Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banks
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Altres documents de l'autoria: Tortosa-Ausina, Emili; Grifell-Tatje, E.; Armero, Carmen; Conesa, David
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
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http://dx.doi.org/10.1016/j.ejor.2006.11.035 |
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Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banksData de publicació
2008Editor
ElsevierISSN
0377-2217Cita bibliogràfica
European Journal of Operational Research, 184, 3, p. 1062-1084Tipus de document
info:eu-repo/semantics/articleParaules clau / Matèries
Resum
Hypothesis testing and statistical precision in the context of non-parametric efficiency and productivity measurement have been investigated since the early 1990s. Recent contributions focus on this matter through the ... [+]
Hypothesis testing and statistical precision in the context of non-parametric efficiency and productivity measurement have been investigated since the early 1990s. Recent contributions focus on this matter through the use of resampling methods-i.e., bootstrapping techniques. However, empirical evidence is still practically non-existent. This gap is more noticeable in the case of banking efficiency studies, where the literature is immense. In this work, we explore productivity growth and productive efficiency for Spanish savings banks over the (initial) post-deregulation period 1992-1998 using Data Envelopment Analysis (DEA) and bootstrapping techniques. Results show that productivity growth has occurred, mainly due to improvement in production possibilities, and that mean efficiency has remained fairly constant over time. The bootstrap analysis yields further evidence, as for many firms productivity growth, or decline, is not statistically significant. With regard to efficiency measurement, the bootstrap reveals that the disparities in the original efficiency scores of some firms are lessened to a great extent. © 2006 Elsevier B.V. All rights reserved. [-]
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