منابع رشد بهره وری بانک های چین در طول 2002-2009: مشاهده توده ای
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|11772||2012||10 صفحه PDF||سفارش دهید||8730 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Banking & Finance, Volume 36, Issue 7, July 2012, Pages 1997–2006
This study investigates the sources of bank productivity growth in China over the period 2002–2009. In order to perform this research, we propose an advanced index – input slack-based productivity index (ISP) – a model that disaggregates total factor productivity growth into each input productivity change. Funds, capital, and employees are chosen as the inputs, whereas loans and other earning assets are outputs in this study. Our results show that technological gains transcend the efficiency regressions and result in total factor productivity growth. More specifically, technical progress in capital productivity reveals the dominant force behind the total factor technical change and productivity improvement. In addition, this paper uses these disaggregation terms to find out the competitive advantages and disadvantages of input usages for each Chinese bank. These findings indicate that the ISP index provides more insights than traditional total factor productivity indices.
In the past three decades, China’s banking system has reformed gradually and gained remarkable successes in many respects. The total assets of the banking industry are over RMB 60 trillion, or 300 times that in 1978.1 In November 2009 the capital adequacy ratio and the provision coverage of the banking industry were over 10% and 150%, respectively. Chinese banks in recent years have raised their importance in the world banking system. For example, Industrial and Commercial Bank of China, China Construction Bank, Agricultural Bank of China, and Bank of China are four of the largest 10 banks in the world. Moreover, financial reforms have made efficiency and productivity improvements in the banking sector (Chen et al., 2005 and Matthews et al., 2009). This paper investigates the total factor productivity (TFP) changes and disaggregates the sources of productivity change in China’s banking industry from 2002 to 2009. This research period is meaningful for Chinese banks, because China has entered the World Trade Organization (WTO) in December 2001. In addition, China’s ‘Big Four’ state-owned banks (SOBs) have been partially privatized to take on minority foreign ownership since 2005. However, the academic literature related to bank productivity mainly focuses on US and European banks, using the Malmquist productivity index and Luenberger productivity index approaches. One of the first studies to investigate productivity change in the banking industry is Berg et al. (1992), who employ the Malmquist index for productivity growth and find that the source of productivity growth is efficiency improvement in Norway’s banks during 1980–1989. Other evidence indicates that productivity growth is mainly driven by technical change for the American (Alam, 2001 and Mukherjee et al., 2001), European, and Japanese banks (e.g., Casu et al., 2004, Koutsomanoli-Filippaki et al., 2009, Barros et al., 2010 and Assaf et al., 2011) by applying the Malmquist index or Luenberger index. However, only a few research studies have taken a look at the productivity growth of Chinese banks, such as Kumbhakar and Wang, 2007 and Matthews et al., 2009 and Matthews and Zhang (2010). These studies generally conclude that a positive TFP growth is dominantly driven by technical progress in China’s banking industry and the TFP growth rate of joint-stock banks (JSBs) is higher than SOBs. In summary, prior literature adopts the Malmquist productivity index (MPI) or Luenberger productivity index (LPI) to investigate the change of TFP, efficiency change, and technical change. Unfortunately, these two indices are aggregative and do not simultaneously deal with the TFP growth and the productivity change of a single factor under a total factor framework, meaning insights may be lacking if we want to investigate the productivity change of one particular factor among all input factors (such as labor, capital, and fund inputs). This paper tries to overcome the disadvantage of the total factor productivity index and introduces an index to measure the productivity change of an individual factor under a total factor framework. The proposed index herein, the so-called input slack-based productivity index (ISP), uses a Färe–Lovell efficiency measure to extend the traditional Luenberger productivity index and finds the strongly efficient vector for each input. This index then can be decomposed into particular input efficiency change and input technical change, meaning that we can discuss the sources of individual input productivity. Furthermore, we show that the TFP change is the average of the productivity change of an individual input. It is meaningful that we can explore the sources of each bank’s TFP growth, efficiency change, and technical progress. The remainder of this paper is organized as follows. Section 2 reviews financial reform in China’s banking industry and the literature on efficiency and productivity improvements of Chinese banks. Section 3 illustrates our proposed total factor input productivity index. Section 4 interprets the data sources and variables’ descriptions. Section 5 provides the empirical results and Section 6 concludes this paper.
نتیجه گیری انگلیسی
This paper investigates the sources of productivity growth for 19 Chinese banks during the period 2002–2009. Because employees, funds, and capital are the main resources (inputs) within a bank’s operation, this study also analyzes the productivity changes of these inputs. Unfortunately, commonly-used productivity measures, i.e., Luenberger and Malmquist productivity indices, are aggregated indices that do not understand the productivity changes of each input factor directly. Therefore, this paper proposes an advanced measure – input slack-based productivity index (ISP) – which combines the feature of the Färe–Lovell efficiency measure into the Luenberger productivity index, to deal with our research topic. The proposed ISP index has two advantages over traditional indices. First, ISP rapidly calculates total factor productivity growth and decomposes TFP growth into the productivity changes of each input. Second, ISP measures TFP growth as the arithmetic mean of each input’s productivity change. Thus, we find out the major forces behind TFP growth. The empirical findings are briefly summarized as follows. First, from the viewpoint of China’s whole banking industry, our results present that the industry gains total factor productivity growth with a total of 29.84% over the research period. It is found that the main force behind TFP growth is attributed to technology progress with a total of 40.84%. More specifically, our ISP index shows that the technical improvement of capital productivity is the major source of Chinese banks’ TFP growth. Second, with respect to the bank group level, joint-stock banks reveal the highest TFP growth rate, followed by city commercial banks and state-owned banks. Again, the TFP growth of joint-stock banks is mainly driven by capital productivity enhancement. Third, comparing to other Chinese banks, Bank of Beijing gains the highest TFP growth rate with an average of 8.12% annually. Bank of Beijing also presents relatively higher growth rates and efficiency levels in all input usages, especially for labor input. This advanced index herein can accordingly provide more useful insights than traditional productivity indices. We consider that the ISP index cannot only be used to examine banking issues, but also be applied to other research topics that target the disaggregation terms of TFP growth. We do suggest that the ISP index can be improved and extended through some aspects of future research. For example, this paper does not analyze what determinants affect the fluctuations of those disaggregation terms of TFP and input productivity growths, which should be an interesting topic for any following works. Technically, the ISP index proposed in this study is structured based on the concept of constant-to-scale (CRS). More fascinating decomposition terms can be accomplished if future studies extend the ISP index to a variable return to scale assumption.