基于数据包络分析(DEA)的巴西联邦大学效率研究

João Paulo Araujo dos Santos, Luiz Honorato Da Silva Júnior, A. Nunes
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引用次数: 0

摘要

本文采用数据包络分析(DEA)方法,采用产出导向的DEA BCC模型,对2012 - 2018年巴西联邦大学的技术效率水平进行了分析。TCU制定的四个管理指标作为输入,两个质量指标作为输出。为此,我们选取了56所联邦大学作为样本。考虑到标准效率得分,结果表明,在分析的年份里,技术效率水平很高。2012年联邦大学的平均技术效率为93.7%,有17所高效大学,2018年为94.5%,有18所高效大学。2015年和2016年表现突出,分别有28所和27所高效大学,其全国平均水平为96.1%。然而,由于非常小或非常大的单位的BCC模型的仁慈,一些大学可能默认被认为是高效的。从2012年到2018年,Malmquist指数显示,大学生产率提高了2.2%,其中追赶效应(1.8%)所占比例较大,前沿转移效应(0.4%)所占比例较小。此外,1.8%的相对效率的提高主要是由于纯技术效率的提高(1.1%),而规模效率的提高所占的比例较低(0.7%)。然而,尽管生产力有所提高,但仍有提高质量和改进资源管理的余地,以便减少浪费。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficiency in Brazilian Federal Universities: a study with Data Envelopment Analysis (DEA)
This paper aimed to analyze the level of technical efficiency of Brazilian federal universities, from 2012 to 2018, using the Data Envelopment Analysis (DEA) method, with an output-oriented DEA BCC model. Four management indicators instituted by TCU were used as inputs and two quality indicators, as outputs. For that, a sample of 56 federal universities was used. Considering the standard efficiency scores, the results indicated high levels of technical efficiency during the years under analysis. The average technical efficiency of federal universities was 93.7% in 2012, with 17 efficient universities, and 94.5% in 2018, with 18 efficient universities. The years 2015 and 2016 stood out, with 28 and 27 efficient universities respectively, whose national averages were 96.1%. However, due to the benevolence of the BCC model with very small or very large units, some universities may have been considered efficient by default. From 2012 to 2018, the Malmquist Index indicated an increase in the productivity of universities by 2.2%, which occurred in a greater proportion due to the catch-up effect (1.8%) and in a lower proportion by the frontier shift effect (0.4%). In addition, the increase of 1.8% in relative efficiency occurred in a greater proportion due to the increase in pure technical efficiency (1.1%) and in a lower proportion due to the increase in scale efficiency (0.7%). Yet, despite the increase in productivity, there is still room to achieve better quality results and to improvements in the management of resources, in order to reduce waste.
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