{"title":"Evaluation of Operating Efficiency of Agricultural Listed Enterprises Based on DEA-Tobit Two Stage Model","authors":"Liping Yan","doi":"10.2991/MASTA-19.2019.8","DOIUrl":null,"url":null,"abstract":"This paper used DEA model to evaluate the operating efficiency of sample enterprises, and analyzed the influencing factors of business efficiency through Tobit regression. The results of DEA evaluation show that the operating efficiency of the listed agricultural enterprises is low and the difference between enterprises is obvious. The Northeast comprehensive efficiency, pure technical efficiency and scale efficiency is highest; Forestry, animal husbandry, farming, fishery and agricultural service industry’s comprehensive efficiency, pure technical efficiency decrease in turn; In addition to the low scale efficiency of fisheries, the rest of the industry scale efficiency is almost the same. Tobit regression analysis shows that the age, scale of enterprises and the nature of controlling shareholders are negatively related to business efficiency, and total assets return rate and ownership concentration are positively related to business efficiency. Introduction Meng Lingjie et al. (2005) found that the average efficiency of the sample companies is low, and is influenced by factors such as the company's operation time and business direction[1].Wang Qian and Qin Fu (2009) use the DEA model to evaluate the efficiency of the 42 listed agricultural enterprises in China in 2007 and carry out the projection analysis[2].Du Chuanzhong et al. (2009) used Malmquist efficiency index to evaluate the dynamic change of enterprise's efficiency level[3].Yuan Bin et al. (2015)evaluated the input-output efficiency of 109 agricultural industrialization leading enterprises in Nanjing in 2012.It was found that the productivity of the agricultural leading enterprises was \"U\" distribution with the upgrading of the grade, and the influence factors of the efficiency difference were analyzed by the Quantile regression[4].Wang Liming and Wang Yubin (2015) concluded that the efficiency of food leading enterprises is generally low, but it shows an upward trend and the difference of the comprehensive efficiency of enterprises in different regions is obvious[5]. The above research results have played an useful reference for the correct evaluation of the efficiency level of Chinese listed agricultural enterprises and the efficiency promotion of the listed agricultural enterprises. However, most of these studies lack systematic and in-depth analysis.Based on the comprehensive evaluation of the operational efficiency of agricultural listed companies by using the DEA model, this paper makes a thorough and comprehensive analysis of the comprehensive efficiency, scale efficiency and technical efficiency of enterprises from the perspective of enterprise size, industry category and regional distribution. Data Sources and Research Methods Data Sources In this paper, 62 companies with agriculture, forestry, animal husbandry and fishery as the main business activities and engaged in the processing of agricultural and sideline products in the Shanghai and Shenzhen A stock market in 2016 are the research samples. After removing samples from ST enterprises and data that did not meet the requirements of analysis, the final valid samples are 52. Shown in Table 1 and Table 2. International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019) Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Intelligent Systems Research, volume 168","PeriodicalId":103896,"journal":{"name":"Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/MASTA-19.2019.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
基于DEA-Tobit两阶段模型的农业上市企业经营效率评价
本文采用DEA模型对样本企业的经营效率进行评价,并通过Tobit回归分析企业经营效率的影响因素。DEA评价结果显示,上市农业企业的经营效率较低,企业间差异明显。东北地区综合效率、纯技术效率和规模效率最高;林、牧、农、渔业和农业服务业的综合效率、纯技术效率依次下降;除了渔业规模效率低外,其他行业的规模效率几乎相同。Tobit回归分析表明,企业年龄、规模、控股股东性质与经营效率负相关,总资产收益率、股权集中度与经营效率正相关。孟令杰等(2005)发现样本公司的平均效率较低,受公司经营时间和经营方向等因素的影响[1]。王茜和秦富(2009)利用DEA模型对2007年中国42家农业上市企业的效率进行了评价,并进行了预测分析[2]。杜传忠等(2009)用Malmquist效率指数来评价企业效率水平的动态变化[3]。袁斌等(2015)对2012年南京市109家农业产业化龙头企业的投入产出效率进行了评价。研究发现,农业龙头企业生产率随等级提升呈“U”型分布,并采用分位数回归分析效率差异的影响因素[4]。王黎明、王玉斌(2015)得出食品龙头企业效率总体较低,但呈上升趋势,不同地区企业综合效率差异明显[5]。以上研究成果为正确评价我国农业上市企业效率水平,促进农业上市企业效率提升起到了有益的借鉴作用。然而,这些研究大多缺乏系统和深入的分析。本文在运用DEA模型对农业上市公司经营效率进行综合评价的基础上,从企业规模、行业类别、区域分布等角度对企业的综合效率、规模效率和技术效率进行了深入、全面的分析。数据来源与研究方法数据来源本文以2016年沪深A股市场62家以农林牧渔业为主要经营活动,从事农副产品加工的公司为研究样本。除去ST企业的样本和不符合分析要求的数据后,最终有效样本为52个。如表1和表2所示。建模、分析、仿真技术与应用国际会议(MASTA 2019)版权所有©2019,作者。亚特兰蒂斯出版社出版。这是一篇基于CC BY-NC许可(http://creativecommons.org/licenses/by-nc/4.0/)的开放获取文章。智能系统研究进展,第168卷
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