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From partial derivatives of DEA frontiers to marginal products, marginal rates of substitution, and returns to scale

机译:从DEA前沿的偏导数到边际产品,边际替代率和规模收益

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摘要

The characterization of a technology, from an economic point of view, often uses the first derivatives of either the transformation or the production function. In a parametric setting, these quantities are readily available as they can be easily deduced from the first derivatives of the specified function. In the standard framework of data envelopment analysis (DEA) models these quantities are not so easily obtained. The difficulty resides in the fact that marginal changes of inputs and outputs might affect the position of the frontier itself while the calculation of first derivatives for economic purposes assumes that the frontier is held constant. We develop here a procedure to recover first derivatives of transformation functions in DEA models and we show how we can evacuate the problem of the (marginal) shift of the frontier. We show how the knowledge of the first derivatives of the frontier estimated by DEA can be used to deduce and compute marginal products, marginal rates of substitution, and returns to scale for each decision making unit (DMU) in the sample. (C) 2016 Elsevier B.V. All rights reserved.
机译:从经济的角度来看,对技术的表征通常使用转换或生产函数的一阶导数。在参数设置中,这些量很容易获得,因为可以很容易地从指定函数的一阶导数中推导出来。在数据包络分析(DEA)模型的标准框架中,很难轻易获得这些数量。困难在于这样一个事实,即投入和产出的边际变化可能会影响边界本身的位置,而出于经济目的计算一阶导数时,则假定边界保持不变。我们在这里开发一种程序来恢复DEA模型中变换函数的一阶导数,并且我们展示了如何避免边界的(边际)转移问题。我们展示了如何通过DEA估算的边界一阶导数的知识可用于推导和计算样本中每个决策单元(DMU)的边际乘积,边际替代率以及规模收益。 (C)2016 Elsevier B.V.保留所有权利。

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