FCSM 2022:

Estimating Missing Race and Ethnicity Data with 

Surname and Geolocation

Jeff Tessin 

Assistant Director (Statistics)

Applied Research and Methods Group

U.S. Government Accountability Office (GAO)

Washington, DC

tessinj@gao.gov | 202-512-4232

October 2022
The Problem

How do we analyze how federal programs work across racial 

and ethnic groups, when agencies do not measure race and 

ethnicity?

• Race/ethnicity is not explicitly needed to administer many 

programs. Measurement may enable or suggest discrimination.

• Yet, race/ethnicity often matters. Race-blind administration is not 

necessarily equitable.
The Problem

Many researchers and policymakers grapple with a lack of 

race and ethnicity in administrative data.

• Health disparity research: COVID-19 cases, cardiovascular health 

outcomes, quality of health care

• Voter registration and voting activity
3 Types of Solutions

• Direct measurement

• Linkage

• Imputation
Lit Review: Imputation Methods

• Literature Search Parameters: 

• Material types: scholarly/peer reviewed material, conference 

papers, working papers, nonprofit/think tank publications

• Publication date range: 2006 - 2021

• Databases searched: Scopus, ProQuest, EBSCO, Harvard 

Think Tank

• Imputation Methods Identified by Literature: 

• Statistical modeling (e.g., GLMs)

• Data mining and machine learning algorithms

• Probabilistic classifiers 

• Bayesian Improved Surname Geocoding
BISG: Bayesian Improved Surname Geocoding
BISG: Bayesian Improved Surname Geocoding

Source: RAND (link)

• Has been applied to financial, healthcare, voter registration, 

marriage license datasets

• Used by the Federal Reserve, Consumer Financial Protection 

Bureau, Medicare/Medicaid

• Needs only two commonly measured variables to impute with 

reasonable accuracy
BISG: Bayesian Improved Surname Geocoding

Auxiliary Dataset

Person Observed 

Race Predictor

1 Asian Group 1

2 Asian Group 1

3 Asian Group 1

4 Black Group 2

5 Black Group 2

Target Dataset

Person

Imputed 

Probability 

Asian

Imputed

Probability 

Black

Predictor

1 100% 0% Group 1

Asian Black Row 2 0% 100% Group 2 Total

Group 1

(Row pct)

3

(100%)

0

(0%) 3

Group 2

(Row pct)

0

(0%)

2

(100%) 2
BISG: Bayesian Improved Surname Geocoding

Many surnames can predict race/ethnicity with minimal error

Surname Probability 

Multiracial

% American 

Indian/

Alaskan 

Native

% Asian / 

Pacific 

Islander

% Black % Hispanic % White % Other

SANTUARIO 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0%

HERNANDEZA 0.0% 0.0% 0.0% 0.3% 99.4% 0.3% 0.0%

ZHEN 0.5% 0.0% 98.6% 0.0% 0.3% 0.6% 0.5%

ZHOU 0.5% 0.1% 98.2% 0.2% 0.1% 1.0% 0.6%
BISG: Bayesian Improved Surname Geocoding

Some surnames are less predictive (more equally represented among 

each group)

Top 5 Names in the 2010 Census, by Race

Surname Probability 

Multiracial

% American 

Indian/

Alaskan 

Native

% Asian / 

Pacific 

Islander

% Black % Any 

Hispanic % White % Other

SMITH 2.2% 0.9% 0.5% 23.1% 2.4% 70.9% 3.1%

JOHNSON 2.6% 0.9% 0.5% 34.6% 2.4% 59.0% 3.5%

WILLIAMS 2.8% 0.8% 0.5% 47.7% 2.5% 45.8% 3.6%

BROWN 2.6% 0.9% 0.5% 35.6% 2.5% 58.0% 3.4%

JONES 2.6% 1.0% 0.4% 38.5% 2.3% 55.2% 3.6%

Source: 2010 Census Surname Table (https://www.census.gov/topics/population/genealogy/data/2010_surnames.html)
BISG: Bayesian Improved Surname Geocoding

Adding residential geolocation improves predictions, due to clustering among 

some groups with less predictive names.

Source: Census (link)
BISG: Bayesian Improved Surname Geocoding

• Estimate by combining surname and geolocation 

probabilities from 2010 Census, using Bayes Rule:

• G = {block groups}

• R = {racial/ethnic groups}

• S = {surnames}

• i = {1, 2, ..., N people} (1)

(2)
BISG: Bayesian Improved Surname Geocoding

• Validate predictions against self-reported race/ethnicity

• 2010 Census

• Mortgage applications

• Custom surveys (healthcare)

• Generally high probability/certainty, though less accurate for 

Blacks and Whites than Asians and Hispanics
BISG: Bayesian Improved Surname Geocoding

Source: Elliott, et al. 2009
BISG: Application to COVID Tax Credits

• What options are available to examine disparities in the proportion of 

businesses benefiting from selected tax provisions by the sex, race, 

or ethnicity of the business owner? (GAO-22-104582)

• Scope: CARES Act tax relief provisions for employers and selfemployed business owners; businesses of interest were small, 

single-owner firms
BISG: Application to COVID Tax Credits

• Problem: IRS doesn’t measure race/ethnicity!

• According to IRS officials, race/ethnicity is not collected 

because it is not needed for administration of tax code.

• Treasury recently announced that they will begin equity 

analyses of tax policies.
BISG: Application to COVID Tax Credits

• Two input data files from 2010 Census (most recent):

• Summary File 1: race distribution by block group

• Surname Table: race distribution by surname

• Extracted surnames and addresses from various tax filings

• Geocoded addresses into block groups using default SAS 

process (PROC GEOCODE). Provides a useable Census block 

group for roughly 85% of available addresses
BISG: Application to COVID Tax Credits

• Due to measurement constraints, collapsed groups into: 

Hispanic and Non-Hispanic Asian, Black, White, and Other

• Estimation involves algebra and can be implemented in 

various software

• No validation data
BISG: Application to COVID Tax Credits

• Imputed race/ethnicity using best available combination of 

name and location.

• High rate of matching to surname and Census block 

group location, but not 100%

• When location was unavailable, used name alone

• When names don’t appear in Census data, use 

probabilities for a residual “All Other Names” group.
BISG: Application to COVID Tax Credits
BISG: Application to Flood Insurance Data

• Ongoing work analyzing how revised flood risk rating 

system affects FEMA policyholders by race and ethnicity

• Policyholder data have surname and address

• BISG estimates feasible, largely due to reliable block 

groups

• Surveys, linkage too expensive or infeasble
BISG: Application to SSA Data

• Evaluating the quality of SSA service delivery during COVID 

pandemic, especially by race and ethnicity

• Analyzing self-reported data from SSA:

• Missing for many applicants

• Before 2009: measured “Black,” “White,” and “Other” via 

paper form

• After 2009: measured OMB categories
BISG: Application to SSA Data

• Experimenting with BISG, applied to benefit claims

• Surnames and addresses available from SSA

• Uncertain feasibility and accuracy

• Possible error from surrounding block group, due to 

institutional living
Implications

• Greater interest in measuring race/ethnicity among program 

participants

• Imputation can enrich many administrative datasets with 

limited direct measurement.
Implications

• BISG predicts with minimal error, using only two variables 

that agencies often do collect reliably.

• Enables analysis of race/ethnicity when direct measurement 

is not desirable, possible, or affordable
Implications

• Estimates vary in quality. Imputation error exists.

• Can reduce risk by using only the estimates with high 

confidence

• Sensitivity analysis

• Validate with self-reported data, when possible

• Must assume that responses to the decennial census 

resemble responses that specific populations would give.
Implications

• Can’t measure small or detailed racial/ethnic groups (e.g., 

Irish Catholic Whites, Multiracial Hispanics)

• Latest Census data (2010) are old. Need updates and revalidation.

• Predictive power may decrease with more nuanced 

identification patterns and less residential segregation.
Resources

• Elliott, et al., “Using the Census Bureau’s Surname List to 

Improve Estimates of Race/Ethnicity and Associated Disparities,” 

Health Services, Outcomes, and Research Methods (2009) 9: 69-

83.

• Haas, et al., “Imputation of race/ethnicity to enable measurement 

of HEDIS performance by race/ethnicity,” Health Services 

Research (2019) 54:13–23.
Thanks for listening!