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Autores
Orientador(es)
Resumo(s)
Public procurement plays a critical role in ensuring transparency and efficiency in government
spending. In Brazil, recent regulatory advances such as the implementation of the National
Public Procurement Platform (PNCP) have expanded the availability of procurement data,
enabling new forms of analysis. Motivated by concerns over inefficiencies, fragmentation, and
limited competition, this study applies Network Analysis Theory (NAT) to examine structural
patterns in Brazilian federal procurement between 2022 and mid-2024. Procurement
relationships are modeled as bipartite graphs linking public agencies to suppliers, and metrics
such as degree, betweenness, closeness, and eigenvector centrality are used alongside
community detection algorithms. The analysis reveals persistent supplier concentration,
cyclical contracting aligned with fiscal calendars, and growing network fragmentation over
time. Central positions are consistently held by a small group of actors, while most remain
marginal, suggesting low institutional integration and potential barriers to competition. The
study demonstrates the scalability and reproducibility of NAT applied to large procurement
datasets and proposes integrating real-time network monitoring into PNCP to enhance
transparency. These findings contribute to the field of data-driven public procurement by
illustrating how analytical tools can complement legal reforms to strengthen accountability
and promote more equitable public contracting in Brazil and similar contexts.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
Palavras-chave
Public procurement Network analysis Contracting networks Centrality measures Community structure SDG 8 - Decent work and economic growth SDG 10 - Reduced inequalities SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals
