Inefficiency Effects Model Research Articles

Technical efficiency of carp production in Nepal: An application of stochastic frontier production function approach*

Modern aquaculture is a relatively new activity among Nepalese farmers and a small contributor to the economy. Given the abundance of water resources and fish species, rising demand for fish, and its high profitability, aquaculture has potential for future expansion if it is given appropriate attention from the government. In Nepal, productivity in aquaculture is much lower compared to other countries in the region, which suggests that there is potential for increased fish production through technological progress and improvement in farm‐level technical efficiency. However, no formal analysis has yet been conducted to assess the productive performance of Nepalese aquaculture and its potential for future improvement. Against this background, this paper examines the technical efficiency and its determinants for a sample of fish pond farms from the Tarai region of the country using a stochastic production frontier involving a model for technical inefficiency effects. The estimated mean technical efficiency is 77%, with intensive farms being more efficient than extensive farms. The adoption of regular fish, water, and feed management activities has a strong positive effect on technical efficiency.

Technical Inefficiency Effects Among Paddy Farmers in the Villages of the ‘Office du Niger’, Mali, West Africa

A stochastic frontier production function incorporating a model for technical inefficiency effects (Battese and Coelli, 1995) is applied to field data on paddy farmers from 29 villages in the ‘Office du Niger’ in Mali. Four ‘conventional factors’ (land, labor, fertilizer and machinery) are considered as inputs of production. The technical inefficiency effects in the stochastic frontier were related to firm-specific variables, institutional factors, social organisation, ecological considerations and health factors. Data were obtained from an economic survey conducted during two consecutive agricultural seasons (1989 and 1990) on 844 farms of the Office du Niger. The null hypothesis of the absence of technical inefficiency effects was rejected. A supportive ‘institutional environment’ and a coherent organisation of land use were the best correlates of technically efficiency. The social environment was also found to contribute to technical efficiency of the paddy farmers: within the village, the greater the degree of ethnic cohesion, the greater the efficiency of the farmers. Health status of households had an effect in that ‘healthy’ families tended to be more technically efficient than ‘unhealthy’ ones. Farmers with more extensive sorghum cultivation were less efficient as paddy farmers. These results may help agricultural policy makers formulate strategies. Technical efficiency may be improved by intensifying agricultural training regarding one specific crop and, through the control of parasitic diseases which place a burden on family households.

Functional Forms of Stochastic Frontier Production Functions and Models for Technical Inefficiency Effects: A Comparative Study for Wheat Farmers in Pakistan

This paper considers translog and Cobb-Douglas stochastic frontiers in which the technical inefficiency effects are defined by three different models. The models involved are the time-varying inefficiency model, proposed by Battese and Coelli (1992), the inefficiency effects model for panel data, proposed by Battese and Coelli (1995), and the non-neutral frontier model, proposed by Huang and Liu (1994). Technical change is also accounted for in the frontier models. Predicted technical efficiencies of the wheat farmers and estimates of the elasticities of wheat production with respect to the different inputs and the returns-to-scale parameter are compared under the different model specifications.

IDENTIFICATION OF FACTORS WHICH INFLUENCE THE TECHNICAL INEFFICIENCY OF INDIAN FARMERS

The agricultural production of Indian farmers is investigated using a stochastic frontier production function which incorporates a model for the technical inefficiency effects. Farm‐level data from the International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT) are used. Variables considered in the model for the inefficiency effects include the age and level of education of the farmers, farm size and the year of observation. The parameters of the stochastic frontier production function are estimated simultaneously with those involved in the model for the inefficiency effects. This approach differs from the usual practice of predicting farm‐level inefficiency effects and then regressing these upon various factors in a second‐stage of modelling. The results indicate that the above factors do have a significant influence upon the inefficiency effects of farmers in two of the three villages considered.

A model for technical inefficiency effects in a stochastic frontier production function for panel data

A stochastic frontier production function is defined for panel data on firms, in which the non-negative technical inefficiency effects are assumed to be a function of firm-specific variables and time. The inefficiency effects are assumed to be independently distributed as truncations of normal distributions with constant variance, but with means which are a linear function of observable variables. This panel data model is an extension of recently proposed models for inefficiency effects in stochastic frontiers for cross-sectional data. An empirical application of the model is obtained using up to ten years of data on paddy farmers from an Indian village. The null hypotheses, that the inefficiency effects are not stochastic or do not depend on the farmer-specific variables and time of observation, are rejected for these data.