End-to-End Python implementation of Muço’s (2025) corruption measurement framework. Combines NLP pipeline (regex extraction, Porter stemming, TF-IDF), PCA-based dimensionality reduction, and fixed-effects OLS to quantify institutional quality from Brazilian audit reports. Includes supervised learning robustness checks and LOO sensitivity analysis.
natural-language-processingtext-miningtext-classificationscikit-learnnltkeconometricssupervised-learningdimensionality-reductionprincipal-component-analysisfixed-effectspolitical-economytext-as-databrazilian-datagovernment-transparencyportuguese-nlpresearch-replicationcorruption-measurementdictionary-based-classificationinstitutional-qualityaudit-analysis
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Updated
Dec 14, 2025 - Jupyter Notebook