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Technical Efficiency in the Angolan Banking Sector with the B-convexity Model

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  • Carlos P. Barros
  • Qi Bin Liang
  • Nicolas Peypoch

Abstract

This paper analyses technical efficiency in Angolan banks from 2005 to 2010 with an innovative production frontier model, the B -convexity model. The intermediate approach is adopted. It is observed that the efficiency increases over the observation period, according to the international experience, market share and local markets. Policy implications indicate that competition and governance should be promoted in order to increase efficiency.

Suggested Citation

  • Carlos P. Barros & Qi Bin Liang & Nicolas Peypoch, 2014. "Technical Efficiency in the Angolan Banking Sector with the B-convexity Model," South African Journal of Economics, Economic Society of South Africa, vol. 82(3), pages 443-454, September.
    • Handle: RePEc:bla:sajeco:v:82:y:2014:i:3:p:443-454
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    Cited by:

    1. Wanke, Peter & Azad, M.D. Abul Kalam & Barros, C.P., 2016. "Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach," Research in International Business and Finance, Elsevier, vol. 36(C), pages 485-498.
    2. Wanke, Peter & Maredza, Andrew & Gupta, Rangan, 2017. "Merger and acquisitions in South African banking: A network DEA model," Research in International Business and Finance, Elsevier, vol. 41(C), pages 362-376.
    3. Chen, Zhongfei & Matousek, Roman & Wanke, Peter, 2018. "Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines☆," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 71-86.
    4. Peter Wanke & Carlos P. Barros & Md. Abul Kalam Azad & Dercio Constantino, 2016. "The Development of the Mozambican Banking Sector and Strategic Fit of Mergers and Acquisitions: A Two†Stage DEA Approach," African Development Review, African Development Bank, vol. 28(4), pages 444-461, December.
    5. Coert Erasmus, 2014. "An Empirical Study of Bank Efficiency in South Africa Using the Standard and Alternative Approaches to Data Envelopment Analysis (DEA)," Journal of Economics and Behavioral Studies, AMH International, vol. 6(4), pages 310-317.
    6. Carlos Pestana Barros & Silvestre Dumbo & Peter Wanke, 2014. "Efficiency Determinants and Capacity Issues in Angolan Insurance Companies," South African Journal of Economics, Economic Society of South Africa, vol. 82(3), pages 455-467, September.
    7. Carlos Pestana Barros & Zorro Mendes, 2016. "Assessing the competition in Angola’s banking industry," Applied Economics, Taylor & Francis Journals, vol. 48(30), pages 2785-2791, June.
    8. Peter Wanke & Carlos Barros & Nkanga Pedro João Macanda, 2016. "Predicting Efficiency in Angolan Banks: A Two-Stage TOPSIS and Neural Networks Approach," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 461-483, September.
    9. Barros, C.P. & Emrouznejad, Ali, 2016. "Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banksAuthor-Name: Wanke, Peter," European Journal of Operational Research, Elsevier, vol. 249(1), pages 378-389.
    10. Carlos Pestana Barros & Emanuel Reis Leão & Nkanga Pedro João Macanda & Zorro Mendes, 2016. "A Bayesian Efficiency Analysis of Angolan Banks," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 484-498, September.

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    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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