Global Crisis and its Effect on Turkish Banking Sector: A Study with Data Envelopment Analysis

Şahin, Güller; Gokdemir, Levent; Öztürk, Doğan

doi:10.1016/s2212-5671(16)30174-5

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…Advanced statistical modelling is typically carried out for benchmarking organisations/institutions, e.g., multilevel modelling. However, the main reasons to use DEA method are as follows: (1) ‘DEA method enables to compare variables with “the best value” whereas statistical methods analyse the variables based on “average value” using central tendency approach’, 4 and (2) There are multiple inputs and outputs, where DEA is the only methodology that is able to deal with such datasets. In this study, we developed a data envelopment analysis (DEA) based modelling to determine the efficiency levels of countries (out of the selected 16 nations) with respect to healthcare systems preparedness (in the form of availability of resources, i.e.…”

Section: Introductionmentioning

confidence: 99%

Healthcare systems and Covid19: Lessons to be learnt from efficient countries

Ordu

Akin

Demir

2021

Health Planning & Management

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Background The novel coronavirus is rapidly spreading over the world and puts the health systems of countries under intense pressure. High hospitalization levels due to the pandemic outbreak have caused the intensive care units to work above capacity. Purpose A data envelopment analysis (DEA) based modelling approach was developed to evaluate the effectiveness of regions (i.e. city, country or clinical commissioning groups) against the pandemic outbreak. The objective is to enable related authorities better manage the struggle against the outbreak and put in place the emergency action plans immediately. Methodology/Approach DEA method was used to measure the efficiency scores of countries. Super efficiency DEA method was also applied to countries based on the level of efficiencies they have achieved. Sixteen countries were selected that have been facing with Covid19 pandemic outbreak for at least 5 consecutive weeks after their 100th confirmed case. Results A total of 80 DEA models were developed, that is, 16 DEA models for each week. The percentage of efficient countries decreased dramatically over time, from 43.75% in the first week to 25% in the fifth week. Unlike most European countries, China and South Korea increased their effectiveness after first week of implementing all the necessary measures. Conclusion This study sheds light into better understanding the effectiveness of policies adopted by countries and their management strategy in dealing with Covid19 pandemic. Our model will enable political leaders to identify inadequate policies as quickly as possible and learn from their peers for more effective decisions.

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Section: Introductionmentioning

confidence: 99%

Healthcare systems and Covid19: Lessons to be learnt from efficient countries

Ordu

Akin

Demir

2021

Health Planning & Management

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“…Their conclusion is that there is a huge room for improvement in terms of OTE, PTE and SE that can be attained by commercial banks in the UK. Research also exists on Australian banks (Avkiran, 2000), African banks (Banya and Biekpe, 2018), Brazilian banks (Wanke and Barros, 2014), Turkish banks (Sahin et al , 2016; Kutlar et al , 2017) and banks of South-East Asian countries like Vietnamese banks (Stewart et al , 2016) and banks from Asian countries including China (Avkiran and Morita, 2010; Wang et al , 2014; Silva et al , 2017; Chen et al , 2018), Japan (Ohsato and Takahashi, 2015; Fukuyama and Matousek, 2017), Taiwan (Liu, 2018), Hong Kong (Phan et al , 2018) and Korean banks (Sufian and Habibullah, 2009).…”

Section: Literature On Efficiency In Bankingmentioning

confidence: 99%

Efficiency analysis of Indian banking industry over the period 2008–2017 using data envelopment analysis

Tamatam

Dutta

et al. 2019

BIJ

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Purpose The purpose of this paper is to estimate the relative efficiencies of banks of the Indian domestic banking sector by employing various models of data envelopment analysis (DEA) using the panel data of the recent decade (2008–2017). The paper provides a comparative analysis of these models based on the efficiency outputs. It compares the performance of banks based on their ownership and sizes and studies the decade-long trend of productivity using Malmquist indices. Design/methodology/approach This paper estimates overall technical, pure technical and scale efficiencies of 21 public sector banks and 17 private banks. It compares the descriptive statistics of efficiency estimates found out through 18 different DEA models and compares them using two non-parametric statistical tests. It studies the difference in efficiencies based on ownership and size by applying the same statistical tests. It employs the Malmquist index method to study the technological and technical progress in the banks’ productivity over the decade of FY 2008–FY 2017. Findings During FY 2016–2017, only 9 out of 38 banks were overall technically efficient with the whole sample having a mean overall technical inefficiency of 5 percent with scale inefficiency contributing more than pure technical inefficiency. The comparative study ascertains that private sector and public sector banks (PSBs) possess efficiencies that are similar based on super-efficiency slack-based model – variable returns to scale and non-oriented, a model that the authors argue to be the most suitable for the real-life business banking scenarios whereas the private sector banks possess better efficiency than the PSBs. The Malmquist indices prove that private sector banks have a higher increase in productivity based on both technological progress and efficiency improvements whereas PSBs had a loss of efficiency and comparatively less improvement in technology. Research limitations/implications This study has a limitation of choosing a single model of inputs and outputs. Improved insights can be drawn by employing more models based on different inputs and outputs. Further, relevance of each input and output can be examined using a regression-based feedback mechanism (Ouenniche and Carrales, 2018). The influence of environmental factors on the efficiencies can be studied using second-stage regression models and the relationship between efficiency scores and financial ratios can be examined. Originality/value This study is based on the panel data of the recent decade (2008–2017) and provides insights into the efficiency scenario of the Indian banking industry and how it changed over the past decade, to the leadership of banks, the banking regulators and the policy makers. The comparative analysis of DEA models based on a sample of Indian banks is first of its kind in the Indian context and helps the researchers to select an appropriate model and delve into further research on the same.

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“…It is suitable for various forms of input and output and has high practicability. The Malmquist index based on the DEA model is also widely used [27][28][29]. The first step is to calculate the distance function using DEA, and then calculate and decompose the Malmquist index using the distance function.…”

Section: Dea-malmquist Indexmentioning

confidence: 99%

Urban Total Factor Productivity: Does Urban Spatial Structure Matter in China?

Liu

Jiancheng

et al. 2019

Sustainability

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With the rapid development of urbanization in China, urban spatial form has increasingly gained research attention. In this study, the DEA (data envelopment analysis)-Malmquist index model and a panel data model are used to examine the relationship between the urban spatial form and total factor productivity (TFP) of 30 provincial cities in China. Our method of measuring urban spatial form is different from the current entropy method, but we use remote sensing GIS (Geographic Information System) technology to measure the relevant data on urban compactness and urban elongation. The average values of urban compactness and urban elongation first rise, then fall, and then rise again, and there are alternate situations of urban compact development and urban sprawl and expansion. Furthermore, there is a significant positive correlation between urban compactness and TFP. Therefore, cities with high urban compactness can promote TFP. In addition, there is a significant negative correlation between urban extension rate and TFP, indicating that an increase in urban elongation has a restraining effect on TFP. Finally, the average TFP of each city shows a fluctuating trend of rising first and then declining, which is determined mainly by technological change and efficiency change. These results are expected to provide a scientific basis for the development of urban agglomerations in China.

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Global Crisis and its Effect on Turkish Banking Sector: A Study with Data Envelopment Analysis

Cited by 10 publications

References 4 publications

Healthcare systems and Covid19: Lessons to be learnt from efficient countries

Healthcare systems and Covid19: Lessons to be learnt from efficient countries

Efficiency analysis of Indian banking industry over the period 2008–2017 using data envelopment analysis

Urban Total Factor Productivity: Does Urban Spatial Structure Matter in China?

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