Distribution Systems and Efficiency of Life Insurers in Korea
DOI:
https://doi.org/10.58886/jfi.v20i3.5414Keywords:
Data Envelopment Analysis, Efficiency, Distribution Systems, Insurance CompaniesAbstract
This study investigates the technical efficiencies of all life insurance companies in Korea using data environment analysis (DEA) for the sample of 2006 – 2017. During the sample period studied, new life policy sales by cyber marketing and traditional face-to-face sales have significantly increased and sales by other distribution methods have parred or decreased. The estimates of average technical efficiency measures of Korean life insurers are about 18 percent higher than those of foreign life insurers. Among competing regression models, a random effects model is found to be an appropriate model and shows that cyber marketing and tele-marketing have a statistically significant positive impact on insurers’ efficiency, while there is a statistically significant negative relationship between the capital input and the efficiency. The findings of this suggest that insurers should strive to increase their operational efficiency by reevaluating and restructuring their distribution channels.
References
Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. DOI: https://doi.org/10.1287/mnsc.30.9.1078
Berger, A.N., Cummins, J.D., & Weiss, M.A. (1997). The coexistence of multiple distribution systems for financial services: The case of property-liability insurance. Journal of Business, 70(4), 515–546. DOI: https://doi.org/10.1086/209730
Boonyasai, T., Grace, M.F. & Skipper, Jr., H.D. (2002). The Effect of Liberalization and Deregulation on Life Insurer Efficiency (CEAR Working Paper No. 02-2). Atlanta, GA: Center for Risk Management and Insurance Research.
Boussofiane, A., Dyson, R.G., & Thanassoulis, E. (1991). Applied Data Envelopment Analysis. European Journal of Operational Research, 52(1), 1-15. DOI: https://doi.org/10.1016/0377-2217(91)90331-O
Bowlin, W.F., (1998). Measuring Performance: An Introduction to Data Envelopment Analysis (DEA). The Journal of Cost Analysis & Management, 15(2), 3-27. DOI: https://doi.org/10.1080/08823871.1998.10462318
Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2(6), 429-444. DOI: https://doi.org/10.1016/0377-2217(78)90138-8
Cho, Y. (2019). 2020nyeon bo-heom-san-eop jeon-mang-gwa gwa-je [2020 Insurance Industry Outlook and Issues]. Seoul, Korea: Korea Insurance Research Institute.
Coelli, T.J. (1996). A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program. Armidale: University of New England,
Coelli, T., Rao, P., & Battese, G.E. (1998). An introduction to efficiency and productivity analysis. Boston: Kluwer Academic Publishers. DOI: https://doi.org/10.1007/978-1-4615-5493-6
Choi, B.P., Park, J., & Ho, C. (2016). Liquidity transformation: an examination of US life insurers. Managerial Finance, 42(7), 618-634. DOI: https://doi.org/10.1108/MF-11-2015-0302
Cummins, J.D., Dionne, G., Gagné, R., and Nouira, A. (2006). Efficiency of Insurance Firms with Endogenous Risk Management and Financial Intermediation Activities. (CRCRM Working Paper 06-06). Montreal: Canada Research Chair in Risk Management.
Cummins, J.D., Tennyson, S., & Weiss, M.A. (1999). Consolidation and efficiency in the US life insurance industry. Journal of Banking and Finance, 23(2-4), 325–357. DOI: https://doi.org/10.1016/S0378-4266(98)00089-2
Cummins J.D., & Weiss M.A. (2000). Analyzing Firm Performance in the Insurance Industry Using Frontier Efficiency and Productivity Methods. In: Dionne G. (Ed.) Handbook of Insurance, (pp. 767-829). New York: Springer DOI: https://doi.org/10.1007/978-94-010-0642-2_24
Cummins, J.D., Weiss, M., & Zi, (1999). Organizational form and efficiency: The coexistence of stock and mutual property-liability insurers. Management Science, 45(9), 1254–1269. DOI: https://doi.org/10.1287/mnsc.45.9.1254
Cummins, J.D., & Zi, H. (1998). Comparison of frontier efficiency methods: An application to the US life insurance industry. Journal of Productivity Analysis, 10(2), 131–152. DOI: https://doi.org/10.1023/A:1026402922367
Dumm, R.E., & Hoyt, R.E. (2003). Insurance Distribution Channels: Markets in Transition. Journal of Insurance Regulation, 22(1), 27-47.
Dyson, R.G., Allen, R., Camanho, A.S., Podinovski, V.V., Sarrico, C.S., & Shale, E.A., (2001). Pitfalls and Protocols in DEA. European Journal of Operational Research, 132(2), 245-259. DOI: https://doi.org/10.1016/S0377-2217(00)00149-1
Farrell, M.J. (1957). The Measurement of productive efficiency. Journal of the Royal Statistical Society, Series A (General), 120(3), 253–290. DOI: https://doi.org/10.2307/2343100
Gardner, L.A., & Grace, M.F. (1993). X-Efficiency in the US life insurance industry. Journal of Banking and Finance, 17(2-3), 497-510. DOI: https://doi.org/10.1016/0378-4266(93)90048-I
Golany, B., & Roll, Y. (1989). An Application Procedure for DEA. Omega, 17(3), 237-250. DOI: https://doi.org/10.1016/0305-0483(89)90029-7
Gong B., & Sickles R. (1992). Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data. Journal of Econometrics, 51(1-2), 259-284. DOI: https://doi.org/10.1016/0304-4076(92)90038-S
Greene, W., & Segal, D. (2004). Profitability and Efficiency in the U.S. Life Insurance Industry. Journal of Productivity Analysis, 21(3), 229-247. DOI: https://doi.org/10.1023/B:PROD.0000022092.70204.fa
Hjalmarsson, L., Kumbhakar, S.C. & Heshmati A. (1996). DEA, DFA and SFA: A comparison. Journal of Productivity Analysis 7(2-3), 303–327. DOI: https://doi.org/10.1007/BF00157046
Jeon, Y., Hwang, J., Byun, H., Jung, W., Park, S., Ki, S., Lee, S., and Choi, W. (2013). 2013nyeon bo-heom-so-bi-ja seol-mun-jo-sa [2013 Survey of Insurance Consumers]. Seoul, Korea: Korea Insurance Research Institute.
Jeong, W., Kim, S., and Park, J. (2018). pan-mae-chae-neol byeon-hwa-ga bo-heom-san-eob-e mi-chi-neun yeong-hyang [Impact of distribution channels in the insurance industry]. Seoul, Korea: Korea Insurance Research Institute.
Joskow, P. (1973). Cartels Competition and Regulation in the Property Liability Insurance Industry. Bell Journal of Economics and Management Science, 4(2), 375-427. DOI: https://doi.org/10.2307/3003047
KIRI (2019). 2019 Korean Insurance Industry (KIRI August 2019), Seoul: Retrieved from http://kiri.or.kr/eng/pdf/Korean%20Insurance%20Industry%202019.pdf
Kumbhakar, S.C., & Lovell, C.K. (2000). Stochastic Frontier Analysis: An Econometric Approach. Cambridge: Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781139174411
Lin, T.T., Lee, C.C., & Chiu T.F., (2009). Application of DEA in analyzing a bank’s operating performance. Expert Systems with Applications, 36(5), 8883-8891. DOI: https://doi.org/10.1016/j.eswa.2008.11.018
Park, J., Lee S., & Kang, H. B. (2009). The insurance distribution systems and efficiency in the property-casualty insurance industry. Managerial Finance, 35(8), 670-681. DOI: https://doi.org/10.1108/03074350910967213
Park, J., & Park, J. (2015). Enterprise Resource Planning and Efficiency: Evidence from Korean Property/Casualty Insurance Companies. Managerial Finance, 41(4), 405-415. DOI: https://doi.org/10.1108/MF-07-2014-0182
Regan, L., & Tennyson, S. (1996). Agent discretion and the choice of insurance marketing system. Journal of Law and Economics, 39(2), 637-666. DOI: https://doi.org/10.1086/467361
Sun, L., & Chang, T. P. (2011). A comprehensive analysis of the effects of risk measures on bank efficiency: Evidence from emerging Asian countries. Journal of Banking & Finance, 35(7), 1727-1735. DOI: https://doi.org/10.1016/j.jbankfin.2010.11.017
Staib, D., Tschekassin, O., & Puttaiah, M.H. (2019). World insurance: the great pivot east continues (Sigma publication No. 3/2019), Zurich: Retrieved from https://www.swissre.com/institute/research/sigma-research/sigma-2019-03.html
Yungert, A.M. (1993). The Measurement of Efficiency in Life Insurance: Estimates of a Mixed Normal- Gamma Error Model. Journal of Banking and Finance, 17(2-3), 483-496. DOI: https://doi.org/10.1016/0378-4266(93)90047-H
