Proposing a Model of Efficiency Evaluation Based on the Adjusted Range Measurement Model and Weight Restrictions (Case Study: Branches of Iran Insurance Company)

Document Type : Research Paper

Authors

1 Associate Prof. of Accounting, Faculty of Management, University of Tehran, , Iran

2 PhD Candidate of Operations Research, Farabi Campus, University of Tehran, , Iran

3 PhD Candidate of Industrial Engineering, Iran University of Science & Technology, , Iran

Abstract

Objective: One serious drawback of the application of data envelopment analysis in insurance firm efficiency evaluation has been the absence of decision-maker and experts’ opinions, allowing total freedom while allocating weights to input and output data of insurance firms under analysis. This allows insurance firms to achieve artificially highly efficient grants by ignoring some important inputs and outputs.
Methods: The most widespread method for considering decision-maker and experts’ opinions in data envelopment analysis models is, perhaps, the weight restrictions inclusion. Weight restrictions allow for the integration of decision maker and experts’ opinions and controlling the range of weight changes in Data Envelopment analysis. Therefore, in this paper, an adjusted range measurement model considering the weight restrictions once and not considering weight restrictions later is used.
Results: The average efficiency of the adjusted range measurement model with and without considering the weight restrictions is 0.927 and 0.959, respectively. The number of efficient branches in the presence of weight restrictions has decreased from 71 units to 24 units representing the impact of these restrictions.
Conclusion: This article intended to propose a new data envelopment model regarding an adjusted range measure and considering the weight restrictions and the model was tested on Iran insurance company.

Keywords


Asgary, GH.H., Beygi, M., & Yaghoubi. (2016). Evaluation of the efficiency and ranking of insurance branches Using Data Envelopment Analysis Technique. International Conference on Management and Accounting, Tehran, Amozesh Allie Nikan Institute. (in Persian)
Azar, A., & Daneshvar, M. (2007). A review of the methods of assessing the performance of insurance branches. Insurance Journal, 22(2), 123- 152. (in Persian)
Bal, H., Örkcü, H. H., & Çelebioğlu, S. (2010). Improving the discrimination power and weights dispersion in the data envelopment analysis. Computers & Operations Research, 37(1), 99-107.
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.
Bikker, J. A., & Gorter, J. (2011). Restructuring of the Dutch nonlife insurance industry: consolidation, organizational form, and focus. Journal of Risk and Insurance, 78(1), 163-184.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of econometrics, 30(1), 91-107.
Cooper, W. W., Park, K. S., & Pastor, J. T. (1999). RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis, 11(1), 5-42.
Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses: with DEA-solver software and references. Springer Science & Business Media.
Cummins, J. D., & Weiss, M. A. (2013). Analyzing firm performance in the insurance industry using frontier efficiency and productivity methods. In Handbook of insurance (pp. 795-861). Springer New York.
Delhausse, B., Fecher, F., Pestieau, P. (1995). Measuring Productive Performance in the Non-Life Insurance Industry: The Case of French and Belgian Markets. Tijdschrift voor Economie en Management, 40 (1), 47–69.
Daniyali-Dah-hooz, M., & Ketabi, S. (2014). Evaluation of the effectiveness of insurance branches using Data Envelopment Analysis (DEA) (Case study: Branches of Insurance Company in the Southern Provinces of Iran). Quarterly Journal Productivity management, 7(1), 71-94. (in Persian)
Ebadi, J., & Bagherzadeh, H.A. (2008). Examination of technical efficiency and economies of scale in selected public and private insurance companies. Journal of Economic Research (Tahghighat-e-Eghtesadi), 43(3)205-229. (in Persian)
Eling, M., & Schaper, P. (2017). Under pressure: how the business environment affects productivity and efficiency of European life insurance companies. European Journal of Operational Research, 258(3), 1082-1094.
Fecher, F., Perelman, S., & Pestieau, P. (1991). Scale economies and performance in the French insurance industry. The Geneva Papers on Risk and Insurance-Issues and Practice, 16(3), 315-326.
Fukuyama, H. (1997). Investigating productive efficiency and productivity changes of Japanese life insurance companies. Pacific-Basin Finance Journal, 5(4), 481-509.
Gharagozlo, A. (2018). Presenting a Super-efficiency model for benchmarking with real situation in Iran Insurance Company. Islamic Azad University, Central Tehran Branch, Faculty Of management, Tehran Province, Master Thesis. (in Persian)
Ghodratian-Kashan, S.A., & Anvary-Rostamy, A.A. (2004). Designing a Comprehensive Model to Evaluate Performance and Rank of a Company. Management Researches in Iran (The Modares Humanity Journal), 8(5), 109-135. (in Persian)
Haghi, M. (2014). Assessing the performance of MA Insurance branches using Data Envelopment Analysis (DEA). International Conference on Business Development and Excellence, Tehran, Modiran Ideh Pardaz Paytakht Viera Institute. (in Persian)
Hanifezadeh, L. (2011). The size and structure of the market and the efficiency of insurance companies in Iran. 3rd National Conference on Data Envelopment Analysis, Firuzkuh, Firuzkuh Islamic Azad University. (in Persian)
Hwang, T., & Gao, S. S. (2005). An empirical study of cost efficiency in the Irish life insurance industry. International Journal of Accounting, Auditing and Performance Evaluation, 2(3), 264-280.
Jafarzadeh, A.H. (2013). Evaluating and Ranking the Branches of Iran Insurance Company Based on Malmquist Index and Data Envelopment Analysis-Free Disposal Hull (DEA-FDH) In the Presence of Weight Restrictions. TehranUniversity. Faculty of Management. Tehran, Master Thesis.
(in Persian)
Jafarzadeh, A.H., Safari, H., & Mehregan, M.R. (2014). Efficiency and Productivity evaluation of Iran Insurance Stock Company's branches based on Data Envelopment Analysis and Malmquist Index in the presence of Weight Restrictions. Journal of Modiriat-E-Farda, 13(4), 109-135. (in Persian)
Javadipour, A. (2013). Efficiency Evaluation and Ranking of the Agencies of Iran Insurance Company based on Data Envelopment Analysis Technique with Weight Restrictions (AR-DEA) and Goal Programming (GP). Tehran University. Aras International Campus. East Azerbaijan Province-Jolfa, Master Thesis. (in Persian)
Kao, C., & Hwang, S. N. (2014). Multi-period efficiency and Malmquist productivity index in two-stage production systems. European Journal of Operational Research, 232(3), 512-521.
Kasman, A., & Turgutlu, E. (2009). Total factor productivity in the Turkish insurance industry. International Journal of the Economics of Business, 16(2), 239-247.
Kessner, E., & Polborn, M. (1999). Eine Effizienzanalyse der deutschen Lebensversicherer—die Best Practice Methode. Zeitschrift für die gesamte Versicherungswissenschaft, 88(2), 469-488.
Kessner, K. (2001). Skaleneffizienz und Produktivitätswachstum in der deutschen Lebensversicherung. Markttransparenz und Produktionseffizienz in der deutschen Lebensversicherung. Dissertation, Ludwig-Maximilians-Universität München.
Li, H., Chen, C., Cook, W. D., Zhang, J., & Zhu, J. (2018). Two-stage network DEA: Who is the leader? Omega, 74, 15-19.
Ma, J., & Chen, L. (2018). Evaluating operation and coordination efficiencies of parallel-series two-stage system: A data envelopment analysis approach. Expert Systems with Applications, 91, 1-11.
Mahlberg, B., & Url, T. (2003). Effects of the single market on the Austrian insurance industry. Empirical Economics, 28(4), 813-838.
Mehregan, M.R. (2004). Quantitative model for organizational performance evaluation (Data Envelopment Analysis). Tehran, Faculty of Management University of Tehran Press.
(in Persian)
Mehregan, M.R., Safari, H., & Jafarzadeh, A.H. (2016). Performance assessment of branches of Iran Insurance Corporation using data envelopment analysis. Journal of Financial Research, 17(2), 393-414. (in Persian)
Mohaghar, A., Jafarzadeh, A.H., Soleimani-Sarvestani, M.H., Moradi-Moghadam, M. (2013). A new AR-Interval Data Envelopment Analysis Model for Supplier Selection. Report and Opinion, 5(5), 1-8.
Mohammadi, A., & Hosainizadeh, S. (2007). Application of Integrated Approaches AHP / DEA in the ranking of insurance agencies. Journal of Economics Research, 7(3), 281-304. (in Persian)
Momeni, M., & Shahkhah, N. (2009). Assessing the efficiency of Iran's insurance companies using the two-stage DEA communication model. Journal of the insurance industry, 28(1-2), 45-72.
(in Persian)
Nadery-Far, A., & Farifteh, Z. (2016). Efficiency assessment of branches of Iran Insurance Corporation using Data Envelopment Analysis (DEA) in Zahedan, International Conference on Management and Accounting, Tehran, Amozesh Allie Nikan Institute. (in Persian)
Norman, M., & Stoker, B. (1991). Data envelopment analysis: the assessment of performance. John Wiley & Sons, Inc.
Omrani, H., Gharizadeh-Beiragh, R., & Shafiei-Kaleibari, S. (2015). Performance Assessment and Ranking of Iranian Insurance Companies by a Combined Model with Experts Preferences. Journal of Industrial Management, 6(4), 791-807. (in Persian)
Palizdar, M.R. (2013). Investigating effect of the participation of managers in budgeting on amount of their managerial performance in Tehran Regional Electricity Company. Islamic Azad University-Central Tehran Branch. Faculty of Management. Tehran, Master Thesis. (in Persian)
Roll, Y., & Golany, B. (1993). Alternate methods of treating factor weights in DEA. Omega, 21(1), 99-109.
Sadraei-Javaheri, A. (2014). Productivity Evaluation of Iranian Insurance IndustryA Non-Parametric Malmquist Approach. Iranian Journal of Economic Research, 18(57), 85-95. (in Persian)
Saen, R. F. (2010). Restricting weights in supplier selection decisions in the presence of dual-role factors. Applied Mathematical Modelling, 34(10), 2820-2830.
Safari, H., Azari, A., & Hosseini, F. (2005). A model for ranking companies based on the EFQM Excellence Model. 4th international industrial engineering conference, Tehran, Tarbiat Modarres University. (in Persian)
Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20.
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73-105.
Soltanpanah, H., Moradi, F., & Bakhsha, N. (2008). Evaluation of the relative efficiency of the branches of Alborz Insurance Company using Data Envelopment Analysis (DEA). Journal of the insurance industry, 22 (4), 151 -177. (in Persian)
 Sueyoshi, T., & Goto, M. (2011). Methodological comparison between two unified (operational and environmental) efficiency measurements for environmental assessment. European Journal of Operational Research, 210(3), 684-693.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509.
Wanke, P., & Barros, C. P. (2016). Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach. Economic Modelling, 53, 8-22.
Yao, S., Han, Z., & Feng, G. (2007). On the Technical Efficiency of China’s Insurance Industry after WTO Accession. China Economic Review, 18(1), 66–86.
Yuengert, A. M. (1993). The measurement of efficiency in life insurance: estimates of a mixed normal-gamma error model. Journal of Banking & Finance, 17(2), 483-496.