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Bank cost efficiency as distribution dynamics: controlling for specialization is important

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Abstract

This paper analyzes the dynamics of cost efficiency scores in the Spanish banking industry during the 1985—1995 period and the way in which such dynamics are influenced by specialization. Efficiency is estimated by a nonparametric approach, and a model of distribution dynamics has been applied to assess the evolution of efficiency scores over time. Results show that efficiency at the industry level (mean effciency) has increased importantly and that, considering how the entire distribution evolves, there are some important patterns that the mean and the standard deviation fail to uncover, such as multi-modality. In addition, more light is shed on efficiency dynamics when each firm’s output mix is controlled for, as efficiency scores tend to become closer much faster.

Suggested Citation

  • Emili Tortosa-Ausina, 2003. "Bank cost efficiency as distribution dynamics: controlling for specialization is important," Investigaciones Economicas, Fundación SEPI, vol. 27(1), pages 71-96, January.
    • Handle: RePEc:iec:inveco:v:27:y:2003:i:1:p:71-96
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    Cited by:

    1. Minzhi Wu & Emili Tortosa-Ausina & Paula Cruz-García, 2024. "The impact of diversification on the profitability and risk of Chinese banks: evidence from a semiparametric approach," Empirical Economics, Springer, vol. 67(6), pages 2565-2606, December.
    2. Jose Pastor & Lorenzo Serrano, 2006. "The Effect of Specialisation on Banks' Efficiency: An International Comparison," International Review of Applied Economics, Taylor & Francis Journals, vol. 20(1), pages 125-149.
    3. Minzhi Wu & Emili Tortosa-Ausina, 2020. "Bank Diversification and Focus in Disruptive Times: China, 2007–2018," Working Papers 2020/21, Economics Department, Universitat Jaume I, Castellón (Spain).
    4. Fadzlan SUFIAN, 2009. "Analysis Of The Relevance Of Non-Traditional Activities Items Upon The Efficiency Of Chinese Banks: A Non-Stochastic Frontier DEA Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 4, pages 83-102, November.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • L5 - Industrial Organization - - Regulation and Industrial Policy

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