Returns to scale and most productive scale size in DEA with negative data

2016 ◽  
Vol 255 (2) ◽  
pp. 545-558 ◽  
Author(s):  
Biresh K Sahoo ◽  
Mohammad Khoveyni ◽  
Robabeh Eslami ◽  
Pradipta Chaudhury
Keyword(s):  
Returns To Scale ◽  
Negative Data ◽  
Scale Size ◽  
Most Productive Scale Size

scholarly journals Structural efficiency in Lithuanian family farms

2015 ◽  
Vol 37 (4) ◽  
pp. 462-479
Author(s):  
Tomas Baležentis
Keyword(s):  
Returns To Scale ◽  
Scale Efficiency ◽  
Family Farms ◽  
Technical Inefficiency ◽  
Structural Efficiency ◽  
Scale Size ◽  
Level Data ◽  
Farm Accountancy Data Network ◽  
Most Productive Scale Size ◽  
Research Period

Along with firm-specific technical inefficiency, sector-specific structural inefficiency might induce losses in productivity. This paper therefore aims to identify the trends in structural efficiency in Lithuanian family farms. Specifically, the four farming types are considered, namely cereal farming, field cropping, dairying, and mixed farming. Farm-level data from Farm Accountancy Data Network are used for the analysis. The research period spans over the years 2004–2011. The trends in technical and scale efficiency are presented. Furthermore, the prevailing returns to scale are discussed thus offering insights into the most productive scale size and deviations from it in Lithuanian family farms. Finally, the dynamics in structural efficiency are discussed. The results indicate that the aggregate output of certain farming types could be augmented by some 20–25% due to reallocation of inputs among farms. Anyway, technical inefficiency remains the major driver of structural inefficiency.

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

2022 ◽  
pp. 1-11
Author(s):  
Hooshang Kheirollahi ◽  
Mahfouz Rostamzadeh ◽  
Soran Marzang
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Random Inputs ◽  
Dea Model ◽  
Scale Size ◽  
Stochastic Dea ◽  
Probability Constraints ◽  
Most Productive Scale Size ◽  
Decision Making Units ◽  
Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

scholarly journals DEA Window Analysis Efficiency Measurement of Selected Nigerian Bank

2021 ◽  
Vol 4 (1) ◽  
pp. 176-184
Author(s):  
IJ DIKE
Keyword(s):  
Returns To Scale ◽  
Balance Sheet ◽  
Scale Model ◽  
Scale Efficiency ◽  
Average Efficiency ◽  
Window Analysis ◽  
Scale Size ◽  
Constant Returns To Scale ◽  
Dea Window Analysis ◽  
Variable Returns To Scale

This study analyzes the performance efficiency of six selected banks in Nigeria for the period 2010 – 2016. DEA window analysis was employed to establish the performance efficiency of the selected banks. The analysis is based on panel data for the period under review. The result of the DEA window analysis for the reviewed period showed that the average efficiency scores under constant returns to scale ranged from 84% to 91%. Under the variable returns to scale, the average efficiency scores ranged from 91% to 95%. The average inefficiency of the selected Nigeria commercial banks under the constant returns to scale model was in the range 9 – 16%. This inefficiency could be attributed to the excess of customers deposits on the balance sheet of the selected banks. The average scale efficiency for the banks was 93%. Guaranty Trust Bank was the most efficient bank on all measures. United Bank for Africa was the most inefficient bank under constant returns to scale and variable returns to scale. It was however, more scale efficient than three other banks, an indication that its inefficiency cannot be attributed to inappropriate scale size.

Estimating most productive scale size with imprecise-chance constrained input–output orientation model in data envelopment analysis

2012 ◽  
Vol 63 (1) ◽  
pp. 254-261 ◽  
Author(s):  
R. Eslami ◽  
M. Khodabakhshi ◽  
G.R. Jahanshahloo ◽  
F. Hosseinzadeh Lotfi ◽  
M. Khoveyni
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Input Output ◽  
Orientation Model ◽  
Scale Size ◽  
Most Productive Scale Size ◽  
Constrained Input

An inverse semi-oriented radial data envelopment analysis measure for dealing with negative data

2020 ◽  
Vol 31 (4) ◽  
pp. 505-516
Author(s):  
Mojtaba Ghiyasi ◽  
Ning Zhu
Keyword(s):  
Data Envelopment Analysis ◽  
Global Financial Crisis ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Negative Data ◽  
Dea Model ◽  
Proposed Model ◽  
Scale Properties ◽  
The Global Financial Crisis ◽  
Special Case

Abstract The conventional inverse data envelopment analysis (DEA) model is only applicable to positive data, while negative data are commonly present in most real-world applications. This paper proposes a novel inverse DEA model that can handle negative data. The conventional inverse DEA model is a special case of our model as our model is more general in terms of returns-to-scale properties. The proposed model is used to evaluate the efficiency of the Chinese commercial banks after the global financial crisis, where negative outputs existed. We show that our model is feasible in the presence of negative data and generates empirical findings that are consistent with reality.

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