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This paper dissects the potential of state-of-the-art computational analysis to promote the investigation of government’s administrative decisions and politics. The Executive Branch generates massive amounts of textual data comprising daily decisions in several levels and stages of the law and decree-making processes. The use of automated text analysis to explore this data based on the substantive interests of scholars runs into computational challenges. Computational methods have been applied to texts from the Legislative and Judicial Branches; however, there barely are suitable taxonomies to automate the classification and analysis of the Executive’s administrative decrees. To solve this problem, we put forward a computational framework to analyze the Brazilian administrative decrees from 2000 to 2019. Our strategy to uncover the contents and patterns of the presidential decree-making is developed in three main steps. First, we conduct an unsupervised text analysis through the LDA algorithm for topic modeling. Second, building upon the LDA results, we propose two taxonomies for the classification of decrees: (a) the ministerial coauthorship of the decrees to map policy areas and (b) the decrees’ fields of law based on a tagging system provided by the Brazilian Senate. Using these taxonomies, we compare the performance of three supervised text classification algorithms: SVM, Convolutional Neural Network, and Hierarchical Attention Network, achieving F1-scores of up to 80% when automatically classifying decrees. Third, we analyze the network generated by links between decrees through centrality and clustering approaches, distinguishing a set of administrative decisions related to the president’s priorities in the economic policy area. Our findings confirm the potential of our computational framework to explore N-large datasets, advance exploratory studies, and generate testable propositions in different research areas. They advance the monitoring of Brazil’s administrative decree-making process that is shaped by the president’s priorities and by the interplay among cabinet members.

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value.