Replication Data for: Measuring the impact of campaign finance on congressional voting: A machine learning approach

Replication data for the paper: "Measuring the impact of campaign finance on congressional voting: A machine learning approach" Includes: * metadata for legislators and bills, * text embeddings for legislative summaries (sourced from ProPublica Congress Database). Includes 768d LongFormer embeddings and 2d embeddings for visualization (UMAP and Isomap), * legislator embeddings: 100d PCA on legislators' financial disclosures, as well as 2d visualization embeddings (UMAP and Isomap), * scripts for running the classification and RSA analyses. Up to 100d embeddings are provided from the output of PCA for both bills and legislators. See README.ipynb for a tour of the datasets as well as starter code.