  Prediction of individual's race and ethnicity plays an important role in
social science and public health research. Examples include studies of racial
disparity in health and voting. Recently, Bayesian Improved Surname Geocoding
(BISG), which uses Bayes' rule to combine information from Census surname files
with the geocoding of an individual's residence, has emerged as a leading
methodology for this prediction task. Unfortunately, BISG suffers from two
Census data problems that contribute to unsatisfactory predictive performance
for minorities. First, the decennial Census often contains zero counts for
minority racial groups in the Census blocks where some members of those groups
reside. Second, because the Census surname files only include frequent names,
many surnames -- especially those of minorities -- are missing from the list.
To address the zero counts problem, we introduce a fully Bayesian Improved
Surname Geocoding (fBISG) methodology that accounts for potential measurement
error in Census counts by extending the naive Bayesian inference of the BISG
methodology to full posterior inference. To address the missing surname
problem, we supplement the Census surname data with additional data on last,
first, and middle names taken from the voter files of six Southern states where
self-reported race is available. Our empirical validation shows that the fBISG
methodology and name supplements significantly improve the accuracy of race
imputation across all racial groups, and especially for Asians. The proposed
methodology, together with additional name data, is available via the
open-source software WRU.
