  We provide the largest compiled publicly available dictionaries of first,
middle, and last names for the purpose of imputing race and ethnicity using,
for example, Bayesian Improved Surname Geocoding (BISG). The dictionaries are
based on the voter files of six Southern states that collect self-reported
racial data upon voter registration. Our data cover a much larger scope of
names than any comparable dataset, containing roughly one million first names,
1.1 million middle names, and 1.4 million surnames. Individuals are categorized
into five mutually exclusive racial and ethnic groups -- White, Black,
Hispanic, Asian, and Other -- and racial/ethnic counts by name are provided for
every name in each dictionary. Counts can then be normalized row-wise or
column-wise to obtain conditional probabilities of race given name or name
given race. These conditional probabilities can then be deployed for imputation
in a data analytic task for which ground truth racial and ethnic data is not
available.
