What Causes the Efficiency and the Technology Gap Under Different Ownership Structures in the Chinese Banking Industry?

Lee, Chi‐Chuan; Huang, Tai–Hsin

doi:10.1111/coep.12409

Cited by 15 publications

(4 citation statements)

References 81 publications

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“…These results were in line with many previous studies focused on analyzing cost efficiency of Chinese banks, such as Chen et al [28]. Nevertheless, our findings were contrary to Lee and Huang [56], who showed that JSCBs and RCBs were more cost efficient compared with CCBs and the four biggest SOCBs. The different conclusions may have been due to the different sample of banks selected, different methodologies applied and different periods covered.…”

Section: The Lerner Index (Li) and Scale Economies (Sc)supporting

confidence: 89%

A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry

Liu

Rahman

et al. 2020

Mathematics

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The paper develops a simultaneous equations stochastic frontier model (SFM) with dependent random noise and inefficiency components of individual equations as well as allowing dependence across all equations of the model using copula functions. First, feasibility of our developed model was verified via two simulation studies. Then the model was applied to assess cost efficiency and market power of the banking industry of China using a panel data of 37 banks covering the period 2013–2018. Results confirmed that our simultaneous SFM with dependent random noise and inefficiency components outperformed its predecessor, which is a simultaneous SFM with dependent composite errors but with independent random noise and inefficiency components of individual SFMs as well as the conventional single-equation SFM. Apart from the statistical and computational superiority of our developed model, we also see that Chinese banks in general have a high level of cost efficiency and that competition in the banking industry of China mainly exists in state-owned banks and joint stock banks. Presence of economies of scales as well as diseconomies of scales were found in different banks. Also, the state-owned banks embraced most sophisticated technologies thereby allowing them to operate with the highest level of cost efficiency.

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Section: The Lerner Index (Li) and Scale Economies (Sc)supporting

confidence: 89%

A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry

Liu

Rahman

et al. 2020

Mathematics

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“…To control the effects of bank characteristics on bank competition, we follow the banking literature to incorporate various control variables. Firstly, we control for bank efficiency, which assessed through cost efficiency, as indicated by prior studies (Lee et al , 2021; Lee and Huang, 2019; Girardone et al , 2004; Košak et al , 2009; Phan et al , 2016). This metric gauges the variance in costs between a bank’s actual expenditures and the optimal costs of comparable outputs, assuming cost minimisation and encompassing both technical and allocative efficiency (Fungáčová et al , 2013; Abid and Goaied, 2017; Kallel et al , 2019).…”

Section: Methodsmentioning

confidence: 99%

Banking competition in Indonesia: does Fintech lending matters?

Dilla,

Shahrin,

Zainir

2024

JFEP

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Purpose This paper aims to examine how the rise of financial technology (Fintech) lending affects bank competition. Moreover, this study also identifies the structure of Indonesian commercial banking sector and the different behaviour of competition among bank groups (based on their size, type and ownership) and the joint impact of COVID-19 due to the rise of Fintech lending. Design/methodology/approach Using an unbalanced panel data set of 118 commercial banks in Indonesia over the period 2018–2022, both static panel and 2SLS/IV data analysis were used and found that random effect model is the best model. Findings The results show that the Indonesian commercial banking sector can be considered as monopolistic competition. Moreover, using the Lerner index reveals that the entry of the Fintech lenders increases bank competition. Furthermore, there were different responses to the impact of Fintech lending on bank competition among state-owned banks, private banks, regional development banks and foreign banks. Greater efficiency and stability lead to greater market power. In the meantime, higher level of asset growth, capitalisation and cost-to-income ratio increase the competition. Lastly, higher bank credit growth and lower inflation boost overall bank competitiveness. Practical implications This study highlights some policy recommendations for commercial banks to be aware of the coming of Fintech lenders because they have started to increase the market competition. The government should create a more collaborative ecosystem between banks and Fintech lending to anticipate unhealthy competition. Originality/value This study will contribute to the literature by expanding the determinants of bank competition by considering the rise of Fintech lending in the market.

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“…Este enforque se emplea en el caso en el que las embarcaciones de una flota operen con distintas tecnologías, v. g. distintos artes de pesca. Este método requiere identificar a priori j grupos conocidos de buques que operan bajo la misma y diferente tecnologías intra e intergrupos respectivamente 4 ; y estimar una frontera estocástica producción para cada uno de ellos en una primera etapa; y a continuación, en una segunda etapa, estimar con todas las embarcaciones otra frontera de producción, denominada metafrontera, que envuelva a las fronteras de los j grupos mediante métodos de programación matemática (Battese et al, 2004) o de fronteras estocásticas (Huang et al, 2014;Alem et al, 2018;Lee y Huang, 2018). Este procedimiento permite además obtener puntuaciones de eficiencia técnica comparables intra e inter grupos; y una medida de la brecha tecnológica existente entre los distintos grupos de embarcaciones.…”

Section: = ( )unclassified

Eficiencia técnica de pesquerías con heterogeneidad inobservada

Espino¹,

Hoyo²

2019

SAE

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Fisheries production is subject to a significant variability caused no only by the stochastic nature of fisheries due to uncontrolled environmental and biological conditions, but also by factors related to production activity. It is necessary to take into consideration all these factors to avoid biases on production model estimates. This paper aims to go through this variability in Stochastic Frontier Analysis to account for observed and unobserved heterogeneity together with technical efficiency and randomness. This paper exemplifies the application of a Latent Class Stochastic Frontier model to the anchovy fishery of Southeastern Black Sea to account for production frontier heterogeneity. Results show a mean level of technical efficiency of 55%, which is higher than those produced by the standard stochastic frontier model. Moreover, results allow identifying two latent classes in the fleet. They also provide sound scientific advice for de management of the fishery.

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What Causes the Efficiency and the Technology Gap Under Different Ownership Structures in the Chinese Banking Industry?

Cited by 15 publications

References 81 publications

A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry

A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry

Banking competition in Indonesia: does Fintech lending matters?

Eficiencia técnica de pesquerías con heterogeneidad inobservada

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