Estrutura produtiva, complexidade econômica e desigualdade de renda nas unidades da federação brasileira nas duas primeiras décadas dos anos 2000

• Ano de defesa: 2023
• Autor(a) principal: Giomo, Danniele
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Economia
• Palavras-chave em Português: Complexidade econômica; Desigualdade de renda; Estrutura produtiva; Regiões brasileiras; Economic complexity; Rent inequality; Productive structure; Brazilian regions; CNPQ::CIENCIAS SOCIAIS APLICADAS::ECONOMIA::CRESCIMENTO, FLUTUACOES E PLANEJAMENTO ECONOMICO::CRESCIMENTO E DESENVOLVIMENTO ECONOMICO; CNPQ::CIENCIAS SOCIAIS APLICADAS::ECONOMIA::ECONOMIA REGIONAL E URBANA::ECONOMIA REGIONAL; CNPQ::CIENCIAS SOCIAIS APLICADAS::ECONOMIA::METODOS QUANTITATIVOS EM ECONOMIA; CNPQ::CIENCIAS SOCIAIS APLICADAS::ECONOMIA::ECONOMIA INDUSTRIAL::MUDANCA TECNOLOGICA; CNPQ::CIENCIAS SOCIAIS APLICADAS::ECONOMIA::ECONOMIA INTERNACIONAL
• Link de acesso: https://repositorio.ufu.br/handle/123456789/37933; http://doi.org/10.14393/ufu.te.2023.159

Considering that Brazil is one of the most unequal countries in the world, the main objective of this work is to understand the effect of economic complexity on income inequality at a regional level in Brazil in the first decades of the 2000s. To fulfill this objective, in Chapter 4, econometric analyzes using the Fixed Effect Panel (EF) and Pooled Mean Group Autoregressive Distributed Lags (ARDL/PMG) methods for the Brazilian states from 2002 to 2019. In these models, the explanatory variables Economic Complexity Index were used and participation of industrial jobs, the Gini Index calculated by the IBGE was used as the dependent variable, and the GDP per capita, the average years of study, the resident population, the percentage distribution of the white resident population, the participation of agriculture in employment and participation of services in employment. In addition, models were estimated using the EF method with the same explanatory and control variables, but with a new dependent variable – the Gini index calculated without the social benefits granted by the federal government, for the period from 2012 to 2019. They were also analyzed descriptive statistics, in chapters 2 and 3, to understand at the national and regional level, what the dynamics of the productive structure, economic complexity and income inequality in the first two decades of the 21st century, the variables used were: participation of the productive sectors in the GDP and total employment; participation of the manufacturing and extractive industry in the national GDP; industrial transformation value of CNAE sectors by technological intensity; exports and trade balance by technological intensity; Economic Complexity Index; Gini Index and simulated Gini Index without benefits. Furthermore, in chapter 1, a dialogue was reviewed and established between theories on economic development, structuralism and regional income inequality, and the recent methodology of economic complexity. The evidence found suggests that economic complexity and industrial employment are important to explain income inequality in the Brazilian Federation Units and that a reduction in economic complexity, as well as in relative industrial employment, increases income inequality in these regions. Based on these results, if one seeks to reduce income inequality in the Units of the Brazilian Federation, one must increase the economic complexity and industrial jobs in these places.