The use of DEA as a tool to evaluate public expenditure on education: an analysis of the cities of the state of Rio de Janeiro

Abstract

The aim of this article is to evaluate the effi ciency of cities of the State of Rio de Janeiro in relation to municipal expenditures on education, as well as to identify the reasons that lead to ineffi ciency. In order to achieve the initial goal, this study uses DEA technique. The DMUs are 69 cities of the State of Rio de Janeiro. This paper uses following variables as inputs: municipal expenditure on elementary school, number of teachers of 9 th grade of elementary school and number of students enrolled in 9 th grade of elementary school. The following variables are used to compose output information: number of students who achieved advanced or profi cient level in Portuguese and number of students who achieved advanced or profi cient level in Mathematics. All data refer to the year 2013. In this study, output-oriented BCC model is used and, for its application, SIAD V3.0 software was used. GIS-DEA integration and some variables external to modelling were used to identify aspects that led to the ineffi ciency of some DMUs. This study concludes that cities that displayed a good performance are located in the least economically developed regions and that teachers' average salary is an important motivational factor.

Goal programming associated with the non-archimedean infinitesimal: a case study applied in the agricultural sector

“…Therefore, the method proposed by Yamada et al (1994) and Entani et al (2002) was not able to solve the problem of the distribution of unrealistic weights of the inputs and outputs of the DEA method (BCC-input). On the other hand, the Inverted Frontier was still applied together with the classic DEA since it is able to recognize benchmark units which represent potential management references (Mota & Meza, 2020).…”

The performance of regional governments under the results-based budgeting framework: A two-stage sectoral analysis

“…The regions marked by social inequalities presented low indexes of efficiency. Thus, one may conclude that the lack of worthy housing, the high rates of criminality, and the misdistribution of services can influence students' performance in a negative way (Mota & Meza, 2020). It is interesting to note the case of the region of Moquegua, which has experienced a significant improvement in its educational quality in recent years.…”

Data-Driven Evaluation and Optimisation of Livelihood Improvement Efficiency

“…Most current scholars discuss education, health, and employment when exploring the efficiency of livelihood improvement [30][31][32][33] but vary in how they further refine the indicators. For example, various indicators have been constructed for educational improvement efficiency: Andonova et al constructed public expenditure as an input indicator and set output indicators involving secondary education graduates, secondary education completion rate, and total net enrolment rate, among others [3]; Tulio et al constructed evaluation indicators of the number of teachers and students, and the achievements of the students [34]; Cossani et al used the area of teaching facilities, academic staff, and operating costs, among others, as input indicators and the number and quality of publications as output indicators to measure the efficiency of the inputs and outputs of higher education [35]; Azar et al used public expenditure on education per capita as an input indicator and 'average years of schooling' and 'population with secondary education as the highest educational level attained' as output indicators [36]; and Afonso et al adopted school buildings per capita and primary school enrolment as output indicators [37]. Regarding social security and employment, Giovannae et al used childbearing services, kindergartens, services for minors, leisure facilities, and care facilities for older and migrant population groups as indicators [4], whereas the evaluation indicators designed by Yangming Hu et al include coverage of endowment insurance and minimum living allowance, employment rate, and the gap between urban and rural areas [38].…”