Avaliação da Eficiência dos Gastos das Instituições Federais de Ensino Superior Brasileiras

Rolim, Luckas Fernandes; Almeida, Aléssio Tony Cavalcanti de; Filho, Stélio Coêlho Lombardi; Júnior, Otoniel Rodrigues dos Anjos

doi:10.22478/ufpb.2238-104x.2021v11n1.50628

Cited by 3 publications

(7 citation statements)

References 22 publications

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“…Finally, Rolim et al (2020) evaluated the technical efficiency of federal universities, based on data from 2015, using the DEA-BCC model oriented to output. Current Cost (CC), the number of professors in activity, and the number of employees were used as inputs, and the number of students enrolled and the General Course Index (IGC) were used as outputs.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Thus, as a starting point, 12 management indicators used to assess the performance of federal universities were previously considered as inputs, and as outputs, two indicators that express the quality results of these institutions were considered. Subsequently, taking into account the correlations between inputs and outputs (APPENDIX A), as well as other studies carried out in the Brazilian context TURRIONI, 2006;COSTA et al, 2012;NUINTIN, 2014;COSTA et al, 2015;MARTÍNEZ COHEN;PAIXÃO;OLIVEIRA, 2018;ROLIM et al, 2020), four input variables and two output variables were selected for the model, in order to achieve a reasonable level of discrimination, since it is recommended that the number of DMUs should be at least equal to twice the product between inputs and outputs, according to Dyson et al (2001).…”

Section: Selection Of Variablesmentioning

confidence: 99%

“…The first input (Current Cost -CC) is related to the maintenance cost of federal universities, including expenses with professors and employees in activity, and other expenses, such as electricity, water, warehousing and cleaning materials, student assistance and outsourcing services (BRASIL, 2006;ROLIM et al, 2020). However, for this research, the costs related to hospital complexes were disregarded, since not all federal universities have university hospitals.…”

Section: Selection Of Variablesmentioning

confidence: 99%

“…The third input is the Number of Equivalent Employees (NFE), which considers civil servants and outsourced employees, whose function is to offer administration and support services to teaching and research activities. When calculating the indicator, those who work 40 hours/week are assigned a weight of 1.00; to those who work 30 hours/week, weight 0.75; and to those who work 20 hours/week, weight 0.50 (BRASIL, 2006;ROLIM et al, 2020). Finally, the fourth input (Faculty Qualification Index -IQCD) refers to the weighted average of faculty qualification, obtained through the following weights: 5 for Doctoral Degree/PhD, 3 for Master's Degree, 2 for Lato Sensu Postgraduate Degree, and 1 for Undergraduate Degree (BRASIL, 2006).…”

Section: Selection Of Variablesmentioning

confidence: 99%

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Efficiency in Brazilian Federal Universities: a study with Data Envelopment Analysis (DEA)

2022

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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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Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Selection Of Variablesmentioning

confidence: 99%

Section: Selection Of Variablesmentioning

confidence: 99%

Section: Selection Of Variablesmentioning

confidence: 99%

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Efficiency in Brazilian Federal Universities: a study with Data Envelopment Analysis (DEA)

2022

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“…Nesse sentido, as ações vinculadas ao REUNI buscavam atender a um compilado de demandas que propunham o aumento da eficiência das universidades, ou seja, o aprimoramento da forma de utilização dos recursos disponibilizados com o objetivo de MOREIRA et al, 2019;MELONIO;LUCAS, 2020;ROLIM et al, 2020;SILVA et al, 2021).…”

Section: Introductionunclassified

Avaliação do Programa Reuni: Um Estudo com as Universidades Federais do Nordeste

Lós¹,

Oliveira²,

Rita

et al. 2022

RFSA

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As políticas públicas de expansão do acesso e qualidade do ensino superior no Brasil tiveram marco a partir do ano de 2003. Uma das políticas desenvolvidas com este objetivo foi o Programa de Apoio a Planos de Reestruturação e Expansão das Universidades Federais, conhecido como REUNI. Nos anos de 2008 a 2012 foram empenhados 4,2 bilhões de reais para atender os objetivos do programa que continham, entre eles, o aumento de vagas e a otimização de recursos estruturais e humanos nas universidades. Este artigo visa avaliar a eficiência técnica de 15 universidades selecionadas da região nordeste do Brasil, por meio de Análise Envoltória de Dados no modelo BCC, observando a relação entre os recursos utilizados pelas instituições e os resultados obtidos nos indicadores de taxa de sucesso na graduação (TSG) e na relação aluno x professor (RAP) nos anos de 2009 a 2012. A pesquisa é classificada metodologicamente como descritiva e se deu através da análise de dados secundários obtidos através do sistema SIOP e relatórios de gestão das universidades. Os resultados demonstraram as universidades com os melhores desempenhos, com destaque para UFAL, UFPE e UFRN que foram as únicas a alcançar a fronteira de eficiência composta normalizada. Também foi possível identificar as universidades menos eficientes de cada ano e quais as melhorias necessárias para que possam atingir a fronteira de eficiência. Além disso, foi possível constatar um crescimento gradual da RAP durante os anos, porém, o mesmo não ocorreu com a TSG.

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The measurement, level, and influence of resource allocation efficiency in universities: empirical evidence from 13 “double first class” universities in China

Chen,

Sun

et al. 2024

Humanit Soc Sci Commun

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China’s higher education system is shifting from quantitative expansion to connotative development to advance its quality. Since 2015, Chinese governments have been implementing a strategic policy for higher education called “double first-class”, which aims to promote a number of Chinese top universities to construct world-class universities or to establish world-class disciplines. “Double first-class” universities have received a large amount of educational resources through this policy. Taking advantage of resources efficiently is an important element in promoting the development of higher quality higher education. However, research on resource allocation in China’s “double first-class” universities is incomplete. Current research has not clarified the level of resource allocation efficiency or the factors affecting China’s “double first-class” universities. With the help of the superefficient data envelopment analysis (DEA)-Malmquist–Tobit model, this study actively explores the current status of the resource allocation efficiency of China’s “double first-class” universities to fill this gap in the field. Specifically, the development level and change trend of the resource allocation efficiency of 13 “double first-class” universities in China from 2015 to 2019 were measured with the help of the superefficient DEA-Malmquist model. The internal and external factors affecting the resource allocation efficiency of “double first-class” universities are also analysed with the help of the Tobit model. The overall level of resource allocation efficiency of “double first-class” universities is high, but the internal variability is large. From the perspective of efficiency decomposition, it is found that both technical efficiency change (EFch) and technical progress efficiency (TEch) play important roles in improving the total factor productivity (TFP) of resource allocation. Compared with TEch, EFch plays a more significant pulling role. This study confirms that the factors affecting resource allocation efficiency are complex. Among them, the regional economic environment, faculty title structure, and degree of international exchange have significant roles in promoting the resource allocation efficiency of “double first-class” universities, but local financial support and the time of policy implementation have certain negative effects.

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Avaliação da Eficiência dos Gastos das Instituições Federais de Ensino Superior Brasileiras

Cited by 3 publications

References 22 publications

Efficiency in Brazilian Federal Universities: a study with Data Envelopment Analysis (DEA)

Efficiency in Brazilian Federal Universities: a study with Data Envelopment Analysis (DEA)

Avaliação do Programa Reuni: Um Estudo com as Universidades Federais do Nordeste

The measurement, level, and influence of resource allocation efficiency in universities: empirical evidence from 13 “double first class” universities in China

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