Project Title :
Study Area : Bangladesh
Bangladesh is predominantly an agricultural economy. About 80 percent of total cultivable land is diverted to rice production . The share of agriculture to GDP is about 37 percent of which 30 percent comes from rice. Still Bangladesh has a chronic shortage of foodgrain. Bangladesh has to import about two millions tons of foodgrain per year to meet her domestic consumption. If one knows the existing efficiency level of farmers in using the inputs for rice production then government can take viable plans to increase the rice production up to the maximum level. If farmers are found to be technically inefficient, production can be increased to a large extent with the existing level of inputs and available technology by rearranging input combinations. On the other hand, if farmers are technically efficient, then the government has to increase investment and has to adopt new technology in order to increase production to meet her consumption needs. The objective of this study is to determine the status of technical, allocative and economic efficiency for a sample of rice farmers. This is because determining the efficiency status of farmers are very important for policy purposes. Efficiency also is a very important factor of productivity growth. In an economy where resources are scarce and opportunities for new technologies are lacking, inefficiency studies will be able to show that it is possible to raise productivity by improving efficiency without the resource base or developing new technology. It also helps determine the under utilization or over utilization of factor inputs.
The measurement of the productive efficiency of a farm relative to other farms or to the 'best practice' in an industry has long been of interest to agricultural economists. From a theoretical point of view, there has been a spirited exchange about the relative importance of the various components of firm efficiency(leibenstein 1966, 1978; Comanor and leibenstein; Stigler). From an applied perspective, measuring efficiency is important because this the first step in a process that might lead to substantial resource savings.These resource savings have important implications for both policy formulation and firm management.
The current interest in efficiency measurement finds it's origin in a pioneering paper published by M. J. Farrel over 40 years ago. Farrell (1957) suggested a method of measuring the technical efficiency of a firm in an industry by estimating the production function of firms which are 'fully-efficient'(i.e., a frontier production function). The approach proposed by Farrell distinguishes between technical and allocative efficiency where the former refers to the ability of a firm to produce maximum possible output with a minimum quantity of inputs, given technology; the latter refers to the choice of the optimal input proportions given relative prices. Economic or total efficiency is the product of technical and allocative efficiency. Farrell's model which is known as a deterministic non-parametric frontier (Forsund, Lovell, and Schmidt), attributes any deviation from the frontier to inefficiency and imposes no functional form on the data.
Many subsequent papers have applied and extended Farrell's ideas. This literature may be roughly divided into two groups according to the method chosen to estimate the frontier production function, namely mathematical programming versus econometric estimation. The mathematical programming approach to frontier estimation is usually termed Data Envelopment Analysis (DEA). Coelli (1995a) outlines some of the literature on this approach. The primary criticism of the DEA approach is that measurement errors can have a large influence upon the shape and positioning of the estimated frontier.
A deficiency characterizing all deterministic frontier models is their sensitivity to extreme observations. Aigner, Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977) independently proposed the stochastic frontier production to account for the presence of measurement error in production in the specification and estimation of frontier production functions and to ameliorate the extreme observation problem. The advantage of the stochastic frontier over the deterministic frontier is that farm-specific efficiency and random error effect can be separated. Stochastic frontier production functions have two error terms, one is symmetric which to account for factors such as measurement error in the output variable, luck, weather, etc. and the other is a one-sided component which captures the effects of inefficiency relative to the stochastic frontier. An extension by Jondraw et al. has solved the previous inability of deriving individual firm efficiency measures from stochastic frontiers.
This attempt will contribute to the literature on farm level efficiency measurement by extending Kopp and Diewert's decomposition technique from a deterministic to a stochastic model. This stochastic formulation yields technical, economic and allocative efficiency measures that are free from distortions, stemming from statistical noise, inherent in deterministic models. In addition , the model makes possible a comprehensive efficiency analysis relying only on the econometric estimation of a production frontier, which is helpful because the farm-level price data required to estimate dual (cost and profit ) models are often unavailable or inadequate (Quiggin and Bui-lan).
The specific objectives of the study are as follows: