  In the absence of sensitive race and ethnicity data, researchers, regulators,
and firms alike turn to proxies. In this paper, I train a Bidirectional Long
Short-Term Memory (BiLSTM) model on a novel dataset of voter registration data
from all 50 US states and create an ensemble that achieves up to 36.8% higher
out of sample (OOS) F1 scores than the best performing machine learning models
in the literature. Additionally, I construct the most comprehensive database of
first and surname distributions in the US in order to improve the coverage and
accuracy of Bayesian Improved Surname Geocoding (BISG) and Bayesian Improved
Firstname Surname Geocoding (BIFSG). Finally, I provide the first high-quality
benchmark dataset in order to fairly compare existing models and aid future
model developers.
