Correspondence Analysis of Government Expenditure Patterns
This work addresses government transparency issues for researchers and policymakers, particularly in developing nations, but is incremental as it applies existing neural network methods to a new dataset.
The paper tackles the problem of analyzing government expenditure patterns by introducing a neural network-based approach for visualizing outlying expenses, using a dataset of over 7 million expenses from Brazilian congress members to create a benchmark for machine learning in government transparency.
We analyze expenditure patterns of discretionary funds by Brazilian congress members. This analysis is based on a large dataset containing over $7$ million expenses made publicly available by the Brazilian government. This dataset has, up to now, remained widely untouched by machine learning methods. Our main contributions are two-fold: (i) we provide a novel dataset benchmark for machine learning-based efforts for government transparency to the broader research community, and (ii) introduce a neural network-based approach for analyzing and visualizing outlying expense patterns. Our hope is that the approach presented here can inspire new machine learning methodologies for government transparency applicable to other developing nations.