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A stochastic frontier production function is defined for panel data on firms, in which the non-negative technical inetGciency 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 inefTiciency 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.

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Frontier production functions are important for the prediction of technical efficiencies of individual firms in an industry. A stochastic frontier production function model for panel data is presented, for which the firm effects are an exponential function of time. The best predictor for the technical efficiency of an individual firm at a particular time period is presented for this time-varying model. An empirical example is presented using agricultural data for paddy farmers in a village in India.

A rm ida le, NS WIn this paper recent developments in the estimation of frontier functions and the measurement of efficiency are surveyed, and the potential applicability of these methods in agricultural economics is discussed. Frontier production, cost and profit functions are discussed, along with the construction of technical, allocative, scale and overall efficiency measures relative to these estimated frontiers. The two primary methods of frontier estimation, econometric and linear programming, are compared. A survey of recent applications of frontier methods in agriculture is also provided.

In this paper we examine levels and trends in agricultural output and productivity in 93 developed and developing countries that account for a major portion of the world population and agricultural output. We make use of data drawn from the Food and Agriculture Organization of the United Nations and our study covers the period 1980-2000. Due to the non-availability of reliable input price data, the study uses data envelopment analysis (DEA) to derive Malmquist productivity indexes. The study examines trends in agricultural productivity over the period. Issues of catch-up and convergence, or in some cases possible divergence, in productivity in agriculture are examined within a global framework. The paper also derives the shadow prices and value shares that are implicit in the DEA-based Malmquist productivity indices, and examines the plausibility of their levels and trends over the study period.