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Multi-Directional Meta-Frontier DEA Model for Total Factor Productivity Growth in the Chinese Banking Sector: A Disaggregation Approach
Volume 31, Issue 1 (2020), pp. 185–204
Ning Zhu   Tomas Baležentis   Zhiqian Yu   Wenjie Wu  

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https://doi.org/10.15388/20-INFOR397
Pub. online: 23 March 2020      Type: Research Article      Open accessOpen Access

Received
1 March 2019
Accepted
1 November 2019
Published
23 March 2020

Abstract

Departing from conventional TFP index without variable-specific analysis, this paper applies a novel Malmquist productivity index on the basis of the multi-directional efficiency analysis to investigate not only the overall total factor productivity growth, but also the variable-specific productivity growth in the Chinese banking sector. Moreover, considering heterogenous types of banks, the metafrontier framework is taken into account. It is found that the total factor productivity tended to decline in the Chinese banking during 2005–2015 with technological change being the main source of regress. The large state-owned commercial banks performed better than the small-medium commercial banks in terms of total factor productivity growth.

References

 
Asmild, M., Matthews, K. (2012). Multi-directional efficiency analysis of efficiency patterns in Chinese banks 1997–2008. European Journal of Operational Research, 219, 434–441.
 
Asmild, M., Hougaard, J.L., Kronborg, D., Kvist, H.K. (2003). Measuring inefficiency via potential improvement. Journal of Productivity Analysis, 19(1), 59–76.
 
Asmild, M., Baležentis, T., Hougaard, J.L. (2016). Multi-directional productivity change: MEA-Malmquist. Journal of Productivity Analysis, 46, 109–119.
 
Avkiran, N.K. (2011). Association of DEA super-efficiency estimates with financial ratios: investigating the case for Chinese banks. Omega, 39, 323–334.
 
Battese, G.E., Rao, D.S.P. (2002). Technology potential, efficiency and a stochastic metafrontier function. International Journal of the Economics of Business, 1(2), 87–93.
 
Battese, G.E., Rao, D.S.P., CJ, O. (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.
 
Belas, J., Gavurova, B., Kocisova, K., Delibasic, M. (2018). The relationship between asset diversification and the efficiency of banking sectors in EU countries. Transformations in Business & Economics, 17, 479–496.
 
Berg, S.A., FR, F., Jansen, E.S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980–89. The Scandinavian Journal of Economics, 94, 211–228.
 
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.
 
Bogetoft, P., Hougaard, J.L. (1999). Efficiency evaluations based on potential (non-proportional) improvements. Journal of Productivity Analysis, 12(3), 233–247.
 
Cai, Y.Z., Guo, M.J. (2009). Empirical study on total factor productivity of China’s listed commercial banks. Economics Research Journal, 9, 52–65.
 
Casu, B., Girardone, C., Molyneux, P. (2004). Productivity change in European banking: A comparison of parametric and non-parametric approaches. Journal of Banking and Finance, 28(10), 2521–2540.
 
Casu, B., Ferrari, A., Zhao, T.S. (2013). Regulatory reform and productivity change in Indian banking. The Review of Economics and Statistics, 95(3), 1066–1077.
 
Chambers, R.G., Chung, Y., Färe, R. (1996). Benefit and distance function. Journal of Economic Theory, 70, 407–419.
 
Chang, T.P., Hu, J.L., Chou, R.Y., Sun, L. (2012). The source of bank productivity growth in China during 2002–2009: a disaggregation view. Journal of Banking and Finance, 36, 1997–2006.
 
Chen, K.H. (2012). Incorporating risk input into the analysis of bank productivity: application to the Taiwanese banking industry. Journal of Banking and Finance, 36(7), 1911–1927.
 
Chen, K.H., Yang, H.Y. (2011). A cross-country comparison of productivity growth using the generalised metafrontier Malmquist productivity index: with application to banking industries in Taiwan and China. Journal of Productivity Analysis, 35(3), 197–212.
 
Chiu, C.R., Chiu, Y.H., Chen, Y.C., Fang, C.L. (2016). Exploring the source of metafrontier inefficiency for various bank types in the two-stage network sstem with undesirable output. Pacific-Basin Finance Journal, 36, 1–13.
 
Chovancova, B., Gvozdiak, V., Rozsa, Z., Rahman, A. (2019). An exposure of commercial banks in the terms of an impact of government bondholding with the context of its risks and implications. Montenegrin Journal of Economics, 15(1), 173–188.
 
Coelli, T.J., Rao DS O’Donnell CJ, P., Battese, G.E. (2005). An Introduction to Efficiency and Productivity Analysis. Springer Science-Business Media, Inc., New York.
 
Degl’Innocenti, M., Kourtzidis, S.A., Sevic, Z., Tzeremes, N.G. (2017). Bank productivity growth and convergence in the European Union during the financial crisis. Journal of Banking and Finance, 75, 184–199.
 
Drake, L., Hall, M.J.B., Simper, R. (2006). The impact of macroeconomic and regulatory factors on bank efficiency: a non-parametric analysis of Hong Kong’s banking system. Journal of Banking and Finance, 27(5), 891–917.
 
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 and Finance, 72(S), 70–80.
 
Färe, R., Lovell, C.A.K. (1978). Measuring the technical efficiency of production. Journal of Economic Theory, 19(1), 150–162.
 
Färe, R., Grosskopf, S., Morris, M., Zhang, Z. (1994). Productivity growth technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.
 
Fethi, M.D., Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: a survey. European Journal of Operational Research, 204, 189–198.
 
Fujii, H., Managi, S., Matousek, R. (2014). Indian bank efficiency and productivity changes with undesirable outputs: a disaggregated approach. Journal of Banking and Finance, 38, 41–50.
 
Goldsmith, W. (1969). Financial Structure and Development. Yale University Press.
 
Grifell-Tatjé, E., Lovell, C.A.K. (1997). The sources of productivity change in Spanish banking. European Journal of Operational Research, 98(2), 364–380.
 
Gurly, J.G., Shaw, E.S. (1960). Money in a Theory of Finance. The Brookings Institution Press.
 
Hayami, Y. (1969). Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics, 51(3), 564–575.
 
Hayami, Y., Ruttan, V. (1970). Agricultural productivity differences among countries. American Economic Review, 60(5), 895–911.
 
Holvad, T., Hougaard, J.L., Kronborg, D., Kvist, H.K. (2004). Measuring inefficiency in the Norwegian bus industry using multi-directional efficiency analysis. Transportation, 31, 349–369.
 
Iannotta, G., Nocera, G., Sironi, A. (2013). The impact of government ownership on bank risk. Journal of Financial Intermediation, 22, 152–176.
 
Kaminskyi, A., Versal, N. (2018). Risk management of dollarization in banking: case of post-soviet countries. Montenegrin Journal of Economics, 14(3), 21–40.
 
Kevork, I.S., Pange, J., Tzeremes, P., Tzeremes, N.G. (2017). Estimating malmquist productivity indexes using probabilistic directional distances: an application to the European banking sector. European Journal of Operational Research, 261(3), 1125–1140.
 
King, G., Levine, R. (1993). Finance and growth: Schumpeter might be right. Quarterly Journal of Economics, 108(3), 717–738.
 
Koetter, M., Poghosyan, T. (2009). The identification of technology regimes in banking: implications for the market power-fragility nexus. Journal of Banking & Finance, 33(8), 1413–1422.
 
Kontolaimou, A., Tsekouras, K. (2010). Are cooperatives the weakest link in European banking? A non-parametric metafrontier approach. Journal of Banking and Finance, 34(8), 1946–1957.
 
Koutsomanoli-Filippaki, A., Margarits, D., Staikouras, C. (2012). Profit efficiency in the European union banking industry: a directional technology distance function approach. Journal of Productivity Analysis, 37(3), 277–293.
 
Kumar Sharma, S., Dalip, R. (2014). Efficiency and productivity analysis of Indian banking industry using Hicks-Moorsteen approach. International Journal of Productivity and Performance Management, 63(1), 57–84.
 
Kumbhakar, S.C., Wang, D. (2007). Economic reforms, efficiency and productivity in Chinese banking. Journal of Regulatory Economics, 32, 105–129.
 
Lee, C.C., Huang, T.H. (2016). Productivity changes in pre-crisis Western European banks: does scale effect really matter? The North American Journal of Economics and Finance, 36, 29–48.
 
Matthews, K., Zhang, N. (2010). Bank productivity in China 1997–2007: measurement and convergence. China Economic Review, 21, 617–628.
 
Matthews, K., Zhang, X., Guo, J. (2009). Nonperforming loans and productivity in Chinese banks, 1997–2006. Chin Economy, 42(2), 30–47.
 
O’Donnell, C.J. (2012). An aggregate quantity framework for measuring and decomposing productivity change. Journal of Productivity Analysis, 38, 255–272.
 
O’Donnell, C.J., Rao, D.S.P., Bettese, G.E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics, 34(2), 231–255.
 
Oh, D.H. (2010). A global Malmquist-Luenberger productivity index. Journal of Productivity Analysis, 34, 183–197.
 
Oh, D.H., Lee, J.D. (2010). A metafrontier approach for measuring Malmquist productivity index. Empirical Economics, 38(1), 47–64.
 
Park, K.H., Weber, W.L. (2006). A note on efficiency and productivity growth in the Korean banking industry, 1992–2002. Journal of Banking and Finance, 30(8), 2371–2386.
 
Pasiouras, F. (2008). International evidence on the impact of regulations and supervision on banks’ technical efficiency: an application of two-stage data envelopment analysis. Review of Quantitative Finance and Accounting, 30, 187–223.
 
Portela, M.C.A., Thanassoulis, E. (2010). Malmquist-type indices in the presence of negative data: an application to bank branches. Journal of Banking and Finance, 34(7), 1472–1483.
 
Radojicic, M., Savic, G., Jeremic, V. (2018). Measuring the efficiency of banks: the bootstrapped I-distance GAR DEA approach. Technological and Economic Development of Economy, 24(4), 1581–1605.
 
Song, M., Zhu, S., Wang, J., Zhao, J. (2020). Share green growth: Regional evaluation of green output performance in China. International Journal of Production Economics, 219, 152–163.
 
Sufian, F. (2009). The impact of off-balance sheet items on banks’ total factor productivity: empirical evidence from the Chinese banking sector, American Journal of Finance and Accounting, 1(3), 213–238.
 
Tone, K. (2001). A slack-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509.
 
Wang, K., Huang, W., Wu, J., Liu, Y.N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5–20.
 
Wang, S., Chen, M., Song, M. (2018). Energy constraints, green technological progress and business profit ratios: evidence from big data of Chinese enterprises. International Journal of Production Research, 56(8), 2963–2974.
 
Zeng, S., Xiao, Y. (2018). A method based on TOPSIS and distance measures for hesitant fuzzy multiple attribute decision making. Technological and Economic Development of Economy, 24(3), 905–919.
 
Zeng, S., Chen, S.M., Kou, L.W. (2019). Multiattribute decision making based on novel score function of intuitionistic fuzzy values and modified VIKOR method. Information Sciences, 488, 76–92.
 
Zhu, N., Wang, B., Wu, Y.R. (2015). Productivity, efficiency, and non-performing loans in the Chinese banking industry. Social Science Journal, 52, 468–480.
 
Zhu, Q., Wu, J., Song, M. (2018). Efficiency evaluation based on data envelopment analysis in the big data context. Computers & Operations Research, 98, 291–300.
 
Zhu, N., Wu, Y.R., Wang, B., Yu, Z.Q. (2019). Risk preference and efficiency in Chinese banking. China Economic Review, 53, 324–341.
 
Zieschang, K.D. (1984). An extended Farrell technical efficiency index. Journal of Economic Theory, 33, 387–396.

Biographies

Zhu Ning
ningzhu@scut.edu.cn

N. Zhu obtained his PhD in economics from Jinan University, China, in 2016. He visited the University of Copenhagen, Denmark, during 2014–2016. He is a distinguished research fellow at School of Economics and Commerce, South China University of Technology, China. His research interests include efficiency and productivity analysis, banking management.

Baležentis Tomas
tomas@laei.lt

T. Baležentis is a research professor at Lithuanian Institute of Agrarian Economics and a professor at Vilnius University (Lithuania). He holds PhD degrees from Vilnius University and University of Copenhagen. Dr. Baležentis has published over 120 papers in SSCI/SCI journals. The research activities of Dr. Baležentis span over multi-criteria decision making, agricultural economics, energy economics, and managerial economics. He has published in European Journal of Operational Research, Decision Sciences, Fuzzy Sets and Systems, Energy Economics, Energy Policy and Resource and Environmental Economics.

Yu Zhiqian
yzq_8866@hotmail.com

Z. Yu obtained her PhD in economics from Jinan University, China, in 2014. She visited Business School of Copenhagen, Denmark, during 2012–2013. She is currently an associate professor at School of Economics and Statistics, Guangzhou University, China. Her research interests include efficiency and productivity analysis, public economics.

Wu Wenjie
caswwj@foxmail.com

W. Wu obtained his PhD in economic geography from London School of Economics and Political Science, the UK, in 2013. He is currently a professor at College of Economics, Jinan University, China. His research interests include urban development, spatial economics.


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Keywords
data envelopment analysis total factor productivity multi-directional efficiency analysis metafrontier Chinese banking

Funding
The authors gratefully acknowledge financial supports from the National Social Science Foundation of China (19CJY061), the National Natural Science Foundation of China (71703040), the Humanities and Social Research Project of Ministry of Education of China (17YJC790215, 17JZD013, 18YJC790207), the Natural Science Foundation of Guangdong Province (2018A0303130230), and the Fundamental Research Funds for the Central Universities (2019MS079).

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