Returns to Scale as an Established Scaling Indicator: Always a Good Advisor?

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Andreas Dellnitz ◽  
Wilhelm Rödder
Keyword(s):  
Data Envelopment Analysis ◽  
Real World ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Data Envelopment ◽  
Productivity Improvement ◽  
Increasing Returns To Scale ◽  
Numerical Examples ◽  
Real World Application ◽  
A Company

AbstractIn data envelopment analysis (DEA), returns to scale (RTS) are a widely accepted instrument for a company to reveal its activity scaling potentials. In the case of increasing returns to scale (IRS), a company learns that upsizing activities improves its productivity. For decreasing returns to scale (DRS), the instrument likewise should depict a downsizing force, again for improving productivity. Unfortunately, here the classical RTS concept shows misbehavior. Under certain circumstances, it is the wrong indicator for scaling activities and even hides respective productivity improvement potentials. In this paper, we study this phenomenon, using the DEA concept, and illustrate it via little numerical examples and a real-world application consisting of 37 Brazilian banks.

Research of the Construction Industry Efficiency in China — Based on DEA Panel Data Approach

2013 ◽  
Vol 689 ◽  
pp. 105-109 ◽  
Author(s):  
Wei Zhong Zhou ◽  
Chun Lu Liu
Keyword(s):  
Data Envelopment Analysis ◽  
Panel Data ◽  
Construction Industry ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Data Envelopment ◽  
Increasing Returns To Scale ◽  
Dea Model ◽  
Net Profit ◽  
The Mean

The efficiency of the construction industry is analyzed based on provinces panel data in China in this paper. The Mean Number of Employee and the Mean Completed Investment are used as inputs. The Mean Actual Sales of Commercial Houses and the Mean Net Profit are used as outputs. Data Envelopment Analysis (DEA) model is used to measure the efficiency of the construction industry. Shanghai and Zhejiang are found technically efficient. Shandong is scale efficient but technology efficiency is lower. There are two provinces are decreasing returns to scale and other provinces are increasing returns to scale. On the whole, the technology efficiency of the construction industry of China is lower. Based on the conclusions, the paper proposes some suggestions to improve the efficiency of the construction industry in China.

scholarly journals ARE VACCINATION SITES IN BANGLADESH SCALE EFFICIENT?

2003 ◽  
Vol 19 (4) ◽  
pp. 692-697 ◽  
Author(s):  
Vivian Valdmanis ◽  
Damian Walker ◽  
Julia Fox-Rushby
Keyword(s):  
Data Envelopment Analysis ◽  
Cost Effectiveness ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Data Envelopment ◽  
Dhaka City ◽  
Increasing Returns To Scale ◽  
Multiple Outputs ◽  
Expanded Program On Immunization ◽  
Constant Returns To Scale

Objectives: The overall aim of this study is to discern whether and to what degree vaccination sites exhibit constant returns to scale.Methods: Data Envelopment Analysis is used to compare all the facilities in the sample in terms of input costs used to produce multiple outputs. The application considers the Expanded Program on Immunization (EPI), which operated in Dhaka City, Bangladesh, during 1999.Results: A preponderance of EPI sites were determined to be operating at increasing returns to scale.Conclusions: Our findings question the applicability of cost-effectiveness analyses that assume constant returns to scale.

scholarly journals EVALUATION OF PLASTERING CREW PERFORMANCE IN BUILDING PROJECTS USING DATA ENVELOPMENT ANALYSIS

2014 ◽  
Vol 22 (6) ◽  
pp. 926-940 ◽  
Author(s):  
Ibrahim Halil GEREK ◽  
Ercan ERDIS ◽  
Gulgun MISTIKOGLU ◽  
Mumtaz A. USMEN
Keyword(s):  
Data Envelopment Analysis ◽  
Management Practices ◽  
Research Question ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Building Projects ◽  
Unit Costs ◽  
Using Data ◽  
A Company ◽  
Variable Returns To Scale

The research question addressed in this study was how the performance of construction crews working in a certain project or locality could be evaluated, ranked and improved. To develop and demonstrate the relevant framework, data envelopment analysis (DEA) was applied to establish the relative efficiency of plastering crews working in building projects located in different cities around Turkey. Data were collected from 40 crews of varying characteristics, and their technical efficiency scores were computed using the Banker, Charnes and Cooper (BCC) model, which is based on variable returns-to-scale (VRS). The model yields efficiency scores that range between 0 and 1, and a company or crew is considered efficient if its score is 1.0 (100%). Efficient and inefficient crews were identified and ranked on this basis in the study. Cross tabulation analyses were subsequently conducted to gain further insights into the relationships between the efficiency scores and input factors of numbers of skilled and unskilled laborers, daily labor unit costs, work hours, average age of crew members, total crew experience, plastering location, plastering technique, and plaster type. No discernible relationship could be identified between the efficiency scores and productivity outputs of the crews. It was found that plastering technique, plastering location, and total crew experience had a significant association with crew efficiency. Efficiency improvement strategies identified included training, hiring experienced plasterers, adopting more advanced plastering technology, implementing better jobsite management practices, and enhancing workers’ knowledge, skills and attitude towards productivity and quality.

scholarly journals A DEA approach for merging dairy farms

2021 ◽  
Author(s):  
Sebastian Lozano ◽  
Belarmino Adenso-Diaz
Keyword(s):  
Standard Deviation ◽  
Technical Efficiency ◽  
Returns To Scale ◽  
Dairy Farms ◽  
Herd Size ◽  
Increasing Returns ◽  
Data Envelopment ◽  
Efficiency Improvement ◽  
Increasing Returns To Scale ◽  
Different Response

This paper proposes a model for determining the most advantageous merger within a set of dairy farms. It uses data envelopment analysis (DEA) to estimate the total technical efficiency improvement that the merger would produce and for decomposing it into a learning effect and a pure merger effect. A design of experiments has also been carried to test the effects of various factors (the total number of farms, the standard deviation of herd size, the percentage of farms exhibiting increasing returns to scale, the standard deviation of the current technical efficiency of the farms) on different response variables (the percentage of farms involved in the merger, the reduction of herd size and the efficiency improvement obtained by the merger). The results show that the disparity in the herd size of the farms in a region and the percentage of farms that exhibit increasing returns to scale increase the number of farms that enter into the most advantageous merger. The disparity of herd size also increases the number of cows that are not needed after the merger. Finally, the expected efficiency improvement increases with the total number of farms.

A model to evaluate the effects of the returns to scale on the inverse data envelopment analysis

2021 ◽  
Author(s):  
M. Ebrahimzade Adimi ◽  
M. Rostamy-Malkhalifeh ◽  
F. Hosseinzadeh Lotfi ◽  
R Mehrjoo
Keyword(s):  
Data Envelopment Analysis ◽  
Returns To Scale ◽  
Data Envelopment

scholarly journals Does Farm Size and Specialization Matter for Productive Efficiency? Results from Kansas

2011 ◽  
Vol 43 (4) ◽  
pp. 515-528 ◽  
Author(s):  
Amin W. Mugera ◽  
Michael R. Langemeier
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Production Technology ◽  
Returns To Scale ◽  
Productive Efficiency ◽  
Farm Size ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Sample Period ◽  
Analysis Techniques

In this article, we used bootstrap data envelopment analysis techniques to examine technical and scale efficiency scores for a balanced panel of 564 farms in Kansas for the period 1993–2007. The production technology is estimated under three different assumptions of returns to scale and the results are compared. Technical and scale efficiency is disaggregated by farm size and specialization. Our results suggest that farms are both scale and technically inefficient. On average, technical efficiency has deteriorated over the sample period. Technical efficiency varies directly by farm size and the differences are significant. Differences across farm specializations are not significant.

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