##  1 Introduction

In every state, otherwise voting-eligible individuals incarcerated pretrial or
to serve a misdemeanor sentence remain legally entitled to vote while
incarcerated (The Sentencing Project, 2020, 2022). The vast majority of the
approximately 650,000 individuals incarcerated in county jails on any given
day are being held pretrial or to serve misdemeanor sentences (Sawyer and
Wagner, 2022). Anecdotal evidence suggests, however, that many of those
incarcerated in county jails are not being given adequate opportunities to
exercise their right to vote (The Sentencing Project, 2020). Concerns have
also been raised about possible racial disparities in ballot access for those
incarcerated in county jails (The Sentencing Project, 2022). Yet we lack
reliable causal estimates of the impacts of being incarcerated in a county
jail on the exercise of the right to vote, and of any racial heterogeneity in
those impacts.

Some recent papers have sought to estimate the impacts of prison and jail
incarceration on individuals’ post-release voting behavior (Gerber et al.,
2017; White, 2019b; McDonough, Enamorado and Mendelberg, 2022). White and
Nguyen (2022) describe the extent of voting from prison in Maine and
Vermont—the two states permitting individuals serving felony sentences to vote
from prison—but do not estimate the causal impacts of prison incarceration on
voting from prison.

We leverage new data on daily individual-level jail records from the Jail Data
Initiative and exploit the timing of incarceration to estimate the causal
effects of being incarcerated in a county jail during 2020 general election
voting days on the probability of voting. We probabilistically match 944,985
individual-level booking records from 936 jail rosters with 195,655,326 voter
records from 42 corresponding statewide files for a period of 180 days
centered on Election Day (November 3, 2020). We identify individuals whose
periods of jail incarceration began during 2020 voting days in their state
(either mail-in or in-person), or within windows ranging between 7 and 42 days
after Election Day. Our primary analyses are conducted within the sample of
jailed individuals who match to voter records with match probability $p>0.75$;
we also replicate analyses for the sample with match probability $p>0.95$. We
interpret matches as indicating registered voters who were incarcerated in
county jails during our time period of interest. We source information about
jailed registered voters from both jail and voter records. For post-Election
Day control group windows ranging between 7 and 42 days, we conduct a series
of balance tests on individual-level and booking-level characteristics,
including age, race, gender, partisanship, and number and kind of booking
charges, to identify the pre-Election Day treatment group windows within which
individuals booked into county jails during 2020 voting days (either mail-in
or in-person) were observably indistinguishable (joint F-test p-value $>$
0.10) from individuals booked into the same jails during post-Election Day
control group windows. For records with match probability $p>0.75$, the
balanced samples range in size from 57,821 (28-day control group window) to
103,091 (42-day control group window).

Our identification strategy rests on the assumption that individuals booked
into the same jail within a narrow temporal window, who are indistinguishable
on observed characteristics, are also indistinguishable on unobserved
characteristics. If this assumption is correct, we can use the 2020 turnout of
individuals booked into county jails just after the last 2020 voting day as a
valid counterfactual for the turnout we likely would have observed among
individuals booked into the same jails during 2020 voting days, had they not
been incarcerated during voting days.

Turnout among registered voters in 2020 is estimated by the U.S. Census Bureau
to have been
91.9%.111https://www.census.gov/content/dam/Census/library/publications/2022/demo/p20-585.pdf
Registered voters booked into county jails just after the last 2020 voting day
had much lower average turnout in 2020, relative to the general population,
underscoring the importance of identifying an appropriate counterfactual
turnout rate for those jailed during 2020 voting days. For the sample with
match probability $p>0.75$, turnout among registered voters who were booked
into county jails within 7 – 42 days after 2020 Election Day ranged between
34.9% and 35.7% in our samples, or between 56% and 62% less than the
registered voter turnout observed in the U.S. population. With a less
appropriate control group, we might mistakenly attribute some of the decreased
turnout among those jailed prior to the election to the effects of jail
incarceration, rather than to a lower propensity to vote.

Within our balanced samples, we estimate the impacts of (1) being booked into
a county jail during 2020 voting days (either mail-in or in-person) for any
length of time and (2) being booked into a county jail during 2020 voting days
for some proportion of voting days on the probability that a registered voter
voted in the 2020 election. In models that include all individual- and
booking-level covariates along with jail and week fixed effects, we find that
registered voters who were booked into county jails during 2020 voting days
for any length of time were on average 3.0 – 3.3 percentage points or 8.6% –
9.3% less likely to have voted in 2020, relative to registered voters who were
booked into the same jails within 7 – 42 days after Election Day (avg. effect
magnitude = 9.0%; p $<$ 0.01).

The impacts of incarceration on voting from jail in 2020 increased with the
duration of incarceration. Including all individual- and booking-level
covariates along with jail and week fixed effects, registered voters who were
booked into county jails during 2020 voting days for the full span of voting
days in their state were on average 13.7 – 22.9 percentage points or 38.4% –
65.2% less likely to have voted in 2020, relative to registered voters who
were booked into the same jails within 7 – 42 days after Election Day (avg.
effect magnitude = 46.1%; p $<$ 0.01). These estimates imply that registered
voters who were booked into county jails during 2020 voting days for the full
span of voting in their state voted from jail at rates ranging between 12.2%
and 22.0%. In a series of placebo tests, we find no consistent effects of the
timing of individuals’ 2020 bookings on their probabilities of having voted in
the 2012 or 2016 elections. All estimates are of comparable magnitude in the
sample of linked records with match probability $p>0.95$.

We also explore racial heterogeneity in the effects of incarceration on voting
from jail in 2020, finding that the effect of jail incarceration was
significantly larger for Black registered voters, relative to white registered
voters. White registered voters booked into county jails just after Election
Day 2020 had turnout rates ranging between 37.7% and 38.3%, considerably
higher than the turnout rates for Black registered voters booked into county
jails just after Election Day 2020, which ranged between 27.8% and 28.2%. Jail
incarceration also had less of a negative effect on turnout for white
registered voters. Including all individual- and booking-level covariates
along with jail and week fixed effects, white registered voters who were
booked into county jails during 2020 voting days for the full set of voting
days were on average 12.5 – 22.8 percentage points or 32.6% – 60.2% less
likely to have voted in 2020, relative to white registered voters who were
booked into the same jails within 7 – 42 days after Election Day (avg. effect
magnitude = 39.8%; p $<$ 0.01). These estimates imply that white registered
voters booked into county jails during 2020 voting days for the full set of
voting days voted from jail at rates ranging between 15.1% – 25.8%. The
estimated negative effect of incarceration on voting from jail was an
additional 5.0 – 11.8 percentage points or 38.2% – 55.0% larger for Black
registered voters (avg. effect magnitude = 45.9%; p $<$ 0.05). The total
estimated effect of jail incarceration for Black registered voters booked into
county jails during 2020 voting days for the full span of voting days was a
17.9 – 34.6 percentage point or 61.3% – 121.8% decrease in turnout, relative
to Black registered voters who were booked into the same jails within 7 – 42
days after Election Day (avg. effect magnitude = 77.9%). These estimates imply
that Black registered voters booked into county jails during 2020 voting days
for the full set of voting days voted from jail at rates ranging between 0.0%
and 11.3%. Estimates of racially heterogeneous effects are of comparable
magnitude when we restrict the sample to states that report voter race
(eliminating the records for which race is predicted by L2).

We also explore the effects of jail incarceration on voter registration
behavior, and on voting behavior unconditional on registration status. Jail
incarceration in 2020 had inconsistent effects on voter registration. The
effects of jail incarceration on turnout in 2020 unconditional on voter
registration are of similar magnitude to the effects estimated from the sample
of registered voters only.

The nature of our data prevent us from exploring the precise mechanisms
generating both the overall decreases in turnout for individuals booked into
county jails during 2020 voting days, and the substantially larger decreases
for Black individuals. Nevertheless, at a minimum our findings reveal a
pressing need for states and counties to take steps to ensure that voting-
eligible incarcerated individuals are given adequate opportunities to exercise
their constitutional right to vote, and to address troubling racial
disparities in the exercise of that right.

##  2 Incarceration and Voting

Whether and by how much being incarcerated in a county jail interferes with an
individual’s exercise of the right to vote, and whether there are any racial
disparities in the effects of incarceration on voting from jail, are empirical
questions. We lack systematic evidence on these questions.

Some researchers have sought to estimate the post-release impacts of periods
of prison and jail incarceration on individuals’ exercise of their right to
vote (Gerber et al., 2017; White, 2019b; McDonough, Enamorado and Mendelberg,
2022). Gerber et al. (2017) used state prison and sentencing records to
investigate the impacts of prior periods of prison incarceration on post-
release voting in the 2012 election, finding few differences in turnout
between those incarcerated and released from state prison between 2008 and
2012 and either observably comparable individuals in the Pennsylvania voter
files, or observably comparable convicted defendants who received sentences
other than prison incarceration. Gerber et al. (2017) also found few
differences in post-release turnout between those convicted defendants
sentenced to jail incarceration on their most serious charge and those
receiving non-carceral sentences.

White (2019b) addressed potential bias arising from selection into jail
incarceration by estimating the effects of a sentence to jail incarceration on
post-release 2012 turnout using a design based on the as-if random assignment
of first-time misdemeanor defendants to judges in Harris County, Texas. White
(2019b) found a 13 percentage point average decrease in post-release 2012
turnout among marginal Black misdemeanor defendants sentenced between 2008 and
2012, and negligible effects for marginal white defendants.

McDonough, Enamorado and Mendelberg (2022) used a similar design to estimate
impacts from pretrial jail incarceration between 2008 and 2012 in Philadelphia
County on post-release 2012 turnout, leveraging the assignment of defendants
to bail judges to attempt to identify causal effects. McDonough, Enamorado and
Mendelberg (2022) found a 39% average reduction in post-release voter turnout
among marginal defendants incarcerated pretrial before the 2012 election, with
effects appearing in the year prior to the election and with larger effects
for Black defendants.222The assignment of bail judges to cases in Philadelphia
County during this period is not independent of case and defendant
characteristics (p $<$ 0.001), raising the possibility that non-randomly
assigned case and defendant characteristics may account for the larger
estimated reduction in post-release turnout, relative to White (2019b).

Incarceration may affect turnout not only after a defendant is released, but
also, for those defendants incarcerated during voting days, during periods of
incarceration (The Sentencing Project, 2020, 2022). White and Nguyen (2022)
merged state prison records with voter file records in Maine and Vermont—the
two states in which individuals incarcerated for felony convictions are
legally entitled to vote while incarcerated—finding that 8% and 6% of those
incarcerated in state prison voted while incarcerated in 2018 in Vermont and
Maine, respectively. White and Nguyen (2022) did not attempt to estimate the
causal impacts of incarceration on the incidence of voting from prison.
McDonough, Enamorado and Mendelberg (2022) attempted to distinguish the
incapacitation effect of jail incarceration on Election Day 2012 from the
post-release effects of prior periods of jail incarceration, but their design
and data did not allow them to cleanly identify an incapacitation effect.

To date we lack any reliable causal estimates of whether and by how much being
incarcerated in a county jail interferes with an individual’s exercise of the
right to vote. Individuals incarcerated in county jails are, with few
exceptions, not rendered ineligible to vote because of their jail
incarceration.333The vast majority of those held in county jails are being
held pretrial or for the purpose of serving a sentence following a misdemeanor
conviction, neither of which is disenfranchising in any state. Like
individuals not incarcerated in county jails, those incarcerated in county
jails may be ineligible to register and vote for reasons unrelated to their
current incarceration, including citizenship status and prior felony
convictions. For more information on enfranchisement status within
incarcerated populations, see The Sentencing Project (2020). Anecdotal reports
suggest that county jails nonetheless often fail to provide incarcerated
individuals with the ballot access to which they are constitutionally entitled
(The Sentencing Project, 2020, 2022). However, we lack reliable estimates of
the magnitude of the impacts of jail incarceration on voting from jail. We
also lack any systematic estimates of racial disparities in these impacts. The
lack of rigorous evidence about these impacts impoverishes policy debates.

We probabilistically match daily incarceration records from county jails in
the United States to voting records from the 2020 general election, estimating
the impacts of jail incarceration on turnout for those whose periods of jail
incarceration began during 2020 voting days, relative to those whose periods
of jail incarceration began just after the last 2020 voting day.

##  3 Data

###  3.1 Jail Incarceration Data

We source daily individual-level jail incarceration data from the Jail Data
Initiative (JDI), a project of New York University’s Public Safety
Lab.444https://jaildatainitiative.org/. Daily JDI jail records are scraped
from county, sheriff, and corrections websites posting public rosters of all
incarcerated individuals on (at least) a daily basis. By Fall 2020, the JDI
project was scraping daily jail rosters from more than 1,100 county jails.
County jails are not included in the JDI data if they do not post online jail
rosters (e.g., all jails in the state of Alaska), or if their states have
unified correctional systems (e.g., Connecticut and Hawaii).

Daily jail roster records are converted into booking records by identifying
unique individuals and unique periods of incarceration within each facility.
Unique individuals are identified using a variety of fields including booking
number, person identification number, name, and date of birth, depending on
field availability. Unique periods of incarceration or booking records are
identified using the dates on which unique individuals enter and exit jail
rosters (unique individuals may have multiple booking records in the same
county). Demographic and bail/bond fields are standardized across facilities.
Charge descriptions contained in text fields are classified into charge
categories using the Text-Based Offense Classification algorithm developed by
the Criminal Justice Administrative Records System at the University of
Michigan (Choi et al., 2022).

The JDI bookings database contains daily jail incarceration data unprecedented
in granularity and national scope. While the Bureau of Justice Statistics
produces a 5-year Census of Jails—most recently in 2019—and an intervening
Annual Survey of Jails, these data are only based on single-day point-in-time
snapshots of jail populations (Zhen Zeng and Minton, 2021).

For our main analyses of the effects of jail incarceration on the turnout of
registered voters, we include as covariates JDI data on the length of each
booking in days, number of charges associated with each booking, and
indicators for whether the most serious CJARS charge category associated with
each booking was violent, property, drug, public order, or DUI, with criminal
traffic as the excluded charge category.555The CJARS Text-Based Offense
Classification algorithm uses the Uniform Crime Classification Standard (UCCS)
(Choi et al., 2022). We determine most serious charge using a hierarchical
ranking of top-level UCCS codes based on the following order of severity, from
most to least serious: violent, property, drug, public order, DUI, criminal
traffic. For our analyses of the effects of jail incarceration on voter
registration status, and on turnout unconditional on registration status, we
also include as covariates JDI data on gender, age, and race. Further
information on JDI data collection and standardization is provided in Appendix
C.

###  3.2 Voter File Data

We source voter file data from the L2 voter database licensed to New York
University. These data contain voting history and demographic information for
registered voters.666https://l2-data.com/our-data/. L2 data were pulled in
early 2021. For our main analyses of the effects of jail incarceration on the
turnout of registered voters, we include as covariates L2 data on age, gender,
race/ethnicity (both reported and predicted), and party
registration/identification (both reported and predicted).

L2 race and ethnicity categories are both reported directly by states and,
where not reported by states, predicted by L2 from other features available in
the data. We map L2 race/ethnicity values to non-Hispanic white (European),
Black (Likely African-American) and Other (East and South Asian, Hispanic and
Portuguese, or Other). We replicate our analyses of racially heterogeneous
effects of jail incarceration using only states that directly report race data
to L2.

L2 party categories are likewise both reported directly by states and, where
not reported by states, predicted by L2 from other features available in the
data. We map the L2 party categories to Democratic, Republican, and Non-
Partisan or Other (a group that includes Non-Partisan, Other, and 54 other
minor party descriptions). Further information on L2 voter data is provided in
Appendix C.

###  3.3 Matching Jail Records to Voter File Records

We initially constructed a sample of 944,985 JDI jail booking records from 936
counties for individuals who were booked into county jails on dates within a
+/- 90-day window around Election Day 2020 (August 5, 2020 to February 1,
2021). To make probabilistic record linkage tractable, we created age strata
for JDI bookings and blocked on L2 within-state matching pools of +/- 2 years.
Where age was missing for JDI booking records, we matched across full-state L2
voter files. We further restricted blocks to L2 surname soundex matches of
incarcerated individuals.

Data completeness varies by jail roster. Due to the variety in data quality,
we employed a relaxed, Java-implemented modification of the probabilistic
matching algorithm used by McDonough, Enamorado and Mendelberg (2022), itself
an instance of the Fellegi-Sunter/Expectation Maximization linkage model as
developed by Enamorado, Fifield and Imai (2019). In lieu of labeled matches we
produced a baseline averaged Jaro-Winkler distance between name components,
and considered all scores above a threshold to be matches for parametric
initialization. We then vectorized similarities of name, gender, and in-block
age as ternary (to account for missingness and variation in reporting of name
components), and FIPS codes as binary (within-state). We ran samples from this
pool of match-candidate pairs through a standard expectation maximization
process (Winkler, 2000) to stabilize parameters, and then conducted our final
linkage, keeping only highest-scoring matches per booked individual and
discarding low-probability matches.

We followed a similar strategy of probability match score re-weighting based
on first and last name frequencies to that employed by Enamorado, Fifield and
Imai (2019) and McDonough, Enamorado and Mendelberg (2022), and then further
thresholded matched results. We also removed any rows indicating multiple
voter matches per booked individual, where multiple matched bookings exhibited
temporal overlap, or where booking records indicate that individuals were
younger than 18 years old. Our primary analysis sample includes linked records
that exceed a match probability of 0.75; we also replicate analyses with
linked records that exceed a match probability of 0.95.

###  3.4 2020 Voting Days

In response to the COVID-19 pandemic, many states enacted emergency measures
that impacted voting days and methods during the 2020 general election. Some
states suspended requirements for requesting absentee mail ballots, instead
permitting no-excuse absentee ballot requests; others automatically sent out
mail-in absentee ballots to registered voters for the first time, or extended
early in-person voting days (Weiser et al., 2020). There was a 105% increase
in voting by mail from the 2016 general election to the 2020 general election,
and a 37% increase in early in-person voting (Scherer, 2021).

Mail-in ballot and early in-person voting days varied both across and within
states. For each state we record the earliest of the two dates on which voters
could begin (1) returning the mail-in ballots mailed out by county boards of
elections, or (2) voting in person. Earliest mail-in ballot dates range from
September 4, 2020 (60 days prior to Election Day) in North Carolina to October
16, 2020 (19 days prior to Election Day) in Washington state. Earliest in-
person voting dates range from September 14, 2020 (50 days prior to Election
Day) in Pennsylvania to Election Day itself in several states (National
Conference of State Legislatures, 2022; Higgins and Rattner, 2020). By taking
the earlier of these two dates per state, we ensure our treatment windows
cover the complete legal voting periods of each state.

##  4 Analysis

Individuals incarcerated in jails or prisons likely differ along a number of
dimensions, relative to unincarcerated individuals. These differences may be
related to individuals’ propensity to vote. Of particular concern, individuals
at high risk for incarceration may have low propensity to vote, leading
researchers to overestimate the impacts of incarceration on voting.

Gerber et al. (2017) sought to address selection into incarceration by
controlling for observed characteristics correlated with both risk of
incarceration and propensity to vote. However, this strategy does not address
the possible presence of unobserved characteristics related to both risk of
incarceration and propensity to vote. White (2019a) and McDonough, Enamorado
and Mendelberg (2022) addressed selection into jail incarceration by
leveraging the potentially as-if random assignment of cases to trial and bail
judges. However, this strategy does not permit clean identification of the
effects of incarceration on voting from jail, as whether an individual remains
incarcerated during legal voting periods is affected by factors other than a
judge’s bail determination or sentencing decision (McDonough, Enamorado and
Mendelberg, 2022).

We leverage the timing of jail bookings to address selection into jail
incarceration. Individuals booked into county jails during legal voting days
in 2020 are likely very similar to individuals booked into those same jails
just after the last legal voting day (November 3, 2020 in all states). In our
design, incarcerated individuals are effectively randomized into treatment and
control groups based on the timing of their jail incarceration, with treated
individuals booked into county jails during legal voting days, and control
individuals booked into those same jails just after Election Day. This
identification strategy has been successfully deployed in a number of recent
papers (White, 2019b; Mueller-Smith and Schnepel, 2021; Anker, Doleac and
Landersø, 2021). Following this literature, we report balance tests to confirm
the similarity of treatment and control groups.

Incarceration during 2020 voting days likely had a larger effect on the voting
behavior of registered voters than on the registration (and possibly voting)
behavior of unregistered voters. We initially restrict our attention to
estimating the effect of jail incarceration on the voting behavior of
registered voters booked into county jails during our period of interest,
using a match probability of $p>0.75$ as indicating a jailed registered voter.
We replicate analyses for records that have a match probability of $p>0.95$.
We then estimate the effects of jail incarceration on both registration and
voting behavior unconditional on registration status.

###  4.1 Balance Tests

We expect that registered voters booked into county jails during 2020 voting
days will have similar individual- and booking-level characteristics as those
booked into the same jails just after the close of legal voting (November 3,
2020 in all states). However, in 2020 voting in some states commenced as early
as 60 days prior to Election Day (mail-in voting in North Carolina).
Registered voters booked into county jails 60 days prior to November 3, 2020
may not have been comparable to registered voters booked into those same jails
just after November 3, 2020.

To identify temporal windows within which individual- and booking-level
characteristics are balanced across treatment and control groups, we estimated
a series of balance tests for windows of varying lengths including both 2020
voting days and post-Election control days. We assessed balance on a variety
of individual- and booking-level characteristics, including age (from L2, in
years), race/ethnicity (from L2, indicators for Black and non-Hispanic white),
party registration/identification (from L2, indicators for Democratic and
Republican registration), gender (from L2, indicator for male), number of
charges (from JDI), and indicators for whether the top charge against the
booked individual was a DUI, drug, property, public order, or violent charge
(from JDI). Samples include linked records (registered voters booked into
county jails during the specified time periods) with match probability
$p>0.75$. We excluded observations missing data on any covariates, or where
voting records indicated that an individual did not register to vote until
after November 3, 2020. All balance tests included jail-level fixed effects
and clustered standard errors on jail facilities.

Figure 1 reports estimates of balance on individual- and booking-level
characteristics for post-Election Day control windows ranging between 7 and 42
days and treatment windows containing legal voting days in 2020. For each
post-Election Day control window we initially tested for balance (joint
p-value $>$ 0.10) using treatment windows containing the full set of voting
days in each state in 2020. We sequentially dropped the earliest voting day in
the sample and repeated the balance test, continuing until we reached a
treatment window of 7 days prior to Election Day. Consistent with
expectations, treatment group windows closer to Election Day are more
consistently balanced with post-Election Day control groups. However, some
post-Election Day control groups (7 days, 21 days, and 42 days) are balanced
for the full set of 2020 voting days, and stay balanced for all subsets of
voting days. Other post-Election Day control groups (14 days, 28 days, and 35
days) are only consistently balanced with subsets of 2020 voting days that are
closer to Election Day.

Table 1 reports estimates of balance on individual- and booking-level
characteristics for post-Election Day control windows ranging between 7 and 42
days and the largest treatment windows for which the joint F-test p-values for
that treatment window and for all smaller treatment windows are greater than
0.10. For example, for a 7-day control window, we secure a balanced sample
with a 60-day treatment window (p $<$ 0.61). The joint F-test p-values for all
treatment windows less than 60 days are also greater than 0.10 for this
control group. The treatment group contains bookings beginning on September 4
through November 3 in North Carolina, where absentee ballot mail-out began on
September 4; bookings beginning on October 16 through November 3 in Washington
state, where both absentee ballot mail-out and early in-person voting began on
October 16; and so on. The control window contains bookings beginning on
November 4 through November 10. The outcome is whether a booking occurred
during a treatment window (1) versus a control window (0). All models include
jail-level fixed effects; robust standard errors clustered on jails are
reported in parentheses.

The L2 data include a ballot return date field indicating early voting. We can
use this field to assess the coverage of our treatment windows. Figure 2
reports the cumulative distribution of ballot return over time. The smallest
treatment window among the samples in Table 1 (17 voting days—beginning
October 17—with a control group of 28 post-Election days) captures
approximately 70% of ballot return.

###  4.2 Descriptive Statistics

Table 2 reports summary statistics for treatment and control groups for each
of the balanced samples identified in Table 1. 73.1% – 73.7% of our samples
are male. 24.6% - 27.1% of our samples are Black, 59.3% – 60.7% are white, and
12.8% – 15.1% are other race. The average age in our sample ranges between
37.2 – 37.4 years. 19.2% - 20.4% of our samples are Republicans, 40.9% – 41.9%
are Democrats, and 37.8% – 40.0% are nonpartisan or other party.

28.3% – 29.8% of those in our samples are incarcerated for public order top
charges; 27.4% – 29.2% are incarcerated for violent top charges; 19.4% – 20.2%
are incarcerated for property top charges; 12.7% – 13.8% are incarcerated for
drug top charges; 6.2% – 6.7% are incarcerated for DUI top charges; 3.2% –
3.5% are incarcerated for criminal traffic top charges. Average number of
charges is 2.5 charges across all samples. Average length of jail
incarceration ranges from 33.9 – 37.5 days. The average proportion of 2020
voting days during which individuals are confined ranges between 0.14 – 0.21.

Turnout among registered voters who were booked into county jails within 7 –
42 days after 2020 Election Day ranged between 34.9% and 35.7% in our samples,
or between 56% and 62% less than the registered voter turnout observed in the
U.S.
population.777https://www.census.gov/content/dam/Census/library/publications/2022/demo/p20-585.pdf.
As noted earlier, without an appropriate control group, we might mistakenly
attribute some of the lower turnout of those incarcerated during 2020 voting
days to the effects of jail incarceration, rather than to a lower propensity
to vote.

###  4.3 Estimating Average Treatment Effects

Table 3 reports OLS estimates of the effects of (1) being booked into a county
jail during 2020 voting days (either mail-in or in-person) for any length of
time on a registered voter’s probability of voting in the 2020 election, with
and without covariates, and (2) being booked into a county jail during 2020
voting days for some proportion of voting days on a registered voter’s
probability of voting in the 2020 election, with and without covariates. For
example, if a registered voter is incarcerated in North Carolina from
September 4, 2020 to January 1, 2021, the proportion of voting days confined =
1.0, as the individual is incarcerated during 60 out of all 60 possible voting
days in North Carolina. If a registered voter is confined in Washington state
from November 3, 2020 to December 10, 2020, the proportion of voting days
confined = 0.053, as the individual is incarcerated during 1 out of 19
possible voting days in Washington state. Models are estimated on the samples
reported in Table 1. All models include jail and week-of-year fixed effects;
robust standard errors two-way clustered at the jail and week-of-year level
are reported in parentheses.

As reported in Table 3, in models that include all individual- and booking-
level covariates along with jail and week fixed effects, we find that
registered voters who were booked into county jails during 2020 voting days in
their state for any length of time were on average 3.0 – 3.3 percentage points
or 8.6% – 9.3% less likely to have voted in 2020, relative to registered
voters who were booked into the same jails within 7 – 42 days after Election
Day (avg. effect magnitude = 9.0%; p $<$ 0.01).

The impacts of incarceration on voting from jail in 2020 increased with the
duration of incarceration. As reported in Table 3, including all individual-
and booking-level covariates along with jail and week fixed effects,
registered voters who were booked into county jails during 2020 voting days in
their state for the full duration of voting days were on average 13.7 - 22.9
percentage points or 38.4% – 65.2% less likely to have voted in 2020, relative
to registered voters who were booked into the same jails within 7 – 42 days
after Election Day (avg. effect magnitude = 46.1%; p $<$ 0.01).

###  4.4 Placebo Tests

Table 4 reports placebo tests of the effects on 2016 and 2012 voter turnout of
a booking occurring during a pre-Election Day window in 2020, relative to a
post-Election Day window. The 2016 placebo samples include registered voters
in each of our balanced samples who were registered to vote on both Election
Day 2020 and Election Day 2016 (November 8, 2016). The 2016 placebo tests
report OLS estimates of the effect of a 2020 booking occurring in a pre-
Election Day 2020 window on a registered voter’s probability of voting in the
2016 election, for the registered voters in each of the balanced samples
reported in Table 1. The 2012 placebo samples include registered voters in
each of our balanced samples who were registered to vote on both Election Day
2020 and Election Day 2012 (November 6, 2012). The 2012 placebo tests report
OLS estimates of the effect of a 2020 booking occurring in a pre-Election Day
2020 window on a registered voter’s probability of voting in the 2012
election, for the registered voters in each of the balanced samples reported
in Table 1.

As reported in Table 4, and as expected, there are no consistent effects of
pre-Election Day jail incarceration in 2020 on registered voters’ turnout in
2016 or 2012. The effects of 2020 jail incarceration on 2016 turnout are very
slightly positive but consistently insignificant. The effects of 2020 jail
incarceration on 2012 turnout are very slightly negative but generally
insignificant, reaching p $<$ 0.05 in only one balanced sample. By contrast,
the effects of 2020 jail incarceration on 2020 turnout among registered voters
who were also registered to vote in 2016 and/or 2012 are consistently
negative, large, and significant, ranging between 2 – 4 percentage point or
5.0% – 8.8% decreases (p $<$ 0.01).

###  4.5 Racially Disparate Effects of Jail Incarceration

Concerns have also been raised about possible racial disparities in ballot
access for those incarcerated in county jails (The Sentencing Project, 2020,
2022). We explore racial heterogeneity in the effects of incarceration on
voting from jail in 2020 by interacting our two treatment variables (a binary
indicator for having been booked into a county jail during 2020 voting days
for any length of time, and the proportion of 2020 voting days during which a
registered voter booked into a county jail during 2020 voting days was
incarcerated) with the indicator for Black registered voters from the L2 voter
files. For ease of interpretation we retain only non-Hispanic white and Black
voters from the matched sample for the interaction models. All models include
all covariates along with jail and week-of-year fixed effects; robust standard
errors are two-way clustered at the jail and week-of-year level.

Table 5 reports estimates. The effect of jail incarceration on voting from
jail in 2020 was significantly larger for Black registered voters, relative to
white registered voters. Including all individual- and booking-level
covariates along with jail and week fixed effects, white registered voters who
were booked into county jails during 2020 voting days for the full set of
voting days were on average 32.6% – 60.2% less likely to have voted in 2020,
relative to white registered voters who were booked into the same jails within
7 – 42 days after Election Day (avg. effect magnitude = 39.8%; p $<$ 0.01).
The estimated negative effect of incarceration on voting from jail was 38.2% -
55.0% larger for Black registered voters (avg. effect magnitude = 45.9%; p $<$
0.05). The average total effect magnitude for Black registered voters of being
booked into a county jail during 2020 voting days for the full set of voting
days was a 77.9% decrease in turnout, relative to Black registered voters
booked into the same jails within 7 – 42 days after Election Day.

The L2 race and ethnicity categories include both reported and predicted
race/ethnicity. Table 1 restricts the analyses reported in Table 5 to only
those states that report race/ethnicity to L2, excluding observations where
race/ethnicity is predicted by L2 using other features available in the data.
Effect magnitudes and significance levels are similar for both white and Black
registered voters.

###  4.6 Match Probability $p>0.95$

The sample of linked records used in our main analyses have match
probabilities that exceed 0.75. In Appendix A we also replicate our primary
analyses using only linked records with match probabilities that exceed 0.95.
Figure A.1 reports for this sample estimates of balance on individual- and
booking-level characteristics for post-Election Day control windows ranging
between 7 and 42 days and treatment windows containing legal voting days in
2020. Table A.1 reports for this sample estimates of balance on individual-
and booking-level characteristics for post-Election Day control windows
ranging between 7 and 42 days and the largest treatment windows for which the
joint F-test p-values for that treatment window and for all smaller treatment
windows are greater than 0.10. Balanced samples are considerably smaller than
those for linked records with match probabilities that exceed 0.75. Figure A.2
reports the distribution of ballot return and the date of the start of the
smallest treatment window among the samples in Table A.1. Table A.2 reports
summary statistics for the balanced samples reported in Table A.1.

Table A.3 reports estimates of the effect of jail incarceration on the voting
behavior of registered voters, for the sample of linked records with match
probabilities exceeding 0.95. Effect magnitudes are larger than those reported
for the sample of linked records with match probabilities exceeding 0.75. In
models that include all individual- and booking-level covariates along with
jail and week fixed effects, registered voters in this sample who were booked
into county jails during 2020 voting days in their state for any length of
time were on average 3.9 – 4.5 percentage points or 13.1% – 14.5% less likely
to have voted in 2020, relative to registered voters who were booked into the
same jails within 7 – 42 days after Election Day (avg. effect magnitude =
13.9%; p $<$ 0.01).

In the balanced samples that have coverage on registered voters who were
incarcerated during the full span of 2020 voting days in their states
(excluding the balanced sample including only 7 days pre- and post-Election
Day), effect magnitudes are again larger than those reported for the sample of
linked records with match probabilities exceeding 0.75. Including all
individual- and booking-level covariates along with jail and week fixed
effects, registered voters in this sample who were booked into county jails
during 2020 voting days in their state for the full duration of voting days
were on average 21.3 - 30 percentage points or 67.9% – 98% less likely to have
voted in 2020, relative to registered voters who were booked into the same
jails within 14 – 42 days after Election Day (avg. effect magnitude = 80.8%; p
$<$ 0.01).

Table A.4 reports no consistent effects of pre-Election Day jail incarceration
in 2020 on registered voters’ turnout in 2016 or 2012, for the sample of
linked records with match probabilities exceeding 0.95. The effects of 2020
jail incarceration on 2016 turnout are very slightly positive and significant
in two out of six models at p $<$ 0.05; the effects of 2020 jail incarceration
on 2012 turnout are consistently insignificant. The effects of 2020 jail
incarceration on 2020 turnout among registered voters who were also registered
to vote in 2016 and/or 2012 are consistently negative, large, and significant,
with magnitudes consistent with those observed for the sample of linked
records with match probabilities exceeding 0.75.

###  4.7 The Effect of Jail Incarceration on Registration

Jail incarceration may also affect voting behavior by reducing the ability of
jailed individuals to register to vote. If this were the case in 2020, then
our estimates of the effect of jail incarceration on the voting behavior of
registered voters in 2020 understate that effect.

In Appendix B we replicate our analyses for the full sample of jailed
individuals during our time periods of interest, both registered and
unregistered. Figure B.1 reports for this sample estimates of balance on
individual- and booking-level characteristics for post-Election Day control
windows ranging between 7 and 42 days and treatment windows containing legal
voting days in 2020. Individual- and booking-level characteristics for this
sample are sourced only from the JDI database in order to have coverage on
both unregistered and registered voters. Table B.1 reports the balance tests
for the largest treatment windows for which the joint F-test p-values for that
treatment window and for all smaller treatment windows are greater than 0.10.
These windows are considerably smaller than the windows within which we obtain
balanced samples of registered voters, limiting our ability to make inferences
about the impacts of being incarcerated for the full span of legal voting days
in 2020.

Table B.2 reports summary statistics for the balanced samples reported in
Table B.1. Pre-Election Day voter registration rates among individuals who
were booked into county jails within 7 – 42 days after 2020 Election Day
ranged between 29% and 29.4% in our samples. 2020 turnout rates in these same
post-Election day samples, unconditional on registration status, ranged
between 8.1% and 8.4%.

Table B.3 reports estimates of the effect of jail incarceration during 2020
voting days on the probability that an individual was registered to vote by
Election Day 2020 (whether a jailed individual matched to L2 records with
match probability $p>0.95$ and was registered to vote before Election Day
2020), for the balanced samples in Table B.1. In models that include all
individual- and booking-level covariates along with jail and week fixed
effects, there is a very small positive effect (0.02%) of being jailed for any
length of time before Election Day on the probability of being registered to
vote by Election Day ($p<0.05$). The sign of the effect flips when
incarceration is measured as proportion of voting days incarcerated. Estimates
of the effects of jail incarceration on registration behavior in 2020 using
this continuous measure of incarceration are large, negative, and significant
at p $<$ 0.01. However, because no balanced samples that include both
registered and unregistered voters have coverage on individuals who were
incarcerated during the full span of 2020 voting days in their states, we
refrain from interpreting the effect magnitudes for this measure of
incarceration.

Table B.4 reports estimates of the effect of jail incarceration during 2020
voting days on turnout unconditional on registration status, for the balanced
samples in Table B.1. Effect magnitudes for the impacts of being incarcerated
for any length of time before Election Day are comparable to those estimated
only on samples of registered voters. In models that include all individual-
and booking-level covariates along with jail and week fixed effects,
individuals in this sample who were booked into county jails during 2020
voting days in their state for any length of time were on average 0.6 – 0.7
percentage points or 7.2% – 8.3% less likely to have voted in 2020, relative
to individuals who were booked into the same jails within 7 – 42 days after
Election Day (avg. effect magnitude = 7.8%; p $<$ 0.01). Estimates of the
effects of jail incarceration on voting behavior in 2020 using the continuous
measure of incarceration are large, negative, and significant at p $<$ 0.01.
Again, however, because no balanced samples that include both registered and
unregistered voters have coverage on individuals who were incarcerated during
the full span of 2020 voting days in their states, we refrain from
interpreting the effect magnitudes for this measure of incarceration.

##  5 Conclusion

In every state, otherwise voting-eligible individuals incarcerated pretrial or
to serve a misdemeanor sentence remain legally entitled to vote while
incarcerated (The Sentencing Project, 2020, 2022). The vast majority of the
approximately 650,000 individuals incarcerated in county jails on any given
day are being held pretrial or to serve misdemeanor sentences (Sawyer and
Wagner, 2022). Anecdotal evidence suggests, however, that many of those
incarcerated in county jails are not being given adequate opportunities to
exercise their right to vote (The Sentencing Project, 2020). Concerns have
also been raised about possible racial disparities in ballot access for those
incarcerated in county jails (The Sentencing Project, 2022). Yet we lack
reliable causal estimates of the impacts of being incarcerated in a county
jail on the exercise of the right to vote, and of any racial heterogeneity in
those impacts.

We leverage new data on daily individual-level jail records and exploit the
timing of incarceration to estimate the causal effects of jail incarceration
on voting from jail in 2020. We find that registered voters booked into county
jails for the full duration of 2020 voting days were on average 46% less
likely to vote in 2020, relative to registered voters booked into the same
jails within 7 – 42 days after Election Day. The estimated negative effect of
incarceration on voting from jail was much larger for Black registered voters,
who were 78% less likely to vote in 2020 if booked into county jails for the
full duration of 2020 voting days, relative to Black registered voters booked
into the same jails just after Election Day. Placebo tests indicate no effects
of 2020 jail incarceration on the 2012 or 2016 turnout of registered voters.
We find few effects of jail incarceration on voter registration in 2020, and
effect sizes of comparable magnitude for turnout unconditional on registration
status. Our findings reveal the pressing need to enable voting-eligible
incarcerated individuals to exercise their constitutional right to vote, and
to address troubling racial disparities in the effect of jail incarceration on
the exercise of the right to vote.

## References

  * (1)
  * Anker, Doleac and Landersø (2021) Anker, Anne Sofie Tegner, Jennifer L. Doleac and Rasmus Landersø. 2021\.  “The Effects of DNA Databases on the Deterrence and Detection of Offenders.” American Economic Journal: Applied Economics 13(4):194–225.
  * Choi et al. (2022) Choi, Jay, David Kilmer, Michael Mueller-Smith and Sema A. Taheri. 2022\.  Hierarchical Approaches to Text-based Offense Classification. Working paper University of Michigan.
  * Enamorado, Fifield and Imai (2019) Enamorado, Ted, Benjamin Fifield and Kosuke Imai. 2019.  “Using a Probabilistic Model to Assist Merging of Large-Scale Administrative Records.” American Political Science Review 113(2):353–371.
  * Gerber et al. (2017) Gerber, Alan S., Gregory A. Huber, Marc Meredith, Daniel R. Biggers and David J. Hendry. 2017.  “Does Incarceration Reduce Voting? Evidence about the Political Consequences of Spending Time in Prison.” The Journal of Politics 79(4):1130–1146.
  * Higgins and Rattner (2020) Higgins, Tucker and Nate Rattner. 2020.  “When does vote by mail and early voting start? A state by state guide.” https://www.cnbc.com/2020/09/04/election-early-absentee-mail-voting-every-state.html.
  * McDonough, Enamorado and Mendelberg (2022) McDonough, Anne, Ted Enamorado and Tali Mendelberg. 2022.  “Jailed While Presumed Innocent: The Demobilizing Effects of Pretrial Incarceration.” The Journal of Politics .
  * Mueller-Smith and Schnepel (2021) Mueller-Smith, Michael and Kevin Schnepel. 2021.  “Diversion in the Criminal Justice System.” The Review of Economic Studies 88(2):883–936.
  * National Conference of State Legislatures (2022) National Conference of State Legislatures. 2022.  “Table 7: When States Mail Out Absentee/Mail Ballots.” https://www.ncsl.org/research/elections-and-campaigns/vopp-table-7-when-states-mail-out-absentee-ballots.aspx.
  * Sawyer and Wagner (2022) Sawyer, Wendy and Peter Wagner. 2022.  “Mass Incarceration: The Whole Pie 2022.” https://www.prisonpolicy.org/reports/pie2022.html.
  * Scherer (2021) Scherer, Zachary. 2021.  “Majority of Voters Used Nontraditional Methods to Cast Ballots in 2020.” https://www.census.gov/library/stories/2021/04/what-methods-did-people-use-to-vote-in-2020-election.html.
  * The Sentencing Project (2020) The Sentencing Project. 2020.  “Voting in Jails.” https://www.sentencingproject.org/publications/voting-in-jails/.
  * The Sentencing Project (2022) The Sentencing Project. 2022.  “Voting in Jails: Advocacy Strategies to #UnlocktheVote.” https://www.sentencingproject.org/publications/voting-in-jails-advocacy-strategies-to-unlockthevote/.
  * Weiser et al. (2020) Weiser, Wendy R., Eliza Sweren-Becker, Dominique Erney and Anne Glatz. 2020\.  “Mail Voting: What Has Changed in 2020.” https://www.brennancenter.org/our-work/research-reports/mail-voting-what-has-changed-2020.
  * White (2019a) White, Ariel. 2019a.  “Family Matters? Voting Behavior in Households with Criminal Justice Contact.” American Political Science Review 113(2):607–613.
  * White (2019b) White, Ariel. 2019b.  “Misdemeanor Disenfranchisement? The Demobilizing Effects of Brief Jail Spells on Potential Voters.” American Political Science Review 113(2):311–324.
  * White and Nguyen (2022) White, Ariel and Avery Nguyen. 2022.  “How Often Do People Vote While Incarcerated? Evidence from Maine and Vermont.” Journal of Politics 84(1):568–572.
  * Winkler (2000) Winkler, William E. 2000.  Using the EM Algorithm for Weight Computation in the Fellegi-Sunter Model of Record Linkage. In Proceedings of the Section on Survey Research Methods, American Statistical Association.  pp. 667–671.
  * Zhen Zeng and Minton (2021) Zhen Zeng, Ph.D. and Todd D. Minton. 2021.  Jail Inmates in 2019. Technical Report NCJ 255608 U.S. Department of Justice: Bureau of Justice Statistics.

## Tables and Figures

Figure 1: Balance Tests (Joint F-test P-values)

  * •

Note: This figure reports estimates of balance on individual- and booking-
level characteristics for post-Election Day control windows ranging between 7
and 42 days and treatment windows containing legal voting days in 2020, for
the sample of matched records (registered voters booked into county jails
during this time period) with match probability $p>0.75$. For each post-
Election Day control window we initially tested for balance using treatment
windows containing the full set of voting days in each state in 2020,
sequentially dropping the earliest voting day in the sample and repeating the
balance test until we reached a treatment window of 7 days prior to Election
Day.

Figure 2: Distribution of Early Ballot Return

  * •

Note: This figure reports the cumulative distribution of early ballot return
in the L2 voter file data for 2020, where available. The smallest treatment
window in the samples reported in Table 1 begins on October 17, indicated with
a vertical dashed line. Approximately 70% of 2020 ballots were returned after
October 17.

Table 1: Balance Tests

| Control: | 7 days | 14 days | 21 days | 28 days | 35 days | 42 days
---|---|---|---|---|---|---|---
| Treatment: | 60 days | 53 days | 60 days | 17 days | 31 days | 60 days
Age (L2) |  | -0.000 | -0.000 | -0.000 | -0.000 | 0.000 | 0.000
|  | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
Black (L2) |  | 0.008 | 0.005 | 0.010 | 0.004 | 0.004 | 0.002
|  | (0.006) | (0.006) | (0.007) | (0.008) | (0.007) | (0.007)
White (L2) |  | 0.000 | -0.001 | 0.001 | -0.010 | -0.003 | -0.001
|  | (0.005) | (0.005) | (0.005) | (0.006) | (0.006) | (0.005)
Male (L2) |  | 0.002 | 0.007** | 0.006 | 0.006 | 0.003 | 0.004
|  | (0.003) | (0.004) | (0.004) | (0.005) | (0.004) | (0.004)
Democrat (L2) |  | -0.007* | -0.002 | -0.002 | -0.003 | 0.002 | -0.001
|  | (0.004) | (0.004) | (0.004) | (0.005) | (0.004) | (0.004)
Republican (L2) |  | -0.004 | -0.005 | -0.006 | -0.005 | -0.005 | -0.005
|  | (0.004) | (0.005) | (0.005) | (0.006) | (0.005) | (0.005)
Number of Charges (JDI) |  | 0.000 | 0.000 | 0.000 | -0.001 | -0.000 | -0.000
|  | (0.000) | (0.000) | (0.000) | (0.001) | (0.001) | (0.001)
Top Charge: DUI (JDI) |  | 0.003 | -0.015 | -0.009 | -0.002 | -0.003 | -0.011
|  | (0.010) | (0.011) | (0.011) | (0.014) | (0.012) | (0.011)
Top Charge: Drug (JDI) |  | 0.009 | -0.002 | -0.002 | -0.008 | 0.000 | -0.007
|  | (0.010) | (0.010) | (0.010) | (0.014) | (0.012) | (0.010)
Top Charge: Property (JDI) |  | 0.005 | -0.008 | -0.005 | -0.008 | -0.003 | -0.009
|  | (0.009) | (0.010) | (0.010) | (0.013) | (0.012) | (0.010)
Top Charge: Public Order (JDI) |  | 0.007 | -0.007 | -0.002 | -0.008 | 0.004 | -0.002
|  | (0.009) | (0.010) | (0.010) | (0.013) | (0.011) | (0.010)
Top Charge: Violent (JDI) |  | 0.007 | -0.014 | -0.007 | -0.015 | -0.011 | -0.016*
|  | (0.008) | (0.010) | (0.010) | (0.012) | (0.011) | (0.010)
Observations |  | 63000 | 70854 | 79854 | 57821 | 81683 | 103091
Joint F-Test p-value |  | 0.614 | 0.101 | 0.372 | 0.162 | 0.131 | 0.182

  * •

Note: This table reports estimates of balance on individual- and booking-level
characteristics for post-Election Day control windows ranging between 7 and 42
days and treatment windows containing legal voting days in 2020, for the
sample of matched records (registered voters booked into county jails during
the specified time periods) with match probability $p>0.75$. As reported in
Figure 1, for each control group window we initially tested for balance using
treatment windows containing the full set of voting days in each state in
2020, sequentially dropping the earliest voting day in the sample and
repeating the balance test until we reached a treatment window of 7 days prior
to Election Day. This table reports balance for the largest treatment windows
for which the joint F-test p-values for that treatment window and for all
smaller treatment windows were greater than 0.10. The control window contains
bookings beginning on November 4 through November 10. The outcome is whether a
booking occurred during a treatment window (1) versus a control window (0).
All models include jail-level fixed effects; robust standard errors clustered
on jails are reported in parentheses. ***p $<$ 0.01, **p $<$ 0.05, *p $<$
0.10.

Table 2: Summary Statistics

Treatment/Control: |  T |  C |  T |  C |  T |  C |  T |  C |  T |  C |  T |  C
---|---|---|---|---|---|---|---|---|---|---|---|---
Days: |  60d |  7d |  53d |  14d |  60d |  21d |  17d |  28d |  31d |  35d |  60d |  42d
Age (L2) |  37.178 |  37.365 |  37.175 |  37.391 |  37.178 |  37.275 |  37.152 |  37.217 |  37.187 |  37.241 |  37.178 |  37.232
Black (L2) |  0.271 |  0.246 |  0.270 |  0.249 |  0.271 |  0.247 |  0.255 |  0.247 |  0.267 |  0.248 |  0.271 |  0.253
White (L2) |  0.601 |  0.604 |  0.602 |  0.603 |  0.601 |  0.603 |  0.597 |  0.605 |  0.593 |  0.607 |  0.601 |  0.601
Male (L2) |  0.734 |  0.736 |  0.733 |  0.731 |  0.734 |  0.733 |  0.737 |  0.734 |  0.735 |  0.734 |  0.734 |  0.734
Democrat (L2) |  0.417 |  0.419 |  0.418 |  0.411 |  0.417 |  0.410 |  0.408 |  0.408 |  0.415 |  0.409 |  0.417 |  0.410
Republican (L2) |  0.200 |  0.203 |  0.199 |  0.204 |  0.200 |  0.204 |  0.192 |  0.203 |  0.194 |  0.202 |  0.200 |  0.201
Non-Partisan or Other Party (L2) |  0.383 |  0.378 |  0.383 |  0.385 |  0.383 |  0.386 |  0.400 |  0.389 |  0.391 |  0.389 |  0.383 |  0.389
Number of Charges (JDI) |  2.507 |  2.539 |  2.499 |  2.502 |  2.507 |  2.501 |  2.466 |  2.520 |  2.494 |  2.504 |  2.507 |  2.514
Top Charge: Violent (JDI) |  0.275 |  0.279 |  0.274 |  0.288 |  0.275 |  0.285 |  0.284 |  0.289 |  0.278 |  0.289 |  0.275 |  0.292
Top Charge: Property (JDI) |  0.194 |  0.200 |  0.195 |  0.198 |  0.194 |  0.199 |  0.202 |  0.201 |  0.199 |  0.199 |  0.194 |  0.198
Top Charge: Drug (JDI) |  0.138 |  0.131 |  0.138 |  0.128 |  0.138 |  0.131 |  0.127 |  0.128 |  0.133 |  0.129 |  0.138 |  0.131
Top Charge: Public Order (JDI) |  0.298 |  0.288 |  0.298 |  0.287 |  0.298 |  0.287 |  0.290 |  0.287 |  0.294 |  0.286 |  0.298 |  0.283
Top Charge: DUI (JDI) |  0.062 |  0.067 |  0.063 |  0.067 |  0.062 |  0.066 |  0.065 |  0.063 |  0.063 |  0.065 |  0.062 |  0.065
Top Charge: Criminal Traffic (JDI) |  0.033 |  0.035 |  0.033 |  0.032 |  0.033 |  0.032 |  0.033 |  0.032 |  0.034 |  0.033 |  0.033 |  0.032
Length of Stay (days) (JDI) |  37.458 |  37.207 |  37.527 |  35.561 |  37.458 |  35.104 |  33.885 |  34.566 |  36.559 |  34.279 |  37.458 |  35.559
Confined During Voting Days |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000
Proportion of Voting Days Confined |  0.215 |  0.000 |  0.215 |  0.000 |  0.215 |  0.000 |  0.143 |  0.000 |  0.192 |  0.000 |  0.215 |  0.000
Turnout |  0.323 |  0.349 |  0.322 |  0.352 |  0.323 |  0.354 |  0.316 |  0.351 |  0.321 |  0.356 |  0.323 |  0.357
Observations |  53995 |  9005 |  53507 |  17347 |  53995 |  25859 |  23947 |  33874 |  40047 |  41636 |  53995 |  49096

  * •

Note: This table reports mean values of individual- and booking-level
characteristics for the balanced samples reported in Table 1, which comprise
matched records (registered voters booked into county jails during the
specified time periods) with match probability $p>0.75$, by treatment and
control groups.

Table 3: The Effect of Incarceration on Voting From Jail

| Control: | 7 days | 14 days | 21 days
---|---|---|---|---
| Treatment: | 60 days | 53 days | 60 days
Confined During Voting Days |  | -0.029*** | -0.031*** | -0.031***
|  | (0.002) | (0.002) | (0.002)
with covariates |  | -0.030*** | -0.031*** | -0.032***
|  | (0.002) | (0.002) | (0.002)
Proportion of Voting Days Confined |  | -0.169*** | -0.166*** | -0.166***
|  | (0.021) | (0.021) | (0.020)
with covariates |  | -0.151*** | -0.143*** | -0.142***
|  | (0.018) | (0.019) | (0.018)
Mean Proportion of Voting Days Confined |  | 0.215 | 0.215 | 0.215
Max. Proportion of Voting Days Confined |  | 1.000 | 1.000 | 1.000
Mean Control Turnout |  | 0.349 | 0.352 | 0.354
Observations |  | 63000 | 70854 | 79854
| Control: | 28 days | 35 days | 42 days
| Treatment: | 17 days | 31 days | 60 days
Confined During Voting Days |  | -0.030*** | -0.032*** | -0.032***
|  | (0.002) | (0.002) | (0.002)
with covariates |  | -0.032*** | -0.033*** | -0.033***
|  | (0.002) | (0.002) | (0.002)
Proportion of Voting Days Confined |  | -0.274*** | -0.204*** | -0.162***
|  | (0.043) | (0.023) | (0.019)
with covariates |  | -0.229*** | -0.175*** | -0.137***
|  | (0.040) | (0.020) | (0.018)
Mean Proportion of Voting Days Confined |  | 0.143 | 0.192 | 0.215
Max. Proportion of Voting Days Confined |  | 0.947 | 1.000 | 1.000
Mean Control Turnout |  | 0.351 | 0.356 | 0.357
Observations |  | 57821 | 81683 | 103091

  * •

Note: This table reports OLS estimates of (1) the effect of being booked into
a county jail during 2020 voting days for any length of time on a registered
voter’s probability of voting in the 2020 election, with and without
covariates; and (2) the effect of the proportion of 2020 voting days during
which a registered voter booked into a county jail during voting days was
confined on their probability of voting in the 2020 election, with and without
covariates, for samples of matched records (registered voters booked into
county jails during the specified time periods) with match probability
$p>0.75$. Models are estimated on samples as described in the notes to Table
1. All models include jail and week-of-year fixed effects; robust standard
errors two-way clustered at the jail and week-of-year level are reported in
parentheses. ***p $<$ 0.01, **p $<$ 0.05, *p $<$ 0.10.

Table 4: Placebo Tests

|  | 2016 Placebo |  | 2012 Placebo |
---|---|---|---|---|---
Control/Treatment: (7 days, 60 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.005 | -0.020*** | -0.012 | -0.024***
|  | (0.007) | (0.007) | (0.008) | (0.009)
Mean Control Turnout |  | 0.442 | 0.399 | 0.554 | 0.450
Observations |  | 40693 | 40693 | 28388 | 28388
Control/Treatment: (14 days, 53 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.004 | -0.025*** | -0.007 | -0.030***
|  | (0.005) | (0.006) | (0.006) | (0.007)
Mean Control Turnout |  | 0.442 | 0.404 | 0.544 | 0.456
Observations |  | 45732 | 45732 | 31873 | 31873
Control/Treatment: (21 days, 60 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.004 | -0.027*** | -0.009 | -0.033***
|  | (0.005) | (0.005) | (0.006) | (0.006)
Mean Control Turnout |  | 0.441 | 0.406 | 0.544 | 0.459
Observations |  | 51471 | 51471 | 35862 | 35862
Control/Treatment: (28 days, 17 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.002 | -0.034*** | -0.011* | -0.040***
|  | (0.005) | (0.005) | (0.007) | (0.007)
Mean Control Turnout |  | 0.443 | 0.404 | 0.544 | 0.457
Observations |  | 37135 | 37135 | 25644 | 25644
Control/Treatment: (35 days, 31 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.003 | -0.032*** | -0.009* | -0.037***
|  | (0.004) | (0.004) | (0.005) | (0.005)
Mean Control Turnout |  | 0.445 | 0.409 | 0.546 | 0.462
Observations |  | 52505 | 52505 | 36358 | 36358
Control/Treatment: (42 days, 60 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.001 | -0.030*** | -0.010** | -0.036***
|  | (0.004) | (0.004) | (0.005) | (0.005)
Mean Control Turnout |  | 0.445 | 0.410 | 0.546 | 0.463
Observations |  | 66386 | 66386 | 46045 | 46045

  * •

Note: This table reports placebo tests of the effects of 2020 jail
incarceration on 2016 and 2012 voter turnout. The 2016 placebo samples include
registered voters in each of our balanced samples who were registered to vote
on both Election Day 2020 and Election Day 2016 (November 8, 2016). The 2016
placebo tests report OLS estimates of the effect of a 2020 booking occurring
in a pre-Election Day 2020 window on a registered voter’s probability of
voting in the 2016 election, for the registered voters in each of the balanced
samples reported in Table 1. The 2012 placebo samples include registered
voters in each of our balanced samples who were registered to vote on both
Election Day 2020 and Election Day 2012 (November 6, 2012). The 2012 placebo
tests report OLS estimates of the effect of a 2020 booking occurring in a pre-
Election Day 2020 window on a registered voter’s probability of voting in the
2012 election, for the registered voters in each of the balanced samples
reported in Table 1. All models include jail fixed effects; robust standard
errors clustered at the jail level are reported in parentheses. ***p $<$ 0.01,
**p $<$ 0.05, *p $<$ 0.10.

Table 5: Racial Disparities in the Effect of Incarceration on Voting From Jail
(With Covariates)

| Control: | 7 days | 14 days | 21 days
---|---|---|---|---
| Treatment: | 60 days | 53 days | 60 days
Confined During Voting Days |  | -0.039*** | -0.042*** | -0.042***
|  | (0.004) | (0.005) | (0.005)
Confined $\times$ Black |  | -0.003 | 0.003 | -0.001
|  | (0.006) | (0.007) | (0.008)
Proportion of Voting Days Confined |  | -0.135*** | -0.131*** | -0.128***
|  | (0.017) | (0.017) | (0.017)
Proportion Confined $\times$ Black |  | -0.060** | -0.050** | -0.055**
|  | (0.024) | (0.023) | (0.023)
Mean Proportion of Voting Days Confined Black |  | 0.209 | 0.209 | 0.209
Max. Proportion of Voting Days Confined Black |  | 1.000 | 1.000 | 1.000
Mean Proportion of Voting Days Confined White |  | 0.218 | 0.218 | 0.218
Max. Proportion of Voting Days Confined White |  | 1.000 | 1.000 | 1.000
Mean Control Turnout Black |  | 0.278 | 0.279 | 0.285
Mean Control Turnout White |  | 0.377 | 0.380 | 0.380
Observations |  | 54741 | 61414 | 69084
| Control: | 28 days | 35 days | 42 days
| Treatment: | 17 days | 31 days | 60 days
Confined During Voting Days |  | -0.041*** | -0.042*** | -0.042***
|  | (0.003) | (0.004) | (0.004)
Confined $\times$ Black |  | -0.005 | -0.006 | -0.003
|  | (0.004) | (0.007) | (0.006)
Proportion of Voting Days Confined |  | -0.228*** | -0.160*** | -0.125***
|  | (0.036) | (0.022) | (0.016)
Proportion Confined $\times$ Black |  | -0.118*** | -0.088*** | -0.054**
|  | (0.030) | (0.014) | (0.022)
Mean Proportion of Voting Days Confined Black |  | 0.124 | 0.178 | 0.209
Max. Proportion of Voting Days Confined Black |  | 0.947 | 1.000 | 1.000
Mean Proportion of Voting Days Confined White |  | 0.148 | 0.196 | 0.218
Max. Proportion of Voting Days Confined White |  | 0.947 | 1.000 | 1.000
Mean Control Turnout Black |  | 0.284 | 0.291 | 0.292
Mean Control Turnout White |  | 0.379 | 0.382 | 0.383
Observations |  | 49257 | 70041 | 89010

  * •

Note: This table reports OLS estimates of (1) the effect of being booked into
a county jail during 2020 voting days for any length of time on a registered
voter’s probability of voting in the 2020 election, interacted with an
indicator for whether the individual is Black (0 = non-Hispanic white), with
covariates; and (2) the effect of the proportion of 2020 voting days during
which a registered voter booked into a county jail during voting days was
confined on their probability of voting in the 2020 election, interacted with
an indicator for whether the individual is Black, with covariates. Models are
estimated on samples as described in the notes to Table 1, including only
Black and white individuals. All models include jail and week-of-year fixed
effects; robust standard errors two-way clustered at the jail and week-of-year
level are reported in parentheses. ***p $<$ 0.01, **p $<$ 0.05, *p $<$ 0.10.

Racial Disparities in the Effect of Incarceration on Voting From Jail (With
Covariates)
States Reporting Race Control: 7 days 14 days 21 days Treatment: 60 days 53
days 60 days Confined During Voting Days -0.044*** -0.047*** -0.047*** (0.008)
(0.008) (0.008) Confined $\times$ Black 0.008 0.012 0.008 (0.008) (0.008)
(0.010) Proportion of Voting Days Confined -0.131*** -0.126*** -0.126***
(0.029) (0.029) (0.028) Proportion Confined $\times$ Black -0.084*** -0.070***
-0.073*** (0.020) (0.021) (0.021) Mean Proportion of Voting Days Confined
Black 0.197 0.196 0.197 Max. Proportion of Voting Days Confined Black 1.000
1.000 1.000 Mean Proportion of Voting Days Confined White 0.210 0.210 0.210
Max. Proportion of Voting Days Confined White 1.000 1.000 1.000 Mean Control
Turnout Black 0.293 0.291 0.298 Mean Control Turnout White 0.379 0.384 0.385
Observations 28382 31616 35843 Control: 28 days 35 days 42 days Treatment: 17
days 31 days 60 days Confined During Voting Days -0.049*** -0.049*** -0.045***
(0.007) (0.007) (0.007) Confined $\times$ Black 0.008 0.005 0.003 (0.010)
(0.008) (0.009) Proportion of Voting Days Confined -0.335*** -0.192***
-0.121*** (0.072) (0.033) (0.027) Proportion Confined $\times$ Black -0.076
-0.083*** -0.072*** (0.075) (0.025) (0.021) Mean Proportion of Voting Days
Confined Black 0.111 0.165 0.197 Max. Proportion of Voting Days Confined Black
0.581 0.968 1.000 Mean Proportion of Voting Days Confined White 0.116 0.175
0.210 Max. Proportion of Voting Days Confined White 0.581 1.000 1.000 Mean
Control Turnout Black 0.295 0.305 0.308 Mean Control Turnout White 0.382 0.385
0.388 Observations 24709 35881 45868

  * •

Note: This table reports OLS estimates of (1) the effect of being booked into
a county jail during 2020 voting days for any length of time on a registered
voter’s probability of voting in the 2020 election, interacted with an
indicator for whether the individual is Black (0 = non-Hispanic white), with
covariates; and (2) the effect of the proportion of 2020 voting days during
which a registered voter booked into a county jail during voting days was
confined on their probability of voting in the 2020 election, interacted with
an indicator for whether the individual is Black, with covariates. Models are
estimated on samples as described in the notes to Table 1, including only
Black and white individuals, and including only states that report voter race
to L2. All models include jail and week-of-year fixed effects; robust standard
errors two-way clustered at the jail and week-of-year level are reported in
parentheses. ***p $<$ 0.01, **p $<$ 0.05, *p $<$ 0.10.

##  Appendix A Appendix: Registered Voters; Match Probability $p>0.95$

Figure A.1: Balance Tests (Joint F-test P-values)
Match Probability $p>0.95$

  * •

Note: This figure reports estimates of balance on individual- and booking-
level characteristics for post-Election Day control windows ranging between 7
and 42 days and treatment windows containing legal voting days in 2020, for
the sample of matched records (registered voters incarcerated in county jails)
with match probability $p>0.95$. For each post-Election Day control window we
initially tested for balance using treatment windows containing the full set
of voting days in each state in 2020, sequentially dropping the earliest
voting day in the sample and repeating the balance test until we reached a
treatment window of 7 days prior to Election Day.

Figure A.2: Distribution of Early Ballot Return
Match Probability $p>0.95$

  * •

Note: This figure reports the cumulative distribution of early ballot return
in the L2 voter file data for 2020, where available. The smallest treatment
window in the samples reported in Table A.1 begins on October 27, indicated
with a vertical dashed line. Approximately 30% of 2020 ballots were returned
after October 27.

Table A.1: Balance Tests
Match Probability $p>0.95$

| Control: | 7 days | 14 days | 21 days | 28 days | 35 days | 42 days
---|---|---|---|---|---|---|---
| Treatment: | 7 days | 60 days | 60 days | 18 days | 19 days | 31 days
Age (L2) |  | 0.000 | -0.000 | -0.000 | -0.000 | 0.000 | 0.000
|  | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
Black (L2) |  | 0.036** | 0.012* | 0.020** | 0.019* | 0.013 | 0.012
|  | (0.017) | (0.007) | (0.008) | (0.010) | (0.009) | (0.008)
White (L2) |  | 0.012 | 0.002 | 0.006 | -0.001 | -0.005 | 0.003
|  | (0.014) | (0.006) | (0.006) | (0.007) | (0.006) | (0.006)
Male (L2) |  | -0.003 | 0.007* | 0.005 | 0.005 | 0.004 | 0.001
|  | (0.009) | (0.004) | (0.004) | (0.005) | (0.005) | (0.005)
Democrat (L2) |  | -0.026** | -0.005 | -0.007 | -0.008 | -0.010* | -0.005
|  | (0.011) | (0.005) | (0.005) | (0.006) | (0.005) | (0.005)
Republican (L2) |  | -0.000 | -0.005 | -0.005 | -0.005 | -0.005 | -0.008
|  | (0.012) | (0.006) | (0.006) | (0.007) | (0.007) | (0.006)
Number of Charges (JDI) |  | -0.001 | 0.000 | -0.000 | -0.001 | -0.001 | -0.000
|  | (0.001) | (0.000) | (0.000) | (0.001) | (0.001) | (0.001)
Top Charge: DUI (JDI) |  | 0.029 | -0.017 | -0.012 | -0.007 | 0.000 | -0.010
|  | (0.027) | (0.013) | (0.013) | (0.017) | (0.015) | (0.013)
Top Charge: Drug (JDI) |  | 0.052** | 0.000 | -0.001 | 0.002 | 0.010 | -0.002
|  | (0.026) | (0.012) | (0.012) | (0.015) | (0.015) | (0.013)
Top Charge: Property (JDI) |  | 0.044* | -0.004 | -0.003 | -0.002 | 0.008 | -0.005
|  | (0.026) | (0.011) | (0.012) | (0.015) | (0.014) | (0.013)
Top Charge: Public Order (JDI) |  | 0.041 | -0.006 | -0.003 | -0.004 | 0.009 | 0.000
|  | (0.025) | (0.011) | (0.011) | (0.014) | (0.014) | (0.012)
Top Charge: Violent (JDI) |  | 0.056** | -0.011 | -0.008 | -0.013 | -0.002 | -0.015
|  | (0.024) | (0.011) | (0.011) | (0.014) | (0.013) | (0.012)
Observations |  | 15312 | 54252 | 60834 | 45375 | 52980 | 68785
Joint F-Test p-value |  | 0.122 | 0.164 | 0.205 | 0.113 | 0.188 | 0.193

  * •

Note: This table reports estimates of balance on individual- and booking-level
characteristics for post-Election Day control windows ranging between 7 and 42
days and treatment windows containing legal voting days in 2020,for the sample
of matched records (registered voters incarcerated in county jails) with match
probability $p>0.95$. As reported in Figure A.1, for each control group window
we initially tested for balance using treatment windows containing the full
set of voting days in each state in 2020, sequentially dropping the earliest
voting day in the sample and repeating the balance test until we reached a
treatment window of 7 days prior to Election Day. This table reports balance
for the largest treatment windows for which the joint F-test p-values for that
treatment window and for all smaller treatment windows were greater than 0.10.
The control window contains bookings beginning on November 4 through November
10. The outcome is whether a booking occurred during a treatment window (1)
versus a control window (0). All models include jail-level fixed effects;
robust standard errors clustered on jails are reported in parentheses. ***p
$<$ 0.01, **p $<$ 0.05, *p $<$ 0.10.

Table A.2: Summary Statistics
Match Probability $p>0.95$

Treatment/Control: |  T |  C |  T |  C |  T |  C |  T |  C |  T |  C |  T |  C
---|---|---|---|---|---|---|---|---|---|---|---|---
Days: |  7d |  7d |  60d |  14d |  60d |  21d |  18d |  28d |  19d |  35d |  31d |  42d
Age (L2) |  36.706 |  36.744 |  36.650 |  36.846 |  36.650 |  36.771 |  36.634 |  36.712 |  36.642 |  36.714 |  36.659 |  36.705
Black (L2) |  0.269 |  0.258 |  0.289 |  0.260 |  0.289 |  0.260 |  0.271 |  0.261 |  0.272 |  0.261 |  0.284 |  0.267
White (L2) |  0.595 |  0.593 |  0.587 |  0.594 |  0.587 |  0.592 |  0.586 |  0.593 |  0.586 |  0.596 |  0.581 |  0.589
Male (L2) |  0.721 |  0.718 |  0.716 |  0.712 |  0.716 |  0.715 |  0.719 |  0.716 |  0.720 |  0.717 |  0.717 |  0.718
Democrat (L2) |  0.404 |  0.423 |  0.422 |  0.413 |  0.422 |  0.414 |  0.409 |  0.412 |  0.409 |  0.413 |  0.419 |  0.414
Republican (L2) |  0.177 |  0.184 |  0.182 |  0.188 |  0.182 |  0.187 |  0.176 |  0.186 |  0.176 |  0.185 |  0.177 |  0.185
Non-Partisan or Other Party (L2) |  0.419 |  0.394 |  0.396 |  0.399 |  0.396 |  0.400 |  0.414 |  0.402 |  0.415 |  0.402 |  0.405 |  0.401
Number of Charges (JDI) |  2.441 |  2.567 |  2.489 |  2.504 |  2.489 |  2.505 |  2.463 |  2.517 |  2.480 |  2.495 |  2.477 |  2.502
Top Charge: Violent (JDI) |  0.298 |  0.283 |  0.284 |  0.296 |  0.284 |  0.295 |  0.292 |  0.301 |  0.291 |  0.299 |  0.287 |  0.302
Top Charge: Property (JDI) |  0.193 |  0.195 |  0.190 |  0.191 |  0.190 |  0.193 |  0.197 |  0.194 |  0.197 |  0.194 |  0.194 |  0.192
Top Charge: Drug (JDI) |  0.127 |  0.129 |  0.135 |  0.127 |  0.135 |  0.129 |  0.127 |  0.125 |  0.127 |  0.126 |  0.131 |  0.127
Top Charge: Public Order (JDI) |  0.289 |  0.288 |  0.292 |  0.283 |  0.292 |  0.282 |  0.285 |  0.282 |  0.286 |  0.281 |  0.289 |  0.278
Top Charge: DUI (JDI) |  0.064 |  0.068 |  0.065 |  0.071 |  0.065 |  0.069 |  0.067 |  0.067 |  0.067 |  0.068 |  0.065 |  0.068
Top Charge: Criminal Traffic (JDI) |  0.029 |  0.036 |  0.033 |  0.032 |  0.033 |  0.032 |  0.032 |  0.032 |  0.032 |  0.033 |  0.033 |  0.032
Length of Stay (days) (JDI) |  32.268 |  35.361 |  35.452 |  33.670 |  35.452 |  33.653 |  32.461 |  33.263 |  32.963 |  32.893 |  34.859 |  34.165
Confined During Voting Days |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000
Proportion of Voting Days Confined |  0.088 |  0.000 |  0.206 |  0.000 |  0.206 |  0.000 |  0.146 |  0.000 |  0.153 |  0.000 |  0.186 |  0.000
Turnout |  0.264 |  0.297 |  0.269 |  0.305 |  0.269 |  0.308 |  0.267 |  0.306 |  0.267 |  0.308 |  0.269 |  0.311
Observations |  8060 |  7252 |  40962 |  13290 |  40962 |  19872 |  19414 |  25961 |  20568 |  32412 |  30515 |  38270

  * •

Note: This table reports mean values of individual- and booking-level
characteristics for registered voters in the balanced samples reported in
Table A.1, containing matched records (registered voters incarcerated in
county jails) with match probability $p>0.95$, by treatment and control
groups.

Table A.3: The Effect of Incarceration on Voting From Jail
Match Probability $p>0.95$

| Control: | 7 days | 14 days | 21 days
---|---|---|---|---
| Treatment: | 7 days | 60 days | 60 days
Confined During Voting Days |  | -0.036*** | -0.041*** | -0.042***
|  | (0.002) | (0.002) | (0.002)
with covariates |  | -0.039*** | -0.043*** | -0.043***
|  | (0.002) | (0.002) | (0.002)
Proportion of Voting Days Confined |  | -0.710*** | -0.259*** | -0.256***
|  | (0.067) | (0.023) | (0.023)
with covariates |  | -0.592*** | -0.213*** | -0.209***
|  | (0.051) | (0.021) | (0.020)
Mean Proportion of Voting Days Confined |  | 0.088 | 0.206 | 0.206
Max. Proportion of Voting Days Confined |  | 0.421 | 1.000 | 1.000
Mean Control Turnout |  | 0.297 | 0.305 | 0.308
Observations |  | 15312 | 54252 | 60834
| Control: | 28 days | 35 days | 42 days
| Treatment: | 18 days | 19 days | 31 days
Confined During Voting Days |  | -0.039*** | -0.039*** | -0.042***
|  | (0.002) | (0.002) | (0.002)
with covariates |  | -0.042*** | -0.043*** | -0.045***
|  | (0.002) | (0.002) | (0.002)
Proportion of Voting Days Confined |  | -0.374*** | -0.349*** | -0.299***
|  | (0.045) | (0.045) | (0.025)
with covariates |  | -0.300*** | -0.278*** | -0.242***
|  | (0.037) | (0.037) | (0.021)
Mean Proportion of Voting Days Confined |  | 0.146 | 0.153 | 0.186
Max. Proportion of Voting Days Confined |  | 1.000 | 1.000 | 1.000
Mean Control Turnout |  | 0.306 | 0.308 | 0.311
Observations |  | 45375 | 52980 | 68785

  * •

Note: This table reports OLS estimates of (1) the effect of being booked into
a county jail during 2020 voting days for any length of time on a registered
voter’s probability of voting in the 2020 election, with and without
covariates; and (2) the effect of the proportion of 2020 voting days during
which a registered voter booked into a county jail during voting days was
confined on their probability of voting in the 2020 election, with and without
covariates, for the sample of matched records (registered voters incarcerated
in county jails) with match probability $p>0.95$. Models are estimated on
samples as described in the notes to Table A.1. All models include jail and
week-of-year fixed effects; robust standard errors two-way clustered at the
jail and week-of-year level are reported in parentheses. ***p $<$ 0.01, **p
$<$ 0.05, *p $<$ 0.10.

Table A.4: Placebo Tests
Match Probability $p>0.95$

|  | 2016 Placebo |  | 2012 Placebo |
---|---|---|---|---|---
Control/Treatment: (7 days, 7 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.024** | -0.026** | 0.005 | -0.025**
|  | (0.010) | (0.010) | (0.013) | (0.012)
Mean Control Turnout |  | 0.400 | 0.339 | 0.525 | 0.386
Observations |  | 9575 | 9575 | 6488 | 6488
Control/Treatment: (14 days, 60 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.010* | -0.031*** | -0.002 | -0.034***
|  | (0.006) | (0.006) | (0.008) | (0.008)
Mean Control Turnout |  | 0.403 | 0.351 | 0.516 | 0.397
Observations |  | 34029 | 34029 | 23132 | 23132
Control/Treatment: (21 days, 60 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.008 | -0.035*** | -0.003 | -0.041***
|  | (0.005) | (0.005) | (0.007) | (0.007)
Mean Control Turnout |  | 0.404 | 0.355 | 0.515 | 0.403
Observations |  | 38117 | 38117 | 25914 | 25914
Control/Treatment: (28 days, 18 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.011** | -0.035*** | -0.002 | -0.039***
|  | (0.005) | (0.006) | (0.007) | (0.007)
Mean Control Turnout |  | 0.406 | 0.353 | 0.516 | 0.401
Observations |  | 28325 | 28325 | 19123 | 19123
Control/Treatment: (35 days, 19 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.009* | -0.038*** | -0.002 | -0.042***
|  | (0.005) | (0.006) | (0.007) | (0.007)
Mean Control Turnout |  | 0.408 | 0.355 | 0.517 | 0.404
Observations |  | 33079 | 33079 | 22325 | 22325
Control/Treatment: (42 days, 31 days) |  | 2016 | 2020 | 2012 | 2020
Confined During Voting Days |  | 0.006 | -0.038*** | -0.003 | -0.042***
|  | (0.004) | (0.004) | (0.006) | (0.006)
Mean Control Turnout |  | 0.408 | 0.358 | 0.518 | 0.406
Observations |  | 42934 | 42934 | 28987 | 28987

  * •

Note: This table reports placebo tests of the effects of 2020 jail
incarceration on 2016 and 2012 voter turnout. The 2016 (2012) placebo samples
include registered voters in each of our balanced samples who were registered
to vote on both Election Day 2020 and Election Day 2016 (Election Day 2012).
The 2016 (2012) placebo tests report OLS estimates of the effect of a 2020
booking occurring in a pre-Election Day 2020 window on a registered voter’s
probability of voting in the 2016 (2012) election, for the registered voters
in each of the balanced samples reported in Table A.1. All models include jail
fixed effects; robust standard errors clustered at the jail level are reported
in parentheses. ***p $<$ 0.01, **p $<$ 0.05 , *p $<$ 0.10.

##  Appendix B Appendix: Registered and Unregistered Voters

Figure B.1: Balance Tests (Joint F-test P-values)
Registered and Unregistered Voters

  * •

Note: This figure reports estimates of balance on individual- and booking-
level characteristics for post-Election Day control windows ranging between 7
and 42 days and treatment windows containing legal voting days in 2020, for
the full sample of individuals booked into jail during this time period. For
each post-Election Day control window we initially tested for balance using
treatment windows containing the full set of voting days in each state in
2020, sequentially dropping the earliest voting day in the sample and
repeating the balance test until we reached a treatment window of 7 days prior
to Election Day.

Table B.1: Balance Tests
Registered and Unregistered Voters

| Control: | 7 days | 14 days | 21 days | 28 days
---|---|---|---|---|---
| Treatment: | 7 days | 13 days | 12 days | 11 days
Age (JDI) |  | 0.000 | 0.000 | 0.000 | 0.000
|  | (0.000) | (0.000) | (0.000) | (0.000)
Black (JDI) |  | 0.006 | 0.014 | 0.010 | 0.007
|  | (0.012) | (0.009) | (0.008) | (0.007)
White (JDI) |  | -0.005 | 0.005 | 0.004 | 0.003
|  | (0.011) | (0.009) | (0.008) | (0.007)
Male (JDI) |  | -0.001 | -0.001 | -0.000 | -0.000
|  | (0.006) | (0.005) | (0.004) | (0.004)
Number of Charges (JDI) |  | -0.001 | -0.000 | -0.000 | -0.001*
|  | (0.000) | (0.001) | (0.001) | (0.000)
Top Charge: DUI (JDI) |  | 0.022 | -0.004 | -0.004 | -0.006
|  | (0.020) | (0.016) | (0.014) | (0.013)
Top Charge: Drug (JDI) |  | 0.032* | 0.021 | 0.012 | 0.003
|  | (0.020) | (0.015) | (0.013) | (0.012)
Top Charge: Property (JDI) |  | 0.033* | 0.014 | 0.005 | -0.005
|  | (0.018) | (0.014) | (0.013) | (0.012)
Top Charge: Public Order (JDI) |  | 0.029* | 0.018 | 0.012 | 0.003
|  | (0.018) | (0.014) | (0.012) | (0.011)
Top Charge: Violent (JDI) |  | 0.041** | 0.018 | 0.013 | -0.003
|  | (0.018) | (0.014) | (0.013) | (0.011)
Observations |  | 38623 | 72096 | 86248 | 96446
Joint F-Test p-value |  | 0.172 | 0.103 | 0.203 | 0.115

  * •

Note: This table reports estimates of balance on individual- and booking-level
characteristics for post-Election Day control windows ranging between 7 and 28
days and treatment windows containing legal voting days in 2020, for the full
sample of individuals booked into jail during this time period. As reported in
Figure B.1, for each control group window we initially tested for balance
using treatment windows containing the full set of voting days in each state
in 2020, sequentially dropping the earliest voting day in the sample and
repeating the balance test until we reached a treatment window of 7 days prior
to Election Day. This table reports balance for the largest treatment windows
for which the joint F-test p-values for that treatment window and for all
smaller treatment windows were greater than 0.10. The outcome is whether a
booking occurred during a treatment window (1) versus a control window (0).
All models include jail-level fixed effects; robust standard errors clustered
on jails are reported in parentheses. ***p $<$ 0.01, **p $<$ 0.05, *p $<$
0.10.

Table B.2: Summary Statistics
Registered and Unregistered Voters

Treatment/Control: |  T |  C |  T |  C |  T |  C |  T |  C
---|---|---|---|---|---|---|---|---
Days: |  7d |  7d |  13d |  14d |  12d |  21d |  11d |  28d
Age (JDI) |  35.708 |  35.742 |  35.749 |  35.750 |  35.755 |  35.697 |  35.708 |  35.669
Black (JDI) |  0.343 |  0.325 |  0.344 |  0.328 |  0.345 |  0.328 |  0.340 |  0.328
White (JDI) |  0.582 |  0.594 |  0.582 |  0.592 |  0.581 |  0.592 |  0.584 |  0.592
Male (JDI) |  0.776 |  0.776 |  0.772 |  0.773 |  0.773 |  0.773 |  0.773 |  0.774
Number of Charges (JDI) |  2.716 |  2.778 |  2.739 |  2.777 |  2.744 |  2.777 |  2.723 |  2.812
Top Charge: Violent (JDI) |  0.261 |  0.253 |  0.260 |  0.259 |  0.259 |  0.257 |  0.259 |  0.264
Top Charge: Property (JDI) |  0.207 |  0.207 |  0.204 |  0.206 |  0.203 |  0.208 |  0.205 |  0.209
Top Charge: Drug (JDI) |  0.142 |  0.145 |  0.145 |  0.144 |  0.145 |  0.145 |  0.143 |  0.141
Top Charge: Public Order (JDI) |  0.321 |  0.318 |  0.322 |  0.315 |  0.323 |  0.315 |  0.320 |  0.313
Top Charge: DUI (JDI) |  0.042 |  0.045 |  0.042 |  0.046 |  0.042 |  0.046 |  0.044 |  0.045
Top Charge: Criminal Traffic (JDI) |  0.027 |  0.032 |  0.028 |  0.029 |  0.028 |  0.029 |  0.029 |  0.028
Length of Stay (days) (JDI) |  41.127 |  44.463 |  43.036 |  42.640 |  42.681 |  42.187 |  40.931 |  41.502
Confined During Voting Days |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000 |  1.000 |  0.000
Proportion of Voting Days Confined |  0.091 |  0.000 |  0.133 |  0.000 |  0.127 |  0.000 |  0.117 |  0.000
Registered by Election Day |  0.292 |  0.290 |  0.291 |  0.292 |  0.290 |  0.294 |  0.292 |  0.292
Voted |  0.073 |  0.081 |  0.071 |  0.083 |  0.072 |  0.084 |  0.073 |  0.084
Observations |  20501 |  18122 |  37287 |  34809 |  34371 |  51877 |  30669 |  65777

  * •

Note: This table reports mean values of individual- and booking-level
characteristics for individuals in the balanced samples reported in Table B.1,
comprising both registered and unregistered voters booked into county jails
during the specified time periods, by treatment and control groups.

Table B.3: The Effect of Incarceration on Registering From Jail

| Control: | 7 days | 14 days | 21 days | 28 days
---|---|---|---|---|---
| Treatment: | 7 days | 13 days | 12 days | 11 days
Confined During Voting Days |  | 0.011*** | 0.011*** | 0.010*** | 0.011***
|  | (0.002) | (0.001) | (0.001) | (0.001)
with covariates |  | 0.007*** | 0.007*** | 0.006*** | 0.006***
|  | (0.002) | (0.001) | (0.001) | (0.001)
Proportion of Voting Days Confined |  | -0.335*** | -0.278*** | -0.287*** | -0.296***
|  | (0.083) | (0.043) | (0.042) | (0.044)
with covariates |  | -0.184*** | -0.163*** | -0.168*** | -0.174***
|  | (0.068) | (0.032) | (0.030) | (0.032)
Mean Proportion of Voting Days Confined |  | 0.091 | 0.133 | 0.127 | 0.117
Max. Proportion of Voting Days Confined |  | 0.421 | 0.737 | 0.684 | 0.632
Mean Control Registration Rate |  | 0.290 | 0.292 | 0.294 | 0.292
Observations |  | 38623 | 72096 | 86248 | 96446

  * •

Note: This table reports OLS estimates of (1) the effect of being booked into
a county jail during 2020 voting days for any length of time on an
individual’s probability of being registered to vote, with and without
covariates; and (2) the effect of the proportion of 2020 voting days during
which an individual booked into a county jail during voting days was confined
on their probability of being registered to vote, with and without covariates,
where registration is defined as matching to L2 records with match probability
$p>0.95$. Models are estimated on samples as described in the notes to Table
B.1. All models include jail and week-of-year fixed effects; robust standard
errors two-way clustered at the jail and week-of-year level are reported in
parentheses. ***p $<$ 0.01, **p $<$ 0.05, *p $<$ 0.10.

Table B.4: The Effect of Incarceration on Voting From Jail
Unconditional on Registration Status

| Control: | 7 days | 14 days | 21 days | 28 days
---|---|---|---|---|---
| Treatment: | 7 days | 13 days | 12 days | 11 days
Confined During Voting Days |  | -0.005*** | -0.005*** | -0.005*** | -0.005***
|  | (0.001) | (0.001) | (0.001) | (0.001)
with covariates |  | -0.006*** | -0.007*** | -0.007*** | -0.007***
|  | (0.001) | (0.001) | (0.001) | (0.001)
Proportion of Voting Days Confined |  | -0.278*** | -0.206*** | -0.217*** | -0.239***
|  | (0.040) | (0.033) | (0.034) | (0.031)
with covariates |  | -0.215*** | -0.154*** | -0.162*** | -0.181***
|  | (0.033) | (0.028) | (0.028) | (0.025)
Mean Proportion of Voting Days Confined |  | 0.091 | 0.133 | 0.127 | 0.117
Max. Proportion of Voting Days Confined |  | 0.421 | 0.737 | 0.684 | 0.632
Mean Control Turnout |  | 0.081 | 0.083 | 0.084 | 0.084
Observations |  | 38623 | 72096 | 86248 | 96446

  * •

Note: This table reports OLS estimates of (1) the effect of being booked into
a county jail during 2020 voting days for any length of time on an
individual’s probability of voting in the 2020 election, with and without
covariates; and (2) the effect of the proportion of 2020 voting days during
which an individual booked into a county jail during voting days was
incarcerated on their probability of voting in the 2020 election, with and
without covariates, unconditional on registration status. Models are estimated
on samples as described in the notes to Table B.1. All models include jail and
week-of-year fixed effects; robust standard errors two-way clustered at the
jail and week-of-year level are reported in parentheses. ***p $<$ 0.01, **p
$<$ 0.05, *p $<$ 0.10.

##  Appendix C Appendix: Data and Matching

###  C.1 Jail Data Initiative Booking Records

Our data on jail bookings are sourced from the Jail Data Initiative (JDI)
NoSQL database of booking records888For detailed documentation of the JDI
pipeline and database architecture, see
https://jaildatainitiative.org/documentation. We initially sampled all
individuals in the JDI database with jail booking dates from August 5, 2020 to
February 1, 2021. These dates span 90 days on each side of 2020 Election Day
(November 3, 2020) and include all legal voting days in 2020 (the earliest of
which was September 4, 2020 in North Carolina, for absentee mail-in voting, 60
days prior to Election Day), and a post-Election Day period encompassing all
control windows we employ. This sample includes 944,985 bookings. For each of
these bookings we retrieved individual- and booking-level standardized fields
from the JDI database as detailed below.

Name Jail rosters may report a single name string, and/or first name, middle
name, middle initial, and/or surname. In order to compare name components as
part of the match process, we split names into components using the TupiLabs
Java HumanNameParser
package999https://github.com/tupilabs/HumanNameParser.java.. Additionally, we
create soundex codes for surnames, using the Apache Soundex Java
class101010https://commons.apache.org/proper/commons-
codec/apidocs/org/apache/commons/codec/language/Soundex.html..

Length of Stay Length of stay is used as a booking-level covariate in analyses
of the effects of jail incarceration. Among the initial sample of 944,985
bookings, length of stay ranges from 1 to 736 days, and the mean length of
stay is 44.48 days. Length of stay is used as a booking-level covariate in
analyses of the effects of jail incarceration.

Age Jail rosters may report age, year of birth, and/or date of birth. The JDI
booking data report a standardized age field constructed from these original
fields. Among the initial sample of 944,985 bookings, age ranges from 13 to
95, and the mean age is 35.7. The proportion without reported age is 0.19. Age
is used as an individual-level covariate in analyses that include both
unregistered and registered voters. We exclude from those samples booking
records without reported age.

Gender Jail rosters may report sex and/or gender. The JDI database reports a
standardized sex-gender field from these original fields. Values (e.g., “F”,
“Female”, “FEM”, etc.) are manually encoded as male, female, trans, non-binary
or unknown. Among the initial sample of 944,985 bookings, the proportion
without reported or encoded sex-gender is 0.24. Among those with encoded sex-
gender, the proportion male is 0.78 and the proportion female is 0.23. 17
records (0.00) are encoded as non-binary or trans; these are re-categorized as
unknown for matching with L2 voter records. Encoded sex-gender is used as an
individual-level covariate in analyses that include both unregistered and
registered voters. We exclude from those samples booking records without
encoded sex-gender.

Number of Charges Jail rosters may or may not report charges for each booking;
charges may appear as a single string in an unparseable format. Among the
initial sample of 944,985 bookings, the proportion with no determinable number
of charges is 0.10. Among those with parseable charges, the mean number of
charges per booking is 2.6. Number of charges is used as a booking-level
covariate in analyses of the effects of jail incarceration. We exclude from
all samples booking records without parseable charge records.

Top Charge For each booking with parseable charge description(s) as specified
above, the JDI database reports the highest-probability-match Uniform Crime
Classification Standard (UCCS) code from the Text-Based Offense Classification
(TOC) algorithm developed by the Criminal Justice Administrative Records
System at the University of Michigan111111For detailed information about the
CJARS TOC model, see https://cjars-toc.isr.umich.edu/.. In some cases, the
charge string is not sufficiently legible for the model to produce a match
classification. The JDI database assigns a “top” charge type for the booking
from among the least granular classification codes (“offense type” codes, with
values 1-6 corresponding to the charge categories outlined below), using a
hierarchical ordering of classification codes by severity. This order of
severity, from most to least serious, is: violent, property, drug, public
order, DUI, criminal traffic. Among the initial sample of 944,985 bookings,
the proportion with no determinable top charge type is 0.2. Among those with
encoded top charge types, the proportions of each type are: violent (0.21),
property (0.17), drug (0.12), public order (0.25), DUI (0.04), and criminal
traffic (0.03). Top charge indicators are used as booking-level covariates in
analyses of the effects of jail incarceration. We exclude from all samples
booking records without parseable charge records.

County FIPS Code The JDI database reports the unique five-digit Federal
Information Processing Standard Publication 6-4 (FIPS) county-identifying code
from the Bureau of Justice Statistics 2013 Census of Jails corresponding to
the county in which a jail is
located.121212https://nvlpubs.nist.gov/nistpubs/Legacy/FIPS/fipspub6-4.pdf,
https://doi.org/10.3886/ICPSR36128.v4. Among the initial sample of 944,985
bookings, there are 985 unique county FIPS codes.

###  C.2 L2 Voter Records

We sourced voter records from the L2 voter files licensed to New York
University. The records used here were pulled in Spring 2021. The voter
records from which we derive match candidates include 195,655,326 registered
voters in the 42 states for which we have jail booking records.

Age Among the initial sample of 195,655,326 voters, age ranges from 18 to 100,
and the mean age is 50.07. The proportion without reported age is 0.02. L2 age
is used as an individual-level covariate in analyses of the effects of jail
incarceration on the voting behavior of registered voters. We exclude from
those samples records without L2 age.

Gender Among the initial sample of 195,655,326 voters, the proportion without
reported gender is 0.01. Among those with reported gender, the proportion male
is 0.47 and the proportion female is 0.53. L2 gender is used as an individual-
level covariate in analyses of the effects of jail incarceration on the voting
behavior of registered voters. We exclude from those samples records without
L2 gender.

Race Among the initial sample of 195,655,326 voters, the broadest L2
race/ethnicity field may take one of the following values: “East and South
Asian,” “European,” “Hispanic and Portuguese,” “Likely African-American,” or
“Other.” In states where these fields are not reported, L2 uses an algorithmic
process to predict race/ethnicity, relying on a proprietary database of name-
ethnicity mappings, with additional census block and secondary in-block
surname assessment. We re-categorize L2 race/ethnicity into one of three
possible values: (non-Hispanic) “white” if L2 ethnicity value is “European,”
(non-Hispanic) “Black” if L2 ethnicity value is “Likely African-American,” and
“Other” if L2 ethnicity value is “East and South Asian,” “Hispanic and
Portuguese” or “Other.” Among our sample of voters, the proportion with
unknown/unpredicted race is 0.09. Among those with known/predicted race, the
proportion white is 0.68, the proportion Black is 0.12, and the proportion
Other is 0.20. L2 race/ethnicity is used as an individual-level covariate in
analyses of the effects of jail incarceration on the voting behavior of
registered voters. We exclude from those samples records without L2
race/ethnicity.

Party L2 reports voter party registration data where available. Where party
registration data are not available, L2 models “likely” party identification
(https://www.l2datamapping.com/help?ds=VM_US#data__party). The party
registration/identification field may take one of 57 values or “unknown.” We
re-categorize the party registration/identification field into one of three
possible values: “Democratic,” “Republican”, and “Non-Partisan or Other”.
Among our sample of voters, the proportion with unknown party
registration/identification is 0.00. Among those with known/predicted party,
the proportion Democratic is 0.41, the proportion Republican is 0.32, and the
proportion Non-Partisan or Other is 0.28. L2 party registration/identification
is used as an individual-level covariate in analyses of the effects of jail
incarceration on the voting behavior of registered voters. We exclude from
those samples records without L2 party registration/identification.

Registration Date Voter registration date is used for sample inclusion in
turnout analysis. L2 reports a calculated registration date field that reduces
missingness in reported registration date, and captures the earliest
registration date for an individual, since official registration dates are
often reset when individuals move between voting districts. Among the initial
sample of 195,655,326 voters, the mean calculated voter registration date is
March 6, 1990.

Voting Indicators L2 reports binary fields that indicate whether registered
voters voted. We record such fields for the 2020, 2016 and 2012 general
elections. Among the initial sample of 195,464,995 registered voters, the mean
registered voter turnout for the 2020 general election was 0.75, the mean
registered voter turnout for the 2016 general election was 0.59, and the mean
registered voter turnout for the 2012 general election was 0.50.

County FIPS Code Among the initial sample of 195,655,326 voters there are
3,004 unique county FIPS codes.

###  C.3 Probabilistic Record Linkage

Using the pools of booking and voter records described above, we conduct
probabilistic record linkage using the Fellegi-Sunter/Expectation Maximization
linkage model enumerated by McDonough, Enamorado and Mendelberg (2022) and
Enamorado, Fifield and Imai (2019).

Match-candidate booking rows appear as follows (note: full name and soundex
code are used for blocking and initialization, but not for matching):

Booking ID | FIPS | Age | Gender | First | Middle | Last | Full Name | Soundex
---|---|---|---|---|---|---|---|---
12a3b… | 11217 | 30 |  | Jane | A | Doe | Jane A Doe | D000

Match-candidate voter rows appear as follows:

Voter ID | FIPS | Age | Gender | First | Middle | Last | Full Name | Soundex
---|---|---|---|---|---|---|---|---
LALAL1… | 11217 | 29 | M | John | Adam | Doe | John Adam Doe | D000

We create blocks for matching as below. Importantly, we only run our match
program within-state; if an individual is booked in Alabama but is registered
in the Wyoming voter file, we do not consider them for matching.

  1. 1.

For each unique age value among match-candidate bookings, we select only
match-candidate registered voters such that the absolute value of the
difference (L1 norm) of their age and the current age is $<=$ 2 years (a
blocked age range of 5 years). The intuition for this age bounding leniency is
that jail rosters may not consistently update recorded age for booked
individuals over time, and the scope of the JDI database is 2 years. For
bookings with unknown age, we do not block on voter age.

  2. 2.

For each match-candidate booking with the current age value, we select only
match-candidate registered voters with equivalent surname soundex codes.

Our sampling process runs over a random selection of within-state match-
candidate pairs. We compute a binary match indicator field for parametric
initialization by calculating the average of the Jaro-Winkler similarities of
first name, last name, and full name, considering matches to be any rows with
resultant average $>$ 0.88. We also output numeric similarity vectors for each
match-candidate pair. These are defined by the following logic:

  1. 1.

FIPS: 1 if FIPS values are equal; else 0.

  2. 2.

Age: 2 if the absolute value of the difference of age between booking and
registered voter is $<=$ 1; 1 if either age is missing; else 0.

  3. 3.

Gender: 2 if booking and registered voter gender values are equal; 1 if either
gender is missing; else 0.

  4. 4.

First Name: 2 if Jaro-Winkler similarity of booking and registered voter first
name is $>$ 0.94; 1 if Jaro-Winkler similarity of booking and registered voter
first name is $>$ 0.88 and $<=$ 0.94; else 0.

  5. 5.

Middle Name: 2 if (a) one value is an initial and the other is a complete
middle name, and the initial equals the first letter of the complete middle
name or (b) both values are complete middle names, and their Jaro-Winkler
similarity is $>$ 0.94; 1 if (a) either middle name is missing or (b) both
values are complete middle names, and their Jaro-Winkler similarity is $>$
0.88 and $<=$ 0.94; else 0.

  6. 6.

Last Name: 2 if Jaro-Winkler similarity of booking and registered voter last
name is $>$ 0.94; 1 if Jaro-Winkler similarity of booking and registered voter
last name is $>$ 0.88 and $<=$ 0.94; else 0.

For the synthetic booking and registered voter row examples above, sampling
output would appear as follows:

Booking ID | Registered Voter ID | FIPS | Age | Gender | First | Middle | Last | Average | Match
---|---|---|---|---|---|---|---|---|---
12a3b… | LALAL1… | 1 | 2 | 1 | 0 | 2 | 2 | 0.83 | 0

Sampling results are used to initialize parameters for Fellegi-Sunter
calculation. Subsequently, iterative expectation maximization is conducted to
stabilize parameter estimates from 50 re-samples of 1 million match-candidate
pairs each. Following the full linkage process, we retain only the registered
voter match(es) for each booking with the highest probability score(s), and
discard all matches with probability scores $<$ 0.5. We then conduct in-
block/in-state term frequency re-weighting of first and last names on match
records as described by Enamorado, Fifield and Imai (2019) and Winkler (2000).
The final steps of our matching process are exclusions using the following
logic:

  1. 1.

We exclude any rows where re-weighted match probability is $<0.75$.

  2. 2.

We exclude all rows for any booking that is matched to more than one unique
voter ID.

  3. 3.

We exclude all rows for any registered voter ID that is matched to overlapping
bookings (where booking start and end dates are determined as described in
Section 3.1 of this report).

  4. 4.

We exclude all rows where booking-reported age is $<$ 18.

  5. 5.

We exclude all rows where voter registration date is after Election Day
(November 3, 2020).

In total, out of our initial sample of 944,985 jail bookings we match to
331,703 registered voter records. As a final step to enable measurement of
covariates related to charges against booked individuals, we subset this pool
of matches to include only the 305,239 individuals detained in facilities
whose rosters report charge information.