Measuring Corruption from Text Data

Using Brazilian municipal audit reports, I construct an automated corruption index that combines a dictionary of audit irregularities with principal component analysis. The index validates strongly against independent human coders, explaining 71-73 \% of the variation in hand-coded corruption counts in samples where coders themselves exhibit high agreement, and the results are robust within these validation samples. The index behaves as theory predicts, correlating with municipal characteristics that prior research links to corruption. Supervised learning alternatives yield nearly identical municipal rankings ($R^{2}=0.98$), confirming that the dictionary approach captures the same underlying construct. The method scales to the full audit corpus and offers advantages over both manual coding and Large Language Models (LLMs) in transparency, cost, and long-run replicability.