Cost efficiency of African insurance companies using a finite mixture model

Abstract

This paper evaluates the operational practices by African insurance companies from Angola and Mozambique, using a finite mixture model that allows controlling for unobserved heterogeneity. More precisely, a stochastic frontier latent class model is adopted in this research to estimate the cost frontiers for each of the different technologies embedded in this heterogeneity. This model not only enables the identification of different groups of African insurance companies from Angola and Mozambique, but it also permits the analysis of their cost efficiency. The results indicate the existence of three different technology groups in the sample, suggesting the need for different business strategies. The policy implications are also derived.

Revenue efficiency evaluation of life insurance companies in India: identification of leaders and laggards

“…Siddiqui (2020) indicated that the state life insurer i.e. Life Insurance Corporation (LIC) had been efficient for the entire time period of study from -2013to 2016-2017. Tone and Sahoo (2005 reported huge increase in cost efficiency of Life Insurance Corporation of India over the period 1982-2001 due to modernization of operations.…”

“…, 2008; Eling and Luhnen, 2008; Davutyan and Klumpes, 2008; Asghar and Afza, 2010; Barros et al. , 2010; Ansah-Adu et al , 2012; Barros and Wanke, 2016; Jaloudi, 2019; Ilyas and Rajasekaran, 2019, b). However, the studies conducted jointly on life and non-life insurances have been criticized as both arenas do not have similar restrictions and regulations (Barros et al.…”

“…Researchers have evaluated efficiency and productivity for both life and non-life insurance companies (Fecher et al, 1991;Fecher et al, 1993;Donni and Hamende, 1993;Donni and Fecher, 1997;Berger et al, 2000;Chaffai and Ouertani, 2002;Cummins et al, 2004;Ennsfellner et al, 2004;Barros et al, 2005;Badunenko et al, 2006;Cummins and Rubio-Misas, 2006;Barros and Obijiaku, 2007;Fenn et al, 2008;Eling and Luhnen, 2008;Davutyan and Klumpes, 2008;Asghar and Afza, 2010;Barros et al, 2010;Ansah-Adu et al, 2012;Barros and Wanke, 2016;Jaloudi, 2019;Ilyas and Rajasekaran, 2019, b). However, the studies conducted jointly on life and non-life insurances have been criticized as both arenas do not have similar restrictions and regulations (Barros et al, 2005).…”

Dynamic cost productivity and economies of scale of Ghanaian insurers

“…There is ample room for research on non-European countries, especially on developing ones such as Ghana. There are a number of other efficiency studies in a developing country context (Barros et al 2014b;Barros and Wanke 2014, 2016 but the focus of this study is on cost productivity.…”

Efektywność techniczna zakładów ubezpieczeń w dziale ubezpieczeń non-life

“…W pierwszym przyjęto założenie dla składnika nieefektywności u i , że nie zmienia się w czasie (the time-invariant inefficiency), a w drugim, że zmienia się w czasie (the time-variant inefficiency). Przeciętną efektywność techniczną ubezpieczycieli autorzy oszacowali na poziomie 42-43%.Barros i Wanke16 , stosując funkcję kosztów metody SFA, zanalizowali ubezpieczycieli działu ubezpieczeń non-life Angoli i Mozambiku w latach 2002-2012. Autorzy wykazali, że średnie wartości efektywności kosztowej ubezpieczycieli kształtowały się w granicach 30-44%.Alhassan i Biepke17 , wykorzystując roczne dane z 80 zakładów ubezpieczeń non-life w Afryce południowej w latach 2007-2012 oraz stosując techniki metody SFA, oszacowali ich efektywność kosztów oraz efektywność zysków.…”