Avaliação de políticas públicas: eficiência das universidades federais e identificação de benchmarks por meio de análise envoltória de dados

• Ano de defesa: 2019
• Autor(a) principal: Encinas, Rafael
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Planejamento e Governança Pública; UTFPR
• Palavras-chave em Português: Universidades e faculdades públicas - Brasil; Eficiência organizacional; Política pública - Avaliação; Ensino superior - Controle de qualidade - Indicadores; Benchmarking (Administração); Análise de envoltória de dados; Programação linear; Correlação (Estatistica); Modelos matemáticos; Administração pública; Public universities and colleges - Brazil; Organizational effectiveness; Public policy - Rating of; Education, Higher - Quality control - Directories; Benchmarking (Management); Data envelopment analysis; Linear programming; Correlation (Statistics); Mathematical models; Public administration; CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::ADMINISTRACAO PUBLICA; Planejamento Urbano e Regional
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/4588

The objective of this research was to evaluate the efficiency of federal universities and to identify the most efficient ones that can be used as benchmarks for the replication of good management and governance practices. The evaluation of public policies is essential for the accountability of public organizations and efficiency becomes an even more relevant principle in a situation of scarcity of resources, imposed by the budget ceiling established by the Constitutional Amendment nº 95/2016 and budgetary restrictions that threaten the autonomy of federal public higher education. The Data Envelopment Analysis (DEA) consists of a mathematical technique that uses linear programming to estimate a non-parametric production boundary from a set of input and output data. Eight models of DEA were executed, four with orientation to inputs, in which the objective was to verify to what extent the institutions justify their costs in front of the presented results; and four with product orientation, in order to identify which universities are able to present better results against the available inputs. In both orientations, the models of constant and variable returns of scale were compared; and for both were executed superefficient DEA models, with the objective to differentiate the universities considered efficient. Output variables were selected that indicated both the number of products generated – number of graduate students and intellectual production – and their quality – Indicator of Difference between Observed and Expected Performances (IDD) and Capes Concept. Among the input variables, in the orientation to inputs were used the total expenses of the universities; in product orientation, indicators were used that sought to reflect the quantity and quality of the inputs, such as the number of undergraduate and postgraduate students in the institutions, the number of teachers, the qualification of the teaching staff and the average grade of the courses in the offered conditions dimension of the Preliminary Concept of the Course (CPC). In each model, an efficiency ranking of federal universities was generated. For the analysis of the results, the universities were divided into two groups, using cluster analysis technique, and the rankings were analyzed using a Spearman correlation technique. It was verified that the rankings generated by the DEA are robust, being more influenced by the relation between “students graduated” and “total expenses”, in the input orientation; and by the relationship between the outputs “graduate students” and “intellectual production”, and the inputs “students enrolled” and “teachers”, in output orientation. In the comparison between constant and variable returns to scale models, there was a significant increase in efficiency scores, which indicates that scale is a relevant factor for the efficiency of universities, and other studies are necessary to better understand this impact. It was also verified that the DEA indicates as benchmarks among the efficient universities those that are similar to the inefficient ones, because 86% of the weight of the targets came from institutions of the same group. There is also ample space for improving the efficiency of federal universities, with the possibility of reducing almost R$ 2 billion in spending, which represents 21% of the expenses of inefficient universities. The same happens with the possibility of an increase in the number of students graduated in 13%, intellectual production in 23% and quality in 10%.