Nonparametric Analysis of Technical and Allocative Efficiencies in Production
生产中技术效率与配置效率的非参数分析AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要用利润最大化与生产可能集约束，从企业投入、产出和价格观测推导效率界限，对应非参数结构估计方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
生产中技术效率与配置效率的非参数分析
- 研究问题
- 当企业投入、产出与价格观测不能全部被利润最大化解释时，如何识别可理性化的企业，并界定技术与配置效率？
- 方法与思路
- 作者在包含全部观测的闭、凸、单调生产可能集中，确定可共同弱理性化的最大企业集合，并据此限定可容许的生产可能集。
- 材料陈述的贡献
- 摘要称可求技术、配置与总体效率的最紧上下界，并证明技术效率的最紧上界与数据包络分析计算的数值相同。
- 阅读关注点
- 核对利润最大化理性化条件、最大企业集合的构造和上下界的紧性，尤其看数据包络分析对应技术效率上界的论证。
- 阅读边界
- 摘要中的集合符号存在转录损坏；仅凭摘要不能核验界限求解方法、计算成本及对生产可能集形状约束的敏感性。
原文摘要
In this paper we extend Varian's (1984) nonparametric production analysis to situations when the set of observed output, input, and price data is not consistent with profit maximization for at least one firm. In such cases, Varian's results imply that no production possibility set containing all observations can rationalize the observed data. We identify each firm whose performance, given the prices faced by it, may be found consistent with profit maximization relative to some production possibility set containing all observed output-input vectors. We show that the set 4' of all such firms can itself be weaklv rationalized in the sense that there exists a (closed, convex, and monotone) production possibility set that contains all the observations, and relative to which the performance of all the firms in the set 8O is consistent with profit maximization given their respective prices. By definition, firms not included in this largest set d of efficient observations unambiguously deviate from profit maximizing behavior for any production possibility set containing all observations. We follow Farrell (1957) and analyze these deviations into technical and allocative efficiency measures, considering as admissible all closed, convex, and monotone production possibility sets relative to which the performance of each firm in the set g remains consistent with profit maximization. We then describe nonparametric methods for determining the tightest upper and lower bounds on the technical, allocative, and aggregate efficiency measures evaluated relative to all such admissible production possibility sets. It is seen that the tightest upper bound on the technical efficiency measure is the same as the value computed by the nonparametric efficiency evaluation technique known as data envelopment analysis, thus establishing a link between this literature in management science/operations research and the nonparametric production analysis in economics.