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Technical, allocative and cost efficiency in the Australian general insurance industry

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  • Andrew C. Worthington
  • Emily V. Hurley

Abstract

Data envelopment analysis is used to calculate technical, allocative and cost efficiency indices for a sample of fifty-three Australian general insurers. The inputs used are labour, physical capital (in the form of both information technology and plant and equipment) and financial capital. The outputs are net premium revenues for housing-related insurance, transport-related insurance, indemnity-related insurance and other insurance, along with investment revenue. The results indicate that the major source of overall cost inefficiency would appear to be allocative inefficiency, rather than technical inefficiency, and that the largest twenty percent of insurers are significantly more efficient than the remaining firms. A second-stage analysis uses limited dependent variable regression techniques to relate efficiency scores to financial and non-financial information. Cost efficiency appears to be closely related to asset size, the proportion of non-premium income, and participation in compulsory third party (CTP) markets, but not to stock exchange listing or product range.

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File URL: http://external-apps.qut.edu.au/business/documents/discussionPapers/2000/Worthington_Hurley_74.pdf
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Bibliographic Info

Paper provided by School of Economics and Finance, Queensland University of Technology in its series School of Economics and Finance Discussion Papers and Working Papers Series with number 074.

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Date of creation: 20 Feb 2000
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Handle: RePEc:qut:dpaper:074

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Keywords: Data envelopment analysis; Technical; allocative and cost efficiency; general;

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References

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  1. J. David Cummins & Hongmin Zi, 1997. "Comparison of Frontier Efficiency Methods: An Application to the U.S. Life Insurance Industry," Center for Financial Institutions Working Papers 97-03, Wharton School Center for Financial Institutions, University of Pennsylvania.
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  3. Yuengert, Andrew M., 1993. "The measurement of efficiency in life insurance: Estimates of a mixed normal-gamma error model," Journal of Banking & Finance, Elsevier, vol. 17(2-3), pages 483-496, April.
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  10. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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  14. Andrew M. Yuengert, 1993. "The measurement of efficiency in life insurance estimates of a mixed normal-gamma error model," Research Paper 9308, Federal Reserve Bank of New York.
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Cited by:
  1. Don U.A. Galagedera, 2004. "A Survey On Investment Performance Appraisal Methods With Special Reference To Data Envelopment Analysis," Finance 0406013, EconWPA.
  2. Kathleen Goffey & Andrew Worthington, 2002. "Motor Vehicle Usage Patterns in Australia: A Comparative Analysis of Driver, Vehicle & Purpose Characteristics for Household & Freight Travel," School of Economics and Finance Discussion Papers and Working Papers Series 117, School of Economics and Finance, Queensland University of Technology.

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