What contributes to total factor productivity growth in the Chinese banking sector?



Published Mar 1, 2018


We propose a new metafrontier, non-radial, biennial Luenberger productivity indicator to evaluate the total factor productivity growth of the Chinese banking sector, during the period of 2004–2012. The bootstrapping approach is also taken into account to introduce the statistical inference of the total factor productivity, and its components. It is found that the overall Chinese banking sector operated well with an average growth rate of 5.4%, where technological progress was the driving force promoting the development of the Chinese banking sector during the earlier studied period, and efficiency gains outperformed technological progress during the later studied period. We investigated three banking groups, state-owned commercial banks and joint-stock commercial banks depending on their technological progress, but city commercial banks were dominated by efficiency gains. Regarding the productivity growth gap, the metafrontier productivity growth gap and efficiency change gap appeared to show gradual convergences, but the technological change gap maintained the width at a certain extent.

Copyright © 2018 The Author(s). Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract 23 | PDF Downloads 18



total factor productivity, metafrontier, biennial technology, Directional distance function, data envelopment analysis, bootstrapping approach

Avkiran, N. K. 2011. Association of DEA super-efficiency estimates with financial ratios: iInvestigating the case for Chinese banks, Omega 39(3): 323–334. https://doi.org/10.1016/j.omega.2010.08.001

Barros, C. P.; Managi, S.; Matosek, R. 2012. The technical efficiency of Japanese banks: Nnon-radial directional performance measurement with undesirable output, Omega 40(1): 1–8. https://doi.org/10.1016/j.omega.2011.02.005

Battese, G. E.; Rao, D. S. P. 2002. Technology potential, efficiency and a stochastic metafrontier function, International Journal of Business and Economics 1(2): 87–93.

Battese, G. E.; Rao, D. S. P.; O’Donnell, C. J. 2004. A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies, Journal of Productivity Analysis 21 (1): 91–103. https://doi.org/10.1023/B:PROD.0000012454.06094.29

Ben Neceur, S.; Ben-Khedhiri, H.; Casu, B. 2009. What drives the efficiency of selected MENA banks? A meta-frontier analysis, No. 499, Working Papers from Economic Research Forum. Available from Internet: http://erf.org.eg/wp-content/uploads/2014/08/499.pdf

Berger, A. N.; Humphrey, D. B. 1997. Efficiency of financial institutions: International survey and directions for future research, European Journal of Operational Research 98(2): 175–212. https://doi.org/10.1016/S0377-2217(96)00342-6

Bos, J. W. B.; Schmiedel, H. 2007. Is there a single frontier in a single European banking market? Journal of Banking & Finance 31 (7): 2081–2102. https://doi.org/10.1016/j.jbankfin.2006.12.004

Casu, B.; Ferrari, A.; Zhao, T. S. 2013. Regulatory reform and productivity change in Indian banking, Review of Economics and Statistics 95(3): 1066–1077. https://doi.org/10.1162/REST_a_00298

Chambers, R. G.; Chung, Y.; Färe, R. 1996. Benefit and distance function, Journal of Economic Theory 70(2): 407–419. https://doi.org/10.1006/jeth.1996.0096

Chang, T. P.; Hu, J. L.; Chou, R. Y.; Sun, L. 2012. The source of bank productivity growth in China during 2002–2009: Aa disaggregation view, Journal of Banking & Finance 36(7): 1997–2006. https://doi.org/10.1016/j.jbankfin.2012.03.003

Chen, K. H.; Yang, H. Y. 2011. A cross-country comparison of productivity growth using the generalised metafrontier Malmquist productivity index: Wwith application to banking industries in Taiwan and China, Journal of Productivity Analysis 35(3): 197–212. https://doi.org/10.1007/s11123-010-0198-7

Drake, L.; Hall, M. J. B.; Simper, R. 2006. The impact of macroeconomic and regulatory factor on bank efficiency: Aa non-parametric analysis of Hong Kong’s banking system, Journal of Banking & Finance 27(5): 891–917. https://doi.org/10.1016/j.jbankfin.2005.03.022

Duygun, M.; Sena, V.; Shaban M. 2016. Trademarking activities and total factor productivity: Some evidence for British commercial banks using a metafrontier approach, Journal of Banking & Finance 72(S): 70–80. https://doi.org/10.1016/j.jbankfin.2016.04.017

Färe, R.; Grosskopf, S.; Norris, M.; Zhang, Z. 1994. Productivity growth, technical progress, and efficiency change in industrialized countries, American Economic Review 84(1): 66–83.

Fu, T.-T.; Juo, J.-C.; Chiang, H.-C.; Yu, M.-M.; Huang, M.-Y. 2016. Risk-based decompositions of the meta profit efficiency of Taiwanese and Chinese banks, Omega 62: 34–46. https://doi.org/10.1016/j.omega.2015.08.007

Fujji, H.; Managi, S.; Matousek, R. 2014. Indian bank efficiency and productivity changes with undesirable outputs: Aa disaggregated approach, Journal of Banking & Finance 38(1): 41–50. https://doi.org/10.1016/j.jbankfin.2013.09.022

Hayami, Y. 1969. Sources of agricultural productivity gap among selected countries, American Journal of Agricultural Economics 51 (3): 564–575. https://doi.org/10.2307/1237909

Hayami, Y.; Ruttan, V. M. 1970. Agricultural productivity differences among countries, American Economic Review 60 (5): 895–911.

Huang, T. H.; Chiang, L. C.; Chen, K. C. 2011. An empirical study of bank efficiencies and technology gaps in European banking, The Manchester School 79(4): 839–860. https://doi.org/10.1111/j.1467-9957.2010.02178.x

Kontolaimou, A.; Kostas, T. 2010. Are cooperatives the weakest link in European banking? A non-parametric metafrontier approach, Journal of Banking & Finance 34(8): 1946–1957. https://doi.org/10.1016/j.jbankfin.2010.01.003

Kumbhakar, S. C.; Wang, D. 2007. Economic reforms, efficiency and productivity in Chinese banking, Journal of Regulatory Economics 32(2): 105–129. https://doi.org/10.1007/s11149-007-9028-x

Lee, C.-C.; Huang, T.-H. 2017. Cost efficiency and technological gap in Western European banks: A stochastic metafrontier analysis, International Review of Economics & Finance 48: 161–178. https://doi.org/10.1016/j.iref.2016.12.003

Matthews, K.; Zhang, N. 2010. Bank productivity in China 1997–2007: measurement and convergence, China Economic Review 21(4): 617–628. https://doi.org/10.1016/j.chieco.2010.06.004

Matthews, K.; Zhang, X.; Guo, J. 2009. Nonperforming loans and productivity in Chinese banks, 1997–2006, The Chinese Economy 42(2): 30–47. https://doi.org/10.2753/CES1097-1475420202

O’Donnell, C. P.; Rao, D. S. P.; Battese, G. E. 2008. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios, Empirical Economics 34(2): 231–255. https://doi.org/10.1007/s00181-007-0119-4

Oh, D. H.; Lee, J. D. 2010. A metafrontier approach for measuring Malmquist productivity index, Empirical Economics 38(1): 47–64. https://doi.org/10.1007/s00181-009-0255-0

Pasiouras, F. 2008. International evidence on the impact of regulations and supervision on banks’ technical efficiency: Aan application of two-stage data envelopment analysis, Review of Quantitative Finance and Accounting 30(2): 187–223. https://doi.org/10.1007/s11156-007-0046-7

Pastor, J.; Asmild, M.; Lovell, C. A. K. 2011. The biennial Malmquist productivity change index, Socio-Economic Planning Sciences 45(1): 10–15. https://doi.org/10.1016/j.seps.2010.09.001

Pastor, J.; Lovell, C. A. K. 2005. A global Malmquist productivity index, Economics Letters 88(2): 266–271. https://doi.org/10.1016/j.econlet.2005.02.013

Portela, M.; Thanassoulis, E. 2010. Malmquist-type indices in the presence of negative data: Aan application to bank branches, Journal of Banking & Finance 34(7): 1472–1483. https://doi.org/10.1016/j.jbankfin.2010.01.004

Ray, S. C.; Desli, E. 1997. Productivity growth, technical progress and efficiency change in industrialized countries: comment, American Economic Review 87(5): 1033–1039.

Simar, L.; Wilson, P. 1998. Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models, Management Science 44(1): 49–61. https://doi.org/10.1287/mnsc.44.1.49

Sufian, F. 2009. The impact of off-balance sheet items on banks’ total factor productivity: Eempirical evidence from the Chinese banking sector, American Journal of Finance and Accounting 1(3): 213–238. https://doi.org/10.1504/AJFA.2009.026482

Tone, K. 2001. A slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research 130 (3): 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5

Tulkens, H.; Vanden Eeckaut, P. 1995. Non-parametric efficiency, progress and regress measures for panel data: methodological aspects, European Journal of Operational Research 80(3): 474–499. https://doi.org/10.1016/0377-2217(94)00132-V

Zhu, N.; Wang, B.; Wu, Y. R. 2015. Productivity, efficiency, and non-performing loans in the Chinese banking industry, The Social Science Journal 52(4): 468–480. https://doi.org/10.1016/j.soscij.2014.10.003