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


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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.

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.

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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
    • 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
    • Conceptualization
      Kishor Meher
    • Formal Analysis
      Kishor Meher, Maheswaran Muthuraman
    • Project administration
      Kishor Meher
    • Software
      Kishor Meher
    • Supervision
      Kishor Meher, Sanjay Kumar Satapathy
    • Writing – review & editing
      Kishor Meher
    • Data curation
      Abebe Asfawu, Maheswaran Muthuraman
    • Funding acquisition
      Abebe Asfawu, Maheswaran Muthuraman, Sanjay Kumar Satapathy
    • Investigation
      Abebe Asfawu
    • Methodology
      Abebe Asfawu
    • Resources
      Abebe Asfawu, Maheswaran Muthuraman
    • Writing – original draft
      Abebe Asfawu
    • Validation
      Maheswaran Muthuraman, Sanjay Kumar Satapathy
    • Visualization
      Maheswaran Muthuraman, Sanjay Kumar Satapathy