stood at the average annual growth of per cent. An average premium value of Rs.5.17 lakhs collected per year per agent with a co-variation of per cent. The average productivity of a single agent of ING life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. And an average premium value of Rs.2.29 lakhs collected per year per agents with a co-variation of per cent. The average productivity of a single agent of HDFC life insurance was as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of 1.39 per cent. An average premium value of Rs.3.38 lakhs collected per year by per agents with a co-variation of per cent. The average productivity of a single agent of MAXNEW life insurance was Rs lakhs in and it increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. And an average premium value of Rs.5.57 lakhs collected per year per agents with a co-variation of 62.41per cent. 126

6 The average productivity of a single agent of Reliance life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of Rs.1.30 lakhs were collected per year per agent with a co-variation of per cent. The average productivity of a single agent of TATA life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. TATA collected an average premium of Rs.2.71 lakhs per year per agent with a co-variation of per cent. The average productivity of a single agent of MET life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. An average premium value of Rs.2.82 lakhs collected per year per agent with a co-variation of per cent. The average productivity of a single agent of SBI life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. SBI collected an average premium of Rs lakhs per year per agent with a co-variation of per cent. The average productivity of a single agent of BAJAJ life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. Its average premium of Rs.3.17 lakhs collected per year per agent with a co-variation of per cent. The average productivity of a single agent of KOTAK life insurance was Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. An average premium of Rs.5.84 lakhs collected per year per agent with a co-variation of per cent. Thus, it has been concluded that the overall gross premium productivity of agent of life insurance companies in India, MET life insurance and Bajaj life insurance have highest growth of per cent per annum. Table 5.2 depicts the gross premium productivity per branch of life insurance companies in India from the study period to

7 TABLE: 5.2 GROSS PREMIUM PRODUCTIVITY PER BRANCH OF LIFE INSURANCE COMPANIES IN INDIA ( in Rs.lakhs) Year LIC BIRLA ICICI ING HDFC MAX NEW Reliance TATA MET SBI Bajaj Kotak Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs Mean SD CV(%) CGR Source: Compiled from IRDA Annual Reports of Various Years 128

8 The above Table 5.2 indicates gross premium productivity per branch. LIC recorded of Rs lakhs in and increased to Rs lakhs of premium collected per branch in with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs lakhs premium collected per branch with a co-variation of per cent. Gross premium productivity per branch per year of BIRLA life insurance recorded Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of 7.15 per cent. BIRLA collected an average branch premium of Rs lakhs with a co-variation of per cent. Gross premium productivity per branch per year of ICICI life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual negative growth of 2.27 per cent. And an average branch premium of Rs lakhs collected per branch with a co-variation of per cent. Gross premium productivity per branch per year of ING life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. ING collected an average branch premium of Rs lakhs with a co-variation of per cent. Gross premium productivity per branch per year of HDFC life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average negative annual growth of 6.67 per cent. Its average branch premium of Rs lakhs collected per year with a co-variation of 34.81per cent. Gross premium productivity per branch per year of MAX NEW life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of 9.64 per cent. MAX NEW life insurance collected an average branch premium of Rs lakhs with a co-variation of per cent. Gross premium productivity per branch per year of Reliance life insurance recorded as Rs.1.65 lakhs in and increased to Rs lakhs in with yearly 129

9 fluctuation it stood at the average annual growth of per cent. And an average branch premium of Rs lakhs collected per branch with a co-variation of per cent. Gross premium productivity per branch per year of TATA life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of 4.71 per cent. And an average branch premium of Rs lakhs collected per branch with a co-variation of per cent. Gross premium productivity per branch per year of MET life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly changes it stood at the average annual growth of per cent. MET collected an average branch premium of Rs lakhs per year with a co-variation of per cent. Gross premium productivity per branch per year of SBI life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. And an average branch premium of Rs lakhs collected per branch with a co-variation of per cent. Gross premium productivity per branch per year of BAJAJ life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly fluctuation it stood at the average annual growth of per cent. And an average branch premium of Rs lakhs collected per branch with a co-variation of per cent. Gross premium productivity per branch per year of KOTAK life insurance recorded as Rs lakhs in and increased to Rs lakhs in with yearly increased it stood at the average annual growth of per cent. And an average branch premium of Rs lakhs collected per branch with a co-variation of per cent. Thus, it has been concluded that the overall gross premium productivity per branch of life insurance companies in India, MET life insurance has the highest growth of per cent per annum. Table 5.3 shows the gross premium productivity per policy of life insurance companies in India from the study period to

10 CHART 5.1 BRANCH PRODUCTIVITY OF LIFE INSURANCE COMPANIES IN INDIA FROM 2001/ / Rs.in Lakhs Productivity of branches Source: Compiled from IRDA Annual Reports of Various Years 131

11 TABLE: 5.3 GROSS PREMIUM PRODUCTIVITY PER POLICY OF LIFE INSURANCE COMPANIES IN INDIA ( in Rs.lakhs) Year LIC BIRLA ICICI ING HDFC MAX NEW Reliance TATA MET SBI Bajaj Kotak Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs Mean SD CV(%) CGR Source: Compiled from IRDA Annual Reports of Various Years 132

12 The above Table 5.3 indicates that gross premium productivity per policy. LIC policy productivity recorded Rs.0.29 lakhs in to Rs.0.55 lakhs in per policy and with yearly fluctuation it stood at the average annual growth of 9.64 per cent. LIC collected an average of Rs.0.33 lakhs per policy per year with a co-variation of per cent. BIRLA life insurance policy productivity recorded Rs.0.03 lakhs in to Rs.0.57 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.0.36 lakhs premium was collected per policy every year with a co-variation of per cent. ICICI life insurance policy productivity recorded Rs.0.04 lakhs in to Rs.1.32 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. ICICI collected an average of Rs.0.33 lakhs per policy per year with a co-variation of per cent. ING life insurance policy productivity recorded Rs.0.01 lakhs in to Rs.0.62 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.0.30 lakhs premium was collected per policy every year with a co-variation of per cent. HDFC life insurance policy productivity recorded Rs.0.03 lakhs in and increased to Rs.0.11 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.0.34 lakhs premium was collected per policy every year with a co-variation of per cent. MAX NEW life insurance policy productivity recorded Rs.0.04 lakhs in to Rs.0.71 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.0.27 lakhs premium was collected per policy every year with a co-variation of per cent. RELIANCE life insurance policy productivity recorded Rs.0.02 lakhs in to Rs.0.35 lakhs in and with yearly fluctuation. An average of Rs.0.20 lakhs premium was collected per policy every year with a co-variation of per cent. 133

13 TATA life insurance policy productivity recorded Rs.0.02 lakhs in to Rs.0.72 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. TATA collected an average of Rs.0.33 lakhs per policy per year with a co-variation of per cent and a co-variation of per cent. MET policy productivity recorded Rs.0.02 lakhs in to Rs.1.26 lakhs in and with yearly changes its average premium per policy collected was Rs.0.40 lakhs with a co-variation of per cent. SBI life insurance policy productivity recorded Rs.0.03 lakhs in to Rs.1.37 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. SBI collected an average of Rs.0.48 lakhs per policy per year with a co-variation of per cent. BAJAJ life insurance policy productivity recorded Rs.0.01 lakhs in to Rs.0.62 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.0.28 lakhs premium was collected per policy every year and a co-variation of per cent. KOTAK life insurance policy productivity recorded Rs.0.03 lakhs in to Rs.1.15 lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.0.54 lakhs premium was collected per policy every year with a co-variation of per cent. Thus, it has been concluded that the overall gross premium productivity per policy of life insurance companies in India, ING life insurance has the highest growth of per cent per annum. Table 5.4 reflects the gross premium contribution to capital productivity of life insurance companies in India from the study period to

14 CHART 5.2 POLICIES PRODUCTIVITY OF LIFE INSURANCE COMPANIES IN INDIA FROM 2001/ / Rs. in Lakhs Productivity of policies Source: Compiled from IRDA Annual Reports of Various Years 135

15 TABLE: 5.4 GROSS PREMIUM CONTRIBUTION TO CAPITAL PRODUCTIVITY OF LIFE INSURANCE COMPANIES IN INDIA ( in Rs.lakhs) Year LIC BIRLA ICICI ING HDFC MAX NEW Reliance TATA MET SBI Bajaj Kotak Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs Mean SD CV(%) CGR Source: Compiled from IRDA Annual Reports of Various Years 136

16 The above Table 5.4 indicates the gross premium contribution to capital productivity. LIC capital productivity recorded Rs lakhs in and increased to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs lakhs premium was collected from capital average with a co-variation of per cent. BIRLA life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An amount of Rs.2.15 lakhs average premium was collected from capital with a co-variation of per cent. ICICI life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.5.98 lakhs premium was collected from capital average with a co-variation of per cent. ING life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.1.16 lakhs premium was collected from capital average with a co-variation of per cent. HDFC life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.2.12 lakhs premium was collected from capital average with a co-variation of per cent. MAXNEW life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.1.61 lakhs premium was collected from capital average with a co-variation of per cent. RELIANCE life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly increased it stood at the average annual growth of per cent. RELIANCE collected a mean value of Rs.2.13 lakhs from capital with a co-variation of per cent. 137

17 TATA life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.1.57 lakhs premium was collected from capital average with a co-variation of per cent. MET life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.0.79 lakhs premium was collected from capital average with a co-variation of per cent. SBI life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. SBI collected a mean value of Rs.3.63 lakhs from capital with a co-variation of per cent. BAJAJ life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs lakhs premium was collected from capital average with a co-variation of per cent. KOTAK life insurance capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of per cent. An average of Rs.2.86 lakhs premium was collected from capital average with a co-variation of per cent. Thus, it has been concluded that the overall gross premium contribution to capital productivity of life insurance companies in India, Reliance life insurance and Bajaj life insurance have the highest growth of per cent per annum. Table 5.5 depicts the agent contribution to capital productivity of life insurance companies in India from the study period to

18 TABLE: 5.5 AGENT CONTRIBUTION TO CAPITAL PRODUCTIVITY OF LIFE INSURANCE COMPANIES IN INDIA ( in Rs.lakhs) Year LIC BIRLA ICICI ING HDFC MAX NEW Reliance TATA MET SBI Bajaj Kotak Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs. Rs Mean SD CV(%) CGR Source: Compiled from IRDA Annual Reports of Various Years 139

19 The above Table 5.5 indicates the agent contribution to capital productivity. (Capital intensity) LIC was recorded as Rs lakhs in to Rs lakhs in There is no change every year due to additional capital not introduced. BIRLA life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average negative annum growth of per cent. Its average mean value showed Rs.2.90 lakhs per year. ICICI life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly changes it stood at the negative average annual growth of per cent. Its average mean value showed Rs.1.36 lakhs per year. ING life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of 8.79 per cent. ING average mean value showed Rs.2.90 lakhs per year. HDFC life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the negative average annum growth of per cent. HDFC showed an amount of Rs.2.06 lakhs as average premium from the capital contribution to agents. MAXNEW life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average negative annual growth of 8.79 per cent. Its average mean value showed Rs.4.37 lakhs per year. RELIANCE life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annum growth of per cent. TATA life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual growth of 1.15 per cent. Its average mean value showed Rs.1.71 lakhs per year. 140

20 MET life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the negative average annual growth of per cent. Its average mean value showed Rs.6.90 lakhs per year. SBI life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the negative average annual growth of per cent. Its average mean value showed Rs.7.02 lakhs per year. BAJAJ life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average annual negative growth of per cent. Its average mean value showed Rs.0.59 lakhs per year. KOTAK life insurance agent contribution to capital productivity recorded Rs lakhs in to Rs lakhs in and with yearly fluctuation it stood at the average negative annual growth of per cent. Its average mean value showed Rs.3.13 lakhs per year. Thus, it has been concluded that the overall agent contribution to capital productivity of life insurance companies in India, Reliance life insurance has the highest growth of per cent per annum. 141

21 CHART 5.3 PRODUCTIVITIES OF AGENTS, CAPITAL AND CAPITAL INTENSITY OF LIFE INSURANCE COMPANIES IN INDIA FROM 2001/ / Rs. in Lakhs Prodictivity of Agents Prodictivity of capital Capital Intensity Source: Compiled from IRDA Annual Reports of Various Years 142

22 5.3 TESTING OF HYPOTHESIS As Cummins et al. (2006) suggest that efficiency measures are useful to make relative comparisons of the performance among insurance companies and the components of efficiency across time. Based on Cummins et al suggestion a hypothesis is framed to measure the productivity of the sample life insurance companies in this study. Ho 2 : The productivity of the life insurance companies are not significantly influenced by its operational parameters. 5.4 MULTIPLE REGRESSION ANALYSIS The regression model was performed to evaluate association gross premium and productivity of the life insurance companies. To check whether there is multi-collinearity in the model the simple correlation coefficients between the explanatory variables have been examined. Total premium income is one of the important and main indicators of the performance of the life insurance business. This variable is considered as the dependent variables (input variable) and productivity variables are considered as the independent variables. The dependent variable was gross premium and the independent variables are: X 1 -Gross premium productivity of agent, X 2 -Gross premium productivity per branch, X 3 -Gross premium productivity per policy, X 4 -Gross premium contribution to capital productivity, X 5 -Agent contribution to capital productivity, Table 5.6(a,b&c) shows the value of constant and coefficient value of each attributes productivity of select life insurance companies. 143

23 Factors Gross premium productivity of agent Gross premium productivity per branch Gross premium productivity per policy Gross premium contribution to capital productivity Agent contribution to capital productivity TABLE: 5.6(a) ASSOCIATION BETWEEN PROFITABILITY AND GROSS PREMIUM FACTORS LIC BIRLA ICICI ING β t Sig β t Sig β t Sig β t Sig R R F Significance Level of Significance: 5 per cent 144

24 Factors Gross premium productivity of agent Gross premium productivity per branch Gross premium productivity per policy Gross premium contribution to capital productivity Agent contribution to capital productivity TABLE: 5.6(b) ASSOCIATION BETWEEN PROFITABILITY AND GROSS PREMIUM FACTORS HDFC MAX NEW Reliance TATA β t Sig β t Sig β t Sig β t Sig R R F Significance Level of Significance: 5 per cent 145

25 Factors Gross premium productivity of agent Gross premium productivity per branch Gross premium productivity per policy Gross premium contribution to capital productivity Agent contribution to capital productivity TABLE: 5.6(c) ASSOCIATION BETWEEN PROFITABILITY AND GROSS PREMIUM FACTORS MET SBI Bajaj Kotak β t Sig β t Sig β t Sig β t Sig R R F Significance Level of Significance: 5 per cent 146

26 The above Table 5.6(a,b c) represents the statistical relevance of the econometric test conducted. LIC: The result of econometric test establishes.914 i.e., 91 per cent of correlation between the variables tested in the case of LIC. The R 2 value at.835 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. BIRLA life insurance: The result of econometric test establishes.940 i.e., 94 per cent of correlation between the variables tested in the case of BIRLA. The R 2 value at.883 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. ICICI life insurance: The result of econometric test establishes.988i.e. 98 per cent of correlation between the variables tested in the case of ICICI. The R 2 value at.976 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. ING life insurance: The result of econometric test establishes.936 i.e, 93 per cent of correlation between the variables tested in the case of ING. The R 2 value at.876 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross 147

27 premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. HDFC life insurance: The result of econometric test establishes.832 i.e, 83 per cent of correlation between the variables tested in the case of HDFC. The R 2 value at.692 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. MAX NEW life insurance: The result of econometric test establishes.984 i.e, 98 per cent of correlation between the variables tested in the case of MAX NEW. The R 2 value at.968 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. Reliance life insurance: The result of econometric test establishes.942 i.e, 94 per cent of correlation between the variables tested in the case of Reliance. The R 2 value at.887 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is rejected and it is concluded that the productivity of the life insurance companies is significantly influenced by its operational parameters. 148

28 TATA life insurance: The result of econometric test establishes.961 i.e, 96 per cent of correlation between the variables tested in the case of TATA. The R 2 value at.923 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. MET life insurance: The result of econometric test establishes.982 i.e, 98 per cent of correlation between the variables tested in the case of MET. The R 2 value at.964 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. SBI life insurance: The result of econometric test establishes.949 i.e, 94 per cent of correlation between the variables tested in the case of SBI. The R 2 value at.900 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have 90 per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. Bajaj life insurance: The result of econometric test establishes.901i.e, 90 per cent of correlation between the variables tested in the case of SBI. The R 2 value at.811states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have 149

29 81.10 per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. Kotak life insurance: The result of econometric test establishes.986 i.e, 98 per cent of correlation between the variables tested in the case of SBI. The R 2 value at.972 states that all the five independent variables tested i.e., gross premium productivity of agent, gross premium productivity per branch, gross premium productivity per policy, gross premium contribution to capital productivity and agent contribution to capital productivity have per cent influence on the dependent variable gross premium. Therefore, the hypothesis is accepted and it is concluded that the productivity of the life insurance companies is not significantly influenced by its operational parameters. 5.5 COMPARATIVE STATEMENT Table 5.7 clearly shows a comparison between select life insurance players in India regarding Gross premium productivity of agent, Gross premium productivity per branch, Gross premium productivity per policy, Gross premium contribution to capital productivity and Agent contribution to capital productivity. Table 5.7 shows the comparative statement of compound growth rate for measuring Productivity factor. 150

30 TABLE: 5.7 COMPARATIVE STATEMENT OF PRODUCTIVITY OF SELECT LIFE List of Life Insurance Gross premium productivity of agent INSURANCE COMPANIES IN INDIA Gross premium productivity per branch Productivity Level Gross premium productivity per policy Gross premium contribution to capital productivity ( in percentage) Agent contribution to capital productivity LIC Birla ICICI ING HDFC Max New Reliance Tata Met SBI Bajaj Kotak Source: Computed From Base Data From Table 5.7 the productivity efficiency of the twelve sample life insurance companies was analyzed for a period of ten years from to Gross premium underwriting by the LIC agent was recorded as 2.32 per cent per annum, whereas as the agent s productivity of Birla was observed to be 1.85 per cent and HDFC with a low margin of 1.85 per cent per annum. ANP Sanmar being merged overtaken by Reliance life insurance. The researcher could not compute its agents performance. From the detailed analysis it has further been inferred that the agent s productivity of TATA and Bajaj recorded a highest growth rate of per cent and other sample private players managed a business growth of around 20 to 28 per cent as an average. 151

31 The complementary to the agent productivity the branch-wise productivity of twelve life insurance companies were computed and it has been observed that MET life insurance had recorded the highest branch business at per cent as ICICI life insurance and HDFC life insurance had recorded a negative growth. LIC s branch-wise business was noted as per cent per annum. The other life insurance companies had an average branch business of 4.71 to per cent. Policy-wise premium collection of LIC had registered a growth of 9.64 per cent per annum. Whereas its private competitors had a business relegation around per cent to per cent per policy at the (CGR). It indicates that opening of insurance sectors to private players had created a very tough competition to the monopoly LIC that had a sound customer base in terms of policy distribution. Gross premium capital contribution of LIC recorded an annual growth of per cent. In fact, the data analysis in the previous Chapter IV, it has been observed that total expenses met by the LIC fall with the Insurance Regulation Act and it implies that a portion of the surplus premium had been transferred to capital reserve of the company. In the case of the eleven private insurance companies though they were very young, statured after 2000 and meet enormous expenses in term of commission, infrastructure establishment and building of distribution networks, they were able to contribute around per cent to per cent towards capital resources per annum. 5.6 SUMMARY With the liberalization and entry of private companies in insurance, the Indian insurance sector has started showing signs of significant change. From the data analysis it has been inferred that within a short span of time, private life insurance companies have registered a positive sign of productivity in terms of agent and branch expansion. The productivity ratio reflects the efficiency in operations, which ultimately indicates the management efficiency and soundness of the sample life insurance companies as good both in the case of LIC and other eleven private life insurance companies. 152