The sources of bank productivity growth in China during 2002–2009: A disaggregation view

Chang, Tzu‐Pu; Hu, Jin-Li; Chou, Ray Y.; Sun, Lei

doi:10.1016/j.jbankfin.2012.03.003

Cited by 94 publications

(65 citation statements)

References 29 publications

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“…Later literatures also show that Luenberger index is more robust than Malmquist index since arithmetic index can also allow extreme values such as zero while a geometric index cannot [7]. Therefore, most up-to-date researchers recommend to use the Luenberger indicator instead of the Malmquist index [40][41][42]. For instance, Chang and Hu [7] used the Luenberger indicator to define and compute the total-factor energy productivity index for Chinese regions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, it can only be seen as measuring the dynamic change in total factor green productivity performance, not the specific carbon emissions performance changes. Chang et al [41] proposed an advanced approach named slack-based Luenberger indicator which is a non-radial approach to handle this shortcoming. For environmental studies, Zhou et al [43] proposed the non-radial directional distance function to deal with the nonzero slack variables problem.…”

Section: Literature Reviewmentioning

confidence: 99%

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Dynamic total factor carbon emissions performance changes in the Chinese transportation industry

2015

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Dynamic total factor carbon emissions performance changes in the Chinese transportation industry

2015

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“…Earlier work, however, has largely neglected the organizational heterogeneity of China's banking sector, consisting of an increasingly diversified financial system combining fully stateowned policy banks (PBs) with state-commercial banks with majority state ownership (SOCBs), joint stock banks with minority state shares (JSCBs), and collectively owned rural credit cooperatives (RCCs). Microlevel studies highlight differences in profitability and efficiency among these different banking institutions (Ariff and Can 2008;Berger et al 2009;Matthews and Zhang 2010;Chang et al 2012). We take these findings as our point of departure to explore China's finance-growth nexus by treating each organizational segment of the banking industry as an aggregate measure respon-sible for one share in China's lending portfolio.…”

Section: Introductionmentioning

confidence: 99%

Lending for growth? A Granger causality analysis of China’s finance–growth nexus

2016

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China's banking sector is dominated by four distinct organizational forms: policy banks (PBs), state-owned commercial banks (SOCBs), joint stock commercial banks (JSCBs), and rural credit cooperatives (RCCs). Economic analyses have especially focused on the development of bank efficiency and profitability over time. The equally important question, which of China's banking institutions promote economic growth, has not been explored using macroeconomic data. Our study uses a novel data set covering the period 1997-2008 and employs Granger causality tests to estimate the finance-growth nexus of each of these bank types. Our results show that SOCBs and RCCs do not Granger-cause GDP growth and that SOCBs even have a negative effect on manufacturing growth. By contrast, PBs and JSCBs promote economic growth.

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“…This is because, in practice, a bank uses a lot of multiple inputs to generate income in the form of profits. For analysis of banking efficiency, Ariff and Luc (2008) and Chang et al (2012) used labour and physical capital as inputs, whereas other earning assets (i.e., investments) and total loans were used as outputs. The other earning assets can be disaggregated into short-and long-term investments (Luo & Yao 2010).…”

Section: An Application To Efficiency Estimates Of Commercial Banks Imentioning

confidence: 99%

Quantitative efficiency assessment based on the dynamic slack-based network data envelopment analysis for commercial banks in Ghana

Zhou

Amowine

Huang

2018

SAJEMS

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Background: Gains in bank efficiency improvement have widely been regarded as one of the most effective and efficient means of ensuring sustainability of a financial system. Aim: This article proposes a relative dynamic two-stage network data envelopment analysis model for measurement of bank efficiency based on the slack-based measure. Setting: Twenty-seven banks in Ghana during the period of 2009–2014. Methods: By considering simultaneous processes within the framework of two-stage data envelopment analysis, the slack-based measure approach identifies the sources of inefficiency in the banks. Results: In the empirical analysis, non-performing loans are an undesirable output in one production process, which should also be treated as a carry-over factor; that is to say, some non-performing loans from the preceding year can be collected in the current year. The carry-over factors should be used to indicate the presence of performance gaps that exist in the banks. The proposed model was used to measure the efficiency of the 27 banks in Ghana during the period of 2009–2014. We also present useful suggestions for improvement in bank efficiency based on the empirical results. Conclusion: The 27 main commercial banks in Ghana are far from efficient. For all banks, the efficiency score in the second stage is much higher than that of the first stage. That means more attention should be paid to the first stage of production in order to increase the banks’ efficiency.

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The sources of bank productivity growth in China during 2002–2009: A disaggregation view

Cited by 94 publications

References 29 publications

Dynamic total factor carbon emissions performance changes in the Chinese transportation industry

Dynamic total factor carbon emissions performance changes in the Chinese transportation industry

Lending for growth? A Granger causality analysis of China’s finance–growth nexus

Quantitative efficiency assessment based on the dynamic slack-based network data envelopment analysis for commercial banks in Ghana

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