Electoral crime: Using machine-learning algorithms to extract data from unstructured court documents
This study investigates how convictions for electoral crimes affect politicians’ chances of running for office and being re-elected, using novel data from electoral courts in Brazil. Exploiting random assignment of judges, it identifies the causal impact of enforcement on political careers and electoral integrity.
Project
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Aires-Syed-Policy-Brief-February-2026.pdf
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- Automated, machine-learning-based models have come a long way in extracting useful data from unstructured documents.
- Relative to manual extraction by experts, the model has an accuracy of over 70% for two-thirds of the questions.
- Even for complicated legal language, the model seems to understand the context and is able to classify the documents into different legal categories with more than 80% accuracy.
Using the data extracted using our Machine-Learning pipeline, we analyse the effects of a guilty verdict in election-related cases. Although the estimates are not precise because of data limitations, the preliminary results, based on manual extraction, can be summarised as follows: a guilty verdict leads to:
- Lower likelihood of being a candidate in future elections.
- Lower likelihood of being elected, conditional on being a candidate in future elections.