From Legal Theory to Practical Application:
A How-To for Performing Vote
Dilution Analyses∗
M. V. Hood III, University of Georgia
Peter A. Morrison, Morrison & Associates
Thomas M. Bryan, Bryan GeoDemographics
Objectives. The Supreme Court opinion in Thornburg v. Gingles three decades ago established a threeprong test whereby a vote dilution claim can be substantiated. This article provides practitioners
and social scientists with a working understanding of the operational steps involved in analyzing a
vote dilution claim. Methods. A brief primer is offered on how to translate the Gingles preconditions
into a set of practical, real-world tests. At each stage, we buttress these explanations with examples
from actual court proceedings. Results. This primer furnishes readers with the basic knowledge
necessary to carry out a vote dilution analysis under the current legal standard. Conclusion. While
the generic process for conducting a test of vote dilution has been well-defined by decades of case
law, practitioners should be mindful that some aspects of these procedures will continue to be
affected by future court proceedings.
The 1965 Voting Rights Act has profoundly altered the American political landscape
(see Berman, 2015). The challenges to election procedures and practices it prompted
now hinge centrally on successfully prosecuting a claim under Section 2 of the Act.1
The Supreme Court opinion in Thornburg v. Gingles three decades ago established a
three-prong test whereby a vote dilution claim can be substantiated. Translating judicial guidance into real-world empirical analysis, however, is not always a straightforward
process.
To fill this gap, we present a primer on how to transform the three Gingles preconditions
into a set of practical real-world tests. Our aim is to provide a working understanding of the
operational steps involved in analyzing a vote dilution claim. This how-to practicum will
illustrate the uses of demographic and statistical analyses to inform legal proceedings. We
begin by providing the necessary legal background for experts undertaking a vote dilution
analysis. We then consider each of the three necessary preconditions, termed the Gingles
prongs, which a plaintiff must satisfy to sustain a vote dilution claim. In describing the
∗
Direct correspondence to M. V. Hood III, Department of Political Science, University of Georgia, 104
Baldwin Hall, Athens, GA 30602 th@uga.edu. M. V. Hood III and Peter A. Morrison served as expert
witnesses in Rios-Andino v. Orange County.
1
In 2013, the U.S. Supreme Court in Shelby County v. Holder declared the coverage formula in Section 4
of the Voting Rights Act unconstitutional. These formulas determine, in turn, which jurisdictions are subject
to Section 5. Until Congress revises Section 4, Section 5 will continue to be unenforceable. As of this writing,
it is unlikely that Congress will soon amend the coverage formulas in Section 4. There is both an interparty
disagreement over what form a new coverage formula might take and intraparty dissention given that different
formulas will produce a varying mix of covered jurisdictions. For an introduction to the previous Section 4
formulas and an interactive look at what jurisdictions would be covered under potential formulas, see “The
Formula Behind the Voting Rights Act.” New York Times June 22, 2013.
SOCIAL SCIENCE QUARTERLY
C 2017 by the Southwestern Social Science Association
DOI: 10.1111/ssqu.12405
2
Social Science Quarterly
process for evaluating each prong, we draw on actual cases to illustrate accepted techniques
experts typically employ.2
Legal Background
Largely as a consequence of the 1965 Voting Rights Act, the reenfranchisement of black
Americans in the South resulted in substantial increases in black registration and turnout in
the region (Bullock and Gaddie, 2009; Hood et al., 2012). Subsequent to these numerical
gains following removal of formal barriers to the ballot box, a new issue surfaced: minority
vote dilution. Davidson makes a clear distinction between disenfranchisement and dilution:
the later can take place even if the former is not present. He defines vote dilution as
“a process whereby election laws or practices, either singly or in concert, combine with
systematic bloc voting among an identifiable group to diminish the voting strength of at
least one other group” (Davidson, 1994:22).
Section 2 of the Voting Rights Act anticipated the potential for minority vote dilution
in 1965:
No voting qualification or prerequisite to voting, or standard, practice, or procedure shall
be imposed or applied by any State or political subdivision to deny or abridge the right of
any citizen of the United States to vote on account of race or color.3
Vote dilution has a multitude of potential causes, both blatant and subtle. They include
electoral systems (at-large or multimember districts), how election districts are drawn
(redistricting), the structure of elections (majority-vote requirements), voter requirements
(presentation of government-issued photo identification), or how elections are administered
(constraints on would-be voters, such as the length of the early in-person voting period).
Each can abridge the right to vote specifically among members of certain groups.
Section 2 of the Voting Rights Act (unlike Section 5) applies to jurisdictions nationwide
and can be used to challenge existing electoral systems.4 Section 2, however, was viewed as
little more than a redundancy to guarantees provided in the U.S. Constitution (14th and
15th Amendments) prior to actions on the part of Congress in 1982 (Lowenstein et al.,
2012). In 1980, the Court ruled in City of Mobile v. Bolden that it was necessary to prove
that a jurisdiction had intentionally erected an electoral system designed to dilute minority
voting strength in order for a cause on the part of a plaintiff to be upheld.5 Two years
later Congress responded to the Court’s interpretation of Section 2 by amending the law,
supplanting the intent standard with one based solely on effects. The 1982 amendment
to Section 2 of the VRA allowed both the Department of Justice and private plaintiffs to
challenge a host of election devices as having the effect of diluting minority voting strength.
Following the 1982 amendments to Section 2, the Supreme Court clarified its interpretation of this component of the Voting Rights Act as it applies to vote dilution involving
election districts.6 In doing so, the Court also articulated a three-part test that plaintiffs
must meet to sustain a vote dilution claim (see Bullock, 2010):
2
Detailed applications of these methodologies are provided in a separate electronic appendix to this article.
See also Morrison (1994).
3
Amendments to the Voting Rights Act in 1975 added language defining minorities as a protected class
(Bullock and Gaddie, 2009). Statute text from the Office of the Clerk, U.S. House of Representatives at http://
library.clerk.house.gov.
4
Note that unlike Section 5 litigation, the burden of proof falls on the plaintiff in a Section 2 claim.
5
City of Mobile v. Bolden, 446 U.S. 55 (1980).
6
See Thornburg v. Gingles, 478 U.S. 30 (1986).
From Legal Theory to Practical Application
3
1. The minority group must be of sufficient size and geographically compact enough to
allow for the creation of a single-member district for the group in question.
2. It must be demonstrated that the minority group is politically cohesive.
3. It must further be demonstrated that the candidate of choice for the minority group
is typically defeated by the majority voting bloc.
To prevail on a vote dilution claim, plaintiffs must present convincing evidence for all
three preconditions. If, for example, the racial minority were not sufficiently numerous to
constitute the majority in a single-member district for the jurisdiction in question, then the
Court would be unable to grant relief. Likewise, proving the existence of racially polarized
voting would not, of itself, constitute vote dilution. It must also be demonstrated that such
bloc voting typically results in the defeat of the minority group’s preferred candidate of
choice.7
The 1982 VRA amendments and subsequent Supreme Court interpretations led to a
torrent of challenges nationwide in jurisdictions with a sizable proportion of minority
residents. These initial challenges targeted multimember, at-large, and mixed election
systems. Legal challenges have ranged across all manner of office holding, from city council,
school board, county commission, and judicial offices to state legislator. Section 2 vote
dilution cases have challenged district compositions under single-member plans as well.
For example, although most state legislative plans have shifted from using multimember
districts to reliance on the single-member variety, such plans nevertheless have also been
challenged under Section 2 (Weber, 2012).
Establishing the three Gingles prongs is the primary path to sustain a vote dilution
claim. More often than not, meeting these three prongs will likely enable a plaintiff to
prevail at trial. Practitioners must understand both the legal foundation underlying each
precondition and accepted measurement approaches for establishing it. Accordingly, we
now turn to a primer on how to translate these legal preconditions into a set of practical
tests.
The First Gingles Precondition
The first prong poses the question: Can the minority group in question constitute
the majority of eligible voters in a hypothetical demonstration district? A demonstration
district merely establishes the possibility of forming an aggregation of contiguous territory
that would encompass the necessary number of total residents (e.g., one-fifth of a city’s
population with a five-member elected city council) and would sufficiently concentrate
the minority group to comprise the majority of that territory’s eligible voters.8 The two
relevant populations here are (1) all residents irrespective of age, citizenship, and felony
status and (2) all eligible voters.
Can a Majority-Minority District be Drawn?
We begin with a simple illustration. Historically, minorities and immigrants have established recognizable local neighborhoods in American cities (Logan and Zhang, 2004).
7
Additional evidence of lingering effects of previous discrimination, known as a totality-of-the-circumstances
test, also can be used by the Court upon meeting the requirements as laid out by Gingles (see Bullock, 2010).
8
Contiguous territory means that a pedestrian within the district could walk to any other point within the
district without leaving the district.
4
Social Science Quarterly
These racial/ethnic neighborhoods typically emerge where people of the same background cluster and live together. The City of Gainesville, Georgia exemplifies this coalescence of an immigrant group. Gainesville has registered a sharp increase in Hispanics, altering the city’s demographic landscape and buttressing Hispanics’ presence
among its residents. The impetus behind that increase—employment opportunities in
local poultry processing—drew adult Hispanic workers who are not yet citizens and therefore ineligible to vote. As a consequence, Hispanics by 2010 comprised 42 percent of
Gainesville’s residents, but a mere 12 percent of those eligible to vote (citizens 18 and
older).
Gainesville’s voters elect its five city council members at-large. A potential plaintiff
demanded replacement with a single-member election system, claiming that the existing
system prevents the Hispanic community from electing candidates of its choice. The city’s
initial line of defense centered on the first Gingles precondition: Are Hispanic eligible
voters sufficiently numerous and geographically compact to constitute a majority of eligible
voters within a hypothetical single-member election district encompassing one-fifth of the
city’s population? The stark disparity between Hispanics’ demographic presence (among all
residents) and their electoral presence among adult citizens cast doubt on that likelihood.
A useful starting point is simply to gauge the mathematical possibility of meeting this
first precondition (see Table 1):
1. For each census block in the jurisdiction, we calculate the percentage of the citizen
voting-age population (CVAP) who are Hispanic (or the subject minority).
2. Next, we rank all census blocks in descending order of that percentage, starting with
the highest percentage Hispanic.
3. Next, we calculate the cumulative percentage of the jurisdiction’s total population,
citizen voting-age population, Hispanic citizen voting-age population, and the Hispanic
share of CVAP.
4. Note the row at which the cumulative total population approaches 20 percent of the
city’s 35,446 residents (19.2 percent in Table 1).
5. For this row, we note that the Hispanic share of CVAP (46.3 percent) defines the
arithmetic upper limit of Hispanics’ share of CVAP in any potential district drawn to
encompass nearly 20 percent of the total resident population. As seen here, that upper
limit is well below 50 percent, proving that it is arithmetically impossible to form any
district (regardless of the contiguity of its blocks) affording Hispanics a majority of a
district’s eligible voters.
In rare instances such as this, the first Gingles precondition can be proven arithmetically impossible. Oftentimes, though, a majority-minority district is numerically possible.
In that case, other geographic concerns enter the equation, such as whether these census blocks and block groups could be aligned so as to create a contiguous district (Butler
and Cain, 1992). In addition to geographic contiguity, a court takes into account numerous other “traditional districting criteria” relevant when considering a district’s constitutionality (see Bullock, 2010 and Butler and Cain, 1992 for a discussion of these
criteria).
One such criteria is a district’s geographic compactness. A compact district minimizes
the distance between all the parts of a constituency (Butler and Cain, 1992:157).9 Where
race is an important consideration, the courts have viewed bizarrely shaped districts with
low levels of compactness as a warning sign that the district may be an unconstitutional
9
For an introduction to district compactness and its measurement, see Butler and Cain (1992).
From Legal Theory to Practical Application
TABLE 1
Evaluating the Arithmetic Possibility of Forming One Majority-Hispanic District in a Five-District Plan
Total Population
Census Block
131390011012000
131390011013027
131390010031008
131390011012006
131390010031014
131390011012023
131390011012061
131390011013028
131390012012046
131390010032033
131390011021004
131390008003041
131390010023015
131390011013002
131390012013029
131390011013024
131390011013062
131390011011047
131390005002039
131390011013068
131390011013030
131390011013000
131390010032010
131390011011032
131390011011031
131390011013010
131390011013008
131390011013070
CVAP (2009–2013)
Cumulative CVAP (2009–2013)
Number
Cumulative
Percentage
Number
Hispanic
Percentage of
Hispanic
Number
Hispanic
Percentage of
Hispanic
12
9
9
21
6
7
5
41
17
12
7
14
5
13
3
6
7
57
8
3
6
5
2
8
5
69
104
26
0.0
0.1
0.1
0.1
0.2
0.2
0.2
0.3
0.4
0.4
0.4
0.5
0.5
0.5
0.5
0.5
0.5
0.7
0.7
0.7
0.8
0.8
0.8
0.8
0.8
1.0
1.3
1.4
2
1
2
3
1
1
1
6
2
4
2
6
2
2
2
1
1
4
2
1
1
1
0
0
0
7
12
3
2
1
2
3
1
1
1
6
2
4
2
6
2
2
2
1
1
4
2
1
1
1
0
0
0
7
11
3
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
99.0
97.7
97.3
2
3
5
8
9
10
11
17
19
23
25
31
33
35
37
38
39
43
45
45
46
47
47
47
48
55
67
70
2
3
5
8
9
10
11
17
19
23
25
31
33
35
37
38
39
43
45
45
46
47
47
47
48
55
67
69
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
99.9
99.5
99.4
5
continued
6
TABLE 1
Continued
Total Population
Census Block
Cumulative CVAP (2009–2013)
Number
Cumulative
Percentage
Number
Hispanic
Percentage of
Hispanic
Number
Hispanic
Percentage of
Hispanic
104
47
476
20
22
24
57
7
1357
298
2
105
126
1252
81
86
199
67
115
205
7
190
442
17
307
45
1.7
1.8
3.1
3.2
3.3
3.3
3.
3.5
7.3
8.2
8.2
8.5
8.8
12.4
12.6
12.8
13.4
13.6
13.9
14.5
14.5
15.0
16.3
16.3
17.2
17.3
39
27
146
5
2
10
29
3
546
130
2
44
55
406
53
47
69
41
48
117
6
91
141
5
141
13
33
19
100
4
1
6
16
1
281
65
1
22
26
187
24
20
28
15
17
40
2
30
46
2
44
4
84.5
72.1
68.9
68.6
67.4
63.7
55.5
53.2
51.4
49.9
49.4
49.3
46.4
46.1
45.9
41.7
40.1
36.6
35.3
34.5
33.7
33.4
32.7
31.4
31.1
30.6
108
135
281
286
288
298
326
329
875
1,005
1,007
1,051
1,106
1,513
1,565
1,613
1,682
1,723
1,771
1,888
1,894
1,985
2,126
2,130
2,272
2,284
102
121
222
225
227
233
249
250
531
596
597
618
644
831
856
875
903
918
935
975
977
1,008
1,054
1,055
1,099
1,103
94.1
89.7
78.9
78.7
78.6
78.2
76.2
76.0
60.7
59.3
59.3
58.8
58.2
55.0
54.7
54.3
53.7
53.3
52.8
51.7
51.6
50.8
49.6
49.5
48.4
48.3
←15.0% of Total
Population
continued
Social Science Quarterly
131390007011012
131390013013026
131390011021024
131390014031030
131390011012035
131390009003027
131390010022029
131390014031003
131390007012033
131390010023021
131390004001010
131390009003029
131390007012011
131390011021023
131390009004007
131390010041026
131390007011014
131390010041024
131390009003030
131390013013064
131390013013066
131390004004049
131390011021000
131390012012003
131390010022001
131390014031011
CVAP (2009–2013)
From Legal Theory to Practical Application
TABLE 1
Continued
Total Population
CVAP (2009–2013)
Cumulative CVAP (2009–2013)
Census Block
Number
Cumulative
Percentage
Number
Hispanic
Percentage of
Hispanic
Number
Hispanic
Percentage of
Hispanic
131390009003026
131390011011061
131390011013012
131390008002005
131390008002012
131390009004008
131390006002085
131390009004013
131390010023020
131390007012018
131390004004046
131390011011064
131390011013052
Total, City of
Gainesville
57
59
57
55
169
67
73
123
709
236
115
86
73
35,446
17.5
17.7
17.8
18.0
18.5
18.6
18.9
19.2
21.2
21.9
22.2
22.4
22.6
100.0
27
6
8
19
54
38
8
67
316
75
49
10
10
17,940
8
2
2
5
14
10
2
16
77
18
12
2
2
2,066
29.8
28.8
27.0
27.0
26.5
25.9
25.8
24.4
24.3
24.2
23.7
23.4
23.1
11.5
2,311
2,317
2,325
2,344
2,398
2,437
2,445
2,511
2,827
2,902
2,951
2,961
2,971
17,940
1,111
1,113
1,115
1,120
1,134
1,144
1,146
1,163
1,239
1,257
1,269
1,271
1,274
2,066
48.1
48.0
48.0
47.8
47.3
47.0
46.9
46.3
43.8
43.3
43.0
42.9
42.9
11.5
←19.2% of Total
Population
SOURCES: U.S. Census Bureau, 2010 Census PL94-191 block data (using post-2010 city boundaries); 2013 American Community Survey five-year file, Tables 05003
and 05003I, allocating block group data to individual blocks based on 2010 block-level VAP full-count data.
7
8
Social Science Quarterly
racial gerrymander.10 Even a majority-minority district that is mathematically possible
may be viewed as constitutionally questionable if it is noncontiguous and/or is not
compact.
Evaluating a Demonstration District
Only rarely can a defendant rule out the mathematical possibility of forming a majorityminority district. Typically, the plaintiff will have crafted an illustrative majority-minority
district, which can then be evaluated by experts on behalf of the defendant governmental
entity. While district apportionment is based on the official decennial census count of total
population, it may be necessary in a Section 2 claim to evaluate a hypothetical district
based on additional criteria. Among such criteria might be the actual voting strength of
a minority group, based on its share of the voting-age population (VAP) or the citizen
voting-age population (CVAP). The latter may be particularly salient for racial/ethnic
groups composed of large percentages of noncitizens. One might draft a hypothetical
majority-minority district based on that group’s share of the total population, but such
a district might fail to function as a majority-minority group based on other measures.
In this section, we examine how experts evaluate such criteria and distinguish the key
considerations and issues that arise.
A vote dilution challenge to the six-member Board of County Commissioners (BCC) in
Orange County, Florida exemplifies the circumstance in which experts employ competing
methodologies to gauge the voting strength of a minority group within a district. The six
members of Orange County’s BCC are elected from single-member districts. Following
the 2010 Census, the county adopted a redistricting plan that adjusted existing district
boundaries to the changed distribution of the county’s population. A group of plaintiffs
then sued the county, alleging that the enacted redistricting plan violated Section 2 of the
VRA. Below, we focus on the first Gingles precondition, involving the plaintiffs’ claim that
the adopted plan failed to create a majority-Hispanic commission district in a county that
had registered substantial Hispanic population growth.11
Plaintiffs specifically asserted that Commission District 3 could have been drawn to
afford Hispanics a majority of the district’s eligible voters. Their expert constructed an
illustrative six-district plan in which Hispanics purportedly constituted a majority (50.19
percent) of the CVAP in District 3. The defendant’s expert disputed this claim, asserting
that what the demonstration district encompasses is less than a majority. The opposing
conclusion hinged on a methodological point, which exemplifies the judgment an expert
must exercise in evaluating a vote dilution claim.
Both experts drew on the same two bodies of U.S. Census Bureau data routinely used
for redistricting applications: (1) the 2010 decennial PL94-171 data, furnishing the official
“complete count” of the voting-age population of individual areas as small as a census
block; and (2) the 2008–2012 five-year American Community Survey file (ACS 2008–
2012), furnishing the Bureau’s official estimate of the citizen voting-age population of
individual areas as small as a census block group. The Census Bureau publishes the five-year
American Community Survey file precisely for applications such as redistricting, which
need maximum spatial resolution.
10
See Bullock (2010) and Pildes and Niemi (1993) on the constitutionality of districts and the issue of
compactness.
11
Rios-Andino v. Orange County, 51 F. Supp.3d 1215 (M.D. Florida 2014).
From Legal Theory to Practical Application
9
A demonstration district (like the one here) is composed of numerous geographic units
for which the Census Bureau reports population data. The first, and smallest, unit is
the census block (typically corresponding to a city block in urban settings). The second
geographic unit is the census block group (BG), made up of multiple census blocks.12
The plaintiffs’ expert deemed citizenship rates for Hispanics and non-Hispanics to be
reliable only at the county level. Accordingly, he calculated the countywide proportion of
all voting-age persons and Hispanic voting-age persons who were citizens. These countywide proportions were then applied to the corresponding total and Hispanic voting-age
populations residing within the illustrative District 3. This estimation technique (hereafter,
Method 1) assumes that citizenship rates are invariant across subareas within the county.
Were that assumption invalid, the resulting estimates would be inaccurate.
For a large and diverse area such as Orange County, it is quite plausible that the proportion
of adults who are citizens would vary geographically, and that proved to be the case. The
defendant’s expert adopted an alternative approach to capture this spatial variation. That
approach (hereafter, Method 2) leveraged data from the American Community Survey,
along with the decennial “complete count” population data, to estimate citizenship down
to the BG level.
The boundary of plaintiffs’ demonstration District 3 encompassed both whole BGs
and some individual census blocks. This distinction is noteworthy because the Census
Bureau’s official CVAP estimate from the ACS is published for whole BGs, not individual
census blocks. For a district composed of both individual blocks and whole BGs, standard
demographic practice favors allocating the total CVAP of a parent BG to those individual
blocks within the district based on the VAP counted in each block.13
For example, assume a BG is composed of two blocks (A and B) and contains 500
voting-age persons. If 400 reside in Block A and the other 100 reside in Block B, then
one assigns 80 percent of the BG’s CVAP to Block A and the other 20 percent to Block
B. The rationale here is that the known distribution of VAP (from the decennial census)
best reflects the distribution of CVAP across the individual blocks of a given BG. Likewise,
one allocates the Hispanic CVAP of a parent BG to those individual blocks based on the
fraction of Hispanic VAP in each block. To derive the Hispanic share of CVAP for District
3 using this methodology, one tabulates the Hispanic CVAP estimate and the total CVAP
estimate for all BGs and blocks within District 3, and then divides the former by the latter.
Table 2 compares the estimates of the Hispanic share of CVAP for District 3 using
these two methods. Using Method 1 (assuming Hispanic citizenship rates are invariant
across Orange County), Hispanics would comprise 50.19 percent of the total CVAP of
the illustrative District 3. Using Method 2 (accounting for variations in the citizenship
share across BGs), Hispanics would constitute less than a majority (48.04 percent) of
District 3.14 Depending on which method is employed, then, Hispanics may or may not
comprise a majority of District 3 CVAP. If Hispanics are not a voting-eligible majority
within the demonstration district, then the district cannot actually function as a majorityminority district. Clearly, underlying technical assumptions can make for legally significant
differences that support opposite conclusions on issues of law. Best practices call for using
12
See 2010 Geographic Terms and Concepts for more discussion on the geographic hierarchy the Census
employs (https://www.census.gov/geo/reference/terms.html).
13
This methodology accords with standard demographic practice for apportioning the population of a
geographic unit (e.g., block group) among its subareas (e.g., blocks). Its logic follows the U.S. Census Bureau’s
procedures and adheres to a key principle that “all of the estimates we produce must be consistent across
geography.” See U.S. Census Bureau (2014:2).
14
One could calculate confidence intervals for these estimates as well. Even if the CVAP estimate for a group
constitutes a majority, it is possible that the confidence interval may fall below the 50 percent threshold level.
10
Social Science Quarterly
TABLE 2
Hispanics’ Share of Voting-Age Citizens in Demonstration District 3 (Orange County, Florida):
Comparison of Countywide Approximation and Block-Group-Level Estimates
Method 1:
Hispanic
Total
Method 2:
Hispanic
Total
Voting Age
Population
ACS Estimate of
Citizenship (%)
Citizens of
Voting Age
Percent of
CVAP
73,854
135,248
76.00
82.69
56,129
111,833
50.19
—-
78,855
135,251
73.28
83.30
54,124
112,663
48.04
—-
the most precise data available and formulating defensible assumptions when constructing
demonstration districts.
The Second and Third Gingles Preconditions
The second and third Gingles prongs are typically examined together to detect the
presence of conditions resulting in minority vote dilution for the election system under analysis. How does one translate the concept of vote dilution into a real-world test?
The second prong concerns the degree to which the minority group in question is politically cohesive. Put another way, does a clear candidate of choice exist for minority
voters?
How exactly does one define one or another group’s “clear candidate of choice”? A standard
definition is: it is the candidate who received a majority of the vote (50.01 percent) from
the minority group in question. If a clear candidate of choice can be discerned, one next
proceeds to evaluate the third prong: determining whether the minority candidate of choice
was defeated by the majority (white) voting bloc. The presence of both these conditions
for a given contest—a clear minority candidate of choice who was defeated by a majority
bloc vote—demonstrates an instance of minority vote dilution.15
The Gingles test established by the Court makes clear that plaintiffs must show a pattern
of vote dilution. What constitutes a pattern? The language used by the Court adds the
qualifier typically—meaning the minority candidate of choice is typically defeated by the
majority voting bloc. Operationally, one can define typically as meaning “more often than
not.” Accordingly, a plaintiff’s expert must demonstrate that both prongs two and three are
sustained in a numerical majority of cases considered for a vote dilution claim to have any
merit.
In analyzing the second and third Gingles prongs, the first task involves determining
the appropriate universe of elections to be analyzed. Election contests can be categorized
based on four criteria: type, time, overlap, and candidates. Courts view those races directly
15
For the second prong, attention may focus on the presence and/or degree of polarization present between
the minority and the white voting blocs. Note, however, that the presence of polarization (even where strong)
between a majority and minority group alone is insufficient to establish claims of vote dilution. Polarization
in the vote dilution context may be thought of as the degree of support of a racial/ethnic group for a candidate
measured against the level of support of another racial/ethnic group for the same candidate. For example, if
91.0 percent of black voters in a congressional district voted for the Democratic candidate, while 34.0 percent
of white voters did so, the level of polarization would be 57.0 (91.0 – 34.0).
From Legal Theory to Practical Application
11
pertaining to the office in question (termed endogenous elections) as being the most probative. As noted, however, it takes a series of elections to detect the existence of a typical
pattern of vote dilution. It may be necessary then to expand the scope of inquiry to include
other types of elections (exogenous) as well.
The relevant universe of elections should include both contested general and primary
elections (not uncontested elections, which reveal nothing about any group’s ability to elect
a candidate of choice). Consider a majority-black legislative district. Here, black voters will
likely support the Democratic nominee in the general election; yet the black community’s
candidate of choice in such a district quite often is determined at the primary stage. As
such, primary elections can have even more probative value than general elections.
There is no hard-and-fast rule concerning how many election cycles one should use when
conducting a vote dilution analysis. Contemporary elections are considered more relevant;
however, it is a common practice to analyze historic elections as well, in order to define
a sufficiently large universe of relevant elections to analyze. It is not uncommon to use a
10-year timeframe for identifying relevant elections.
Overlap is another characteristic that bears on the selection of exogenous election contests
for analysis. If one is analyzing a particular congressional district, it is important to consider
the geographic congruity of an exogenous election with the district under challenge.16 For
example, in deciding whether to include a state house election, one should take into account
how congruent the house district is geographically with the congressional district under
examination. Is the state house district wholly encompassed within the district or only
partially contained? The closer the geographic congruence, the more relevant the election
insofar as the behavior exhibited is that of essentially the same voters.17
Finally, one must also consider the race/ethnicity of the candidates running for election.
Of the elections available for analysis, the more relevant are those that feature a minority
candidate from the racial/ethnic group suing the jurisdiction in question.18 For example,
in a vote dilution suit brought by Latino voters, one would seek election contests featuring
Hispanic candidates, while also keeping in mind the other criteria previously discussed.19
Analyzing any election entails estimating the share of the vote by the subject minority
group that favored each candidate in a particular election contest, as well as the share of
the majority block vote that opposed a minority-favored candidate. This type of analysis
derives inferences about individual-level voting preferences from aggregate-level data—
typically precincts or VTDs (voting districts), which are the smallest geographic areas for
which election return data are generally reported. Racial/ethnic data must then be matched
to such election precincts in order to produce estimates of voting behavior based on these
characteristics. For some jurisdictions, this is straightforward, while for others it can be
painstaking. It is also critical to assure that the geographic unit to which the election return
data refer is congruent with the geographic unit whose voters one has characterized with
separate demographic data.
16
Here, GIS software can be used to calculate the percentage of overlap between two election districts.
As a further refinement, one might analyze the results of a statewide gubernatorial contest by subsetting
the results to correspond to just those precincts housed within the congressional districts being analyzed.
18
Determining a candidate’s race/ethnicity may not be simple and straightforward. For example, a candidate
named Mary Garcia Jones may or may not self-identify as Hispanic. Here, a researcher can draw upon a variety
of sources: candidate guides, campaign materials, websites, or newspaper articles documenting self-identified
race or ethnicity. Researchers might also turn to individuals familiar with the political setting under study
(local political activists).
19
As with the choice of exogenous races, one must also decide whether an election contest that does not
feature a minority candidate per se still warrants inclusion because it featured an identifiable minority-favored
white candidate of choice.
17
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Social Science Quarterly
Data quality may vary greatly across jurisdictions. For some areas, one may be able to
obtain registration or even turnout data by race/ethnicity summarized at the precinct level.
For others, one may be forced to use Census data to characterize the voting-age population
or citizen voting-age population, aggregated from small units such as census blocks to
match election precincts.20 Given a choice, the order of preference for data type would be
turnout, otherwise registration, otherwise CVAP or VAP.21
The courts have recognized several different statistical methods for deriving inferences
about individual-level voting preferences from aggregate-level data. They include homogenous precinct analysis, ecological regression (sometimes referred to as Goodman’s double
regression), and ecological inference (EI) (King, 1997). In certain circumstances, a particular method may be unsuitable, given the available data. For example, homogenous
precinct analysis would be inapplicable in a jurisdiction lacking any precincts that contain
an overwhelming majority (usually 90 percent or more) of a specific racial/ethnic group.
The accompanying supporting information provides a detailed tutorial on implementing
these statistical methods, along with illustrations.
Having derived estimates of vote choice by race/ethnicity, the analyst can bring that
evidence to bear on the second Gingles prong. Again, one way to operationalize political
cohesion is to determine for each contest analyzed if there is a clear candidate of choice
for voters of the racial/ethnic group in question. Specifically, did the vote share from the
minority voting bloc reach a simple majority for any of the candidates in the race? For
example, if one’s estimates show that 54.0 percent of black voters supported the black
candidate in a particular election contest, that estimate reveals a clear candidate of choice
among black voters.22 At this juncture, the analyst must categorize each contest according
to the presence or absence of a clear candidate of choice among the minority group of
interest.
Having categorized election contests this way, the analyst next can determine if said
candidate won the election. A simple summary table can be used to report the relevant
details:
1. The number of election contests analyzed.
2. The number of contests with an identifiable candidate of choice by minority voters.
3. The percentage of those contests where the minority candidate of choice was defeated.
This last percentage is the critical one for a vote dilution claim. As related to the second
and third prongs, it answers the question: Is the minority candidate of choice typically
defeated by the majority voting (white) bloc in the election system under challenge?
Prongs Two and Three: An Example from Orange County, Florida
We refer again to Rios-Andino v. Orange County to illustrate testing the second and
third Gingles prongs. At the time of litigation, county commissioners were elected from
20
These data can often be obtained from state redistricting offices or their equivalent. At the local level, larger
jurisdictions will often have a dedicated GIS office housing these data. Barring these possibilities, researchers
may be forced to produce these calculations using Census data and GIS shapefiles of voting precincts.
21
Recently, another alternative has emerged to derive racial estimates by geographic unit. This technique
makes use of various algorithms to predict an individual’s race based on surname and geocoding (see Imai and
Khanna, 2016). Using this method one could assign a race to registrants in a voter file where this quantity is
not present and then aggregate these individuals by a geographic unit such as a voting precinct. Of course,
since one must estimate vote choice, an ecological estimation technique must still be used.
22
Again, one generally would expect the candidate of choice for a racial/ethnic group to be of the same
race or ethnicity as the group in question; however, it is certainly possible for a candidate of choice to be from
another racial/ethnic group, including an Anglo (non-Hispanic) majority.
From Legal Theory to Practical Application
13
TABLE 3
2012 Primary for Orange County Commissioner—District 3
Candidate
Michael Aviles (H)
Eric Armando Lasso (H)
Pete Clark
Lui Damiani∗
Lydia Pisano
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
10.72
15.19
22.95
31.10
20.05
45.4
10.1
19.8
11.7
13.0
3.1
16.5
24.5
35.2
20.7
14.2
15.9
25.9
13.9
30.1
16.9
14.1
15.2
35.6
18.2
NOTES: Entries represent vote percentages estimated by ecological inference.
H, Hispanic candidate; ∗ = Winner.
TABLE 4
2010 Primary for Orange County Commissioner—District 4
Candidate
Mayra Uribe (H)∗
Jennifer Thompson∗
Lydia Pisano
Pete Clarke
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
18.27
48.31
17.79
15.64
64.0
9.9
11.9
14.2
14.1
46.9
21.1
18.0
4.7
79.7
6.6
8.9
3.3
88.0
2.9
5.9
NOTES: Entries represent vote percentages estimated by ecological inference.
H, Hispanic candidate; ∗ = Winner; Hispanic candidate of choice in bold font.
TABLE 5
2010 General for Orange County Commissioner—District 4
Candidate
Mayra Uribe (H)
Jennifer Thompson∗
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
28.99
71.01
78.4
21.6
19.5
80.5
19.1
80.9
14.9
85.1
NOTES: Entries represent vote percentages estimated by ecological inference.
H, Hispanic candidate; ∗ = Winner; Hispanic candidate of choice in bold font.
six single-member districts in nonpartisan elections. Under this election scheme, an initial
contest is held during the primary election period. If no candidate in the race receives a
majority of the votes cast, a run-off election is then held during the general election to
determine the winner.
Our focus centers on eight county commission races held from 2000 to 2012, involving
a total of 10 Hispanic candidates. These eight endogenous contests each contained at
least one Hispanic candidate. Estimates were derived using precinct-level turnout data by
race/ethnicity.23 As the electorate in Orange County includes more than one racial/ethnic
group, the voting behavior of Anglos, blacks, Hispanics, and others could be estimated
using a variant of the EI technique.24
Examples of the analysis undertaken are shown in Tables 3–10. Table 3 reveals the results
of the 2012 primary race for County Commission District 3, a five-person race featuring
two Hispanic candidates. The Hispanic vote is fractured among the five candidates and, as
23
In Florida, the race/ethnicity of registrants and voters is recorded.
EI estimates derived from the ei.RxC procedure in R. For more information on this EI variant, see Rosen
et al. (2001).
24
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Social Science Quarterly
TABLE 6
2008 General for Orange County Commissioner—District 3
Candidate
Mildred Fernandez (H)∗
John Kelly Harris
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
56.75
43.25
77.3
22.7
43.1
56.9
52.5
47.5
71.5
28.5
NOTES: Entries represent vote percentages estimated by ecological inference.
H, Hispanic candidate; ∗ = Winner; Hispanic candidate of choice in bold font.
TABLE 7
2006 Primary for Orange County Commissioner—District 4
Candidate
Martin Collins
J. P. Quinones (H)
Linda Stewart∗
Jennifer Thompson
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
5.33
11.78
51.24
31.64
3.6
69.1
19.1
8.2
4.9
1.2
62.9
31.0
13.1
56.9
7.4
22.6
21.2
17.5
4.0
57.4
NOTES: Entries represent vote percentages estimated by ecological inference.
H, Hispanic candidate; ∗ = Winner; Hispanic candidate of choice in bold font.
TABLE 8
2004 Primary for Orange County Commissioner—District 3
Candidate
Mildred Fernandez (H)∗
Lui Damiani∗
Larry Calabretta
Jeremy Markman
John Kelly Harris
Cheryl Taubensee
Jonathan D. Cook
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
24.33
21.40
17.13
12.96
9.87
7.54
6.78
79.8
3.3
5.5
1.1
4.9
1.6
3.8
10.1
26.8
19.7
18.1
9.1
7.9
8.3
16.5
6.6
19.6
7.2
12.7
26.6
10.7
12.5
18.6
19.7
7.8
22.3
7.9
11.2
NOTES: Entries represent vote percentages estimated by ecological inference.
H, Hispanic candidate; ∗ = Winner; Hispanic candidate of choice in bold font.
TABLE 9
2004 General for Orange County Commissioner—District 3
Candidate
Mildred Fernandez (H)∗
Lui Damiani
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
50.93
49.07
88.7
11.3
31.1
68.9
74.5
25.5
62.4
37.6
NOTES: Entries represent vote percentages estimated by ecological inference.
H, Hispanic candidate; ∗ = Winner; Hispanic candidate of choice in bold font.
a result, no clear candidate of choice emerges. While the two Hispanic candidates together
received an estimated 55.5 percent of the Hispanic vote, neither candidate alone had a
clear majority of Hispanic votes.
Table 4 shows the results of the 2010 nonpartisan primary race for County Commission
District 4, a four-person race featuring one Hispanic candidate, Mayra Uribe. Here, Uribe
From Legal Theory to Practical Application
15
TABLE 10
2000 Primary for Orange County Commissioner—District 3
Candidate
Mary Johnson (H)∗
Lou Pendas (H)
Larry Calabretta
Actual (%)
Hispanic (%)
Anglo (%)
Black (%)
Other (%)
50.72
33.04
16.24
47.1
43.4
9.5
52.1
30.7
17.1
78.9
8.5
12.7
33.0
44.7
22.3
NOTES: Entries represent vote percentages estimated by ecological inference.
H: Hispanic candidate. ∗ =Winner.
TABLE 11
Summary of Results—Orange County Commission Races Analyzed
Contests
Number of races analyzed
No clear Hispanic candidate of choice
Clear Hispanic candidate of choice
Hispanic candidate of choice wins
Hispanic candidate of choice defeated
Frequency
Percent
8
2
6
4
2
—25.0
75.0
50.0
25.0
was the clear candidate of choice for Hispanics, receiving an estimated 64.0 percent of
their vote. The same evaluation is undertaken for each of the remaining six contests under
analysis (see Tables 5–10). Once completed, a summary compilation of results (see Table
11) can expose any pattern of vote dilution based on the elections analyzed in Tables 3
through 10.
In six of the eight contests (75 percent) our analyses reveal a clear candidate of choice by
Hispanic voters. In four of these six races, the Hispanic candidate of choice won election.
In 25 percent of the contests analyzed, the Hispanic candidate of choice was defeated. Two
of these losses are attributable to an Anglo voting bloc defeating the Hispanic candidate of
choice. Is there a clear pattern of Hispanic vote dilution in Orange County commission
races? No, because in 75 percent of the contests analyzed there is no evidence of minority
vote dilution. Stating these results with reference to Prong 3, Hispanic candidates running
for Orange County Commission seats have not typically been defeated by an Anglo voting
bloc (which has on occasion occurred). Thus, objective analyses failed to substantiate
the first and the third prongs of the Gingles test. Accordingly, plaintiffs failed to prevail
because they could not demonstrate the existence of conditions associated with all three
prongs.
Additional Issues to Consider
While some components of a Section 2 vote analysis are well established, other aspects
have yet to be fully resolved through the legal process. Two noteworthy issues are: (1) Exactly
who should be counted when one engages in drawing district boundary lines? and (2) How
can vote dilution analysis techniques described above assist in making a determination
concerning the percentage of a racial or ethnic minority that is constitutionally permissible
when creating a districting plan? Both these issues are discussed in the accompanying
supporting information.
16
Social Science Quarterly
Discussion and Conclusion
For about three decades, Section 2 of the Voting Rights Act has been used as an effective
means to combat minority vote dilution. With Section 5 of the Act presently unenforceable,
Section 2 has taken center stage as a litigation tool in this area of the law. For at least the short
term, voting rights cases are essentially synonymous with Section 2 claims, underscoring
the need to understand how to conduct a vote dilution analysis.
Since the Thornburg decision and subsequent opinions, a standard formula has informed
vote dilution claims. In this article, we have endeavored to provide practitioners and social
scientists with a rudimentary understanding of the operational steps involved in analyzing
a vote dilution claim. Further, we also develop a generalizable road map using examples
from actual cases in order to demonstrate how to empirically examine the three prongs of
the Gingles test as established by the U.S. Supreme Court.
To be sure, the methods we detail will not cover every contingency that may arise related
to a vote dilution case. As well, the exact process will continue to be refined through future
legal proceedings. Our hope is that this workshop article will nevertheless place any analyst
on firm footing when it comes to carrying out a vote dilution analysis and understanding
the issues surrounding such an analysis.
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Supporting Information
Additional supporting information may be found in the online version of this article at the
publisher’s website:
Appendix A: Additional Issues for Consideration
Appendix B: Techniques to Estimate Candidate Vote Shares by Race/Ethnicity