Using First Name Information to Improve Race and Ethnicity Classification

This article uses a recent first name list to develop an improvement to an existing Bayesian classifier, namely the Bayesian Improved Surname Geocoding (BISG) method, which combines surname and geography information to impute missing race/ethnicity. The new Bayesian Improved First Name Surname Geocoding (BIFSG) method is validated using a large sample of mortgage applicants who self-report their race/ethnicity. BIFSG outperforms BISG, in terms of accuracy and coverage, for all major racial/ethnic categories. Although the overall magnitude of improvement is somewhat small, the largest improvements occur for non-Hispanic Blacks, a group for which the BISG performance is weakest. When estimating the race/ethnicity effects on mortgage pricing and underwriting decisions with regression models, estimation biases from both BIFSG and BISG are very small, with BIFSG generally having smaller biases, and the maximum a posteriori classifier resulting in smaller biases than through use of estimated probabilities. Robustness checks using voter registration data confirm BIFSG's improved performance vis-a-vis BISG and illustrate BIFSG's applicability to areas other than mortgage lending. Finally, I demonstrate an application of the BIFSG to the imputation of missing race/ethnicity in the Home Mortgage Disclosure Act data, and in the process, offer novel evidence that the incidence of missing race/ethnicity information is correlated with race/ethnicity.本文采用最新的姓名名录，对现有贝叶斯分类器——贝叶斯改进姓氏地理编码（Bayesian Improved Surname Geocoding, BISG）方法进行改进。该方法通过结合姓氏与地理信息，对缺失的种族/族裔信息进行插补。本文提出的新型贝叶斯改进名-姓氏地理编码（Bayesian Improved First Name Surname Geocoding, BIFSG）方法，通过一组包含大量自行报告种族/族裔的抵押贷款申请人样本完成验证。相较于BISG，BIFSG在所有主要种族/族裔类别下均展现出更优的准确率与覆盖度。尽管整体改进幅度相对有限，但改进效果最为显著的群体为非西班牙裔黑人——这一群体正是BISG性能表现最弱的对象。当利用回归模型估计种族/族裔对抵押贷款定价与承保决策的影响时，BIFSG与BISG均仅产生极小的估计偏差，且BIFSG的偏差整体更小；同时，相较于使用估计概率的方法，采用最大后验分类器可进一步降低偏差。通过选民登记数据开展的稳健性检验，进一步验证了BIFSG相较于BISG的性能优势，并证实BIFSG可应用于抵押贷款放贷场景之外的其他领域。最后，本文展示了BIFSG在《住房抵押贷款披露法》（Home Mortgage Disclosure Act, HMDA）数据缺失种族/族裔信息插补中的应用，并在此过程中提供了新颖的实证证据，表明缺失种族/族裔信息的发生率与种族/族裔本身存在相关性。