No public surveys, no data? A proposal for income forecast in brazilian municipalities

Ricardo Limongi; Rafael Martins Ronqui; Pedro Paulo Coelho; Eduardo de Rezende Francisco

doi:10.5585/2023.22993

Authors

Ricardo Limongi Federal University of Goiás and Federal University of Uberlândia / Goiânia - GO https://orcid.org/0000-0003-3231-7515
Rafael Martins Ronqui Fundação Getulio Vargas / School of Business Administration of São Paulo, São Paulo - SP http://orcid.org/0000-0002-9588-5926
Pedro Paulo Coelho Fundação Getulio Vargas / School of Business Administration of São Paulo, São Paulo - SP http://orcid.org/0000-0001-6238-4749
Eduardo de Rezende Francisco Fundação Getulio Vargas / School of Business Administration of São Paulo, São Paulo - SP http://orcid.org/0000-0001-8895-2089

DOI:

https://doi.org/10.5585/2023.22993

Keywords:

forecast income, spatial statistics, public data, public policies, census

Abstract

Objective: Due to the lack of regularity from the census in Brazil, the proposal to use alternative indicators is relevant. The population's income, primary census information, is a variable used in studies in different areas such as public policies, forecasting, and planning a new business. However, on average, this information is released every ten years in Brazil; thus, it is necessary to explore frequency variables to estimate the population's income. In this sense, this study proposes a predictive income model based on technological and communication aspects.

Method: We choose two variables: internet and cable TV access. Our study included the analysis of the 5570 Brazilian municipalities through linear (OLS) and spatial models (Spatial Auto-Regressive [SAR] and Geographically Weighted Regression [GWR]).

Results: The results with the spatial models showed better results. The autoregressive spatial regression (SAR) presented a more significant explained variance (R² = 0.51) than the linear model (R² = 0.41) and the GWR model (R² = 0.44), which indicates a significant spatial dependence and contribution of the geographic perspective in modeling and explaining the phenomenon.

Conclusion: The results were found to contribute to the development of public policies in regions with difficult access to information on the population's income and with managers and companies in the technology area that seek to plan the improvement and expansion of the provision of digital communication services through a model updated with secondary public data.

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Author Biographies

Ricardo Limongi, Federal University of Goiás and Federal University of Uberlândia / Goiânia - GO

PhD in Business Administration in the line of Marketing Strategies from EAESP/FGV, with a doctoral internship at Cornell University under the supervision of Vithala Rao. Permanent Professor and Coordinator of the Graduate Program in Business Administration at UFG. Associate Editor of the Accounting, Management and Governance Magazine (CGG). He coordinates the MBA in Strategic Marketing. Leads research topic in the Marketing Division since 2019 at ANPAD - National Association of Graduate Studies and Research in Administration. His research has already been nominated and/or awarded by the international database Emerald (2015/2017) and scientific events such as SEMEAD (2013) and EMA (2014/2018). He had projects approved in Scientific Notices by the Goiás State Research Support Foundation (FAPEG) and by CNPQ. His research is linked to themes such as Behavioral Economics and Performances Applied to Marketing; Econometric Modeling; Experiments in Marketing and Machine Learning. He acts as coordinator of ADMKT - Teaching, Research and Extension Group in Marketing and Data Analytics (https://admkt.face.ufg.br/), created in 2012, and certified by CNPq.

Rafael Martins Ronqui, Fundação Getulio Vargas / School of Business Administration of São Paulo, São Paulo - SP

Professional Master in Management for competitiveness. Four graduate degrees. Graduated in computer engineering. Computer Technician. I have worked for over 20 years with IT, supply processes and eight years as an undergraduate professor. Ten years with SAP at Nestlé, working with some modules, currently MM and transportation, with local and global projects. I'm writing an article on the implementation of agile projects, looking for quality, another one on circular economy focused on helping the collector with IT support and published one on Big Data with NoSQL and Redis. Digital transformation projects, Power BI, RPA and Compliance. Project management. Coordination of the SAP automation process - GRC, Nestlé and Coca-Cola FEMSA. Development and implementation of systems aimed at the areas of supply chain, sales, marketing, legal and finance with more than a thousand users. Java Developer. Active participation in meetings with other markets in English and Spanish. Management of collaborators and third parties. Responsible for the transportation module in SAP, acting not only in the purchasing part, but also in what involves the other parts of the process (sales and finance). High knowledge in changing and creating master data of suppliers, materials, customers, products and data cleansing. ABAP development specifications with code and requirements. Systems with data in the cloud. IT Careers Lecturer. Use of the Scrum and PMI methodology, at Nestlé and in the classroom. Knowledge of other agile methodologies, such as SAFe. Specialist in object-oriented Java and WEB languages such as HTML 5, JavaScript, PHP and CSS. Advanced knowledge of SQL and Power BI. Supplier development. Microsoft teams and Zoom. Using the R language for analytics and big data. ChatBot implementation with curation, customer experience and big data. Use of R language

Pedro Paulo Coelho, Fundação Getulio Vargas / School of Business Administration of São Paulo, São Paulo - SP

Graduated in Economic Sciences (bachelor's degree) at the Pontifical Catholic University of São Paulo (2015) and graduated (bachelor's degree) in Geography at the University of São Paulo (2015). He is currently a Master's student in Public Administration and Government (CMAPG) at FGV-EAESP and professor of Geography at Colégio Bandeirantes. He created and developed economics and politics teaching projects at the same institution and at Colégio Ítaca (São Paulo) from 2012 to 2016. He is interested in interdisciplinary themes related to the areas of educational and socio-environmental public policies, human geography, education and industry economics public.

Eduardo de Rezende Francisco, Fundação Getulio Vargas / School of Business Administration of São Paulo, São Paulo - SP

He holds a PhD (2010) and a Master's (2006) in Business Administration from Fundação Getulio Vargas - EAESP and holds a bachelor's degree in Computer Science from the Institute of Mathematics and Statistics of the University of São Paulo (1999). He has been Vice-Coordinator of the Undergraduate Course in Business Administration at FGV EAESP since February 2019, responsible for the academic management of double degrees (with Economics and Law) and double degrees with international partner schools. He is a Career Professor at the Department of Technology and Data Science (TDS) at FGV EAESP, where he has been teaching since 2011. He teaches courses related to GeoAnalytics, Data Science, Artificial Intelligence, Big Data, Business Analytics, Applied Statistics and Spatial Statistics. He is the coordinator of the FGV Continuing Education course (FGV PEC) called Geographic Intelligence for Decision Support. He was a visiting researcher at the Department of Information Science at the University of Otago, in New Zealand. Has experience in the area of predictive models for Microcredit, Marketing, Applied Statistics, Geostatistics, Demography and Geotechnologies in general, with emphasis on Data Mining, working mainly on the following topics: Microcredit, Geomarketing, Business Intelligence, Customer Satisfaction and Integration of Technologies de Informação.
He was a professor at ESPM from 2014 to 2018. He coordinated the MBA in Big Data Applied to Marketing (EAD modality) and the Short Course "Big Data Analytics in Decision Making" at ESPM in September 2016 to December 2018. He was professor of the NDE of the Information Systems Courses (TECH) at ESPM (coordinator of the Digital Business Intelligence track) from 2014 to 2018, of the Executive MBA and of the Professional Master's Degree in Consumer Behavior (MPCC) at ESPM from 2015 to 2018 and the Graduate Course in Business Administration in 2018. He was academic coordinator of the International Module Consumer Behavior: Big Data of ESPM, conducted in January 2018 in France, Luxembourg and the United Kingdom. He is a founding partner and responsible for Business Analytics and Spatial Statistics at GisBI, a group that studies and promotes integration between Business Intelligence and GIS (since 2012). He has been a member of the Board of Trustees of Fundação SEADE (São Paulo State Data Analysis System) since September 2016.

References

Abreu, L. C. de, Elmusharaf, K., & Siqueira, C. E. G. (2021). A time-series ecological study protocol to analyze trends of incidence, mortality, lethality of COVID-19 in Brazil. Journal of Human Growth and Development, 31(3), 491–495. https://doi.org/10.36311/jhgd.v31.12667

Anselin, L., & Florax, R. (2012). New directions in spatial econometrics. Springer Science & Business Media.

Boing, A. F., Boing, A. C., & Subramanian, S. (2021). Inequalities in the access to healthy urban structure and housing: An analysis of the Brazilian census data. Cadernos de Saúde Pública, 37, e00233119. https://doi.org/10.1590/0102-311X00233119

Brasil, A. (2022, January 25). Coleta de dados do Censo Demográfico 2022 começa em 1o de agosto. Agência Brasil. https://agenciabrasil.ebc.com.br/geral/noticia/2022-01/coleta-do-censo-demografico-2022-comeca-em-1o-de-agosto

Carlsson-Szlezak, P., Reeves, M., & Swartz, P. (2020). What coronavirus could mean for the global economy. Harvard Business Review, 3(10).

Cavalcante, P., & Lotta, G. S. (2021). Boundary-Crossing Strategies: Managing Macro Policies in a Federal Government. Revista de Administração Contemporânea, 25, e200012. https://doi.org/10.1590/1982-7849rac2021200012.en

Francisco, E. D. R. (2010). INDICADORES DE RENDA BASEADOS EM CONSUMO DE ENERGIA ELÉTRICA: ABORDAGENS DOMICILIAR E REGIONAL NA PERSPECTIVA DA ESTATÍSTICA ESPACIAL. https://doi.org/10.13140/RG.2.2.36634.03524

Greene, W. H. (2000). Econometric analysis 4th edition. International Edition, New Jersey: Prentice Hall, 201–215.

IBGE. (2021, December 13). IBGE reafirma plena confiança no orçamento de R$ 2,292 bilhões para o Censo 2022. https://www.ibge.gov.br/novo-portal-destaques/32553-ibge-reafirma-plena-confianca-no-orcamento-de-r-2-292-bilhoes-para-o-censo-2022.html

IBGE. (2023). IBGE | Portal do IBGE | IBGE. https://www.ibge.gov.br/

Jannuzzi, P. de M. (2018). A importância da informação estatística para as políticas sociais no Brasil: Breve reflexão sobre a experiência do passado para considerar no presente. SciELO Brasil. https://doi.org/10.20947/S0102-3098a0055

Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625–636. https://doi.org/doi.org/10.1080/13658816.2019.1684500

Kabudula, C. W., Houle, B., Collinson, M. A., Kahn, K., Tollman, S., & Clark, S. (2017). Assessing Changes in Household Socioeconomic Status in Rural South Africa, 2001–2013: A Distributional Analysis Using Household Asset Indicators. Social Indicators Research, 133(3), 1047–1073. https://doi.org/10.1007/s11205-016-1397-z

Lascala, A. J., Silva, B. M. A., & de Rezende Francisco, E. (2018). Organização partidária e votos no Legislativo: Estudo das organizações municipais do PT e PSDB no estado de São Paulo a partir da composição e influência geográfica Party organization and votes in the legislature: Study of the municipal organizations of the PT and PSDB in Sao Paulo state from the composition and geographical influence. Revista Brasileira de Pesquisas de Marketing, Opinião e Mídia, 11(2), 175–189.

Lotta, G., & Pires, R. (2023). Public Policy Implementation in a Context of Extreme Inequality: Between Universalist Ambitions and Practical Selectivity. https://doi.org/10.1108/978-1-80262-655-120231019

Lü, G., Batty, M., Strobl, J., Lin, H., Zhu, A.-X., & Chen, M. (2019). Reflections and speculations on the progress in Geographic Information Systems (GIS): A geographic perspective. International Journal of Geographical Information Science, 33(2), 346–367. https://doi.org/10.1080/13658816.2018.1533136

Mari, A. (2020, November 10). Digital Services Surge Among Poor Brazilians Amid Pandemic. Forbes. https://www.forbes.com/sites/angelicamarideoliveira/2020/11/10/digital-services-surge-among-poor-brazilians-amid-pandemic/

Rehman, A. U., Saleem, R. M., Shafi, Z., Imran, M., Pradhan, M., & Alzoubi, H. M. (2022). Analysis of Income on the Basis of Occupation using Data Mining. 1–4. https://doi.org/10.1109/ICBATS54253.2022.9759040

Sami, J. (2011). Multivariate cointegration and causality between exports, electricity consumption, and real income per capita: Recent evidence from Japan. International Journal of Energy Economics and Policy, 1(3), 59–68.

Santos, M. (1978). Por uma geografia nova da crítica da geografia a uma geografia crítica.

Santos, M. (2002). A natureza do espaço: Técnica e tempo, razão e emoção (Vol. 1). Edusp.

Santos, M. (2012). Metamorfoses do espaço habitado: Fundamentos teóricos e metodológicos da geografia.

Santos, M., & Silveira, M. L. (2001). O Brasil: Território e sociedade no

início do século XXI.

Schabenberger, O., & Gotway, C. A. (2017). Statistical methods for spatial data analysis. CRC press.

SEADE. (2021). Conjunto de dados—SEADE Repositório. https://repositorio.seade.gov.br/dataset/

Silva, J. J. O., Zerboni, F., & Prado, M. (2012). Lubrax by Petrobras. Emerald Emerging Markets Case Studies, 2(8), 1–25. https://doi.org/10.1108/20450621211291798

Toledo Villacís, M. (2021). Estrategias post-COVID 19 para reactivar el Turismo local en el Ecuador: Caso provincia de Tungurahua. Green World Journal, 2021, Vol. 4, Num. 1-003, p. 1-12. https://dspace.uib.es/xmlui/handle/11201/155324

Walker, K. (2023). Analyzing us census data: Methods, maps, and models in R. CRC Press.

Widjaja, T., & Gregory, R. W. (2020). Monitoring the Complexity of IT Architectures: Design Principles and an IT Artifact. Journal of the Association for Information Systems, 21(3), 4. https://doi.org/10.17705/1jais.00616

World, H. O. (2023). Global research on coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov