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GROWING RACIAL DISPARITIES IN VOTER TURNOUT, 2008–2022  
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Kevin Morris, Coryn Grange
March 2, 2024
Brennan Center for Justice
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_ The gap is increasing nationwide, especially in counties that had
been subject to federal oversight until the Supreme Court invalidated
preclearance in 2013. _

, Doug Chayka

 

Introduction

The gap in voter participation between Black and white Americans
decreased following the civil rights revolution of the 1960s.
Unfortunately, our research shows that for more than a decade, this
trend has been reversing. This report uses data to which few previous
researchers have had access to document the racial turnout gap in the
21st century.

The racial turnout gap — or the difference in the turnout rate
between white and nonwhite voters — is a key way of measuring
participation equality. We find that the gap has consistently grown
since 2012 and is growing most quickly in parts of the country that
were previously covered under Section 5 of the 1965 Voting Rights Act,
which was suspended by the Supreme Court in its 2013 decision
in _Shelby County v. Holder_. footnote1_0cgd98z
[[link removed]]1
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Section 5 of the Voting Rights Act required jurisdictions with a
history of racial discrimination in voting to “preclear” any
changes to their voting policies and practices with the U.S.
Department of Justice (or federal courts). In the Supreme
Court’s _Shelby County _decision, Chief Justice John Roberts,
writing for the majority, argued that Congress had not established
that the formula used to determine the jurisdictions that would be
subject to preclearance (found in Section 4b) was reflective of
current political realities and that the formula was thus
unconstitutional. While the Court agreed that the original coverage
formula’s reliance (in part) on low turnout was justified in the
1960s and 1970s, the narrow majority concluded that contemporary
turnout gaps should be used to assess current coverage under Section
4b. The Court relied heavily on turnout rates to substantiate its
argument, writing that in the 2012 presidential election,
“African-American voter turnout has come to exceed white voter
turnout in five of the six States originally covered by §5.” But
this interpretation of the data was far too narrow: the low turnout
gaps in 2012 were likely due to Barack Obama’s presidential
candidacy and did _not_ demonstrate that preclearance was no longer
needed. footnote2_aaxdcoe
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[[link removed]] That
moment, on its own, was unrepresentative of the general pattern
showing a sustained, and now growing, racial turnout gap.

In this report, we assess how the racial turnout gap has evolved in
the decade since the Court’s decision. We find that while the gap is
growing virtually everywhere, _Shelby County_ had an independent
causal impact in regions that were formerly covered under Section 5.
By 2022, our primary models indicate that the white–Black turnout
gap in these regions was about 5 percentage points greater than it
would have been if the Voting Rights Act were still in full force, and
the white–nonwhite gap was about 4 points higher. Put differently:
the turnout gap grew almost _twice as quickly_ in formerly covered
jurisdictions as in other parts of the country with similar
demographic and socioeconomic profiles.

Recent scholarship finds that restrictive voting laws generally limit
the turnout of voters of color the most. footnote3_g6dy9ec
[[link removed]]3
[[link removed]] But
while the research documents the effects of individual policies like
polling place consolidation and voter identification laws, less is
known about how the effects of these policies compound as more
restrictions on voting are enacted. footnote4_irlcmlr
[[link removed]]4
[[link removed]] Moreover,
many policies and practices that drive voting are not codified in
state law. Take, for instance, voter list maintenance practices:
following the _Shelby County _decision, jurisdictions that
previously had been required to preclear any changes to voting with
the federal government dramatically increased the rate at which they
removed voters, even if state laws governing list maintenance did not
change. footnote5_joqwjqo
[[link removed]]5
[[link removed]] We
cannot identify and measure the impact of each individual change to
voting policies and practices across the country, but the racial
turnout gap necessarily takes account of _all_ changes in voting
policy, statutory or otherwise. Our unique data set, collected from
nearly 1 billion vote records, allows us to conduct this analysis for
the first time.

This report uses voter file snapshots from shortly after each of the
past eight federal elections from Catalist and L2 to estimate turnout
rates by race. Catalist and L2 are respected firms that sell voter
file data to campaigns, advocacy groups, and academic institutions.
Our conclusions based on this body of information about
individual-level turnout behavior far surpasses what previous
researchers have been able to establish working from limited survey
data. We show that the racial turnout gap has grown everywhere. In all
regions, the gap in the 2022 midterms was larger than in any midterm
since at least 2006. In 2022, white Americans voted at higher rates
than nonwhite Americans in every single state besides Hawaii.
Moreover, the turnout gap cannot be entirely explained by
socioeconomic differences — in income or education level
— between Americans of different races and ethnicities.

That gap costs American democracy millions of ballots that go uncast
by eligible voters. It also has significant consequences for political
candidates and their campaigns. In 2020, if the gap had not existed, 9
million more ballots would have been cast — far more than the 7
million by which Joe Biden won the national popular vote. In 32
states, the number of “uncast” ballots due to the turnout gap was
larger than the winning presidential candidate’s margin of
votes. footnote6_jihqe5n
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[[link removed]] That’s
not to say that the racial turnout gap necessarily changed electoral
outcomes in any given state, but the immensity of this figure does put
the magnitude of the turnout gap into greater perspective. The gap
matters for our political system.

Given that the racial turnout gap is growing around the country,
including in regions that weren’t covered by Section 5, _Shelby
County_’s impact is not immediately clear. The widening of the gap
nationally can’t be directly attributed to the Supreme Court’s
decision, though the Court perhaps emboldened jurisdictions that were
not subject to preclearance to enact new restrictive
policies. footnote7_jxi46sc
[[link removed]]7
[[link removed]] However,
the turnout gap — especially the white–Black turnout gap — is
growing more quickly in counties that were formerly subject to Section
5 than in other, comparable parts of the country. A variety of
statistical approaches support the conclusion that this more rapid
growth in the turnout gap is attributable to the Supreme Court’s
decision in _Shelby County_.

In addition, the effect of _Shelby County _has been growing over
time; the decision did not result in a one-time increase. Instead, the
difference between formerly covered and other jurisdictions was larger
in 2022 than in any election since the decision was handed down.
Meanwhile, with the federal government unable to protect the political
rights of people of color using the full power of the Voting Rights
Act, the laws and practices that would have been subject to
preclearance continue to accumulate. footnote8_y35l0f7
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I. Methodology

To calculate turnout rates in this report, we rely on data from the
registered voter files. Current academic scholarship indicates that
the voter file data from states with self-reported racial
identification is superior to the data collected by the Current
Population Survey, which has been used in much of the existing
research on the racial turnout gap and actually understates the
magnitude of the turnout gap. footnote1_l0huqzw
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[[link removed]] Even
the best political opinion surveys are often biased when it comes to
self-reported turnout — some respondents falsely report that they
voted, and others misremember whether they participated, leading to
incorrect estimates of turnout. footnote2_nofhbbs
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Voter files, on the other hand, are government administrative records
of who participated and are free of response or sampling bias. While
other academic surveys like the Cooperative Election Study have begun
validating respondents’ reported turnout history in recent years,
the voter files offer an unparalleled look at the U.S.
electorate. footnote3_xstlpfb
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Voter File Data

All told, we analyze nearly 1 billion voter file
records. footnote4_gzrgytc
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[[link removed]] This
study is, to the best of our knowledge, the first to use such a large
set of registered voter files to estimate turnout rates. Specifically,
we analyze snapshots of the registered voter file from every state
from the past eight federal elections. Each snapshot includes a record
of every voter registered in the state at that time. These snapshots
were each collected shortly after the election in question, offering
an accurate picture of participants in each of the
elections. footnote5_6uqof8m
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[[link removed]] For
the 2008–2012 elections, we rely on snapshots provided by Catalist;
for the 2014–2022 elections, we use records from L2. There is no
reason that obtaining data from different vendors would impact any
results we present in the body of this report. One potential concern
could arise from different racial predictions from the vendors, but in
no case do we rely on proprietary racial categorization. Instead, in
all years and from both vendors, we rely solely on either
self-reported racial data or on consistent, open-source methodologies
discussed below. footnote6_nauo3hk
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We refrain from analyzing _registration_ rates calculated from the
voter files. Such files contain some amount of deadwood — that is,
voters who are registered but no longer eligible to vote (perhaps
because they have moved or passed away). If racial groups have
different levels of deadwood, we would have biased registration rates.
Moreover, states conduct voter list maintenance (the removal of
ineligible voters) at different times. Comparing the total number of
registrants in two states in the spring of an odd-numbered year might
be less an indication of underlying registration rates than of the
timing of this routine administrative list maintenance. Neither of
these issues is likely to impact _turnout_ rates estimated from the
voter file. These records indicate whether each person actually cast a
ballot. What’s more, voters who participate in an election are
unlikely to be removed from the rolls as part of systematic voter list
maintenance the following spring, when our snapshots were collected:
states generally remove individuals due to
nonparticipation. footnote7_0kuo7j4
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Voters’ Race and Racial Turnout Rates

Most states do not include self-reported racial identification in
their voter files. footnote8_b863wpw
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[[link removed]] For
these states, we use Bayesian Improved Surname Geocoding (BISG), an
approach that incorporates two different data sources to predict each
voter’s race. footnote9_y6khg4u
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[[link removed]] The
first is the racial composition of a voter’s neighborhood, in this
case census block groups. The second is the racial distribution of
surnames from the Census Bureau. Every 10 years, the Census Bureau
publishes data on the racial identifications of Americans with
different surnames. For instance, in the 2010 census, 92 percent of
respondents with the last name Martinez identified as Latino, and 89
percent of respondents with the last name Wood identified as white.
Using both data sources, BISG estimates the likelihood that a voter is
Black, white, Latino, Asian, or “some other
race.” footnote10_o4luzfr
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[[link removed]] BISG
is widely used among academic researchers and has been accepted by
courts as a valid basis for evaluating a number of concepts, including
the presence of racially polarized voting. footnote11_zltc1wu
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Throughout this report, we slightly modify the canonical version of
BISG, which uses the racial characteristics of the total population
(from the decennial census) of a voter’s block
group. footnote12_paobnhg
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[[link removed]] We
use geographic population characteristics to estimate the
characteristics of voters; thus, the more similar the geographic
population we use is to the pool of registered voters, the better we
can predict race. The total population can skew estimates where it is
different from the citizen voting-age population (CVAP) — for
instance, in areas with large noncitizen immigrant populations. We
therefore use the CVAP from the five-year American Community Survey
(ACS) estimate ending with each election year as our target population
for the BISG analyses. In the technical appendix accompanying this
report, we show that using CVAP results in better estimates (in states
with self-reported race) and that our primary results hold when using
total or total adult population.

We calculate turnout rates by dividing the number of ballots cast by
members of each racial group by the CVAP from the ACS five-year
estimates ending in each election year. footnote13_tifa4ax
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[[link removed]] The
Census Bureau publishes CVAP at the block-group level, a low
geographic level that roughly corresponds to neighborhoods. (The
median block group had a population of 1,248 in
2021.) footnote14_6z15rw5
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[[link removed]] In
conjunction with the geocoded voter file, we produce detailed turnout
estimates for very low geographic units across the
nation. footnote15_p6205u4
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[[link removed]] We
also aggregate up to higher geographic levels like counties and
states.

Calculating turnout as the share of citizens of voting age in each
racial group who participate — and not as the share of registered
voters in each group — follows the definition provided by Bernard
Fraga in his book, _The Turnout Gap_. footnote16_0uzpsqj
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[[link removed]] We
calculate the turnout gap in the same way, by subtracting the turnout
rate of each group from the turnout rate of white Americans.

Adjusting the Turnout Gap

In addition to looking at the raw turnout gap, we also present results
weighting the gap by the nonwhite share of the population in each
state. This lets us determine how much higher overall turnout would
have been had nonwhite voters participated at the same rate as white
voters and compare the gap’s impact on statewide turnout across
states with different racial characteristics. Such estimates rely on
two measures. The first is the size of the racial turnout gap. The
greater the distance between white and nonwhite turnout, the higher
the weighted turnout gap. The second is the relative size of the
nonwhite population in a given jurisdiction. Those where the
population is less white will have a higher weighted turnout gap.
Weighting the turnout gap allows us to compare the impact of the gap
on statewide turnout in different sorts of states.

We do not mean to imply that large racial turnout gaps do not matter
where minority populations are small. For example, Native American
turnout rates are lower than those of other groups, a result of
centuries of racially discriminatory policymaking. footnote17_gajknnq
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[[link removed]] However,
the Native American population in most states is not large enough to
depress overall statewide turnout. Different measures are clearly
needed to capture the participatory implications of large turnout gaps
on small populations. Despite this limitation, however, weighting the
turnout gap offers a way of identifying the states where racial
turnout gaps are meaningfully depressing overall turnout numbers.

We weight a jurisdiction’s turnout gap by estimating the
jurisdiction’s racial turnout gap and multiplying it by the nonwhite
share of the population. Consider, for example, a hypothetical state
where white turnout is 60 percent, nonwhite turnout is 50 percent, and
20 percent of the CVAP is nonwhite. The turnout gap is 10 percentage
points (60 percent – 50 percent), and the weighted gap is 2
percentage points (10 percentage point turnout gap × 20 percent
nonwhite population share). In other words, statewide turnout in this
state would have been 2 percentage points higher in the absence of the
turnout gap.

II. Participation Rate Differences Across Time

In the analyses that follow, we examine how turnout rates and gaps
have evolved since 2008. Data of this kind is not available prior to
2008, making that the earliest year for which voter file snapshots can
be used on a nationwide scale. While the Obama presidency probably
reduced racial turnout gaps early in our study period, our results
indicate that the gap has widened ever since 2014, when a nonwhite
presidential candidate was not temporarily reducing these disparities.

General Turnout Gap

Figure 1 plots the national turnout rates among Asian, Black, Latino,
and white voters — the ethnic/racial groups for which BISG provides
reliable estimates. As figure 1 makes clear, turnout for white and
Black voters in the 2008 and 2012 elections, with Obama at the top of
the ticket, reached near parity. While turnout rates for Asian and
Latino voters lagged white and Black voters, the overall
white–nonwhite turnout gap was narrower during these years than in
the decade that followed.

As we discussed above, the majority of the Court in _Shelby
County _pointed to the narrow turnout gaps in the 2008 and 2012
presidential elections to argue against the continued necessity of
Section 5 of the Voting Rights Act. Of course, political science
research has long established that Black voters participate at higher
rates when Black candidates are on the ballot; this, as much as
anything else, was the likely explanation for the near parity in those
years. footnote1_xughlni
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[[link removed]] Figure
1 makes clear just how narrow the Court’s argument was. In the 2010
election, when Section 5 was still in full force, the white–Black
turnout gap was 8 percentage points — four times the size of the gap
in 2008. By pointing only to presidential elections with a Black
candidate, it focused on elections where factors unrelated to voting
rights (temporarily) reduced the racial turnout gap.

While turnout rates have collectively improved since 2012, white
turnout has increased the most: from the 2012 to 2020 presidential
elections, white turnout rose by 10 percentage points while overall
nonwhite turnout went up by less than 8 points. Similarly, from the
2014 to 2022 midterm elections, white turnout rose by 13 points while
nonwhite turnout increased by only 8 points. Much of the increase in
the gap was concentrated in 2022, perhaps due to the highly
contentious round of redistricting leading into that year’s
election. All told, the white–nonwhite turnout gap increased from 10
points to 12 points between 2012 and 2020.

The shifts in national turnout rates among different racial groups
raise many questions. Black voters, for instance, are generally
concentrated in the Northeast and the South, while Latino and Asian
communities are larger on the West Coast. Are the differences in
racial turnout rates just _regional_ differences? Are voters on the
West Coast less likely to participate overall, regardless of their
race? Figures 2 and 3 plot the turnout rates for each racial group
within each of the country’s broadly defined regions: Northeast,
South, Midwest, and West. footnote2_j6mc187
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Figures 2 and 3 make clear that most of the racial turnout gap is not
explained by regional differences. Within each region, white turnout
exceeded that of other groups in every year apart from the 2008 and
2012 elections in the South, where Black turnout slightly exceeded
white turnout. footnote3_mysb0wn
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Americans with less education, less money, and fewer resources are
less likely to participate in elections. footnote4_c0ydkkf
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[[link removed]] The
opportunity cost of participating can be higher for Americans with
fewer resources. footnote5_bb28d53
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[[link removed]] Traveling
to a polling place, for instance, is harder for people without access
to a car; the time cost might be compounded for an individual required
to take unpaid time off work to vote. Further, individuals juggling
multiple jobs or child-care responsibilities, or who face other
demands on their time, might forget to register to vote prior to the
deadline. Policies that make it more difficult to vote fall hardest on
the people with the fewest resources to dedicate to voting.

Economically disadvantaged voters might also abstain from
participating because of alienation from government and a political
system that in many ways fails to reflect their policy
preferences. footnote6_a1ljnj7
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[[link removed]] Regressive
policies, such as campaign finance rules that favor wealthy donors and
corporate entities or aggressive partisan gerrymandering, send
messages to voters that politicians do not care about their needs. As
Soss and Jacobs observe, policies that do not address voters’
pressing challenges can “foster atomized publics with little sense
of what they have in common and at stake in politics and
government.” footnote7_duyndc0
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[[link removed]] The
same is true when voters think of the government as something that
happens _to_, and not _with_, them. In some communities, for
example, a constant and aggressive police presence teaches citizens
that government is something imposed on them, not something that they
can control. footnote8_9ueww8k
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As a result of centuries of racially discriminatory policymaking,
including when only white people were permitted by law to vote or make
policy, racial and ethnic minorities are over-represented in
populations where economic and other social precarities are
common. footnote9_cbkj529
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[[link removed]] Given
that social disadvantages can undermine democratic participation, do
socioeconomic factors explain the racial turnout gap? They do explain
some of it: turnout in the bottom income quartile in 2022 was 32
percent, compared with 58 percent in the top income quartile. The
bottom quartile was also considerably less white (the CVAP was 53
percent white compared with 72 percent white in the top quartile). But
we find that there are turnout gaps between racial groups living in
socioeconomically similar neighborhoods, which indicates that these
characteristics can’t entirely explain such gaps.

While the voter file does not include information about voters’
economic status or education, ACS five-year estimates from the Census
Bureau reveal the income and education characteristics of the
neighborhoods in which they live. We break out turnout gaps by census
tract in figures 4 and 5 to test whether neighborhood characteristics
influence turnout. footnote10_xrgtiki
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[[link removed]] We
first plot the turnout gap for different races in neighborhoods based
on the median household income, with the first quartile being the
lowest-income neighborhoods and the fourth quartile being the highest.

Figure 4 makes immediately clear that the turnout gap is not driven
simply by the fact that voters of color live in lower-income
neighborhoods: a persistent turnout gap has grown steadily in each
income quartile over the past decade. Outside the highest-income
areas, the white–Black turnout gap closed prior to 2014, though it
has subsequently grown. While white–nonwhite turnout rates
approached parity in the early parts of the past decade among voters
living in low-income neighborhoods, the same is not true in
high-income neighborhoods, which have consistently had the largest
turnout gaps. The white–nonwhite turnout gap exceeded 15 percentage
points in 2022’s midterm election among voters living in the
highest-income parts of the country. footnote11_ln3x7qd
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The trends in the white–Asian turnout gap, broken out by income,
tell a different story. As figure 1 shows, the overall white–Asian
turnout gap narrowed from 14 points in 2016 to just 8 points in 2020.
Figure 4 shows, however, that increased participation rates were
largely concentrated among Asian voters living in high-income
neighborhoods. For Asian Americans living in the lowest-income
neighborhoods, the gap grew between 2016 and 2020.

Neighborhood estimates of education level similarly cannot fully
explain the turnout gap, as seen in figure 5. When we split tracts
into quartiles based on the proportion of the adult population that
has at least a bachelor’s degree, turnout gaps remain for all
groups. Similar to the trends across income level, the
white–nonwhite turnout gap is largest among voters living in the
highest-educated neighborhoods. And, while the gaps may be smaller in
lower-education neighborhoods, those are also the neighborhoods where
the gap is growing most rapidly. Further, reductions in the
white–Asian turnout gap are almost entirely concentrated among
voters in the highest-educated neighborhoods. While the white–Asian
gap is substantially larger than that of other racial and ethnic
groups among voters living in all but the most educated areas, it has
consistently been close to or smaller than the white–Latino gap in
high-education neighborhoods.

Weighted Turnout Gaps

Figure 6 shows how the turnout gap impacted statewide turnout in the
2020 presidential (left-hand panel) and 2022 midterm (right-hand
panel) elections. We break states out according to whether they were
entirely, partially, or not covered by the preclearance condition of
the Voting Rights Act prior to _Shelby County_. Nationally, turnout
would have been 4 percentage points higher in 2020 and 6 percentage
points higher in 2022 if nonwhite voters had participated at the same
rate as white voters. These figures are particularly striking
considering that turnout in these elections was at near-record highs;
in fact, turnout in 2020 was the highest in at least a century. And
yet, had voters of color participated at the same rates as white
voters in 2020, 9.3 million more ballots would have been cast, and in
2022 that figure would have been 13.9 million. White turnout exceeded
nonwhite turnout in every single state except Hawaii in 2022.

Figure 6 indicates that the weighted turnout gap was not uniformly
distributed across states. It was largest in Alaska in 2020 and
Florida in 2022. New Mexico and Texas had the second- and
third-largest gap in both elections. These states are home to large
nonwhite populations, so their presence at the top is unsurprising
given that the relative size of the nonwhite population directly
contributes to the influence of the racial turnout gap on overall
participation rates. Another striking feature of this figure, however,
is the concentration of high weighted gaps in states in the West;
generally speaking, the impact of the racial turnout gap on statewide
turnout was larger in states where Latinos make up a large share of
the nonwhite population. This corresponds with results presented in
the previous section: although Latino turnout rates were not markedly
different in different regions, Latinos make up a larger share of the
population in the West, exerting a larger influence on statewide
turnout in those states.

Figure 6 also makes clear just how distinct the states formerly
covered by Section 5 of the Voting Rights Act remain. The formerly
covered states have large nonwhite populations and large turnout gaps,
leading to some of the largest statewide turnout distortions in the
nation. Put differently, a decade after _Shelby County_, the turnout
gap continues to have a disproportionate impact in precisely the parts
of the country that were once covered due to their histories of
racially discriminatory voting practices.

Figures 7 and 8 break down the weighted turnout gaps in 2020 and 2022,
respectively, based on which group formed the largest nonwhite racial
or ethnic group in the state. The weighted gap is consistently highest
in states where Latinos were the largest nonwhite group. Once again,
the impact of the racial turnout gap on statewide participation rates
is highest in the parts of the country that were covered under Section
5 of the Voting Rights Act. (In these charts, “other” includes all
states where a group other than Black or Latino Americans is the
single largest nonwhite group.)

Figure 9 shows how the weighted gap has evolved over the past 15
years. We break the trends out into four major regions. The figure
indicates that the weighted gap has grown nearly everywhere, just as
the raw racial turnout gap has. By way of reminder, the growth in the
weighted gap is driven both by changes in the turnout gap and by
changes in the nonwhite share of the population; if the turnout rate
is constant but the nonwhite share of the population grows, the effect
of the turnout gap on statewide turnout increases.

III. The Effects of _Shelby County v. Holder_

_See the academic working paper
[[link removed]] for a
more in-depth discussion of the theory, methods, and results included
in this section._

Prior to 2013, states and localities with a history of racial
discrimination in their voting practices were required to clear any
changes to their electoral policies before they could go into effect.
Over the past decade, since the Supreme Court suspended preclearance,
nearly 30 laws that make voting more difficult have gone into effect
in states formerly covered under Section 5. footnote1_ib7d7ss
[[link removed]]37
[[link removed]]

These formal changes in laws may be just the tip of the iceberg.
County-level administrators have a great amount of discretion over how
elections are run, deciding such things as the movement or even
closure of polling places. footnote2_3l96a9g
[[link removed]]38
[[link removed]] Such
discretionary modifications are not reflected in changes to statewide
voting law, but they would have been subject to preclearance in
covered jurisdictions prior to the _Shelby County _decision.

Because jurisdictions are no longer required to report and submit
these changes to the federal government for analysis of their
potentially discriminatory effects, researchers have struggled to
assess the total impact this Supreme Court decision has had on voters
of color. By evaluating the decision’s effects on the racial turnout
gap, we are able to provide at least one measure that necessarily
takes account of _all _changes in voting, whether statutory or
otherwise. Our unique data set allows us to conduct this analysis for
the first time.

As we showed in the previous sections, places formerly covered by
Section 5 had the highest weighted turnout gaps in 2020 and 2022. But
that doesn’t necessarily prove that the elimination of the
preclearance regime _caused_ the gaps in these places to grow;
it’s possible that these places already had higher than average
turnout gaps prior to 2013, for instance, or that the gaps in places
with large Black populations would have increased the most over the
past decade even if the preclearance system had continued.

To test the effect of the _Shelby County _decision more directly, we
calculate the white–nonwhite and white–Black turnout gap for every
county in the country for each election between 2008 and
2022. footnote3_j8t980s
[[link removed]]39
[[link removed]] But
the counties formerly covered by Section 5 differed socioeconomically
in important ways from the rest of the country. footnote4_oaptksi
[[link removed]]40
[[link removed]] They
were, for instance, on average 16.7 percent Black, compared with just
3.4 percent for non-covered counties. Covered counties voted for
Barack Obama at higher rates, and were also younger, than uncovered
counties. Because of these differences, we might expect the turnout
gap to evolve in formerly covered counties in the post–_Shelby
County _period in distinct ways from the rest of the country. Take,
for instance, the Black share of the population. Given our expectation
that Obama’s candidacy reduced the white–Black turnout gap, we
would expect the turnout gap to grow the most quickly in the
post-Obama era in areas with large Black populations. Put differently,
there might have been forces other than _Shelby
County _disproportionately increasing the turnout gap in formerly
covered jurisdictions.

To account for the differences between covered and non-covered
counties, we use a tool called entropy balancing. This lets us weight
the counties that were not covered so that they resemble the covered
ones, based on 2012 (that is, pre-_Shelby County_) characteristics.
For a much more detailed discussion of our methodology, a balance
table, and various robustness checks, see the appendix.

Figure 10 plots the trends in the white–Black turnout gap over time
for counties covered under Section 5 and the (weighted) ones that were
not. The white–Black gap before _Shelby County_ was more than 3
points higher in covered counties than in counties that were not
covered. By way of reminder, the Supreme Court wrote in _Shelby
County_ that the turnout gaps in formerly covered jurisdictions
appeared to be in line with the rest of the country. While there was
some truth to that point, it ignored the important socioeconomic
differences between this region and the rest of the country. Figure 10
indicates that — after accounting for these differences —
conditions in Section 5 jurisdictions were considerably worse than in
the rest of the country even before _Shelby County_.

While the figure visually indicates that the turnout gaps might have
grown more in places formerly covered by Section 5 than in
others, _Shelby County _is clearly not the sole driver of the
increasing turnout disparities. That’s not necessarily surprising:
as discussed above, new restrictive voting laws have gone into effect
all around the country over the past decade, not only in formerly
covered states, and this could be responsible for some of the upward
trends in the gap.

However, the Supreme Court decision could be exacerbating underlying
trends. To test this possibility, we use a
“difference-in-differences” design. footnote5_rxrqham
[[link removed]]41
[[link removed]] We
begin from the assumption that the turnout gaps in covered and
non-covered counties would have evolved in parallel if the Court
hadn’t invalidated Section 4b, net of controlling for other relevant
characteristics. The plausibility of this assumption is bolstered by
the fact that, as figure 10 shows, the gaps went up and down in
virtual lockstep _prior_ to 2013. This doesn’t mean that the gaps
in the two sets of counties would have been the same; as figure 10
makes clear, the formerly covered counties had higher gaps even prior
to _Shelby County _(once we weighted the other counties
appropriately). If the post–_Shelby County _differences between
covered and non-covered counties increased to a great enough extent,
we could conclude that _Shelby County_ had a causal impact on the
turnout gap.

Our statistical models (which include county and year fixed effects)
indicate that _Shelby County_ caused a statistically significant
increase in both the white–Black and the white–nonwhite turnout
gaps. In the non-covered counties, the white–nonwhite and
white–Black turnout gaps grew by 5 and 6 percentage points between
2012 and 2022, respectively; in the covered counties, however, the
comparable figures were 9 and 11 points, respectively. In other words,
by 2022, the white–nonwhite turnout gap grew about 4 points larger
and the white–Black gap 5 points larger in the formerly covered
counties than they would have if _Shelby County_ hadn’t been
handed down. They grew at a substantially quicker pace than similar,
non-covered counties. Over the post-treatment period as a whole, the
average treatment effect on the treated counties was about 2 points,
which is statistically significant at the 99 percent confidence level.

In addition to these _overall_ effects, we also conclude that the
effects of _Shelby County_ were largest in exactly the sorts of
counties we would expect. We start from the observation that _Shelby
County_ could have had different effects in different sorts of
counties. Many counties were fully covered under Section 5 of the
Voting Rights Act; any changes to their local election practices
needed to be precleared by the federal government. There were,
however, other counties that were not covered by Section 5, but where
the decision might still have had an impact: non-covered counties in
states that were partially covered by Section 5. That’s because the
Supreme Court ruled in _Monterey County v. Lopez_ that
all _statewide _voting policies were subject to review if even a
single county in the state was covered by Section
5. footnote6_i1iiq79
[[link removed]]42
[[link removed]] In
Florida, for instance, only five counties were formally covered by
preclearance. Nevertheless, Section 5 blocked the state’s 2002 House
district maps. These uncovered counties in partially covered states
could therefore make local decisions without getting preclearance from
the federal government, but state policies impacting the
administration of elections in these counties were subject to such
approval. Because _Shelby County_ didn’t impact these uncovered
counties as much, we would expect the decision to have a muted effect
in these places.

Table 1 indicates that the effect of _Shelby County_ was indeed
muted in counties that were not covered by Section 5 but were in
partially covered states. In fact, the coefficients on State Covered
× Post_ Shelby County _are not statistically significant in the
white–nonwhite gap model. We do, however, find that _Shelby
County_ meaningfully increased the turnout gaps in counties where
both state _and_ local practices were subject to preclearance.

Our second extension deals with Section 5 objection letters from the
years prior to _Shelby County_. Before Section 4b was invalidated,
localities would receive an “objection letter” from the federal
government if a proposed change was not cleared under the preclearance
condition. Put differently, these objection letters identified
policies with racially disparate impacts and stopped them from going
into effect. We would expect that _Shelby County _would have a
larger effect in counties that tried to enact a racially regressive
policy in the years when they were still covered under Section 5 of
the Voting Rights Act. To avoid the possibility that objection letters
are simply identifying the counties that were directly covered by
Section 5, we do not include the uncovered counties in partially
covered states in this analysis (these counties did not need to
preclear changes and thus would not have received objection letters).

Table 2 indicates that this was the case. _Shelby County _did
increase the white–nonwhite turnout gap even in counties without an
objection letter. But the gaps went up considerably more in the
counties that did have an objection letter: by an additional 1.8
points (for the white–Black gap) and 1.6 points (for the
white–nonwhite gap).

That the causal effect of _Shelby County_ on the white–nonwhite
turnout gap is significant only in the fully covered counties, and not
in the uncovered counties in partially covered states, underscores the
importance of local election administration for participation rates.
So too does our finding that the gap increase was concentrated in
counties that tried to implement discriminatory changes under Section
5. County-level coverage, not constraints on statewide policy, appear
to have been the drivers of post-_Shelby County_ turnout gap
increases.

In the appendix, we show that the finding that _Shelby
County_ increased the turnout gaps is robust to many robustness
checks.

Conclusion

If the United States wants to make good on its foundational claims of
a democratic system of governance open to all citizens, it must find
ways to close the racial turnout gap. Wider now than at any point in
at least the past 16 years, the gap costs millions of votes from
Americans of color all around the country. Perhaps most worrisome of
all, the gap is growing most quickly in parts of the country that were
previously covered under the preclearance regime of the 1965 Voting
Rights Act until the disastrous _Shelby County_ ruling.

This report gives us a better look at the contours of the racial
turnout gap than ever before and throws the severity of the problem
into stark relief. We urge scholars to continue to study the myriad
drivers of the turnout gap, from statewide policies to local election
practices, from language barriers to disaffection from the criminal
justice system; without a full understanding of the causes, we cannot
develop solutions that will permanently ensure political
representation for Americans of all races.

Importantly, as we’ve shown, socioeconomics can’t fully explain
the gap; the gap remains in high- and low-income neighborhoods alike.
We do, however, prove one of the causes of the increasing racial
turnout gap: the Supreme Court’s ruling in _Shelby County_. There
is no doubt that the end of federal preclearance in regions with
histories of racial discrimination increased the racial turnout gap.
We argue that this is due to changes both in state policy and in local
election practices. A fully functional Section 5 of the Voting Rights
Act would improve conditions in areas where racial discrimination
remains in voting policy. We urge Congress to pass the John R. Lewis
Voting Rights Advancement Act to update and restore the preclearance
regime for the 21st century.

Endnotes

* footnote1_0cgd98z
[[link removed]]
1
[[link removed]] 

 
[[link removed]]

Shelby County v. Holder, 570 U.S. 529 (2013).

* footnote2_aaxdcoe
[[link removed]]
2
[[link removed]]
 
[[link removed]]

Lawrence Bobo and Franklin D. Gilliam, “Race, Sociopolitical
Participation, and Black Empowerment,” _American Political Science
Review_ 84, no. 2 (1990): 377–93, [link removed];
and Ebonya Washington, “How Black Candidates Affect Voter
Turnout,” _Quarterly Journal of Economics_ 121, no. 3 (2006):
973–98, [link removed].

* footnote3_g6dy9ec
[[link removed]]
3
[[link removed]]
 
[[link removed]]

Anna Baringer, Michael C. Herron, and Daniel A. Smith, “Voting by
Mail and Ballot Rejection: Lessons from Florida for Elections in the
Age of the Coronavirus,” _Election Law Journal: Rules, Politics,
and Policy_ 19, no. 3 (2020):
289–320, [link removed]; Bernard L. Fraga
and Michael G. Miller, “Who Do Voter ID Laws
Keep from Voting?,” _Journal of Politics_ 84, no. 2 (2022):
1091–1105, [link removed]; John Kuk, Zoltan Hajnal,
and Nazita Lajevardi, “A Disproportionate Burden: Strict Voter
Identification Laws and Minority Turnout,” _Politics, Groups, and
Identities_ 10, no. 1 (2022):
126–34, [link removed]; and Enrijeta
Shino, Mara Suttmann-Lea, and Daniel A. Smith, “Determinants of
Rejected Mail Ballots in Georgia’s 2018 General
Election,” _Political Research Quarterly_ 75, no. 1 (2022):
231–43, [link removed].

* footnote4_irlcmlr
[[link removed]]
4
[[link removed]]
 
[[link removed]]

Kevin Morris and Peter Miller, “Authority After the Tempest:
Hurricane Michael and the 2018 Elections,” _Journal of
Politics_ 85, no. 2 (2023):
405–20, [link removed]; and Fraga and Miller,
“Who Do Voter ID Laws Keep from Voting?” 

* footnote5_joqwjqo
[[link removed]]
5
[[link removed]]
 
[[link removed]]

Jonathan Brater et al., _Purges: A Growing Threat to the Right to
Vote_, Brennan Center for Justice,
2018, [link removed].

* footnote6_jihqe5n
[[link removed]]
6
[[link removed]]
 
[[link removed]]

David Wasserman et al., “2020 Popular Vote Tracker,” _Cook
Political Report_,
2020, [link removed].

* footnote7_jxi46sc
[[link removed]]
7
[[link removed]]
 
[[link removed]]

Further, by putting the burden on advocates to monitor changes in
policy and bring Section 2 cases in all 50 states, the decision made
it more likely that a change in a non-covered jurisdiction would go
unnoticed or unchallenged. Section 2 prohibits any electoral practice
that minimizes the voting strength of a racial or ethnic group.

* footnote8_y35l0f7
[[link removed]]
8
[[link removed]]
 
[[link removed]]

J. Morgan Kousser and others have documented the central role that the
federal government must play in promoting and safeguarding multiracial
democracy in the United States. See J. Morgan Kousser,
_Colorblind Injustice: Minority Voting Rights and the Undoing of the
Second Reconstruction _(Chapel Hill, NC: University of North Carolina
Press,
2000), [link removed]
[[link removed]]; and
Jacob Grumbach, _Laboratories Against Democracy: How National Parties
Transformed State Politics_ (Princeton, NJ: Princeton University
Press, 2022), [link removed].

* footnote1_l0huqzw
[[link removed]]
9
[[link removed]]
 
[[link removed]]

Stephen Ansolabehere, Bernard L. Fraga, and Brian F. Schaffner, “The
Current Population Survey Voting and Registration Supplement
Overstates Minority Turnout,” _Journal of Politics_ 84, no. 3
(2022): 1850–55, [link removed].

* footnote2_nofhbbs
[[link removed]]
10
[[link removed]]
 
[[link removed]]

Ted Enamorado and Kosuke Imai, “Validating Self-Reported Turnout by
Linking Public Opinion Surveys with Administrative
Records,” _Public Opinion Quarterly_ 83, no. 4 (2019):
723–48, [link removed].

* footnote3_xstlpfb
[[link removed]]
11
[[link removed]]
 
[[link removed]]

These voter files do not indicate for_ whom _someone voted; ballots
are secret in the United States. Instead, they indicate whether
someone voted and, in some states and years, how the ballot was cast
(in person or via the mail).

* footnote4_gzrgytc
[[link removed]]
12
[[link removed]]
 
[[link removed]]

The snapshots we leverage collectively have 1.5 billion records; this
report, however, looks only at the individuals who voted in a
particular federal general election.

* footnote5_6uqof8m
[[link removed]]
13
[[link removed]]
 
[[link removed]]

In the technical appendix accompanying this report, we report the date
of each snapshot. Though the voter files are the best available data,
they are not perfect. Voter files are constantly in flux. For
instance, it can take states a handful of months to record
participation in the registered voter file. Moreover, states are
constantly “cleaning” their voter files and removing ineligible
voters. By the time a complete set of participants is included in the
file, other voters may have died, moved away, or been removed from the
file for another reason. Thus no 100 percent accurate voter file
exists that captures all participants and includes all individuals
registered as of a given election. See Seo-young Silvia Kim and
Bernard Fraga, “When Do Voter Files Accurately Measure Turnout? How
Transitory Voter File Snapshots Impact Research and Representation,”
American Political Science Association, APSA Preprints, Version 1,
September 14, 2022, [link removed].

* footnote6_nauo3hk
[[link removed]]
14
[[link removed]]
 
[[link removed]]

In many states, voters’ state identification numbers are reported by
both Catalist and L2. Using the state ID number, along with voters’
house number and ZIP code, we identify 94 million voters who did not
move between the 2012 and 2014 elections. The correlation coefficients
(an estimate of the “fit” of these data sets) on the predicted
probability of being white, nonwhite, Black, or Latino are all 0.97
(it is 0.93 for probability of being Asian). Given that voters’
racial estimates are updated each year as the racial composition of
the citizen voting-age population in an assigned block group changes,
we would expect a correlation coefficient approaching, but not
exactly, 1. As such, we conclude that the files are highly comparable
and that combining these files improves the power of our analyses and
does not bias our results. In addition, the parallel trends assumption
(that is, that the turnout gaps in covered and non-covered counties
would have evolved in parallel if the Court hadn’t invalidated
Section 4b) means that changing data vendors does not bias our causal
estimates of the effect of_ Shelby County_ on the turnout gap, so
long as differences between vendors are unrelated to coverage status.
Among this set of voters, the average change in the predicted
probability of being white decreased by 0.5 percentage points for
voters in covered and uncovered states alike between 2012 and 2014,
indicating that our results are not being driven by the crossover from
Catalist to L2 in 2014.

* footnote7_0kuo7j4
[[link removed]]
15
[[link removed]]
 
[[link removed]]

According to the National Voter Registration Act, voters can be
removed from the rolls only under specific circumstances if the state
doesn’t have personalized information indicating a change in
eligibility. Generally, voters must fail to respond to a postcard and
fail to participate in two federal election cycles before they can be
removed. Thus, many individuals removed after a given election will be
those who did not vote. For a detailed discussion of how list
maintenance impacts voter file data, see Kim and Fraga, “When Do
Voter Files Accurately Measure Turnout?”

* footnote8_b863wpw
[[link removed]]
16
[[link removed]]
 
[[link removed]]

The exceptions are Alabama, Florida, Georgia, Louisiana, North
Carolina, and South Carolina.

* footnote9_y6khg4u
[[link removed]]
17
[[link removed]]
 
[[link removed]]

Kosuke Imai and Kabir Khanna, “Improving Ecological Inference by
Predicting Individual Ethnicity from Voter Registration
Records,” _Political Analysis_ 24, no. 2 (2016):
263–72, [link removed].

* footnote10_o4luzfr
[[link removed]]
18
[[link removed]]
 
[[link removed]]

Following BISG’s categorization, we consider Latino or Hispanic
voters to be nonwhite in all cases. Throughout our analyses, we
aggregate up the posterior probabilities rather than assigning voters
a discrete race. Thus, if we had 10 voters who were each predicted to
be Black with 40 percent certainty and white with 60 percent
certainty, we would assume (in aggregate) that we had four Black and
six white voters. Discrete assignment would assume that we had 10
white voters, the most likely racial category for each of them. It is
worth noting that the surname data provided by the Census Bureau and
incorporated into the BISG algorithm does not report whether an
individual is “some other race.” Instead, the developers of the
BISG algorithm combine the “Non-Hispanic American Indian and Alaska
Native Alone” and “Non-Hispanic Two or More Races” to create the
“some other race” category. Because the “other” category
returned by BISG does not correspond exactly to “other” as defined
in, e.g., the Census Bureau’s CVAP data, at no point do we present
turnout estimates of the “other” category. Wherever we present the
overall nonwhite turnout rates (or the white–nonwhite gap),
“nonwhite” is calculated by subtracting the estimated number of
white ballots (or CVAP) from the total number of ballots (CVAP), thus
sidestepping this issue.

* footnote11_zltc1wu
[[link removed]]
19
[[link removed]]
 
[[link removed]]

Christian R. Grose, Expert Report of Christian R. Grose, Ph.D., La
Union Del Pueblo Entero et al. v. Gregory w. Abbott et al., No.
5:21-CV-0844-XR (W.D. Tex 2022); Loren Collingwood, Expert Report of
Loren Collingwood, Ph.D., LULAC Texas et al. v. John Scott et al., No.
1:21-cv-786-XR (W.D. Tex 2022); Jacob M. Grumbach and Alexander Sahn,
“Race and Representation in Campaign Finance,” _American
Political Science Review_ 114, no. 1 (2020):
206–21, [link removed]; and Kevin DeLuca
and John A. Curiel, “Validating the Applicability of Bayesian
Inference with Surname and Geocoding to Congressional
Redistricting,” _Political Analysis_ 31, no. 3 (2023):
465–71, [link removed].

* footnote12_paobnhg
[[link removed]]
20
[[link removed]]
 
[[link removed]]

Imai and Khanna, “Improving Ecological Inference.”

* footnote13_tifa4ax
[[link removed]]
21
[[link removed]]
 
[[link removed]]

The Census Bureau did not begin reporting CVAP numbers until 2009, and
the 2022 numbers will not be available until early 2024. Therefore,
the denominators for 2008 turnout are the five-year 2009 CVAP
estimates, while those for 2022 turnout are the 2021 estimates.

* footnote14_6z15rw5
[[link removed]]
22
[[link removed]]
 
[[link removed]]

U.S. Census Bureau, “American Community Survey 5-Year Data
(2009–2022),” accessed July 24,
2023, [link removed].

* footnote15_p6205u4
[[link removed]]
23
[[link removed]]
 
[[link removed]]

This approach has been used in recent political science scholarship.
Kevin T. Morris and Kelsey Shoub, “Contested Killings:
The Mobilizing Effects of Community Contact with Police Violence,”
_American Political Science Review_ (2023):
1–17, [link removed]; Eitan D. Hersh and
Clayton Nall, “The Primacy of Race in the Geography of Income-Based
Voting: New Evidence from Public Voting Records,” _American Journal
of Political Science_ 60, no. 2 (2016):
289–303, [link removed]; Wendy K. Tam Cho,
James G. Gimpel, and Iris S. Hui, “Voter Migration and the
Geographic Sorting of the American Electorate,” _Annals of the
Association of American Geographers_ 103, no. 4 (2013):
856–70, [link removed]; and Jacob R.
Brown and Ryan D. Enos, “The Measurement of Partisan Sorting for 180
Million Voters,”_ Nature Human Behaviour _5, no. 8 (2021):
998–1008, [link removed]–021–01066-z
[[link removed]].

* footnote16_0uzpsqj
[[link removed]]
24
[[link removed]]
 
[[link removed]]

Bernard L. Fraga, _The Turnout Gap: Race, Ethnicity, and Political
Inequality in a Diversifying America_ (Cambridge: Cambridge
University Press, 2018), 12, [link removed].

* footnote17_gajknnq
[[link removed]]
25
[[link removed]]
 
[[link removed]]

National Congress of American Indians, “Every Native Vote Counts:
Fast Facts,”
2020, [link removed];
and James Thomas Tucker, Jacqueline De León, and Dan
McCool, _Obstacles at Every Turn: Barriers to Political Participation
Faced by Native American Voters_, Native American Rights Fund,
2020, [link removed].

* footnote1_xughlni
[[link removed]]
26
[[link removed]]
 
[[link removed]]

Bobo and Gilliam, “Race, Sociopolitical Participation, and Black
Empowerment”; and Washington, “How Black Candidates Affect Voter
Turnout.”

* footnote2_j6mc187
[[link removed]]
27
[[link removed]]
 
[[link removed]]

We divide states into regions as follows. Northeast: Connecticut,
Maine, Massachusetts, New Hampshire, New Jersey, New York,
Pennsylvania, Rhode Island, and Vermont. South: Alabama, Arkansas,
Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana,
Maryland, Mississippi, North Carolina, Oklahoma, South Carolina,
Tennessee, Texas, Virginia, and West Virginia. Midwest: Illinois,
Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North
Dakota, Ohio, South Dakota, and Wisconsin. West: Alaska, Arizona,
California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico,
Oregon, Utah, Washington, and Wyoming.

* footnote3_mysb0wn
[[link removed]]
28
[[link removed]]
 
[[link removed]]

See also Fraga, _The Turnout Gap_, 110.

* footnote4_c0ydkkf
[[link removed]]
29
[[link removed]]
 
[[link removed]]

Fraga, _The Turnout Gap_; Yeaji Kim, “Absolutely Relative: How
Education Shapes Voter Turnout in the United States,” _Social
Indicators Research_ 168 (2023):
447–69, [link removed]–023–03146–1
[[link removed]]; Alexander K. Mayer,
“Does Education Increase Political Participation?,”_ Journal of
Politics_ 73, no. 3 (2011):
633–45, [link removed]; Robert Paul
Hartley, “Unleashing the Power of Poor and Low-Income Americans:
Changing the Political Landscape,” Poor People’s Campaign: A
National Call for Moral Revival, August
2020, [link removed]
[[link removed]];
Henry E. Brady, Sidney Verba, and Kay Lehman Schlozman, “Beyond SES:
A Resource Model of Political Participation,” _American
Political __Science Review_ 89, no. 2 (1995):
271–94, [link removed]; and Zachary Markovich and
Ariel White, “More Money, More Turnout? Minimum Wage Increases and
Voting,”_ Journal of Politics_ 84, no. 3 (2022):
1834–38, [link removed].

* footnote5_bb28d53
[[link removed]]
30
[[link removed]]
 
[[link removed]]

Scot Schraufnagel, Michael J. Pomante, and Quan Li, “Cost of Voting
in the American States: 2022,” _Election Law Journal: Rules,
Politics, and Policy_ 21, no. 3 (2022):
220–28, [link removed].

* footnote6_a1ljnj7
[[link removed]]
31
[[link removed]]
 
[[link removed]]

Suzanne Mettler and Mallory SoRelle, “Policy Feedback Theory,”
chapter 3 in _Theories of the Policy Process_, Christopher M. Weible
and Paul A. Sabatier, eds. (New York: Routledge,
2018), [link removed]–4
[[link removed]]; Suzanne Mettler and Joe
Soss, “The Consequences of Public Policy for Democratic Citizenship:
Bridging Policy Studies and Mass Politics,” _Perspectives on
Politics_ 2, no. 1 (2004):
55–73, [link removed]; and Joe Soss and
Lawrence R. Jacobs, “The Place of Inequality: Non-participation in
the American Polity,” _Political Science Quarterly_ 124, no. 1
(2009):
95–125, [link removed]–165x.2009.tb00643.x
[[link removed]].

* footnote7_duyndc0
[[link removed]]
32
[[link removed]]
 
[[link removed]]

Soss and Jacobs, “The Place of Inequality,” 110.

* footnote8_9ueww8k
[[link removed]]
33
[[link removed]]
 
[[link removed]]

Monica C. Bell, “Police Reform and the Dismantling of Legal
Estrangement,” _Yale Law Journal_ (2017):
2054–2150, [link removed]; Brie McLemore,
“Procedural Justice, Legal Estrangement, and the Black People’s
Grand Jury,” _Virginia Law Review_ 105, no. 2 (2019):
371–95, [link removed]; Robert J. Sampson
and Dawn Jeglum Bartusch, “Legal Cynicism and (Subcultural?)
Tolerance of Deviance: The Neighborhood Context of Racial
Differences,” _Law and Society Review _32, no. 4 (1998):
777–804, [link removed]; and Amy E. Lerman and
Vesla M. Weaver, _Arresting Citizenship: The Democratic Consequences
of American Crime Control_ (Chicago: University of Chicago Press,
2014).

* footnote9_cbkj529
[[link removed]]
34
[[link removed]]
 
[[link removed]]

Richard Rothstein, _The Color of Law: A Forgotten History of How Our
Government Segregated America_ (New York: Liveright Publishing,
2017), [link removed]
[[link removed]]; Jacob W. Faber, “We
Built This: Consequences of New Deal Era Intervention in America’s
Racial Geography,” _American Sociological Review_ 85, no. 5
(2020): 739–75, [link removed]; Daniel
Aaronson et al., “The Long-Run Effects of the 1930s HOLC
‘Redlining’ Maps on Place-Based Measures of Economic Opportunity
and Socioeconomic Success,” _Regional Science and Urban
Economics_ 86 (2021):
103622, [link removed]; and
Solomon Greene, Margery Austin Turner, and Ruth Gourevitch, “Racial
Residential Segregation and Neighborhood Disparities,” US
Partnership on Mobility from Poverty, August 29,
2017, [link removed].

* footnote10_xrgtiki
[[link removed]]
35
[[link removed]]
 
[[link removed]]

U.S. Census Bureau, “Census Tracts,” accessed January 5,
2023, [link removed] (explaining
that “census tracts are small, relatively permanent statistical
subdivisions of a county. [They] average about 4,000 inhabitants.”).

* footnote11_ln3x7qd
[[link removed]]
36
[[link removed]]
 
[[link removed]]

While there is recent scholarship arguing that BISG misclassifies
nonwhite individuals as white in wealthy areas, we show in the
appendix that the same relationships between socioeconomic
characteristics and turnout gaps remain when looking only at states
with self-reported race. See Lisa P. Argyle and Michael Barber,
“Misclassification and Bias in Predictions of Individual Ethnicity
from Administrative Records,” _American Political Science
Review_ (May 15, 2023):
1–9, [link removed].

* footnote1_ib7d7ss
[[link removed]]
37
[[link removed]]
 
[[link removed]]

Jasleen Singh and Sara Carter, “States Have Added Nearly 100
Restrictive Laws Since SCOTUS Gutted the Voting Rights Act 10 Years
Ago,” Brennan Center for Justice, June 23,
2023, [link removed].

* footnote2_3l96a9g
[[link removed]]
38
[[link removed]]
 
[[link removed]]

Ariel R. White, Noah L. Nathan, and Julie K. Faller, “What Do I Need
to Vote? Bureaucratic Discretion and Discrimination by Local Election
Officials,” _American Political Science Review_ 109, no. 1 (2015):
129–42, [link removed]; and Markie
McBrayer, R. Lucas Williams, and Andrea Eckelman, “Local Officials
as Partisan Operatives: The Effect of County Officials on Early Voting
Administration,” _Social Science Quarterly_ 101, no. 4 (2020):
1475–88, [link removed].

* footnote3_j8t980s
[[link removed]]
39
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The weighted turnout gap is driven in part by the nonwhite share of
the population in a given jurisdiction. Given that _Shelby
County_ could not realistically have impacted this characteristic, we
do not test the impact of the Court’s decision on the weighted
turnout gap. We focus in this section on the white–nonwhite and
white–Black gaps for two reasons. First, most of these regions were
covered under Section 5 specifically because of discrimination against
Black Americans. Second, Black Americans make up half of the nonwhite
population in these counties, compared with just 25 percent in the
rest of the country (see table A5 in the appendix). The relatively
small size of the other groups makes studying their specific gaps more
statistically challenging.

* footnote4_oaptksi
[[link removed]]
40
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[[link removed]]

Throughout this section, we include in the covered group counties that
were not covered but whose state’s policies were subject to
preclearance (because another county in the state was covered), unless
otherwise noted.

* footnote5_rxrqham
[[link removed]]
41
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[[link removed]]

Brantly Callaway and Pedro H. C. Sant’Anna,
“Difference-in-Differences with Multiple Time Periods,” _Journal
of Econometrics_ 225, no. 2 (2021):
200–230, [link removed].

* footnote6_i1iiq79
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42
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Monterey County v. Lopez, 525 U.S. 266 (1999).

_KEVIN MORRIS is a Senior Research Fellow, Voting Policy Scholar and
Manager with the Democracy Program, specializing in voting rights and
elections. His research focuses on how restrictive voting laws limit
access to the polls, how election administration influences turnout,
and the impacts of the criminal legal system on American democracy.
His academic work has been published in the American Political
Science Review, the Journal of Politics, and other leading journals._

_Prior to joining the Brennan Center, Dr. Morris worked as an economic
researcher associate at the Federal Reserve Bank of New York, and an
economist at the Port Authority of New York and New Jersey. He has a
PhD in Sociology from the CUNY Graduate Center, a BA in economics from
Boston College, and a master’s degree in urban planning from NYU’s
Wagner School._

_CORYN GRANGE is a research associate with the Voting Rights Program.
Previously, Coryn was a fellow and consultant for the Brennan Center,
focusing on the racial turnout gap as well as the Freedom to Vote Act.
Prior to joining the Brennan Center, she worked in development for an
International education nonprofit, geomatics, and research in charter
school statutory law. She has a BA in political science form CUNY
Hunter College and a master of public administration degree with a
specialization in public policy analysis from NYU’s Wagner Graduate
School of Public Service. _

_THE BRENNAN CENTER FOR JUSTICE is a nonpartisan law and policy
institute.  We strive to uphold the values of democracy. We stand
for equal justice and the rule of law. We work to craft and advance
reforms that will make American democracy work, for all._

_Donate to Brennan Center.
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