Summer 2014

Using publicly available 

information to proxy for 

unidentified race and 

ethnicity 

A methodology and assessment
2 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

Table of contents

Table of contents.........................................................................................................2

1. Executive summary..............................................................................................3

2. Introduction...........................................................................................................4

3. Using census geography and surname data to construct proxies 

for race and ethnicity ...........................................................................................5

3.1 Data sources.............................................................................................. 7

3.2 Constructing the BISG probability...........................................................8

4. Assessing the ability to predict race and ethnicity: an application 

to mortgage data ................................................................................................12

4.1 Composition of lending by race and ethnicity ....................................... 14

4.2 Predicting race and ethnicity for applicants ...........................................15

5. Conclusion ..........................................................................................................23

6. Technical Appendix A: Constructing the BISG probability.............................24

7. Technical Appendix B: Receiver Operating Characteristics and 

Area Under the Curve.........................................................................................28

8. Technical Appendix C: Additional tables .........................................................33
3 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

1. Executive summary

The Consumer Financial Protection Bureau (CFPB) is charged with ensuring that lenders are 

complying with fair lending laws and addressing discrimination across the consumer credit 

industry. Information on consumer race and ethnicity is required to conduct fair lending 

analysis of non-mortgage credit products, but auto lenders and other non-mortgage lenders are 

generally not allowed to collect consumers’ demographic information. As a result, substitute, or 

“proxy” information is utilized to fill in information about consumers’ demographic 

characteristics. In conducting fair lending analysis of non-mortgage credit products in both 

supervisory and enforcement contexts, the Bureau’s Office of Research (OR) and Division of 

Supervision, Enforcement, and Fair Lending (SEFL) rely on a Bayesian Improved Surname 

Geocoding (BISG) proxy method, which combines geography- and surname-based information 

into a single proxy probability for race and ethnicity. This paper explains the construction of the 

BISG proxy currently employed by OR and SEFL and provides an assessment of the 

performance of the BISG method using a sample of mortgage applicants for whom race and 

ethnicity are reported. Research has found that this approach produces proxies that correlate 

highly with self-reported race and national origin and is more accurate than relying only on 

demographic information associated with a borrower’s last name or place of residence alone. 

The Bureau is committed to continuing our dialogue with other federal agencies, lenders, 

advocates, and researchers regarding the methodology.
4 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

2. Introduction

The Equal Credit Opportunity Act (ECOA) and Regulation B generally prohibit a creditor from 

inquiring “about the race, color, religion, national origin, or sex of an applicant or any other 

person in connection with a credit transaction”1 with a few exceptions, including for applications 

for home mortgages covered under the Home Mortgage Disclosure Act (HMDA).2 Information 

on applicant race and ethnicity, however, is often required to conduct fair lending analysis to 

identify potential discriminatory practices in underwriting and pricing outcomes.3 

Various techniques exist for addressing this data problem. Demographic information that 

reflects applicants’ characteristics—for example, whether or not an individual is White—can be 

approximated by constructing a proxy for the information. A proxy may definitively assign a 

characteristic to a particular applicant—an individual is classified as being either White or nonWhite—or may yield an assignment that is probabilistic—an individual is assigned a probability, 

ranging from 0% to 100%, of being White. When characteristics are not reported for an entire 

population of individuals, as is usually the case for non-mortgage credit products, techniques 

focused on approximating the demographic data generally require relying on additional sources 

of data and information to construct proxies.

 

1

12 C.F.R. § 1002.5(b).

2

12 C.F.R. § 1002.5(a)(2) and 12 C.F.R. § 1002.13. For HMDA and its implementing regulation, Regulation C, see 29 

U.S.C § 2801-2810 and 12 C.F.R. Part 1003. For the Regulation B provisions concerning requests for information 

generally, see 12 C.F.R. § 1002.5.

3 The ECOA makes it unlawful for “any creditor to discriminate against any applicant, with respect to any aspect of a 

credit transaction (1) on the basis of race, color, religion, national origin, sex or marital status, or age (provided the 

applicant has the capacity to contract); (2) because all or part of the applicant’s income derives from any public 

assistance program; or (3) because the applicant has in good faith exercised any right under the Consumer Credit 

Protection Act.” 15 U.S.C. § 1691(a).
5 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

3. Using census geography and 

surname data to construct 

proxies for race and ethnicity

In a variety of settings, including the analysis of administrative health care data and the 

evaluation of fair lending risk in non-mortgage loan portfolios, researchers, statisticians, and 

financial institutions often rely on publicly available demographic information associated with 

an individual’s surname and place of residence from the U.S. Census Bureau to construct 

proxies for race and ethnicity when this information is not reported. A proxy for race and 

ethnicity may be based on the distribution of race and ethnicity within a particular geographic 

area. Similarly, a proxy for race and ethnicity may be based on the distribution of race and 

ethnicity across individuals who share the same last name. Traditionally, researchers and 

statisticians have relied on information associated with either geography or surnames to develop 

proxies.4

A research paper by Elliott et al. (2009) proposes a method to proxy for race and ethnicity that 

integrates publicly available demographic information associated with surname and the 

geographic areas in which individuals reside and generates a proxy that is more accurate than 

those based on surname or geography alone.5 The method involves constructing a probability of 

 

4 For example, in conducting fair lending analysis of indirect auto lending portfolios, the Federal Reserve relies on the 

U.S. Census Bureau’s Spanish Surname List to proxy for Hispanic borrowers. Information on the Federal Reserve’s 

methodology is available at: http://www.philadelphiafed.org/bank-resources/publications/consumer-complianceoutlook/outlook-live/2013/indirect-auto-lending.cfm.

5 Marc N. Elliott et al., Using the Census Bureau’s Surname List to Improve Estimates of Race/Ethnicity and 

Associated Disparities, HEALTH SERVICES & OUTCOMES RESEARCH METHODOLOGY (2009) 9:69-83.
6 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

assignment to race and ethnicity based on demographic information associated with surname 

and then updating this probability using the demographic characteristics of the census block 

group associated with place of residence. The updating is performed through the application of a 

Bayesian algorithm, which yields an integrated probability that can be used to proxy for an 

individual’s race and ethnicity. Elliott et al. (2009) refer to this method as Bayesian Improved 

Surname Geocoding (BISG).

The Office of Research (OR) and the Division of Supervision, Enforcement, and Fair Lending 

(SEFL) employ a BISG proxy methodology for race and ethnicity in our fair lending analysis of 

non-mortgage credit products that relies on the same public data sources and general methods

used in Elliott et al. (2009).6 The following sections describe these public data sources, explain 

the construction of the BISG proxy, identify any differences from the general methods used by 

Elliott et al. (2009), and provide an assessment of the performance of the BISG proxy.

Statistical analysis based on proxies for race and ethnicity is only one factor taken into account 

by OR and SEFL in our fair lending review of non-mortgage credit products. This paper 

describes the methodology currently employed by OR and SEFL but does not set forth a 

requirement for the way proxies should be constructed or used by institutions supervised and 

regulated by the CFPB.7 Finally, our proxy methodology is not static: it will evolve over time as 

enhancements are identified that improve accuracy and performance.

 

6 We also rely on a proxy for sex based on publicly available data from the Social Security Administration, available at: 

http://www.ssa.gov/oact/babynames/limits.html. The focus of this paper, however, is on the BISG methodology 

and the construction of the proxies for race and ethnicity.

7 The federal banking regulators have made it clear that proxy methods may be used in fair lending exams to estimate 

protected characteristics where direct evidence of the protected characteristic is unavailable. The CFPB adopted the 

Interagency Fair Lending Examination Procedures as part of its CFPB Supervision and Examination Manual. See 

CFPB Supervision and Examination Manual, Part II, C, ECOA, Interagency Fair Lending Examination Procedures at

19, available at http://files.consumerfinance.gov/f/201210_cfpb_supervision-and-examination-manual-v2.pdf

(explaining that “[a] surrogate for a prohibited basis group characteristic may be used” in a comparative file review 

and providing examples of surname proxies for race/ethnicity and first name proxies for sex).
7 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

3.1 Data sources

3.1.1 Surname

Information used to calculate the probability of belonging to a specific race and ethnicity given 

an individual’s surname is based on data derived from Census 2000 that was released by the 

U.S. Census Bureau in 2007.8 This release provides each surname held by at least 100 

enumerated individuals, along with a breakdown of the percentage of individuals with that 

name belonging to one of six race and ethnicity categories: Hispanic; non-Hispanic White; nonHispanic Black or African American; non-Hispanic Asian/Pacific Islander; non-Hispanic 

American Indian and Alaska Native; and non-Hispanic Multiracial. These categories are 

consistent with 1997 Office of Management and Budget (OMB) definitions.9,10 In total, the list 

provides 151,671 surnames, covering approximately 90% of the U.S. population. Word et al. 

(2008) provides a detailed description of how the census surname list was constructed and 

describes the routines used to standardize surnames appearing on the list.11

3.1.2 Geography

Information on the racial and ethnic composition of the U.S. population by geography comes 

from the Summary File 1 (SF1) from Census 2010, which provides counts of enumerated 

 

8 The data and documentation are available at: http://www.census.gov/genealogy/www/data/2000surnames/. The 

most recent census year for which the surname list exists is 2000. We will rely on more current data when it 

becomes available. 

9 This classification holds Hispanic as mutually exclusive from the race categories, with individuals identified as 

Hispanic belonging only to that category, regardless of racial background. The Census relies on self-identification of 

both race and ethnicity when determining race and ethnicity for these individuals, with an exception made for 

classification to the “Some Other Race” category. In Census 2000, some individuals identifying as “Some Other 

Race” also specified a Hispanic nationality (e.g., Salvadoran, Puerto Rican); in these instances, the Census identified 

the respondent as Hispanic. OMB definitions are available at: 

http://www.whitehouse.gov/omb/fedreg_1997standards.

10 In the census surname data, the Census Bureau suppressed exact counts for race and ethnicity categories with 2-5 

occurrences for a given name. Similarly to Elliott et al. (2009), in these cases we distribute the sum of the 

suppressed counts for each surname evenly across all categories with missing nonzero counts.

11 Word, D.L., Coleman, C.D., Nunziata, R., Kominski, R., Demographic aspects of surnames from Census 2000. 

Available at: http://www.census.gov/genealogy/www/data/2000surnames/surnames.pdf.
8 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

individuals by race and ethnicity for various geographic area definitions, with census block 

serving as the highest level of disaggregation (the smallest geography).12 In the decennial Census 

of the Population, the Census Bureau uses a classification scheme for race and ethnicity that 

differs slightly from the scheme used by OMB. Census treats Hispanic as an ethnicity and the 

other OMB categories as racial identities. However, Census does report population counts by 

race and ethnicity in a way that allows for the creation of race and ethnicity population totals 

that are consistent with the OMB definition.13 Our method relies on race and ethnicity 

information for the adult (age 18 and over) population at the census block group, census tract, 

and 5-digit zip code levels, as discussed in the next section.14,15

3.2 Constructing the BISG probability

Constructing the BISG proxy for race and ethnicity for a given set of applicants requires place of 

residence (address) and name information for those applicants, the census surname list, and 

census demographic information by census block group, census tract, and 5-digit zip code. The 

process occurs in a number of steps: 

1. Applicants’ surnames are standardized and edited, including removing special characters 

and titles, such as JR and SR, and parsing compound names. 

 

12 The hierarchy of census geographic entities, from smallest to largest, is: block, block group, tract, county, state, 

division, region, and nation. Block group level information appears in Table P9 (“Hispanic or Latino, and Not 

Hispanic or Latino by Race”) in the SF1. Table P11 in the SF1 provides similar counts for the restricted population of 

individuals 18 and over. The public can access these data in a variety of ways, including through the American 

FactFinder portal at: http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.

13 In the 2010 SF1, Census produced tabulations that report counts of Hispanics and non-Hispanics by race. These 

tabulations include a “Some Other Race” category. As in Elliott et al. (2009), we reallocate the “Some Other Race” 

counts to each of the remaining six race and ethnicity categories using an Iterative Proportional Fitting procedure to 

make geography based demographic categories consistent with those on the census surname list.

14 Throughout this paper, we use 5-digit zip code, when referring to zip code demographics, as a synonym for ZIP 

Code Tabulation Areas (ZCTAs) as defined by the U.S. Census Bureau. More information on the construction of 

ZCTAs is available at: https://www.census.gov/geo/reference/zctas.html. 

15 From the SF1, we retain population counts for the contiguous U.S., Alaska, and Hawaii in order to ensure 

consistency with the population covered by the census surname list.
9 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

2. Standardized surnames are matched to the census surname list. For applicants with 

compound surnames, if the first word of the compound surname successfully matches to 

the surname data, it is used to calculate the surname based probability. If the first word 

does not match, the second word is then tried. For example, if an applicant’s last name is 

Smith-Jones, the demographic information associated with Smith is used if Smith 

appears on the name list. If Smith does not appear on the name list, then the information 

associated with Jones is used if Jones is on the list. 

3. For each name that matches the census surname list, the probability of belonging to a 

given racial or ethnic group (for each of the six race and ethnicity categories) is 

constructed. The probability is simply the proportion (or percentage) of individuals who 

identify as being a member of a given race or ethnicity for a given surname. For example, 

according to the census surname list, 73% of individuals with the surname Smith report 

being non-Hispanic White; thus, for any individual with the last name Smith, the 

surname-based probability of being non-Hispanic White is 73%. For applications with 

names that do not match the census surname list, a probability is not constructed. These 

records are excluded in subsequent analysis.16 Given that approximately 10% of the U.S. 

population is not included on the census surname list, one would reasonably expect 

roughly a 10% reduction in the number of records in a proxied dataset due to nonmatches to the census surname list.

4. Applicant address information is standardized in preparation for geocoding. 

Standardization includes basic checks such as removing non-numeric characters from 

zip codes, making sure zip codes with leading zeroes are accurately identified, and 

ensuring address information is in the correct format, for example, that house number, 

street, city, state, and zip code are appropriately parsed into separate fields.

5. Addresses are mapped into census geographic areas using a geocoding and mapping 

software application.17 The geocoding application used by OR and SEFL in building the 

 

16 Elliott et al. (2009) retain records in their assessment data that do not appear on the surname list. To do so, they 

use the distribution of race and ethnicity appearing on the name list and the national population counts in the 

Census 2000 SF1 to characterize the unlisted population. OR and SEFL continue to evaluate the approach 

undertaken by Elliott et al. (2009) and may adopt a method for proxying the unlisted surname population in future 

updates to the proxy methodology.

17 We currently use ArcGIS Version 10.1 with Street Map Premium 2011 Release 3 to geocode data when building the 

proxy. We may rely on updated releases as they become available or may move to different geocoding technology in 

the future. The BISG proxy methodology does not require the use of a specific geocoding technology.
10 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

proxy identifies the geographic precision to which an address is geocoded, and the 

precision of geocoding determines the precision of the demographic information relied 

upon.18 For addresses that are geocoded to the latitude and longitude of an exact street 

address (often referred to as a “rooftop”), information on race and ethnicity for the adult 

population residing in the census block group containing the street address is used; if the 

census block group has zero population, information for the census tract is used. For 

addresses that are geocoded to street name, 9-digit zip code, and 5-digit zip code, the 

race and ethnicity information for the adult population residing in the 5-digit zip code is 

used. Addresses that cannot be geocoded or that can be geocoded only to a geographical 

area that is less precise than 5-digit zip code (for example, city or state) are excluded in 

subsequent analysis.

6. For geocoded addresses, the proportion (or percentage) of the U.S. adult population for 

each race and ethnicity residing in the geographic area containing the address or 

associated with the 5-digit zip code is calculated.

7. Bayes Theorem is used to update the surname-based probabilities constructed in Step 3 

with the information on the concentration of the U.S. adult population constructed in 

Step 6 to create a probability—a value between, or equal to, 0 and 1—of assignment to 

each of the 6 race and ethnicity categories. These proxy probabilities can be used in 

statistical analysis aimed at identifying potential differences in lending outcomes.

Appendix A provides the mathematical formula associated with Step 7 and an example of the 

construction of the BISG proxy probabilities for an individual with the last name Smith residing 

in California. The statistical software code, written in Stata, and the publicly available census 

data files used to build the BISG proxy are available at: https://github.com/cfpb/proxymethodology. Because OR and SEFL currently use ArcGIS to geocode address information when 

building the proxy, the geocoding of address information must occur before running the Stata 

code that builds the BISG proxy. The use of alternative geocoding applications may return 

slightly different geocoding results and, therefore, may yield different BISG probabilities than 

those generated using ArcGIS. 

Steps 1 through 7 describe the general process currently undertaken by OR and SEFL to 

construct proxies for race and ethnicity for fair lending analysis. Unique features of a dataset 

 

18 The precision of the geocoding is driven by the availability of address information and the geocoding software 

application’s assessment of the quality of address information provided.
11 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

under review, for example, the quality of surname data and the ability to match individuals to 

the census surname list, or the quality of address information and the ability to geocode to an 

acceptable level of precision, may lead to a modification of the general methodology, as 

appropriate.
12 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

4. Assessing the ability to 

predict race and ethnicity: an 

application to mortgage data

Elliott et al. (2009) demonstrate, using health plan enrollment data with reported race and 

ethnicity, that the BISG proxy methodology is more accurate than either the traditional 

surname-only or geography-only methodologies. In this section, we discuss a similar validation 

of the BISG proxy in the mortgage lending context. 

To assess the performance of the BISG proxy in this context, the geography-only, surname-only, 

and BISG proxies for race and ethnicity were constructed for applicants appearing in a sample of 

mortgage loan applications in 2011 and 2012 for which address, name, and race and ethnicity 

were reported.19,20 These data were provided to the CFPB by a number of lenders pursuant to the 

CFPB’s supervisory authority. Applications with surnames that did not match the surname list 

 

19 The geography-only probability proxy is constructed in a manner that is similar to the construction of the surnameonly proxy. For each geocoded address, the probability of belonging to a given racial or ethnic group (for each of the 

six race and ethnicity categories) is constructed. The probability is simply the proportion (or percentage) of 

individuals who identify as being a member of a given race or ethnicity who reside in the block group, census tract, 

or area corresponding to the 5-digit zip code, depending on the precision to which an applicant’s address is 

geocoded.

20 The reported race and ethnicity used in the assessment are derived from the HMDA reported race and ethnicity 

contained in the mortgage data sample. Ethnicity (Hispanic) and race—American Indian/Alaska Native, Asian, 

Black or African American, Native Hawaiian or Other Pacific Islander, and White—are reported at the applicant 

level. For a given applicant, up to five races may be reported. The reported HMDA race and ethnicity are used to 

classify applicants in a manner consistent with the six mutually exclusive race and ethnicity categories defined by 

the Office of Management and Budget and used on the census surname list. Applications for which race or ethnicity 

information was not provided were omitted from the initial sample.
13 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

and with addresses that could not be geocoded to at least the 5-digit zip code were omitted from 

the analysis. Table 1 shows that for the initial sample of 216,798 mortgage applications, 26,363 

applications—approximately 12% of the initial sample—were omitted from the analysis, 

resulting in a final sample of 190,435.

TABLE 1: MORTGAGE LOAN SAMPLE

Not Geocoded Geocoded

Surname did not match 8 26,297

Surname did match 58 190,435

For each applicant, three probabilities of assignment to each of the six race and ethnicity 

categories were constructed: a probability based on census race and ethnicity information 

associated with geography (geography-only); a probability based on census race and ethnicity 

information associated with surname (surname-only); and the BISG probability based on census 

race and ethnicity information associated with surname and geography (BISG). As previously 

discussed, the probabilities themselves may be used to proxy for race and ethnicity by assigning 

to each record a probability of belonging to a particular racial or ethnic group. These 

probabilities can be used to estimate the number of individuals by race and ethnicity and to 

identify potential disparities in outcomes through statistical analysis.

Assessing the accuracy of the proxy involves comparing a probability that can range between 0 

and 1 (a continuous measure) to reported race and ethnicity classifications that, by definition, 

take on values of only 0 or 1 (a dichotomous measure). Accuracy can be evaluated in at least two 

ways: (1) by comparing the distribution of race and ethnicity across all applicants based on the 

proxy to the distribution based on reported characteristics and (2) by assessing how well the 

proxy is able to sort applicants into the reported race and ethnicity categories. The tendency for 

low values of the proxy to be associated with low incidence of individuals in a particular racial or 

ethnic group and for high values of the proxy to be associated with high incidence is measured 

by the correlation between the proxy and reported classification for a given race and ethnicity. 

Additional diagnostic measures, such as Area Under the Curve (AUC) statistics, reflect the 

extent to which a proxy probability accurately sorts individuals into target race and ethnicity 

and provides a statistical framework for assessing improvements in sorting attributable to the 

BISG proxy. Section 4 provides an evaluation of the use of the BISG probability proxy and 
14 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

assesses performance relative to reported race and ethnicity, illustrating the merits of relying on 

the BISG probability proxy rather than on a proxy based solely on information associated with 

geography or surname alone.

4.1 Composition of lending by race and 

ethnicity

Table 2 provides the distribution of reported race and ethnicity (Reported) and the distributions 

based on the BISG, surname-only, and geography-only proxies. For the Reported row, the 

percentage in each cell is calculated as the sum of the reported number of individuals in each 

racial or ethnic group divided by the number of applicants in the sample (multiplied by 100). 

For the proxies, the percentage is simply the sum of the probabilities for each race and ethnicity 

divided by the number of applicants in the sample (multiplied by 100). For example, two 

individuals each with a 0.5 probability of being Black and a 0.5 probability of being White would 

contribute a count of 1 to both the Black and the White totals.

TABLE 2: DISTRUBUTION OF LOANS BY RACE AND ETHNICITY21

Classifier 

or Proxy Hispanic White Black Asian/Pacific Islander

American 

Indian/Alaska 

Native

Multiracial

Reported 5.8% 82.9% 6.2% 4.5% 0.1% 0.4%

BISG 6.1% 79.7% 7.5% 5.0% 0.2% 1.4%

Surnameonly 7.4% 75.4% 10.0% 4.9% 0.6% 1.7%

Geography

-only 7.2% 78.6% 8.1% 4.8% 0.3% 1.0%

 

21 In this table and in subsequent tables, we refer only to the race for a non-Hispanic race group. For instance, the 

“White” category refers to “Non-Hispanic White.”
15 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

As the table indicates, all three proxies tend to approximate the reported population race and 

ethnicity. However, each also tends to underestimate the population of non-Hispanic Whites 

and overestimate the other race and ethnicity categories, which may reflect differences between 

the racial and ethnic composition of the census based populations used to construct the proxies 

and the racial and ethnic composition of individuals applying for mortgages. 

Importantly, however, the BISG proxy comes closer to approximating the reported race and 

ethnicity than the traditional proxy methodologies, with the only exception being for 

Asian/Pacific Islanders and Multiracial. Though we see small absolute gains in accuracy from 

use of a BISG proxy for some groups relative to the traditional methods of proxying, these gains 

frequently represent a sizeable improvement in terms of relative performance. For example, the 

gap between reported race and estimated race for non-Hispanic Whites shrinks by 1.1% (from 

82.9% – 78.6% = 4.3% to 82.9% – 79.7% = 3.2%) when moving from a geography-only to the 

BISG proxy. Given the initial gap of 4.3% this represents an almost 25% reduction in the 

difference between estimated and reported race. The gaps for non-Hispanic Black, non-Hispanic 

American Indian/Alaska Native, and Hispanic shrink in a similar manner. For non-Hispanic 

Asian/Pacific Islander, the gap between estimated and reported totals increases by 0.2% in 

absolute terms compared to the geography-only alternative and by 0.1% compared to the 

surname-only alternative. For the non-Hispanic Multiracial category, the BISG proxy does 

slightly better than the surname-only and slightly worse than the geography-only proxy in 

approximating the reported percentage.

4.2 Predicting race and ethnicity for 

applicants

4.2.1 Correlations between the proxy and reported race and 

ethnicity

Table 3 provides the correlations between reported race and ethnicity and the BISG, surnameonly, and geography-only proxies. 
16 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 3: CORRELATIONS BETWEEN PROXY PROBABILITY AND REPORTED RACE AND ETHNICITY

Proxy Hispanic White Black Asian/Pacific 

Islander

American 

Indian/Alaska 

Native

Multiracial

BISG 0.81 0.77 0.70 0.83 0.06 0.05

Surname-only 0.78 0.66 0.40 0.81 0.03 0.05

Geographyonly 0.45 0.54 0.58 0.38 0.05 0.03

Correlation is a statistical measure of the relationship between different variables—in this case 

the race and ethnicity proxy and an applicant’s reported race and ethnicity. Positive values 

indicate a positive correlation (as one variable increases in value, so does the other), negative 

values imply negative correlation (as one variable increases in value, the other decreases), and 0 

indicates no statistical relationship. By definition, a correlation coefficient of 0 means that the 

proxy probability has no predictive power in explaining movement in the reported value, while a 

coefficient of 1 means that an increase in the proxy probability perfectly predicts increases in the 

reported values. Higher values of the correlation measure indicate a stronger ability to 

accurately sort individuals both into and out of a given race and ethnicity classification. 

Correlations associated with the BISG proxy probabilities for Hispanic and non-Hispanic White, 

Black, and Asian/Pacific Islander are large and suggest strong positive co-movement with 

reported race and ethnicity. This means, for example, that the Hispanic proxy value is higher on 

average for individuals who are reported as Hispanic than for those who are not. For nonHispanic American Indian/Alaska Native and the Multiracial classifications, correlations are 

positive but close to zero for all proxy methods, suggesting a low degree of power in predicting 

reported race and ethnicity for these two groups. 

Looking across the rows in Table 3, correlations associated with the BISG are higher than those 

associated with the surname-only and geography-only proxies, notably for non-Hispanic Black 

and non-Hispanic White, reflecting the increase in the strength of the relationship between the 

proxy and reported characteristic from the integration of information associated with surname 

and geography in the BISG proxy. These results align closely with those found in Elliott et al. 
17 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

(2009), which, as previously noted, assessed the BISG proxy using national health plan 

enrollment data.22

4.2.2 Area Under the Curve (AUC)

While correlations illustrate the overall extent of co-movement between the proxies and 

reported race and ethnicity, it is also important to assess the extent to which the proxy 

probabilities successfully sort individuals into each race and ethnicity. 

A statistic that can be used to calculate this is called the Area Under the Curve (AUC), which 

represents the likelihood that the proxy will accurately sort individuals into a particular racial or 

ethnic group.23 For example, if one randomly selects an individual who is reported as Hispanic 

and a second individual who is reported as non-Hispanic, the AUC represents the likelihood that 

the randomly selected individual reported as Hispanic has a higher proxy value of being 

Hispanic than the randomly selected individual reported as non-Hispanic. The AUC can be used 

to test the hypothesis that one proxy is more accurate than another at sorting individuals in 

order of likelihood of belonging to a given race and ethnicity. An AUC value of 1 (or 100%) 

reflects perfect sorting and classification, and a value of 0.5 (or 50%) suggests that the proxy is 

only as good as a random guess (e.g., a coin toss). 

Table 4 provides the results of statistical comparisons of the geography-only, surname-only, and 

BISG probabilities. The AUC statistics associated with the BISG proxy for Hispanic and nonHispanic White, Black, and Asian/Pacific Islander are large and exceed 90%. For instance, the 

AUC statistic associated with the BISG proxy for non-Hispanic Black is 0.9540, suggesting that 

95% of the time, a randomly chosen individual reported as Black will have a higher BISG 

probability of being Black than a randomly chosen individual reported as non-Black.

 

22 Table 4 of Elliott et al. (2009): Non-Hispanic White (0.76); Hispanic (0.82); Black (0.70); Asian/Pacific Islander 

(0.77); American Indian/Alaska Native (0.11); and Multiracial (0.02).

23 The AUC is based on the Receiver Operating Characteristic (ROC) curve, which plots the tradeoff between the true 

positive rate and the false positive rate for a given proxy probability over the entire range of possible threshold 

values that could be used to classify individuals with certainty to the race and ethnicity being proxied. See Appendix 

B for more detail on the construction of the ROC curves and calculation of the AUC.
18 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 4: LIKELIHOOD OF ASSIGNMENT OF HIGHER PROXY PROBABILITY FOR GROUP MEMBERSHIP 

GIVEN THAT BORROWER IS REPORTED AS MEMBER OF GROUP (AREA UNDER THE CURVE STATISTIC)

Proxy Hispanic White Black Asian/Pacific 

Islander

American 

Indian/Alaska 

Native

Multiracial

BISG 0.9446 0.9430 0.9540 0.9723 0.6840 0.6846

Geographyonly 0.8386 0.8389 0.8959 0.8359 0.6574 0.6015

Surnameonly 0.9302 0.8968 0.8678 0.9651 0.5907 0.7075

p-value, H0: 

BISG=Geo <0.0001 <0.0001 <0.0001 <0.0001 0.0262 <0.0001

p-value, H0: 

BISG=Name <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0289

For each of these four race and ethnicity categories, the AUC for the BISG proxy probability is 

statistically significantly larger than the AUC for the surname-only and geography-only 

probabilities, suggesting that, at or above the 99% level of statistical significance, the BISG more 

accurately sorts individuals than the traditional proxy methodologies.24 The greatest 

improvements in the AUC are associated with the BISG proxy for non-Hispanic White and 

Black, as the AUC is considerably higher than the AUCs associated with the geography-only and 

surname-only proxies. For Hispanic and non-Hispanic Asian/Pacific Islander, this improvement 

is only marginal relative to the performance of the surname-only proxy. Performance for nonHispanic American Indian/Alaska Native and Multiracial, while generally improved by the use 

of the BISG proxy probabilities, is weak overall regardless of proxy choice, with only an 18% 

improvement in sorting over a random guess. These results suggest that proxies based on census 

geography and surname data are not particularly powerful in their ability to sort individuals into 

these two race and ethnicity categories.

 

24 The p-values for the tests of equivalence of the AUC statistics for the BISG and geography-only proxies and the 

BISG and surname-only proxies for each race and ethnicity appear in the last two rows of Table 4.
19 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

4.2.3 Classification over the range of proxy values

The BISG proxy’s ability to sort individuals is made clear through an evaluation of the number of 

applicants falling within ranges of proxy probability values. For example, for 10% bands of the 

BISG proxy probability for Hispanics, Table 5 provides: the number of total applicants (column 

1); the estimated number of Hispanic applicants based on the summation of the BISG 

probability (column 2); the number of reported Hispanic applicants (column 3); the number of 

reported non-Hispanic White applicants (column 4); and the number of reported other 

minority, non-Hispanic applicants (column 5). A few results are worth noting.

TABLE 5: CLASSIFICATION OVER RANGE OF BISG PROXY FOR HISPANIC

Hispanic 

BISG Proxy 

Probability 

Range

Total 

Applicants

(1)

Estimated 

Hispanic 

(BISG) 

(2)

Reported 

Hispanic

 

(3) 

Reported 

White 

(4) 

Reported 

Other 

Minority 

(5) 

0% - 10% 176,116 1,129 1,677 153,974 20,465

10% - 20% 1,720 240 163 1,207 350

20% - 30% 653 163 130 414 109

30% - 40% 541 189 147 312 82

40% - 50% 557 251 226 261 70

50% - 60% 597 328 279 258 60

60% - 70% 802 522 455 263 84

70% - 80% 1,135 853 766 286 83

80% - 90% 1,788 1,529 1,347 347 94

90% - 100% 6,526 6,312 5,883 534 109

Total 190,435 11,516 11,073 157,856 21,506

*Estimated Hispanic (BISG) is calculated as the sum of the BISG probabilities for being Hispanic within the corresponding proxy 

probability range.
20 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

First, the distribution of the BISG proxy probability is bimodal with concentrations of total 

applicants for low (e.g., 0%-20%) and high (e.g., 80%-100%) values of the proxy, which 

illustrates the sorting feature of the proxy. Reported Hispanic applicants are concentrated 

within high values of the proxy. For example, 65% ((1,347+5,883)/11,073) of reported Hispanic 

applicants (column 3) have BISG proxy probabilities greater than 80%; this concentration is 

mirrored by the estimated number of Hispanic applicants (column 2), 68% of whom have BISG 

proxy probabilities greater than 80% ((1,529+6,312)/11,516). While the BISG proxy may assign 

high values to some non-Hispanic applicants, 98% ((153,974+1,207)/157,856) of the reported 

non-Hispanic White and 97% ((20,465+350)/21,506) of the reported other non-Hispanic 

minority borrowers have Hispanic BISG proxy probabilities that are less than 20%.

Second, there are reported Hispanic applicants over the full range of values of the BISG proxy; 

this is also reflected by the estimated counts in column 2. For example, there are 597 applicants 

with BISG proxy values between 50% and 60%, of whom 279 are reported as being Hispanic, 

while the BISG proxy estimate of the number of Hispanic applicants in this range—calculated by 

summing probabilities for individuals within this probability range—is 328.

As suggested by Table 5 the BISG proxy tends to overestimate the number of Hispanic 

applicants for the mortgage pool under review. In the final row of column (3) we see that the 

total number of reported Hispanic applicants is 11,073. The estimated total number of Hispanic 

applicants—calculated as the sum of the BISG probabilities for Hispanic applicants—is 11,516 

(column 2), which overestimates the number of Hispanic applicants by 4%. This overestimation 

may reflect, as discussed in Section 4.1, the use of demographic information based on the 

population at large to proxy the characteristics of mortgage applicants. According to the 2010 

Census of Population, 14% of the U.S. adult population was Hispanic; 67% non-Hispanic White; 

12% non-Hispanic Black; 5% Asian/Pacific Islander; and 1% American Indian/Alaska Native. 

According to the 2010 HMDA loan application data for all reporting mortgage originators, only 

7% of applicants for home mortgages were Hispanic; 80% non-Hispanic White; 6% nonHispanic Black; 6% Asian/Pacific Islander; and less than 1% American Indian/Alaska Native.25

Mortgage borrowers tend to be disproportionately non-Hispanic White and, in particular, 

underrepresent Hispanic and non-Hispanic Blacks relative to the population of the U.S.

 

25 The HMDA distributions for race and ethnicity are based only on applicant information for which race and 

ethnicity is reported and for applications that were originated, approved but not accepted, and denied by lenders.
21 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

OR and SEFL rely directly on the BISG probability in our fair lending related statistical analyses. 

In contrast, some practitioners rely on the use of a probability proxy and a threshold rule to 

classify individuals into race and ethnicity. When a threshold rule is used, individuals with 

proxy probabilities equal to and greater than a specific value, for example 80%, are considered 

to belong to a group with certainty, while all others are considered non-members with certainty. 

Consider two individuals who are assigned BISG probabilities of being non-Hispanic Black: 

individual A with 82% and individual B with 53%. The application of an 80% threshold rule for 

assignment would force individual A’s probability to 100% and classify that individual as being 

Black and force individual B’s probability to 0% and classify that individual as being non-Black. 

The threshold rule removes the uncertainty about group membership at the cost of decreased 

statistical precision, with that precision deteriorating with decreases in the proxy’s ability to 

create separation across races and ethnicity. In situations in which researchers can obtain clear 

separation between groups—for instance, situations for which the probabilities of assignment 

tend to be very close to 0 or 1—the consequences of using a threshold assignment rule, beyond 

simple measurement error, would be minor. However, when insufficient separation exists—for 

example, when there are a significant number of individuals with probabilities between 20% and 

80% of belonging to a particular group—the use of thresholds can artificially bias, usually 

downward, estimates of the number of individuals belonging to particular racial and ethnic 

groups and potentially attenuate estimates of differences in outcomes between groups.

Table 5 makes clear the consequence of applying a threshold rule to the BISG proxy probability 

to force classification with certainty. If an 80% threshold rule is applied, the estimated number 

of Hispanic applicants is 8,314—the sum of all applicants in column (1) with a BISG probability 

equal to or greater than 80%—which underestimates the reported number of 11,073 Hispanic 

applicants by 25%. The underestimation is driven by the failure to count the large number of 

individuals in column (3) who are reported as being Hispanic in the mortgage sample but for 

whom the BISG probability of assignment is less than 80%.

It is worth noting that the application of an 80% threshold rule to classify individuals also yields 

false positives: individuals who are reported as being non-Hispanic but, nonetheless, are 

assigned BISG proxy probabilities of being Hispanic equal to or greater than 80%. For the 

mortgage pool under review, 881 applicants who are reported as being non-Hispanic White and 

203 applicants who are reported as being some other minority would be classified as Hispanic 

by an 80% threshold rule. The false positive rate associated with these 1,084 observations is 

0.6%, measured as the number of false positives (1,084) as a percentage of the total number of 

false positives plus the 178,278 true negative reported non-Hispanics with BISG probabilities 
22 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

less than 80%. The false discovery rate for these same 1,084 observations is 13%, measured as 

the number of false positives (1,084) as a percentage of 8,314 applicants identified as Hispanic 

by the 80% threshold rule.

Classification and misclassification tables for the other five race and ethnicity categories appear 

in Appendix C.
23 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

5. Conclusion

Information on consumer race and ethnicity is generally not collected for non-mortgage credit 

products. However, information on consumer race and ethnicity is required to conduct fair 

lending analysis. Publicly available data characterizing the distribution of the population across 

race and ethnicity on the basis of geography and surname can be used to develop a proxy for 

race and ethnicity. Historically, practitioners have relied on proxies based on geography or 

surname only. A new approach proposed in the academic literature—the BISG method—

combines geography- and surname-based information into a single proxy probability. In 

supervisory and enforcement contexts, OR and SEFL rely on a BISG proxy probability for race 

and ethnicity in fair lending analysis conducted for non-mortgage products.

This paper explains the construction of the BISG proxy currently employed by OR and SEFL and 

provides an assessment of the performance of the BISG method using a sample of mortgage 

applicants for whom race and ethnicity are reported. Our assessment demonstrates that the 

BISG proxy probability is more accurate than a geography-only or surname-only proxy in its 

ability to predict individual applicants’ reported race and ethnicity and is generally more 

accurate than a geography-only or surname-only proxy at approximating the overall reported 

distribution of race and ethnicity. We also demonstrate that the direct use of the BISG 

probability does not introduce the sample attrition and significant underestimation of the 

number of individuals by race and ethnicity that occurs when commonly-relied-upon threshold 

values are used to classify individuals into race and ethnicity categories.

OR and SEFL do not require the use of or reliance on the specific proxy methodology put forth 

in this paper, but we are making available to the public the methodology, statistical software 

code, and our understanding of the performance of the methodology for a pool of mortgage 

applicants in an effort to foster transparency around our work. The methodology has evolved 

over time and will continue to evolve as enhancements are identified that improve accuracy and 

performance. Finally, the Bureau is committed to continuing our dialogue with other federal 

agencies, lenders, advocates, and researchers regarding the methodology.
24 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

6. Technical Appendix A: 

Constructing the BISG 

probability

For race and ethnicity, demographic information associated with surname and place of 

residence are combined to form a joint probability using the Bayesian updating methodology 

described in Elliott, et al. (2009). For an individual with surname s who resides in geographic 

area g: 

1. Calculate the probability of belonging to race or ethnicity r (for each of the six race and 

ethnicity categories) for a given surname s. Call this probability p(r|s).

2. Calculate the proportion of the population of individuals in race or ethnicity r (for each 

of the six race and ethnicity categories) that lives in geographic area g. Call this 

proportion q(g|r).

3. Apply Bayes’ Theorem to calculate the likelihood that an individual with surname s living 

in geographic area g belongs to race or ethnicity r. This is described by

 ( |g s) 

 ( |s) (g| )

∑ 

where R refers to the set of six OMB defined race and ethnicity categories. To maintain the 

statistical validity of the Bayesian updating process, one assumption is required: the probability 

of residing in a given geography, given one’s race, is independent of one’s surname. For 

example, the accuracy of the proxy would be impacted if Blacks with the last name Jones 

preferred to live in a certain neighborhood more than both Blacks in general and all people with 

the last name Jones.
25 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

Suppose we want to construct the BISG probabilities on the basis of surname and state of 

residence for an individual with the last name Smith who resides in California.26 Table 6 

provides the distribution across race and ethnicity for individuals in the U.S. with the last name 

Smith.27 For individuals with the surname Smith, the probability of being non-Hispanic Black, 

based on surname alone, is simply the percentage of the Smith population that is non-Hispanic 

Black: 22.22%.

TABLE 6: DISTRIBUTION OF RACE AND ETHNICITY FOR INDIVIDUALS IN THE U.S. POPULATION WITH THE

SURNAME SMITH

Race/Ethnicity Distribution

Hispanic 1.56%

White 73.35%

Black 22.22%

Asian/Pacific Islander 0.40%

American Indian/Alaska Native 0.85%

Multiracial 1.63%

To update the probabilities of assignment to race and ethnicity, the percentage of the U.S. 

population residing in California by race and ethnicity is calculated. These percentages appear in 

Table 7.

 

26 In the example, we choose to use state to make the example easy to understand. In practice, a finer level of 

geographic detail is used as discussed earlier.

27 “Smith” is the most frequently occurring surname in the 2000 Decennial Census of the Population. There are 

2,376,206 individuals in the 2000 Decennial Census of Population with the last name “Smith” according to the 

surname list (http://www.census.gov/genealogy/www/data/2000surnames/).
26 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 7: POPULATION RESIDING IN CALIFORNIA AS A PERCENTAGE OF THE TOTAL U.S. POPULATION 

BY RACE AND ETHNICITY

Race/Ethnicity U.S. Population California 

Population

% of U.S. 

Population 

Residing in 

California

Hispanic 33,346,703 9,257,499 27.76%

White 157,444,597 12,461,055 7.91%

Black 27,464,591 1,655,298 6.03%

Asian/Pacific Islander 11,901,269 3,968,506 33.35%

American Indian/Alaska Native 1,609,046 126,421 7.86%

Multiracial 2,797,866 490,137 17.52%

Total 234,564,071 27,958,916 11.92%

Given the information provided in these two tables, we can now construct the probability that 

Smith’s race is non-Hispanic Black, given surname and residence in California using Bayes’ 

Theorem. The probability of being non-Hispanic Black for the surname Smith (22.22%) is 

multiplied by the percentage of the non-Hispanic Black population residing in California 

(6.03%) and then divided by the sum of the products of the surname-based probabilities and 

percentage of the population residing in California for all six of the race and ethnicity categories: 

 

 

This same calculation is performed for the remaining race and ethnicity categories. Table 8

provides the surname-only and updated BISG probabilities for all six race and ethnicity 

categories for individuals with the last name Smith residing in California.
27 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 8: SURNAME-ONLY AND BISG PROBABILITIES FOR "SMITH" IN CALIFORNIA

Race/Ethnicity Surname-only BISG

Hispanic 1.56% 5.37%

White 73.35% 72.00%

Black 22.22% 16.61%

Asian and Pacific Islander 0.40% 1.65%

American Indian/Alaska Native 0.85% 0.83%

Multiracial 1.63% 3.54%

The impact of the adjustment of the surname based probabilities is readily apparent: the 

surname probability is weighted downward or upward depending on the degree of 

overrepresentation or underrepresentation of the population of a given race and ethnicity in 

California relative to the percentage of the U.S. population residing in California. For example, 

just under 12% of the U.S. population resides in California but nearly 28% of Hispanics in the 

U.S. reside in California. Knowing that Smith resides in California and that California is more 

heavily Hispanic than the nation as a whole leads to an increase in the probability that Smith is 

Hispanic compared to the probability calculated based on surname information alone.
28 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

7. Technical Appendix B: 

Receiver Operating 

Characteristics and Area 

Under the Curve

One way to characterize the proxy’s ability to sort individuals into race and ethnicity is to plot 

the Receiver Operating Characteristic (ROC) curve. The ROC curve is constructed by applying a 

threshold rule for classification to each race and ethnicity, where probabilities above the 

threshold yield classification to a given race and ethnicity and those below do not, and then 

plotting the relationship between the false positive rate and the true positive rate over the range 

of possible threshold values.

Figures 1 through 6 show the ROC curves for the geography-only, name-only, and BISG 

probabilities by race and ethnicity. In each plot, the true positive rate is measured on the y-axis 

and the false positive rate is measured on the x-axis.28 The slope of the ROC curve represents the 

tradeoff between identifying true positives at the expense of increasing false positives over the 

range of possible threshold values. The ROC curve for a perfect proxy—one that could classify 

individuals into and out of a given race and ethnicity with no misclassification—moves along the 

edges of the figure from (0,0) to (0,1) to (1,1). The closer that the ROC curve is to the left and 

upper edge of the plot area, the better the proxy is at correctly classifying individuals. A proxy 

 

28 The true positive rate is defined as the ratio of the number of applicants correctly classified into a reported race and 

ethnicity by a given threshold divided by the total number applicants reporting the race and ethnicity; the false 

positive rate is defined as the ratio of applicants incorrectly classified into a reported race and ethnicity by a given 

threshold divided by the total number of applicants not reporting the race and ethnicity. 
29 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

that provides no useful information instead moves along the 45-degree line that runs through 

the middle of the figure. Movement along this line implies that a proxy measure has no ability to 

meaningfully identify more true members of a group without simultaneously identifying a 

similar proportion of non-members.

The graphs demonstrate that for Hispanic and non-Hispanic White, Black, and Asian/Pacific 

Islander, the BISG proxy is generally associated with a higher ratio of true positives to false 

positives across all possible threshold values, as shown by the general tendency for BISG’s ROC 

curve to be located to the left and above of the ROC curves for the surname-only and geographyonly proxies. The BISG proxy’s overall ability to improve sorting, relative to the surname-only or 

geography-only proxy, is especially notable for non-Hispanic Whites and Blacks. The AUC 

statistic discussed in Section 4.2.2 simply represents the area beneath the ROC curve and above 

the x-axis.

FIGURE 1: RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES FOR NON-HISPANIC WHITE
30 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

FIGURE 2: RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES FOR NON-HISPANIC BLACK

FIGURE 3: RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES FOR HISPANIC
31 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

FIGURE 4: RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES FOR NON-HISPANIC ASIAN/PACIFIC

FIGURE 5: RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES FOR NON-HISPANIC NATIVE
32 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

FIGURE 6: RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES FOR NON-HISPANIC MULTIRACIAL
33 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

8. Technical Appendix C: 

Additional tables

TABLE 9: CLASSIFICATION OVER RANGES OF BISG PROXY FOR NON-HISPANIC WHITE

White BISG 

Proxy 

Probability 

Range

Total 

Applicants

(1)

Estimated 

White (BISG)

 

(2)

Reported White

(3) 

Reported 

Minority 

(4) 

0% - 10% 20,108 506 2,114 17,994

10% - 20% 3,995 582 937 3,058

20% - 30% 2,738 680 962 1,776

30% - 40% 2,483 867 1,206 1,277

40% - 50% 2,748 1,240 1,596 1,152

50% - 60% 3,346 1,847 2,196 1,150

60% - 70% 4,480 2,927 3,477 1,003

70% - 80% 7,105 5,363 5,851 1,254

80% - 90% 15,620 13,409 14,201 1,419

90% - 100% 127,812 124,411 125,316 2,496

Total 190,435 151,832 157,856 32,579
34 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 10: CLASSIFICATION OVER RANGES OF BISG PROXY FOR NON-HISPANIC BLACK

Black BISG 

Proxy 

Probability 

Range

Total 

Applicants 

(1) 

Estimated 

Black 

(BISG) 

(2)

Reported 

Black 

(3) 

Reported 

White 

(4)

Reported 

Other 

Minority 

(5) 

0% - 10% 160,733 1,859 1,466 139,684 19,583

10% - 20% 9,742 1,387 941 8,403 398

20% - 30% 4,916 1,207 906 3,814 196

30% - 40% 3,101 1,072 726 2,242 133

40% - 50% 2,229 997 738 1,408 83

50% - 60% 1,680 922 736 877 67

60% - 70% 1,417 920 765 596 56

70% - 80% 1,407 1,057 963 391 53

80% - 90% 1,517 1,293 1,222 241 54

90% - 100% 3,693 3,548 3,408 200 85

Total 190,435 14,262 11,871 157,856 20,708
35 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 11: CLASSIFICATION OVER RANGES OF BISG PROXY FOR NON-HISPANIC ASIAN/PACIFIC 

ISLANDER

Asian/

Pacific 

Islander 

BISG Proxy 

Probability 

Range

Total 

Applicants 

(1)

Estimated 

Asian and 

Pacific 

Islander 

(BISG) 

(2)

Reported 

Asian and 

Pacific 

Islander 

(3)

Reported 

White 

(4)

Reported 

Other 

Minority 

(5)

0% - 10% 178,533 867 861 154,872 22,800

10% - 20% 1,536 216 234 890 412

20% - 30% 657 160 147 366 144

30% - 40% 492 170 157 247 88

40% - 50% 385 174 145 176 64

50% - 60% 361 199 168 139 54

60% - 70% 411 267 223 156 32

70% - 80% 649 488 421 180 48

80% - 90% 1,268 1,085 923 270 75

90% - 100% 6,143 5,941 5,367 560 216

Total 190,435 9,567 8,646 157,856 23,933
36 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 12: CLASSIFICATION OVER RANGES OF BISG PROXY FOR NON-HISPANIC AMERICAN 

INDIAN/ALASKA NATIVE

American 

Indian/Alaska 

Native BISG 

Proxy 

Probability 

Range

Total 

Applicants 

(1)

Estimated 

American 

Indian/Alaska 

Native (BISG) 

(2)

Reported 

American 

Indian/Alaska 

Native 

(3)

Reported 

White 

(4)

Reported 

Other 

Minority 

(5)

0% - 10% 190,212 377 238 157,680 32,294

10% - 20% 137 19 3 106 28

20% - 30% 38 9 2 30 6

30% - 40% 12 4 1 9 2

40% - 50% 15 7 1 13 1

50% - 60% 6 3 0 6 0

60% - 70% 5 3 1 4 0

70% - 80% 4 3 1 3 0

80% - 90% 1 1 1 0 0

90% - 100% 5 5 0 5 0

Total 190,435 431 248 157,856 32,331
37 USING PUBLICLY AVAILABLE INFORMATION TO PROXY FOR UNIDENTIFIED RACE AND ETHNICITY

TABLE 13: CLASSIFICATION OVER RANGES OF BISG PROXY PROBABILITIES FOR NON-HISPANIC 

MULTIRACIAL

Multiracial 

BISG Proxy 

Probability 

Range

Total 

Applicants 

(1)

Estimated 

Multiracial 

(BISG) 

(2)

Reported 

Multiracial 

(3)

Reported 

White 

(4)

Reported 

Other 

Minority 

(5)

0% - 10% 187,964 2,102 682 156,439 30,843

10% - 20% 1,615 224 34 937 644

20% - 30% 443 107 8 255 180

30% - 40% 199 68 5 115 79

40% - 50% 113 50 9 47 57

50% - 60% 56 31 3 34 19

60% - 70% 33 21 0 18 15

70% - 80% 9 7 0 8 1

80% - 90% 3 2 0 3 0

90% - 100% 0 0 0 0 0

Total 190,435 2,612 741 157,856 31,838