Assessment of measurement and ranking of technical efficiencies of Ethiopian general insurers
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DOIhttp://dx.doi.org/10.21511/ppm.18(4).2020.27
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Article InfoVolume 18 2020, Issue #4, pp. 334-350
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The non-life insurance companies indemnify the properties from the risk of being damaged due to unforeseen events like natural calamity or accidents. The probability of bankruptcy is imminent on account of large, unprecedented claims. As a risk saver of various society stakeholders, these insurers must be efficient while managing the insurance business. The present research thrusts upon to evaluate the efficiency and decomposition that would further direct the insurers towards achieving optimal scale. Thus, the captioned research aims to measure and rank the technical efficiency of the general insurance firms of Ethiopia and evaluate and analyze their relative efficiencies. The research adopts a quantitative approach and deploys descriptive analysis by a panel data of 17 Ethiopian general insurers for the period 2005-2016 on the input-output-oriented approach of Data Envelopment Analysis (DEA). The data of general insurance are obtained using stratified sampling from the mix of life and general category. The inputs employed are total expenses, total liabilities, and shareholder’s fund, while net premiums earned and income from investments are used as outputs. The findings reveal that the public insurer is technically efficient by operating at an optimal scale as compared to all private insurers who, in turn, experience pure technical inefficiency to scale inefficiency due to poor management practices and erroneous utilization of input materials. Increasing Returns to Scale (IRS) witnessed a major form of scale inefficiency in 2016. Private insurers should increase capital and size of assets, cost efficiency, and improve key management skills.
Acknowledgment
The authors express their thanks of gratitude for the support extended by Ethiopia’s insurance companies’ officials to provide the hard copies of published annual reports up to 2016 as the secondary data are not available after that year’s analysis.
- Keywords
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JEL Classification (Paper profile tab)G22, L25, C33
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References44
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Tables10
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Figures3
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- Figure 1. Relative technical efficiency score under CCR model
- Figure 2. Relative technical efficiency under BCC model
- Figure 3. Average TE, PTE, and SE
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- Table 1. Descriptive analysis of inputs and outputs of general insurers
- Table 2. Technical efficiency of insurance companies under the CRS (CCR model)
- Table 3. Company-wise rank and relative efficiency of the insurance companies under the Constant Returns to Scale (CCR model)
- Table 4. Technical efficiency of insurance companies under the VRS (BCC model)
- Table 5. Company-wise rank and relative efficiencies of the insurance companies under VRS (BCC model)
- Table 6. Decomposition of year-wise Overall Technical Efficiency (OTE), Pure Technical Efficiency (PTE), and Scale Efficiency (SE)
- Table 7. Decomposition of firm-wise technical efficiency for 2016
- Table A1. Ethiopian general insurance companies, their establishment period and observations
- Table B1. The selected variables of inputs and outputs along with definition
- Table C1. Nature of returns to scale from 2005 to 2016
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- Abduh, M., Omar, M., & Tarmizi, R. (2012). The Performance of the insurance industry in Malaysia: Islamic vis-a-vis conventional insurance. Journal of Islamic Banking and Finance, 29(4), 40-49.
- Al-Amri, K., Gattoufi, S., & Al‐Muharrami, S. (2012). Analyzing the technical efficiency of insurance companies in GCC. The Journal of Risk Finance, 13(4), 362-380.
- Ali, A. I., & Seiford, L. M. (1993). The Mathematical Programming Approach to Efficiency Analysis. In H. O. Fried & S. S. Schmidt (Eds.), The Measurement of Productive Efficiency: Techniques and Applications (pp. 120-159). New York: Oxford University Press.
- Ansah-Adu, K., Andoh, C., & Abor, J. (2013). Evaluating the cost efficiency of insurance companies in Ghana. The Journal of Risk Finance, 13(1), 61-76.
- Banker, R. D., Charnes, A., & Cooper, W. (1984). Some models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092.
- Barros, C. P., & Obijiaki, E. L. (2007). Technical Efficiency of Nigerian Insurance Companies (Working Paper No. 018/2007/DE/UECE). School of Economics and Management, Technical University of Lisbon.
- Bauer, P., Berger, A., Ferrier, G., & Humphrey, D. B. (1998). Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods. Journal of Economics and Business, 50(2), 85-114.
- Bawa, S., K. & Navjeet, K. (2014). An Analysis of Efficiency-Profitability Relationship: A Study of Indian Public General Insurers. Paradigm, 18(1), 51-72.
- Berger, A. N., & Humphrey, D. B. (1992). Measurement and efficiency issues in commercial banking. In Z. Griliches (Ed.), Output Measurement in the Service Sectors (pp. 245-300). University of Chicago Press.
- Berger, A., & Mester, L. (2008). Inside the Black Box: What Explains Differences in the Efficiencies of Financial Institutions? Journal of Banking & Finance, 21(7), 895-947.
- Brockett, P. L., Cooper, W. W., Golden, L. L., Rousseau, J. J., & Wang, Y. (1998). Evaluating solvency versus efficiency performance and different forms of organization and marketing in US property-liability insurance companies. European Journal of Operation Research, 154(2), 492-514.
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the Efficiency of Decision-Making Units. European Journal of Operations Research, 2(6), 429-444.
- Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (1995). Data Envelopment Analysis: Theory, Methodology, and Applications. Boston, MA: Kluwer Academic Publishers.
- Chen, B., Powers, M. R., & Qiu, J. (2009). Life-insurance efficiency in China: a comparison of foreign and domestic firms. China & World Economy, 17(6), 43-63.
- Coelli, T. J. (1996). A Guide to DEAP version 2.1: A Data Envelopment Analysis (Computer) Program (CEPA Working Paper No. 96/08). Centre for Efficiency and Productivity Analysis.
- Coelli, T. J., Rao, P., & Battese, G. E. (1998). An Introduction to Efficiency and Productivity Analysis. New York: Springer Science.
- Creswell, W. (2009). Research design: quantitative, qualitative, and mixed methods approach (3rd ed.). Sage Publications, California.
- Cummins, J. D., & Santomero, A. (1999). Changes in the Life Insurance Industry: Efficiency, Technology, and Risk Management. Kluwer Academic Publishers, Boston.
- Cummins, J. D., & Weiss, M. A. (2000). Analyzing Firm Performance in the Insurance Industry Using Frontier Efficiency and Productivity Methods. In G. Dionne (Ed.), Handbook of Insurance Economics (pp. 767-829). Boston: Kluwer Academic Publishers.
- Diacon, S. R., Starkey, K., & O’Brien, C. (2002). Size and efficiency in European long-term insurance companies: an international comparison. Geneva Papers on Risk and Insurance, 27(3), 444-466.
- Dutta, A., & Sengupta, P. P. (2011). Efficiency Measurement of Indian Life Insurance Industry in Post-Reforms Era. Global Business Review, 12(3), 415-430.
- Eling, M., & Luhnen, M. (2010). Efficiency in the International Insurance Industry: A Cross-Country Comparison. Journal of Banking and Finance, 34(7), 1497-1509.
- Fare, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production Frontier. Cambridge University Press.
- Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253-290.
- Feyen, E., Lester, R. & Rocha, R. (2011). What drives the development of the insurance sector? An empirical analysis based on a panel of developed and developing countries (Policy Research Working Papers).
- Gollani, B., & Roll, Y. (1989). An application procedure for DEA. Omega International Journal of Management Sciences, 17(3), 237-50.
- 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.
- Kounetas, K., & Tsekouras, K. (2007). Measuring scale efficiency change using a Translog distance function. International Journal of Business and Economics, 6(1), 63-69.
- Kumbhakar, S. C. (1987). Production frontiers and panel data: an application to US class 1 railroad. Journal of Business and Economic Statistics, 5(2), 249-255.
- Leverty, J. T., & Grace, M. F. (2010). The robustness of output measures in property-liability insurance efficiency studies. Journal of Banking and Finance, 34(7), 1510-1524.
- Lovell, C. K. (1993). Production Frontiers and Productive Efficiency. In H. O. Fried & S. S. Schmidt (Eds.), The Measurement of Productive Efficiency: Techniques and Applications (pp. 3-67). New York: Oxford University Press.
- Marwa, N., & Aziakpono, M. (2016). Technical and Scale Efficiency of Tanzanian Saving and Credit Cooperatives. The Journal of Developing Areas, 50(1), 29-46.
- Meher, K., & Getaneh, H. (2019). Impact of determinants of financial distress on the financial sustainability of Ethiopian commercial banks. Banks and Bank Systems, 14(3), 187-201.
- Meher, K., & Zewudu, T. (2020). Determinants of Firm’s Internals & Macroeconomic Factors on Financial Performance of Ethiopian Insurers. DLSU Business & Economics Review, 29(2), 71-80.
- National Bank of Ethiopia. (n.d.). Annual Reports of National Bank of Ethiopia.
- Noulas, A. G., Hatzigayios, T., Lazaridis, J., & Lyroudi, K. (2001). Non-parametric production frontier approach to the study of the efficiency of non-life insurance companies in Greece. Journal of Financial Management and Analysis, 14(1), 19-26.
- Owusu-Ansah, E., Dontwi, I., Seidu, B., Abudulai, G., & Sebil, C. (2010). Technical efficiencies of Ghanaian general insurers. American Journal of Social and Management Sciences, 1(1), 75-87.
- Saad, N. M. (2012). An analysis of the efficiency of takaful and insurance companies in Malaysia: a non-parametric approach. Review of Integrative Business & Economics Research, 1(1), 33-56.
- Seiford, L. M. (1996). Data Envelopment Analysis: The Evolution of State of the Art (1978–1995). Journal of Productivity Analysis, 7(2-3), 99-138.
- Seiford, L. M., & Thrall, R. M. (1990). Recent Developments in DEA: The Mathematical Approach to Frontier Analysis. Journal of Econometrics, 46(1-2), 7-38.
- Sinha, R. P. (2015). A Dynamic DEA Model for Indian Life Insurance Companies. Global Business Review, 16(2), 258-269.
- Tone, K., & Sahoo, B. K. (2000). Evaluating cost efficiency and returns to scale in the life insurance corporation of India using data envelopment analysis. Socio-Economic Planning Sciences, 39(4), 261-285.
- Udaibir, S. D., Nigel, D., & Podpiera, R. (2003). Insurance and issues in soundness (IMF Working Paper No. WP/03/138).
- 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.