Subsidy to public inspections: Identification of Municipalities with discrepantspendings in Basic Education
DOI:
https://doi.org/10.55532/1806-8944.2022.158Keywords:
Clustering of municipalities, Anomaly detection, Public spending, Basic educationAbstract
Public spending must be constantly monitored by government agencies. In this context, the application of technology to produce strategic information that supports actions to combat corruption and mismanagement of public resources becomes essential. With the availability of the Information System on Public Budgets in Education (SIOPE), the present work employs data mining techniques for the detection of atypical expenses in Elementary Education, performed by municipalities in 2018 - which may constitute occasional events (such as works in schools) or represent evidence of irregularities. Clustering of municipalities and anomaly detection algorithms were applied to a group of similar municipalities. The results (if the municipality is anomalous and its degree of anomaly) can be added to the planning of control actions and, also, support the adoption of appropriate measures by other instances, such as the Ministry of Education and social control councils.
References
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BRAZIL. Law no. 11.494, of June 20, 2007. Regulates the Fund for Maintenance and Development of Basic Education and Valorization of Education Professionals - FUNDEB. Brasília, 2007. Available at: <http://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11494.htm>. Accessed on: 19 Nov 2019.
BRAZIL, Comptroller General of the Union. Inspection Report No. 201602219 - Fund for Maintenance and Development of Basic Education and Valorization of Education Professionals (Fundeb). Available at <https://auditoria.cgu.gov.br/download/12682.pdf>. Accessed on: 19 Nov 2019a.
BRAZIL, Comptroller General of the Union. Inspection Report No. 201602218 - Fund for Maintenance and Development of Basic Education and Valorization of Education Professionals (Fundeb). Available at <https://auditoria.cgu.gov.br/download/12685.pdf>. Accessed on: 19 Nov 2019b.
BRAZIL, Comptroller General of the Union. Annual Audit Report No. 201900673 - National Fund for the Development of Education - Fiscal Year 2018. Available at: <https://auditoria.cgu.gov.br/download/13670.pdf>. Accessed 20 Dec 2019c.
BRAZIL, Comptroller General of the Union. Guidelines for monitoring the actions of the Fund for Maintenance and Development of Basic Education and for Valuing Education Professionals (FUNDEB). Available at <https://www.cgu.gov.br/Publicacoes/controle-social/arquivos/fundeb2012.pdf>. Accessed on: 20 Dec 2019d.
BRAZIL, Office of the Comptroller General. Understand the indicators (Vulnerability Matrix). Available at: <https://www.cgu.gov.br/assuntos/auditoria-e-fiscalizacao/programa-de-fiscalizacao-em-entes-federativos/1-ciclo/1o-ciclo/entenda-os-indicadores>. Accessed 21 Dec 2019e.
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BRAZIL, Comptroller General of the Union. Ordinance No. 3,553, of November 12, 2019. Approves the Internal Regulations and the Demonstrative Chart of Commissioned Positions and Functions of Trust of the Office of the Comptroller General of the Union - CGU and makes other provisions. Available at <http://www.in.gov.br/web/dou/-/portaria-n-3.553-de-12-de-novembro-de-2019-227654932>. Accessed on 02 Feb 2020b.
BRAZIL, National Fund for Education Development. SIOPE Portal: Information System on Public Budgets in Education. Available at: <https://www.fnde.gov.br/fnde_sistemas/siope>. Accessed 06 Aug 2019.
BRAZIL, Brazilian Institute of Geography and Statistics. Population Estimates. Available at: <https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?=&t=resultados>. Accessed 08 Oct 2019.
BRAZIL, Anísio Teixeira National Institute for Educational Studies and Research. IDEB Results. Available at: <http://portal.inep.gov.br/web/guest/educacao-basica/ideb/resultados>. Accessed 08 Oct 2019a.
BRAZIL, Anísio Teixeira National Institute for Educational Studies and Research. IDEB Results. Municipalities - Regular Elementary School - Early Years.
Available at: <http://download.inep.gov.br/educacao_basica/portal_ideb/planilhas_para_download/2017/ disclosure_initial_years_municipalities2017-updated-Jun_2019.xlsx>. Accessed 08 Oct 2019b.
BRAZIL, Anísio Teixeira National Institute for Educational Studies and Research. IDEB Results. Municipalities - Regular Elementary School - Final Years.
Available from: <http://download.inep.gov.br/educacao_basica/portal_ideb/planilhas_para_download/2017/divulgacao_anos_finais_municipios2017-atualizado-Jun_2019.xlsx>. Accessed 08 Oct 2019c.
BRAZIL, Anísio Teixeira National Institute for Educational Studies and Research. IDEB Results. Municipalities - High School. Available at: <http://download.inep.gov.br/educacao_basica/portal_ideb/planilhas_para_download/2017/divulgacao_ensino_medio_municipios2017-atualizado-Jun_2019.xlsx>. Accessed 08 Oct 2019d.
BRAZIL, National Institute of Educational Studies and Research Anísio Teixeira. Instructions for using the Microdata of the 2018 Basic Education Census. Available at: <http://download.inep.gov.br/microdados/microdados_educacao_basica_2018.zip>. Accessed 12 Oct 2019e.
BRAZIL, National Institute for Educational Studies and Research Anísio Teixeira. Transition Rates 2014/2015. Available at: <http://portal.inep.gov.br/web/guest/indicadores-educacionais>. Accessed 12 Oct 2019f.
BRAZIL, National Institute of Educational Studies and Research Anísio Teixeira. Statistical Notes - Census of Basic Education 2019. Available at: <http://portal.inep.gov.br/censo-escolar>. Accessed on 02 Feb 2020.
BRAZIL, Ministry of Education. SIOPE Municipal 2018: User Guidelines Manual. Available at: https://www.fnde.gov.br/index.php/centrais-de-conteudos/publicacoes/category/139-siope?download=11869:manual-siope-municipal. Accessed 06 Aug. 2019.
BRAZIL, Ministry of Planning, Development and Management. Ordinance #42, of April 14, 1999. Updates the breakdown of spending by functions and makes other provisions. Available at <http://www.orcamentofederal.gov.br/orcamentos-anuais/orcamento-1999/Portaria_Ministerial_42_de_140499.pdf>. Accessed on 02 feb 2020.
Ministry of Planning, Development and Management. Federal Budget Secretariat. Technical manual of budget - MTO 2017. Brasília, 2016. Available at: <http://www.orcamentofederal.gov.br/informacoes-orcamentarias/manual-tecnico/mto_2017-1a-edicao-versao-de-06-07-16.pdf>. Accessed on 02 Feb 2020.
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CHAPMAN, Pete et al. CRISP-DM 1.0: Step-by-step data mining guide. Technical report.
The CRISP-DM consortium, 2000. Available at: <https://pdfs.semanticscholar.org/5406/1a4aa0cb241a726f54d0569efae1c13aab3a.pdf>. Accessed on: 29 Jan 2020.
GOLDSTEIN, Markus and UCHIDA, Selichi. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLoS ONE, 11(4): e0152173, April, 2016. Available at: <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152173>
Accessed 23 Dec 2019.
GOIX, Nicolas. How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms? 2016. Available at: <https://arxiv.org/abs/1607.01152> Accessed on: 25/02/2020.
HE, Zengyou; XIAOFEI, Xu and SHENGCHUN, Deng. Discovering cluster-based local outliers. Pattern Recognit. Lett., vol. 24, pp. 1641-1650, 2003.
IBM. IBM SPSS Modeler CRISP-DM Guide. Available at: <https://www.ibm.com/support/knowledgecenter/SS3RA7_18.2.1/modeler_crispdm_ddita/clementine/crisp_help/crisp_overview.html>, 2019. Accessed 29 Jan 2020.
KRIEGEL, Hans-Peter, SCHUBERT, Matthias, and ZIMEK, Arthur. Angle-based outlier detection in high-dimensional data. In Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining (pp. 444-452). Association for Computing Machinery. Las Vegas, NV, 2008.
LEEK, Jeff. The Elements of Data Analytic Style. Leanpub, Victoria British Columbia, 2015.
PEDREGOSA, Fabian et all. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011. volume 12, p. 2825-2830.
PROJECT JUPYTER. Project Jupyter®. Available at: <https://jupyter.org>. Accessed 30 Aug 2019.
PYTHON, Software Foundation. Python. Available at: <https://www.python.org/>. Accessed 30 Aug 2019.
QUEIROZ, Christina. FAPESP Research: Complex Gears. Fed by tax revenue, education funding schemes such as Fundeb, which expires in 2020, pose a challenge to the federal government. Available at: https://revistapesquisa.fapesp.br/2019/03/12/engrenagem-complexa>. Accessed on 02 Feb 2020.
SEABORN. SEABORN statistical data visualization. Available at <https://seaborn.pydata.org/index.html>. Accessed 30 Aug 2019.
SEN, Soumya. Intercluster and Intracluster Distance. Available at: <https://www.geeksforgeeks.org/ml-intercluster-and-intracluster-distance>. Accessed 20 Dec 2019.
SRIVASTAVA, Shobhit. Feature Scaling in Scikit-learn. Available at: <https://medium.com/analytics-vidhya/feature-scaling-in-scikit-learn-b11209d949e7>
Accessed 20 Dec 2019.
THE PANDAS PROJECT. Pandas - Python Data Analysis Library. Available at: <https://pandas.pydata.org>. Accessed 30 Aug 2019.
THESING, Ana Paula. Analytics and Big Data are powerful weapons against corruption. 2019. Available at: <https://www.itforum365.com.br/analytics-e-big-data-sao-poderosas-armas-contra-a-corrupcao>. Accessed on 20 Feb 2020.
Published
02-03-2022
How to Cite
Machado, R. G. (2022). Subsidy to public inspections: Identification of Municipalities with discrepantspendings in Basic Education. CADERNOS DE FINANÇAS PÚBLICAS, 22(01). https://doi.org/10.55532/1806-8944.2022.158
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