Análise bibliométrica das teses de doutorado em economia do Caen/UFC

• Autor(a) principal: Cesar, Lucas Coelho Lenz
• Data de Publicação: 2021
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/64058
• topic: Literatura Econômica, Teses de Doutorado, CAEN/UFC, Código JEL

This project sought to build a database containing the doctorate thesis presented at CAEN since the first doctorate class in 2000 and classify them by JEL codes. These codes are utilized to classify the economic scientific production in different areas within the literature. Later, it identified, through the Word Clouds software, what are the most studied topics at UFC (Federal University of Ceara) doctorate program. Besides that, the study was able to separate the academic production by gender and check if there was a difference in the literary trend defended in the thesis between the first and second decade of the program’s existence. In all the works, a great utilization of mathematical and quantitative methods was observed. In the theses presented by male students, it was observed a great occurrence of studies about Development and Growth, Economics of Crime, and Analysis of Education and Inequality. In the ones presented by the females, it was observed a great interest in areas about Gender Economics, Education, Employment and Unemployment, Public Policies, and Economic Growth in open economies. In the first decade, the most studied topics were econometric models conjunctly with empirical analysis of economic Growth. In the second decade of the doctorate program, the focus shifts to subjects that discuss Inflation, Unemployment, Education and Public Policies with a higher dedication to State and Municipal Government analysis. In general, it is possible to conclude that the spectrum of topics in Economics is broadly studied by doctorate students at CAEN, and, by the amount of econometric studies presented, it is possible to infer that the program’s concludents go through a deep learning in econometrics.