Do bottom-up and independent agricultural cooperatives really perform better? Insights from a technical efficiency analysis in Ethiopia

Agrekon ◽  
2019 ◽  
Vol 59 (1) ◽  
pp. 93-109 ◽  
Author(s):  
Tafesse W. Gezahegn ◽  
Steven Van Passel ◽  
Tekeste Berhanu ◽  
Marijke D’haese ◽  
Miet Maertens
Keyword(s):  
Technical Efficiency ◽  
Efficiency Analysis ◽  
Agricultural Cooperatives ◽  
Bottom Up

scholarly journals Accounting for rainfall and the length of growing season in technical efficiency analysis

2018 ◽  
Vol 20 (4) ◽  
pp. 2583-2608
Author(s):  
Yiorgos Gadanakis ◽  
Francisco José Areal
Keyword(s):  
Technical Efficiency ◽  
Physical Environment ◽  
Farming Systems ◽  
Growing Season ◽  
Efficiency Analysis ◽  
Performance Measurements ◽  
East Anglia ◽  
Dea Model ◽  
Efficiency Ranking ◽  
Business Survey

Abstract The physical environment of farming systems is rarely considered when conducting farm level efficiency analysis, which is likely to lead to bias of performance measurements based on benchmarking methods such as Data Envelopment Analysis (DEA). We incorporate variations of the physical environment (rainfall and length of growing season) through the specifications of the linear programming in DEA to investigate performance measurement bias. The derived technical efficiency estimates are obtained using a sub-vector DEA which ensures farms are compared in a homogenous environment (i.e. accounting for differences in rainfall levels amongst distinct farm units). We use the Farm Business Survey to analyse a representative sample of 245 cereal farms in the East Anglia region between 2009 and 2010. Efficiency rankings obtained from a standard DEA model and a non-discretionary DEA model that incorporates the variations in the physical environment. We show that incorporating rainfall and the length of the growing season as non-discretionary inputs into the production function had significantly altered the farm efficiency ranking between the two models. Hence, to improve extension services to farmers and to reduce biased estimates of farm technical efficiency, variations in environmental conditions need to be integral to the analysis of efficiency.

scholarly journals Corporate ownership and technical efficiency analysis in the Spanish real estate sector

2007 ◽  
Vol 4 (2) ◽  
pp. 100-113 ◽  
Author(s):  
Douglas Nanka-Bruce
Keyword(s):  
Real Estate ◽  
Technical Efficiency ◽  
Shadow Economy ◽  
Panel Data Analysis ◽  
Efficiency Analysis ◽  
News Article ◽  
Corporate Ownership ◽  
Spanish Economy ◽  
The Real Estate ◽  
Real Estate Sector

The real estate sector keeps contributing significantly to the Spanish economy. A recent news article reports the existence of inefficiencies in the nature and delivery of new properties. We investigate the technical efficiency of this sector using a non-parametric “reasonable” benchmarking frontier, acknowledging the marked influence of the sector’s shadow economy. We then relate the results applying a panel data analysis to the shareholding concentration and identity of firm ownership. We find no systematic support for the effect of corporate ownership on technical efficiency

scholarly journals Labour efficiency in construction industry in Europe based on frontier methods: data envelopment analysis and stochastic frontier analysis

2017 ◽  
Vol 23 (6) ◽  
pp. 787-795 ◽  
Author(s):  
Joanicjusz NAZARKO ◽  
Ewa CHODAKOWSKA
Keyword(s):  
Data Envelopment Analysis ◽  
Construction Industry ◽  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Efficiency Analysis ◽  
European Countries ◽  
Personnel Cost ◽  
Data Envelopment ◽  
Frontier Analysis

The primary problems pertaining to productivity or – more precisely – efficiency are: how to define it and how to measure it. This article studies technical efficiency in Stochastic Frontier Analysis (SFA) – the input-oriented frontier model – in the construction industry and compares it with Data Envelopment Analysis (DEA) results. The models ex­plored in this paper were constructed on the basis of two outputs and personnel cost as an input. The research sample consisted of European countries. The aim was to determine whether there are substantial differences in estimation of ef­ficiency derived from those two alternative frontier approaches. The comparison of results according to the models may translate into higher reliability of the undertaken labour efficiency analysis in construction and its conclusions. Although the results are not characterized by high compatibility, the conducted analysis indicated the most attractive countries taking into account labour cost to profit and turnover ratios of enterprises. One of the determinants which should not be ignored when analysing the labour efficiency is the level of development of a country; however, it is not the sole factor affecting the efficiency of the sector.

scholarly journals The efficiency analysis of organic and conventional olive farms: Case of Turkey

2010 ◽  
Vol 56 (No. 2) ◽  
pp. 89-96 ◽  
Author(s):  
M.M. Artukoglu ◽  
A. Olgun ◽  
H. Adanacioglu
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Efficiency Analysis ◽  
Data Envelopment ◽  
Average Efficiency ◽  
Total Potential ◽  
Input And Output ◽  
Study Results ◽  
Crs Model ◽  
Potential Improvement

This paper investigates technical and economically efficiency of 62 organic and 62 conventional olive producing farms in Turkey. According to the study results; by using the CRS model which is input and output-oriented, the average technical efficiency of organic olive farms is 67.68%, the average technical efficiency of conventional olive farms is 47.93%. The technical efficiency of the output-oriented VRS model is 74.78%, and the technical efficiency of the input-oriented VRS model is 93.46%. Also, considering the same model, the average efficiency of the conventional olive farms in the input and output are 59.58% and 94.97%, respectively. Therefore, according to the Data Envelopment Analysis, the technical efficiency in conventional olive farms is less than in the organic ones. When the farms have been evaluated one by one in the light of the total potential improvement values, inputs and outputs, improvement is needed in all values.

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