#SOFTWARE FRONTIER 4.1 FULL#

Potential output producible from the capacity base with variable inputs If variable inputs are assumed to be approximated by the

#SOFTWARE FRONTIER 4.1 FULL#

Thus variable effort, from its full utilization level is sometimes termed an If capacity isДefined only in terms of capital inputs, the implied variation in output, and Variable inputs (usually days, or variable В“effortВ”). InД®stimating the full utilization production frontier, a distinction must be madeД«etween inputs comprising the capacity base (usually capital inputs), and While the techniques have been developed primarily to estimateД®fficiency, they can be readily modified to represent capacity utilization. AДЇirm is technically efficient if its output level is on the frontier, which Measure of its output as a proportion of the corresponding frontier output. Technical efficiency of the jth firm is therefore a relative If u jД®quals zero, then TE equals one, and production is said to be technicallyД®fficient.

Then technical efficiency (TE) can be given by:ДЄnd is constrained to be between zero and one in value. While the efficient level of production (i.e. Given that the production of each firm j can be V j and u j are assumed to be independentlyДЄnd identically distributed (iid) with variance Representing the technical inefficiency of firm j. (white noise) error term and u j is a one-sided error

J, x is a vector of factor inputs, v j is the stochastic The relationship between observed production and some ideal or potentialДЄ general stochastic production frontier model can be given To measure the relative efficiency of certain groups or a set of practices from В“inefficientВ”, so from an estimated production frontier it is possible Observations within the frontier are deemed Noise is accommodated, since the estimation procedures are stochastic, but anДЄdditional one-sided error represents any other reason firms would be away from Output for a given set of inputs along a ray from the origin point. It therefore indicates the maximum potential In contrast, estimation of the productionДЇrontier assumes that the boundary of the production function is defined by Thus assumed to be random, and are likely to be associated with mis- or (average) firm therefore defines the frontier. Lindner, 1990) and translog production functions (Squires, 1987 Pascoe andДЄn implicit assumption of production functions is that allДЇirms are producing in a technically efficient manner, and the representative Production functions (Hannesson, 1983), CES production functions (Campbell and The individual boat level or total fishery level. Of the factors of production through estimating production functions at either A number of studies have estimated the relative contributions If estimatedД®conometrically from data on observed outputs and input usage, it indicates theДЄverage level of outputs that can be produced from a given level of inputs The underlying theoryДЄ production function defines the technological relationshipД«etween the level of inputs and the resulting level of outputs. Outputs that take zero values (Paul, Johnson and Frengley, 2000). Requires stochastic multiple output distance functions, and raises problems for Model multiple output technologies, doing so is somewhat more complicated, A disadvantage of the technique is that, although it can Measure is more consistent with the potential harvest under В“normalВ” Over DEA is that random variations in catch can be accommodated, so that the

A potential advantage of the stochastic production frontier approach Modification of the inputs incorporated in the production (or distance)ДЇunction. However, the technique also can be applied to capacity estimation through Stochastic production frontiers were initially developed forД®stimating technical efficiency rather than capacity and capacity utilization. APPENDIX C: STOCHASTIC PRODUCTION FRONTIERS

Software frontier 4.1