Efficiency Analysis of Life Insurance Companies in Thailand

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1 Efficiency Analysis of Life Insurance Companies in Thailand Li Li School of Business, University of the Thai Chamber of Commerce 126/1 Vibhavadee_Rangsit Rd., Dindaeng, Bangkok 10400, Thailand Tel: (662) ;

2 1 Efficiency Analysis of Life Insurance Companies in Thailand Abstract This paper studies the efficiency of 24 life insurance companies in Thailand during the period and the data envelopment analysis is utilized to measure each company s efficiency. The annual average efficiency scores are found to be as follows: technical efficiency 0.68, allocative efficiency 0.43, cost efficiency 0.28, pure technical efficiency 0.77 and scale efficiency The results also reveal that foreign ownership is significantly positively related to allocative, technical and cost efficiencies, company size and human capital are significantly positively related to all 5 types of efficiency, whereas the real gross domestic product growth rate is significantly negatively related to technical, pure technical and scale efficiencies. JEL classification code: G2;G22; Keywords: Efficiency, Life insurance, Thailand 1. Introduction According to the report from Department of Insurance (2007), Thailand s life insurance penetration rate was 2.35% in 2005 which was lower than the world average of 4.34% and Asia average of 5.16%. The life insurance density was US$56.4 in Thailand in 2005, which was again lower than the world average of US$299.5 and Asia average of US$ Further, Watson, Farley and Williams (2007) predict that the 60 years or older would be about 23% of the entire adult population by the year These numbers indicate that the life insurance industry in Thailand will have great opportunity to grow. However, recent world financial or economic crisis forced regulators to set more stringent rules on financial institutions to prevent problems in the future. For instance, under the supervision of Office of Insurance

3 Commissioner, capital requirement will be reinforced and the risk based capital adequacy is introduced. Therefore, it is forecasted that there will be more consolidation of life insurance companies in the near future and more foreign insurers will hold stakes in Thai insurers. As a consequence, there will be more competitions in the life insurance industry, which motivates the author to study the efficiency, especially the cost and scale efficiency, of life insurance companies in Thailand. This study will help life insurance companies to know themselves better in the aspects of cost, technical, allocative, pure technical, and scale efficiencies. Further, this paper attempts to identify possible determinants of the efficiency, such as foreign ownership percentage, size, human capital, real gross domestic product (GDP) growth rate, etc. The results can also help the authority to better regulate the life insurance industry in Thailand. The rest of the paper is organized as follows: section 2 reviews the literature and section 3 details the data and methodology. Results are discussed in section 4 and section 5 concludes Literature review There are many studies in the literature on efficiency of the insurance or the life insurance industry and the efficiency is usually measured by using different approaches such as the stochastic frontier analysis (SFA), data envelopment analysis (DEA), total factor productivity (TFP), etc. Cummins and Zi (1996) measure the cost efficiency of 445 U.S. life insurance companies over the period using both econometric (SFA) and mathematical programming (DEA) approaches. Cost efficiency is then broken into technical and allocative efficiencies; pure technical and scale efficiencies are also studied. It is found that the choice of methodology does have a significant effect on efficiency results. The 5-year averages of

4 cost efficiency, technical efficiency and allocative efficiency are measured as 0.46, 0.58 and 0.82 respectively and there is more than 63 percent of the life insurance companies demonstrating increasing returns to scale and 31 percent demonstrating decreasing returns to scale. Rai (1996) studies the cost efficiency of insurance companies in 11 countries over the period The paper measures the X-inefficiency from both the stochastic frontier model and the distribution-free model. Life and nonlife premiums are chosen as output and 3 inputs are labor, capital, and benefits and claims. The author argues that benefits and claims paid during the year should be defined as input because they form an integral and substantive part of total costs. The results indicate that the inefficiencies are different by country (companies in the United Kingdom have the lowest efficiency whereas companies in Finland and France have the highest efficiency), size (small companies are more cost efficient than large companies on average) and specialization (specialized companies are more cost efficient than the combined companies). The efficiency of 84 life insurance companies in France over the period is studied by Fecher et al. (1993). For the DEA approach, outputs are gross premiums and financial returns (net returns on financial investment) to reflect the two main functions of a life insurance company: to service the insurance contracts and to manage the savings; and inputs are labor, equipment and buildings. The efficiency indicators are found to be low and dispersed. However, the dispersion can be narrower after controlling for variations in scale, ownership, distribution, reinsurance, and claims ratios. Fenn et al. (2006) employ the SFA approach to measure the cost and profit efficiencies of European insurance companies in 14 countries using a panel data from the Standard and Poor s Enrothesys data set for the period The results indicate that the company size and market share are significant factors affecting the company s cost and profit 3

5 efficiencies. It is also found that larger firms, and those with high market shares, tend to have less cost efficiency and high profit efficiency. Yao et al. (2007) utilize the DEA approach to evaluate the technical efficiency of a panel data set of 22 insurance companies in China over the period after the World Trade Organization (WTO) accession. Two outputs are premiums and investment income and three inputs are capital, labor, and payments and benefits. It is concluded that the average efficiency score is ranged from as low as 0.27 to as high as 1 and the technical efficiency of the insurance company is affected by company size, ownership structure, mode of business and human capital. Karim and Jhantasana (2005) evaluate the cost efficiency and its relationship with profitability of 25 life insurance companies in Thailand over the period The cost inefficiency is measured by stochastic cost frontier approach. It is found that the life insurance industry in Thailand is on average 82 to 140 percent cost inefficient and the inefficiency is negatively correlated with return on equity (ROE) and return on assets (ROA) ratios. Hence the paper suggests that increasing efficiency should be the main priority for the life insurance industry in Thailand. Further, the paper suggests that consolidating large number of smaller insurance companies should be high on the agenda since the mean inefficiency is negatively related to size. Another paper investigates the 20 insurance companies (both life and non-life) that are members of the Stock Exchange of Thailand (SET) in the first quarter of 2005 and listed in the SET in 1998 or prior to 1998 (Chansarn, 2005). The study period is and the efficiency is measured by Total Factor Productivity (TFP) assuming standard Cobb Douglas production function. It is demonstrated that the TFP growth in the insurance sector remained constant over the study period. Also there is evidence supporting that domestic and foreign insurance companies are not different in efficiency. 4

6 5 3. Data and methodology 3.1 Data The scope of this research is to study the efficiency of 24 life insurance companies in Thailand during the period Annual data of insurance companies are compiled from Life Insurance Annual Statistics Report (The Thai Life Assurance Association, various issues) and the website of Office of Insurance Commissioner The foreign ownership data are collected from the Ministry of Commerce and the real GDP data are collected from the International Financial Statistics (International Monetary Fund, various issues). 3.2 Methodology The first part of the study is to measure the five types of efficiency of 24 insurance companies using the DEA approach based on Coelli (1996). The DEA linear programming models are solved to measure each insurance company s cost, technical, pure technical, allocative and scale efficiencies, and to investigate the sources of scale inefficiency. To compute the five types of efficiency scores, input and output variables are first selected. Input selection does not raise much problem when studying the efficiency of life insurance companies and commonly selected inputs are capital, labor and materials/building/equipments (Cummins and Zi, 1996; Fecher et al., 1993; Yao et al., 2007). However, it is still controversial in selecting output variables when studying the efficiency of life insurance companies. Cummins and Zi (1993) argue that life insurers generally provide two principal services: risk bearing/risk pooling services and intermediation services. Therefore, two types of output variables should be incurred benefit payments (proxy for risk bearing/risk pooling services) and additions-to-reserves (proxy for intermediation services). Whereas Yao et al. (2007) argue that the objective in efficiency analysis is to maximize the

7 output but the normal practice for a life insurer is to minimize the benefit payments (insurance claims). Therefore, it seems that benefit payments are more suitable to be classified as input when analyzing the efficiency of a life insurer. Fecher et al. (1993) measure the output of insurance company by gross premiums and financial returns where gross premiums concern the technical activity (the ability to market products, to select clients, and to accept carrying risk) and financial returns reflect the financial activity (the ability to invest the necessary reserves in a proper manner) of a life insurance company. Hence, this paper will consider all of the above arguments and choose two output variables as 1) net written premiums and 2) net investment and other income to represent the two main functions (sources of revenue) of the insurance company: serving the insurance customers and managing the savings. Three input variables are chosen similar to Rai (1996): labor (total number of employees), capital cost (total expenses subtract total salaries, welfares and commissions and brokerages), and benefits and claims. Following Rai (1996), the price of labor can be calculated by dividing total salaries, welfares and commissions and brokerages by the number of employees; the price of capital can be measured by the ratio of capital cost to total assets; and the price of benefits and claims is the ratio of total benefits and claims paid during the year to total assets. The second part of the study intends to find the determinants of the efficiency of life insurance companies in Thailand. Further, following Yao et al. (2007), possible determinants of efficiency or independent variables for the regression are: foreign ownership percentage, size (total assets), and human capital (percentage of staff with higher education). One new variable, real GDP growth rate is added in this study to capture the effect of economy growth on life insurance. Hence, each type of the efficiency is regressed using the following model: 6 Efficiency score it = α + β 1 foreignownership it + β 2 size it + β 3 humancapital it + β 4 Rgdpg t + ε (1)

8 7 where the dependent variable is the efficiency score of the life insurance company i at year t, and independent variables are as follows: foreignownership stands for the foreign ownership percentage of a life insurance company, size is the natural logarithm of the total assets of a life insurance company, humancapital is the percentage of employees with higher education of a life insurance company, Rgdpg is the annual real GDP growth rate of Thailand. Since the dependent variable is the efficiency score which ranges from 0 to 1, the censored Tobit regression is chosen to analyze the determinants according to Grigorian and Manole (2002) and Sufian and Abdul Majid (2007). 4. Empirical results Table 1 shows the descriptive statistics of life insurance companies in Thailand over the period from 2001 to 2008: two outputs, three inputs, three input prices and average net income and total assets. All of the financial data are adjusted by the GDP deflator and the standard deviation of each mean is also shown in the table. The average total assets of life insurance companies in Thailand has been increasing throughout the eight year period from Baht million in year 2001 to Baht million in year 2008 which is roughly tripled. Further, average net written premiums is nearly doubled from Baht million in 2001 to Baht million in 2008 suggesting the rapid growth of life insurance industry in Thailand. Average net investment and other income increased year by year without exception from year 2001 of Baht million to year 2008

9 Average net written premiums (million Baht) Average net investment and other income (million Baht) Table 1: Descriptive statistics of life insurance companies in Thailand Average number of employees Average capital cost (million Baht) Average total benefit payments (million Baht) Average price of labor (million Baht /employee) Average price of capital Average price of benefits and claims Average net income (million Baht) Average total assets (million Baht) ( ) ( ) (657.95) (287.92) ( ) (1.17) (0.11) (0.08) (621.49) ( ) ( ) ( ) (652.13) (270.32) ( ) (1.35) (0.12) (0.14) (759.43) ( ) ( ) ( ) (657.25) (349.47) ( ) (1.41) (0.16) (0.12) (999.88) ( ) ( ) ( ) (669.55) (298.75) ( ) (1.22) (0.08) (0.07) ( ) ( ) ( ) ( ) (674.74) (351.83) ( ) (1.00) (0.14) (0.09) ( ) ( ) ( ) ( ) (691.19) (346.43) ( ) (0.96) (0.07) (0.09) ( ) ( ) ( ) ( ) (720.53) (381.81) ( ) (1.16) (0.08) (0.09) (941.43) ( ) ( ) ( ) (737.31) (414.82) ( ) (1.04) (0.07) (0.08) (573.21) ( ) Overall average ( ) ( ) (672.37) (336.47) ( ) (1.15) (0.11) (0.10) (926.21) ( ) Note: Standard deviations are in the parentheses. Financial data are adjusted by the GDP deflator.

10 of Baht million which is also nearly doubled. However, the seemingly high standard deviation indicates diverse market shares of different life insurance companies in Thailand. The trend of average number of employees is increasing slowly year by year. By 2008, the average number of employees in a life insurance company in Thailand was 602 with a high standard deviation of 737, which again indicates the divergent sizes of different life insurance companies. The trend of average price of labor is not clear and the trend of average price of capital is declining from 10 percent to only 6 percent over the eight year period. Furthermore, the average price of benefits and claims increased slightly from 10 percent to 13 percent over the eight year period. The annual average net income has been increasing year by year from Baht million in 2001 and peaked in 2005 at the level of Baht million and then fell to Baht million in This may be in line with the economic growth in Thailand. Table 2 lists the annual average efficiency scores of life insurance companies in Thailand. The overall annual average efficiency scores of life insurance companies in Thailand are measured as follows: technical efficiency, allocative efficiency, cost efficiency, pure technical efficiency and scale efficiency. The above results indicate that the life insurance companies in Thailand need to improve their cost efficiency (which is consistent with the results of Karim and Jhantasana, 2005) where the cost inefficiency is found to be dominated by the allocative inefficiency and the technical inefficiency is dominated by the pure technical inefficiency. Table 3 displays the sources of scale inefficiency of life insurance companies in Thailand. It can be seen from the table that the major source of scale inefficiency is increasing returns to scale. In total, there are 50.5 percent of life insurance companies in Thailand demonstrating increasing returns to scale, 31.3 percent demonstrating constant returns to scale and 18.2 percent demonstrating decreasing returns to scale.

11 Table 2: Annual average efficiency scores of life insurance companies in Thailand 10 Technical efficiency Allocative efficiency Cost efficiency Pure technical efficiency Scale efficiency overall average Table 3: Sources of scale inefficiency of life insurance companies in Thailand Number of life insurance companies with IRS (Increasing returns to scale) Number of life insurance companies with CRS (Constant returns to scale) Number of life insurance companies with DRS (Decreasing returns to scale) Total Total Percentage (%) Since there are only 178 observations having the data of foreign ownership, so the regression is conducted twice: one with the foreign ownership variable and one without the foreign ownership variable. The results of Tobit regressions (Table 4) reveal that foreign ownership is significantly positively related to allocative, technical and cost efficiencies at the 5 percent

12 11 or 1 percent level, which is consistent with the global advantage hypothesis (Berger, et al., 2000) that foreign institutions would bring advanced technology or management skills to the local organization. Size and human capital are found to be significantly positively related to all 5 types of efficiency at the 1 percent level, which is consistent with Karim and Jhantasana (2005) and Yao et al. (2007). It might imply that the bigger size would lead to economies of scale or scope and human capital would increase efficiency. On the other hand, the real GDP growth rate is significantly negatively related to the technical, cost, pure technical, and scale efficiencies at the 1 or 10 percent level. This result could indicate that poor economic situation would motivate life insurance companies to pay more attention on input cutting and efficiency. 5. Conclusion This research studies the efficiency of 24 life insurance companies in Thailand during the period The DEA approach is applied to measure 5 aspects of efficiency: cost, technical, allocative, pure technical and scale. To measure the efficiency, two output variables are chosen as 1) net written premiums and 2) net investment and other income to represent the two main functions of the insurance company: serving the insurance customers and managing the savings; and three input variables are chosen as labor, capital cost, and benefits and claims. The annual average efficiency scores of life insurance companies in Thailand are measured as follows: technical efficiency, allocative efficiency, cost efficiency, pure technical efficiency and scale efficiency. The above results indicate that the life insurance companies in Thailand need to improve their cost efficiency where the cost inefficiency is dominated by the allocative inefficiency. It is also found that the technical inefficiency is dominated by the pure technical inefficiency and the major source of scale inefficiency is increasing returns to scale.

13 Table 4: Results of Tobit regressions Dependent variable Allocative efficiency Technical efficiency Cost efficiency Pure technical efficiency Scale efficiency Independent variables Constant *** *** *** *** 0.467*** 0.403*** 0.303*** 0.262*** (0.009) (0.000) (0.924) (0.498) (0.000) (0.000) (0.001) (0.005) (0.003) (0.008) Foreignownership 0.326*** 0.278** 0.337*** (0.001) (0.042) (0.000) (0.133) (0.148) Size 0.053*** 0.064*** 0.082*** 0.093*** 0.072*** 0.086*** 0.031*** 0.040*** 0.080*** 0.087*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.004) (0.000) (0.000) (0.000) Humancapital 0.304*** 0.438*** 0.467*** 0.566*** 0.350*** 0.487*** 0.416*** 0.491*** 0.253*** 0.286*** (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.001) (0.000) Rgdpg *** *** * *** *** *** *** (0.436) (0.229) (0.000) (0.000) (0.095) (0.112) (0.005) (0.001) (0.000) (0.000) R-squared Observations Note: Foreignownership is the foreign ownership percentage of a life insurance company; Size is the natural logarithm of the total assets. Humancapital is the percentage of the employees with higher education. Rgdpg stands for the real GDP growth rate. P-values are in the parentheses. *, **, and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively.

14 The results of Tobit regressions reveal that foreign ownership is significantly positively related to allocative, technical and cost efficiencies at the 5 percent or 1 percent level. Size and human capital are found to be significantly positively related to all 5 types of efficiency at the 1 percent level. The real GDP growth rate, however, is significantly negatively related to the technical, pure technical and scale efficiencies at the 1 percent level. This result could indicate that poor economic condition would motivate life insurance companies to pay more attention on input cutting and efficiency. To sum up, based on the findings of this study, life insurance companies in Thailand should increase their foreign ownership percentage, consolidate or merge each other and increase the percentage of human capital to improve their efficiency. Acknowledgement The author acknowledges the financial support of the University of the Thai Chamber of Commerce for this research. References Berger, A. N., DeYoung, R., Genay, H. and Udell, G. F. (2000), Globalization of Financial Institutions: Evidence from Cross-Border Banking Performance, Brookings Wharton Papers on Financial Services, Vol. 3, pp Chansarn, S. (2005), The Efficiency in Thai Financial Sector after the Financial Crisis, Munich Personal RePEc Archive Paper, No Coelli, T. (1996), A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program, CEPA Working Paper, University of New England.

15 14 Cummins, J. D. and Zi, H. (1996), Measuring Cost Efficiency in the U.S. Life Insurance Industry: Econometric and Mathematical Programming Approaches, Working paper, The Wharton Financial Institutions Center. Department of Insurance (2007), Annual report 2006, Ministry of Commerce, Thailand. Fecher, F., Kessler, D., Perelman, S. and Pestieau, P. (1993), Productive Performance of the French Insurance Industry, The Journal of Productivity Analysis, Vol.4, pp Fenn, P., Vencappa, D., Diacon, S., Klumpes, P. and O Brien, C. (2006), Market Structure and the Efficiency of European Insurance Companies: A Stochastic Frontier Analysis, Working paper, Nottingham University Business School. Grigorian, D. A., and Manole, V. (2002), Determinants of Commercial Bank Performance in Transition: An Application of Data Envelopment Analysis, Policy Research Working Paper 2850, the World Bank. International Monetary Fund, various issues. International Financial Statistics. Karim, M. Z. A. and Jhantasana, C. (2005), Cost Efficiency and Profitability in Thailand s Life Insurance Industry: A Stochastic Cost Frontier Approach, International Journal of Applied econometrics and Quantitative Studies, Vol.2-4, pp Rai, A. (1996), Cost Efficiency of International Insurance Firms, Journal of Financial Services Research, Vol. 10, pp Sufian, F., and Abdul Majid, M. Z. (2008), Bank Ownership, Characteristics and Performance: A Comparative Analysis of Domestic and Foreign Islamic Banks in Malaysia, Munich Personal RePEc Archive Paper, No

16 15 The Thai Life Assurance Association, various issues, Life Insurance Annual Statistic Report. Watson, Farley and Williams (2007), Life Insurance in Thailand- the Road Ahead, Yao, S., Han Z. and Feng, G. (2007), On Technical efficiency of China s Insurance Industry after WTO Accession, China Economic Review, Vol. 18, pp

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