Efficiency of insurance companies: Application of DEA and Tobit analyses

Grmanová, Eva; Strunz, Herbert

doi:10.14254/2071-8330.2017/10-3/18

Cited by 52 publications

(32 citation statements)

References 14 publications

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“…Return on the assets variable highlight the role of profitability in enhancing the chance that insurer being efficient, where the result indicates that ROA increases the chance of being efficient by (2.46) times. The result consistent with the finding of (Gramanova and Strunz, 2017) (Diacon, 2001).…”

Section: Logit Modelsupporting

confidence: 92%

The Efficiency of Jordan Insurance Companies and its Determinants using DEA, Slacks, and Logit models

Jaloudi¹

2019

TR1001

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This study aims to evaluate the technical efficiency in the Jordan insurance market, and examine the internal and external determinants that appear to affect the technical efficiency of the insurance companies. The study used panel data for 22 insurance companies operating inside Jordan over the period 2000-2016. Data Envelopment Analysis used to evaluate the technical Efficiency Scores, Slacks based and Logit models to examine the efficiency determinants. The study found that there is a slight development of technical efficiency for the Jordanian insurance companies during the study period. In addition, there is a substantial efficiency difference between insurance companies each year, and there is a variation at the level of efficiency for each company in each year. The results also showed that owners' equity are among the most important internal determinants of companies' efficiency, and there is a significant correlation between type, size, and return on assets of the insurer and its efficiency. This study provides insurance management with relevant indicators that would guide them to make efficient use of the resource base. The period of study also covers the period following the adoption of the Insurance Law and the issuance of most of the legislation related to the work of insurance companies.

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Section: Logit Modelsupporting

confidence: 92%

The Efficiency of Jordan Insurance Companies and its Determinants using DEA, Slacks, and Logit models

Jaloudi¹

2019

TR1001

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“…This research result has suggested that the "indemnities paid by the insurance companies negatively affected the liquidity but with a lag of two periods after changes in indemnities". Furthermore, Grmanová & Strunz (2017) studying the efficiency of insurers was conducted on the application of DEA and Tobit analyses. This research determined the relationship between technical efficiency and profitability (ROA, ROE and the size of assets) of insurers.…”

Section: Literature Reviewmentioning

confidence: 99%

General trends and competitiveness of Australian life insurance industry

Arych¹,

Darcy²

2020

Journal of International Studies

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This paper considers financial and economic indicators of Australian life insurance industry. The analysis shows the main characteristics of the current life insurance market in Australia and its factors (determinants) that will guide the life insurance market in the future. The authors have defined and calculated the main indicators of competitiveness for Australian life insurance industry (growth rates of the life insurers number'; density of insurance; penetration rates; concentration ratios; Herfindahl-Hirschman index; integrated assessment of competitiveness). In this paper, we analyzed the general trends and competitiveness of Australian life insurance market for the 1997-2017 period. The results of the Herfindahl-Hirschman index calculations, based on the net policy revenue, demonstrate that Australian life insurance industry competition is high and there is a slight concentration at this market. Herfindahl-Hirschman index for other indicators shows that competition within life insurance industry is defined as weak and underdeveloped. In addition, the analysis shows that for Australian life insurance market there are statistically significant and directly proportional impacts of, firstly, population on life insurance premiums; secondly, the number of life insurers on life insurance penetration rate via gross written premium; thirdly, the number of life insurance companies on life insurance penetration rate via assets of life insurers; and, lastly, life insurance companies' assets on gross written life insurance premiums.

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“…DEA, introduced by Charnes et al (1978), is one of the most applied methods in the efficiency area (Eling and Luhnen, 2008). It was used in a wide range of areas such as banking (Wang et al, 2013), fisheries (Hoff, 2007) and insurance markets (Eling and Luhnen, 2008;Yakob et al, 2014, Grmanova andStrunz, 2017). Moreover, it has been developed into a further variant such as BCC, introduced by Banker et al (1984) or the slacks-based measure (SBM) model, introduced by Tone (2001).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The data set of the 20 biggest German life insurers, fundamental for this study form, were measured on the "actuarial reserves" in 2017 over the last ten years 2008-2017(Bureau van Dijk, 2018. The underlying data is from the Moody's Analytics InsuranceFocus database of Bureau van Dijk.…”

Section: Research Sample and Datamentioning

confidence: 99%

Investment Efficiency of Life Insurance Companies in Germany: Application of a Two-Stage SBM

Krupa¹,

Farbarzevics

Salame

2019

CER

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Purpose -To prove the robustness of the efficiency-measuring model against potentially system-relevant disturbances to company variables such as SIZE, ROA, solvency and organizational form. Methodology -In the first stage, the established model is applied using the SBM to measure insurance efficiency. The underlying data sets are from the twenty biggest life insurance companies (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) in Germany. In the second stage, the established model is examined for its robustness against disturbance variables. Several disturbance variables are introduced individually to the system and examined for their influence by three econometric methods, Tobit regression, OLS and the fixed-effect model. This approach allows a comparative analysis of the results with respect to the systemic relevance of every added variable. In the end, the accuracy of the second stage is compared through the Spearman test. Findings -The comparative analysis of all three econometric techniques brought ROA as an efficiency-influencing variable. Furthermore, both proved econometric models Tobit and OLS are SBM-suitable with cross-sectional data. Further evidence for SBM compatibility are found for Tobit and the fixed-effect model with panel data.

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Efficiency of insurance companies: Application of DEA and Tobit analyses

Cited by 52 publications

References 14 publications

The Efficiency of Jordan Insurance Companies and its Determinants using DEA, Slacks, and Logit models

The Efficiency of Jordan Insurance Companies and its Determinants using DEA, Slacks, and Logit models

General trends and competitiveness of Australian life insurance industry

Investment Efficiency of Life Insurance Companies in Germany: Application of a Two-Stage SBM

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