IN THE UNITED STATES DISTRICT COURT 

FOR THE SOUTHERN DISTRICT OF FLORIDA 

MIAMI DIVISION 

Case No. 1:22-cv-24066-KMM 

GRACE, INC., et al.,

Plaintiffs,

v.

CITY OF MIAMI,

Defendant.

 

/

EXPERT REPORT OF DR. BRYANT MOY 

[02/10/2023] 

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Racially Polarized Voting in Miami, Florida

Bryant J. Moy, PhD

February 10, 2023

1 Executive Summary

In this report, I examine past election data from the City of Miami, Florida, to determine whether and the

extent to which racially polarized voting exists. Racially polarized voting (RPV) exists if minority voters

systematically prefer one candidate and the majority ethnic group preferences another. I examine twenty

elections between 2017 to 2021. Of the twenty elections, six were endogenous citywide elections, and fourteen

were exogenous elections from the federal, county, or state levels. I conclude the following:

• Racially polarized voting exists in ten of the twenty elections studied.

• For endogenous (municipal) elections, two of six exhibited signs of racially polarized voting. In those

contests, the Latino-preferred candidate prevailed over the Anglo-preferred candidate.

• For exogenous elections, eight of the fourteen exhibited signs of racially polarized voting. In five of those

eight contests, the Latino-preferred candidate won, blocking either the Anglo-preferred candidate, the

Black-preferred candidate or both.

• The Latino-preferred candidate won the majority of polarized races at 70% (7/10). Black- and Anglopreferred candidates won 50% (4/8) and 33% (3/9) of the polarized contests they were involved in.

• Black and Anglo voters tended to have the same preferred candidates in six of the ten races with

racially polarized voting. Alternatively, Black and Latino voters had the same candidate once in

polarized races.

2 Background and Qualifications

I am a Data Science Faculty Fellow at the Center for Data Science and a Visiting Assistant Professor in the

Wilf Family Department of Politics at New York University.1 I received a Ph.D. in Political Science from

Washington University in St. Louis in 2022. My concentration in graduate school was American Politics

and Political Methodology.

My current area of expertise is related to local government, race and ethnic politics, and the use of

advanced statistical models to understand political phenomena. My research has been published in the

Journal of Experimental Political Science and Political Behavior. Other writings have appeared in the

Oxford Bibliographies in Political Science and the Political Science Educator.

My research has won the Best Poster Award from the Society of Political Methodology, and I have

received the Susan Clarke Young Scholar Award from the Urban and Local Politics Section of the American

Political Science Association. In addition, I provide a copy of my curriculum vitae in the Appendix of this

report.

1. I have also accepted an appointment as a tenure-track Assistant Professor in the Wilf Family Department of Politics at

New York University starting in 2024.

2

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3 Racially Polarized Voting

Racially polarized voting occurs when a minority group votes for one candidate and the dominant racial or

ethnic group votes for an opposing candidate. For the City of Miami, Florida, we are interested in three

ethnic groups: Anglos, Blacks, and Latinos. Indeed, according to the 2020 Census, Latinos of any race make

up 70% of the population, while non-Hispanic whites – Anglos – and non-Hispanic Blacks make up 14% and

12%, respectively.2

I classify the candidates as Latino-, Black-, or Anglo-preferred if there is sucient evidence that the

ethnic group votes in a cohesive block. For this report, I use the 60% threshold of support as a sign of

cohesive voting. In other words, if a candidate receives higher than 60% support among members of the

same ethnic group, that candidate is that group’s preferred candidate. If racially polarized voting exists, I

would expect to see the ethnic groups have di↵erent preferred candidates in large numbers.

To assess racially polarized voting patterns, I will rely on ecological inference. In the next section, I detail

ecological inference and my approach.

4 Methodology: Ecological Inference

Researchers typically examine patterns of racial polarization by inferring individual voting behavior from

aggregate data – also known as ecological inference. We infer an individual’s voting behavior by examining

voting patterns within and between precincts. Ecological inference estimates racial group-level preferences

from aggregate precinct data.

I conduct this analysis using two approaches. First, I examine each election and present a bivariate

scatterplot between the ethnic composition of the electorate and candidate vote share. In this analysis, each

dot represents a precinct. The x-axis will indicate the electorate’s composition, and the y-axis will indicate

the candidates’ vote share. I draw a fitted line and display the correlation coecient and the corresponding

p-value, which will indicate whether the correlation is statistically significant. For a racial group to have a

preferred candidate, I expect the fitted line to fall over the 60% threshold of candidate vote share when the

precincts are racially homogeneous. The fitted line extrapolates to racially homogeneous precincts even when

no observed precinct exists. Second, I run an iterated ecological inference algorithm using eiCompare, which

estimates a candidate’s support among each ethnic group.3 This method is widely accepted to estimate

candidate support among ethnic or racial groups (Collingwood et al. 2020; King and Roberts 2016; Lau,

Moore, and Kellermann 2020).

Researchers typically use the voting age population or citizen voting age population estimate derived

from the U.S. Census and predict the racial composition of voters in a given geographic area (i.e., precincts).

Fortunately, Miami-Dade County provides a publicly available count of registered voters by precinct and

racial/ethnic group using the information on their voter file.4 Using the Miami-Dade County data and voter

file is preferable to the U.S. Census and prediction approach because it provides a more accurate measure

of the racial composition of the electorate.

5 List of Elections Analyzed and Additional Statistics

I examine twenty elections, including six municipal-level (endogenous) contests and fourteen non-municipal

(exogenous) contests. Endogenous elections originate from the city itself. This includes races for City

Mayor and City Commissioner. Exogenous races are contests that overlap with Miami precincts but do not

originate at the city level. Examples of exogenous races include contests for the President, Governor, and

County Mayor. Analyzing exogenous races – alongside endogenous one – are essential because they provide

2. The data comes from the U.S. Census Bureau. The table is entitled, “Hispanic or Latino, and Not Hispanic or Latino by

Race” in the Decennial Census.

3. The eiCompare (Collingwood et al. 2020) R package relies and builds upon two other packages for ecological inference:

“ei” (King and Roberts 2016) and“eiPack” (Lau, Moore, and Kellermann 2020) I include this analysis only for elections where

these quantities can be calculated reliably. In all cases, the substantive results from the scatterplots and ecological inference

packages are the same.

4. Miami-Dade County’s Elections Department Data:https://www.miamidade.gov/elections/voter-statistics-current-archive.

html

3

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additional information about the nature of racially polarized voting. The exogenous contests examined in

this report are similar to the city-level races in that most are non-partisan local contests. Yet, we benefit from

the varying levels of competitiveness found in exogenous races. Indeed, most of the endogenous municipal

contests were non-competitive. Thus, it is probative to endogenous and exogenous elections.

In my sample of elections, I include all municipal (endogenous) elections from 2017 to 2021. Beyond

municipal elections, I include exogenous contests that have sucient overlap with Miami precincts. In Table

1 I provide a full list of elections analyzed.

Table 2 shows the composition of the 2020 citizen voting age population using the district line from the

2013 plan. Similarly, Table 3 shows the 2020 citizen voting age population using the district lines from the

current 2022 enacted plan.

Table 1: List of Elections Analyzed

Year Election Endo/Exo Oce

2021 Municipal Endogenous City Mayor

2021 Municipal Endogenous City Comm. Dist. 3

2021 Municipal Endogenous City Comm. Dist. 5

2017 Municipal Endogenous City Mayor

2017 Municipal Endogenous City Comm. Dist. 3

2017 Municipal Endogenous City Comm. Dist. 4

2020 General Exogenous Congress, 24

2020 General Exogenous County Comm. Dist. 3

2020 General Exogenous County Mayor

2020 General Exogenous Clerk of the Court

2020 General Exogenous President

2020 Primary Exogenous County Property Appraiser

2020 Primary Exogenous County Judge, Grp. 24

2020 Primary Exogenous County Judge, Grp. 9

2020 Primary Exogenous Circuit Judge, Grp. 75

2020 Primary Exogenous Circuit Judge, Grp. 67

2020 Primary Exogenous Circuit Judge, Grp. 65

2020 Primary Exogenous Circuit Judge, Grp. 57

2020 Primary Exogenous Circuit Judge, Grp. 55

2018 General Exogenous Governor

Table 2: Citizen Voting Age Population by District, 2013 Plan

District Map/Plan Anglo Black Latino

District 1 2013 4.2% 7.6% 87.4%

District 2 2013 36.8% 9.2% 48.7%

District 3 2013 9.5% 4.2% 85.8%

District 4 2013 7.3% 1% 90.8%

District 5 2013 8% 59.8% 32.3%

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Table 3: Citizen Voting Age Population by District, 2022 Enacted Plan

District Map/Plan Anglo Black Latino

District 1 2022 5% 8.1% 86%

District 2 2022 40.4% 8.7% 44%

District 3 2022 9.9% 3.9% 85.6%

District 4 2022 8.2% 1.3% 89.6%

District 5 2022 9% 58.1% 30.8%

Table 4: 2020 Citizen Voting Age Population

City Anglo Black Latino

Miami 14% 17% 67%

6 Does RPV Exist Across Elections?

In this section, I examine twenty races in the City of Miami. The first six are municipal-level endogenous

races, including the mayor and city commissioners. The next fourteen races are exogenous and include races

for federal oce (i.e., Congress and President), county oces (i.e., county commission, county mayor, county

judge, and property appraiser), and state government (Governor).

6.1 Election 1: Mayor 2021

The 2021 Miami mayoral contest was between five candidates: Francis Suarez, Max Martinez, Marie Exantus,

Anthony Dutrow, and Francisco Pichel. Francis Suarez won the race with overwhelming support by receiving

78.6% of the vote, with the next closest candidate – Max Martinez – receiving only 11.6% of the vote. Figure

1 shows the bivariate relationship between the precinct’s demographic composition and Suarez’s support.

First, we can examine the fitted line in the scatterplot and extrapolate the estimated vote share of the

candidate if there were homogeneous precincts (i.e., a precinct with all Anglos, Blacks, or Latinos). Across

all groups, Suarez would have received higher than 60% of the vote. Second, these results are verified using

the ecological inference algorithm to estimate the candidate’s vote share if only one race or ethnic group

voted (Collingwood et al. 2020; King and Roberts 2016; Lau, Moore, and Kellermann 2020). Indeed, Francis

Suarez is estimated to receive over 70% support from all racial groups.

I find no evidence of racially polarized voting in this election.

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Figure 1: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = − 0.22, p = 0.015

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Francis X. Suarez

R = − 0.31, p = 0.00036

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Francis X. Suarez

R = 0.48, p = 2e−08

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Francis X. Suarez

6

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Figure 2: Estimated Candidate Support by Race/Ethnicity

0.1%

71%

0.3%

4.4%

31.2%

AnthonyMelvinDutrow

FranciscoPichel

FrancisXSuarez

MarieFrantzExantus

MaxMartínez

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Anglo

11.9%

70.4%

2.3%

12.7%

1.3%

AnthonyMelvinDutrow

FranciscoPichel

FrancisXSuarez

MarieFrantzExantus

MaxMartínez

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Black

1.5%

86.5%

2%

1.6%

8.4%

AnthonyMelvinDutrow

FranciscoPichel

FrancisXSuarez

MarieFrantzExantus

MaxMartínez

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Latino

7

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6.2 Election 2: District 3 2021

In 2021, the City of Miami held an election for District 3 City Commissioner between four candidates: Joe

Carollo, Andriana Oliva, Quinn Smith, and Miguel Soliman. Joe Carollo won with 64.4% of the vote, while

Quinn Smith received the second most votes at 21.8%. Figure 3 depicts the bivariate relationship between the

racial composition of the electorate and candidate choice. The Anglo-preferred candidate is Quinn Smith, as

shown by the positive relationship between Anglo share of the electorate and Smith vote share. In contrast,

as the share of Anglo voters increases, the share of Carollo votes declines. The Latino-preferred candidate is

Joe Carollo. As the Latino share of the electorate increases, the share of Carollo votes increase, and the share

of Smith votes decrease. I find no relationship between the Black share of the electorate and candidate vote

choice. This finding (or lack of one) is driven by the low proportions of Black voters within the commission

district. Indeed, the Black share of the electorate is lower than 5% across every precinct in this district.

I find evidence that racially polarized voting exists in this district between Anglos (who preferred Smith)

and Latinos (who preferred Carollo). Joe Carollo – the Latino-preferred candidate – won the race.

Figure 3: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = − 0.0096, p = 0.97

0.4

0.6

0.8

0.00 0.01 0.02 0.03

Black Share of the Electorate

Joe Carollo

R = 0.044, p = 0.87

0.1

0.2

0.3

0.4

0.5

0.00 0.01 0.02 0.03

Black Share of the Electorate

Quinn Smith

R = − 0.84, p = 2.3e−05

0.3

0.5

0.7

0.9

0.0 0.1 0.2 0.3 0.4

Anglo Share of the Electorate

Joe Carollo

R = 0.89, p = 1.7e−06

0.2

0.4

0.6

0.0 0.1 0.2 0.3 0.4

Anglo Share of the Electorate

Quinn Smith

R = 0.82, p = 5.2e−05

0.2

0.4

0.6

0.8

0.5 0.6 0.7 0.8 0.9

Latino Share of the Electorate

Joe Carollo

R = − 0.88, p = 4.2e−06

0.1

0.2

0.3

0.4

0.5

0.5 0.6 0.7 0.8 0.9

Latino Share of the Electorate

Quinn Smith

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6.3 Election 3: District 5 2021

The City of Miami held an election for District 5 City Commissioner between seven candidates: Francois

Alexandre, Zico Fremont, Michael Hepburn, Christine King, Revran Lincoln, Stephanie Thomas, and Je↵rey

Watson. Christine King won with 64.92% of the vote, with Je↵rey Watson receiving the second most votes

at 15.81%. Figure 4 depicts the bivariate relationship between electorate demographics and candidate vote

choice. The Black-preferred candidate and the Latino-preferred candidate was Christine King. I find no

evidence that Anglo support for any candidate reached the 60% threshold.

I find no evidence of racially polarized voting in this election.

Figure 4: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.44, p = 0.0085

0.0

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Christine King

R = −0.14, p = 0.42

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Jeffrey Watson

R = −0.71, p = 2.8e−06

0.0

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Christine King

R = 0.59, p = 0.00025

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Jeffrey Watson

R = 0.11, p = 0.54

0.0

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75

Latino Share of the Electorate

Christine King

R = −0.35, p = 0.04

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75

Latino Share of the Electorate

Jeffrey Watson

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6.4 Election 4: Mayor 2017

Miami held an election for Mayor in 2017 between four candidates: Francis Suarez, Williams Armbrister,

Christian Canache, and Cynthia Jaquith. Francis Suarez won with 85.81% of the vote, with Cynthia Jaquith

receiving 5.47% of the vote. Figure 5 depicts the bivariate relationship between electorate demographics and

candidate choice. Across all racial and ethnic groups, Suarez was the preferred candidate.

I find no evidence of racially polarized voting in this contest.

Figure 5: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.27, p = 0.0027

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6 0.8

Anglo Share of the Electorate

Francis X. Suarez

R = 0.4, p = 8.2e−06

0.00

0.05

0.10

0.15

0.20

0.0 0.2 0.4 0.6 0.8

Anglo Share of the Electorate

Cynthia Jaquith

R = −0.57, p = 1.9e−11

0.5

0.6

0.7

0.8

0.9

1.0

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Francis X. Suarez

R = 0.23, p = 0.012

0.00

0.05

0.10

0.15

0.20

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Cynthia Jaquith

R = 0.67, p < 2.2e−16

0.5

0.6

0.7

0.8

0.9

1.0

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Francis X. Suarez

R = −0.47, p = 9.8e−08

0.00

0.05

0.10

0.15

0.20

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Cynthia Jaquith

10

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Figure 6: Estimated Candidate Support by Race/Ethnicity

7%

12.2%

71.3%

7.6%

ChristianCanache

CynthiaJaquith

FrancisXSuarez

WilliamsAlfredArmbristerSr

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Anglo

3.1%

8.5%

70.9%

19.3%

ChristianCanache

CynthiaJaquith

FrancisXSuarez

WilliamsAlfredArmbristerSr

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Black

2.2%

3.3%

92.3%

2%

ChristianCanache

CynthiaJaquith

FrancisXSuarez

WilliamsAlfredArmbristerSr

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Latino

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6.5 Election 5: City Commissioner District 3 2017

Miami held an election for District 3 City Commissioner on December 7th between seven candidates. This

contest proceeded to a runo↵ election between Joe Carollo and Alfonzo Leon. I analyze the runo↵ election.

Joe Carollo won with 52.7% of the vote. Alfonzo Leon was the Anglo- and Black-preferred candidate. Both

groups were cohesive in their support (95.7% and 99%). The Latino-preferred candidate was Joe Carollo.

I estimate the Latino support for Carollo to be 60.8%, which is near the threshold. It’s important to note

that most precincts in this district had a Latino super-majority. Indeed, no precinct in this district had less

than 50% Latino share of the electorate.

I find evidence of racially polarized voting in this contest. The Latino-preferred candidate prevailed.

Figure 7: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = − 0.21, p = 0.42

0.2

0.4

0.6

0.000 0.005 0.010 0.015 0.020 0.025

Black Share of the Electorate

Joe Carollo

R = 0.21, p = 0.42

0.4

0.6

0.8

0.000 0.005 0.010 0.015 0.020 0.025

Black Share of the Electorate

Alfonso "Alfie" Leon

R = −0.85, p = 1.7e−05

0.0

0.2

0.4

0.6

0.0 0.1 0.2 0.3 0.4

Anglo Share of the Electorate

Joe Carollo

R = 0.85, p = 1.7e−05

0.3

0.5

0.7

0.9

0.0 0.1 0.2 0.3 0.4

Anglo Share of the Electorate

Alfonso "Alfie" Leon

R = 0.86, p = 7.6e−06

0.0

0.2

0.4

0.6

0.6 0.7 0.8 0.9

Latino Share of the Electorate

Joe Carollo

R = −0.86, p = 7.6e−06

0.3

0.5

0.7

0.9

0.6 0.7 0.8 0.9

Latino Share of the Electorate

Alfonso "Alfie" Leon

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Figure 8: Estimated Candidate Support by Race/Ethnicity

95.7%

2.6%

AlfonsoLeon

JoeCarollo

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Anglo

99%

2.3%

AlfonsoLeon

JoeCarollo

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Black

39.4%

60.8%

AlfonsoLeon

JoeCarollo

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

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6.6 Election 6: District 4 2017

Miami held an election for District 4 Commissioner between three candidates: Manolo Reyes, Ralph Rosado,

and Denise Turros. Manolo Reyes won the election with 56.74% of the vote, with Ralph Rosado receiving

the second most votes at 36.15%. Figure 9 depicts the bivariate associations between the racial composition

of the electorate and vote choice. No candidates were deeply preferred by any racial group, as shown by the

lack of relationships throughout Figure 9. District 4 is a predominantly Latino district with all precincts

having more than 70% Latino electorate.

I find no evidence of racially polarized voting in this election.

Figure 9: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.047, p = 0.84

0.4

0.5

0.6

0.7

0.0000 0.0025 0.0050 0.0075

Black Share of the Electorate

Manolo Reyes

R = 0.038, p = 0.87

0.3

0.4

0.5

0.0000 0.0025 0.0050 0.0075

Black Share of the Electorate

Ralph Rosado

R = 0.04, p = 0.87

0.050

0.075

0.100

0.0000 0.0025 0.0050 0.0075

Black Share of the Electorate

Denise Galvez Turros

R = −0.018, p = 0.94

0.4

0.5

0.6

0.7

0.05 0.10 0.15 0.20

Anglo Share of the Electorate

Manolo Reyes

R = −0.091, p = 0.7

0.3

0.4

0.5

0.05 0.10 0.15 0.20

Anglo Share of the Electorate

Ralph Rosado

R = 0.36, p = 0.12

0.025

0.050

0.075

0.100

0.125

0.05 0.10 0.15 0.20

Anglo Share of the Electorate

Denise Galvez Turros

R = 0.1, p = 0.66

0.4

0.5

0.6

0.7

0.75 0.80 0.85 0.90 0.95

Latino Share of the Electorate

Manolo Reyes

R = −0.0013, p = 1

0.3

0.4

0.5

0.75 0.80 0.85 0.90 0.95

Latino Share of the Electorate

Ralph Rosado

R = − 0.36, p = 0.12

0.025

0.050

0.075

0.100

0.125

0.75 0.80 0.85 0.90 0.95

Latino Share of the Electorate

Denise Galvez Turros

6.7 Election 7: Congress 24 2020

The northern part of Miami sat in Florida’s 24th Congressional District prior to the 2022 redistricting.

I examine precincts in the City of Miami. In the 2020 election, there were three candidates in the race:

Frederica Wilson (Democrat), Lavern Spicer (Republican), and Christina Olivo (Independent). Frederica

Wilson was the preferred candidate among Black voters. I do not find evidence that Anglo or Latino voters

had a preferred candidate. As Figure 11 shows, support for Frederica Wilson was only greater than 60%

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among Black voters, even though all racial groups nominally supported Wilson. While the Miami portion of

this congressional district was majority Black, Wilson won the plurality of the votes in both the Anglo and

Latino majority precincts. Wilson won Miami precincts with 76.5% of the vote.

I find no evidence of racially polarized voting across the Miami precincts in this election.

Figure 10: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.65, p = 4.7e−08

0.0

0.3

0.6

0.9

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Frederica Wilson

R = − 0.21, p = 0.12

0.00

0.05

0.10

0.15

0.20

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Christine Alexandria Olivo

R = − 0.61, p = 4.1e−07

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Lavern Spicer

R = − 0.11, p = 0.44

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Frederica Wilson

R = 0.12, p = 0.38

0.00

0.05

0.10

0.15

0.20

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Christine Alexandria Olivo

R = 0.087, p = 0.52

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Lavern Spicer

R = − 0.29, p = 0.026

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Frederica Wilson

R = 0.31, p = 0.02

0.00

0.05

0.10

0.15

0.20

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Christine Alexandria Olivo

R = 0.24, p = 0.071

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Lavern Spicer

15

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Figure 11: Estimated Candidate Support by Race/Ethnicity

6.2%

49.9%

46.1%

ChristineAlexandriaOlivo

FredericaWilson

LavernSpicer

0.0 0.2 0.4 0.6

Estimated Candidate Support: Anglo

2.4%

95.9%

3.4%

ChristineAlexandriaOlivo

FredericaWilson

LavernSpicer

0.0 0.3 0.6 0.9

Estimated Candidate Support: Black

8.2%

54.8%

36.3%

ChristineAlexandriaOlivo

FredericaWilson

LavernSpicer

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

16

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6.8 Election 8: County Commission 3 2020

The exogenous election of County Commissioner District 3 was between Keon Hardemon and Gepsie Metellus.

If the election were held in Miami precincts, Hardemon would have won with 66.7%. The Black-preferred and

Latino-preferred candidate was Keon Hardemon, and the Anglo-preferred candidate was Gepsie Metellus.

I find evidence that racially polarized voting exists between the Miami precincts of County Commission

3. The Black and Latino preferred candidate (Keon Hardemon) won against the Anglo-preferred candidate

(Gepsie Metellus).

Figure 12: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.7, p = 2.3e−08

0.1

0.2

0.3

0.4

0.5

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Gepsie M. Metellus

R = 0.7, p = 2.3e−08

0.5

0.6

0.7

0.8

0.9

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Keon Hardemon

R = 0.65, p = 3.4e−07

0.1

0.2

0.3

0.4

0.5

0.6

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Gepsie M. Metellus

R = −0.65, p = 3.4e−07

0.4

0.5

0.6

0.7

0.8

0.9

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Keon Hardemon

R = 0.38, p = 0.0077

0.1

0.2

0.3

0.4

0.5

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Gepsie M. Metellus

R = −0.38, p = 0.0077

0.5

0.6

0.7

0.8

0.9

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Keon Hardemon

17

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Figure 13: Estimated Candidate Support by Race/Ethnicity

64.8%

35.6%

GepsieMetellus

KeonHardemon

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Anglo

17.4%

82.3%

GepsieMetellus

KeonHardemon

0.00 0.25 0.50 0.75

Estimated Candidate Support: Black

34.5%

65.6%

GepsieMetellus

KeonHardemon

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Latino

18

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6.9 Election 9: County Mayor 2020

The 2020 Miami-Dade County Mayor race was between five candidates: Daniella Levine Cava, Esteban Bovo,

Alex Penelas, Xavier Suarez, and Monique Barley. During the runo↵ election, Daniella Levine Cava won

with 54% of the vote. I analyze the runo↵ results in the Miami precincts. Figure 14 depicts the bivariate

association between the electorate’s racial composition and the candidate’s vote share. The Black- and

Anglo-preferred candidate was Daniella Levine Cava. The Latino-preferred candidate was Esteban Bovo.

Latino support for Bovo is estimated near the 60% threshold.

I find evidence of racially polarized voting in this contest. The Black- and Anglo-preferred candidate

won.

Figure 14: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.73, p < 2.2e−16

0.0

0.3

0.6

0.9

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Daniella Levine Cava

R = −0.73, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Esteban Bovo

R = 0.19, p = 0.026

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Daniella Levine Cava

R = −0.19, p = 0.026

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Esteban Bovo

R = −0.78, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Daniella Levine Cava

R = 0.78, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Esteban Bovo

19

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Figure 15: Estimated Candidate Support by Race/Ethnicity

75.2%

24.9%

DaniellaLevineCava

EstebanBovo

0.00 0.25 0.50 0.75

Estimated Candidate Support: Anglo

99.9%

0.1%

DaniellaLevineCava

EstebanBovo

0.0 0.3 0.6 0.9

Estimated Candidate Support: Black

38.3%

61.8%

DaniellaLevineCava

EstebanBovo

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

20

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6.10 Election 10: Clerk 2020

The contest for the County Clerk of the Courts was between two candidates: Harvey Ruvin (Democrat) and

Rubin Young (Independent). I use Miami precincts in this analysis. Harvey Ruvin won with 76.5% of the

vote in Miami precincts. All racial and ethnic groups preferred Harvey Ruvin.

I find no evidence of racially polarized voting in this contest.

Figure 16: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.57, p = 1.4e−12

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Harvey Ruvin

R = −0.57, p = 1.4e−12

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Rubin Young

R = 0.095, p = 0.28

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Harvey Ruvin

R = −0.095, p = 0.28

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Rubin Young

R = −0.61, p = 9.2e−15

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Harvey Ruvin

R = 0.61, p = 9.2e−15

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Rubin Young

21

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Figure 17: Estimated Candidate Support by Race/Ethnicity

79.6%

20.5%

HarveyRuvin

RubinYoung

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Anglo

95.7%

4.3%

HarveyRuvin

RubinYoung

0.0 0.3 0.6 0.9

Estimated Candidate Support: Black

64.8%

35.2%

HarveyRuvin

RubinYoung

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

22

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6.11 Election 11: President 2020

The 2020 Presidential race was primarily between Donald Trump (Republican) and Joseph Biden (Democrat). I analyze Miami precincts only. Biden won Miami precincts with 59% of the vote. The Anglo

and Black-preferred candidate was Joseph Biden, while the Latino-preferred candidate was Donald Trump.

Latino cohesion was near the 60% threshold. Black and Anglo support for Biden was cohesive at an estimated

95% and 80%, respectively.

I find evidence of racially polarized voting in this contest. The Black and Anglo-preferred candidate

(Biden) won.

Figure 18: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.69, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Donald J. Trump

R = 0.68, p < 2.2e−16

0.0

0.3

0.6

0.9

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Joseph R. Biden

R = −0.23, p = 0.0091

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Donald J. Trump

R = 0.22, p = 0.0095

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

Joseph R. Biden

R = 0.7, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Donald J. Trump

R = −0.7, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Joseph R. Biden

23

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Figure 19: Estimated Candidate Support by Race/Ethnicity

19.6%

80.1%

1.8%

DonaldTrump

JosephBiden

Other_cand

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Anglo

4.7%

95%

1.3%

DonaldTrump

JosephBiden

Other_cand

0.0 0.3 0.6 0.9

Estimated Candidate Support: Black

60.2%

38.6%

0.8%

DonaldTrump

JosephBiden

Other_cand

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

24

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6.12 Election 12: County Property Appraiser 2020

The County Property Appraiser election was between Pedro Garcia and Marisol Zenteno. While the Anglopreferred candidate was Marisol Zenteno, I fail to find evidence that Latinos or Black greatly preferred

either. In other words, while Latinos nominally supported Garcia (58%) and Blacks supported Zenteno

(53%), neither Latino nor Black preferred a candidate over 60% (See Figure 21). Zenteno won Miami

precincts with 51% of the vote.

I find no evidence of racially polarized voting in this contest.

Figure 20: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.11, p = 0.23

0.4

0.6

0.8

1.0

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Marisol Zenteno

R = −0.11, p = 0.23

0.0

0.2

0.4

0.6

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Pedro J. Garcia

R = 0.55, p = 1.5e−11

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Marisol Zenteno

R = −0.55, p = 1.5e−11

0.0

0.2

0.4

0.6

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Pedro J. Garcia

R = −0.49, p = 4.2e−09

0.4

0.6

0.8

1.0

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Marisol Zenteno

R = 0.49, p = 4.2e−09

0.0

0.2

0.4

0.6

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Pedro J. Garcia

25

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Figure 21: Estimated Candidate Support by Race/Ethnicity

81.4%

18.4%

MarisolZenteno

PedroGarcia

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Anglo

53.5%

46.4%

MarisolZenteno

PedroGarcia

0.0 0.2 0.4 0.6

Estimated Candidate Support: Black

41.3%

58.3%

MarisolZenteno

PedroGarcia

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

26

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6.13 Election 13: County Judge 24 2020

The County Judge Group 24 election was between Christine Bandin and Shaun Spector. Analyzing Miami

precincts only, Christine Bandin won the race with 78% of the vote. Bandin was the preferred candidate by

all racial and ethnic groups.

I find no evidence of racially polarized voting in this contest.

Figure 22: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.45, p = 1.6e−07

0.7

0.8

0.9

1.0

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Christine Bandin

R = 0.45, p = 1.6e−07

0.0

0.1

0.2

0.3

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Shaun Spector

R = −0.11, p = 0.21

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Christine Bandin

R = 0.11, p = 0.21

0.0

0.1

0.2

0.3

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Shaun Spector

R = 0.46, p = 4.1e−08

0.7

0.8

0.9

1.0

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Christine Bandin

R = −0.46, p = 4.1e−08

0.0

0.1

0.2

0.3

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Shaun Spector

27

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Figure 23: Estimated Candidate Support by Race/Ethnicity

75.4%

24.2%

ChristineBandin

ShaunSpector

0.00 0.25 0.50 0.75

Estimated Candidate Support: Anglo

70.2%

29.6%

ChristineBandin

ShaunSpector

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Black

81.8%

18.2%

ChristineBandin

ShaunSpector

0.00 0.25 0.50 0.75

Estimated Candidate Support: Latino

28

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6.14 Election 14: County Judge 9 2020

The County Judge (Group 9) election was between Joseph Mansfield and Miguel Mirabal. In the Miami

precincts, Miguel Mirabal won the race with 53% of the vote. The Black and Anglo-preferred candidate was

Mansfield, while the Latino-preferred candidate was Mirabal.

I find evidence of racially polarized voting in this contest with the Black and Anglo-preferred candidates

losing.

Figure 24: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.75, p < 2.2e−16

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Joseph J. Mansfield

R = −0.75, p < 2.2e−16

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Miguel 'Mike' Mirabal

R = 0.21, p = 0.016

0.2

0.4

0.6

0.8

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Joseph J. Mansfield

R = −0.21, p = 0.016

0.2

0.4

0.6

0.8

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Miguel 'Mike' Mirabal

R = −0.89, p < 2.2e−16

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Joseph J. Mansfield

R = 0.89, p < 2.2e−16

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Miguel 'Mike' Mirabal

29

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Figure 25: Estimated Candidate Support by Race/Ethnicity

71.8%

28.7%

JosephMansfield

MiguelMirabal

0.00 0.25 0.50 0.75

Estimated Candidate Support: Anglo

81.2%

18.8%

JosephMansfield

MiguelMirabal

0.00 0.25 0.50 0.75

Estimated Candidate Support: Black

27%

73%

JosephMansfield

MiguelMirabal

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Latino

30

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6.15 Election 15: Circuit Judge 75 2020

The Circuit Judge (Group 75) race was between Rosy Aponte and Dava Tunis. Aponte won the Miami

precincts with 56% of the vote. I find no clear evidence that Black voters preferred Aponte to Tunis. Aponte

support among Black voters was 58%, which is lower than the 60% threshold. The Anglo-preferred candidate

was Dava Tunis. The Latino-preferred candidate was Rosy Aponte.

I find evidence of racially polarized voting in this contest. The Latino-preferred candidate won.

Figure 26: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.042, p = 0.64

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Dava J. Tunis

R = −0.042, p = 0.64

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Rosy Anette Aponte

R = 0.73, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Dava J. Tunis

R = −0.73, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Rosy Anette Aponte

R = −0.52, p = 3e−10

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Dava J. Tunis

R = 0.52, p = 3e−10

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Rosy Anette Aponte

31

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Figure 27: Estimated Candidate Support by Race/Ethnicity

92.3%

7.8%

DavaTunis

RosyAnetteAponte

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Anglo

42%

58.4%

DavaTunis

RosyAnetteAponte

0.0 0.2 0.4 0.6

Estimated Candidate Support: Black

25.2%

75%

DavaTunis

RosyAnetteAponte

0.00 0.25 0.50 0.75

Estimated Candidate Support: Latino

32

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6.16 Election 16: Circuit Judge (Group 67) 2020

The Circuit Judge (Group 67) contest was between Marcia Hansen and Mavel Ruiz. In Miami precincts,

Mavel Ruiz won with 56.8% of the vote. The Black-preferred candidate was Hansen, while the Latinopreferred candidate was Ruiz. Anglos slightly preferred Ruiz (55%), but the evidence is inconclusive as it

did not reach the threshold of 60%.

I find evidence of racially polarized voting in this contest. The Latino-preferred candidate won.

Figure 28: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.64, p = 5.7e−16

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Marcia Giordano Hansen

R = −0.64, p = 5.7e−16

0.00

0.25

0.50

0.75

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Mavel Ruiz

R = −0.013, p = 0.88

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Marcia Giordano Hansen

R = 0.013, p = 0.88

0.00

0.25

0.50

0.75

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Mavel Ruiz

R = −0.64, p = 8.8e−16

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Marcia Giordano Hansen

R = 0.64, p = 8.8e−16

0.00

0.25

0.50

0.75

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Mavel Ruiz

33

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Figure 29: Estimated Candidate Support by Race/Ethnicity

43.9%

54.9%

MarciaGiordanoHansen

MavelRuiz

0.0 0.2 0.4 0.6

Estimated Candidate Support: Anglo

71.7%

28.3%

MarciaGiordanoHansen

MavelRuiz

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Black

32.1%

68%

MarciaGiordanoHansen

MavelRuiz

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Latino

34

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6.17 Election 17: Circuit Judge (Group 65) 2020

The Circuit Judge (Group 65) election was between Denise Martinez-Scanziani and Thomas Rebull. If the

contest was held in only Miami precincts, Martinez-Scanziani would have won with 51% of the vote. The

Black-preferred candidate was Rebull, winning 59.8% of their vote. I do not find clear evidence that Latinos

or Anglos had a preferred candidate. As shown in Figure 31, Anglos supported Rubell at 51.2%, and Latinos

preferred Martinez-Scanziani at 56.5%. In all cases, support for the preferred candidate did not reach 60%.

I find no evidence of racially polarized voting in this contest.

Figure 30: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.37, p = 1.8e−05

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Denise Martinez−Scanziani

R = 0.37, p = 1.8e−05

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Thomas J. Rebull

R = −0.15, p = 0.092

0.00

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Denise Martinez−Scanziani

R = 0.15, p = 0.092

0.00

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Thomas J. Rebull

R = 0.43, p = 4e−07

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Denise Martinez−Scanziani

R = −0.43, p = 4e−07

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Thomas J. Rebull

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Figure 31: Estimated Candidate Support by Race/Ethnicity

48.8%

51.2%

DeniseMartinezScanziani

ThomasRebull

0.0 0.2 0.4 0.6

Estimated Candidate Support: Anglo

40.4%

59.8%

DeniseMartinezScanziani

ThomasRebull

0.0 0.2 0.4 0.6

Estimated Candidate Support: Black

56.5%

43.8%

DeniseMartinezScanziani

ThomasRebull

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

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6.18 Election 18: Circuit Judge (Group 57) 2020

The Circuit Judge (Group 57) election was between Carmen Cabarga and Roderick Vereen. Carmen Cabarga

won Miami precincts with 56.6% of the vote. The Black and Anglo-preferred candidate was Roderick Vereen,

while the Latino-preferred candidate was Carmen Cabarga.

I find evidence of racially polarized voting in this contest. The Latino-preferred candidate won.

Figure 32: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.78, p < 2.2e−16

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Carmen Cabarga

R = 0.78, p < 2.2e−16

0.00

0.25

0.50

0.75

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Roderick 'Rod' Vereen

R = −0.23, p = 0.0082

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Carmen Cabarga

R = 0.23, p = 0.0082

0.0

0.2

0.4

0.6

0.8

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Roderick 'Rod' Vereen

R = 0.93, p < 2.2e−16

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Carmen Cabarga

R = −0.93, p < 2.2e−16

0.0

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Roderick 'Rod' Vereen

37

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Figure 33: Estimated Candidate Support by Race/Ethnicity

32.8%

67.5%

CarmenCabarga

RoderickVereen

0.00 0.25 0.50 0.75

Estimated Candidate Support: Anglo

17.7%

82.4%

CarmenCabarga

RoderickVereen

0.00 0.25 0.50 0.75

Estimated Candidate Support: Black

80.9%

19.2%

CarmenCabarga

RoderickVereen

0.00 0.25 0.50 0.75

Estimated Candidate Support: Latino

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6.19 Election 19: Circuit Judge (Group 55) 2020

The Circuit Judge (Group 55) contest was between Olanike Adebayo and Joe Perkins. Adebayo won Miami

precincts with 51.4% of the vote. The Black and Anglo-preferred candidate was Adebayo. While both

groups have higher than 60% cohesion in voting, the cohesion rate among Blacks were on the lower end of

the spectrum (60.04%). The Latino-preferred candidate was Joe Perkins.

I find evidence of racially polarized voting in this contest. The Black and Anglo-preferred candidate won

the Miami precincts.

Figure 34: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = −0.27, p = 0.0021

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Joe Perkins

R = 0.27, p = 0.0021

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Olanike 'Nike' Adebayo

R = −0.5, p = 2.8e−09

0.00

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Joe Perkins

R = 0.5, p = 2.8e−09

0.00

0.25

0.50

0.75

1.00

0.0 0.2 0.4 0.6

Anglo Share of the Electorate

Olanike 'Nike' Adebayo

R = 0.61, p = 2.8e−14

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Joe Perkins

R = −0.61, p = 2.8e−14

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

Olanike 'Nike' Adebayo

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Figure 35: Estimated Candidate Support by Race/Ethnicity

15.4%

84.7%

JoePerkins

OlanikeAdebayo

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Anglo

40.1%

60.4%

JoePerkins

OlanikeAdebayo

0.0 0.2 0.4 0.6

Estimated Candidate Support: Black

63.4%

37.1%

JoePerkins

OlanikeAdebayo

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

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6.20 Election 20: Governor 2018

The 2018 gubernatorial race was primarily between Ron Desantis (R) and Andrew Gillum (D). If the contest

was held in only Miami precincts, Gillum would have won with 65% of the vote. In Figure 36, I plot the share

of the electorate by ethnicity, and vote shares of the top two vote receiving candidates. There’s sucient

evidence that Blacks and Anglos preferred Andrew Gillum to Ron DeSantis. However, Latinos support was

split between DeSantis at 52.6% and Gillum at 45.4%. Thus, Latino support was not cohesive.

I find no evidence of racially polarized voting in this contest.

Figure 36: Scatterplot: Race/Ethnic Composition by Candidate Vote Share

R = 0.13, p = 0.14

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

ANDREW GILLUM

R = −0.12, p = 0.17

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Anglo Share of the Electorate

RON DESANTIS

R = 0.62, p = 3.3e−15

0.0

0.3

0.6

0.9

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

ANDREW GILLUM

R = −0.62, p = 4.1e−15

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

RON DESANTIS

R = −0.73, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

ANDREW GILLUM

R = 0.72, p < 2.2e−16

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Latino Share of the Electorate

RON DESANTIS

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Figure 37: Estimated Candidate Support by Race/Ethnicity

75.2%

1.1%

0.9%

0.3%

23.8%

0%

0.1%

AndrewGillum

BruceStanley

DarcyRichardson

KyleKCGibson

RonDesantis

RyanFoley

WRITEIN

0.0 0.2 0.4 0.6 0.8

Estimated Candidate Support: Anglo

96.5%

0.3%

0.5%

0.3%

3.4%

0.2%

0.1%

AndrewGillum

BruceStanley

DarcyRichardson

KyleKCGibson

RonDesantis

RyanFoley

WRITEIN

0.00 0.25 0.50 0.75 1.00

Estimated Candidate Support: Black

45.4%

0.3%

0.8%

0.3%

52.6%

0.2%

0.1%

AndrewGillum

BruceStanley

DarcyRichardson

KyleKCGibson

RonDesantis

RyanFoley

WRITEIN

0.0 0.2 0.4 0.6

Estimated Candidate Support: Latino

7 Threshold Analysis

The analysis thus far provides evidence of racially polarized voting. With a sample of twenty elections in the

City of Miami between 2017 and 2021, I found ten contests showing discernible patterns of racially polarized

voting. In this section, I estimate the proportion of Black, Anglo, and Latino registered voters required for

their preferred candidate to prevail.

To estimate this threshold proportion, I need turnout by ethnicity, the proportion of registered voters by

ethnicity, the estimated support of the group-preferred candidate, and the total number of registered voters

in the area. These numbers come from two sources: (1) the turnout rates and the registered voter rate are

taken from the voter file, and (2) the estimated support for the group’s preferred candidate is derived from

ecological inference. Using these quantities, I estimate the proportion of registered voters needed to elect the

group-preferred candidate. In other words, this analysis will show how the Black-preferred candidate would

have done if the share of Black registered voters varied. I do this analysis for all ethnic groups that have a

preferred candidate.

It is important to note that I use registration numbers rather than citizen voting age population. Using

registration rates by ethnicity provides a more accurate depiction of racially polarized voting at this level.

Generally, the citizen voting age population is less precise at the precinct level, where much of our analysis

primarily takes place. For example, a few precincts have more registered voters of a particular race than the

estimated citizen voting age population.

In Figure 38, I plot the relationship between the number of registered voters in the precinct and the

CVAP. They are correlated between .97 and .98. Furthermore, in Figure 39, I plot the same relationship

using the group’s share of registered voters and the group’s share of CVAP. I again find a high correlation

between the variables. Thus, I use the registered voters for my analysis. For reference, if you want to

calculate the estimated share of Black CVAP from the Black share of registered voters, you will subtract 0.2

percentage points from the Black share of registered voters. For Anglos, you would subtract two percentage

points from the Anglo share of registered voters to estimate the Anglo CVAP. Lastly, to estimate Latino

42

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CVAP, you would add seven percentage points to the Latino CVAP.5

Figure 38: Registration and 2019 CVAP: Miami Precincts

R = 0.98, p < 2.2e−16

0

1000

2000

0 500 1000 1500 2000

Black Registered Voters

Black 2019 CVAP

R = 0.96, p < 2.2e−16

0

500

1000

1500

2000

0 500 1000 1500 2000

Anglo Registered Voters

Anglo 2019 CVAP

R = 0.97, p < 2.2e−16

0

1000

2000

3000

4000

0 1000 2000 3000

Latino Registered Voters

Latino 2019 CVAP

R = 0.95, p < 2.2e−16

0

2000

4000

6000

0 1000 2000 3000 4000

Total Registered Voters

Total 2019 CVAP

5. Note that for Latinos, the share of Latino registered voters underestimates the CVAP.

43

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Figure 39: Share of Registered Voters and Share of 2019 CVAP: Miami Precincts

R = 0.99, p < 2.2e−16

0.00

0.25

0.50

0.75

0.00 0.25 0.50 0.75

Share of Black Registered Voters

Share of Black 2019 CVAP

R = 0.93, p < 2.2e−16

0.0

0.2

0.4

0.6

0.0 0.2 0.4 0.6

Share of Anglo Registered Voters

Share of Anglo 2019 CVAP

R = 0.97, p < 2.2e−16

0.25

0.50

0.75

1.00

0.25 0.50 0.75 1.00

Share of Latino Registered Voters

Share of Latino 2019 CVAP

I run and interpret results for the ten contests exhibiting racially polarized voting patterns. For ease

and reliability of computation, I estimate the vote share as if only three ethnic groups were voting: Blacks,

Anglos, and Latinos.

Interpreting the Threshold Plots. If the contest had a group-preferred candidate, I estimate the

proportion of registered voters needed for that candidate to be elected with a majority vote. The x-axis is

the group’s share of the registered voters. The y-axis is the preferred candidate’s estimated vote share. The

black line is how the share of registered voters translates to a candidate’s vote share. I draw a dashed line

across the 50% vote share to indicate when the candidate reaches a majority. I draw a vertical red line at

the point where the candidate is elected.

The blue line will show the composition of the voting electorate. For example, the point at which the grey

and blue lines intersect should be interpreted as the electorate’s composition when their preferred candidate

wins. Furthermore, when the blue line is above the black line, the ethnic group’s preferred candidate is

receiving above-average support from other groups. We expect this to be the case when two groups share

the same preferred candidate.

7.1 City Commissioner District 3 2021

The contest for City Commissioner District 3 in 2021 showed patterns of racially polarized voting. The

Anglo-preferred candidate was Quinn Smith, while the Latino-preferred candidate was Joe Carollo. Joe

Carollo won the contest. Latinos made up 84% of the registered voters in this district. As shown in Figure

40, Latinos would need to be 61% of the registered voters for their preferred candidate (Carollo) to win.

For the Anglo-preferred candidate to prevail, they would need to 48% of the registered voting population

(See Figure 41). Anglos are currently 12% in this district. While there is slight evidence that Black’s

preferred Smith to Carollo, they did not make up a sucient amount in any precinct to provide reliable

results.

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Figure 40: Latino-Preferred Candidate Carollo

0.0

0.2

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

% Latino Registered Voters

Carollo's Vote Share (Latino Pref. Candidate)

Figure 41: Anglo-Preferred Candidate Smith

0.25

0.50

0.75

0.00 0.25 0.50 0.75 1.00

% Anglo Registered Voters

Smith's Vote Share (Anglo Pref. Candidate)

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7.2 City Commissioner District 3 2017

The contest for City Commissioner District 3 in 2017 showed patterns of racially polarized voting. The

Latino-preferred candidate was Joe Carollo, while the Anglo- and Black-preferred candidate was Alfonso

Leon. The Latino-preferred candidate prevailed. Latino’s make up 79% of the district. As shown in Figure

42, Latinos need to make up about 77% of the registered voters for their candidate to receive 50% of the

vote. When the Latino-preferred candidate wins, the Latino share of the electorate is about 83%.

For the Anglo-preferred candidate to prevail, Anglos need to make up 72% of the registered voters (See

Figure 43). When the Anglo-preferred candidate wins, the Anglo share of the electorate is 19%. For the

Black-preferred candidate to prevail, Blacks need to make up 26% of the registered voters (See Figure 44).

When the Black-preferred candidate wins, the Black composition of the electorate is about 10%. This

suggests that the Black-preferred candidate has larger than average support from other racial groups. In

this case, the Anglo- and Black-preferred candidates are the same.

Figure 42: Latino-Preferred Candidate Carollo

0.40

0.45

0.50

0.55

0.60

0.65

0.5 0.6 0.7 0.8 0.9 1.0

% Latino Registered Voters

Carollo's Vote Share (Latino Pref. Candidate)

Figure 43: Anglo-Preferred Candidate Leon

0.40

0.45

0.50

0.55

0.60

0.65

0.0 0.1 0.2 0.3 0.4 0.5

% Anglo Registered Voters

Leon's Vote Share (Anglo Pref. Candidate)

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Figure 44: Black-Preferred Candidate Leon

0.4

0.5

0.6

0.7

0.0 0.1 0.2 0.3 0.4 0.5

% Black Registered Voters

Leon's Vote Share (Black Pref. Candidate)

7.3 County Commissioner District 3

The contest for County Commissioner District 3 in 2020 showed patterns of racially polarized voting. The

Black-preferred and Latino-preferred candidate was Keon Hardemon. The Anglo-preferred candidate was

Gepsie Metellus. Across all levels of Black share and Latino share of registered voters, the Black preferred

candidate prevails (See Figures 45 and 46). The Anglo-preferred candidate needs Anglo voters to make up

60% of the registered voter population to win a majority. When the Anglo-preferred candidate wins, they

will make up 63% of the voting electorate.

Figure 45: Black-Preferred Candidate

0.5

0.6

0.7

0.8

0.00 0.25 0.50 0.75 1.00

% Black Registered Voters

Hardemon's Vote Share (Black Pref. Candidate)

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Figure 46: Latino-Preferred Candidate

0.50

0.55

0.60

0.65

0.00 0.25 0.50 0.75 1.00

% Latino Registered Voters

Hardemon's Vote Share (Latino Pref. Candidate)

Figure 47: Anglo-Preferred Candidate

0.3

0.4

0.5

0.6

0.00 0.25 0.50 0.75 1.00

% Anglo Registered Voters

Metellus' Vote Share (Anglo Pref. Candidate)

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7.4 County Mayor 2020

The contest for County Mayor in 2020 showed signs of racially polarized voting. The Black and Anglopreferred candidate was Levine Cava while the Latino-preferred candidate was Bovo. For the Black-preferred

candidate to prevail, Blacks must make up 8% of the registered voter population. Similarly, the Anglopreferred candidate must make up 2% of the registered voting population to prevail. For the Latino-preferred

candidate to prevail, they must make up 74% of the registered voter population.

Figure 48: Black-Preferred Candidate

0.4

0.5

0.6

0.7

0.00 0.05 0.10 0.15 0.20 0.25

% Black Registered Voters

Levine Cava's Vote Share (Black Pref. Candidate)

Figure 49: Anglo-Preferred Candidate

0.45

0.50

0.55

0.60

0.00 0.05 0.10 0.15

% Anglo Registered Voters

Levine Cava's Vote Share (Anglo Pref. Candidate)

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Figure 50: Latino-Preferred Candidate

0.3

0.4

0.5

0.6

0.5 0.6 0.7 0.8 0.9 1.0

% Latino Registered Voters

Bovo's Vote Share (Latino Pref. Candidate)

7.5 Presidential 2020

The Presidential race in 2020 showed signs of racially polarized voting. The Black- and Anglo-preferred

candidate was Biden. The Latino-preferred candidate was Trump. For the Black-preferred candidate to

win, the Black share of the registered voter population must reach 5%. Similarly, the Anglo share of the

registered voter population must reach 4% for the Anglo-preferred candidate to win. For the Latino-preferred

candidate to win, Latinos must make up 77% of the registered voter population.

Figure 51: Black-Preferred Candidate

0.45

0.50

0.55

0.60

0.65

0.00 0.05 0.10 0.15 0.20 0.25

% Black Registered Voters

Biden's Vote Share (Black Pref. Candidate)

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Figure 52: Anglo-Preferred Candidate

0.45

0.50

0.55

0.60

0.00 0.05 0.10 0.15

% Anglo Registered Voters

Biden's Vote Share (Anglo Pref. Candidate)

Figure 53: Latino-Preferred Candidate

0.3

0.4

0.5

0.6

0.5 0.6 0.7 0.8 0.9 1.0

% Latino Registered Voters

Trump's Vote Share (Latino Pref. Candidate)

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7.6 County Judge (Group 9) 2020

The County Judge (Group 9) election showed signs of racially polarized voting. The Black and Anglopreferred candidate was Mansfield. The Latino-preferred candidate was Mirabal. For the Black-preferred

candidate to win, the Black share of the registered voter population must reach 30%. Similarly, the Anglo

share of the registered voter population must reach 33% for the Anglo-preferred candidate to win. For the

Latino-preferred candidate to win, Latinos must make up 77% of the registered voter population.

Figure 54: Black-Preferred Candidate

0.4

0.5

0.6

0.7

0.8

0.00 0.25 0.50 0.75 1.00

% Black Registered Voters

Mansfield's Vote Share (Black Pref. Candidate)

Figure 55: Anglo-Preferred Candidate

0.4

0.5

0.6

0.7

0.00 0.25 0.50 0.75 1.00

% Anglo Registered Voters

Manfield's Vote Share (Anglo Pref. Candidate)

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Figure 56: Latino-Preferred Candidate

0.3

0.4

0.5

0.6

0.7

0.00 0.25 0.50 0.75 1.00

% Latino Registered Voters

Mirabal's Vote Share (Latino Pref. Candidate)

7.7 Circuit Judge 75

The Circuit Judge (Group 75) contest showed signs of racially polarized voting. The Anglo-preferred candidate was Dava Tunis, and the Latino-preferred candidate was Aponte. While there is suggestive evidence

that Black’s preferred Aponte to Tunis, the results did not meet the 60% threshold. As such, I analyze the

Anglo and Latino composition of the registered population. For the Anglo-preferred candidate to win, the

Anglo share of the registered voter population must reach 32%. For the Latino-preferred candidate to win,

Latinos must make up 44% of the registered voter population.

Figure 57: Anglo-Preferred Candidate

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

% Anglo Registered Voters

Tunis' Vote Share (Anglo Pref. Candidate)

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Figure 58: Latino-Preferred Candidate

0.3

0.4

0.5

0.6

0.7

0.00 0.25 0.50 0.75 1.00

% Latino Registered Voters

Aponte's Vote Share (Latino Pref. Candidate)

7.8 Circuit Judge (Group 67) 2020

The Circuit Judge (Group 67) contest showed signs of racially polarized voting. The Black-preferred candidate Marcia Giordano Hansen, while the Latino-preferred candidate was Mavel Ruiz. There’s suggestive evidence that Anglo’s preferred Ruiz to Hansen, but Anglo cohesion did not reach 60%. For the Black-preferred

candidate to prevail, Blacks must make up 43% of the registered population. For the Latino-preferred candidate to win, they must make up 29% of the registered population.

Figure 59: Black-Preferred Candidate

0.4

0.5

0.6

0.7

0.00 0.25 0.50 0.75 1.00

% Black Registered Voters

Hansen's Vote Share (Black Pref. Candidate)

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Figure 60: Latino-Preferred Candidate

0.5

0.6

0.00 0.25 0.50 0.75 1.00

% Latino Registered Voters

Ruiz's Vote Share (Latino Pref. Candidate)

7.9 Circuit Judge 57

The Circuit Judge (Group 57) contest showed signs of racially polarized voting. The Anglo- and Blackpreferred candidate was Vereen, while the Latino-preferred candidate was Cabarga. For the Black-preferred

candidate to prevail, Blacks must make up 49% of the registered voter population. For the Anglo-preferred

candidate to prevail, Anglos must achieve 38% of the population. For the Latino-preferred candidate to

prevail, they must be 44% of the registered voter population.

Figure 61: Black-Preferred Candidate

0.4

0.5

0.6

0.00 0.25 0.50 0.75 1.00

% Black Registered Voters

Vereen's Vote Share (Black Pref. Candidate)

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Figure 62: Anglo-Preferred Candidate

0.3

0.4

0.5

0.6

0.7

0.8

0.00 0.25 0.50 0.75 1.00

% Anglo Registered Voters

Vereen's Vote Share (Anglo Pref. Candidate)

Figure 63: Latino-Preferred Candidate

0.4

0.6

0.8

0.00 0.25 0.50 0.75 1.00

% Latino Registered Voters

Cabarga's Vote Share (Latino Pref. Candidate)

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7.10 Circuit Judge (Group 55)

The Circuit Judge (Group 55) contest showed signs of racially polarized voting. The Black- and Anglopreferred candidate was Olanike Adebayo, while the Latino-preferred candidate was Joe Perkins.

The Black-preferred candidate prevails when their registration share is 12%. For the Anglo-preferred

candidate to win, they must reach 18% of the registered voter population. For the Latino-preferred candidate

to prevail, the Latino share of the registered voting population must reach 65%.

Figure 64: Black-Preferred Candidate

0.51

0.54

0.57

0.60

0.00 0.25 0.50 0.75 1.00

% Black Registered Voters

Adebayo's Vote Share (Black Pref. Candidate)

Figure 65: Anglo-Preferred Candidate

0.4

0.5

0.6

0.7

0.8

0.00 0.25 0.50 0.75 1.00

% Anglo Registered Voters

Adebayo's Vote Share (Anglo Pref. Candidate)

57

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Figure 66: Latino-Preferred Candidate

0.25

0.35

0.45

0.55

0.00 0.25 0.50 0.75 1.00

% Latino Registered Voters

Perkins' Vote Share (Latino Pref. Candidate)

8 Summary

This report aims to determine whether and to what extent racially polarized voting exists in Miami, Florida.

Using data from Miami-Dade County’s Elections Department, I examine twenty races between 2017 and

2021. Six of the twenty races were endogenous (municipal elections), and fourteen were exogenous.

I evaluate racially polarized voting using two methods. First, I create bivariate scatterplots between the

demographic composition of the turnout and candidate vote share. A group cohesively supports a candidate

if their support – in homogenous precincts – reaches 60% or greater. Second, I use ecological inference from

the eiPack package to estimate the level of support each candidate received from each racial group. Where

the method provides interpretable 95% confidence bands, I display the results. My results are robust across

both methods.

I find evidence of racially polarized voting in half of the contests analyzed. One-third (2/6) of the

endogenous races can be characterized as racially polarized, while 57% (8/14) of exogenous races were

polarized. Of the ten races that exhibited RPV, Latinos prevailed in 70% (7/10) of them. Indeed, Latinos

prevailed at a higher rate than Blacks (4/8) and Anglos (3/9).

It is important to note the coalition formation at the local level. Blacks and Anglos preferred the same

candidate in six RPV contests. In contrast, only one of the RPV contests saw Blacks and Latinos prefer the

same candidate.

Bryant J. Moy, Ph.D.

Date: February 10, 2023

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References

Collingwood, Loren, Ari Decter-Frain, Hikari Murayama, Pratik Sachdeva, and Juandalyn Burke. 2020.

“eiCompare: Compares Ecological Inference, Goodman, Rows by Columns Estimates.”

King, Gary, and Molly Roberts. 2016. “ei: Ecological inference.” ht tps :// C RAN .R-pr oj ect .org/ pack ag

e=ei.

Lau, Olivia, Ryan T. Moore, and Michael Kellermann. 2020. “eiPack: Ecological Inference and Higher Dimension Data Management.” ht tps :// C RAN .R-pr oj ect .org/ pack age=eiP ack .

59

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Bryant J. Moy

Contact

Information

Wilf Family Department of Politics 501-952-8756

19 W. 4th Street (Room 223) bryant.moy@nyu.edu

New York, NY, 10012 www.bryantjmoy.com

Employment New York University, New York City, NY

Assistant Professor, Department of Politics, Starting September 2024

Data Science Faculty Fellow, Center for Data Science, July 2022 -

Visiting Assistant Professor, Department of Politics, July 2022 -

Education Washington University in St. Louis, St. Louis, MO

Ph.D., Political Science, 2022

• Chair: Jacob Montgomery

• Three Essays on Local Government: Responsiveness,

Transparency, and Discriminatory Ordinances

M.A., Political Science, 2018

Arkansas State University, Jonesboro, AR

M.A., Political Science, 2016

• Thesis: Economic Freedom’s Clash with Participatory Democracy

• Committee: (Chair) Rollin Tusalem, Jacob Ausderan, and Hans Hacker

B.A., Political Science, May 2014

• Cum Laude

Research

Interests

Substantive: American Politics, Local/Urban Politics, Race & Ethnic Politics

Methodological: Causal Inference, Experimental Design, Computational Methods

Publications 3. Moy, Bryant J. 2021. “Can Social Pressure Induce Responsiveness? An Open

Records Field Experiment with Mayoral Oces.” Journal of Experimental Political

Science, 8(2), 117-127. doi: 10.1017/XPS.2020.22

2. Gimpel, Jim, Nathan Lovin, Bryant Moy, and Andrew Reeves. 2020. “The

Urban-Rural Gulf in American Political Behavior” Political Behavior, 42, 1343-

1368. doi: 10.1007/s11109-020-09601-w

1. Reeves, Andrew, David Miller, and Bryant J. Moy. 2018. “U.S. Presidential

Campaigns and Their Impact.” Oxford Bibliographies in Political Science, ed. by

L. S. Maisel. Oxford University Press. doi: 10.1093/OBO/9780199756223-0156

Other

Publications

1. O’Brochta, William and Bryant J. Moy. 2021. “Department-Level Graduate

Student Peer Teaching Workshops.” The Political Science Educator. 25(1) 6-8.

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Working Papers 3. Moy, Bryant J. “Responsiveness in the Patchwork of Local Government”

• Won Best Poster Award (Applications). PolMeth XXXVIII. July 2021.

2. Moy, Bryant J. “Racial Threat and Policy Adoption in Local Government: The

Emergence of Criminal Activity Nuisance Ordinances in Ohio Municipalities”

1. Dasanaike, Noah, Jacob Montgomery, Bryant J. Moy, and Santiago Olivella.

“Small-area estimation using Gaussian Process grouped IRT regression and poststratification”

Invited Talks 3. “Racial Threat and the Emergence of Discriminatory Ordinances.” NYU Wagner

Research Seminar, Oct 20, 2022

2. “Responsiveness in the Patchwork of Local Government.” Junior Americanist

Workshop Series (JAWS), December 2021

1. “Responsiveness in the Patchwork of Local Government.” The George Rabinowitz

Seminar Series, American Politics Research Group. University of North Carolina

- Chapel Hill. Oct. 29, 2021.

Conference

Presentation

13. Moy, Bryant J. “Racial Threat and the Emergence of Criminal Activity Nuisance

Ordinances” Local Political Economy Pre-Conference (LPEC), September 2022

12. Moy, Bryant J. “Racial Threat and the Emergence of Criminal Activity Nuisance

Ordinances” American Political Science Association, September 2022

11. Moy, Bryant J. “Racial Threat and the Emergence of Criminal Activity Nuisance

Ordinances” PolMeth XXXIX, July 2022 (Poster)

10. Moy, Bryant J. “Racial Threat and the Emergence of Criminal Activity Nuisance

Ordinances” Midwest Political Science Association, April 2022

9. Moy, Bryant J. “Responsiveness in the Patchwork of Local Government” Southern

Political Science Association, January 14, 2022.

8. Moy, Bryant J. “The Dynamic City: Responsiveness in Local Government?”

American Political Science Association, Oct 1, 2021. (iPoster)

7. Moy, Bryant J.. “Responsiveness in a Fragmented Local Politics” PolMeth

XXXVIII. July 13-16, 2021. (Poster)

6. Dasanaike, Noah, Jacob Montgomery, Bryant J. Moy, and Santiago Olivella.

“Small-area estimation using Gaussian Process grouped IRT regression and poststratification” St. Louis Area Methods Meeting (SLAMM) May 7, 2021.

5. Moy, Bryant J. “The Limited City: Does Dynamic Responsiveness Exist in

Local Government?” Midwest Political Science Association, April 17, 2021.

4. Moy, Bryant J. “Can Social Pressure Induce Responsiveness? An Open Records

Field Experiment with Mayoral Oces.” (Cancelled - COVID) Midwest Political

Science Association, 2020.

3. Rickert, Patrick, Nicholas Waterbury, and Bryant J. Moy. “Changing Principals:

Committee Chair E↵ectiveness in a Partisan Congress” American Political Science

Association. Washington, DC. August 29 - September 1, 2019 (Poster)

2 of 4

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2. Gimpel, Jim, Nathan Lovin, Bryant J. Moy, and Andrew Reeves. “The UrbanRural Gulf in American Political Behavior” Midwest Political Science Association,

Chicago, IL. April 5 - 8, 2018

1. Hacker, Hans J., Lisa Bohn, and Bryant J. Moy. “A Grave Responsibility:

Teaching Social Justice through an Interdisciplinary, Curricular/Extra-Curricular,

Collaborative Experience.” Southwestern Social Science Association. April 2015.

Awards,

Fellowships,

Grants

Award — American Political Science Association (Urban and Local Politics Section)

• Susan Clarke Young Scholars Award 2022

Award — Society for Political Methodology

• Best Poster Award (Applications) July 2021

Fellowship — Washington University in St. Louis

• Graduate Fellowship Fall 2016 - 2022

• The Otto E. Gansow Memorial Scholarship Fall 2017

Fellowship — Institute for Humane Studies

• Summer Graduate Research Fellow $6,000 Summer 2022

• Summer Graduate Research Fellow $5,000 Summer 2021

Fellowship — Mercatus Center

• Bastiat Fellowship $5,000 2021-2022

• Don Lavoie Fellowship $1,250 Spring 2021

Travel Awards — Washington University in St. Louis

• Travel Grant, Department of Political Science $200 Spring 2017

Grants/Awards — Arkansas State University

• Travel Grant, Department of Political Science $500 Spring 2015

• Travel Assistance Award, Graduate School, $400 Spring 2015

• Outstanding M.A. Student Award May 2016

Teaching

Experience

Data Science Faculty Fellow Fall 2022

DS-UA 201: Causal Inference

New York University

Trainer Fall 2019 - Fall 2020

SQL, Relational Databases, and Voter Files

Washington University in St. Louis

Instructor Summer 2019

Introduction to American Politics

Student Evaluation Mean 4.83 (out of 5)

Washington University in St. Louis

Instructor Spring 2015

POSC 2103 - Introduction to United States Government

Student Evaluation Mean 3.56 (out of 4)

Arkansas State University

Teaching

Assistant

Teaching Assistant

• Causal Inference: Panel Data - Short Course (Instructor: Yiqing Xu) Summer 2021

• Business of Elections (Instructor: Andrew Reeves and Steve Malter) Fall 2020

• Privacy in the Digital Age (Instructor: Sunita Parikh) Spring 2020

• Research Design (Instructor: Matthew Gabel) Fall 2019

• Health Politics (Instructor: Darl Lewis) Spring 2019

• Introduction to American Politics (Instructor: Andrew Reeves) Fall 2017, 2018

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Professional

Experience

Data Analyst

• Jon Osso↵ for U.S. Senate 2020-2021

Service • Reviewer: American Political Science Review, American Politics Research, Journal

of Politics, Political Behavior, Political Research Quarterly

• Dean’s Student Advisory (Infrastructure) Committee, Fall 2015

College of Humanities and Social Science, Arkansas State University

• Assistant Coach to the Arkansas State Moot Court Team Fall 2014 - Spring 2016

• Organizer Political Science Department Film Series. Spring 2014

“Trading Places: In a socioeconomic and race perspective”

Technical Skills R, SQL, Stata, LATEX, Qualtrics, Python

Updated: December 19, 2022

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Racially Polarized Voting in Miami, Florida

Bryant J. Moy, PhD

June 12, 2023

Introduction and Scope of Work

My name is Bryant J. Moy, Ph.D., and I am a faculty fellow in the Center for Data Science and a Visiting

Assistant Professor in the Wilf Family Department of Politics at New York University. My qualifications

were further described in my initial report dated February 10, 2023.

I have been asked to analyze two proposed maps and provide my expert opinion on whether Black voters

could elect their preferred candidates in the newly constituted District 5. I was provided a geolocated voter

file and two shape files containing the geographic boundaries of newly constituted District 5.

In this report, I analyze the performance of the Black-preferred candidate in six recent contests from

2022. Next, I re-analyze five contests from 2020 where I previously showed evidence of racial polarization. I

discuss how the newly proposed maps make it easier for Black voters to translate their preferences to higher

vote totals for their preferred candidate.

Summary of Findings

• Black voters in Miami District 5 cohesively support a single candidate in the six recent elections:

Senate, Governor, County Judge Group 5, Chief Financial Ocer, Commissioner of Agriculture, and

Attorney General. Moreover, non-Hispanic white voters’ support for the Black-preferred candidate is

under 50% in all but one of the analyzed contests.

• In the six recent elections, the Black-preferred candidate received the vast majority of the top twocandidate vote share across both newly proposed districts.

• In the re-analysis of five previously polarized contests in 2020, I find that the Black-preferred candidate

would prevail in either of the proposed districts. Moreover, it would be easier for Black voters to

translate their preferences into a higher vote share for their preferred candidate in the two proposed

districts than in the currently constituted one.

1 Black-Preferred Candidates in Recent Elections

In this section, I estimate the extent to which Black voters cohesively support a single candidate and whether

non-Hispanic whites support the Black-preferred candidate. Furthermore, using precinct-level election results, I provide evidence of how the Black-preferred candidate would have performed under the various

District 5 maps. In Table 1, I provide a list of six recently held contests in 2022. I indicate the ethnicity of

the candidate in parentheses: “B” represents Black, “W” represents non-Hispanic white, and “H” represents

Hispanic of any race.

1

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Table 1: List of Elections Analyzed

Race Black-Preferred Candidate Non-Black-Preferred Candidate

US Senate Demings (B) Rubio (H)

Governor Crist (W) DeSantis (W)

Attorney General Ayala (B) Moody (W)

Chief Financial Ocer Hattersley (W) Patronis (W)

Commissioner of Agriculture Blemur (B) Simpson (W)

County Judge 5 Seraphin (B) Diaz de la Portilla (H)

1.1 Estimating Support for the Black-Preferred Candidate By Race

I analyze the extent to which Blacks and whites support di↵erent candidates. As shown in Figure 1, Black

voters cohesively support a single candidate. Indeed, Black voters support their preferred candidate in rates

higher than 80% in all elections analyzed. Non-Hispanic white support for the Black-preferred candidate is

below 50% in all contests but County Judge Group 5.

Figure 1: Estimated Proportion of Support by Race

0.95

0.95

0.94

0.92

0.92

0.91

0.95

0.95

0.94

0.94

0.94

0.93

0.95

0.95

0.94

0.83

0.83

0.83

0.16

0.41

0.42

0.17

0.44

0.45

0.16

0.43

0.45

0.13

0.38

0.4

0.13

0.36

0.38

0.81

0.69

0.69

AG current

AG p1

AG p2

Agr current

Agr p1

Agr p2

CFO current

CFO p1

CFO p2

County_Judge current

County_Judge p1

County_Judge p2

Governor current

Governor p1

Governor p2

Senate current

Senate p1

Senate p2

0.00 0.25 0.50 0.75 1.00

Estimated proportions of Support among Blacks and Whites

Race: Map

2

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1.2 Performance of Black-Preferred Candidate in Recent Elections

Using precinct-level results of six elections held in 2022, I aggregate results to determine how many votes the

Black-preferred candidate would have received within the boundaries of the two newly constituted districts

(See Table 2). The two-candidate vote shares were similar across proposed districts.1 Yet, the Black-preferred

candidate would have prevailed in all contests analyzed at the district-level.

Table 2: Black-Preferred Candidate Performance in Recent Election

Race Map Vote Total2 Black-Pref. # Black-Pref. % Non-Black-Pref. # Non-Black-Pref. %

County Judge Grp 5 Current 6473 5046 77.95% 1427 22%

County Judge Grp 5 1 6567 5124 78% 1443 22%

County Judge Grp 5 2 6698 5220 78% 1478 22%

US Senate Current 14370 11741 81.7% 2629 18.3%

US Senate 1 14483 11784 81.36% 2699 18.6%

US Senate 2 14859 12047 81% 2812 18.9%

Governor Current 14392 11523 80% 2869 20%

Governor 1 14500 11555 80% 2945 20%

Governor 2 14875 11812 79.4% 3063 20.5%

Attorney General Current 14300 11424 80% 2876 20%

Attorney General 1 14418 11462 79.5% 2956 20.5%

Attorney General 2 14793 11730 79.3% 3063 20.7%

CFO Current 14200 11470 80.8% 2730 19.2%

CFO 1 14324 11505 80.3% 2819 19.7%

CFO 2 14696 11764 80% 2932 20%

Comm. of Agriculture Current 14273 11689 81.9% 2584 22.1%

Comm. of Agriculture 1 14396 11724 81.4% 2672 18.6%

Comm. of Agriculture 2 14766 11986 81.2% 2780 18.8%

2 Black-Preferred Candidate Performance in Previously Racially

Polarized Elections

Table 3: Black-Preferred Candidate Performance in Previous RPV Elections

Race Map Vote # Black-Pref. # Black-Pref. % Non-Black-Pref. # Non-Black-Pref. %

President Current 30418 25648 84.3% 4770 15.7%

President 1 34422 28462 82.7% 5960 17.3%

President 2 35032 28935 82.6% 6097 17.4%

County Mayor Current 26889 22159 82.4% 4730 17.6%

County Mayor 1 30509 24845 81.4% 5664 18.6%

County Mayor 2 31059 25262 81.3% 5797 18.6%

County Judge Grp 9 Current 10335 6933 67% 3402 32.9%

County Judge Grp 9 1 11686 7631 65.3% 4055 34.7%

County Judge Grp 9 2 11867 7744 65.2% 4123 34.7%

Circuit Judge Group 57 Current 10654 7380 69% 3274 30.7%

Circuit Judge Group 57 1 12016 8103 67.4% 3913 32.6%

Circuit Judge Group 57 2 12200 8227 67.4% 3973 32.6%

Circuit Judge Group 67 Current 10407 6079 58.4% 4328 41.6%

Circuit Judge Group 67 1 11779 6646 56.4% 5133 43.6%

Circuit Judge Group 67 2 11958 6751 56.4% 5207 43.5%

In this section, I re-analyze five 2020 contests that showed signs of racial polarization in my previous

report: President, County Mayor, County Judge Group 9, Circuit Judge Group 57, and Circuit Judge Group

1. I use the top two candidate vote totals to examine the contest’s Black-preferred and non-Black-preferred candidates.

3

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67. In Table 3, I aggregate the ocial election results for each district and show how many votes the Blackpreferred candidate would have received under each map. Across all contests, the Black-preferred candidate

would have received the majority of the votes in District 5.

Figures 2 - 6 depicts the relationship between the Black share of the electorate and the share that the

Black-preferred candidate received. Each dot represents a precinct and corresponds to the share of Black

registered voters and the two-candidate vote share in that precinct. I report the correlation coecient in

the top left of each figure. These correlation coecients are bounded from -1 to +1. Numbers closer to 1

indicate that Black voters are able to translate their preferences into their preferred candidate’s vote share.

As we see in the figures, Black voters can better translate their preferences into higher vote shares in the

two proposed districts than in the current District 5. For all contests, the correlation coecient is larger

and closer to one in the two proposed districts.

Figure 2: Presidential Election

R = 0.52, p = 0.0022

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Black−Preferred Candidate

Current District 5

R = 0.87, p = 7.7e−10

0.70

0.75

0.80

0.85

0.90

0.95

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 1

R = 0.86, p = 1.6e−09

0.70

0.75

0.80

0.85

0.90

0.95

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 2

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Figure 3: County Mayor

R = 0.47, p = 0.0061

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Black−Preferred Candidate

Current District 5

R = 0.79, p = 3.1e−07

0.65

0.70

0.75

0.80

0.85

0.90

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 1

R = 0.77, p = 5.9e−07

0.65

0.70

0.75

0.80

0.85

0.90

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 2

5

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Figure 4: County Judge Group 9

R = 0.63, p = 0.00013

0.4

0.5

0.6

0.7

0.8

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Black−Preferred Candidate

Current District 5

R = 0.92, p = 3e−12

0.5

0.6

0.7

0.8

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 1

R = 0.91, p = 3.4e−12

0.5

0.6

0.7

0.8

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 2

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Figure 5: Circuit Judge Group 57

R = 0.67, p = 3.2e−05

0.4

0.5

0.6

0.7

0.8

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Black−Preferred Candidate

Current District 5

R = 0.79, p = 3.6e−07

0.6

0.7

0.8

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 1

R = 0.79, p = 2.3e−07

0.6

0.7

0.8

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 2

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Figure 6: Circuit Judge 67

R = 0.52, p = 0.0027

0.2

0.4

0.6

0.00 0.25 0.50 0.75 1.00

Black Share of the Electorate

Black−Preferred Candidate

Current District 5

R = 0.9, p = 3.8e−11

0.45

0.55

0.65

0.75

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 1

R = 0.9, p = 1.7e−11

0.45

0.55

0.65

0.75

0.25 0.50 0.75

Black Share of the Electorate

Black−Preferred Candidate

Proposed Map 2

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Summary

In this report, I analyzed two newly proposed maps for District 5. I analyzed six recent elections and found

that Black support in this district is cohesive. Moreover, in those elections, the Black-preferred candidate

will receive the majority of the votes in the newly proposed districts. Lastly, I re-analyze five contests that

previously showed evidence of racial polarization. The Black-preferred candidate would prevail in either of

the two proposed districts. More importantly, Black voters can better translate their numbers into higher

vote shares for their preferred candidate in the two proposed districts.

Bryant J. Moy, Ph.D.

Date: June 12, 2023

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Supplementary Report:

Racially Polarized Voting in Miami, Florida

Bryant J. Moy, PhD

July 1, 2023

Introduction and Scope of Work

My name is Bryant J. Moy, Ph.D., and I am a faculty fellow in the Center for Data Science and a Visiting

Assistant Professor in the Wilf Family Department of Politics at New York University. My qualifications

were further described in my initial report dated February 10, 2023.

I have been asked to analyze two proposed maps and provide my expert opinion on whether Black voters

could elect their preferred candidates in the newly constituted District 5. The first is Plainti↵’s 4 (“P4”)

and the second is the City of Miami’s proposed map (“City”). I was provided a geolocated voter file and

two shape files containing the geographic boundaries of newly constituted District 5.

In this report, I first provide racial demographic data of registered voters under the current map, the

P4 plan, and the city’s proposal. Second, I analyze the performance of the Black-preferred candidate in six

recent contests from 2022. Lastly, I re-analyze five contests from 2020 where I previously showed evidence

of racial polarization. I discuss how the newly proposed maps make it easier for Black voters to translate

their preferences to higher vote totals for their preferred candidate.

Summary of Findings

• Black voters in the City of Miami cohesively support a single candidate in the six recent elections:

Senate, Governor, County Judge Group 5, Chief Financial Ocer, Commissioner of Agriculture, and

Attorney General. Moreover, non-Black voters’ support for the Black-preferred candidate is under 50%

in all contests. I find patterns of racially polarized voting in these contests.

• In the six recent elections, the Black-preferred candidate received the vast majority of the top twocandidate vote across both newly proposed districts. Moreover, the Black-preferred candidate would

have received more votes under the P4 plan than both the enjoined map and the city’s proposed plan.

• In the re-analysis of five previously polarized contests in 2020, I find that the Black-preferred candidate

would prevail under either of the proposed districts. More importantly, the Black-preferred candidates

under P4 have a marginally higher likelihood of prevailing given the larger makeup of the district

regardless of the Black share of the precinct.

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1 Racial Demographics of Registered Voters in District 5

There are 220,103 registered voters in Miami.1 In Table 1 I show the racial composition of registered voters

under each map’s District 5. The City of Miami has a majority-Hispanic electorate with Anglos (non-Hispanic

whites) constituting 21% of the electorate and Blacks constituting 17% of the electorate.

Table 1: District 5 Racial Composition: Registered Voters

Race Enjoined Enjoined % P4 P4 % City City %

Anglo 6,813 14.0% 7,550 15.0% 6,782 14.0%

Black 28,054 57.6% 28,156 55.5% 27,793 57.0%

Hispanic 13,166 27.0% 14,324 28.0% 13,338 27.0%

AAPI/American Indian 661 1.3% 703 1.4% 713 1.5%

Total 48,694 - 50,733 - 48,626 -

2 Black-Preferred Candidates in Recent Elections

In this section, I analyze six recently help contests in 2022 to assess the extent to which they show patterns

of racially polarized voting: U.S. Senate, Governor, Attorney General, Chief Financial Ocer, Commissioner

of Agriculture, and County Judge Group 5. I estimate the extent to which Black voters cohesively support

a single candidate using bivariate scatterplots. The x-axis corresponds with the Black Share of the Total

Citizen Voting Age Population for 2020, while the y-axis corresponds with the Black preferred candidate’s

vote share within Miami precincts.2 I draw a linear line of best fit through the cluster of precincts. The

positive association means that as the Black share of the Citizen Voting Age Population increases, the

Black-preferred candidate receives a higher share of the vote.

For all contest, when the precinct is homogeneously Black, the identified Black-preferred candidate receives overwhelming support. Similarly, when precincts are homogeneously non-Black, the Black-preferred

candidate fails to receive the majority of the votes on average. All of the six contests analyzed show signs of

racially polarized voting.3

In Table 2, I indicate which individuals are the Black-preferred candidates and include their ethnicity in

parentheses: “B” represents Black, “W” represents non-Hispanic white, and “H” represents Hispanic of any

race.

Table 2: List of Elections Analyzed

Race Black-Preferred Candidate Non-Black-Preferred Candidate

US Senate Demings (B) Rubio (H)

Governor Crist (W) DeSantis (W)

Attorney General Ayala (B) Moody (W)

Chief Financial Ocer Hattersley (W) Patronis (W)

Commissioner of Agriculture Blemur (B) Simpson (W)

County Judge Group 5 Seraphin (B) Diaz de la Portilla (H)

1. Consistent with the first report, I include Black, Anglo, Hispanic, AAPI/American Indian and exclude voters whose race

is recorded as “Other,” “Multi-racial,” or “Unknown” throughout the analysis. Registered voters recorded as both active and

inactive status are included in this analysis.

2. For this analysis, I use the top two candidates only.

3. In 5 of the 6 contest, when the Black share of CVAP is 0, the Black-preferred candidate receives a vote share in the low

40s. In the County Judge Group 5, however, when the Black share of CVAP is 0, the Black-preferred candidate receives 49% of

the vote. While still under 50%, there is suggestive evidence that the County Judge Group 5 may show less racial polarization

than the other contests.

2

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Figure 1: Black Preferred Candidates in Recent Contests

y = 0.416 + 0.63 x, R2 = 0.58

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Demings Vote Share

Senate

y = 0.413 + 0.619 x, R2 = 0.60

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Crist Vote Share

Governor

y = 0.436 + 0.575 x, R2 = 0.57

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Ayala Vote Share

Attorney General

y = 0.419 + 0.615 x, R2 = 0.59

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Hattersley Vote Share

CFO

y = 0.431 + 0.609 x, R2 = 0.58

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Blemur Vote Share

Comm. of Agriculture

y = 0.491 + 0.431 x, R2 = 0.35

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Seraphin Vote Share

County Judge Group 5

2.1 Performance of Black-Preferred Candidate in Recent Elections

Using precinct-level results of six elections held in 2022, I aggregate results to determine how many votes the

Black-preferred candidate would have received within the boundaries of the two newly constituted districts

(See Table 3). The two-candidate vote shares were similar across proposed districts.4 The Black-preferred

candidate would have prevailed in all contests analyzed at the district-level. According to Table 3, the

Black-preferred candidate would receive more votes under the P4 map than both the enjoined and the city’s

plan.

4. I use the top two candidate vote totals to examine the contest’s Black-preferred and non-Black-preferred candidates.

3

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Table 3: Black-Preferred Candidate Performance in Recent Election

Race Map Vote Total Black-Pref. # Black-Pref. % Non-Black-Pref. # Non-Black-Pref. %

County Judge Grp 5 Enjoined 7157 5234 73.1% 1923 26.9%

County Judge Grp 5 P4 7046 5433 77.1% 1613 22.9%

County Judge Grp 5 City 7548 5735 76.0% 1813 24.0%

US Senate Enjoined 16807 12230 72.8% 4577 27.2%

US Senate P4 15942 12718 79.8% 3224 20.2%

US Senate City 17753 13815 77.8% 3938 22.2%

Governor Enjoined 16849 11989 71.2% 4860 28.8%

Governor P4 15966 12462 78.1% 3504 21.9%

Governor City 17782 13530 76.1% 4252 23.9%

Attorney General Enjoined 16660 11979 71.9% 4681 28.1%

Attorney General P4 15876 12403 78.1% 3473 21.9%

Attorney General City 17678 13507 76.4% 4171 23.6%

CFO Enjoined 16554 11908 71.9% 4646 28.1%

CFO P4 15762 12418 78.8% 3344 21.2%

CFO City 17552 13477 76.8% 4075 23.2%

Comm. of Agriculture Enjoined 16607 12182 73.4% 4425 26.2%

Comm. of Agriculture P4 15830 12645 79.9% 3185 20.1%

Comm. of Agriculture City 17608 13743 78.0% 3865 22.0%

3 Black-Preferred Candidate Performance in Previously Racially

Polarized Elections

Table 4: Black-Preferred Candidate Performance in Previous RPV Elections

Race Map Vote # Black-Pref. # Black-Pref. % Non-Black-Pref. # Non-Black-Pref. %

President Enjoined 36848 28308 76.8% 8540 23.2%

President P4 38379 31312 81.6% 7067 18.4%

President City 41234 33233 80.6% 8001 19.4%

County Mayor Enjoined 32852 24968 76.0% 7884 24.0%

County Mayor P4 34145 27473 80.5% 6672 19.5%

County Mayor City 36703 29209 79.6% 7494 20.4%

County Judge Grp 9 Enjoined 12043 7281 60.5% 4762 39.5%

County Judge Grp 9 P4 12798 8198 64.1% 4600 35.9%

County Judge Grp 9 City 13325 8443 63.4% 4882 36.6%

Circuit Judge Group 57 Enjoined 12348 7563 61.2% 4785 38.8%

Circuit Judge Group 57 P4 13140 8741 66.5% 4399 33.5%

Circuit Judge Group 57 City 13685 8976 65.6% 4709 34.4%

Circuit Judge Group 67 Enjoined 12189 6362 52.2% 5827 47.8%

Circuit Judge Group 67 P4 12891 7219 56.0% 5672 44.0%

Circuit Judge Group 67 City 13428 7504 55.9% 5924 44.1%

In this section, I re-analyze five 2020 contests that exhibited signs of racial polarization in my previous

report: President, County Mayor, County Judge Group 9, Circuit Judge Group 57, and Circuit Judge Group

67. In Table 4, I aggregate the ocial election results for each district and show how many votes the Blackpreferred candidate would have received under each map. Across all contests, the Black-preferred candidate

would have received the majority of the votes in District 5.

Figures 2 - 6 depicts the relationship between the Black share of the Citizen Voting Age Population and

the share that the Black-preferred candidate received. I report the linear line of best fit and the R2 in each

graph. As we see in the figures, Black voters are able to translate their preferences into high vote shares

for their preferred candidate. Furthermore, the P4 plan increases the likelihood that the Black-preferred

candidate will prevail over the enjoined map and the plan proposed by the city. Specifically, as shown in the

4

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equation, the intercept is shifted upward which meaning that the Black-preferred candidate is in a better

position to prevail given the larger makeup of the district regardless of the Black share of the precinct. Take

the County Mayor contest for example. The y-intercept shifts from .648 in enjoined map to .716 to the

plainti↵’s plan. This shift is even larger compared with the city’s plan (.638 to .716).

Figure 2: Presidential Election

y = 0.699 + 0.248 x, R2 = 0.79

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

P4

y = 0.629 + 0.345 x, R2 = 0.42

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

City

y = 0.628 + 0.353 x, R2 = 0.74

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

Enjoined

5

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Figure 3: County Mayor

y = 0.716 + 0.178 x, R2 = 0.66

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

P4

y = 0.638 + 0.288 x, R2 = 0.32

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

City

y = 0.648 + 0.288 x, R2 = 0.67

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

Enjoined

6

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Figure 4: County Judge Group 9

y = 0.478 + 0.325 x, R2 = 0.73

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

P4

y = 0.477 + 0.328 x, R2 = 0.73

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

City

y = 0.457 + 0.309 x, R2 = 0.46

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

Enjoined

7

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Figure 5: Circuit Judge Group 57

y = 0.547 + 0.227 x, R2 = 0.60

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

P4

y = 0.506 + 0.287 x, R2 = 0.64

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

City

y = 0.471 + 0.31 x, R2 = 0.55

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

Enjoined

8

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Figure 6: Circuit Judge 67

y = 0.427 + 0.273 x, R2 = 0.67

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

P4

y = 0.438 + 0.26 x, R2 = 0.61

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

City

y = 0.418 + 0.237 x, R2 = 0.33

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Black Share of CVAP

Black−Pref. Candidate

Enjoined

9

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Summary

In this report, I analyzed two newly proposed maps for District 5: plainti↵’s 4 (“P4”) and the city’s plan

(“City”). I analyzed six recent elections and found that Black support is cohesive for a single candidate

and non-Black support for the Black-preferred candidate is under 50%. Moreover, in those elections, the

Black-preferred candidate will receive the majority of the votes in the newly proposed districts. Next, I reanalyzed five contests that previously showed evidence of racial polarization. The Black-preferred candidate

would prevail in either of the two proposed districts. More importantly, Black voters would have seen their

preferred candidate receive a higher vote share under P4 than both the city’s plan and the enjoined plan.

Bryant J. Moy, Ph.D.

Date: July 1, 2023

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