Detecting corruption risk patterns: Analyzing state and municipal contracts in Mexico using hierarchical clustering
DOI:
https://doi.org/10.24965/gapp.11518Keywords:
corruption, public procurement, subnational governments, machine learning, hierarchical clusteringAbstract
Objectives: Corruption in public procurement is a global concern, particularly relevant in contexts such as Mexico. While several studies have examined risk indicators at the national level, applying these models at the subnational level presents significant challenges. This study analyzes more than 150,000 state and municipal contracts financed wholly or partially with resources from Mexico’s national budget line 28 in 2021, based on seven corruption risk indicators. Methodology: The hierarchical clustering technique was used, especially promising for governments with limited institutional capacities, as it does not require extensively labeled data, allows the detection of hidden patterns, and offers accessible interpretations. Results: Through hierarchical clustering, groups of contracts with similar risk profiles were identified. Conclusions: The analysis helps identify dynamics associated with corruption risks in subnational public procurement and opens new possibilities for preventive monitoring in Mexico and comparable contexts.
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