Measuring technical efficiency of insurance companies using dynamic network DEA: an intermediation approach
Abstract
This study measures technical efficiency of the Malaysian insurance companies using a new framework for performance efficiency, built on the intermediation approach, by decomposing the complex service processes of insurance companies into two functional divisions, premium accumulation and investment capability. The study employs a dynamic network data envelopment analysis for performance evaluation of insurer (life, general and composite insurers) and ownership (local and foreign) types, spanning the period 2007–2014. The findings reveal a lack of efficiency in the investment capability function among local insurers as compared to their foreign counterparts. While the composite or non-specialized segment performs better in the investment capability function, the general segment achieves better efficiency in the premium accumulation function. The results suggest the high usage of input quantities and lack of total investment as key reasons for low efficiency, particularly among the local insurers. Implications for business excellence for insurance companies are further discussed.
Keyword : performance evaluation, data envelopment analysis, intermediation approach, dynamic network slacks-based measure, insurance companies
How to Cite
Nourani, M., Devadason, E. S., & Chandran, V. (2018). Measuring technical efficiency of insurance companies using dynamic network DEA: an intermediation approach. Technological and Economic Development of Economy, 24(5), 1909-1940. https://doi.org/10.3846/20294913.2017.1303649
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Cummins, J. D.; Zi, H. 1998. Measuring economic efficiency of the US life insurance industry: econometric and mathematical programming techniques, Journal of Productivity Analysis 10: 131–145. https://doi.org/10.1023/A:1026402922367
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Cooper, W. W.; Seiford, L. M.; Tone, K. 2007. Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver software second edition. Springer US.
Cummins, J. D.; Rubio-Misas, M. 2006. Deregulation, consolidation, and efficiency: evidence from the Spanish insurance industry, Journal of Money, Credit, and Banking 38(2): 323–355. https://doi.org/10.1353/mcb.2006.0029
Cummins, J. D.; Turchetti, G.; Weiss, M. A. 1996. Productivity and technical efficiency in the Italian insurance industry. Wharton School Center for Financial Institutions, University of Pennsylvania, Working Paper.
Cummins, J. D.; Weiss, M. A. 2013. Analyzing firm performance in the insurance industry using frontier efficiency and productivity methods, Handbook of insurance. Springer.
Cummins, J. D.; Weiss, M. A.; Xie, X.; Zi, H. 2010. Economies of scope in financial services: a DEA efficiency analysis of the US insurance industry, Journal of Banking & Finance 34(7): 1525–1539. https://doi.org/10.1016/j.jbankfin.2010.02.025
Cummins, J. D.; Weiss, M. A.; Zi, H. 1999. Organizational form and efficiency: the coexistence of stock and mutual property-liability insurers, Management Science 45(9): 1254–1269. https://doi.org/10.1287/mnsc.45.9.1254
Cummins, J. D.; Xie, X. 2008. Mergers and acquisitions in the US property-liability insurance industry: productivity and efficiency effects, Journal of Banking & Finance 32(1): 30–55. https://doi.org/10.1016/j.jbankfin.2007.09.003
Cummins, J. D.; Zi, H. 1998. Measuring economic efficiency of the US life insurance industry: econometric and mathematical programming techniques, Journal of Productivity Analysis 10: 131–145. https://doi.org/10.1023/A:1026402922367
Ding, C.; He, X. 2004. K-means clustering via principal component analysis, in Proceedings of The Twenty-First International Conference on Machine Learning, 4–8 July 2004, Banff, Alberta, Canada, 29. https://doi.org/10.1145/1015330.1015408
Doherty, N. A. 1980. A portfolio theory of insurance capacity, The Journal of Risk and Insurance 47(3): 405–420. https://doi.org/10.2307/252630
Du, J.; Wang, J.; Chen, Y.; Chou, S.-Y.; Zhu, J. 2014. Incorporating health outcomes in Pennsylvania hospital efficiency: an additive super-efficiency DEA approach, Annals of Operations Research 221(1): 161–172. https://doi.org/10.1007/s10479-011-0838-y
Eling, M.; Luhnen, M. 2010a. Efficiency in the international insurance industry: a cross-country comparison, Journal of Banking & Finance 34(7): 1497–1509. https://doi.org/10.1016/j.jbankfin.2009.08.026
Eling, M.; Luhnen, M. 2010b. Frontier efficiency methodologies to measure performance in the insurance industry: overview, systematization, and recent developments, The Geneva Papers on Risk and Insurance – Issues and Practice 35(2): 217–265. https://doi.org/10.1057/gpp.2010.1
Färe, R.; Grosskopf, S.; Whittaker, G. 2007. Network DEA, Chapter 12 in J. Zhu, W. Cook (Eds.). Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. Springer US. https://doi.org/10.1007/978-0-387-71607-7_12
Färe, R.; Whittaker, G. 1995. An intermediate input model of dairy production using complex survey data, Journal of Agricultural Economics 46(2): 201–213. https://doi.org/10.1111/j.1477-9552.1995.tb00766.x
Farrell, M. J. 1957. The measurement of productive efficiency, Journal of the Royal Statistical Society. Series A (General) 120(3): 253–290. https://doi.org/10.2307/2343100
Fecher, F.; Pestieau, P. 1993. Efficiency and competition in OECD financial services, The Measurement of Productive Efficiency: 374–385.
Golany, B.; Roll, Y. 1989. An application procedure for DEA, Omega 17(3): 237–250. https://doi.org/10.1016/0305-0483(89)90029-7
Hair, J. F.; Black, W. C.; Babin, B. J.; Anderson, R. E. 2009. Cluster analysis, Chapter 9 in Multivariate Data Analysis. 7th ed. Prentice-Hall.
Haugen, R. A.; Kroncke, C. O. 1970. A portfolio approach to optimizing the structure of capital claims and assets of a stock insurance company, The Journal of Risk and Insurance 37(1): 41–48. https://doi.org/10.2307/251517
Huang, L.-Y.; Ma, Y.-L.; Pope, N. 2012. Foreign ownership and non-life insurer efficiency in the Japanese marketplace, Risk Management and Insurance Review 15(1): 57–88. https://doi.org/10.1111/j.1540-6296.2011.01202.x
Huang, W.; Eling, M. 2013. An efficiency comparison of the non-life insurance industry in the BRIC countries, European Journal of Operational Research 226(3): 577–591. https://doi.org/10.1016/j.ejor.2012.11.008
Hwang, S.-N.; Kao, T.-L. 2008. Using two-stage DEA to measure managerial efficiency change of nonlife insurance companies in Taiwan, International Journal of Management and Decision Making 9(4): 377–401. https://doi.org/10.1504/IJMDM.2008.019362
Jain, A. K. 2010. Data clustering: 50 years beyond K-means, Pattern Recognition Letters 31(8): 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Jain, A. K.; Murty, M. N.; Flynn, P. J. 1999. Data clustering: a review, ACM computing surveys (CSUR) 31(3): 264–323.
Kao, C. 2009. Efficiency decomposition in network data envelopment analysis: a relational model, European Journal of Operational Research 192(3): 949–962. https://doi.org/10.1016/j.ejor.2007.10.008
Kao, C.; Hwang, S.-N. 2008. Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan, European Journal of Operational Research 185(1): 418–429. https://doi.org/10.1016/j.ejor.2006.11.041
Kuo, K.-C.; Kweh, Q. L.; Ting, I. W. K.; Azizan, N. A. 2017. Dynamic network performance evaluation of general insurance companies: an insight into risk management committee structure, Total Quality Management & Business Excellence 28(5–6): 542–558. https://doi.org/10.1080/14783363.2015.1100516
Kweh, Q. L.; Lu, W.-M.; Wang, W.-K. 2014a. Dynamic efficiency: intellectual capital in the Chinese non-life insurance firms, Journal of Knowledge Management 18(5): 937–951. https://doi.org/10.1108/JKM-06-2014-0240
Kweh, Q. L.; Lu, W.-M.; Wang, W.-K.; Su, M.-H. 2014b. Life insurance companies’ performance and intellectual capital: a long-term perspective, International Journal of Information Technology & Decision Making 13(4): 755–777. https://doi.org/10.1142/S0219622014500588
Liu, J. S.; Lu, L. Y.; Lu, W.-M.; Lin, B. J. 2013. A survey of DEA applications, Omega 41(5): 893–902. https://doi.org/10.1016/j.omega.2012.11.004
Lu, W.-M.; Kweh, Q. L.; Nourani, M.; Huang, F.-W. 2016. Evaluating the efficiency of dual-use technology development programs from the R&D and socio-economic perspectives, Omega 62: 82–92. https://doi.org/10.1016/j.omega.2015.08.011
Lu, W.-M.; Wang, W.-K.; Kweh, Q. L. 2014. Intellectual capital and performance in the Chinese life insurance industry, Omega 42(1): 65–74. https://doi.org/10.1016/j.omega.2013.03.002
Macminn, R. D.; Witt, R. C. 1987. A financial theory of the insurance firm under uncertainty and regulatory constraints, The Geneva Papers on Risk and Insurance – Issues and Practice: 12(1): 3–20. https://doi.org/10.1057/gpp.1987.1
Mahajan, J. 1991. A data envelopment analytic model for assessing the relative efficiency of the selling function, European Journal of Operational Research 53(2): 189–205. https://doi.org/10.1016/0377-2217(91)90134-H
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