https://repositorio.ufpb.br/jspui/handle/tede/4453 PPGE Thu, 24 Sep 2026 06:56:39 GMT 2026-09-24T06:56:39Z http://repositorio.ufpb.br:443/jspui/retrieve/152795/LOGO_PPGE.jpg https://repositorio.ufpb.br/jspui/handle/tede/4453 https://repositorio.ufpb.br/jspui/handle/123456789/39239 Título: Ensaios sobre Bioeconomia no Brasil Autor(es): Magalhães, Juliane do Carmo Duarte Orientador: Souza, Wallace Patrick Santos de Farias de Abstract: Bioeconomy is a sector with considerable innovation potential due to its applications across a wide range of scientific fields: biology, agronomy, ecology, food sciences, social sciences, and biotechnology. The sector also encompasses various industrial and technological possibilities, including nanotechnology, biotechnology, communication and information technologies, and engineering. Bioeconomy focuses on the value generated by business activities stemming from the innovative and sustainable use of biological resources. This field is seen as a key element in addressing the challenges posed by the changing ways in which human needs are met. Brazilian bioeconomy requires greater legal, administrative, and organizational engagement in order to reach the level of countries that have already consolidated many of their systems, thereby facilitating the transition toward a more economically viable and sustainable economy. This study aims to analyze the impact of entry into the bioeconomy on internal mobility patterns among workers in biofuel production compared to other sectors. Using data from the 2010 Demographic Census, the study proposes to test the hypothesis of greater dynamism in the green sector through a combination of the Propensity Score Matching (PSM) method and entropy balancing. In a second stage, using RAIS data, it aims to analyze the effects on wages resulting from sectoral migration, as well as the direct impact of entry into the biofuel sector. Editor: Universidade Federal da Paraíba Tipo: Tese Fri, 27 Jun 2025 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/39239 2025-06-27T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/39178 Título: Mudança na lei seca e mortes por acidentes de trânsito: evidências para o Brasil Autor(es): Souza, Natália Carneiro de Orientador: Ramalho, Hilton Martins de Brito Abstract: This work aims to analyze the impact of the change that occurred in law 13,546, art. 302 of the “Lei Seca”, in mortality caused by traffic accidents in the period from 2017 to 2019. The change in the law was put into effect in 2018, with the purpose of increasing the penalty for drivers who drove under the influence of alcohol. Data were extracted from SIM-DATASUS and grouped according to external causes of morbidity and mortality. Using the synthetic control method, it was shown that general mortality caused by traffic accidents was not impacted after the intervention. To analyze heterogeneous effects, the cause of treatment was analyzed by region and by weekdays and weekends, to capture the impact in different situations, however the results showed no impact caused by the intervention through the change in the “Lei Seca”. Editor: Universidade Federal da Paraíba Tipo: Dissertação Wed, 29 May 2024 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/39178 2024-05-29T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/38962 Título: Ações afirmativas e desigualdade eleitoral: uma análise da representação racial nas eleições legislativas brasileiras Autor(es): Agostinho, Lucas Daniel da Silva Orientador: Araújo, Jevuks Matheus de Abstract: This study investigates ADPF No. 738/2020, a policy that established the proportional allocation of the Electoral Fund based on the total number of Black candidates nominated by political parties in Brazil’s 2022 legislative elections. The analysis uses data from the Brazilian Superior Electoral Court (TSE) for the years 2018 (pre-policy) and 2022 (post-policy). A logistic regression model with individual- and structural-level covariates— such as campaign financing, incumbency, education, marital status, region, and party ideology—is employed to estimate electoral outcomes. The results indicate that, in 2018, self-declared Black candidates had 24.36% lower odds of being elected compared to white candidates. In 2022, a positive effect is observed; however, it is not statistically significant, even after the implementation of the normative framework. Three robustness checks—a temporal placebo, a falsification test using a non-target group (white candidates), and a specificity test with an irrelevant variable (gender)—support the model’s findings. Although ADPF No. 738 increased the allocation of resources to Black candidacies and the absolute number of elected Black legislators rose by 21.7%, this increase is largely driven by the growth in the number of Black candidates (+13.4%), rather than by greater electoral competitiveness, as the success rate increased by only 0.27 percentage points. Spatial analyses reveal a concentration of resources in the North and Northeast regions, while the South, Southeast, and Center-West maintained historical patterns of resource allocation and political underrepresentation. The study concludes that ADPF No. 738/2020 did not fully achieve its objectives, as it failed to dismantle structural inequalities, indicating the need for complementary policies combining vertical redistribution (resources), horizontal redistribution (state- and region-based criteria), and effective accountability mechanisms. Editor: Universidade Federal da Paraíba Tipo: Dissertação Tue, 20 May 2025 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/38962 2025-05-20T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/38868 Título: Fiscal and Monetary Dominance in Brazil : A Machine Learning DSGE Approach Autor(es): Silva, Kleiton de Luna Souza da Orientador: Aragón, Edilean Kleber da Silva Bejarano Abstract: This dissertation investigates the interactions between fiscal and mone- tary policies in Brazil using a hybrid approach that combines Markov-switching DSGE models (MS-DSGE) and supervised machine learning techniques. The structural model serves as a data-generating process to train classification al- gorithms, which are subsequently applied to real Brazilian time series. Among the tested models, XGBoost demonstrated superior performance, achieving an accuracy of 95% and a Log Loss of 0.17 on the test set. The results reveal a dynamic chronology: Brazil operated under fiscal dominance between 2013 and 2015, transitioning to monetary dominance in the 2015-2017 period fol- lowing the implementation of the expenditure ceiling. An original contribution of this work is the identification of an explosive regime during the pandemic (2020Q3-2021Q3), characterized by the simultaneously active stance of both fiscal and monetary authorities. Robustness analyses indicate that the identifi- cation of fiscal and explosive regimes is resilient to different filtering techniques, whereas monetary dominance appears more sensitive to modeling assumptions. The findings suggest a return to fiscal dominance in the most recent period. Editor: Universidade Federal da Paraíba Tipo: Dissertação Fri, 20 Feb 2026 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/38868 2026-02-20T00:00:00Z