Análisis de la eficiencia de la pesca en estanques en el norte de Bengala Occidental, India: evidencia del análisis de datos DEA

Tunisia olive production fluctuates yearly because it is highly dependent on annual precipitation, and growers need to enhance productivity and efficiency by introducing irrigation. Investigating how irrigation affects the technical efficiency of olive production may contribute to improvement in productivity. This study employs the Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) methods to estimate non-parametric and parametric frontiers for a sample of Tunisian olive orchards. It identifies factors which determine variations in technical and scale efficiencies among orchards. The DEA results show that average output-oriented technical efficiency under constant returns to scale (CRS) and variable returns to scale (VRS) is 8.9 and 17.8%, respectively. The SFA results show that average technical efficiency of the half-normal model with constant returns to scale is estimated at 81.2%, indicating Tunisian olive growers can raise output by an average of 18.8% by improving technology and using fewer inputs. Average technical efficiency in irrigated orchards under the DEA approach was higher than in irrigated ones while irrigated orchards under the SFA approach was less technically efficient than non-irrigated ones. However, the test results of mean difference indicate that average VRS technical and scale efficiencies in irrigated orchards under the DEA approach were not significantly higher than in non-irrigated ones. On the other hand, technical rather than scale inefficiency is the major source of overall inefficiency in irrigated orchards because room for improvement in technical efficiency was larger than in scale efficiency. These results suggest that Tunisian olive growers should raise output and efficiency by introducing more advanced technologies for improving the performance of irrigation systems.   Key words: Olive orchards, technical efficiency, scale efficiency, irrigation, Tunisia.

This study focuses on evaluating the technical and scale efficiencies of smallholder pineapple farmers in Ghana’s Central Region. We surveyed 320 participants selected using random sampling and applied an input-oriented Data Envelopment Analysis (DEA) method to gauge their technical, pure, and scale efficiencies. Our findings indicate that the mean technical efficiency among these farmers is 0.505, with individual scores ranging from 0.079 to 1.000. Notably, 90.82% of the farmers are operating below maximum efficiency levels, suggesting a potential input reduction of up to 49.5% while maintaining current production levels. Relaxing the assumption of constant returns under Variable Returns to Scale (VRS) conditions reveals a notable improvement in technical efficiency, with 10.82% more farmers achieving optimal efficiency levels. Furthermore, our analysis highlights scale inefficiencies, with 67.26% of farmers operating below optimal scale levels. By increasing production by 22.8%, these scale-inefficient farmers could enhance their efficiency and productivity within existing technological frameworks. These findings underscore the importance of collaborative efforts among policymakers, practitioners, and stakeholders within the agricultural value chain to implement interventions such as improving access to technology and innovation for smallholder farmers and making necessary investments in farmer education and training programs to enhance both technical and scale efficiencies in Ghana’s pineapple sector. Such initiatives can drive sustainable growth, improve farmers’ livelihoods, and bolster the sector’s overall competitiveness.

Structural Adjustment and Economic Efficiency of Rice Farmers in Northern Ghana

In data envelopment analysis (DEA) an inefficient unit can be projected onto an efficient target that is far away, i.e. reaching the target may demand large reductions in inputs and increases in outputs. When the inputs and outputs modifications planned are large, it may be troublesome to carry them out all at once. In order to help an inefficient unit reach a distant target, a strategy of gradual improvements with successive, intermediate targets has been proposed. This paper extends such approach to the variable returns to scale (VRS) case. In the VRS scenario we distinguish between units that are technical efficient and those that are not. On the one hand, for those units that are not technical efficient the proposed approach determines successive intermediate targets leading to the technical efficiency frontier, i.e. the priority for those units is to attain technical efficiency. On the other hand, for those units that are technical efficient but not scale efficient the proposed approach computes a sequence of targets ending in the global efficiency frontier, i.e. when technical efficiency is guaranteed the goal is then to attain global efficiency. In both cases, the successive targets are obtained by iteratively solving specific DEA models that take into account given bounds on the rates of change in inputs and outputs that the unit can implement in each step.

Despite the upsurge in research on privatization in recent years, the empirical knowledge of the privatization programme in African is limited. Aside from theoretical predictions, not much is known about the process and outcome of privatization exercises in Africa. This study evaluates technical efficiency in four privatized enterprises in Nigeria. The methodology adopted is Data Envelopment Analysis (DEA). The technical efficiency scores are presented both for the Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) DEA. Relative efficiency was found to be considerably higher in the past privatization period in both the CRS and VRS specifications. These results indicate a substantial improvement in technical efficiency as a result of privatization. African Journal of Economic Policy Vol. 11(1) 2004: 17-34

The present study intended to determine the technical and scale efficiency of sample dairy farms for evaluating their performance. Data Envelopment Analysis (DEA) technique was used to estimate the technical and scale efficiency of 80 each of member and nonmember dairy farms in the Pune district of Maharashtra state during 2019. Technical efficiency score further partitioned into pure technical efficiency and overall technical efficiency. The technical efficiency score was more for member dairy farms as compared to the non-members under the assumption of constant return to scale (CRS) and variable return to scale (VRS). It highlighted that the non-members of dairy cooperatives had more potential to reduce the input use without affecting the output level compared to the member group. It was also observed that the technical efficiency under the CRS assumption was more than VRS for both member and non-member groups. It revealed that the farms were scaled inefficient (SE<1) and not operating at optimal scale. The study further revealed a positive relationship between technical efficiency and herd size. It also revealed that the resource-saving potential due to the scale effect. So, it supported the policy of providing technical advice on the use of feed and fodder resources, better management practices, and increasing the herd size to increase the technical and scale efficiency.

This study estimates technical, allocative, and cost efficiency using cost DEA model under both constant returns to scale (CRS) and variable returns to scale (VRS) respectively using survey data of 70 rice farmers from Kedah, Malaysia. In case of cost efficiency only 4.29% of the farmers were 100% technically efficient under CRS while it is increased into 16.90% under VRS. The average technical, allocative and cost efficiencies were estimated at 0.28, 0.878 and 0.255 respectively under CRS while they were increased into 0.61, 0.883 and 0.533 respectively under VRS.

This paper is focused on the investigation of the competitiveness drivers, namely technical and scale efficiency and technological change, and their relation to the profitability of the Czech food processing companies in the period 2016–2019. This investigation is based on the stochastic frontier modelling of an input distance function in the specification of the four-error-component model. The model is estimated with a multi-step procedure employing the generalized method of moments estimator addressing the potential endogeneity of netputs, and panel data gained from the Bisnode Albertina database. The results revealed (evaluated on the sample mean) that investigated food processing sectors were scale efficient in the analysed period, however, their production technologies exhibited prevailing technological regress. Moreover, the room for almost 17% cost reduction by the technical efficiency improvements was found out in all investigated sectors. Although inter-sectoral differences exist in the scale efficiency, technological change and technical efficiency dynamics, to increase the productivity and competitiveness of food processing companies, it is generally appropriate to focus on technical efficiency and technological change improvements. Both these competitiveness drivers connected with the cost reduction and minimizing of wastage of inputs are achievable through innovations. In general, the basic source of their financing is profit, the achievement of which is supported by cost minimization. However, it was found that sub-sectors, which are linked to sensitive sectors of agricultural production – that means sectors with the lowest national self-sufficiency, the highest level of imports and thus strong cost reduction pressure – have problem to translate the ability to produce efficiently into profitability. Although these food sectors, which have been also facing strong competition for a long time, which leads to significant pressure to reduce costs, achieved the highest technical efficiency, their profitability was lowest from the investigated sectors.

The paper aims to analyze the Technical Efficiency of 70 Commercial banks from 19 African countries from 2009-2020. Using the Data Envelopment Analysis (DEA) method of the two main approaches, Variable Return to Scale (VRS) and Constant Return to Scale (CRS) technique on a Panel Data. We find that African banks have a higher efficacy assessment with the VRS than the CRS technique, thus, with a Pure Technical Efficiency (PTE) score than Technical Efficiency (TE) . Our findings show that the majority of the banks are operating at very low levels of efficiency (not technically efficient), and inability to optimize the conversion of bank assets and liabilities into loan production for customers. Furthermore, the banks are operating inefficiently in scale, economic, and allocative manner due to mismatches in scale of production. Considering these findings, the implications of these inefficiencies extend to the overall economic development and financial stability of the region.

The purpose of this study is to evaluate the technical efficiency, productivity, and determinants of technical inefficiency in local hospitals in Oman, which are facing increasing resource constraints. Effective utilization of hospital resources is crucial for improving service delivery, ensuring equitable access, and maintaining the quality of healthcare. The study employs an input-oriented Data Envelopment Analysis (DEA) approach to assess the technical efficiency of 29 local hospitals under constant returns to scale (CRS), variable returns to scale (VRS), and scale efficiency (SE) using data from 2018. Additionally, a Tobit regression model is used to identify the determinants of hospital inefficiency, and the DEA-based Malmquist Productivity Index (MPI) is applied to panel data from 2015 to 2018 to measure Total Factor Productivity Change (TFPCH). The findings reveal that 75.8% of the local hospitals were technically efficient under VRS and SE assumptions, while 79.3% achieved technical efficiency under the CRS assumption. The average technical efficiency scores under CRS, VRS, and SE were 96%, 97%, and 99%, respectively. The Tobit model indicates that the number of physicians and pharmacists negatively impacts the VRS efficiency score, while the number of outpatient visits has a positive effect. Productivity growth of 18.1% was observed over the study period, mainly driven by a 42.6% increase in technological change. The study concludes that while most local hospitals in Oman are technically efficient, there is still room for improvement. The findings imply that targeted interventions, such as optimizing the allocation of human resources and leveraging technological advancements, could enhance the overall efficiency and productivity of the healthcare system in Oman.

Does Efficiency of Oral and Dental Health Centers Change by the Development Level of Regions?

This paper develops a frontier shadow cost function approach to estimate empirically the effects of technological change, technical and allocative efficiency improvement in Chinese agriculture during the reform period (1980–93). The results reveal that the first phase rural reforms (1979–84) which focused on the decentralization of the production system have had significant impact on technical efficiency but not allocative efficiency. However, during the second phase reforms which was supposed to focus on the liberalization of rural markets, technical efficiency improved very little and allocative efficiency has increased only slightly. Copyright © 2000 John Wiley & Sons, Ltd.

SUMMARYApplying stochastic frontier Cobb–Douglas production function, the study assessed the efficiency of sweet potato (Ipomoea batatas) producers in the Southern region of Ethiopia. The study revealed the existence of fairly large technical inefficiency in sweet potato production. The technical efficiency ranged from 12.6 to 93.7%, with more than half of the producers above the mean efficiency level (66.1%). This suggests that there is room for output gains through technical efficiency improvement. If the average producers in the study region are to achieve the technical efficiency level of the most efficient producer in the sample (93.7%), they can realize nearly 30% output gains. The analysis of allocative efficiency also revealed that sweet potato producers were producing sweet potato with sub-optimal utilization of production inputs, suggesting that potential for output gains remains to be exploited through reconfiguration of the existing resource use. They can make more value out of their sweet potato production by reconfiguring their current utilization of production inputs in favour of more land and manure but less seed rate. Furthermore, age and education are important determinants of the efficiency of sweet potato production. In view of these findings, it is advisable to put in place appropriate extension intervention programmes that enable sweet potato producers to exploit the potential gains in sweet potato output through technical and allocative efficiency improvement.

This paper develops a frontier shadow cost function approach to estimate empirically the effects of technological change, technical and allocative efficiency improvement in Chinese agriculture during the reform period (1980-93). The results reveal that the first phase rural reforms (1979-84) which focused on the decentralization of the production system have had significant impact on technical efficiency but not allocative efficiency. During the second phase reforms which was supposed to focus on the liberalization of rural markets, technical efficiency improved very little and allocative efficiency has increased only slightly, however. In contrast, the rate of technological change continued to increase, although at a declining rate during the second phase reform.

The purpose of this study was to measure the technical and scale efficiency of 128 general hospitals and 69 traditional medicine hospitals and to estimate the possible factors influencing on the efficiency of both general hospitals and traditional hospitals in Inner Mongolia, China. In first part, Input-orientation Data Envelopment Analysis (DEA) was employed to compute technical efficiency and scale efficiency of both general hospital and traditional medicine hospital.In second part, ordinary least square (OLS) was used to determine the factors that affect the efficiency. The DEA analysis showed 86.7% of general hospitals and 66.7% of traditional medicine hospitals were run inefficiency in 2011.Both the two types of hospital, the average variable return to scale (VRS) technical efficiency scores were about50%and 60%, respectively. Thepattern of most general and traditional medicine hospitals scale inefficiency was increasing return to scale. The result of OLS revealed that other personal-physician ratio had a positive relationship with both types of hospital technical efficiency, bed physician ratio;number of doctor in the form of square had a positive relationship with the general hospital technical efficiency score, nurse physician ratio had a negative relationship with the general hospital technical efficiency score. However, all above three variables were insignificant for the traditional medicine hospital technical efficiency scores. Bed occupancy and geographic were insignificant for two types of hospitals’ technical efficiency. For scale efficiency scores, if outpatient visits-physician ratio increase, both two types of hospital scale efficiency were influenced positively, on the contrary, number of beds was found negatively significant. Average of bed day had a negative relationship with the efficiency and significant for the general hospital scale efficiency, but insignificant for the traditional medicine hospital. Traditional medicine hospital in other region were less scale efficient than in the city area.