Using a Probablistic Frontier Production Function to Measure Technical Efficiency

Journal of Political Economy (Impact Factor: 2.9). 02/1971; 79(4):776-94. DOI: 10.1086/259787
Source: RePEc
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    • "Akinwumi and Djato, (1996) defined allocative efficiency as the extent to which farmers make efficient decisions by using inputs up to the level at which their marginal contribution to production value is equal to factor costs. Failure to equate revenue product of some or all factors to their marginal cost is at the very core of economic theory (Timmer, 1971). Similarly, Ali and Byerlee (1991) "
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    ABSTRACT: Rice farming dominates the farming system of Bangladesh, accounting for 75% of gross cropped area. Rice-farming environment is not the same in all parts of the country. Productivity and efficiency of rice production varies from one region to another. This paper is an effort to estimate the level of technical efficiency of rice farms operating in Meherpur district of Bangladesh. The study is mainly based on primary data that are collected from 126 rice farmers of Meherpur district following multistage random sampling technique. The level of technical efficiency of rice farms is estimated applying Translog Stochastic Frontier production function approach. The study found that the average technical efficiency of aus, aman and boro rice farms in Meherpur district are 87.7%, 86.8% and 89.5%, respectively. In addition, the study estimates the determinants of technical efficiency and found that farm size, labor cost, fertilizer and pesticide cost, seed cost, irrigation cost, and ploughing cost have significant contribution in changing the level of technical efficiency of rice production.
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    • "The reason why the above methods are selected is that they are well-developed models with some obvious advantages. The three-stage DEA model proposed by Fried et al. (2002) combines the strong points of the DEA model (Charnes et al., 1978; 1984) and the Stochastic Frontier Analysis (SFA) model (Timmer, 1971). The new model can not only overcome the shortages of calculating efficiency values with the DEA model (not considering the external environment factors have some effects on a Decision Making Unit (DMU)'s efficiency), but also compensate for the fact that the SFA model ignores the influence of random errors. "
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    ABSTRACT: China’s construction industry has constantly been confronted with the problems, such as high resource consumption, serious pollution and low energy efficiency. Thus, improving the energy efficiency of the construction industry and reducing its energy consumption can not only promote the sustainable development of the socio-economy and eco-economy, but also enhance the overall development level of the construction industry. In the context, the objectives are to put forward a set of systematic methodologies for measuring the energy efficiency of the regional construction industry and analyzing its change trends. First, the energy efficiency index system of the construction industry and its influencing factors are constructed through the literature review. Second, two research methods (the three-stage Data Envelopment Analysis (DEA) model and the Data Envelopment Analysis-Discriminant Analysis (DEA-DA) model) are applied to analyze the energy efficiency in 30 provinces of China and the change trends from 2003 to 2011. The results indicate that after eliminating the influence of the environment factors and random errors, the energy efficiency values of the construction industry in most of the provinces were improved. The mean of China’s energy efficiency of the construction industry in each year was approximately 0.92. Except Shandong with the lowest values, the mean of the other provinces was over 0.8, which reflected that the energy management and utilization levels in the construction industry were relative mature. However, the energy efficiency in most of provinces fluctuated constantly during these nine years, with the peak in 2004 and a downward trend in the overall efficiency after 2004. From the regional aspect, the energy efficiency of the construction industry in the eastern, central and western regions decreased successively; as the development level of the local economy had less significant effects on the energy efficiency, the gaps among the three regions were not obvious.
    KSCE Journal of Civil Engineering 04/2015; DOI:10.1007/s12205-015-0553-3 · 0.48 Impact Factor
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    • "farmers attending training on farming techniques are expected to perform better than those who do not (provided that they are operating under otherwise similar conditions). Thus, following Timmer (1971) and Muller (1974), we aim to investigate the variation in TE across various socioeconomic characteristics. Because the dependent variable, the TE score for each farm, is censored between zero and one, we employ the Tobit model to yield unbiased estimates of the characteristic effects: "
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    ABSTRACT: This article investigates the efficiency of intensive, semi-intensive, and extensive shrimp farming practices as well as the difference between the upstream and downstream efficiency of shrimp farms in the Mekong River Delta (MRD), Vietnam. Our article is the first to compare the efficiency of the 3 shrimp practices and investigate the difference between the efficiency of downstream and upstream farms. The efficiency of shrimp farms is measured using group-frontier and meta-frontier analysis on a sample of 292 farms. The results show that, on average, shrimp farms are inefficient; extensive farms are more efficient than intensive and semi-intensive farms; and, controlling for key socio-economic factors, upstream farms are more efficient than downstream farms, suggesting that pollution from upstream farms may influence shrimp farm efficiency. The results give some direction for improvement and some evidence to shrimp farmers and policymakers in the MRD to take the pollution problem seriously and find solutions for more sustainable development.
    Aquaculture Economics & Management 11/2014; 18(4):325–343. DOI:10.1080/13657305.2014.959209 · 1.76 Impact Factor
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