基于DEA和决策树的我国工业劳动生产率影响因素规则提取  

The Rule Extraction for China Industrial Labor Productivity Based on DEA and Decision Tree

作　　者：陶洪[1,2] 徐福缘[2] 

机构地区：[1]绍兴文理学院经济与管理学院.绍兴312000 [2]上海理工大学管理学院,上海200093

出　　处：《上海理工大学学报》2012年第4期394-398,共5页Journal of University of Shanghai For Science and Technology

基　　金：浙江省哲学社会科学规划重点资助项目(08CGJJ001Z);国家自然科学基金资助项目(71171135);上海市重点学科建设资助项目(S30504)

摘　　要：利用数据包络分析将影响劳动生产率增长的因素分解为技术效率、纯技术进步、人均资本的规模效率、资本深化4个方面,并结合分类与规则提取的决策树方法,揭示了各因素影响劳动生产率变动的规律;然后结合我国工业统计数据,分析了我国省域工业劳动生产率的变动特征.研究显示对我国省域劳动生产率差异影响最大的指标是资本深化,其次是技术效率和人均资本的规模效率,而技术进步基本不对省域劳动生产率差异产生影响.Based on data envelope analysis （DEA）, the labor productivity was decomposed into 4 acpects： technical efficiency, pure technical progress, changes of scale efficiency of capital per labor and capital deepening. The rule extraction based on decision tree classification algorithms was used to get the effects of labor productivity＇s affecting factors and to study how to affect provincial industrial labor productivity. The research finds that the most important factor affecting provincial industrial labor productivity is the capital deepening. The second factor is the technical efficiency, and the next factor is the scale efficiency of capital per labor. Pure technical progress almost doesn＇t affect the difference between provincial industrial labor productivities.

关 键 词：劳动生产率 数据包络分析 决策树算法 人均资本 

分 类 号：F404[经济管理—产业经济]