http://repositorio.ufc.br/handle/riufc/137 2026-08-11T21:28:46Z 2026-08-11T21:28:46Z Oliveira, Bruno Marinho Cavalcante de http://repositorio.ufc.br/handle/riufc/87430 2026-08-07T18:34:55Z 2026-01-01T00:00:00Z

Título: Investimentos educacionais e arrecadação tributária por bairro em Fortaleza, análise para o período de 2015-2022 Autor(es): Oliveira, Bruno Marinho Cavalcante de Abstract: This study examines the effects of place-based public investments in educational infrastructure, namely Early Childhood Education Centers and Full-Time Schools, on neighborhood-level tax revenues from the services tax and the tourism-related value-added tax in Fortaleza from 2015 to 2022. The empirical strategy combines an event-study difference-in-differences design with matching on pre-treatment outcome trajectories, building an explicit counterfactual for each treated neighborhood and recovering dynamic effects around investment implementation. The main results show a large and short-lived response for the services tax, with sizable increases at the implementation quarter and in the subsequent quarter, and additional evidence up to roughly one year depending on specification. Robustness checks re-estimate the design under progressively stricter matching vectors that incorporate 2010 Census covariates capturing socioeconomic gradients and urban pressure, confirming the stability of the services-tax dynamics. For the tourism-related value-added tax, the evidence is markedly weaker, with no clearly persistent post-implementation path and higher sensitivity to specification. The findings suggest that localized urban investments can generate short-run fiscal externalities in territorially anchored service tax bases, while effects on more aggregated tax bases appear limited and less tightly linked to the immediate neighborhood environment. Tipo: Dissertação

2026-01-01T00:00:00Z Rocha, Rosiane Maria da Silva http://repositorio.ufc.br/handle/riufc/87423 2026-08-06T19:38:18Z 2025-01-01T00:00:00Z

Título: Relação da governança pública com o desempenho social, econômico e fiscal das capitais brasileiras Autor(es): Rocha, Rosiane Maria da Silva Abstract: The differences in management capacity and results among Brazilian capitals still constitute a central challenge for the effectiveness of public policies and the reduction of social and territorial inequalities. In this context, the study aimed to investigate the relationship between public governance and the social, economic, and fiscal performance of Brazilian capitals, considering associations between indicators, differences according to population size and geographic region, and patterns of similarity among the capitals, in the period from 2017 to 2023. This is an applied, quantitative, descriptive, explanatory, and exploratory research, based on secondary data from the CFA Municipal Governance Index (IGM-CFA), FIRJAN Municipal Development Index (IFDM), Infant Mortality Rate (IMR), Basic Education Development Index (IDEB – 9th grade), Gross Domestic Product per capita (GDP per capita), and FIRJAN Fiscal Management Index (IFGF). Methodologically, descriptive statistics, correlation, panel regression with robust standard errors, mean comparison tests (MANOVA, one-way and Welch ANOVAs, and Tukey HSD and Games-Howell post-hoc tests), and hierarchical cluster analysis were applied. The regression results showed a positive and statistically significant effect of municipal governance on educational (IDEB – 9th grade) and fiscal (IFGF) performance; for municipal development, income, and infant mortality, the coefficients maintained the expected sign, but were not statistically significant at 5%. Mean tests indicated significant differences by size and region. Large capitals showed better results in municipal development, education, income, and health; medium-sized capitals had a relatively favorable position in governance and, above all, in fiscal management; and small capitals had less favorable results, especially in governance, municipal development, health, education, and income. In regional terms, the South and Southeast stood out in governance and municipal development; the South, with the lowest infant mortality rate; The Central-West and Southeast regions registered specific advantages in education; the Central West, Southeast, and South regions presented higher levels of GDP per capita; and, in fiscal management, the North, Northeast, Southeast, and South regions did not differ statistically from each other, while the Central-West revealed relative fragility. Cluster analysis identified three profiles: Capitals with low structural development, with less favorable results in municipal development, health, education, income, and governance, but fiscal management at an acceptable level; Capitals with high structural development, with better relative conditions in municipal development, income, health, and governance, good educational performance, but relatively less favorable fiscal management; and Capitals with intermediate development and educational excellence, characterized by superior educational performance, relatively more favorable fiscal management, and an intermediate position in the other dimensions. The construction of the qualitative governance scale, based on the IGM-CFA score ranges, facilitates the applied reading of municipal governance levels; the typology of clusters and the systematization of strategic subsidies offer a basis for diagnosis and definition of priorities in public management. In summary, the findings allow managers to recognize the weaknesses and strengths of their capital cities and guide decisions according to size, region, and cluster profile, contributing to more effective public policies for society. Tipo: Dissertação

2025-01-01T00:00:00Z Moura, Yure Revélles da Silva http://repositorio.ufc.br/handle/riufc/87420 2026-08-06T19:12:09Z 2026-01-01T00:00:00Z

Título: Ensaios sobre modelos de machine learning aplicados à econômia Autor(es): Moura, Yure Revélles da Silva Abstract: This study brings together three empirical essays that focus on the application of machine learning models to economic forecasting at both the national and regional levels, seeking to evaluate the extent to which these approaches can complement traditional econometric methods. Despite the recent advances in these techniques, important limitations still remain regarding the economic interpretability of the models, the incorporation of regional heterogeneities, and the comparison with structural approaches. The choice of the sample period is justified by its coverage of major consolidations in the Brazilian macroeconomic environment, particularly the adoption of the inflation-targeting regime, while also encompassing distinct economic regimes, episodes of instability, and changes in the conduct of economic policy. In addition, the temporal delimitation was defined according to the availability, compatibility, and standardization of the databases employed throughout the essays. In this context, the first essay presents a national inflation forecasting exercise based on an extensive set of macroeconomic variables. Additionally, the study aims to identify the key predictors of inflation across multiple forecasting horizons. To this end, machine learning models are employed as the main approach and compared with benchmark models. The empirical findings suggest that machine learning models outperform benchmark models across different forecasting horizons. Furthermore, the results provide evidence of the relevance of production and fiscal sectors, labor market conditions, energy costs, and expectations in explaining inflation forecasts over different periods. The second essay proposes a regional inflation forecasting exercise based on an implicit hierarchical structure, incorporating regional elements into the forecasting process. Furthermore, a dynamic decomposition of inflation predictors is performed across regions and forecasting horizons. The results indicate the presence of significant regional heterogeneity across the states of Northeastern Brazil. The dynamic decomposition also reveals that the determinants of regional inflation gradually change over time, exhibiting patterns consistent with economic and political events at both the national and regional levels. The third essay proposes the use of multivariate neural network models to generate dynamic counterfactuals for Brazilian macroeconomic variables based on shocks applied to selected variables. The simulated trajectories are then compared with the dynamic responses obtained from a structural model, allowing an assessment of the extent to which machine learning-based methods can reproduce propagation and persistence patterns commonly found in traditional econometric models. The findings indicate that the multivariate model responds to shocks in a more moderate manner and in closer alignment with the macroeconomic literature. In particular, the model is able to reproduce propagation patterns similar to those generated by the structural model, although such responses arise from statistical relationships learned from the data rather than from explicitly modeled causal mechanisms. In this sense, the estimated counterfactuals exhibited smaller and more gradual responses at the onset of the shock for all variables when compared with the structural model. Taken together, the results reinforce the potential of machine learning methods not only as forecasting tools, but also as complementary instruments to traditional econometric approaches for analyzing dynamic and complex phenomena, especially in environments characterized by high macroeconomic instability. Tipo: Tese

2026-01-01T00:00:00Z Leandro, Leonardo de Amorim http://repositorio.ufc.br/handle/riufc/87377 2026-08-03T17:34:27Z 2025-01-01T00:00:00Z

Título: A relação entre estresse financeiro e percepção de sucesso na carreira: um estudo entre empregados públicos de uma empresa pública de pesquisa brasileira Autor(es): Leandro, Leonardo de Amorim Abstract: This dissertation analyzes the relationship between financial stress and career success perception among public employees of a Brazilian agricultural research company. The study acknowledges that, even in contexts of employment stability and competitive remuneration, financial difficulties can compromise both health and professional experience. Therefore, it aimed to identify how these variables manifest and relate within the organizational environment. The research follows a quantitative approach, with a descriptive and correlational design, based on a population of 7,165 active employees and a sample of 1,001 respondents, distributed among researchers, analysts, technicians, and assistants. Data were collected through a structured questionnaire that included the InCharge Financial Distress/Financial Well-Being Scale (IFDFW), adapted and validated for Brazil, and the Career Success Perception Scale (EPSC). Statistical analyses involved descriptive procedures, reliability tests, Pearson correlation, ANOVA, Student’s t-test, chi-square association tests, and Latent Profile Analysis (LPA). The results revealed that 58.5% of respondents reported low financial stress, while 12.1% experienced high stress. Regarding career success perception, 52.7% reported high levels, especially in dimensions such as identity, competence, and work-life balance. Analyses indicated a significant, yet moderate, correlation between the two variables, showing that higher levels of financial stress are associated with less positive career success perceptions, although not deterministically. This finding confirms that both constructs are multidimensional in nature, influenced by personal, occupational, and contextual variables, reinforcing the complexity of the relationship between finances and career trajectories. Tipo: Dissertação

2025-01-01T00:00:00Z