基于投入距离函数的单要素技术效率测量方法研究  

Measuring Input-specific Technical Efficiency Based on Input Distance Function

作　　者：杨浩然[1,2] Yang Hao-ran(School of Economics,Southwest University of Political Science&Law,Chongqing 401120,China;Center for Institutional Economics,Southwest University of Political Science&Law,Chongqing 401120,China)

机构地区：[1]西南政法大学经济学院,重庆401120 [2]西南政法大学制度经济学研究中心,重庆401120

出　　处：《中国管理科学》2021年第7期238-248,共11页Chinese Journal of Management Science

基　　金：重庆市教委科技项目(KJQN202000305);重庆市教委人文社科项目(21SKGH181);重庆市自然科学基金资助面上项目(cstc2020jcyj-msxmX0945)。

摘　　要：在生产中,由于企业并未掌握关于生产技术的完全信息,因此可能导致不同生产要素具有不同的技术效率水平。而传统的"径向"技术效率测量方法并不能识别生产要素之间的技术效率差异。为解决这一问题,现有的文献中分别提出了基于利润函数和方向距离函数的测量方法。但利润函数的估计需要用到难以收集的价格数据;估计方向距离函数需要事先设定投入要素缩减的方向,而这一方向对于研究者是未知的。基于投入距离函数,本文构建了一个新的可以测量单要素技术效率的框架,且无需价格数据和事先设定"方向"。文章采用贝叶斯方法分两步估计该模型:首先得到模型参数的估计值;其次在模型参数估计值给定的基础上再估计单个要素的技术效率水平。蒙特卡洛模拟分析发现,与直接估计各要素的技术效率的方法相比,这种"两步法"可以更快的实现马尔科夫蒙特卡洛(MCMC)过程的收敛,并能够较为精确的估计各要素的技术效率水平。之后将该方法应用于北京大学企业社会责任调查的数据,估计了资本和劳动力的技术效率水平。结果显示,企业在利用资本中几乎不存在技术效率损失,并且企业间的资本技术效率水平无明显差异。企业技术效率损失主要来自于劳动力利用不足,且企业间劳动力技术效率水平差异较大。平均而言,劳动力的技术效率水平为77%,即在保持产出和资本投入不变的情况下,可以使劳动力的投入下降23%。这个例子表明,本文提出的方法可以识别生产中导致技术效率损失的主要原因,从而有助于找到提升生产效率的解决方案。Due to incomplete information about production technology,firms may use some inputs more efficiently than others,which makes the radial technical efficiency measurement inappropriate.Different approaches have been proposed to reconcile the non-radial nature of technical efficiency and the measurement methodologies.The revenue function based approach proposed by Kumbhakar and Lai(2016)can be applied to measure output-specific technical efficiency.With minor modification,the framework proposed by Kumbhakar and Lai(2016)can be extended to profit function to measure input-specific technical efficiency(ISTE).This approach has limitation in empirical research in that reliable data on input and output prices are not always available.The directional distance function approach can also be used to measure ISTE(Färe and Primont,1995).However,it requires to specify the"directions"toward which inputs can be contracted before data analysis was applied,yet this kind of information is unavailable a priori.Aparicio et al.(2017)proposed a method to measure ISTE based on the principle of least action and the corresponding DEA method was developed to estimate ISTE.It is well known that DEA method is vulnerable to extreme values and it is difficult to distinguish if the distinction between technical efficiency of each input is real or just due to statistical noise.In this paper a new method is proposed to measure input-specific technical efficiency(ISTE)based on input distance function which requires no price information and no need to specify the"direction"a priori.Let x_(j) denote the quantity of input j(j=1,…,K)used in production,y denote output.Let TE_(j)=exp(-η_(j));(0<η_(j)<∞)represents technical efficiency level of input j.Therefore,x_(j)^(0)=x_(j)·TE_(j) gives optimal level of input j while keeping the quantity of output y unchanged.Let X^(0)=(x_(1)^(0),…,x_(K)^(0))be the optimal input vector.Apparent for input distance function we can get:D(Y,X^(0))=sup_(λ){λ:(X^(0)/λ,Y)∈T}=1(1)Suppose Cobb-Douglas function ca

关 键 词：投入距离函数 单要素技术效率 两步法 

分 类 号：F061.1[经济管理—政治经济学] F064.1