scholarly journals Appraising healthcare systems’ efficiency in facing COVID-19 through data envelopment analysis

2021 ◽  
Vol 10 (3) ◽  
pp. 301-310
Author(s):  
Nahia Mourad ◽  
Ahmed Mohamed Habib ◽  
Assem Tharwat
Keyword(s):  
Relative Efficiency ◽  
Statistical Tests ◽  
Internal Validity ◽  
Healthcare Systems ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Medical Practitioners ◽  
Tobit Analysis ◽  
Dea Models ◽  
Tobit Regression Analysis

The healthcare system is a vital element for any community, as it extremely affects the socio-economic development of any country. The current study aims to assess the performance of the healthcare systems of the countries above fifty million citizens in facing the spread of the COVID-19 pandemic since late December 2019. For this purpose, seven scenarios were adopted via the DEA methodology with six variables, which are the number of medical practitioners (doctors and nurses), hospital beds, Conducted Covid-19 tests, affected cases, recovered cases, and death cases. To shed light on the relative efficiency of drivers, the Tobit analysis was used. Besides, the study carried out various statistical tests for the DEA models' findings to validate the choice of the variables and the obtained scores. The DEA results reveal that less than half of the considered countries are relatively efficient. Moreover, the Tobit regression analysis showed that the main impact on the efficiency scores was due to the number of affected and recovered cases. Finally, the results of the tests of Spearman, Mann-Whitney U, and Kruskal-Wallis H indicate the internal validity and robustness of the chosen DEA models. The current study findings raise important implications, which can be helpful for decision makers regarding continuous improvement of performance, in which the findings assert the importance of achieving the best practices regarding relative efficiency through the linkage between the healthcare systems’ resources, and the needed outputs.

Exploring comparative efficiency and determinants of efficiency for major world airlines

2014 ◽  
Vol 21 (4) ◽  
pp. 675-687 ◽  
Author(s):  
Seong-Jong Joo ◽  
Karen L. Fowler
Keyword(s):  
North America ◽  
Technical Efficiency ◽  
Relative Efficiency ◽  
Design Methodology ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Content Type ◽  
Comparative Efficiency ◽  
Major Airlines ◽  
Tobit Regression Analysis

Purpose – For strategic and competitive insights, this paper aims to measure and benchmark comparative operating efficiencies of major airlines in Asia, Europe, and North America. Design/methodology/approach – The authors employ data envelopment analysis for measuring the relative efficiency of 90 airlines in Asia, Europe, and North America. In addition, the authors use Tobit regression analysis for finding determinants of the efficiency. Findings – Results indicate that the efficiency of the airlines in Europe is the lowest among the airlines in these three regions. Efficiency differences between the airlines in Europe and the airlines in the two other regions (Asia and North America) are statistically significant in terms of technical efficiency and pure technical efficiency, but not significant between the airlines in Asia and North America. For the determinants of efficiency, the authors identified that revenues and expenses were significant for explaining efficiency scores of airlines. Research limitations/implications – Further research is needed to explain the findings that airlines in Europe were less efficient than airlines in Asia and North America. In addition, including variables on customer satisfaction in a future study is desirable. Originality/value – Major contributions of this study include measuring the comparative efficiency of major airlines in Asia, Europe, and North America and finding determinants of the efficiency for strategic insights.

scholarly journals Highlighting the Drivers of Italian Diversified Farms Efficiency: A Two-Stage DEA-Panel Tobit Analysis

2021 ◽  
Vol 13 (23) ◽  
pp. 12949
Author(s):  
Luca Romagnoli ◽  
Vincenzo Giaccio ◽  
Luigi Mastronardi ◽  
Maria Bonaventura Forleo
Keyword(s):  
Second Step ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Explanatory Variables ◽  
Farm Diversification ◽  
Tobit Analysis ◽  
Efficiency Performance ◽  
Managerial Implications ◽  
Environmental Goals ◽  
Tobit Regression Analysis

Farm diversification is an important phenomenon in agricultural systems and rural development in Europe, pursuing economic, social and environmental goals. For the sustainability of diversified farms, it is important to analyse some drivers affecting farm efficiency, for instance, socio-economic, technical and policy drivers. The efficiency performance of a panel of Italian farms practising other gainful activities in the period 2012–2017 was investigated and regressed against the drivers that mostly affects farm performances. FADN data and a two-step approach were used. An output-oriented Data Envelopment Analysis was applied; in the second step, efficiency scores were used as a dependent variable in a panel Tobit regression analysis used to determine differences in the significance of drivers. Social, economic, technical and policy drivers were considered as explanatory variables. Results show margins for improving farms performances. The incidence of the output from other gainful activities has been proven to positively affect farms efficiencies, while intermediate costs are the most negatively impacting factor. As regards policy variables and implications, the significance of localization in mountain disadvantaged territories further supports the relevance of EU subsidies in less-favoured areas. Managerial implications in terms of technical, structural and economic indicators can be drawn from study findings.

scholarly journals Has the Efficiency of China’s Healthcare System Improved after Healthcare Reform? A Network Data Envelopment Analysis and Tobit Regression Approach

2019 ◽  
Vol 16 (23) ◽  
pp. 4847 ◽  
Author(s):  
Guangwen Gong ◽  
Yingchun Chen ◽  
Hongxia Gao ◽  
Dai Su ◽  
Jingjing Chang
Keyword(s):  
Data Envelopment Analysis ◽  
Healthcare System ◽  
Health Expenditure ◽  
Healthcare Reform ◽  
Healthcare Systems ◽  
Network Data ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Network Data Envelopment Analysis ◽  
Overall Efficiency

Background: A healthcare system refers to a typical network production system. Network data envelopment analysis (DEA) show an advantage than traditional DEA in measure the efficiency of healthcare systems. This paper utilized network data envelopment analysis to evaluate the overall and two substage efficiencies of China’s healthcare system in each of its province after the implementation of the healthcare reform. Tobit regression was performed to analyze the factors that affect the overall efficiency of healthcare systems in the provinces of China. Methods: Network DEA were obtained on MaxDEA 7.0 software, and the results of Tobit regression analysis were obtained on StataSE 15 software. The data for this study were acquired from the China health statistics yearbook (2009–2018) and official websites of databases of Chinese national bureau. Results: Tobit regression reveals that regions and government health expenditure effect the efficiency of the healthcare system in a positive way: the number of high education enrollment per 100,000 inhabitants, the number of public hospital, and social health expenditure effect the efficiency of healthcare system were negative. Conclusion: Some provincial overall efficiency has fluctuating increased, while other provincial has fluctuating decreased, and the average overall efficiency scores were fluctuations increase.

scholarly journals Performance analysis in the presence of bounded, discrete and flexible measures_

2021 ◽  
Vol 31 (3) ◽  
Author(s):  
Sohrab Kordrostami ◽  
Monireh Jahani Sayyad Noveiri
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Highway Maintenance ◽  
Real World Applications ◽  
The Status ◽  
Dea Models ◽  
Discrete Values ◽  
Flexible Measure ◽  
The Individual ◽  
University Performance

In conventional data envelopment analysis (DEA) models, the relative efficiency of decision making units (DMUs) is evaluated while all measures with certain input and/or output status are considered as continuous data without upper and/or lower bounds. However, there are occasions in real-world applications that the efficiency of firms must be assessed while bounded elements, discrete values, and flexible measures are present. For this purpose, the current study proposes DEA-based approaches to estimate the relative efficiency of DMUs where bounded factors, integer values, and flexible measures exist. To illustrate it, radial models based on two aspects, individual and aggregate, are introduced to measure the performance of entities and to handle the status of the flexible measure such that there are bounded components and discrete data. Applications of approaches proposed in the areas of quality management, highway maintenance patrols, and university performance measurement are given to clarify the issue and to show their practicability. It was found that the introduced procedure can determine practical projection points for bounded measures and integer values (from the individual DMU viewpoint) and can classify flexible measures along with evaluation of DMUs relative efficiency.

A MONTE CARLO EVALUATION OF SEVERAL TESTS FOR THE SELECTION OF VARIABLES IN DEA MODELS

2005 ◽  
Vol 04 (03) ◽  
pp. 325-343 ◽  
Author(s):  
INMACULADA SIRVENT ◽  
JOSÉ L. RUIZ ◽  
FERNANDO BORRÁS ◽  
JESÚS T. PASTOR
Keyword(s):  
Statistical Tests ◽  
Returns To Scale ◽  
Nonparametric Tests ◽  
Model Specification ◽  
Data Envelopment ◽  
Selection Of Variables ◽  
Dea Models ◽  
Observed Behaviour ◽  
Monte Carlo Evaluation ◽  
Selection Of

Data Envelopment Analysis (DEA) is a recently developed methodology that is widely used for estimating relative efficiency scores of Decision Making Units (DMUs) that use several inputs to produce several outputs. Model specification in DEA includes aspects such as the choice of inputs and outputs or the adoption of a returns to scale assumption. As pointed out by many authors, it is obvious that the specification of a model is the key to having reliable efficiency scores. In this paper, we are particularly concerned with the selection of variables in DEA models. To be specific, we investigate the performance of several statistical tests existing in the literature that can be used for the selection of variables. In particular, the behaviour of the well-known tests proposed by Banker2 and the nonparametric tests recently developed by Pastor et al.13 is analyzed in relation to several factors such as sample size, model size, the specification of returns to scale and the type and level of inefficiency. We have drawn some conclusions that will be of help for practical uses, since the observed behaviour of the tests in the different scenarios determined by the specifications of the mentioned factors may provide some useful insight into the choice of an adequate statistical test in the particular context of a given DEA application.

scholarly journals Does Reduction of Material and Energy Consumption Affect to Innovation Efficiency? The Case of Manufacturing Industry in South Korea

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1178 ◽  
Author(s):  
Jaeho Shin ◽  
Changhee Kim ◽  
Hongsuk Yang
Keyword(s):  
Energy Consumption ◽  
Manufacturing Industry ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Major Objective ◽  
Manufacturing Enterprises ◽  
Industry Type ◽  
Show Difference ◽  
Tobit Regression Analysis ◽  
The Relationship

“Reduction of material and energy consumption” (RMEC) exists as a major objective of innovation and it is proved to affect positively to innovation performance from previous literature. Though innovation should be measured in efficiency rather than performance itself, however, the relationship between material and energy reduction on innovation efficiency is still unanswered. In this paper, we analyzed the effect of RMEC on innovation efficiency considering both innovation inputs and outputs. We utilized data of 388 manufacturing enterprises in Korea, and performed data envelopment analysis (DEA) and tobit regression analysis. According to the result, firms show difference by industry type in terms of innovation efficiency and RMEC. Moreover, the effect of RMEC on innovation efficiency turned out to be negative. The result indicates a possibility that input used for innovation might overweigh the output yielded when firms pursue innovation for the RMEC.

scholarly journals Effects of Input Composition on Technical Efficiencies of Textile Industries in Pakistan

2012 ◽  
Vol 51 (2) ◽  
pp. 117-130
Author(s):  
Tariq Mahmood
Keyword(s):  
Data Envelopment Analysis ◽  
Returns To Scale ◽  
Jel Classification ◽  
Manufacturing Industries ◽  
Raw Material ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Analysis Technique ◽  
Tobit Analysis ◽  
Positive Effect

This paper studies the technical efficiencies of the textile manufacturing industries in Pakistan using 5-digit level industry data. Technical efficiencies are computed by the Data Envelopment Analysis technique assuming constant as well as variable returns to scale. The efficiency scores thus obtained are analysed by the TOBIT regression technique to determine how input composition influences these efficiency scores. It is found that imported raw material and machinery exercises a positive effect, whereas non-industrial costs affect technical efficiencies in a negative way. Electricity does not play its due role in affecting technical efficiencies. JEL Classification: C24, D24, L6, O14 Keywords: Technical Efficiency, Data Envelopment Analysis, TOBIT Analysis, Manufacturing Industries

scholarly journals DATA ENVELOPMENT ANALYSIS OF RUBBER SMALLHOLDERS: BCC AND CCR MODELS AND BOOTSTRAPPING TECHNIQUE

2018 ◽  
Vol 6 (5) ◽  
pp. 346-368
Author(s):  
A. ALIYU ◽  
K. BELLO
Keyword(s):  
Data Envelopment Analysis ◽  
Marital Status ◽  
Returns To Scale ◽  
Peninsular Malaysia ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Traditional Concept ◽  
Tobit Regression Analysis ◽  
Socio Demographic Factors ◽  
Constant Returns To Scale

The present study examined the economic efficiency of rubber smallholders in Peninsular Malaysia in a disaggregated form using Banker Charnes and Cooper (BCC) and Charnes Cooper and Rhodes (CCR) models of data envelopment analysis (DEA) as well as their respective bootstrap techniques. Multistage data collection was employed on 327 smallholders among 5 districts of Negeri Sembilan state. However, only 307 observations were used in computing inferential statistics, because the young-age category has been removed. The districts include Seremban, Tampin, Rembau, Kuala Pilah and Jempol. The results revealed that, the mean technical efficiency (TE) under variable returns to scale (VRS) and constant returns to scale (CRS) were 0.95, 0.97 0.96 and 0.45, 0.61, 0.33 for the all-age, matured-age and old-age crops respectively. The findings of the result also disclosed that naïve DEA has higher mean scores than bootstrapped-DEA, thus indicating the presence of bias in the former and absence of bias in the later. Also, the efficiency determinants under VRS and CRS as well as their respective bias-corrected (BC) efficiency scores were also analyzed using Tobit regression analysis against the 15 socio-demographic factors. It was found out that critical factors, common to all the age-categories, include educational level, tapping system and marital status under VRS and BC-VRS assumptions, while under CRS and BC-CRS assumptions include race, tapping system, marital status and farm’s distance. Therefore, education of smallholders should be given more attention to increase efficiency.  The study finally recommends that the traditional concept of computing efficiency or productivity of rubber and other perennial crops in an aggregated form should be complemented with the disaggregated form as this eliminates any bias and gives meaningful results. Improved methods such as bootstrapping should also be used as this only gives what is called bias-corrected efficiency scores. Regarding the determinants, factors such as education, tapping system and farm distance should be given more emphasis.

scholarly journals Technical Efficiency in Public Hospitals in Kenya: A Two –Stage Data Envelopment Analysis

2018 ◽  
Vol 10 (6) ◽  
pp. 141
Author(s):  
Francis Kimani Mwihia ◽  
James Machoki M’ Imunya ◽  
Germano Mwabu ◽  
Urbanus M. Kioko ◽  
Benson B. A. Estambale
Keyword(s):  
Regression Analysis ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Public Hospitals ◽  
Capital City ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Hospital Variation ◽  
Dea Analysis ◽  
Tobit Regression Analysis

The paper uses the DEA technique to estimate efficiency scores in Kenyan public hospitals and then applies the Tobit regression to study inter-hospital variation in the scores. The DEA analysis reveals that small hospitals are more efficient than large hospitals, with efficiency levels ranging from 74-91% in small DMUs and from 57-78% in large DMUs. Tobit regression analysis shows efficiency scores are negatively correlated with the hospital’s distance from the manager’s residence and from the capital city. Internal and external supervisions are suggested as mechanisms for increasing performance of hospitals.

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