Labour-use Efficiency of Assam Gramin Vikash Bank: Branch- and District-level Analysis

2021 ◽  
pp. 232102222110243
Author(s):  
Mohuya Deb Purkayastha ◽  
Joyeeta Deb ◽  
Ram Pratap Sinha
Keyword(s):  
Efficiency Score ◽  
Significant Variable ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Second Stage ◽  
Time Period ◽  
Use Efficiency ◽  
The Mean ◽  
The Impact ◽  
Observation Period

The present study estimated labour-use efficiency of 48 branches of Assam Gramin Vikash Bank at its branch level, covering three districts of Barak Valley, which falls under Silchar region of the bank for the time period from 2010–2011 to 2017–2018. The study applied data envelopment analysis for estimating labour-use efficiency. In the second stage, the study applied censored Tobit regression for determining the impact of several contextual variables on efficiency. The study reveals that the mean labour-use efficiency score of the selected branches is 76% when averaged for the in-sample branches over the observation period. Results of the Tobit regression identified cluster 2 and total business of the branches as the significant factors for determining efficiency and the number of employees as a significant variable influencing inefficiency. JEL Classifications: G2, G20, G21, J3

scholarly journals Measuring the Efficiency of Public Hospitals in Kuwait: A Two-Stage Data Envelopment Analysis and a Qualitative Survey Study

2020 ◽  
Vol 12 (3) ◽  
pp. 121
Author(s):  
Abdullah M. Alsabah ◽  
Hassan Haghparast-Bidgoli ◽  
Jolene Skordis
Keyword(s):  
Technical Efficiency ◽  
Efficiency Score ◽  
Hospital Efficiency ◽  
Public Hospitals ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Factors Affecting ◽  
The Mean ◽  
Technical Efficiency Score

The recent drop in oil prices has challenged public sector financing in Kuwait. Technical and scale efficiency scores for fifteen public hospitals in Kuwait from 2010 to 2014 were estimated using a two-stage data envelopment analysis (DEA). Technical efficiency scores were regressed against institutional characteristics using Tobit regression to investigate the determinants of efficiency differences in hospitals. Semi-structured interviews were also carried out with fourteen public and private hospital managers to qualitatively explore their perceptions and experience about about factors affecting hospital efficiency. The mean technical efficiency score for all hospitals was 85.8%, an improvement of 2% since 2010. The mean pure technical efficiency score was 79.6%, improving from 75% in 2010 to 81.2% in 2014. The mean scale efficiency score was 91.8%, improving from 87.6% in 2010 to 94.2% in 2014. Only three hospitals were constantly technically and scale efficient. Tobit regression showed that hospital efficiency was significantly associated with the average length of patient stay. Hospitals with more than 400 beds were potentially more technically and scale efficient. The qualitative study revealed that external factors affecting efficiency commonly included implemention of legislative changes and decreasing bureaucracy, while internal factors included increasing bed capacity and improving qualifications and training of human resources. Most public hospitals in Kuwait were not technically and scale efficient, but improvements were observed. Potential factors that affected the efficiency of hospitals in Kuwait were identified. These findings are useful to decision-makers in Kuwait for developing strategies to improve public hospital efficiency.

scholarly journals BANKS EFFICIENCY, OWNERSHIP TYPE AND LISTING STATUS IN GULF COOPERATION COUNCIL COUNTRIES: A CROSS-COUNTRIES ANALYSIS

2021 ◽  
pp. 309-319
Keyword(s):  
Data Envelopment ◽  
Islamic Banks ◽  
Tobit Regression ◽  
Ownership Type ◽  
Second Stage ◽  
Gcc Countries ◽  
Using Data ◽  
Tobit Regression Analysis ◽  
Conventional Banks ◽  
The Impact

Using data from 2005-2013, this paper analyzes banks efficiency across the GCC countries. This study examines the efficiency of GCC conventional and Islamic banks across the GCC countries while considering the impact of ownership type and listing status on banks efficiency by employing the Data Envelopment Analysis (DEA) and a second stage Tobit regression analysis with bootstrapping. It is found that GCC conventional banks are by far more efficient than GCC Islamic banks and this conclusion holds across all GCC countries. It is also found that GCC state-owned banks outperform the GCC private-owned banks in general and across all GCC countries; and interestingly, GCC listed banks were less efficient than GCC unlisted banks. More, the main source of inefficiency in GCC banks was the scale inefficiency and GCC banks exhibited a decreasing return to scale. Therefore, GCC policymakers and regulators should not support any expansionary strategy in their banking industry.

Impact of merger on efficiency and productivity : a case study of commercial banks in Malaysia

1970 ◽  
Vol 5 (1) ◽  
pp. 77
Author(s):  
Mahadzir Ismail ◽  
Saliza Sulaiman ◽  
Hasni Abdul Rahim ◽  
Nordiana Nordin
Keyword(s):  
Efficiency Score ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Data Envelopment ◽  
Master Plan ◽  
Before And After ◽  
Domestic Banks ◽  
The Impact ◽  
Efficiency And Productivity

The Financial Master Plan (2001- 2010) aims to enhance the capacity of banking industry so that higher effic iency and productivity can be reaped in the future. This study seeks to determine the impact of merger on the efficiency and productivity ofcommercial banks in Malaysia for the period 1995 until 2005. The study uses a non-parametric approach, nam ely DEA (data envelopment analysis?) to estimate the efficiency scores and to construct the Malmquist productivity index. To enable this estimation, three bank inputs and outputs are used. Amongst the findings are those banks exhibit higher efficiency score after the merger and thefo reign banks are more efficient than the local banks. Productivity of the banks is calculated in both periods, before and after the merger: The results show that, it is the local banks that have improved the most after the merger. The main source of productivity is technical change or innovation. The findings support the existing policy of having larger domestic banks in term of size.

Benchmarking the efficiencies of Indonesia’s municipal water utilities using Stackelberg data envelopment analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 588-609 ◽  
Author(s):  
Andreas Wibowo ◽  
Hans Wilhelm Alfen
Keyword(s):  
Data Envelopment Analysis ◽  
The Body ◽  
Water Utilities ◽  
Data Envelopment ◽  
Two Stage ◽  
Restrictive Assumption ◽  
Content Type ◽  
Second Stage ◽  
Municipal Water ◽  
The Impact

Purpose – The purpose of this paper is to present a yardstick efficiency comparison of 269 Indonesian municipal water utilities (MWUs) and measures the impact of exogenous environmental variables on efficiency scores. Design/methodology/approach – Two-stage Stackelberg leader-follower data envelopment analysis (DEA) and artificial neural networks (ANN) were employed. Findings – Given that serviceability was treated as the leader and profitability as the follower, the first and second stage DEA scores were 55 and 32 percent (0 percent = totally inefficient, 100 percent = perfectly efficient), respectively. This indicates sizeable opportunities for improvement, with 39 percent of the total sample facing serious problems in both first- and second-stage efficiencies. When profitability instead leads serviceability, this results in more decreased efficiency. The size of the population served was the most important exogenous environmental variable affecting DEA efficiency scores in both the first and second stages. Research limitations/implications – The present study was limited by the overly restrictive assumption that all MWUs operate at a constant-return-to-scale. Practical implications – These research findings will enable better management of the MWUs in question, allowing their current level of performance to be objectively compared with that of their peers, both in terms of scale and area of operation. These findings will also help the government prioritize assistance measures for MWUs that are suffering from acute performance gaps, and to devise a strategic national plan to revitalize Indonesia’s water sector. Originality/value – This paper enriches the body of knowledge by filling in knowledge gaps relating to benchmarking in Indonesia’s water industry, as well as in the application of ensemble two-stage DEA and ANN, which are still rare in the literature.

Application of Data Envelopment Analysis and Key Characteristics of Greek Agro-Firms

2015 ◽  
Vol 6 (2) ◽  
pp. 31-39 ◽  
Author(s):  
Christos Lemonakis
Keyword(s):  
Agricultural Sector ◽  
Empirical Studies ◽  
Data Envelopment ◽  
Firm Level ◽  
Efficiency Assessment ◽  
Time Period ◽  
Key Characteristics ◽  
The Government ◽  
Financial Characteristics ◽  
The Impact

The purpose of this study is to investigate key characteristics for the competitiveness in Greek agro-firms during the time period 2004 to 2011, based on firm level financial data. The study attempts to determine the firms' efficiency as well as the impact of exporting activity in agro-firms competitiveness, and more specifically in fisheries, farms with livestock and farms with fruits, vegetables and cereals. Although many empirical studies have been conducted relative to manufacturing firms' financial characteristics, limited research exists on agro-food firms. The use of DEA method seems to be a very useful tool for efficiency assessment and identification of best practices in firms' management for both managers and the Government as well in order to facilitate the growth of the agricultural sector.

Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 536-551 ◽  
Author(s):  
Seyed Hossein Razavi Hajiagha ◽  
Shide Sadat Hashemi ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Content Type ◽  
Fuzzy C Means ◽  
The Impact ◽  
Decision Making Units

Purpose – Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach – To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings – Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications – The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value – The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

Operating Efficiency Analysis of Energy Industries in Taiwan

2013 ◽  
Vol 869-870 ◽  
pp. 612-620 ◽  
Author(s):  
Nattanin Ueasin ◽  
Anupong Wongchai
Keyword(s):  
Regression Model ◽  
Stock Exchange ◽  
Efficiency Score ◽  
Operating Efficiency ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Factors Affecting ◽  
Taiwan Stock Exchange ◽  
Energy Companies ◽  
Tobit Regression Model

The energy business has played an important role in an economic growth of Taiwan because the market share is in the high value that can make a significant contribution towards regional and local employment. However, Taiwan is lack of energy resources, making the country highly relies on an import for more than 98 percent of its all energy. At present, a top priority of the countrys policy is to develop clean, sustainable, independent, and efficient energy in order to eliminate the vulnerability from external disruption. Therefore, this research aims to assess the operating efficiency and to analyze factors affecting the efficiency scores of the registered energy companies in the Taiwan Stock Exchange (TWSE) recorded during 2003-2012. The super-efficiency data envelopment analysis (SE-DEA) was initially applied to reveal the additional efficiency scores, followed by the Tobit regression model used to analyze what factors determine the efficiency scores. The empirical results showed that seven DMUs performed efficiently, ranking from 7.29 to 1.02. The company with the best operating performance was Taiwan Cogeneration Corporation (TCC), while the Great Taipei Gas Corporation (GTG) revealed the worst efficiency score. Furthermore, the Tobit regression model explained that the higher number of the local employees, the greater the efficiency scores were. Besides, the lower number of the shareholders, the greater the efficiency scores were. As a result, the Taiwans government is supposed to encourage all energy companies to have a higher number of local employees and shareholders to increase their efficiency scores.

scholarly journals Effect of Regional Cooperation on Efficiency of Medical Care Delivery in secondary Medical Areas of Japan

2021 ◽  
Vol 13 (10) ◽  
pp. 1
Author(s):  
Hajime Hajime Watanabe ◽  
Asuka Suzuki ◽  
Yoshinori Nakata
Keyword(s):  
Regression Analysis ◽  
Medical Care ◽  
Regional Cooperation ◽  
Care Delivery ◽  
Average Length ◽  
Length Of Hospital Stay ◽  
Efficiency Score ◽  
Tobit Regression ◽  
Medical Area ◽  
The Impact

The Japanese population is aging and requires regional health facilities to cooperate to use medical resources efficiently. This study evaluated the impact of regional cooperation on the efficiency of medical care delivery in secondary medical areas. The discharge adjustment implementation rate of each secondary medical area was used as a proxy for regional cooperation. The study data were obtained from publicly available sources. The efficiency scores of secondary medical areas were calculated using the input-oriented Banker–Charnes–Cooper model for Data Envelopment Analysis. The inputs used were the number of general beds and the average length of hospital stay for each secondary medical area. The outputs used were the number of discharged patients and inpatient medical expenses per person. In addition, the relationship between discharge adjustment implementation rates and efficiency scores were assessed using tobit multiple regression analysis. The models were adjusted for the 7 variables. Ten secondary medical areas had an efficiency score of 1.00 (i.e., highest efficiency). Tobit regression analysis was performed on the 340 secondary medical areas for which efficiency scores were obtained. The discharge adjustment implementation rates and efficiency scores were significantly positively correlated (p = 0.032). While studies that quantitatively evaluate regional cooperation and efficiency are limited, these findings suggest that implementing regional cooperation may improve the efficiency of medical care delivery in secondary medical areas.

scholarly journals The Impact of Climate Change on Hydrological Regime of the Transboundary River Shu Basin (Kazakhstan–Kyrgyzstan): Forecast for 2050

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2800
Author(s):  
Karlygash Kaliyeva ◽  
Petras Punys ◽  
Yermekul Zhaparkulova
Keyword(s):  
Climate Change ◽  
River Flow ◽  
Tien Shan ◽  
Observation Data ◽  
Adaptation Measures ◽  
Transboundary River ◽  
Average Annual Temperature ◽  
The Mean ◽  
The Impact ◽  
Observation Period

The impact of regional climate change on the runoff and the regime of glacier- and snow-fed rivers in the transboundary river Shu basin between Kazakhstan and Kyrgyzstan is investigated. This study covered three of the most representative rivers of the Shu basin. It was based on the weather and gauging stations’ observation data in the river Shu basin — the northern Tien Shan. Based on the trend analysis, an increase in the average annual temperature and river discharge was identified within the observation period as a whole, and for the separate compared periods. Furthermore, the mean annual flow projections were made based on the methodology of the retrospective analysis of runoff and the rate of river flow increase for the observation period, and further extrapolation of data for the forecast period. According to the analysis, the mean annual flow for the considered rivers will be decreased by 25 to 30% on average by 2050. These findings are necessary for elaborating adaptation measures in water allocation for freshwater supply, irrigation and hydropower within this transboundary river.

scholarly journals Long time frames to detect the impact of changing COVID-19 measures, Canada, March to July 2020

2021 ◽  
Vol 26 (40) ◽  
Author(s):  
Jessica E Stockdale ◽  
Renny Doig ◽  
Joosung Min ◽  
Nicola Mulberry ◽  
Liangliang Wang ◽  
...  
Keyword(s):  
Population Level ◽  
Time Frame ◽  
Control Measures ◽  
Type Model ◽  
Time Period ◽  
Long Time ◽  
The Mean ◽  
Reported Data ◽  
Time Frames ◽  
The Impact

Background Many countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions have moved to relax measures, while others have intensified efforts to reduce transmission. Aim We aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases. Methods We examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. We then examined how long it takes to observe this difference, given delays and noise in reported cases. We used a susceptible-exposed-infectious-removed (SEIR)-type model and publicly available data from British Columbia, Canada, collected between March and July 2020. Results It takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20–26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days. Conclusion The time until a change in control measures has an observed impact is longer than the mean incubation period of COVID-19 and the commonly used 14-day time period. Policymakers and practitioners should consider this when assessing the impact of policy changes. Rapid, consistent and real-time COVID-19 surveillance is important to minimise these time frames.

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