Assessment of measurement and ranking of technical efficiencies of Ethiopian general insurers

Abstract

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. AcknowledgmentThe 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.

Literature survey on DEA in the insurance industry with a focus on identification of research hotspots with text mining

“…The eliminated keywords by us are given in Appendix 1e. In this period we have investigated the following issues: how the financial sector reforms affect the efficiency and productivity (Noreen and Ahmad, 2016), efficiency and productivity of property-liability insurers (Cummins and Xie, 2016), efficiencysolvency linkage (Sinha, 2017) the performance evaluation of life, general insurers and its' ownership (Nourani et al 2018), the corporate performance (Tone et al 2019) and the corporate governance effect (board characteristics) on efficiency of life insurance companies (Alhassan and Boakye, 2020), the efficiency and competitiveness (Lee et al 2018), efficiency of the Medicare plans (Brockett et al 2018), efficiency of the publicly held insurance industry (Copeland and Cabanda, 2018), the determinants of the insurance profitability (Camino-Morgo and Bermudez-Barrezueta, 2019), the effect of risk-based capital on efficiency, productivity and competitiveness (Lee et al 2019), the life micro-insurance portfolio of insurance companies (Savitha et al 2019), the relationship between efficiency and profitability of insurance companies (Eling and Jia, 2019), the performance of non-life companies and its determinants (Ilyas and Rajasekaran, 2019), the measure and rank efficiency of general insurance companies (Meher et al 2020), the risk-management efficiency and determinants of non-life insurance companies (Noreen, 2020), private health insurance (Nguyen and Worthington, 2021) and lastly managerial ability (Banker et al 2021).…”