Technical and Scale Efficiency of Selected Insurance Companies in India

Transcription

1 Technical and Scale Efficiency of Selected Companies in India Dr Gagandeep Sharma Assistant Professor, Department of Economics, G.G.D.S.D. College, Sector 32 C, Chandigarh Dr Divya Sharma Assistant Professor, Department of Commerce, D.A.V. College, Sector 10, Chandigarh ABSTRACT sector plays a vital role in the economy. Regulatory Development Authority is the controlling and regulatory body for insurance sector.the insurance companies have to follow the guidelines of Regulatory Development Authority (IRDA). There are around 28 insurance companies in India s insurance sector that compete with each other. The companies need to be efficient to survive in the competitive sector. This paper studies the efficiency of 20 insurance companies for the time period to With the help of Data Envelopment Analysis, technical efficiency of insurance companies has been determined. The inputs used for the study are: Management Expenses, Operating Expenses and Salaries. Premium earned, Investment income, Claims Incurred and Profit after Tax are the outputs. The findings showedthat Life Corporation is the most efficient insurance company. 6 out of 20 companies are found to be operating at IncreasingReturns to Scale (IRS). KEYWORDS: companies, Data Envelopment Analysis, Technical Efficiency, Scale Efficiency INTRODUCTION The insurance sector plays an important role in the economy as it minimizes the risk and mobilizes the resources also. For financial institutions, efficiency implies improved profitability, greater amount of funds channeled in, better prices and services quality for consumers and greater safety in terms of improved capital buffer in absorbing risk. (Berger et al. 1993). The insurance sector of India has witnessed tremendous change. Earlier there were only public insurance companies but with the entry of private insurance companies, the picture of insurance sector changed. Vital changes were brought in by the private players. Various new products and services were launched. Thus the public companies were made to improve their efficiency in order to cope with their competitors. Benchmarking analysis is one of the ways to drive insurance companies towards the frontier of best practices. (Mahlberg and Url, 2003 ).According to Kubai (2011), the insurance industry provides financial security as well as financial intermediation to both individuals and businesses in the economy, hence, improving the nation's financial and economic development. Studying the efficiency of insurance companies is highly important because it helps in determining how insurance companies respond to the various challenges and how many of them are likely to survive those challenges in the event of their occurrence (Berger and Humphrey, 1993). Efficiency is a major concern for the insurance companies. This is so because after the opening up of this sector to the private players, competition has increased manifold. There were only ten life insurance companies in year 2001, 14 till year 2006 and the number has further increased to 24 by the year 2014.The pure technical efficiency is used as an index to capture managerial performance (Gulati,2008). OBJECTIVES OF THE STUDY 1. To analyze the Technical efficiency and Scale efficiency of the insurance companies. 2. To compare the insurance companies on the basis of Technical efficiency and Scale efficiency. REVIEW OF THE STUDIES Efficiency has been the focus of much research in insurance in the past (Fecher, et. al 1993; Gardner and Grace 1993; Fukuyama, 1997; Cummins and Zi, 1998).Fukuyama (1997) studied the efficiency of 25 Japanese life insurance companies over the period of DEA and Malmquist index were used. The 154 Dr Gagandeep Sharma, Dr Divya Sharma

2 inputs used were asset value, number of workers and tied agents or sales representative. The outputs used were insurance reserves loans. Plantinga and Huijgen (1999) evaluated the inves tment performance of the insurance companies by developing the attribution framework model. The model was based on dual benchmark that had two objectives. One was maximization of shareholder value and the second was the protection of the value of policy holders. The benchmark distinguished the surplus driven and liability driven asset. Diacon et al (2002) sampled 450 insurance companies from 15 EU countries for the period and found that insurers in UK, Spain, Sweden and Denmark had higher technical efficiencies than their counterparts in other parts of Europe and that UK insurers seem to have low levels of locative and scale efficiencies.marina Brogi (2003) conducted research in Italy on the topic of regulation, corporate governance and risk management in banks and insurance companies. The objective of the study was to measure the performance of the company and to investigate the role of committees in the risk acceptance. Data sample was taken from 2001 to Tobin s Q and Return on asset ratio was applied to define the models that are onetier, vertical two-tier and horizontal two-tier. Results concluded that models showed to be effective for financial intermediaries. Barros and Obijiaku (2007) evaluated the performance of Nigerian insurance companies from 2001 to 2005 by using Data Envelopment Analysis. They also analysed the situations of these companies in relation to the frontier of best practices. Owusu et.al (2010) used Data Envelopment Analysis (DEA) to evaluate the relative performance of Ghanaian general insurance companies from the year 2002 to The study used debt capital, equity capital and management expenses as inputs and premium, claim and investment income as outputs. It was observed that Ghanaian general insurers operated at an average overall efficiency of 68%, technical efficiency of 87% and scale efficiency of 78%. Also, it was observed that Ghanaian general insurers with higher dimension and market shares tend to have higher efficiency.matsawali et al (2012) described that Takaful insurance system was based on Islamic principles that were mutual assistance and voluntary contribution. The research objective was to know the public preference and understanding between the conventional and Islamic insurance in Brunei. For this purpose a survey was conducted in which questionnaire was prepared. Results concluded that people preferred Takaful to Islamic insurance but they have limited awareness about the Takaful. Afza and Asghar (2012) analyzed the relationship between financial reforms and efficiency in the insurance companies of Pakistan for the period of 2003 to For this purpose Data Envelopment Analysis and Regression Analysis was used to conclude the results. Study was divided in two samples that were life and non life insurers. Financial data was taken from the financial reports of 33 insurers in Pakistan. The analysis results showed that size, profitability, leverage, investments had the main drivers of efficiency. Non life insurers performed better than the life insurers in Pakistan. Claim variables were the positively related with the life and negatively related with the non life insurers. As a whole it showed that financial reforms showed better performance in the scenario of Pakistan. Moghadam et al (2012) compared the performance of insurance companies in Iran from the month of March to December According to them measuring the performance was compulsory to gain competitive advantage in the market and to identify the top competitors for the sake of promotion in insurance companies. Descriptive statistics and ANOVA were used to compare the compensation ratio and the growth rate of manufacturing premium among the insurance companies. Data collection sources were library, computer networks and data base. On the base of ANOVA, results concluded that insurance companies had significant difference in terms of performance.barros et.al (2014) described a variety of approaches used to assess the efficiency of a sample of major insurance companies in Angola between 2003 and The study tried to combine use of neutral networks with DEA results to produce a model for insurance companies performance with effective predictive ability. The findings indicated that older insurance companies with Portuguese origin tend to be more efficient. Results also suggested that opportunities for accommodating future demand appeared to be scarce. Tahira and Arshad (2014) in vestigated the factors like profitability liquidity, risk and solvency and capital adequacy. The data from 4 Takaful and 15 conventional insurance companies were taken from the year 2008 to Descriptive analysis and ANOVA were used to find out the performance of insurance companies. The results showed that Islamic insurance companies performed well. Mathur and Paul (2014) measured the efficiency of 20 non life insurance companies operating in India for the period Data Development Analysis and OLS regression method were used for this purpose. The results showed directional impact the 155 Dr Gagandeep Sharma, Dr Divya Sharma

3 ratios have on the technical efficiency of the insurers. Khan and Noreen (2014) compared the Pakistan s Takaful and conventional insurance companies in terms of efficiency and productivity for the period Data Envelopment Analysis (DEA) technique was used. The results indicated that the insurance industry as a whole was cost inefficient due to high allocative inefficiency. Takaful firms were more efficient as compared to conventional insurance firms. Malmquist productivity index showed a significant improvement in scale efficiency. The study suggested introduction of innovative and diversified products in insurance industry of Pakistan, particularly for Takaful companies. Bawa and Bhagat (2015) determined the efficiency of 13 life insurance companies and also gave the overview of life insurance business for the period to For this purpose Data Envelopment Analysis (DEA) technique was used. Results of the study found that LIC is most efficient in all the years and in private life insurance companies, AVIVA and SBI were efficient in the first four years. In Punjab the life insurance business was operating at an average technical efficiency of 55.0%, pure technical efficiency 67.9% and scale efficiency of 80.5%. Faruk and Rahaman (2015) measured the actual efficiency comparison of 10 conventional life insurance companies from Bangladesh and 5 Takaful life insurance companies from Malaysia. For this purpose Data Envelopment Analysis (DE A) and Malmquist index were used. Babatunde and Haron (2015) investigated the performance of ten Nigerian insurance companies using Data Envelopment Analysis over a period of 2008 to The input employed were commission expenses and management expenses while premium and investment income were used as the output. The results showed that Nigerian insurance industry was less efficient and this was caused by low level of Technical efficiency (EF) change including Technological change (TECH). RESULTS AND DISCUSSION The insurance companies which were functional in the last five years were selected for the study. Thus 20 companies were selected for the study.the secondary data for the companies was taken from their respective sites and IRDA website. The inputs taken for the study are: Management Expenses, Operating Expenses and Salaries. Premium earned, Investment income, Claims incurred and Profit after Tax are the outputs studied. Table 1. Selected Input and Output data of Selected Companies to Company Management Expenses {I} Salaries {I} Operating Expenses {I} Premium Earned {O} Investment Income {O} Claims Incurred {O} Profit after tax {O} Aviva India Bajaj Allianz Life Birla Sun Life Exide Life ICICI Prudential Life IDBI Federal Life Life Max Life PNB Metlife India SBI Life New India Assurance Oriental United India Apollo Munich Health Bharti AXA General IFFCO TOKIO HDFC General Reliance General TATA AIG General Star Health and Allied Dr Gagandeep Sharma, Dr Divya Sharma

4 Table 1 and 2 highlights the descriptive analysis of the inputs and outputs used to analyze the efficiency scores of selected insurance companies.the parameters used to analyze the efficiency are Management Expenses, Operating Expenses, Salaries, Premium Earned, Investment Income, Claims Incurred and Profit after Taxes. HDFC General is having the minimum Management Expenses and SBI Life Company is having the maximum. As regards the salary component Reliance insurance is paying the minimum whereas ICICI Prudential Life is having the maximum budget on salary. Operating expenses are least in case of IFFCO TOKIO and maximum for Oriental Life. Maximum claims have been incurred by Birla Sun Life Company and Maximum Profits are earned by SBI Life Company. Table 2. Descriptive Statistics of Selected Parameters of Selected Companies for the period to Parameters N Minimum Maximum Mean Std. Deviation Management Expenses Salaries Operating Expenses Premium Earned Investment Income Claims Incurred Profit After Tax Table 3 studies the efficiency using Data Envelopment Analysis (DEA) of 20 selected insurance companies operational for the last 5 years ( to ). Table 3 depicts the technical efficiency scores and scale efficiency scores of all the companies. As shown in the table, Life Corporation is found to be technical efficient with highest scores under the Constant Returns Scale Model (CRS). It is evident that other companies i.e. Bajaj Allianz Life, ICICI Prudential Life, SBI Life Company and IFFCO TOKIO are also found relatively efficient. On the other hand, Apollo Munich Health (0.382) is found to be the most inefficient Company. 14 out of 20 companies have been judged as pure technical efficient as shown in Table 3. With the highest benchmark score i.e. 5, Life Corporation is the most efficient bank and Exide Life most inefficient bank on the basis of pure technical efficiency during to The Life Corporation is recognized as most pure technical efficient company during this period because linear combination of this company has been highly used than any other company under Variable Returns Scale Model (VRS). The VRS efficiency result also provide output scale efficiency with CRS efficiency score. A company is considered as scale efficient if its output scale efficiency score is one. 10 out of 20 banks are scale efficient as their output scale efficiency score is equal to 1. Therefore it is clear that commonly there are 10 efficient companies under both CRS and VRS assumptions which are scale efficient. 157 Dr Gagandeep Sharma, Dr Divya Sharma

5 Table 3.Efficiency scores and Returns to scale of Selected Companies ( to ) Companies TE Benchmark PTE Benchmark SE NIRS Returns to Scale Aviva India (0.04) 7 (1.07) 19 (0.16) Bajaj Allianz Life Birla Sun Life Company Limited Exide Life (0.22) 5 (0.01) 7 (0.47) ICICI Prudential Life IDBI Federal Life 158 Dr Gagandeep Sharma, Dr Divya Sharma (0.15) 7 (0.77) 18 (0.08) DRS CRS (1.02) 7 (0.94) DRS (0.22) 5 (0.01) 7 (0.46) 16 (0.31) IRS CRS (0.29) 10 (0.04) 16 (0.25) (0.12) 10 (0.06) 16 (0.64) 17 (0.15) 18 (0.02) IRS Life CRS Max Life (0.46) 7 (0.19) 13 (0.04) 16 (1.74) PNB Metlife India (0.09) 7 (0.34) 16 (0.07) 20 (0.34) DRS (0.09) 7 (0.34) 15 (0.08) 16 (0.15) 20 (0.34) IRS SBI Life CRS New India Assurance CRS Oriental (0.05) 11 (0.24) 13 (0.66) (0.01) 11 (0.37) 13 (0.62) IRS United India CRS Apollo Munich Health Bharti AXA General (0.05) 7 (0.14) 10 (0.00) 19 (0.02) (0.04) 20 (0.02) (0.02) 10 (0.01) 16 (0.54) 17 (0.24) 18 (0.08) 19 (0.12) IRS IRS IFFCO TOKIO CRS HDFC General (0.02) 20 (0.14) Reliance General TATA AIG General Star Health and Allied DRS CRS CRS CRS TE: Technical Efficiency PTE: Pure Technical Efficiency SE: Scale Efficiency NIRS: Non-Increasing Returns to Scale. Further, whether the companies are operating in an area of increasing or decreasing returns to scale can be checked by running an additional DEA problem with Non Increasing Return to Scale (NRIS). If NIRS score and VRS score are unequal for a company then it is increasing returns to scale for that company otherwise it is decreasing returns to scale. Out of 20companies, only 6companies are performing under increasing returns to scale, 10 under constant returns to scale and the remaining 4 are performing under decreasing returns to scale.

6 Exide Life, IDBI Federal Life, PNB Metlife India Company, Oriental Company, Apollo Munich Health and Bharti AXA General have been operating under increasing returns to scale. On the other hand Aviva India, Birla Sun Life Company, Max Life and HDFC General have been operating under decreasing returns to scale. On the basis of scale efficiency, Apollo Munich Health is highly inefficient company and underperforming by 47 percent. CONCLUSION The primary function of insurance is to act as a risk transfer mechanism to provide peace of mind and protect against losses. (Sabbir, 2002). schemes utilize the combination method by persuading a large number of individuals to pool their risk into a large group to minimize overall risk. (Ali, 2000). The main purpose of this paper was to study the efficiency of insurance companies in India. The technical efficiency, pure technical efficiency and scale efficiency for 20 insurers for the financial period to was analyzed. Data Envelopment Analysis was used for this purpose. It was observed that out of insurers, Life Corporation wasfound to be efficient. REFERENCES Afza and Asghar 2012, Financial reforms and efficiency in the insurance companies of Pakistan, African Journal of Business Management, Vol. 6, No.30, pp Ali, M. M. K. 2000, "Provision of micro-insurance for micro finance clients, Microfinance newsletter, Vol. 7, No.1, pp Babatunde, Jaiyeoba Haruna and Haron Razali 2015, "Technical efficiency of Nigerian insurance companies: A Data envelopment Analysis and Latent Growth Curve Modelling Approach", International Journal of Data Envelopment Analysis, Vol. 3, No.2, pp Barros Carlos Pestana and Obijiaku Echika Lemechi 2007, Technical efficiency of Nigerian insurance Companies, Working paper. Barros, Carlos Pestana, Dumbo Silvestre and Wanke Peter 2014, " Efficiency determinants and capacity issues in Angolian companies" South African Journal of Economics. Bawa Sumninder Kaur and Bhagat Nidhi 2015, Efficiency of Life companies operating in Punjab, Pacific Business Review International, Vol. 7, Issue 9, pp Berger, A., and Humphrey, D. 1993, "Efficiency of financial institutions. International survey and directions for future research", European Journal of Operational Research. Berger, A.N. Hunter W.C. and Timme, S.G. 1993, The efficiency of financial intuitions: A Review and Preview of Research past, present and future, Journal of Banking and Finance, Vol. 17, No. (2/3), pp Cummins, J.D. and Zi, H. 1988, "Comparison of Frontier Efficient Methods: An application to the US life insurance industry, Journal of Productivity Analysis, Vol. 10, No.12, pp Faruk, M.d. Omar and Rehaman Arafatur 2015, Measuring efficiency of conventional life insurance companies in Bangladesh and Takaful life insurance companies in Malaysia: A Non-parametric approach, Management Studies and Economic Systems (MSES), Vol. 2, No. 2, pp Fecher, F; Kessler, D; Perelman, S. and Pastieu, P. 1993, "Productive performance of the French Industry, Journal of Productivity Analysis, Vol. 4, pp Fukuyama, H. 1997, "Investigating productive efficiency and productive changes of Japanese life insurance companies," Pacific-Basin Finance Journal, Vol.5, No.4, pp Gardner, A. and Grace, M.F. 1993, "X-efficiency in the US life industry, Journal of Banking and Finance, Vol. 17, pp Gulati, S.K. 2008, "An Examination of Technical, Pure Technical and scale efficiencies in Indian Public sector Banks using Data Envelopment Analysis, Eurasian Journal of Business and Economics, pp Khan Atiquzzafar and Noreen Uzma, 2014, Efficiency measure of insurance v/s Takaful firms using DEA approach: A case of Pakistan, Islamic Economic Studies, Vol. 22, No.1, pp Kubai, P. 2011, "X-efficiency of insurance companies in Kenya, University of Nairobi. Mahlberg, B. and Url, T 2003, The effects of the single market on the Austrian industry, Empirical Economics, Vol. 28, pp Dr Gagandeep Sharma, Dr Divya Sharma

7 Marina Brogi 2003, Regulation, corporate governance and risk management in banks and insurance companies, Department of Banking, and Capital Markets. Mathur Tanuj and Paul Ujjawal Kanti 2014, "Performance appraisal of Indian Non-life Companies: A DEA approach, Universal Journal of Management, Vol. 2, No.5, pp Matsawali et al 2012, A Study on Takaful and Conventional Preferences: The Case of Brunei, International Journal of Business and Social Science, Vol. 3, No. 22. Moghadam et al 2012, Performance comparison of insurance companies, Interdisciplinary Journal of Contemporary Research in Business, Vol. 4, No.7. Owusu-Ansah, E., Dontwi, I.K. Seidu, B., Abudulai, G. and Sebil, C. 2010, "Technical efficiencies of Ghanaian general insurers," American Journal of Social and Management Sciences, Vol. 1, No.1, pp Plantinga and Huijgen 1999, Performance measurement and insurance liabilities, University of Groningen. S R Diacon, K Starkey and C.O. Brien 2002, Size and efficiency in European long term insurance companies, Geneva Papers on, Vol. 27, No.3, pp Sabbir, P. 2002, Takaful and Poverty alleviation, available org/takaful. Tahira Hafiza and Arshad Zeeshan 2014, "Comparative performance of Islamic and Conventional companies in Pakistan, IOSR Journal of Business and Management, Vol. 16, Issue 6, pp Dr Gagandeep Sharma, Dr Divya Sharma