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.
Highlights
This study paper aims to contribute to the literature by analysing the efficiency of a sample of African insurance companies from Angola and Mozambique, as internalisation is a growing trend in this sector (Outreville, 2008)
Latent class models usually classify observations by reducing group variance in order to maximise the value of the total likelihood function
High lambda values for latent class model estimations tell us that randomness is less important than inefficiency, when we want to explain the distance of insurance companies to the frontier
Summary
This study paper aims to contribute to the literature by analysing the efficiency of a sample of African insurance companies from Angola and Mozambique, as internalisation is a growing trend in this sector (Outreville, 2008). There are two major approaches to estimating efficiency levels with respect to productive frontiers: the parametric (stochastic frontier model - SFA) and the non-parametric approach (Data Envelopment Analysis - DEA). While the former is based on econometric techniques and has to specify a functional form for production technology, the latter does not demand the a priori use of a functional form. It is capable of handling multiple outputs. The non-parametric approach, presents some restrictions, such as the vulnerability noted in its outcomes, which are a result of deviant observations
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