Abstract
This work aims to demonstrate the efficiency degree achieved by the Brazilian states in the allocation of public resources for education. We used multivariate statistical analysis, with Data Envelopment Analysis and Multiple Regression, with data from 2001 to 2011. The results show that some states achieved good efficiencies such as Minas Gerais and Rio Grande do Sul. These states succeeded in conciliating reasonable expenses with good scores in education assessments and tests. Maranhao and Para had unsatisfactory scores in teaching, but also lower budgets of public expenditure. Thus, focusing on Brazilian Public Education, the results show the efficiency degree in public spending and education outcomes, using several variables and time periods.
Highlights
The current development level of democratic principles and citizenship requires a review of how information on the activities of the public sector are disclosed to citizens
This study proposes a form of measuring and evaluating the results obtained with the allocation of public funds to education
Data Envelopment Analysis (DEA) is a technique that could help to evaluate efficiency of public expenditure on education, because the technique is based on linear programming designed to measure the performance of Decision Making Units (DMU)
Summary
The current development level of democratic principles and citizenship requires a review of how information on the activities of the public sector are disclosed to citizens. Citizen participation will shortly rise to a level where the mere control and checks for compliance to the laws will no longer be sufficient to meet the information needs. In this context, the search for information on performance becomes a reality for which the public sector must be prepared. The development of new scientific methods can help improve the process of disclosing evidence-based results and of social control. Data Envelopment Analysis (DEA) is a technique that could help to evaluate efficiency of public expenditure on education, because the technique is based on linear programming designed to measure the performance of Decision Making Units (DMU). The results can be compared to an efficiency frontier formed by the best practices of their peers
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