# Race and ethnicity data for first, middle, and last names

arXiv: https://arxiv.org/abs/2208.12443 · HTML: https://mlsanity.com/paper/2208.12443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements](https://mlsanity.com/paper/2205.06129/) (2022)

## Built on by (most cited first, 2 of 2)

- [SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models](https://mlsanity.com/paper/2210.07269/) (2022)
- [Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases](https://mlsanity.com/paper/2305.16577/) (2023)

## Abstract (arXiv)

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.