Finite Mixture Model

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.

[1]  Antonio F. Amores,et al.  Firm efficiency, industry performance and the economy: three-way decomposition with an application to Andalusia , 2014, Efficiency and Input-Output Analyses.

[2]  C. Lovell,et al.  Stochastic Frontier Analysis: Frontmatter , 2000 .

[3]  J. Outreville Foreign Affiliates of the Largest Insurance Groups: Location-Specific Advantages , 2008 .

[4]  C. Uche Government Ownership of Insurance Companies in Nigeria: A Critique , 1999 .

[5]  W. Meeusen,et al.  Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error , 1977 .

[6]  Carlos Pestana Barros,et al.  Evaluating the Efficiency and Productivity of Insurance Companies with a Malmquist Index: A Case Study for Portugal , 2005 .

[7]  Luis R. Murillo-Zamorano Economic Efficiency and Frontier Techniques , 2004 .

[8]  M. Luhnen Determinants of Efficiency and Productivity in German Property-Liability Insurance: Evidence for 1995–2006 , 2009 .

[9]  B. Mahlberg,et al.  Effects of the single market on the Austrian insurance industry , 2003 .

[10]  A. Teixeira,et al.  Corruption and multinational companies’ entry modes. Do linguistic and historical ties matter? , 2012 .

[11]  S. Diacon,et al.  Size and Efficiency in European Long–term Insurance Companies: An International Comparison , 2002 .

[12]  Martin Grace,et al.  X-Efficiency in the US life insurance industry☆ , 1993 .

[13]  Randy I. Anderson,et al.  Production Efficiency in the Austrian Insurance Industry: A Bayesian Examination , 2004 .

[14]  J. Cummins,et al.  Mergers & Acquisitions in the U.S. Property-Liability Insurance Industry: Productivity and Efficiency Effects , 2007 .

[15]  Weili Lu,et al.  Demutualisation, Control and Efficiency in the U.S. Life Insurance Industry , 2011 .

[16]  Martin Eling,et al.  The Performance of Microinsurance Programs: A Data Envelopment Analysis , 2011 .

[17]  Pensions in North Africa: The Need for Reform , 2003 .

[18]  F. Leisch FlexMix: A general framework for finite mixture models and latent class regression in R , 2004 .

[19]  Thomas Url,et al.  Single Market effects on productivity in the German insurance industry , 2010 .

[20]  M. Adams,et al.  The Cost Efficiency of Takaful Insurance Companies , 2010 .

[21]  J. Cummins,et al.  Productivity and Technical Efficiency in the Italian Insurance Industry , 1996 .

[22]  Carlos Pestana Barros,et al.  Efficiency in the Greek insurance industry , 2010, Eur. J. Oper. Res..

[23]  H. Fukuyama Investigating productive efficiency and productivity changes of Japanese life insurance companies , 1997 .

[24]  C. O'Brien,et al.  Market structure and the efficiency of European insurance companies: A stochastic frontier analysis , 2008 .

[25]  S. Steiner,et al.  Participation in Micro Life Insurance and the Use of Other Financial Services in Ghana , 2011 .

[26]  A. Ibiwoye,et al.  Does National Health Insurance Promote Access to Quality Health Care? Evidence from Nigeria , 2008 .

[27]  Olajumoke Olaosebikan The Determinants of the Profitability of Micro-Life Insurers in Nigeria , 2013 .

[28]  Carlos Pestana Barros,et al.  A Malmquist Index for the Greek Insurance Industry , 2010 .

[29]  P. Pestieau,et al.  Productive performance of the French insurance industry , 1993 .

[30]  Fabio Bertoni,et al.  The Productivity of European Life Insurers: Best-Practice Adoption vs. Innovation , 2011 .

[31]  Mohamed Ismail Performance of Data Envelopment and Stochastic Frontier Models , 2012 .

[32]  Pongpitch Petchsakulwong,et al.  The Impact of Corporate Governance on the Efficiency Performance of the Thai Non-Life Insurance Industry , 2010 .

[33]  H. Varian Intermediate Microeconomics: A Modern Approach , 1987 .

[34]  Mary A. Weiss,et al.  Organizational Form and Efficiency: The Coexistence of Stock and Mutual Property-Liability Insurers , 1999 .

[35]  J. David Cummins,et al.  Measuring cost efficiency in the property-liability insurance industry☆ , 1993 .

[36]  G. Battese,et al.  ESTIMATION OF A PRODUCTION FRONTIER MODEL: WITH APPLICATION TO THE PASTORAL ZONE OF EASTERN AUSTRALIA , 1977 .

[37]  Mary A. Weiss,et al.  Analyzing Firm Performance in the Insurance Industry Using Frontier Efficiency Methods , 1998 .

[38]  C. Barrett,et al.  Designing Index‐Based Livestock Insurance for Managing Asset Risk in Northern Kenya , 2013 .

[39]  J. Cummins,et al.  Comparison of Frontier Efficiency Methods: An Application to the U.S. Life Insurance Industry , 1998 .

[40]  S. Kumbhakar,et al.  Efficiency measurement using a latent class stochastic frontier model , 2004 .

[41]  J. Cummins,et al.  The effect of organizational structure on efficiency: Evidence from the Spanish insurance industry , 2004 .

[42]  M. Eling,et al.  Efficiency in the international insurance industry: A cross-country comparison , 2010 .

[43]  A. U.S.,et al.  FORMULATION AND ESTIMATION OF STOCHASTIC FRONTIER PRODUCTION FUNCTION MODELS , 2001 .

[44]  William H. Greene,et al.  Reconsidering heterogeneity in panel data estimators of the stochastic frontier model , 2005 .

[45]  W. Greene Distinguishing between Heterogeneity and Inefficiency: Stochastic Frontier Analysis of the World Health Organization S Panel Data on National Health Care Systems , 2003, Health economics.

[46]  G. Battese,et al.  Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data , 1988 .

[47]  P. Mostert,et al.  The influence of lengTh of relaTionship, gender and age on The relaTionship inTenTion of shorT-Term insurance clienTs: an exploraTory sTudy , 2011 .