OCTOBER 2022

WHITE PAPER

Inferring Race andEthnicity Demographics
from U.S. Census Data:
Testing the Feasibility for Use
in State Court Disparity Analyses
By Miriam Hamilton

Inferring Race & Ethnicity Demographics from U.S. Census Data: Testing the Feasibility for Use in State
Court Disparity Analyses

Introduction
To enable leaders to make data-driven decisions that improve racial equity in the courts, courts must haveaccess to valid race and ethnicity data and regularly use those data to monitor for disparities in court
outcomes. Yet, court researchers, like researchers in numerous fields, face severe obstacles due to thelack of robust data on race and ethnicity. Self-reported data is considered the gold standard for collecting
demographic information.2 However, the collection of these data can be challenging or even impossible insome instances. Self-reported data are limited by nonresponses, as people often refuse to provide these
data.3 Additionally, courts often lack the staff time needed to collect the information. Those courts thatcollect race and ethnicity data frequently rely on observation based on the physical characteristics of anindividual.4 Yet, when jurisdictions rely on observation, the unstandardized reporting becomes unreliable,
creating validity concerns for sharing the data. Additional barriers include the lack of interoperable
systems and other technical issues, as well as various concerns about the data being misused or
misinterpreted.5
Improving data collection is crucial for courts as it is in other fields. A variety of efforts to improve justicesystem data, including court data, is underway nationally. One example is the National Open Court Data
Standards (NODS), initiated by the Conference of State Court Administrators (COSCA) and developedby the National Center for State Courts (NCSC) to support court data creation, sharing, and integration.6
While efforts to improve court data continue in earnest, the need to identify and address racial and ethnic
disparities in court outcomes remains urgent. Researchers are looking for ways to address missing or
poor-quality race and ethnicity data so disparity analyses may be conducted. Thus, techniques to imputedemographic data, including race and ethnicity, have been developed and refined to enable disparity
monitoring and analysis.
Researchers employing methods to impute race/ethnicity data must understand the complex
methodological issues involved in measuring race/ethnicity, as these will inevitably also restrict decisions
about the imputation of such data and the ways the validity of the method should be measured. Adding to1

Approved for publication by the Blueprint for Racial Justice Steering Committee on October 14, 2022.

Kathryn Genthon & Diane Robinson, Collecting Race & Ethnicity Data, National Center for State Courts (2022), https://www.courtstatistics.org/__data/assets/pdf_file/0036/69678/Race_Ethnicity_Data_Collection_4.pdf; Kevin Fiscella & Allen M. Fremont, Use of
Geocoding and Surname Analysis to Estimate Race and Ethnicity, 41 Health Serv. Res.1482, 1496 (2006).
2

Fiscella and Fremont, supra note 2 at 1483; Marc N. Elliott et al., Using the Census Bureau’s surname list to improve estimates ofrace/ethnicity and associated disparities, 9 Health Serv. Outcomes Res. Methodol. 69, 70 (2009).
3

4

Kathryn Genthon & Diane Robinson, supra note 2.

5

Id.

NODS resources are available at www.ncsc.org/nods. Another example is the Justice Counts Initiative, led by the Council for State
Governments and funded by the Bureau of Justice Assistance, which also aims to improve accurate, accessible, and actionable
data across the justice system. An example of a statewide data collection improvement effort is Washington’s program focusing on
use-of-force in law enforcement, initiated in 2021. See Engrossed Second Substitute Senate Bill 5259, http://lawfilesext.leg.wa.gov/
biennium/2021-22/Pdf/Bills/Session%20Laws/Senate/5259-S2.SL.pdf#page=1.
6

Inferring Race & Ethnicity Demographics from U.S. Census Data2the logistical barriers mentioned above, the lack of consistent standards and the confusion about race andethnicity categories pose a critical challenge. This can lead to incomparable datasets across agencies
that collect data and to discrepancies between how people identify and how they self-describe within theconfines of provided categories.7 Keeping these limitations in mind, imputing race and ethnicity data froma source such as the U.S. Census is a promising strategy when good quality administrative data on selfreported race and ethnicity is not available.

Strategies for Imputing Race and Ethnicity
Imputation is a method that involves substituting missing data with data from a similar source that canbe used as a proxy. It is applied in order to “assign” race/ethnicity information to a dataset by estimating
probabilities from other datasets. With either the names, the address-based information, or both,individuals can be assigned a certain race/ethnicity or, better, the probability of possessing that trait.
Estimates derived from these data sources can help researchers define race and ethnicity for use indisparity analyses.8 This paper explores the legitimacy of the approach for use by courts.
Using other recorded personal information such as surnames, common first names, and geocoded
addresses from administrative data files, one can impute aggregate race and ethnicity information for adataset.9 The assumption behind geocoding is that neighborhoods tend to be racially homogenous forcertain races and ethnicities, especially in more segregated regions in the United States. Racially targeted
policies related to redlining, public housing, taxation, and labor unions after Reconstruction and throughmuch of the Twentieth Century led to segregated housing patterns throughout the United States.10
The geocoding technique exploits persistent segregation to impute race. That is, the probability thatsomeone is of a certain race can be constructed by establishing the racial composition of the geographic
area in which the individual resides. This is done by associating the individual’s address with specificcoordinates, a census tract, or simply a ZIP code. Studies using this method alone have often foundthat it is most accurate when identifying Black and White individuals, but rather inaccurate for identifying
Native American and multiracial individuals. It may also lack accuracy in regions with lower residential
segregation.11
Similarly, certain names are more likely to belong to an individual of a certain race/ethnicity than another.
Reference surname lists with the most common names for Hispanics/Latinx and Asians are available
through the U.S. Census and other sources, and other reference name lists continue to be created. This

Kathryn Genthon & Diane Robinson, supra note 2; National Center for State Courts, Racial Justice Organizational Assessment
Toolkit for Courts: Part II.2. Collecting Administrative Data on Race and Ethnicity, (Forthcoming).
7

Jenna R. Sablan, Can You Really Measure That? Combining Critical Race Theory and Quantitative Methods, 56 AM. Educ. Res. J.
178 (2019).
8

9

Allen Fremont et al., When Race/Ethnicity Data Are Lacking, 6 Rand Health Q. 16 (2016).

10

See, e.g., Richard Rothstein, The Color of Law: A Forgotten History of How Our Government Segregated America (2017).

Marc N. Elliott et al., A New Method for Estimating Race/Ethnicity and Associated Disparities Where Administrative Records Lack
Self-Reported Race/Ethnicity, 43 Health Serv. Res. 1722 (2008); Fiscella and Fremont, supra note 2.
11

3

Inferring Race & Ethnicity Demographics from U.S. Census Datamethod has been shown to estimate Hispanic/Latinx proportions relatively effectively.12 It is, however,difficult to obtain lists with a utility for all races including White and Black. Other limitations are thetemporal or regional differences between reference lists and the estimation sample. Often, names canalso be misleading proxies for race, given that surnames are frequently adopted through marriage orreflect only the patrilineal descent.13
To overcome the limitations each of the methods alone faces, researchers developed hybrid imputation
methods, such as the RAND Corporation’s Bayesian Improved Surname Geocoding (BISG) method. Thismethod first uses Census surname lists for six mutually exclusive categories — Hispanic, White, Black,
Asian and Pacific Islander, American Indian/Alaska Native, and Multiracial to calculate the probabilities
of a person’s race given a surname. This probability is then updated by multiplication with the probabilityof census block residence given a certain race.14 Several validation studies working in the realm of
health care, mortgage lending, and voting noted the relative accuracy and strong correlation between
these inferred probabilities and self-reported race/ethnicity.15 Although the extent to which this method
is successful appears to depend somewhat on the sample and geography in question, the evidence
indicates that this is a generally promising approach and can likely be applied in other fields such as thejustice system.16
Nevertheless, it is typically discouraged to use indirect imputation techniques to assign race and ethnicity(or any demographic characteristic) to an individual. What is known as the ecological inference fallacy
consists of thinking that relationships or characteristics observed for groups also hold for individuals in
those groups.17 Thus, while we can estimate the probability of someone from a certain neighborhood
being of a certain race, we cannot predict their race. Instead, the technique can be used to estimate
the aggregate demographic composition of a sample. In study designs where the individual is the unitof analysis, the racial characteristic expressed as a probability can be used for outcome or disparity
analyses.

Bernard Grofman & Jennifer Garcia, Using Spanish Surname Ratios to Estimate Proportion Hispanic in California Cities via
Bayes Theorem*, 96 SOC. SCI. Q. 1511 (2015).
12

Pablo Mateos, A review of name-based ethnicity classification methods and their potential in population studies, 13 Popul. Space
Place 243 (2007).
13

14

Elliott et al., supra note 3.

Dzifa Adjaye-Gbewonyo et al., Using the Bayesian Improved Surname Geocoding Method (BISG) to Create a Working Classification of Race and Ethnicity in a Diverse Managed Care Population: A Validation Study, 49 Health Serv. Res. 268 (2014); Fremont etal., supra note 9; Elliott et al., supra note 11.
15

For instance, two validation studies found that they could predict race successfully based on the correlation to the self-reported
dataset. However, the strength of the correlation varied, ranging from 0.7 to 0.96, see Elliott et al., supra note 3; Fremont et al.,supra note 9; Arthur Baines & Marsha Courchane, Fair Lending: Implications for the Indirect Auto Finance Market, (2014), https://www.crai.com/insights-events/publications/fair-lending-implications-indirect-auto-finance-market/ (last visited Jul 16, 2022). Newer
iterations of the hybrid method also incorporate first names, which increases the ability to identify Black individuals, see Ioan Voicu,
Using First Name Information to Improve Race and Ethnicity Classification, 5 Stat. Public Policy 1 (2018).
16

17

Philip Sedgwick, Understanding the ecological fallacy, 351 BMJ (2015), https://www.bmj.com/content/351/bmj.h4773.

Inferring Race & Ethnicity Demographics from U.S. Census Data

4

A DEEPER DIVE INTO THE TOPIC

Further Considerations for Employing Imputation
Methods to Infer Race and Ethnicity
Race and Ethnicity Categories
 Which race/ethnicity categories are used for
imputation should depend on the geographicand demographic context and the purposeof the study. Researchers must habituallybalance agency preferences based on thestandard categories of a given jurisdiction
with the limitations posed by the availablereference data to be used for imputation.
Recognizing the implications of categorizationdifferences will lead to a more nuanced andtrustworthy interpretation of the results.
Racial/ethnic identities and the categories
to measure them constantly evolve andare dependent on the cultural context. The
methodology of the U.S. Census Bureau, a typicalsource of reference data, has also changed
over the years.18 One significant methodologicalchange (and ongoing debate) involves whether tocollect race and ethnicity information in a single
data field or to collect each separately. Anotherpertinent question is whether to use mutually
exclusive or multiple selected categories. The
latter would account for the multidimensionality of
an individual’s racial/ethnic identity and recognize

the ever-changing nature of these concepts. Using
complex categorizations, however, produces
challenges for comparisons and quantitative
analyses and may create groups too small forstatistically significant estimates.19

Imputing Data That Will Be Comparable Before imputing race/ethnicity data,
researchers must consider the compatibilityof the imputed data with the data that will be
used for comparison in any analysis of theimputed data.
The frequent goal of imputation is to compare
these data with other population datasets. As there
is currently no one universal standard,20 different
administrative systems may employ different
classification systems or use similar terminology
but define the categories differently.21
This poses a challenge when attempting tocompare the imputed data with those of otheragencies, states, or U.S. Census Bureau
information. For instance, if Census data is used
for imputation, it should be expected that the“Other” response category will likely be inflated,
as Hispanics/Latinx whose only “race” identity is

Samantha Viano & Dominique J. Baker, How Administrative Data Collection and Analysis Can Better Reflect Racial and Ethnic
Identities, 44 Rev. Res. Educ. 301, 311 (2020).
18

To overcome this challenge, it might be advisable to focus only on those racial/ethnic groups that are relevant to the inquiry, as
determined by the specific decision-making context and purpose of the study. Not all identities chosen in a multi-select measure will
be equally salient to the individual. See, Kyle L. Marquardt & Yoshiko M. Herrera, Ethnicity as a Variable: An Assessment of Measures and Data Sets of Ethnicity and Related Identities*, 96 Soc. Sci. Q. 689 (2015). Another option is to map specific categories
into broader categories, see Kathryn Genthon & Diane Robinson, Collecting Race & Ethnicity Data, (2022), Court Statistics Project
Brief, https://www.courtstatistics.org/__data/assets/pdf_file/0036/69678/Race_Ethnicity_Data_Collection_4.pdf (last visited Sep 5,2022).
19

NODS recommends the two-question approach to race and Hispanic/Latinx ethnicity, comparable with the U.S. Census categories. Also see Court Statistics Project Brief, Kathryn Genthon & Diane Robinson, supra note 2.
20

21

5

Viano and Baker, supra note 18 at 308.

Inferring Race & Ethnicity Demographics from U.S. Census Data

A DEEPER DIVE INTO THE TOPIC: Further Considerations for Employing Imputation Methods to Infer Race and Ethnicity“Hispanic” or “Latinx” may only select “Hispanic”
within the ethnicity question and will likely choose“other” as race. Such imputed data can then only
be imperfectly compared to administrative datasetsthat use a distinct “Hispanic” or “Latinx” category ina multi-select measure. 22

Choosing Appropriate Geographic Units
for Geocoding Researchers must decide between more
precise but more complicated geocoding
based on small geographic units, and the moreresource-efficient and simple but less precise
geocoding based on ZCTAs.
The success of using geocoding to impute race/
ethnicity depends on relatively segregatedresidential areas. As census blocks, blockgroups, or tracts constitute demographically morehomogenous units than ZIP Code Tabulation Areas(ZCTA), the accuracy of the estimates tends to begreater when geocoding is performed at smaller
geographic levels.23 Each record needs to beprecisely geolocated within Census tracts or blocks
using mapping software that may not be readily
available.
One of the simplest imputation techniques is toinfer the probability of an individual’s race basedon their ZIP code alone. This is not dependent on
mapping software and does not involve additional
steps using name reference lists. Nevertheless,there are caveats. ZIP Codes developed by the
U.S. Postal Service are not geographically definedareas, but instead reflect postal distribution routes.
The U.S. Census Bureau developed ZIP Code
Tabulation Areas (ZCTAs) that approximate thegeographically defined boundaries for each ZIP
Code. However, they are not strictly analogous,
and there will not be a ZCTA match for each ZIP
Code.24 If many records without a match have to
be excluded from the analysis, the estimate of
the racial/ethnic distribution in the dataset will be
skewed if a certain group is more prevalent in the
excluded records.
Furthermore, ZCTAs do not nest within othergeographical units used by the Census and do
not correlate to legal or municipal areas. In fact,
ZCTA’s frequently cross county boundaries and insome instances even state borders. Finally, ZCTA’s
greatly vary in size. This has important implications
for the imputation and comparison of race/ethnicity
data. First, race/ethnicity probabilities that are
inferred based on ZCTA populations and thenaggregated to the county level to compare with theproportions of county populations risk imprecise
aggregation and a comparison that is not truly
based on the same geographical area. Second,as the inferred probabilities are based on ZCTA
populations of various sizes, and the aggregation
then involves averaging these percentages acrosseach county, each average will be based on either
more or less precise probabilities depending on thegeographic unit sizes involved.

Id. at 313. For more details on collecting race/ethnicity data that is compatible with a comprehensive set of standard categories
and on discrepancies that arise when the compatibility is imperfect, see the Racial Justice Organization Assessment Toolkit for
Courts’ Supplement to Part I.1 & I.2: Administrative Data on Race and Ethnicity, National Center for State Courts (forthcoming).
22

Nancy Krieger et al., Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the
choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project, 156 AM. J. Epidemiol.
471 (2002).
23

For instance, P.O. Boxes and single-address ZIP codes for large-volume customers such as commercial addresses, government
entities, or universities will not have a ZCTA match.
24

Inferring Race & Ethnicity Demographics from U.S. Census Data

6

Exploring the Viability of the Strategy for Disparity Analyses
There is little research conducted to date on how the imputation methods perform when using inferred
race/ethnicity in analyses. These techniques always produce some error, and this error might besystematic.25 Even if the inferred records overall are comparable to the true population, analysis
outcomes, such as disparities, may be over- or underestimated. And if there is significant and systematic
error in the imputed demographics, any subsequent analysis based on imputed data will likely produce
different results than the same analyses based on self-reported data. Researchers must understand thelimitations of their inferred data to know the validity of their disparity analyses. Moreover, they will needto provide court leaders and policymakers with the appropriate context if used to inform decision-making.
The case study presented here explores the validity of the geocoding imputation method when examining
the representativeness of jury pools.

Case Study: Representativeness of the Master Jury List in Tennessee
Impartial juries, as guaranteed by the U.S. Constitution, require that the jury pool reflect the demographic
composition of the geographic community within the court’s jurisdiction. The master jury list is the firststep in the lengthy process of creating the jury pool from which juries are selected. As such, it is importantthat the master jury list be as demographically representative as possible to ensure that the resulting jury
pool reflects a fair cross section of the community. Researchers assess representation by comparing thedemographic composition of the master jury list with that of the jury-eligible population. Ideally, they utilize
self-reported race and ethnicity data for such analyses. However, the agencies that maintain juror source
lists often do not collect those data.
Inferring the probability of an individual’s race based on their ZIP code alone is one of the fastest, mostresource-efficient, and most commonly employed imputation methods. In this case study, this method is
cross validated with self-reported data from Tennessee’s juror source list. Tennessee’s juror source list is
the list of licensed drivers and state ID cardholders maintained by the state’s Department of Safety andHomeland Security. It was shared with the National Center for State Courts (NCSC) in August of 2021 toprovide research technical assistance, which included an assessment of the representativeness of thestate master jury list.26 With over 6 million records, this list includes self-reported race/ethnicity for eachrecord. Thus, the original representativeness analyses were based on real, not imputed, data. In thiswhite paper, the results of those original analyses will be compared with the hypothetical results using an
imputation method to infer race and ethnicity.

See Arthur Baines and Marsha Courchane, supra note 16, who found non-random error in the estimates related to factors such as
income levels in the population.
25

Paula Hannaford-Agor, Miriam Hamilton & Erika Bailey, Eliminating Shadows and Ghosts: Findings from a Study of Inclusiveness,
Representativeness, and Record Accuracy in Master Jury Lists and Juror Source Lists in Three States, (September 2022), https://www.ncsc-jurystudies.org/__data/assets/pdf_file/0025/82681/Master-Jury-List.pdf.
26

7

Inferring Race & Ethnicity Demographics from U.S. Census Data

To facilitate comparison, first race and ethnicity for each record were inferred using population data fromthe U.S. Census Bureau for each ZCTA.27 The inferred race/ethnicity probabilities were then aggregatedto the county level. Unfortunately, the racial classification used for the Tennessee list does not adhere to
the U.S. Census classification system. The U.S. Census defines Hispanic/Latino as a separate ethnicity
category, and Hispanics may be of any race. On the other hand, Tennessee’s list includes Hispanic as arace category exclusive to all other races. Additionally, in the Tennessee list, “other” describes all races
other than White, Black, Asian, Hispanic, and Native American, whereas the U.S. Census distinguishes
Native Hawaiian/Pacific Islander and “multiple races” as additional categories to “other.” The disparity
analyses reported in this white paper focus on the following groups: White, Black, and Hispanic/Latinx.
Testing Three Ways to Use U.S. Census Race and Ethnicity Data for Geocoding
Disparity analyses and other demographic comparisons are often limited even when the race data is
known if the racial classification systems used between data sources differ from one another.28 To identify
the best reference dataset among the available options, three datasets are tested. Each set is obtained
from the U.S. Census and presents a slightly different tabulation of the same race and ethnicity data. The
three classification methods with the included race/ethnicity categories are described below.
•

Version 1: Hispanic-inclusive race percentages of the adult population.29
This version uses the probability that an individual is Hispanic/Latinx as an additional ethnicity
category.

Census Tabulation Version 1/3:
Race (including Hispanics/Latinx)
White

Black

NativeAmerican

Asian

Pacific
IslanderBy Gender and Age

Other
race

Multiple
races

Hispanics/Latinx(all races)

By Gender and Age

The source for all data utilized is the American Community Survey (ACS), 5-Year Average, for the Years 2015-2019. It should be
noted that, unlike the decennial census, the ACS estimates population totals. Particularly for very small populations, such as minorities in small ZCTAs, the margin of error can be relatively large. ZCTAs reflect the geographic boundaries of residential ZIP Codes.
27This also leads to a genuine difference at the level of individual data collection. For instance, an individual may identify both as
racially White and ethnically Hispanic/Latinx if given the option, but faced with the choice between the two, may identify as either.
Those who would prefer to select “Hispanic” as race, may opt to select “other.”28

29

Version 1: U.S. Census Table B01001, Version 2: U.S. Census Table B03002; Version 3: U.S. Census Table B01001.

Inferring Race & Ethnicity Demographics from U.S. Census Data

8

•

Version 2: Hispanic-exclusive race percentages of the total population.
This version is based on another tabulation of the same data that breaks down each race category
into Hispanics/Latinx and non-Hispanics.

Census Tabulation Version 2:
Race (including all ages)
White

Black

NativeAmerican

Asian

Pacific
Islander

Other
race

Multiple
races

By Ethnicity: Hispanics/Latinx and Non-Hispanics

Tennessee List Tabulation:
Race
White

Black

NativeAmerican

Asian

Pacific
Islander

Other
race

Multiple
races

This version may allow for a cleaner comparison with the Tennessee classification system. However,this version excludes all Hispanics/Latinx from the race categories, including those who might haveidentified more strongly with another race if asked to choose. The other disadvantage is that these
inferred demographics are based on the total population, including children, whereas the Tennessee
master jury list contains records of adults only.30•

Version 3: Hispanic-inclusive race percentages of the total population.
Unlike Version 1, which also uses Hispanic-inclusive percentages but infers demographics based on
the adult population, this version infers demographics, like Version 2, based on the entire population,
including children. It is hypothesized that this may produce greater error than Version 1’s inclusion of
Hispanics/Latinx in the race categories.31

The original list contained individuals under 18. Because Tennessee law requires that citizens be age 18 or older to qualify forjury service, the list was cleaned and minors were excluded based on the date of birth recorded in the list.
30

For Hispanics, Versions 2 and 3 produce identical results, as both are based on total populations, and the exclusion or inclusionof Hispanics from the other race categories only affects those categories, not the Hispanic percentage itself.
31

9

Inferring Race & Ethnicity Demographics from U.S. Census Data

Methodology
Evaluation of inferred demographics
To evaluate the validity of the imputation method, the three versions of inferred demographics areevaluated and compared in terms of accuracy in a sequence of steps. First, the question of how closely
the estimates each version produces match those derived from the true, self-reported race/ethnicity is
explored via the Pearson’s correlation coefficient.
The next step is to explore the differences between inferred and true county averages of race/ethnicity
and whether they are statistically significant with a Wilcoxon Signed Rank Test. A comparison of thestatewide averages sheds additional light on accuracy and whether the versions over- or underestimate
the prevalence of each race/ethnic category in the dataset. Ideally, inferred race and ethnicity information
would be perfectly correlated with self-reported race and ethnicity. However, as noted earlier, inference isinherently imperfect, so there will be some difference between the data sources. If the difference between
datasets is statistically significant, using an inferred data source for disparity analysis will likewise lead toresults that differ from results based on self-reported data.
A related question is whether these differences might be affected by other factors, such as the overall
size of the racial/ethnic group in a county. This potential relationship is explored via visual analysis of
scatterplots and additional significance testing for the subgroups of each sample.
While it is important to evaluate the performance of the inference method for each race/ethnicity
proportion individually, the accuracy of the overall distribution also needs to be considered. The questionis how the distributions of race/ethnicity in the inferred versions compare with the true distribution
and which version produces the most accurate overall distribution. Following previous research, thisis answered by summarizing the distributional differences across race/ethnic groups and counties bycomputing the weighted average of the deviations, with weights given by the true proportion in eachcategory.32

Evaluation of derived disparity measures
While the first part of the analysis focuses on the accuracy of the estimates and overall distribution ofinferred race/ethnicity, a pressing remaining question is whether the differences are large enough tosubstantially skew subsequent analysis using the inferred demographics as data source. The second
part of the evaluation thus focuses on the bias introduced by the imputation method. To assess how arepresentativeness analysis would be affected by the estimation errors, disparities are calculated between
each set of inferred race/ethnicity percentages and the demographic true composition of the relevant

The evaluation strategies used here largely follow previous research on imputation method performance, see Elliott et al., supra
11; Elliott et al., supra note 3; Voicu, supra note 16; Arthur Baines and Marsha Courchane, supra note 16. However, there are twomain adjustments. First, the unit of analysis here is the county aggregate of inferred individual probabilities rather than the individual
record. Hence, correlational analyses, tests of significance, and the averages of the distributional differences are here all computed based on the county percentages. Second, this study goes a step further by not only evaluating the accuracy of the inferreddemographics but also the bias that might be introduced through imputation through a comparison of disparity analysis results (seesimilarly, Voicu, supra note 16.)
32

Inferring Race & Ethnicity Demographics from U.S. Census Data

10

community, in this case, the jury-eligible county population.33 These disparities can then be comparedwith disparities derived similarly from self-reported data. The demographic composition of the jury-eligible
population is once again based on data collected by the U.S. Census Bureau.34
For the comparison, each of the steps for evaluating the inferred data is repeated in a similar fashion
with the disparity measures to determine how greatly the disparity results differ. The final question to
ask is, “How much estimation error is too much, and how great of a skew in the outcome results isstill acceptable to validate the imputation method?” Unfortunately, this last question depends on thethresholds relevant for the disparity analysis to be performed, which will differ based on the study
question and context.
Courts often measure jury pool representativeness via absolute and comparative disparity, and common
thresholds are 10% for absolute and 50% for comparative disparity (see Box below). These thresholds
are used here to assess in how many counties the results of each version produce disparities above thetypically acceptable threshold. Whether or not the same counties are flagged for exceeding the thresholds(raising representativeness concerns) helps to determine how valid the imputation methods might be.

Measuring Disparity of Jury Pools: Absolute and Comparative Disparity
Absolute disparity is the numerical difference between the percentage of a racial group in
the jury pool and in the community. Comparative disparity, or relative disparity, measures
the percentage by which the group members’ number in the jury pool falls short of or
exceeds their number in the community.
A criminal defendant can establish a prima facie violation of the fair cross-section
requirement when (1) the allegedly excluded group is a distinctive group in thecommunity, (2) the group’s representation in the jury pool is not fair and reasonable inrelation to its representation in the community, and (3) the under-representation of thegroup results from systematic exclusion.35 While there are no specified thresholds forabsolute or comparative disparity, the consensus emerging in case law is that absolutedisparities above 10% and comparative disparities above 50% may be sufficient to showa prima facie violation of the fair cross section requirement.

For purposes of a feasible disparity analysis, a “jury eligible” person is defined here as an adult citizen living in the county servedby the court, although there are additional criteria that determine jury eligibility.
33

Tables B05003, B05003A-I, ACS 5-Year Estimates Detailed Tables, 2019. These race-specific tables contain estimated totals by
age and citizenship status for each county.
34

35

11

As described by the U.S. Supreme Court in Duren v. Missouri, 439 U.S. 357 (1979).

Inferring Race & Ethnicity Demographics from U.S. Census Data

Findings
Comparing Demographics in Versions 1,2, and 3
Correlations
All three sets of inferred demographics were strongly correlated with actual self-reported race/ethnicity
data in the Tennessee juror source list (p<.01). Details are listed in Appendix 1. While differences between
versions were minor, the most accurate estimates, as judging by slightly greater correlation strength,were achieved for Hispanics/Latinx, Whites, and Blacks by using inferred demographics based on total
populations (Versions 2 and 3). This is somewhat counterintuitive given the all-adult master jury list.
For Whites, the correlation to self-reported demographics was strongest when additionally excluding
Hispanics from the race categories (Version 2). For Blacks, this did not make a difference.

Differences between inferred estimates and self-reported data
Comparing the statewide averages of the race/ethnicity probabilities, as shown for Whites, Blacks, andHispanics/Latinx in Table 1, presents a slightly different pattern. Each version leads to averages that arecloser to the self-reported average for one race, but not another, and there is no clear pattern of overor under-estimation. Across versions, the inferred county percentages had a notably smaller range for
Whites and Hispanics than the true demographics had.36

Table 1:Comparison Between Infererred and Self-reported Race & Ethnicity, averaged across 95 TN counties
Median for WhiteMedian for BlackMedian for Hispanic/Latinx

92.5%

2.8%

1.8%

Version 1

93.5%

3.4%

2.2%

Version 2

89.5%

3.4%

2.8%

Version 3

92.6%

3.4%

2.8%

Self-Reported*Differences in race/ethnicity prevalence between the inferred and self-reported samples for this category are
significant at the 1% level.

Although all three versions produced race and ethnicity estimates that are very close to the self-reported
data, as indicated by the strong correlation between them, statistically significant differences between
them can indicate important variance that warrants further analysis before determining suitability for use.
Testing for statistical significance with a Wilcoxon Signed Rank Test reveals that for some races andinference versions the difference to the self-reported data is indeed statistically insignificant.37 Detailed
test results are provided in Appendix 1, Table B.

36

A full table with summary statistics including interquartile ranges is shown in Appendix 1, Table A.

It may be noted that due to the large sample, it might be expected that even minor differences would be statistically significant, as
was the case in previous research for all race categories and methods investigated (Elliott et al., supra note 11; Voicu, supra note
16). Here, the finding that there were instances where the differences lacked significance is thus quite interesting.
37

Inferring Race & Ethnicity Demographics from U.S. Census Data

12

In summary, for Whites, it is Version 3 that produced a statewide median closest to the true medianand inferred percentages that are insignificantly different from the true data. The imputed percentages
for Blacks are indifferent from the true list percentages, regardless of whether Hispanic-inclusive racecategories and/or adult-only populations were used, although each version of imputed Black percentages
was on average overestimated. For Hispanics/Latinx, all inferred percentages were also on average
higher than the true data. Yet, Version 1 produced percentages that are most accurate, and this is also
the only version for which the differences to the self-reported percentages are insignificant.
Based on these findings, using adult populations as reference data (Version 1) seems best suited for
disparity analysis where Hispanics/Latinx are concerned. For Whites, on the other hand, total, Hispanicinclusive populations (Version 3) led to the best estimates. All versions were successful in estimating
Black proportions.

County-specific differences based on racial and ethnic group proportions
Comparing the size of the differences between estimates and true race/ethnicity percentages by county
reveals that there are systematic county-to-county variations. The proportional size of the racial groupin the total county population affected estimation errors for Whites, Asians, and Hispanics/Latinx. For
Whites, where including Latinx in the count led, on average, to overestimated imputed race percentages,
this is particularly true in counties with White populations below 90%, which applies to about 40% of all
95 counties. Appendix 2 Figure A shows the differences between Version 1-inferred and self-reported
demographics by county for Whites. Results in those counties with above 90% White populations aremuch more accurate, i.e., the differences are below five percentage points and statistically insignificant.38
Thus, for counties with proportionately large White populations, including Hispanics/Latinx in the countand using adult-only populations led to a better estimate.
Furthermore, counties with proportionately larger Asian populations produced greater underestimationsof the proportion of Asians in the county’s inferred race dataset (see Appendix 2 Figure E). For counties
with overall larger Hispanic/Latinx populations (above 3% on the master jury list, 29 counties), thetrue Hispanic percentages were significantly higher (median=5.0%) than the inferred percentages(median=4.5% in Version 1). For counties with less than 3% Hispanics/Latinx on the master jury list (66counties), the true list percentages were significantly lower (median=1.4%) than the inferred percentages(median=1.9% in Version 1). Appendix 2, Figure F shows this pattern in more detail. Although the overall
difference was found to be insignificant, the overestimations for counties with smaller Hispanic/Latinx
populations are balanced in this study by the underestimations in counties with larger Hispanic/Latinx
populations.
Caution may thus be warranted when using the inference results for individual counties. As the findings
indicate, counties with proportionately large White populations produced better estimates for Whites. For
Asians, larger Asian populations were underestimated to a greater degree than small county populations.
For Latinx, the very small populations were underestimated even more, while relatively larger populations
were overestimated.

38

13

See details in Appendix 2.

Inferring Race & Ethnicity Demographics from U.S. Census Data

Accuracy of the overall distribution of estimates
To evaluate the relative accuracy of the overall racial/ethnic distribution in each version, the distributional
differences between the sets of inferred and those of the self-reported proportions were summarized
across categories. The deviations were each weighted by a county’s true proportions of the racial/ethnic
group, and the statewide estimation error averages are shown in Appendix 3.
Comparing these, Version 3 is shown to be the most accurate overall with the smallest statewide overall
deviation from the true distribution. Based on the findings described above, this version indeed performed
especially well with Whites and Blacks, only insignificantly overestimating their proportions. On the other
hand, it significantly overestimated the prevalence of Hispanics/Latinx. While all versions performed wellfor Blacks, Hispanics are the only group for which another dataset, Version 1, would be better suited for
disparity analysis given the findings, as the Hispanic proportion is only insignificantly underestimated in
Version 1.
How big the estimation error can be before it is considered too big, may, however, depend on the purposeof the analysis using inferred race and ethnicity. The findings presented thus far help researchers realize
the probability for overestimation or underestimation of each race category depending on the data source
for imputation. This is important when utilizing the inferred estimates to derive disparities that are thenmeasured against certain thresholds for concern, given the potential policy or legal consequences ofa representativeness evaluation. Whether the imputation-derived disparities are more or less likely to
exceed such thresholds compared to disparities derived from known data is addressed in the final section
below.

Disparity Findings Using data from Version 1,2, and 3
Focusing on the question of how great the deviation can be before dismissing the ZIP-code-based
inference method as a viable option, the disparity measures calculated based on inference are evaluatedand compared here with those calculated based on self-reported race and ethnicity.

Differences between disparities derived from estimates and true disparities
It is notable that in most cases the master jury list appears more representative with inferred race
demographics and the distribution of disparity measures across counties is more homogenous than the
results based on the true records would have indicated. In most instances, the disparities have a mean
closer to zero, a smaller range, and fewer outliers compared to the measures derived from self-reported
race percentages. Nevertheless, there are also instances where the disparity is overestimated, such as
for Whites in Version 2. A comparison of summary statistics is shown in Appendix 4.
The relative accuracy of the estimated disparity measures mirrors the results of the comparison ofdemographic proportions between versions.

Inferring Race & Ethnicity Demographics from U.S. Census Data

14

Version 1. Using estimates from Hispanic-inclusive adult populations led to derived disparities that:• are significantly smaller for Whites, making the master jury list appear more representative;• show a very close estimation of the Hispanic/Latinx overrepresentation. (The slightoverestimation is statistically insignificant); and• show a very close estimation of the Black underrepresentation. (However, the true
underrepresentation on the list is accurately expressed by the disparity measures regardless ofthe version, and any deviation is statistically insignificant.)
Version 2. Excluding Hispanics/Latinx from the inferred race demographics led to:• statistically significant over- or underestimation for disparities of all racial groups except Black.
Version 3. Including Hispanics/Latinx in the racial count but deriving estimates from total populations:• works well for Whites, where the slight underestimation of White underrepresentation on the list
is statistically insignificant; but• does not perform as well for Latinx, who would have seemed significantly more overrepresented
on the list than they are.

Accuracy of the overall distribution of disparities across counties
Assessing the overall severity of the disparity inaccuracies across all races in each version, it is again
Version 3 that provides derived disparities that show the smallest statewide overall deviation from thetrue disparity distribution, as shown in Table F, Appendix 4. Yet, comparing the summary statistics ofdisparities for each race and testing for significance of the deviations from the list-based, true disparities,
Version 3 only truly performed well for Whites and Blacks. While Black disparities measured very close to
the true disparities in all versions, unfortunately, there was no version that worked well for inferring bothWhites and Hispanics/Latinx.
Whites are generally underrepresented on the master jury list of this case study. Using adult-only
populations for inferred race and ethnicity data led to Whites not appearing as underrepresented as theyshould. To reflect the true underrepresentation more accurately, using the total populations (Version 3) for
inference helped in this case. As the White birth rate is lower than that of most other races,39
the comparatively small proportion of White children in the total-population estimate relative tothat of other races will lead to a smaller estimated proportion for total Whites than when using
adult-only populations for all races. However, Hispanics/Latinx, who have higher birth rates, arealways overrepresented here.40 Using total population-based inferred data only exaggerates thisoverrepresentation.

Joyce A Martin, Brady E. Hamilton & Michelle J.K. Osterman, Births in the United States, 2020, Data Brief No.418, 1–8 (National
Center for Health Statistics 2021), https://www.cdc.gov/nchs/data/databriefs/db418.pdf.

39

Generally, the original overrepresentation of Hispanics and Asians on the master jury list based on self-reported race and ethnicity
can, in many instances, likely be attributed to high non-citizen rates for these groups. The source list from which potential jurors are
summoned in Tennessee includes non-citizens as citizenship status is unknown at this early point in the jury management process.
40

15

Inferring Race & Ethnicity Demographics from U.S. Census Data

Results for disparity versions by county
A court with county-wide jurisdiction will summon jurors from the jury-eligible population residing
within county borders, and thus each county’s list would need to be assessed individually in terms ofrepresentativeness. The parallel charts in Figure 1 show Hispanic disparities by county for those counties
with Hispanic eligible populations above 2%.

Figure 1. Disparities for Hispanic/Latinx Between Juror Source List and County Populationin 95 Counties, by Data Source
Under- or overrepresentation derived
from self-reported demographics

9Counties with Hispanic
overrepresentation, basedon self-reported data.

8

7

6

Absolute Disparity, in %

5

4

3

2

1

0

1

Derived from selfreported demographics

Derived from inferredadult populations(Version 1)

Derived from inferred
total populations(Version 2/3)

As shown, both versions based on estimates generally led to higher disparity values – in this case, anoverestimation of the overrepresentation of Hispanics/Latinx. However, this is more extreme in the version
based on total populations. Nevertheless, this is the version where for those counties with larger absolutedisparities (dark blue), the disparities derived from total population estimates are most accurate. While
aside from these higher disparity outliers the middle version seems to produce generally closer estimates,
the small range of these estimates is clearly notable. The true disparities have a much greater range andeven include counties with Hispanic/Latinx underrepresentation (i.e., absolute disparity values below 0%).

Inferring Race & Ethnicity Demographics from U.S. Census Data

16

This same result is found for other races too, such as is shown in Appendix 5, Figures G and H for Whites
and Blacks, where the inference-derived disparities have a much smaller range across counties than thetrue disparities do. This is especially evident for the versions including Latinx in the race count.
Looking at individual counties, the true extent of White underrepresentation in the most extreme cases
is only picked up when using total populations and Hispanic-exclusive estimates, despite the statewide
overall performance being better with the Hispanic-inclusive version 3. Furthermore, none of the versions
can reproduce the original absolute disparity findings for the few counties with White overrepresentation
above 3% absolute disparity. For Blacks, this analysis has so far shown that proportions and derived
disparities can be relatively accurately predicted. However, for the few outlier counties where the true
disparities are at potentially concerning levels of underrepresentation, the same counties would not show
similarly high disparities when imputing race.

Quantifying the imputation effect on disparity results
The most relevant effect of the imputation can be found when assessing in a representativeness analysis
how often and in which counties the disparities exceed certain specified thresholds — those commonlyheld thresholds that might raise concerns and potentially warrant action to be taken. Ideally, any analysis
that is based on inferred demographics would identify potential problems for the same counties as ananalysis based on self-reported demographics would have identified. Likewise, it would not identify
problems that would not have been flagged if the true racial and ethnic proportions on the list were
known.
Analyzing the master jury list, a representativeness analysis aims to identify counties in which overor underrepresentation of a distinctive group exceeds 10% absolute disparity and 50% comparative
disparity. Here, all analyses based on inferred race and ethnicity led to an overall more positive picture,with fewer counties identified in the more extreme disparities ranges than should have been identified.
This is true for all racial groups. Appendix 6 details the results and shows the distribution of disparities
within ranges of over- and underrepresentation depending on the list version used for calculating the
disparities for Whites, Blacks, and Hispanics/Latinx.
For Blacks, the original analysis had identified six counties with clear underrepresentation on the jury
list — with above 50% comparative disparities (CD).41 Only one out of these six would have been
identified when using estimated demographics. Considering Black overrepresentation above 50% CD, theestimated versions did not identify the one county correctly where such overrepresented was noted basedon self-reported data. They did, however, incorrectly flag one to two new counties each with seemingly
great overrepresentation above this threshold where such a disparity did not exist based on self-reported
data. Similarly, the lesser underrepresentation ranges (20% - 50% CD) included about 6.5% of thecounties for which measures could be calculated.42 The number of counties identified in these ranges by
the three inference versions range between 3% and 8% of total counties. However, again, these were notnecessarily the same counties that should have been identified in this range.43

41

Comparative disparity is often a better measure for very small populations.

42

For Blacks, this was 62 counties with eligible populations of this race/ethnicity >2%.

The adult-only estimate does not include any of the original counties in this range, and the two total-population estimates identify
only half of the original counties in this range but include other additional counties.
43

17

Inferring Race & Ethnicity Demographics from U.S. Census Data

Whites, never overrepresented based on the original analysis, but severely underrepresented in three
counties, are shown with the same threshold-exceeding underrepresentation in the same counties only
when excluding Hispanics from the estimates (Version 2). Hispanics/Latinx were strongly overrepresented
in most counties according to the original disparity analysis. These counties were each identified as such,or nearly so, in each inference-based analysis. Each version, however, falsely indicated apparent severe
Latinx overrepresentation for at least one additional county, in which, according to the original analysis,
Latinx were even underrepresented on the list. Thus, for these few counties, using inferred ethnicity led
to results that were opposite to the original findings, reversing the direction of the finding from minor
underrepresentation to severe overrepresentation.
Assessing total findings, Hispanics/Latinx were best inferred when based on adult populations, notsurprisingly, as the original list included adults only. Whites, on the other hand, were on average best
inferred when using total population demographics and including White Hispanics in the percentage.
The Black estimates could be inferred most successfully; they are insignificantly different from thetrue race proportion no matter whether adult or total populations are used in the estimate or whether
Hispanic-inclusive racial counts are used. Two factors contributing to these results are, first, differing birthrates which influence the relative race proportions in the adult population versus the total population.
This is important to keep in mind when inferring race and ethnicity for Whites and Hispanics/Latinx inparticular, as total-population counts are not a perfect proxy for inferring adult population probabilities
for these races. Second, Latinx are, on average, quite overrepresented on this list while Whites are, on
average, underrepresented. This fact means that a successful inference method would result in the same
overrepresentation finding for Latinx — one that is not exaggerated by including the relatively larger
proportion of Hispanic/Latinx children in the estimate — and the same underrepresentation finding for
Whites – which a simple address-based inference method appears to underestimate. In the case of thisstudy, including minors in the demographic counts helped to get closer to the true White disparity, skewing
the results toward a lower estimate.44
Importantly, for the few counties with severe over- or underrepresentation and thus outliers in theoriginal distribution of disparities among counties in Tennessee, imputation was unsuccessful inestimating the true proportions. While not an issue for Hispanics, this was particularly notable for Black
underrepresentation. Additionally, even when imputation-derived disparities identified more severe
under- or overrepresentation, it was not necessarily for the same counties that are truly in these ranges
as discovered by the original analysis. The results did not clearly indicate which version might lead to thecorrect identification of those counties with potential representativeness issues. An exception is possibly
Version 2, which identified the three counties with high White underrepresentation.

For this analysis, American Community Survey data was used from approximately the same year (2019) as the year when the records of the master jury list were created, and, as birthdates were known, minors could be excluded. However, this might not always
be possible. Given the effect that including minors in the reference data can have on the accuracy of the inferred demographics,
considering the age composition when choosing reference data is crucial.

44

Inferring Race & Ethnicity Demographics from U.S. Census Data

18

Summary of Key Findings
This case study explored whether imputing race/ethnicity via Zip Code-based geocoding is a viable option
when using results for disparity analysis, utilizing the records on the Tennessee master jury list as an
example. Specifically, this study assessed how close the estimated data were to the true, self-reported
data, comparing three imputation source data versions that either excluded or included Hispanics/Latinx
in the racial count and either used total or adult population as basis for imputation. Disparities derived
from the estimated demographics were then evaluated in terms of their accuracy compared to thosederived from the known data.
We conclude that court researchers can successfully impute race and ethnicity demographic informationfrom Census data using ZIP-code-based geocoding, particularly for White, Black, and Latinx groups.
However, we also conclude that the accuracy of the imputation method varies across jurisdictions, acrossrace and ethnicity categories, and across the versions of Census data used for imputation. As also shown
here, the overall accuracy of the estimated list-wide racial/ethnic distribution does not automatically
include accurate proportions for each group individually. Notably, using the imputed data for disparity
analysis led to results that suggest a more representative master jury list for Blacks than it truly is, as theoutliers with severe under- or overrepresentation were not successfully identified.
The success of the imputation method may be limited to: jurisdictions in which there is a high degreeof neighborhood segregation, original high-quality, residence-only address data used for geocoding,a particular reference data source for each racial group (e.g., total Hispanic-inclusive populations for
Whites, adult-only populations for Hispanics/Latinx), and a small likelihood of outliers in the dataset to be
imputed, i.e., no counties for which the dataset includes a high proportion of individuals of a certain race/
ethnicity living in a ZIP code community where that race/ethnicity has smaller proportions. The successof the method may also be influenced by the overall size of a group’s population in the community.
Estimation errors might be larger when a group’s relative size is very small compared to where the groupis larger, or vice versa. Similarly, variance in birth rates among racial and ethnic groups may have a
different effect on the success of using a particular imputation data source in one jurisdiction compared toanother.
Because of these limitations and influences, we strongly recommend that each jurisdiction entertaining
the use of imputation methods as a technique for obtaining race and ethnicity data consider the findings
from this case study and other research in selecting the parameters of inference methods appropriate
to their local jurisdiction and specific population before using the inferred dataset to conduct disparity
analyses. Such parameters include the format of the U.S. Census tabulations for various geographicalunits, Hispanic-inclusive or exclusive race categories, and age groups among others. We suggest
further research to establish criteria for the types of jurisdictions and populations for which the methods
presented here are most appropriate in order to assess the likely validity and viability of the chosen
method for a specific local jurisdiction and population. In the case studied here, Black estimates could be
inferred most successfully, except for individual outlier counties, regardless of the census data version
used. Yet only a certain version was successful for Whites, and another, less consistently, for Hispanics/
Latinx. Thus, while these findings give insight into which census data might be preferable in the case ofeach race/ethnicity, the findings only apply to the jurisdiction and original dataset of the case study.

19

Inferring Race & Ethnicity Demographics from U.S. Census Data

Implications for Court Researchers
1. Not all race and ethnicity categories can be inferred equally well using the same imputation
methods. The evidence presented here confirms prior research that White and Black proportions
may be most easily inferable with geocoding methods. This is likely because Blacks, more than otherraces, often tend to live in racially concentrated neighborhoods.45 Researchers consistently foundrelatively poor predictability of multiracial and American Indian/Alaska Native people, and this study
confirms this. Here, as in similar research, it might be argued that this caveat to the method might
be disregarded as these populations are usually less than 1% of the overall population. However,as demographics are changing and more people identify as multiracial, the utility of the method
might decline.46 Researchers should consider the geographic context of their study and the salient
demographic group(s) before deciding on methods and reference data.
2. Geocoding-based imputation methods will likely fail when the data to be imputed include
outliers or where the assumption of neighborhood segregation is not given. Inferring race andethnicity based on ZIP codes tends to produce a generally rosier picture, polishing off the rough spots
and presenting a dataset as more representative than it is. Here, this was found by assessing results
for those counties for which the list’s true records do not mirror the community demographics. Using
inferred disparities, these counties appeared to have “better” race/ethnicity counts, i.e., proportions
closer to those found in the community. This will likely always be the case when the records’ race
probabilities are being inferred based on the demographics of the ZCTA community. Unless ZCTAs
show extremely clear racial segregation, the probabilistic nature of the inferred race and ethnicity
proportions will not be sensitive to the unlikely outliers that happen to be on the list. Therefore, when
feasible and available resources allow, opting for more precise methods of imputing race might besensible.
3. How Latinx are counted can lead to complications, such as imperfect comparability between
different classification systems and findings that require complex interpretation. Here, usinga system that includes Hispanics/Latinx in each race category and measures Hispanics/Latinx
as ethnicity to infer probabilities that are then validated by demographics that include Hispanics/
Latinx only as an exclusive race category led to complex findings and was not ideal. This might beunavoidable, given the limited availability of reference data, as discussed below. However, if possible,
carefully considering options given the classification system in use in a local jurisdiction and theavailable other options that might lead to better comparability and more straightforward results isstrongly recommended.
4. A mixed approach using different imputation reference data sources and methods for different
races and ethnic groups might be a promising solution to overcome limitations.
a. Mixed census demographic data sources. Here, the approach of using total populationsand Hispanic-inclusive race categories to infer race yielded the best overall, though not
perfect, results. Since this approach only yielded accurate estimates for Whites and Blacks,

45
Allen M. Fremont et al., Use of geocoding in managed care settings to identify quality disparities, 24 Health Aff. Proj. Hope 516(2005).
46

Viano and Baker, supra note 18.

Inferring Race & Ethnicity Demographics from U.S. Census Data

20

one interesting possibility might be to settle on using this imputation version for inferring
all racial categories but not Latinx. Since Hispanics/Latinx are measured as an additional
ethnicity category, it is possible to use a different dataset, such as adults only, specifically for
Latinx without affecting the percentages of the other racial groups. In most scenarios, there
will not be a validation sample to compare to. Since the comparison data for the jury-eligible
population, which is needed for computing the racial disparities, can be taken from the same
census tabulation, such a mixed approach might lead to the most accurate race and ethnicity
proportions and, subsequently, the most accurate disparity measures.
b. Smaller geographic units. Another approach is to infer race based on smaller geographicalareas, such as census blocks, block groups, or tracts, which are demographically morehomogenous than ZCTA’s but require more complicated geocoding processing. For
demographically more homogenous units, the accuracy of the estimates tends to be greater
as geocoding is performed at more precise geographic levels.47 This may also mitigate thetendency of the ZIP-code-based analysis to “gloss” over small neighborhood enclaves witha large minority population that happens to be represented on the list but would not greatly
influence the ZCTA-wide demographic estimate. Census tracts are equal in population size,
which aids in the correct calculation of averages.
c. Surname analysis for imputing Latinx and Asian proportions. Particularly if the Hispanic/
Latinx or Asian proportion is of concern, using a hybrid method that includes surname lists
might be more effective in producing accurate results for these racial groups that do not tend tolive in segregated neighborhoods.48 Such an approach might be particularly advisable when thelist of records to be inferred is a smaller sample of the population.49
5. Any imputation method will be limited by the availability of reference data. In this case, as
in other studies, the publicly available tabulation formats that the U.S. Census offers restricted
the ways race and ethnicity could be inferred. Ideally, Hispanic-exclusive race categories for adult
populations would have been used. This was not possible for this study. However, if the time framefits, demographic data from the decennial census can be used as it is indeed tabulated in a way thatenables researchers to use Hispanic-exclusive race categories for the adult population only.50
6. Even successful imputation will introduce some bias to the disparity analysis. It is essential toacknowledge that even with a method that has been proven to be valid, the success may vary given
the factors mentioned above, and these influences will likely not be mitigated perfectly. Wheneverfindings of a disparity analysis based on inference are presented, researchers need to be awareof the degree to which the findings are likely more positive than warranted, and that there may

47

Krieger et al., supra note 23.

48

Elliott et al., supra note 3.

The master jury list here presents a fairly inclusive list of the adult population, as an ideal master jury list should be inclusive of
every adult citizen within a given geographic jurisdiction. Hence, using a purely address-based approach was more justifiable in thisstudy than it might be in other scenarios.
49

This is Table P4 of the 2020 Decennial Census. Another advantage to using decennial census data is that it does not rely on
weighting to account for small samples of certain racial groups, like the American Community Survey as a sampling-based survey
does.
50

21

Inferring Race & Ethnicity Demographics from U.S. Census Databe smaller geographic areas (e.g., counties within states, townships within counties) with severe
disparities that are not sufficiently flagged. If an evaluator expects that ground conditions likely lookworse than analysis findings suggest due to the inference method, it might be advisable to use amore stringent standard for raising concerns to avoid missing issues. In this case study, this would
mean lower than 10% absolute disparity and lower than 50% comparative disparity. At the sametime, large relative (comparative) disparity findings should not be misinterpreted as they can easily
become excessively large and misleading for populations that are overall very small.51 Consequently,
an insignificant, small difference between the inferred demographics and the true demographic
proportions may easily influence the disparity measure enough to even reverse the direction fromunderrepresentation to overrepresentation and vice versa, even if the demographic proportions are
estimated seemingly accurately. Due to the sensitivity of the comparative disparity measure for small
populations, any threshold used as a decision standard should be interpreted in a case-by-casemanner.

51

This was the case for Hispanic/Latinx populations in many of the counties presented here.

Inferring Race & Ethnicity Demographics from U.S. Census Data

22

Technical Appendix
APPENDIX 1
Comparison between inferred and self-reported race and ethnicity

Correlations
For Whites, the correlation is strongest52 for Version 2 (excluding Hispanics, total populations). For Blacks,
Version 2 and Version 3 (using total populations) have just a slightly stronger correlation with self-reported
data than Version 1,53 and this is true for Hispanics/Latinx as well.54 For Asians, on the other hand, it
is Version 1 that leads to a higher correlation than Versions 2 and 3.55 Using both Hispanic-inclusive
categories and adult populations also benefitted the accuracy of inferred Native American56 counts.

Table A. Comparison Between Inferred and Self-reported Race and Ethnicity, summarized across 95 TN counties

White

Black

NativeAsian

Hispanic/
Latinx23

Mean %

Standard Deviation %Median %IQR %Version 1

89.6

10.6

93.5

8.0

Version 2

86.2

11.6

89.5

8.7

Version 3

88.7

11.1

92.6

8.1

Self-Reported

88.2

11.9

92.5

10.3

Version 1

7.3

10.2

3.4

7.0

Version 2

7.2

10.4

3.4

6.5

Version 3

7.3

10.5

3.4

6.6

Self-Reported

7.1

10.3

2.8

6.6

Version 1

0.3

0.2

0.3

0.3

Version 2

0.4

0.4

0.4

0.3

Version 3

0.3

0.3

0.3

0.3

Self-Reported

0.2

0.1

0.2

0.1

Version 1

0.7

0.8

0.5

0.7

Version 2

0.4

0.4

0.4

0.3

Version 3

0.7

0.8

0.5

0.6

Self-Reported

1.0

1.0

0.7

0.8

Version 1

2.9

1.9

2.2

1.7

Version 2

3.6

2.4

2.8

2.1

Version 3

3.6

2.4

2.8

2.1

Self-Described

2.9

2.6

1.8

2.4

52

Pearson’s r(93) = .99, p=.000. For Version 1 and Version 3, r(93) = .97, p = .000

53

Pearson’s r(93) = .993, p=.000. For Version 1, r(93)=.992, p=.000.

54

Pearson’s r(93) = .96, p=.000. For Version 1, r(93) = .95, p= .000.

55

Pearson’s r(93) = .86, p=.000. For Version 2 and 3, r(03) = .83, p= .000.

56

Person’s r(93) = .30, p= .003. For Version 2 and 3, r(93) = .27, p= .009, and p= .008.

Appendix 1

APPENDIX 1: Comparison between inferred and self-reported race and ethnicity

Testing Differences for SignificanceA Wilcoxon signed rank test revealed that for some races and inference versions the difference to theself-reported demographics was insignificant, while in other instances the difference was significant. Test
results are provided in Tables B1 and B2.
For Whites, the imputed race percentages of Version 2 were on average lower (median2=89.5%) than
the true list percentages (median=92.5%). Versions 1 and 3 (using Hispanic-inclusive categories)
were both on average higher (median1 = 93.5% and median3 =92.6%), but only Version 3 produced
imputed race and ethnicity proportions that were insignificantly different from the true proportions. The
imputed percentages for Blacks were indifferent from the true list percentages, regardless of the method,
though each version of imputed Black percentages was on average higher than the true percentages.
For Hispanics/Latinx, inferred proportions were on average higher than the true list proportions acrossversions. Yet, the first version derived from adults was the only version for which the differences between
inferred and true list demographics were insignificant.

Table B1. Statistical Significance of the Differences between Self-Reported and Inferred Demographics
White

Black

Native American

Asian

Hispanic/Latinx

Version 1

Significant

Insignificant

Significant

Significant

Insignificant

Version 2

Significant

Insignificant

Significant

Significant

Significant

Version 3

Insignificant

Insignificant

Significant

Significant

Significant

Table B2. Statistical Significance of the Differences between Self-Reported and Inferred Demographics (N=95 counties)
White

Black

Native American

Asian

Hispanic/Latinx

Version 1

Ranked Sumsz Score
p

3396
-4.14
p = .000

2425
-0.54
p = .590

3480
-4.45
p = .000

3688
-5.23
p = .000

2628
-1.29
p = .196

Version 2

Ranked Sumsz Score p

4393-7.843
p = .0002286-0.022
p = .982

3034-2.799p = .005

3779
-5.56
p = .000

4083-6.692
p = .000

Version 3

Ranked Sumsz Scorep2697-1.548p = .1222556-1.024
p = .306

3667-4.035p = .000

3735-5.401
p = .000

4083-6.692
p = .000

Appendix 1

24

APPENDIX 2
Inference accuracy by county

Figure A. Absolute Differences between Inferred (Version 1) White % and True Master Jury List %, by Countyin the Order of White Population Size
12

Difference, in %11
10

11 counties, where difference is > 5%
5 counties, where difference is > 8%
1 county, where difference is > 10%

9
8
7
6
5
4
3
2
1
0

Counties with <90% Whites on the MJL
Figure B. White Proportions on the Juror Source List for Each County:
Self-Reported Versus Inferred Demographics (Version 2)
100

Inferred % White on juror source list

Figures B and C reveal thedifference between using
Hispanic-exclusive or inclusive percentages for
Whites when both are based
on total populations. For
counties with smaller White
populations, as indicated in the
scatterplot by a lower
percentage on the master jury
list (MJL), excluding Hispanics
from the White count (FigureB) led to a closer estimate(though still statisticallysignificant) than when
including Hispanics(Figure C).

90
80
70
60
50
40
30

y= 0.96* [White on juror source list] + 1.1
p=.00020
10y(reference) = 1*[White on juror source list]

0
0

10

20

30

40

50

60

70

80

90

% White on juror source list

25

Appendix 2100

APPENDIX 2: Inference accuracy by county

Figure C. White Proportions on the Juror Source List for Each County:
Self-Reported Versus Inferred Demographics (Version 3)
100
90

Inferred % White on juror source list

For counties with large White
populations, including
Hispanics led to a better
estimate (as indicated by theproximity to the red line).
Excluding them leads
to underestimation of the White
percentage on the list. Using
adult-only, Hispanic-inclusive
populations (Version 1) leads
to a very similar, moreaccurate, estimate.
Furthermore, the differences
are statistically insignificant
when Whites make up morethan 90% on the master jury
list.57

80
70
60
50
40

y= 0.90* [White on juror source list] + 8.9
p=.00030y(reference) = 1*[White on juror source list]

20
10
0
0

10

20

30

40

50

60

70

80

90

100

% White on juror source list
Figure D. Black Proportions on the Juror Source List for Each County:
Self-Reported Versus Inferred Demographics (Version 3)
50

Inferred % Black on juror source list

Black inferred percentages are
not significantly different fromlist percentages, and the sizeof the Black proportion does
not impact the estimation error,
as can be seen in Figure D. As
shown, the fit line for inferredBlack percentages nearlymatches the reference line in
the scatterplot, showing that,on average, the estimate isvery close to the true list
percentages and that this does
not change with increasing or
decreasing Black populations.

40

HAYWOODHENDERSONHENRYHICKMAN
HOUSTON
20
HUMPHREYS
JACKSONy= 0.99* [Black on juror source list] +JEFFERSON
0.3
p=.00010JOHNSONKNOXy(reference) = 1*[Black on juror source list] LAKELAUDERDALE
0
0
10
20
30
40
50 LAWRENCE
LEWIS
LINCOLN% Black on juror source list
LOUDONMACON
MADISON57
A Wilcoxon signed rank test indicates that inferred White percentages in the remaining 56 counties were not statistically
MARIONdifferent from the list percentages (z= -.049, p = .961). However, this result does not hold for the other versions. The differences
remainMARSHALLsignificant in Version 2 when splitting the sample between above and below 90% White populations. This is true also for Version 3,
MAURY
where the differences are only insignificant when including the complete sample.
MCMINNMCNAIRY
Appendix 2
MEIGS
MONROE
30

26

APPENDIX 2: Inference accuracy by county

For Asians, where inference led to underestimated percentages in all versions, the scatterplot in Figure
E reveals that the underestimation is greater for counties with larger Asian populations. However, thedifferences remain statistically significant even when excluding counties with above 1% Asians on the
Master Jury List.

Figure E. Asian Proportions on the Juror Source List for Each County:
Self-Reported Versus Inferred Demographics (Version 3)

Inferred % Asian on juror source list

5

4

3

2

y= 0.65* [Asian on juror source list] + 0.1
p=.0001y(reference) = 1*[Asian on juror source list]
0
0

1

2

3

4

5

6

% Asian on juror source list

27

Appendix 2

HARDINHAWKINSHAYWOODHENDERSONHENRYHICKMAN
HOUSTON
HUMPHREYS
JACKSON
JEFFERSONJOHNSONKNOX
LAKE
LAUDERDALE
LAWRENCE
LEWIS

APPENDIX 2: Inference accuracy by county

In the case of Hispanic/Latinx percentages, using inference Version 1 as an example, where theestimation error was statistically insignificant, the size of a county’s Hispanic population has a clear effecton the direction of the differences between inferred and true data. For counties with overall larger Latinx
populations (above 3% on the master jury list, 29 counties), the true percentages were significantly higher
than the inferred percentages. For counties with less than 3% Latinx on the list (66 counties), the true
list percentages were on average significantly lower than the inferred percentages.58 While Version 1of inferred demographics may seem fairly accurate for Latinx overall, with only insignificant differences,
the scatterplot in Figure F illustrates that caution may be warranted when using the results for individual
counties. The clear overestimations for counties with smaller Hispanic/Latinx populations are balanced inthis study by the underestimations in counties with higher Hispanic/Latinx populations.

Inferred % Hispanic/Latinx on juror source list

Figure F. Hispanic/Latinx Proportions on the Juror Source List for Each County:
Self-Reported Versus Inferred Demographics (Version 1)

12

10

8

6

4

y= 0.68* [Hispanic on juror source list] + 0.86
p=.0002y(reference) = 1*[Hispanic on juror source list]
0
0

2

4

6

8

10

12

14

% Hispanic/Latinx on juror source list

Both differences are statistically significant. For the sample above 3%, HENDERSON
z= -3.56, p= .000. For the sample below 3%, z= -4.55, p=.000. Including children in the estimate does not seem to make a difference.
Versions 2 and 3 show a very similar pattern, though
HENRY
somewhat less pronounced, significantly overestimating Hispanic percentages
in small-population counties, and significantly underHICKMAN
estimating in counties with high populations.
58

HOUSTON
HUMPHREYS
JACKSON
JEFFERSONJOHNSON
KNOX
LAKE

Appendix 228

APPENDIX 3
Overall deviation from self-reported proportions

To summarize the distributional differences between the sets of inferred and those of the self-reported
proportions, total deviation across the five estimated racial/ethnicity proportions were computed, eachweighted by the racial group’s self-reported proportion in the county.59 The statewide averages andranges of the overall weighted deviation for each county are listed in Table C below. Version 3 appears
to be the most accurate overall, with a statewide weighted demographic proportion error of less than two
percent.

Table C. Weighted Average Overall Deviation from Self-Reported Estimates
Version 1(Adults, inclusive race &ethnicity)

Version 2(Total Population,
exclusive categories)

Version 3(Total Population,
inclusive race & ethnicity)

Statewide Mean

2.28%

2.11%

1.95%

Statewide Range

8.99%

9.49%

8.44%

See a similar approach working with dataset-wide proportions rather than county units in Marc N. Elliott et al., A New Method for
Estimating Race/Ethnicity and Associated Disparities Where Administrative Records Lack Self-Reported Race/Ethnicity, 43 HealthServ. Res. 1722–1736 (2008).
59

29

Appendix 3

APPENDIX 4
Disparity Results by Inference Version
In the comparison of absolute disparity measures shown below, negative values reflect
underrepresentation; positive values reflect overrepresentation.

Table D. Absolute Disparities (95 Counties), in %

White

Black

NativeAsian

Hispanic/
LatinxMean %

Standard
Deviation %Median %IQR %Statistical Significance ofDisparity Deviation60

Self-Reported

-1.8

3.8

-103

4.2

Estimate 1

-0.4

1.1

-0.4

1.1

underestimation significant

Estimate 2

-3.9

2.7

-3.4

3.2

overestimation significant

Estimate 3

-1.4

1.4

-1.2

1.7

underestimation insignificant

Self-Reported

-0.4

1.7

0

0.9

Estimate 1

-0.2

0.9

0

0.4

accurate

Estimate 2

-0.2

0.9

-0.1

0.5

slight overestimation insignificant

Estimate 3

-0.2

0.9

-0.1

0.5

slight overestimation insignificant

Self-Reported

-0.1

0.3

-0.1

0.3

Estimate 1

0

0.1

0

0.1

underestimation significant

Estimate 2

0

0.2

0

0.1

underestimation significant

Estimate 3

0

0.2

0

0.1

underestimation significant

Self-Reported

0.5

0.7

0.4

0.6

Estimate 1

0.3

0.3

0.2

0.3

underestimation significant

Estimate 2

0.3

0.4

0.2

0.3

underestimation significant

Estimate 3

0.3

0.4

0.2

0.3

underestimation significant

Self-Reported

1.2

2

0.7

1.8

Estimate 1

1.2

1.1

0.8

0.3

slight overestimation insignificant

Estimate 2

1.9

1.7

1.5

1.6

overestimation significant

Estimate 3

1.9

1.7

1.5

1.6

overestimation significant

Overall, deriving disparities from Hispanic-inclusive, adult populations (Version 1), we would have falsely
assumed a more representative master jury list for Whites, Native Americans, and Asians. For these,
the deviations from the true disparities are statistically significant. Nevertheless, the true Hispanic/Latinx
overrepresentation is very closely estimated, only insignificantly overestimating this disparity.
Excluding Hispanics from the inferred race proportions (Version 2) leads to statistically significant
overestimation of White and Native American underrepresentation and to significant underestimation of
Asian overrepresentation. Even when including Hispanics in the racial count but using total populations(Version 3), Asian and Native American disparities are still underestimated. With this version, Hispanics
would have seemed significantly more overrepresented on the list than they are, but the slightunderestimation of White underrepresentation is statistically insignificant. The true underrepresentationof Blacks on the list is accurately shown by the disparity measures derived from inferred demographics,
regardless of the method, and any deviation is insignificant.
Statistical significance was measured with a Wilcoxon Signed Rank Test. Over-and under-estimation of the disparity measures arehere listed as statistically insignificant when p > .1. Statistically significant measures here were at least significant at the p<.001 level.

60

Appendix 430

APPENDIX 4: Disparity Results by Inference Version

Similarly, comparative disparity measures derived from inferred demographics can be evaluatedagainst the measures derived from self-reported data and tested again for statistical significance of thedifferences to the true disparities. The results mirror those presented for absolute disparities in every celland are shown in Table E.

Table E. Comparative Disparities

White(counties:
95)

Black

(counties:
62*)

Native(counties:
1*)

Asian

(counties:
3*)

Hispanic/
Latinx(counties:
25*)

Mean %

Standard
Deviation %Median %IQR %Statistical Significance ofDisparity Deviation60

Self-Reported

-2.2

4.6

-1.4

4.9

/

Estimate 1

-0.5

1.3

-0.5

1.2

underestimation significant

Estimate 2

-4.5

3.5

-3.7

3.7

overestimation significant

Estimate 3

-1.6

1.8

-1.3

2.0

slight underestimation
insignificant

Self-Reported

-0.6

2.0

0.0

1.5

/

-1.3

8.1

overestimation insignificant

Estimate 1

-1.5

18.1

Estimate 2

-3.8

18.8

-2.6

11.4

overestimation insignificant

Estimate 3

-1.7

20.2

-1.7

9.7

overestimation insignificant

Self-Reported

92.8

/

-92.8

/

/

Estimate 1

-58.4

/

-58.4

/

underestimation significant

Estimate 2

-66.8

/

-66.8

/

underestimation significant

Estimate 3

-66.8

/

-66.8

/

underestimation significant

Self-Reported

120.2

29.2

134.4

/

/

Estimate 1

66.5

15.1

69.8

/

underestimation significant

Estimate 2

68.4

19.9

70.5

/

underestimation significant

Estimate 3

69.4

19.0

70.9

/

underestimation significant

Self-Reported

101.1

84.5

96.5

111.2

/

Estimate 1

75.8

39.5

74.8

53.7

underestimation insignificant

Estimate 2

121.4

58.5

107.3

82.9

overestimation significant

Estimate 3

121.4

58.5

107.3

82.9

overestimation significant* only including counties with eligible populations >2%

Statistical significance was measured with a Wilcoxon Signed Rank Test and is based on the full sample of 95 counties. Overand underestimation of the disparity measures are here listed as statistically insignificant when p >.05.

61

31

Appendix 4

APPENDIX 4: Disparity Results by Inference Version

To assess the disparity inaccuracies across races in each version, the deviations from the true disparities
across races are averaged, each weighted by the eligible population in each county. As was concluded forthe overall deviations of the demographic estimates, Version 3 provides estimated disparities that show
the smallest state-wide overall deviation from the true distribution of disparities. State-wide averages of
the weighted overall deviations for absolute disparities are shown in Table F.

Table F. Weighted Average Overall Deviation from Absolute Disparities Derived from Self-Reported Estimates

Statewide Mean

Version 1(Adults, inclusive race &ethnicity)

Version 2(Total Population,
exclusive categories)

Version 3(Total Population,
inclusive race & ethnicity)

2.35%

2.10%

1.99%

Appendix 432

APPENDIX 5
Disparity Results by County

Figure G shows the distribution of absolute disparities in comparison between versions for Whites. The
inference-derived disparities have a much smaller range across counties than the true disparities do, but
this is especially evident for the methods including Hispanics in the racial count (Versions 1 and 3).
While Version 3 was the version with overall statistically insignificant deviations to true White proportions
and disparities and statewide averages closest to the true averages, this figure illustrates that there
are some counties where the true extent of underrepresentation is only picked up when using total
populations and Hispanic-exclusive categories for imputation. Furthermore, none of the versions canreproduce the original absolute disparity findings for the few counties with White overrepresentation
above 3% absolute disparity.

Figure G. Disparities for Whites Between Juror Source List and County Population in 95 Counties, by Data Source

Counties with White overrepresentation,
based on self-reported data. Not
identified as such with inferred data.

8

6

Underor overrepresentation derived
Under- / overrepresentation based onfrom self-reported demographics-14.304Absolute Disparity, in %

2

0

-2

-4

Counties with White
underrepresentation, based on
self-reported data. Similarly identified
only with one version of inferred data.

-6

-8

-10

-12

-14

Derived fromself-reported
data

33

Appendix 5

Derived frominferred total
populations,excluding
Hispanics

Derived frominferred total
populations,
including
Hispanics

Derived frominferred adult
populations,
including
Hispanics

9.05

APPENDIX 5: Disparity Results by County

For Black demographics, which appeared to be successfully inferable, Figure H depicts a similarly smaller
range of disparities when imputing race. This might be especially problematic for the few outlier counties
where the true disparities are at concerning levels of underrepresentation, but the same counties would
not show similarly high disparities when imputing race.

Figure H. Disparities for Blacks Between Juror Source List and County Populationin 95 Counties, by Data Source
County without disparity. However,Black overrepresentation was
identified using inferred data.

2

1

0

1

Absolute Disparity, in %2

3

Under- or overrepresentation derived
from self-reported demographics

4

Under- / overrepresentation based on

-9.19

2.42

5

Counties with Black
underrepresentation, based on
self-reported data. Not identified
as severe with inferred data.

6

Derived fromself-reported
data

Derived frominferred adult
populations,
including
Hispanics

Derived frominferred total
populations,excluding
Hispanics

Derived frominferred total
populations,
including
Hispanics

Appendix 534

APPENDIX 6Distribution of Disparity Results by Range
Table F shows the distribution of disparities depending on the source data used for Whites, Blacks, andHispanics. For Whites, where the original analysis identified 3 out of 95 counties with more than 10%
absolute disparities, indicating White underrepresentation, only Version 2 led to the identification of thesame three counties.

Table F. Comparison of Representativeness Results for Tennessee Counties, Based on Each Version of Demographics
Disparities based on selfreported demographics…based on imputed
demographics (Version 1)…based on imputed
demographics (Version 2)…based on imputed
demographics (Version 3)

Absolute Disparities for Whites
Less than -10%-33-10% to -5%

11

24

1

-5% to 5%

78

95

68

94

5% to 10%

3

95

95

95

Absolute Disparities for Black-10% to -5%3-5% to 5%92

Absolute Disparities for Hispanic/Latinx-5% to 5%

89

94

88

5% to 10%61

7 (1 new)

95

95

88
7 (1 new)62

Comparative Disparities for Whites (N=95)-20% to 20%

9595Comparative Disparities for Black (N=62*) (and # of flagged counties added when disparities were based on estimates)
Less than -50%

1

6 (5 not flagged in estimates)

11

1 (new)

1 (new)-50% to -40%

1-40% to -20%

32 (new)

5 (3 new)

4 (2 new)-20% to 20%

48

58

54

54

20% to 40%

3
1 (new)

1 (new)

2 (new)

40% to 50%
More than 50%

1

Comparative Disparities for Hispanic/Latinx (N=25*) (# of flagged counties added, or disparity direction changes compared to true disparity)-40% to -20%

2-20% to 20%

2

2

20% to 40%

1

2 (1 reversed direction)

40% to 50%

13More than 50%

19 (2 not be flagged as highin estimates)18(1 reversed direction)

2 (both reversed direction)23(2 new, 2 reversed direction)

2 (both reversed direction)23(2 new, 2 reversed direction)* Only including counties with populations of this race/ethnicity >2%6235This indicates that one of the seven counties within this range was not identified in the same range in the original analysis.

Appendix 6

APPENDIX 5: Disparity Results by County

For Blacks, the absolute disparities calculated based on inferred demographics are smaller than 5% for
all counties, whereas the original analysis had identified three counties with underrepresentation between
5% and 10% absolute disparity. Furthermore, the original analysis had flagged six counties where Blacks
were clearly underrepresented on the jury list with above 50% comparative disparities (CD), which isoften a better measure due to the small overall Black population. Only one out of these six would havebeen identified when using estimated racial counts. The one county flagged for Black overrepresentation
with above 50% CD is not identified at all via the estimated versions. What is more, the disparity
analyses based on inferred race percentages identify one to two new counties each with seemingly great
overrepresentation above the threshold, which were not marked as such in the original analysis. Similarly,
the somewhat less severe underrepresentation ranges of 20% to 40% and 40% to 50% CD included four
counties in the original analysis, only two in estimated Version 1, but six in Version 2, and five in Version
3. Yet, none of the original counties within these ranges are found in the same ranges in Version 1, andonly half are identified in these ranges in Versions 2 and 3. Instead, other counties are identified with
underrepresentation in these ranges.
For Latinx, who have even smaller county populations than Blacks, the absolute disparities were notabove 10% in either analysis. Inference-based Versions 2 and 3 of the analysis identified the same six
counties that the original analysis had identified as between 5% and 10% overrepresentation. However,most of the counties with populations above 2% (high enough to compute comparative disparities) hadvery high comparative disparities, indicating great Hispanic/Latinx overrepresentation on the list. The twocounties that the original analysis had identified with underrepresentation of 20% to 40% are not identified
in any of the other analyses. Those with disparities above the threshold of 50% for overrepresentation,
which are the majority of the 25 counties for which comparative disparities can be calculated, are,however, indeed identified as such, or nearly as such, in each one of the inference-based analyses.
Each of these analyses, nonetheless, flagged at least one additional county with apparently severe
Hispanic/Latinx overrepresentation, while, according to the original analysis, Hispanics/Latinx wereeven underrepresented on the list in these specific counties. Thus, for these few counties, using inferred
ethnicity percentages led to results that were opposite to the original findings, reversing the direction fromminor underrepresentation to severe overrepresentation.

Appendix 636

This white paper was written with support from the State Justice Instituteunder grant number SJI-22P006. The points of view expressed are those of the authorand do not necessarily represent the official position or policies of the State Justice Institute.

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