Chapter 3 Evaluating Efficiencies of Chinese Commercial Banks in the Context
3.2 Literature Review
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The rest of the paper is organized as follows. Section 2 briefly reviews the literature. Section 3 develops an economic model describing a bank’s multistage production processes that lead to the network SFA model. A copula-based likelihood function is then introduced, which embodies the two-stage production processes, on the one hand, and permits us to jointly estimate the production and cost frontiers, on the other. Section 4 defines relevant variables and displays their descriptive statistics.
Section 5 presents the main findings of the empirical results, while the last section concludes the paper.
3.2 Literature Review
During the past two decades, China has undertaken a series of fundamental reforms and restructured its core financial environments by removing so-called protective umbrellas in regulations and enforcing comprehensive government monitoring, market discipline, and corporate governance. A lot of attention has picked up, because the ongoing financial liberalization and related reforms and transformation are regarded as potential factors that have stimulated China’s impressive financial market development (Jiang et al, 2013). A clear and overall picture on the evolution of Chinese banking system during the period 1978-2008 can be obtained in the studies of Jiang et al. (2009) and Huang and Fu (2013). The impact of economic reforms and deregulation on efficiency in this industry has been widely studied by, e.g., Fu and Heffernan (2007), Kumbhakar and Wang (2007), Jiang et al. (2009), and Barros et al.
(2011).
Interest has recently been focused on the ownership-performance nexus of Chinese banks. Most of the previous findings suggest that state-owned banks (SOBs)
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underperform joint stock banks (JSBs). Early SOBs are usually criticized not driven by profit-oriented objective under government intervention which forces them hiring excess staffs for the retired military officers and offering excess lending to state-own enterprises without considering risk management. Fu and Heffernan (2008) find that joint-stock banks have higher X-efficiency than state-owned banks over the period 1985-2002. Berger et al. (2009) examine the cost and profit efficiency of 38 Chinese banks during the period 1994-2003 and conclude that the 4 large stated-owned banks are the least efficient, while foreign banks are the most efficient. Du and Girma (2011) examine the productivity growth and cost efficiency of Chinese banks during 1995-2001 and also find that joint-stock banks outperform state-owned banks. Similarly, Kumbhakar and Wang (2007), Ariff and Can (2008), Lin and Zhang (2009) and Asmild and Matthews (2012) confirm the inferiority of state-owned banks to other types of banks.
However, after a series of reforms and transformations of SOBs by stripping off large amounts of NPLs, engaging ownership reforms, performing public listing in Shanghai and Hong Kong stock exchanges to induce strict corporate governance and strategic alliance with foreign companies to absorb global management skill and experience, recent studies, e.g. Yao et al. (2008) and Zhu et al. (2014), provide some interesting but opposite conclusions that the operations of JSBs are not necessarily superior to those of SOBs. In fact, the obvious advantage of SOBs in the bank-based economy is their strong national wide physical branch networks which seem to surpass the aforementioned side effect in SOBs and finally enhance their operation efficiency.
Hence, the inconsistent results regarding to the ownership issue should verified with more relevant data and more advanced powerful method.
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There exist many studies on Chinese banks’ efficiency that apply different methods and obtain mixed results. Some of them employ SFA, e.g., Kumbhakar and Wang (2007), Fu and Heffernan (2007, 2008, 2009), Yao et al. (2007), Berger et al.
(2009, 2010), Jiang et al. (2009, 2013), Chen and Yang (2011), Du and Girma (2011), Huang and Fu (2013), Tan and Floros (2013), and Dong et al. (2014). Others examine Chinese banks’ efficiency using non-parametric techniques, i.e., DEA, as in Chen et al.
(2005), Ariff and Can (2008), Yao et al. (2008), Luo and Yao (2010), Barros et al. (2011), Asmild and Matthews (2012), Matthews (2013), Dong et al. (2014), and Zhu et al.
(2014). Appendix B. briefly outlines recent studies applying those two methods to probe the Chinese banking industry.
Although those approaches are well-established for measuring efficiency scores, they fail to identify the specific sources of inefficiency embedded in multistage production processes within an organization. To account for this multistage system, Färe and Grosskopf (2000) propose the network DEA model to gauge the overall performance and individual subsectors’ performance of an organization. Many applications and extensive discussions on this model can be found by Lewis and Sexton (2004), Prieto and Zofío (2007), Avkiran (2009), Bogetoft et al. (2009), Chen (2009), Chen et al. (2009), Kao (2009, 2014), Tone and Tsutsui (2009, 2014), Kao and Hwang (2008, 2010), Vaz et al. (2010), Holod and Lewis (2011), Yang and Liu (2012), Lewis et al. (2013), Matthews (2013), Wang et al. (2014), Avkiran (2015), and Zha et al.
(2015), to mention a few. Note that SFA plays no role in this area, which is the main concern of the current paper.
Network DEA is able to decompose all the production processes into a set of subsectors and accounts for internal linkage among those subsectors, whereby some
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outputs of a subunit are regarded as inputs of other subunits. In order to gain further insights into the technical efficiency at each stage, the model requires the availability of disaggregated data specific to inputs employed by different stages. If disaggregated data are unavailable, then one has to estimate the fractions of inputs consumed in each production process. This appears to be difficult to achieve in the context of DEA - e.g.
Holod and Lewis (2011) provide a novel idea to solve the deposit dilemma in the context of network DEA, but they are forced to simplify their model into a single production stage due to the lack of disaggregate data. To deal with this problem, Kao and Hwang (2010) treat the information technology (IT) budget as a shared input by two serial production processes, where the fractions of this budget used are subjectively restricted to be between 0.6 and 0.9. In this paper we propose an economic model that leads to the network SFA framework, characterizes the multistage network production processes, and estimates the fractions of shared inputs used by each production stage.
We attempt to establish a simultaneous equations model that describes the multistage production processes of a firm and allows for dependence among the composed errors of different stages. In this manner, the technical efficiency of each subsector can be evaluated.
To estimate the simultaneous equations model, we suggest employing the copula methods introduced by Sklar (1959), which are a useful tool to handle in a flexible way the co-movement between markets, risk factors, and other relevant variables studied in finance. Copula methods have been widely used in the field of finance - see, for example, Longin and Solnik (2001), Yang et al. (2009), and Steven and Ng (2009).
Several recent studies in the area of SFA have considered the dependence structure between composed errors, e.g. Smith (2008), Shi and Zhang (2011), Carta and Steel (2012), Lai and Huang (2013), Tsay et al. (2013), and Amsler et al. (2014). The salient
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feather of Lai and Huang (2013) is that the composed errors of different equations are correlated with each other. The employment of copula methods assists in deriving the joint distribution from the margins of several error components under a specific copula function. Lai and Huang (2013) apply the Gaussian copula to derive a likelihood function that is later maximized to yield parameter estimates of interest. They also stress that a biased estimator of technical efficiency may be produced if the dependency component is ignored.