Efficiency Determinants and Capacity Issues in Angolan Insurance Companies

Abstract

This paper describes a variety of approaches used to assess the efficiency of a sample of major insurance companies in Angola between 2003 and 2012. Starting out with the bootstrapping technique, several data envelopment analysis (DEA) estimates were generated, allowing the use of confidence intervals and bias correction in central estimates to test for significant differences in efficiency levels and input‐decreasing/output‐increasing potentials. Previous studies have focused on the measurement and explanation of the factors affecting the performance rather than the prediction. The use of neural networks combined with DEA results as part of an attempt to produce a model for insurance companies’ performance with effective predictive ability is investigated. The findings indicate that older insurance companies with Portuguese origin tend to be more efficient. Results also suggest that opportunities for accommodating future demand appear to be scarce.

Why Do Property‐Liability Insurers Destroy Liquidity? Evidence from South Africa

“…This differences in the production cycle and balance sheet structure makes it imperative to undertake this current empirical investigation to expand on the understanding of liquidity creation in insurance markets. Most importantly, evidence from emerging insurance markets appears virtually non‐existent, as microeconomic analysis of insurance markets in Africa have mostly been limited to efficiency (Barros et al ., ; Ansah‐Adu et al ., ; Barros and Wanke, ; Alhassan et al , ; Alhassan and Biekpe, ), profitability (Akotey et al, ; Asare et al ., ), competition (Alhassan and Biekpe, , 2018). The current study provides the first empirical estimate of liquidity de‐creation behaviour and explanatory factors in a developing insurance market.…”

Cost efficiency of insurance firms in Ghana

“…Most of these studies have analyzed efficiency in single country as well as across countries in Africa. The studies by Chaffai and Ouertani (), Barros and Obijiaku (), Barros, Caporale, and Ibiwoye (), Ansah‐Adu et al (), Alhassan and Addison (), Barros, Dumbo, and Wanke (), Barros et al (), Wasseja and Mwenda (), Alhassan and Biekpe (), and Alhassan and Biekpe () among others have focused on individual African countries. For instance, Barros and Obijiaku () and Barros et al () studied the efficiency of insurance companies in Nigeria.…”

“…Their two‐stage regression model confirmed that market share and firm size were positively related to efficiency whereas the ratio of equity to total invested asset was negatively related to cost efficiency in the Ghanaian insurance industry. Barros et al () described a variety of approaches used to assess the efficiency of a sample of major insurance companies in Angola between 2003 and 2012. Barros et al () combined neural networks with DEA results as part of an attempt to produce a model for insurance companies' performance with effective predictive ability.…”

“…Barros et al () described a variety of approaches used to assess the efficiency of a sample of major insurance companies in Angola between 2003 and 2012. Barros et al () combined neural networks with DEA results as part of an attempt to produce a model for insurance companies' performance with effective predictive ability. Their findings indicate that older insurance companies with Portuguese origin tend to be more efficient.…”

Loss reserving behaviour in insurance markets: Evidence from Mauritius

“…The insurance literature in Africa has mostly focused on efficiency analysis (Barros, Ade Ibiwoye, & Managi, for Nigeria; Ansah‐Adu, Andoh, & Abor, for Ghana; Barros & Wanke, for Mozambique); Barros, Dumbo, & Wanke, for Angola and Alhassan & Biekpe, , for South Africa and financial performance (Akotey, Sackey, Amoah, & Manso, , Alhassan, Addisson, & Asamoah, , Asare, Alhassan, Asamoah, & Ntow‐Gyamfi, for Ghana) and competition (Alhassan & Biekpe, , for South Africa).…”