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Measuring lost votes by mail

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The rise of mail balloting has led to concerns that procedural requirements can lead to “lost votes by mail.” We theorize how procedural requirements can affect the incidence and form of lost votes and highlight three measurement issues with equating lost votes and rejected mail ballots. First, coverage: Not all rejected mail ballots are documented. Second, substitution: Some people whose mail ballot is rejected may subsequently successfully vote, in particular if they were notified in time to take action. Third, deterrence: Others may not return their mail ballots if they expect them to be rejected. While rejected mail ballots could over- or underestimate lost votes, a case study of Pennsylvania’s 2022 general election reveals at least 47% more lost votes than rejected mail ballots. These lost votes could prove electorally consequential in Pennsylvania given the number of mail ballots cast and the substantial partisan splits on mail versus in-person ballots.
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SOCIAL SCIENCES
Measuring lost votes by mail
Marc Meredith1*, Michael Morse2, Amaya Madarang1, Katie Steele1
The rise of mail balloting has led to concerns that procedural requirements can lead to “lost votes by mail. We
theorize how procedural requirements can aect the incidence and form of lost votes and highlight three mea-
surement issues with equating lost votes and rejected mail ballots. First, coverage: Not all rejected mail ballots are
documented. Second, substitution: Some people whose mail ballot is rejected may subsequently successfully
vote, in particular if they were notied in time to take action. Third, deterrence: Others may not return their mail
ballots if they expect them to be rejected. While rejected mail ballots could over- or underestimate lost votes, a
case study of Pennsylvania’s 2022 general election reveals at least 47% more lost votes than rejected mail ballots.
These lost votes could prove electorally consequential in Pennsylvania given the number of mail ballots cast and
the substantial partisan splits on mail versus in- person ballots.
INTRODUCTION
e growing use of mail balloting poses a unique challenge for elec-
tion administration. In short, the availability of mail ballots prom-
ises to increase voter participation (1,2) and satisfaction (3). Yet,
precisely because mail ballots are sent to individuals outside a poll-
ing place, states must impose a series of procedural requirements on
mail balloting (4). Further, its very nature makes election adminis-
trators and voters alike dependent on the performance of the United
States Postal Service (USPS) (5,6).
In recent years, the mix of procedural requirements used for mail
balloting across states has become the focus of substantial public
debate. e core dispute centers around the familiar trade- o be-
tween voter access and electoral integrity. In general, procedural
requirements ensure that mail ballots are cast by their intended re-
cipients in a timely manner. However, Stewart (7) developed the
concept of “lost votes by mail” to highlight that these requirements
may prevent eligible voters from successfully casting ballots too.
Building on Stewart (8), we theorize about how particular proce-
dural requirements aect the incidence and form of lost votes by
mail. We focus on three relevant factors: (i) the extent of rejection of
returned mail ballots, (ii) the ease of substitution from a rejected
mail ballot to cast a counted ballot, and (iii) the degree of deterrence
from returning a mail ballot that may otherwise be rejected. We also
consider the circumstances under which these lost votes by mail
could prove electorally consequential.
Given our theory, we argue that previous approaches used to
measure lost votes by mail are incomplete. In a leading study, Stewart
(8) constructs a national estimate of lost votes by mail based in part
on the aggregate number of rejected mail ballots reported in the
Election Administration and Voting Survey (EAVS). Other scholars
have taken an alternative approach, using individual- level adminis-
trative data on mail ballot rejections (912). However, both ap-
proaches necessarily assume that aggregate counts or administrative
data have complete coverage of rejected mail ballots. Further, nei-
ther approach recognizes that rejected mail ballots may understate
or overstate lost votes depending on the relative amount of substitu-
tion and deterrence. Rejected mail ballots will overstate lost votes
when there is more substitution among voters with rejected mail
ballots than deterrence among voters with unreturned mail ballots.
Alternatively, rejections will understate lost votes when there is
more deterrence than substitution. For both reasons, scholars can-
not compare state mail balloting regimes by simply counting rejected
mail ballots.
We use Pennsylvania as a case study to correct the measurement
of lost votes by mail. We gather separate individual- level adminis-
trative data on both the status of requested mail ballots and the turn-
out of registered voters in the state. As a result, we can measure both
substitution, by observing whether voters who submit a rejected
mail ballot go on to cast a valid vote, and deterrence, by modeling
why registrants who request a mail ballot do not return that ballot.
Further, data publicly revealed during recent litigation in Pennsylvania
enable us to both evaluate the coverage of documented, rejected mail
ballots and compare how rates of substitution vary by whether local
election ocials notied voters of rejections. To be clear, the mag-
nitude of lost votes in Pennsylvania reects the specic state and
electoral cycle we study. However, we expect that the incomplete
coverage of rejected mail ballots, the relationship between notice
and substitution, and the relationship between ballot deadlines and
deterrence are each more generalizable.
We make three points about lost votes by mail. First, we demon-
strate that not all rejected mail ballots are documented as such.
While Pennsylvania reported rejecting about 23,000 mail ballots, we
identify at least 5000 additional rejected mail ballots using a variety
of public sources. Second, we show why some documented rejected
mail ballots should not be considered lost votes. Overall, we nd
that about 15% of voters whose mail ballots were rejected nonethe-
less cast a valid vote. We also show that voters are more likely to
avoid a lost vote when counties notify people about rejected mail
ballots with enough time to correct them. ird, we show why some
mail ballots that are not returned, and thus not rejected, should
nonetheless be considered lost votes. In particular, we estimate that
about an additional 11,000 people in Pennsylvania who requested a
mail ballot were deterred from voting because their mail ballots
would have otherwise been rejected as late. We nd more deterrence
for people who request their mail ballots close to the election, and in
particular in the last 2 days before the request deadline.
Together, clarifying the measurement of lost votes helps rene
both the study and assessment of voting by mail. In total, we con-
clude that Pennsylvania had at least 11,000 more lost votes than the
roughly 23,000 rejected mail ballots documented in the EAVS (13).
1Department of Political Science, University of Pennsylvania, Philadelphia, PA USA.
2University of Pennsylvania Carey Law School, Philadelphia, PA, USA.
*Corresponding author. Email: marcmere@ sas. upenn. edu
Copyright © 2024 The
Authors, some rights
reserved; exclusive
licensee American
Association for the
Advancement of
Science. No claim to
original U.S.
Government Works.
Distributed under a
Creative Commons
Attribution
NonCommercial
License 4.0 (CC BY- NC).
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Further, we show how these lost votes could be electorally conse-
quential because many voters in Pennsylvania vote by mail and mail
and in- person voters have distinct preferences. In our conclusion,
we propose a number of ways to reduce lost votes by mail by focus-
ing on the broader system of mail balloting. is includes reducing
the number of decient mail ballots initially received, improving the
identication and notication of remaining deciencies, and mak-
ing it easier to substitute if a ballot is rejected.
Lost votes by mail
We rst consider the theory of a lost vote by mail before evaluating
how lost votes have been measured in the literature.
Theory
e concept of lost votes was rst developed in the wake of the con-
tested 2000 presidential election. An estimated 4 to 6 million in-
person presidential votes were “lost” because of a combination of
registration problems, polling place operations, faulty voting equip-
ment, and confusing ballot design (14).
As vote mode subsequently shied to include substantial voting
by mail, a related concept of lost votes by mail emerged (7). Lost
votes by mail are distinct from lost votes in person because voting by
mail requires a dierent series of steps than voting in person. De-
pending on the state, some registrants must specically request a
mail ballot. States also impose a series of a procedural requirements
on mail ballots to maintain electoral integrity outside of the polling
place. In general, a voter must mark a received mail ballot and po-
tentially place it in an inner envelope, complete an accompanying
adavit, and return it all by the relevant deadline. Election ocials
then determine whether the voter satised the requirements neces-
sary for the ballot to count.
We build on Stewart (8) to theorize about how particular proce-
dural requirements aect the incidence and form of lost votes by
mail. Stewart oered a typology of reasons for lost votes by mail:
votes lost by the postal service, because a mail ballot request or mail
ballot is sent but not received by a voter or election ocial; votes lost
by election ocials, because a mail ballot request is not fullled;
votes rejected, because of the voter adavit, ballot envelope, or bal-
lot deadline; and votes lost at tabulation, in the form of residual
votes (8). In particular, we identify three relevant factors about pro-
cedural requirements that aect the likelihood of a lost vote. As in
Stewart’s typology, the rst is the extent to which returned mail bal-
lots are rejected for failing to satisfy the procedural requirements.
However, not all rejected mail ballots are lost votes because aected
voters sometimes substitute by taking subsequent steps to cast a
counted ballot. Aer experiencing a mail ballot rejection, a voter
might correct their initial mail ballot, submit a new mail ballot, or
shi to voting in person. Further, people may be deterred from re-
turning their mail ballots because they cannot satisfy the procedural
requirements. Although the literature on mail balloting has yet to
account for deterrence, the concept is central to the literature on
voter ID laws. In that context, not all people aected by the ID law
cast a rejected provisional ballot (15,16). Analogously, not all peo-
ple aected by a ballot receipt deadline cast a mail ballot that is re-
jected because it is received too late.
Given our theory, we expect the extent of lost votes to dier
markedly across states. Beyond the number of mail ballots cast by
state, the particular procedural requirements adopted by a state can
generate dierent levels of initial rejection, subsequent substitution,
or deterrence. In short, we theorize that the relative mix of rejection
and deterrence will depend on whether voters can verify procedural
requirements ex ante. We expect relatively more deterrence than re-
jection when voters can verify requirements ex ante, and the con-
verse when voters cannot.
To illustrate this claim, consider the dierent processes used by
states to verify mail voters’ identities. Having some process is neces-
sary when voting outside of a polling place to maintain electoral
integrity. To that end, some states require voters to sign adavits on
mail ballots in the presence of a notary. We expect such a require-
ment to cause relatively high amounts of deterrence, as we anticipate
that many voters who cannot access a notary will stop the process of
mail balloting. Other states verify whether the voter’s signature on
the adavit is suciently similar to their signature on their registra-
tion record. While we expect that few voters are deterred from sub-
mitting mail ballots because they must sign an adavit, signature
matching has the potential to generate a substantial number of
wrongful rejections, which voters are unlikely to anticipate (17).
Similarly, dierent approaches to notifying voters of a mail ballot
rejection and allowing for the correction of an error could aect the
relative level of substitution. For example, some states begin pro-
cessing mail ballots upon receipt or before Election Day, while oth-
ers wait until Election Day. As a result, some people whose mail
ballot is rejected will have more time to take corrective action (18).
For another, states oer voters dierent types of opportunities to
correct any deciency. Some permit voters to quickly take corrective
action by phone or text, others rely on mail or in- person visits, and
still others require either submitting a new ballot or simply voting in
person (19).
Finally, we also expect the electoral consequences of lost votes to
vary by state, even among those with similar procedural require-
ments. In general, two conditions must both be present for an elec-
tion rule to aect election outcomes: e preferences of those
aected and unaected by the rule must be dierent and a sucient
number of people must be aected by the rule (20). Many election
rules plainly fail to satisfy at least one of these conditions, meaning
that the rule is unlikely to be electorally consequential. In the case of
mail balloting, though, table S9 shows increasing vote mode splits,
in which people who vote by mail disproportionately support Dem-
ocratic candidates compared to people who vote in person. e
larger this vote mode split in a state, the greater the potential for
electoral consequences when uniformly applied procedural require-
ments generates lost votes.
Measurement
e EAVS is used to study many aspects of American election ad-
ministration (2124), including lost votes by mail. However, there
are a set of overlapping concerns about the ability of the survey to
measure the full extent of lost votes by mail, as we conceive of it.
In short, the EAVS asks election ocials to provide a battery of
jurisdiction- level statistics. Stewart used the introduction of the
EAVS to study lost votes by mail in the 2008 election (7) and then
updated the analysis for the 2016 election (8).
e latter analysis incorporated information from the EAVS that
about 380,000 mail ballots were reported to not be counted, which
represented about 1.1% of the approximately 33.5 million mail bal-
lots cast (25). It also mentioned, although did not incorporate, the
8.2 million mail ballots reported in the EAVS as unreturned. In a
similar vein, more recent research used the EAVS to conclude that
the national mail ballot rejection rate declined from about 1 to 0.8%
between 2016 and 2020 (26).
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Figure 1 illustrates some limitations of the current use of the
EAVS for the measurement of lost votes. It compares the reported
percent of mail ballots transmitted that were rejected (y axis) or
went unreturned (x axis) in the 2022 general election. In general, we
expect that measurement error causes Fig. 1 to understate the true
rejection rate in many states. e EAVS suers from known issues of
incomplete reporting (27,28). For example, the EAVS reports that
Illinois rejected 1.3% of mail ballots. However, Illinois has publicly
conrmed that its rejection rate was actually twice that (2.6%) (29);
the result is that Illinois looks typical in Fig. 1, yet is actually more
like an outlier.
Even if the EAVS oered an accurate accounting of rejected and
unreturned mail ballots, the type of data illustrated in Fig. 1 cannot
capture the patterns of substitution or deterrence that are relevant
for measuring lost votes. For one, the EAVS does not dierentiate
between people who did and did not take subsequent steps to cast
counted ballots aer mail ballot rejections. However, substitution
could prevent many rejected mail ballots from turning into lost
votes (30). To see why this limitation of the EAVS matters for evalu-
ating mail balloting policies, consider the case of Texas. In 2021,
Texas adopted a new voting law (SB1), which required mail voters to
provide the same identication number on their mail ballot, either a
driver’s license number or the last four digits of a Social Security
number, as they had on their initial voter registration application
(31). SB1 also instituted a ballot curing process (32). Figure 1 shows
that Texas voters experienced one of the highest mail ballot rejec-
tion rates (2.83%) in 2022, the year the law went into eect. How-
ever, we cannot learn how SB1 aected lost votes using EAVS because
the reported rejections do not account for any subsequent substitution.
Similarly, the EAVS also does not allow researchers to learn more
about why mail ballots are going unreturned. Figure 1 shows that
many states experience an order of magnitude more unreturned
mail ballots than rejected mail ballots. is is not only true in states
that conduct vote- by- mail elections but also oen in states in which
registrants must request a mail ballot before the election. us, if
deterrence is causing even a relatively small share of these unre-
turned ballots, it still could be responsible for a substantial share of
the total lost votes by mail. Yet, we have no clear way to dierentiate
between ballots that went unreturned due to deterrence from those
that went unreturned because the person decided to substitute into
in- person voting or was no longer interested in voting.
A nal limitation of any aggregated data on rejected or unre-
turned mail ballots, such as the EAVS, is that it can obscure sub-
groups of voters who disproportionately experience lost votes by
mail. Several recent studies of mail balloting use individual- level
administrative data to overcome this particular limitation. ese
studies generally nd that people with demographics predictive of
lower turnout (e.g., young, non- white, have voted in fewer previous
elections) cast a disproportionate share of rejected mail ballots (9
12). However, this work has not taken the next step of linking ad-
ministrative data on mail ballots with administrative data on turnout.
As a result, it does not account for the substitution and deterrence
necessary to estimate the total number of lost votes by mail.
Pennsylvania case study
We use Pennsylvania as a case study to correct the measurement of
lost votes by mail. We rst discuss the relevance of new data sources,
then detail the state’s particular mail ballot policies, and nally con-
sider the generalizability of our case study.
Data
Several features of Pennsylvanias 2022 general election are help-
ful for measuring lost votes. First, Pennsylvania provides admin-
istrative data on mail ballot outcomes that can be linked with
turnout outcomes. We can thus measure how many people with
rejected or unreturned mail ballots were nonetheless able to suc-
cessfully vote.
Moreover, during the course of federal litigation over mail ballot-
ing rules in place for the election (33), Pennsylvania county boards
of election were required to disclose additional data beyond what is
captured by the EAVS or administrative data. ese disclosures are
helpful for at least two reasons. For one, the responses were provid-
ed by the counties directly, who retain the rejected ballots, rather
than the state. For another, the responses were signed by attorneys
who had to certify, subject to sanction, that the disclosure “is com-
plete and correct” to “the best of the persons knowledge, informa-
tion, and belief formed aer a reasonable inquiry” (34). As a result,
we believe the litigation data allow us to observe the true number of
rejected ballots, even if those ballots were not documented in ad-
ministrative data. Further, information on whether and when coun-
ty election ocials notied voters of mail ballot deciencies allows
us to study how such policies aect whether rejected mail ballots
become lost votes. We further detail both sets of data in Materials
and Methods, including how we obtained the administrative data
from public records requests and the litigation data from a federal
court’s public docket.
Rules
To vote by mail in Pennsylvania, registrants must rst request a mail
ballot. Pennsylvania accepts mail ballot applications up until a week
before Election Day (35). For the mail ballot to be counted, a voter
must sign and date an adavit, place the ballot in an unmarked se-
crecy envelope, and ensure that election ocials receive the ballot
by 8 p.m. on Election Day (36). Under these rules, there are at least
Fig. 1. Voting by mail outcomes by state.
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six causes of mail ballot rejection: (i) an unsigned adavit, (ii) an
undated adavit, (iii) a misdated adavit, (iv) a so- called “naked”
ballot not placed in a secrecy envelope, (v) a ballot placed in a se-
crecy envelope containing markings, and (vi) a late ballot. In addi-
tion, a voter who did not provide their driver’s license number, the
last four digits of their Social Security number, or a copy of their
photo ID with their mail ballot request, must provide a copy of their
ID when returning their mail ballot (37).
ere is no common protocol in Pennsylvania to notify voters of
rejected mail ballots. Instead, county ocials take vastly dierent
approaches. Depending on the county, election ocials might con-
tact voters with a defective mail ballot, post a list of defective mail
ballots, or take no proactive action. Moreover, even counties with a
notice protocol may not discover a mail ballot deciency with
enough time for a voter to take action. Pennsylvania did not allow
election ocials to process mail ballots received during the 2022
general election until Election Day. us, a county’s ability to learn
about deciencies on mail ballots depended on the extent to which
they engaged in what is generally known as “preprocessing.” Prepro-
cessing refers to actions election ocials take before Election Day to
identify problems with mail ballots without opening the mail ballot
return envelopes. Many Pennsylvania counties do not engage in pre-
processing, but some do. During the 2022 general election, the most
common form of preprocessing focused on the adavit on the out-
side of the mail ballot return envelope. Election ocials could iden-
tify an unsigned, undated, or misdated adavit in preprocessing
because doing so does not require opening the return envelope itself
(38). A few enterprising counties also weighed return envelopes to
infer whether it included an inner secrecy envelope (38). We sum-
marize the substantial litigation over Pennsylvania mail ballot rules
in the Supplemental Materials.
Generalizability
As with any single case study, there are questions about how much we
can generalize what we learn about lost votes by mail in Pennsylvania
to other states. For the reasons previously discussed, the coverage
issues that we document in the EAVS are likely present in many
other states, and in particular on questions in which state election
ocials are reporting quantities collected by county ocials. In gen-
eral, we also expect that the rates of substitution and deterrence in
Pennsylvania will be more generalizable than what we learn about
initial rejections or electoral consequences.
We learn about substitution, and how it is aected by notice, by
comparing the rates of substitution in Pennsylvania counties with
and without notice policies. is within- state variation in notice
policies is particularly useful for learning about the eects of notice
regimes while holding xed many of the other policies that are most
likely to aect substitution rates. ese eects are likely to be infor-
mative of how much additional substitution we are likely to observe
in states with notice, but without ballot curing, and states with-
out notice.
e mail ballot request and receipt deadlines that drive deter-
rence in Pennsylvania are also similar to the policies used in other
states. In general, the USPS recommends that mail ballot applica-
tions be completed at least 15 days before Election Day (39). How-
ever, many states do not follow the recommendation (40).
However, the procedural requirements leading to mail ballot re-
jection in Pennsylvania are somewhat distinct from those in other
states. For one, Pennsylvania rejects undated and misdated mail bal-
lots, a policy that best as we can tell is unique to the state. For an-
other, few other states reject mail ballots not returned in a secrecy
envelope, at least as of the 2020 election (41). On the other hand,
Pennsylvania does not engage in signature matching like many oth-
er states (42).
We also suspect that lost votes by mail may be more electorally
consequential in Pennsylvania than most other states, a point we
discuss in detail below. In short, Pennsylvania has the combination
of more mail balloting, more mail ballots turning into lost votes, and
a greater partisan split in mail versus in- person ballots than many
other states.
RESULTS
Our primary dataset is mail ballot applications linked to voter regis-
trations. e mail ballot data allow us to report descriptive statistics
on mail ballot usage overall and by demographics.
Overall, more than 1.4 million Pennsylvania registrants requested
a mail ballot in the 2022 general election. Based on Table 1, the vast
majority (about 87%) of mail ballots were returned. However, about
11% of mail ballots were not returned and about 1.7% were rejected.
Further, among the voters whose mail ballots were rejected, the
most common reasons varied from issues with the adavit (33.7%),
the secrecy envelope (34.8%), and receipt timing (21.2%).
Table 1. Mail ballot applications by status and rejection reason.
n%
All ballots 1,403,760
By mail ballot status
Accepted 1,223,423 87.2%
Not returned 157,170 11.2%
Rejected 23,167 1.7%
By rejection reason
Adavit 7811 33.7%
Secrecy 8072 34.8%
Late 4900 21.2%
Other 2384 10.3%
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Table S6 further details the rates at which mail ballots go unre-
turned and returned mail ballots get rejected by demographics.
Consistent with the literature (912), registrants most likely to be
minorities, young registrants, and people who previously had not
voted by mail experience a disproportionate share of both rejected
and unreturned mail ballots. Table S6 also shows the relative reasons
for rejection dier by group. For example, both young registrants
and rst- time mail voters are particularly likely to have a mail ballot
rejected because it was received late. In contrast, Black registrants
were the most likely to experience a rejection because of an adavit
error or the lack of necessary ID.
ese descriptive statistics on unreturned and rejected mail bal-
lots, though informative, should not be confused with lost votes.
Instead, we oer them to motivate three questions about (i) the cov-
erage of rejected mail ballots as well as the relationship between
both (ii) rejected and (iii) unreturned mail ballots and lost votes. We
then conclude by considering the potential electoral consequences
of lost votes by mail.
Measurement of lost votes
In this subsection, we rst show that the coverage of rejected mail
ballots in Pennsylvanias administrative data is incomplete. We then
measure the extent of substitution following a rejected mail ballot,
including the extent to which notice to voters of a ballot rejection
can avoid a lost vote. Last, we estimate the extent to which mail bal-
lot request and receipt deadlines contributed to deterrence, as voters
may not return a mail ballot in the face of a likely rejection. For all
three reasons, we should not interpret Table 1 as showing that there
were just over 23,000 lost votes by mail. Instead, we will ultimately
conclude that there were likely at least an additional 11,000 lost votes.
Not all rejected mail ballots are documented
We take multiple approaches to estimating the extent to which
Pennsylvanias administrative data on mail ballot rejections is in-
complete. We initially show that aggregate statistics are similar
across the EAVS and state administrative data. However, both
sources are missing two types of mail ballot rejections. For all coun-
ties, we focus on mail ballots rejected because of an issue with the
adavit, which we shorthand as adavit rejections. We compare
adavit rejections as reported by the state in the EAVS to adavit
rejections publicly disclosed by local election ocials in federal liti-
gation. We then focus on select counties and compare all mail ballot
rejections as reported in the EAVS to the same statistics reported
during the county canvass, in local newspapers, or in conversations
with county election ocials. We ultimately conclude that there are
at least 5000 rejected mail ballots in Pennsylvania not otherwise
documented.
EAVS. Table S2 shows how many total mail ballots were rejected,
overall and for specic reasons, in both the EAVS and Pennsylvania
administrative data. e table makes it clear that the state responded
to the EAVS on behalf of counties based on the same administrative
data used to construct Table 1. e state appears to have reported
that there was no data available whenever the administrative data
contained no cases of that type. e concern is that, for some counties
and some types of ballot rejections, there were rejected mail ballots
that were never documented, or only partially documented, in the
administrative data and thus never reported on the EAVS.
More specically, the EAVS reported that 65 of Pennsylvanias 67
counties rejected a total of 23,393 mail ballots. However, there was
substantial heterogeneity across counties in which type of ballot
rejections were documented and thus represented in the total. For
example, only 48 counties reported late mail ballots, only 56 coun-
ties reported mail ballot adavit issues, and only 53 reported secrecy
envelope issues. While nonreporting was more likely in small-
population counties, it also aected counties with substantial popu-
lation too. Allegheny County offers an extreme example of this
phenomenon. On the basis of the EAVS, the second largest county
in the state had no data available on the number of mail ballots re-
jected for secrecy envelope issues. A related challenge is to identify
underreporting. For example, Lehigh County implausibly reported
rejecting just one mail ballot because of a problem with the adavit.
Given both underreporting and nonreporting, the EAVS statistics
must be an undercount of the number of mail ballots rejected over-
all and for specic reasons.
Adavit rejections. We expect more under reporting of misdated
and undated ballots than other types of rejected mail ballots. For one,
it was uncertain whether these ballots would be rejected until just
before Election Day. Relatedly, the state did not provide guidance
about how to record these ballots in the state system until aer a court
determined they would be rejected. eir guidance was to record the
misdated and undated mail ballots in the statewide system using the
code designed to document mail ballots rejected for having unsigned
adavits. is combination of late guidance and atypical documenta-
tion drives our expectation that misdated and undated ballots will be
less likely to be recorded in the state system, and thus also be less likely
to be included in the EAVS than other types of rejected mail ballots.
Litigation over Pennsylvanias mail ballot requirements provides
a vehicle for observing misdated and undated ballots not document-
ed in administrative data or reported in EAVS. As described above
and in Materials and Methods, county election ocials disclosed
counts of misdated and undated mail ballots during the course of
federal litigation. Ultimately, the top of table S3 shows that counties
reported about 10,576 misdated or undated mail ballots. As a point
of comparison, the EAVS only documents 7894 mail ballots being
rejected for any adavit issue.
e states guidance for how to record misdated and undated
ballots makes it impossible to use the litigation data to calculate
precisely how many of these misdated and undated ballots went
undocumented in the state system. Ideally, we would calculate how
many more misdated and undated ballots a county reported in their
response to the discovery request than they recorded in the state sys-
tem. However, the aforementioned documentation protocol means
that ballots recorded as misdated or undated cannot be isolated from
ballots recorded as unsigned. In addition, because the discovery re-
quests did not ask counties to report the number of unsigned ballots,
we do not generally do not know the total number of ballots a county
rejected because of any specic adavit issue.
We can nonetheless use the litigation data to calculate the mini-
mum number of undocumented undated and misdated ballots.
When the dierence between the number of misdated and undated
ballots reported by the county in the discovery request and the
number of adavit rejection recorded in the administrative data is
positive, it represents a lower bound on the number of mail ballot
rejections that are missing in the administrative data. Across all
counties, at least 3017 undated or misdated mail ballots were not
documented in the administrative data. is lower bound assumes
that all of the decient adavits documented in the administrative
data were specically rejected for undated or misdated adavits. In
practice, we know that the opposite was more likely to be true;
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several counties only recorded mail ballots rejected for having un-
signed adavits in the state system. Hence, we assess that the num-
ber of undocumented undated or misdated mail ballots is likely
substantially larger than this lower bound.
e remainder of the table separately considers the coverage of
adavit rejections in the top ve counties by mail ballots returned
and all other counties. Overall, the top ve counties are responsible
for about half of all returned mail ballots. e top ve counties con-
tribute a smaller absolute number of undocumented adavit rejec-
tions, but a slightly higher rate of underreporting than all other
counties.
Large counties. Table S4 takes a complementary approach to
measuring coverage by focusing on the coverage of all mail ballot
rejections, for any reason, but only in the ve counties with the most
mail ballots returned. For each type of mail ballot rejection, it pre-
sents both the quantity as reported in the EAVS and the quantity, if
available, as reported in a county canvass, local newspaper, or directly
from a county election ocial.
For example, there were at least 1702 mail ballot rejections in
Allegheny County not reported in the EAVS. e substantial under-
reporting was the result of the county’s unique approach to curing
defective mail ballots. In short, the county returned defective mail
ballots to voters rather than directly rejecting them and marked
them as pending. For example, the county did not report a single
mail ballot as rejected for a missing secrecy envelope. However, a
news article from 5 days before Election Day reported that the coun-
ty returned 683 mail ballots for that reason (43). While voters may
have cured some of these returned mail ballots, it is likely that even
more defective mail ballots were received in the nal 5 days than
cured. Further, as discussed above, Allegheny County reported re-
jecting 1009 misdated or undated mail ballots in the subsequent
litigation. As a result, we conclude that the total number of lost votes
by mail in Allegheny County is at least the combined 1886 mail bal-
lots mentioned in the newspaper article and litigation.
Overall, table S4 identies about 3200 lost votes not documented
by rejected mail ballots in the EAVS. In addition to the expected a-
davit discrepancies, the EAVS is also missing some mail ballot rejec-
tions for missing secrecy envelopes. For example, in Philadelphia,
the county canvass revealed that there were 1946 mail ballots with
secrecy envelope issues, not the 1820 reported in the EAVS (44).
Similarly, the canvass in Bucks County revealed 551 mail ballots
with secrecy envelope issues, not 65 (45). Montgomery County elec-
tion ocials believe the EAVS correctly captured defective secrecy
envelopes, although they adjusted the number of defective adavits.
Similarly, Chester County supplied individual- level data, which
generally matched the EAVS, but for an adjustment for the number
of late ballots.
Not all rejected mail ballots are lost votes
Coverage concerns aside, the discussion of lost votes typically fo-
cuses on the documented number of rejected mail ballots. However,
rejected mail ballots are not equivalent to lost votes because some
people whose mail ballot is rejected go on to substitute, either by
shiing into in- person voting or correcting their initial mail ballot
by submitting a new one.
To see this, Table 2 details the ultimate turnout outcome of peo-
ple whose mail ballots are rejected. e top row of Table 2 reports
that about 15% of people who cast a rejected mail ballot ultimately
cast a valid vote.
e remaining rows of Table 2 look at how turnout outcomes
vary based on the rejection reason. Our expectation is that substitu-
tion rates will positively associate with the amount of time people
have to take action. On the basis of the particulars of preprocessing
discussed above, this leads us to expect more substitution among
people whose mail ballot is rejected because of an adavit than
people who return a ballot outside of a secrecy envelope. Consistent
with this expectation, we observe that about 23% of people whose
mail ballot is rejected because of an adavit go on to successfully
vote compared to about 16% of people whose mail ballot is rejected
because of a secrecy envelope. We are also not surprised that about
2% of people whose mail ballot is rejected as late go on to vote. at
is because a mail ballot is not marked as late until aer Election Day,
although a voter may anticipate their mail ballot being late and take
further action.
Table 3 further distinguishes between turnout outcomes based
on the local notice policies disclosed by county election ocials
during the federal litigation discussed above. Consistent with our
expectations, it shows that people who return a mail ballot with a
disqualifying adavit were about 14 percentage points more likely
to successfully vote if they lived in a county with a notice program
than if they lived in a county without one. In comparison, the same
quantity is only about 4 percentage points for people who had a
problem with their secrecy envelope. Last, people who submitted
late mail ballots were slightly less likely to successfully vote if they
lived in a county with a notice program than if they lived in a coun-
ty without one, although we suspect this result is an artifact of a data
error in Centre County, which did not have a notication program.
Table S7 reports how substitution aer rejection varies by demo-
graphics. Some of the demographic trends of substitution are simi-
lar to the demographic trends of rejected mail ballots. For example,
young people and rst- time voters are least likely to substitute.
Table 2. Mail ballot rejections by turnout.
Turnout
nIn person By mail % Voted
All rejected ballots 23,167 2507 979 15.0%
By rejection reason
Rejected for adavit 7811 1341 448 22.9%
Rejected for secrecy 8072 840 455 16.0%
Rejected for late 4900 59 40 2.0%
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However, some trends are dierent. Black voters, for example, are
more likely to substitute following a rejection than any other regis-
trants. e diculty with interpreting these descriptive statistics is
that individuals are dierently situated with respect to notice de-
pending on the county that they live in. Across the state, counties
have dierent notice programs, dierent demographic proles, and
attract a dierent amount of campaign outreach. e racial dier-
ence is at least partly attributable to the fact that Black registrants are
more likely to live in jurisdictions with notice programs and benet
from Democratic campaign outreach. is also likely explains, at
least in part, why Democratic registrants are more likely to substi-
tute than Republican registrants.
Some unreturned mail ballots are lost votes
In contrast to rejected mail ballots, unreturned mail ballots do not
typically appear in calculations of lost votes. e diculty of unre-
turned mail ballots for understanding lost votes is that unreturned
ballots represent a combination of three dierent phenomena. First,
there are registrants who abstain from returning a mail ballot be-
cause they are not interested in voting. Second, there are registrants
who substitute into voting in- person despite receiving a mail ballot,
either because they decide they prefer to vote in person or are con-
cerned they will not be able to successfully satisfy the mail ballot
requirements. ird, there are registrants who are deterred from
returning a mail ballot because they are similarly concerned about
satisfying the mail ballot requirements but are not able to substitute
into voting in person. Below, we detail the incidence of both substi-
tution and deterrence among registrants who do not return their
mail ballots. We ultimately conclude that the Election Day receipt
deadline deterred about 11,000 people from voting.
Substitution. Table 4 reports the turnout records of voters who
did not return their requested mail ballots. e rst row focuses on
all requesters, showing that about a third of requesters who do not
return their mail ballots nonetheless vote. By denition, these are
not lost votes.
To test whether the accessibility of in- person voting relates to the
likelihood of substitution, the remaining rows of Table 4 distinguish
between requesters based on where they had their mail ballots sent.
e table groups requesters by whether their ballots were sent to
their zip code of registration (zip), elsewhere in their county of reg-
istration (county), elsewhere in Pennsylvania (PA), to another state
(US), to another country, or to an unknown address. e assump-
tion is that, on average, people will nd it more dicult to vote in
person on Election Day the further they send their ballots from
their home. Consistent with this assumption, Table 4 shows that the
likelihood of substitution decreases when the requesters sent their
mail ballots further aeld.
Figure 2 further illustrates how geography interacts with the tim-
ing of mail ballot requests in shaping the likelihood of substitution.
Figure 2 focuses on mail ballots requested in the last 28 days before
the request deadline. e gure is divided into four panels based on
Table 3. Mail ballot substitution by notice.
Turnout
nIn person By mail % Voted
Rejec ted for adavit
With notice program 4709 1122 228 28.7%
Without notice program 3102 219 220 14.2%
Rejec ted for secrecy
With notice program 5350 779 181 17.9%
Without notice program 2722 61 274 12.3%
Rejec ted for late
With notice program 3573 33 2 1.0%
Without notice program 1327 26 38 4.8%
Table 4. Unreturned mail ballots by turnout.
Among unreturned
Distributed Not returned % No vote % Vote
All mail ballots 1,403,880 157,170 62.4% 37.6%
Where was mail ballot sent?
To zip of registration 1,313,892 138,055 59.3% 40.7%
To rest of county 18,199 4057 69.5% 30.5%
To another PA county 21,743 5308 85.0% 15.0%
To another state 40,88 8648 90.3% 9.7%
To another country 2676 742 95.3% 4.7%
Unknown 6482 360 76.9% 23.1%
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the same geographic variable used in Table 4, excluding the small
number of mail ballots sent internationally or to unknown addresses.
As the panels move from le to right, mail ballots are being sent
further from registrants’ address of registration. All panels highlight
that an increased share of mail ballots go unreturned when requested
closer to the deadline. However, consistent with Table 4, people
who send their mail ballots to their zip code of registration engage
in more in- person substitution than people who send their mail bal-
lots elsewhere. Correspondingly, the gure shows a positive associa-
tion between sending a mail ballot further from an address of
registration and the share of unreturned mail ballots. Together, this
suggests that people who send their mail ballots further aeld are
most aected by the Election Day receipt deadline because they face
the most time pressure and are least able to substitute into in-
person voting.
Deterrence. e previous subsection focused on requesters who
did not return their mail ballots but nonetheless voted. In this sub-
section, we focus on the converse: requesters who did not return their
mail ballots and did not ultimately vote. In theory, nonvoting could be
explained by either abstention or deterrence. However, while the for-
mer should not be considered a lost vote by mail, the latter should be.
Estimating the extent of deterrence among people who do not
return their mail ballots requires us to a formalize a model of why
ballots go unreturned. Our model focuses specically on the subset
of people who requested a mail ballot in the 4 weeks before the
deadline. As previously discussed, Pennsylvania allows people to re-
quest a mail ballot up until 7 days before Election Day, even though
the USPS recommends a deadline of 15 days in states with an Elec-
tion Day receipt deadline (39).
Figure 3 highlights that a substantial number of mail ballot re-
quests come aer the USPS recommended timeline. e gure
counts the number of mail ballots requesters by week of request,
dierentiating between those requesters who successfully voted
(black) from those who did not (gray). e distribution is bimodal
because Pennsylvania registrants can either make an annual request
to receive a mail ballot for all elections that year or request a mail
ballot for a specic election (46). Ultimately, election ocials re-
ceived more than 100,000 mail ballot applications during the week
before the deadline, or less than 2 weeks before Election Day. Of
these applicants, about 16,000 did not successfully vote.
Our model estimates the extent of deterrence by relating patterns
of in- person substitution, discussed above, and nonvoting to varia-
tion in the degree of risk of returning a late mail ballot. More spe-
cically, let
Nt
be the number of people requesting a mail ballot on
day
t
. We observe a set of turnout outcomes,
Yt
, for all of the people
who request mail ballots on day
t
. We dene
Yt
=
(
Y
m
t
,Y
s
t
,Y
a
t)
,
which are the share of these mail ballot requesters at time
t
who
produce a returned mail ballot (
Ym
t
), an in- person vote (
Ys
t
), and ab-
stain (
Ya
t
), respectively.
We expect that the composition of observed turnout outcomes
on day
t
to be a function of the share of people who perceive
heightened risk that their mail ballots will not be received by the
Election Day receipt deadline. Let
π
,π
,π
be a vector of
probabilities representing the likelihood that someone who per-
ceives normal risk returns a mail ballot (
πm
), substitutes into in-
person voting (
πs
), and abstains (
πa
). Let
pt
be the probability that
someone who requests a mail ballot on day
t
faces a heightened
risk of a late mail ballot. When a person faces a heightened risk of
Fig. 2. Share of turnout outcomes by date of mail ballot request.
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returning a late mail ballot, let
δ=(
δ
m
,δ
s
,δ
a)
be the change in the
probability of returning a mail ballot (
δm
), substituting into in-
person voting (
δs
), and abstaining (
δa
) relative to when the person
perceives normal risk. Under this model, the share of people re-
questing mail ballots on day
t
that produce each turnout outcome
is the share under baseline risk of a late mail ballot plus the dier-
ence in the share under heightened risk multiplied by the proba-
bility of heightened risk
Our goal is to estimate the total number of mail ballots that were
deterred because they would have been rejected as late, or
tNtptδa
.
We estimate this quantity using the following steps
1) Identify a
t
such that for
tt
that
pt=0
.
2) Calculate
πs
using mail ballot requests made at
tt
.
3) Construct
pt
δ
s
=Y
s
t
π
s
.
4) Regress
Ya
t
on
Ys
t
to estimate
β=
cov
(
πs+ptδs,πa+ptδa
)
var
(
π
s
+
pt
δ
s)
=δsδavar
(
pt
)
δ
s
2var
(pt)=
δ
a
δs
.
β
captures how many abstentions occur for every one substitu-
tion into in- person voting, among mail ballot requestees who face
heightened risk.
5) We estimate
tptNtδa
using
t>tNtpt
δ
ss
β.
Figure 4 illustrates the rst three steps of our model. e gure
focuses specically on people who expressed interest in voting in
close proximity to the election. It shows the share of mail ballot re-
questees who substitute into in- person voting by the date of their
mail ballot request. To get a baseline rate of substitution into in-
person voting when people do not face a heightened risk of their
mail ballots being received late, we set
t
equal to 11 October and
calculate that about 4.9% of people who requested mail ballots in the
week of 5 October through 11 October ultimately cast an in- person
ballot. We observe a similar rate of substitution into in- person vot-
ing among people who requested mail ballots in the week of 12 October
through 18 October, meaning that
pt
δ
s
is small for
t
in that range.
However, we start observing more substantial
pt
δ
s
for larger
t
. More
than 10% of people who requested mail ballots on the nal 2 days
before the deadline substituted into in- person voting.
e le panel of Fig. 5 visualizes step four of our model. In this
graph, the x axis represents the share of people who requested a mail
ballot on day
t
who substituted into voting in person,
Ys
t
. On the y
axis is the share of people who requested a mail ballot on day
t
who
did not vote,
Ya
t
. Each dot represents the combinations of
Ys
t
and
Ya
t
on every day between 5 October and 1 November, with a clear posi-
tive association between these two variables. e regression line
shows that we estimate a
β
of approximately two, meaning that for
every one percentage point increase in the share of mail ballot re-
questees who substitute into in- person voting we observe about a
corresponding two percentage point increase in the share of mail
ballot requestees who do not cast a counted ballot.
e right panel of Fig. 5 provides a test of the assumption under-
lying our model. Namely, that increased substitution occurs because
mail ballot requestees face heightened risk that their mail ballots
Ym
t
m+ptδm
(1)
Ys
t
s+ptδs
(2)
Ya
t
a+ptδa
(3)
Fig. 3. Mail ballot requesters by week of request.
Meredith et al., Sci. Adv. 10, eadr2225 (2024) 20 December 2024
SCIENCE ADVANCES | RESEARCH ARTICLE
10 of 14
will otherwise be rejected as late. While the x axis remains the same
across the two panels, the y axis is now the share of people who re-
quested a mail ballot on day
t
who cast a mail ballot that was re-
jected as late. Consistent with our expectation, there is a strong
positive association between the number of people substituting into
in- person voting and the share of people returning late mail ballots.
Only about 0.3% of people who requested mail ballots during the
period from 5 October to 11 October returned a late mail ballot,
which is the period during which we assume no one faces a height-
ened risk of returning a late mail ballot. is rate of late ballot return
increases about 10- fold to about 2.3% among people who requested
mail ballots during the nal week before the deadline, which also
was the period when we observe the most in- person substitution.
at said, the observed 0.38 percentage point increase in late ballots
from a 1 percentage point increase in the share of mail ballot re-
questees who substitute into in- person voting is only a fraction of
the increase observed in people who do not vote.
Table S8 shows how we apply what is observed in Figs. 4 and 5
to produce an estimate of the total number of people who were de-
terred from voting by the Election Day receipt deadline. Ultimately,
we estimate that about 11,000 people were deterred, with about
4500 of these deterred ballots being held by people who requested
their mail ballots on the last 2 days before the mail ballot applica-
tion deadline.
Electoral consequences of lost votes
While lost votes by mail are concerning regardless of whether they
aect election outcomes (47), this section assesses whether mail
Fig. 4. Share of in- person substitution by date of mail ballot request.
Fig. 5. In- person substitution and unreturned or late mail ballots.
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SCIENCE ADVANCES | RESEARCH ARTICLE
11 of 14
balloting procedural requirements could have electoral consequenc-
es. We rst consider the consequences in Pennsylvania before again
noting that many states lack the same conditions necessary for lost
votes to change the outcome of an election.
Pennsylvania consequences
Our analysis focuses on registrants who requested a mail ballot but
did not ultimately vote. For ease, we refer to these as registrants with
an unrealized mail vote. We construct a measure of the dierence in
the number of Democratic registrants and Republican registrants
with an unrealized mail vote. We prefer unrealized mail ballots to
rejected mail ballots because of issues with both incomplete cover-
age and deterrence. However, our measure likely overstates the net
partisanship of lost votes by mail because it also includes registrants
who abstain from returning their mail ballots.
While lost mail votes were not electorally consequential state-
wide in 2022, they could be in future statewide elections. Statewide
there were about 51,000 more unrealized mail votes among Demo-
cratic registrants than Republican registrants. Given that the Demo-
cratic candidates for senator and governor won in 2022 by roughly
250,000 and 750,000 votes, respectively, neither race could be af-
fected by unrealized mail ballots. However, Pennsylvania has expe-
rienced several tight statewide elections in the recent past. For
example, the 2021 state supreme court race was decided by about
25,000 votes, while the 2016 presidential race came down to about
45,000. us, it is not far- fetched that Pennsylvania could experi-
ence a close enough statewide race for lost votes by mail to aect
its outcome.
More generally, electoral consequences are about more than state-
wide general elections. Figure 6 expands our focus to state legislative
contests, comparing the vote margin in state legislative races (x axis)
to our measure of dierential partisanship among unrealized mail
votes in the district (y axis). On the basis of the gure, the people at
risk of having experienced lost mail votes are more likely to support
Democratic candidates than members of the broader Pennsylvania
electorate in 196 of the 203 state districts. Crucially, the Democratic
bias in unrealized mail votes is present in the state legislative con-
tests decided by narrow margins. In the ve races in which the mar-
gin of victory was less than 1000 votes, we observe an average of 220
more Democrats than Republicans with unrealized mail votes. In
one race, the Democratic candidate lost by only 76 votes in a contest
with 177 more unrealized mail votes from registered Democrats
than registered Republicans.
Recent postelection challenges to mail ballot rules support our
conclusion that lost votes by mail could be electorally consequential
in particularly close Pennsylvania elections. Consistent with our
state legislative analysis, there have been at least two recent postelec-
tion challenges in which the outcome of an election appears to hinge
on mail ballot rules (48,49).
Consequences in context
e electoral consequences of mail balloting procedural requirements
will necessarily vary by state. e likelihood that mail balloting proce-
dural requirements are electorally consequential is increasing in the
share of voters who cast mail ballots, the magnitude of the vote mode
splits, the likelihood that the requirement causes rejection or deter-
rence, and the share of voters who are not notied when their mail
ballots are rejected.
On the basis of the available evidence, we believe that lost votes by
mail are more likely to be electorally consequential in Pennsylvania than
most other states, although more state- by- state work is necessary to
properly measure lost votes. To start, Pennsylvania has relatively more
mail votes. According to the EAVS, mail votes accounted for about 23%
of all counted votes in Pennsylvanias 2022 general election (13). In con-
trast, the median state had just 12% mail votes. Table S9 shows that the
partisan split in mail versus in- person ballots is greater in Pennsylvania
than in many other states. Further, on the basis of Fig. 1, Pennsylvania
rejects relatively more mail ballots. As explained in more detail above,
Pennsylvania does not have a statewide notice policy, which should
lead to relatively less substitution, and has a receipt rather than post-
mark deadline, which should lead to relatively more deterrence. Finally,
Pennsylvania, unlike many other states, oen experiences competitive
statewide elections. Nonetheless, all states typically have some competi-
tive legislative elections that could be aected when all of these other
conditions are present.
DISCUSSION
We use Pennsylvania as a case study to correct the measurement of
lost votes by mail. To summarize, we rst focus on the coverage of
rejected mail ballots and show that there were at least 5000 rejected
mail ballots in Pennsylvanias 2022 general election not documented
as such. We then consider that some rejected mail ballots are saved
from becoming lost votes and some lost votes are not reected in
rejected mail ballots. With respect to the former, we nd that about
3500, or about 15% of, people whose mail ballot was rejected none-
theless successfully voted. Further, substitution was twice as likely in
counties with notication rather than without. With respect to the
later, we estimate that about 11,000 people did not vote because they
anticipated it would be received too late to count. Assuming that up
to three- quarters of undocumented rejections become lost votes, we
ultimately estimate that there were at least about 11,000 more lost
votes than documented, rejected mail ballots.
Fig. 6. Potential electoral consequences of unrealized mail votes by state district.
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Our specic ndings in Pennsylvania help assess the normative im-
plications of the states mail balloting regime. In general, there are at least
two potential criticisms of our description of votes as “lost.” First, any
fraudulent mail ballots prevented by procedural requirements are by
denition not lost votes. However, mail ballots rejected in Pennsylvania
are unlikely to have been cast by ineligible voters. For example, the date
axed next to a voter’s signature on the mail ballot adavit is not useful
for assessing registrants’ eligibility to vote. Further, secrecy envelopes are
not about eligibility to vote either and, moreover, do little for ballot pri-
vacy now that Pennsylvania processes mail ballots centrally using ma-
chines rather than by poll workers at voters’ polling places. Second,
many of the ballots rejected or deterred by the Election Day receipt
deadline might be considered the “fault” of the registrants because they
waited until so close to Election Day to request the ballots. However, in
the same way that that the CalTech- MIT Voting Technology Project
considered a registration mix- up a lost vote (14), even though the voter
could have updated their address of registration, we consider deterred
votes to be lost votes because there are alternative ways of structuring the
mail balloting systems that would allow most of these ballots to be
counted. While some aspects of our case study reect Pennsylvanias
unique position in the political landscape, our results are both illustrative
and informative of broader dynamics in mail balloting.
One implication of our ndings is that scholars should use more
caution when working with the EAVS. For scholars, it is helpful to
recognize that the EAVS is not so much a simple collection of state
data as an attempt to wrangle state data into uniform, national cat-
egories, a process that is never perfect. Nonetheless, the EAVS may
consider supplementing its instructions to reduce incomplete re-
porting. In Pennsylvanias case, the underreporting reects, in part,
the states particular treatment of misdated and undated ballots.
However, the EAVS appears to undercount mail ballot rejections in
many other states too. Table S5 compares the number of rejected
mail ballots reported in the EAVS in both the 2016 and 2020 general
elections to the number publicly reported by state election ocials
(50). e EAVS exactly matches state reports in 6 of the 21 states
surveyed in 2016 and only 2 of the 23 states surveyed in 2020. e
median dierence between the EAVS and state reports is 2.3% in
2016 and 2.8% in 2020, showing that the EAVS is more oen an
underreport than overreport.
Another implication of our ndings is that people should be
careful when evaluating mail ballot systems based on the share of
rejected mail ballots. For example, the Elections Performance Index
(51,52) ranks states in part based on the number of rejected mail
ballots, in addition to the total number unreturned. Yet various fea-
tures of mail balloting regimes can aect the share of rejected mail
ballots that become lost votes. Given dierences across states, we
interpret comparisons of rejected mail ballots over time and juris-
diction (26) with some caution, because it does not consider, or cor-
rect for, any dierences in coverage, substitution, or deterrence.
A third implication is that lost votes can be reduced by notifying
voters of mail ballot rejections with sucient time to take corrective
action. In general, we observe higher rates of substitution when a
decient ballot could be identied during preprocessing than when
it could not. However, the highest rate of observed substitution is
28% among voters with an adavit error in a county with a notica-
tion program. In contrast, 82% of North Carolina voters who sub-
mitted a decient, but correctable, mail ballot in the 2020 general
election ultimately took such action (30). While there are several
potential explanations for why so many more North Carolinians
took corrective actions than Pennsylvanians, one structural dier-
ence between the two states is that North Carolina had a cure pro-
cess through which voters could take corrective action without
needing to submit a new ballot. As Meredith and Kronenberg (30)
note, there are several reasons why a curing process makes it easier
to take corrective action. At some point in the electoral calendar,
there is not enough time to mail new ballots. Further, some people
vote by mail because they nd it costly to vote in person. In addition,
a curing process can be used to count ballots with errors discovered
on Election Day itself.
Beyond instituting ballot curing, several changes could be made
to states’ electoral systems to further reduce the number of lost votes
by mail. In broad terms, the number of lost votes by mail is a func-
tion of (i) the number of decient mail ballots, (ii) the identication
of deciencies, (iii) notication of deciencies, and (iv) the required
corrective action. In this context, ballot curing reduces the cost of
taking (iv) the required corrective action. However, there are poli-
cies and procedures that can reduce lost votes earlier in the process.
For example, following the 2022 general election, Pennsylvania elec-
tion ocials redesigned mail ballots with an eye on (i) reducing the
number of deciencies (53). Lyons (53) notes that, among other
things, secrecy envelopes are now yellow and watermarked to dis-
courage stray marking, return envelopes highlight where voters
should sign and date adavits, and adavits partially prell the cur-
rent date to remind voters to not write their birthdate. Pennsylvania
ocials have also made it easier to (ii) identify deciencies. For ex-
ample, the state now allows counties to hole punch the return enve-
lope so election workers can identify when the now- yellow secrecy
envelope is missing without canvassing the ballot. A preliminary
evaluation of mail ballots rejections in the 2024 primary election
suggests that these changes modestly reduced the number of reject-
ed mail ballots (54). Beyond these reforms, we also think it would be
helpful to improve (iii) the notication of deciencies by encourag-
ing people requesting a mail ballot to provide election ocials with
contact information, which could then be used to inform them of
any deciency.
Finally, our ndings also highlight the need for more research on
the consequences of deadlines for requesting mail ballots. One rea-
son that voters have little time to correct defective mail ballots is that
many wait until just before the deadline to request a mail ballot.
Moreover, we observed that thousands of people either returned a
mail ballot that was received too late to count or who were deterred
from returning their mail ballot because it was unlikely to be re-
ceived in time to count. Ideally, these registrants would have re-
quested these mail ballots earlier to increase the amount of time
they have to complete the process. For those people who are moti-
vated to take action by deadlines, moving the deadline would likely
reduce the number of lost votes. On the other hand, some people are
not motivated by deadlines as much as they realize late in the elec-
toral calendar that they would like to vote by mail. Ultimately, more
evaluation is needed on how to set mail ballot deadlines to balance
these competing considerations. Such evaluations may nd inspira-
tion from existing work that has considered how voter registration
deadlines aect voter turnout (55).
MATERIALS AND METHODS
Our analysis relies on two dierent individual- level administrative
data sets provided by the Pennsylvania Secretary of States oce
Meredith et al., Sci. Adv. 10, eadr2225 (2024) 20 December 2024
SCIENCE ADVANCES | RESEARCH ARTICLE
13 of 14
through public records requests, as well as aggregate data about mail
ballot rejections and notice procedures made publicly available dur-
ing recent litigation.
Individual- level administrative data
Our primary dataset is individual- level mail ballot applications re-
corded in the Statewide Uniform Registry of Electors (SURE). We
limit the data to approved applications associated with a valid voter
registration number, which did not result in a voided mail ballot. In
the limited circumstances in which a given person was sent multiple
nonvoided mail ballots, we select one mail ballot based on the fol-
lowing rule: We sort the data by mail ballot status, with any rejected
mail ballots rst, any accepted mail ballots second, and any unre-
turned mail ballots last, and take the rst such mail ballot. For ex-
ample, if a person was sent two mail ballots, one of which was
returned and accepted and one of which went unreturned, we would
classify the individual as having an accepted mail ballot. We then
link the mail data to registration records. e linkage is straightfor-
ward because each data source contains registrants’ unique voter
registration number.
Table S1 shows that nearly all records in the SURE data are linked
to a registration record (all but about 31,000 of the roughly 1,435,000
records in the SURE data). e SURE records that do not link to a
registration record likely represent mail ballot applications made by
registrants who had their registrations canceled between when they
applied for the mail ballot and 9 January 2023, when we received the
voter registration data. Given that our primary analysis focuses on
mail ballot applications successfully linked to voter registrations,
table S1 shows that we will slightly underrepresent cases in which
mail ballots went unreturned or were cancelled.
We use several other variables contained in the SURE data and
voter registration records. e SURE data contain information about
when election ocials received a mail ballot application, what ad-
dress the ballot was sent to, and the status of the mail ballot. While
these data also include information about when ballots were mailed
to registrants, we avoid using that information in our analysis be-
cause the lag between the receipt of an application and the mailing of
a ballot varied by county and over time. Separately, the voter regis-
tration data contains registrants’ recorded voting history, including
vote mode, registration address, party of registration, and date of
birth. To proxy for whether someone had the mail ballot sent to
their address of registration or some other address, we compare the
zip code where the mail ballot was sent with the zip code of the reg-
istrant’s registration address (30). If a mail ballot was sent to zip code
other than the zip code of the registration address, we note whether
that zip code is within the registrants’ home county, whether it went
to a zip code within Pennsylvania outside of their home county, or
whether it went to a zip code somewhere else in the US or an inter-
national location.
We supplement our primary dataset by constructing estimates of
registrants’ likely race and ethnicity. To construct these estimates,
we geocode registrants’ addresses of registration in order to obtain
their Census block or Census tract of residence. We combine infor-
mation about registrants’ surnames and the racial demographics of
their Census blocks to generate probabilities that each registrant is
white, Black, Hispanic, Asian, and any other race or ethnicity, using
the method developed by Imai and Khanna (56). We also create a
sixth category for when the geocode is not of sucient quality to
impute race (57).
County- level litigation data
We supplement the individual- level dataset of mail ballot appli-
cations with county- level data on mail ballot rejections. Much
of these data were made publicly available during litigation over
Pennsylvanias mail ballot requirements. During the course of litiga-
tion, the plaintis served on the defendant county election boards
a series of interrogatories about the implementation of mail ballot
requirements in the 2022 general election. e plaintis then at-
tached the county responses to their statement of material facts as
part of their motion for summary judgment. We were not involved
in the litigation but instead downloaded the responses from the
public docket (33).
On the basis of these interrogatories, we identied the number of
undated or misdated mail ballots rejected in each county. We only
counted ballots that were rejected for being undated or misdated if
they would have otherwise counted but for the decient adavit.
us, when a county noted that a ballot was rejected because the
adavit was undated and it was submitted outside of a secrecy enve-
lope or was subsequently cured, we did not count it.
We also use these interrogatories to measure whether a county
had a program in place to notify voters if they returned a mail ballot
with a disqualifying deciency. In theory, Pennsylvania’s statewide
SURE system would attempt to notify voters in all counties when a
county entered the status of the mail ballot as rejected in the system.
However, that notication system was of limited utility. It only
worked for voters with an email address associated with their voter
registration and then only if election ocials logged the rejected
mail ballot before Election Day. In addition, because Pennsylvania
law forbid canvassing mail ballots before Election Day, this could
only happen if a county engaged in preprocessing. As a result, we
did not consider the 43 counties using only the SURE system to be a
specic program of notifying voters of ballot issues in advance of the
election. Instead, we only considered a county to have a specic no-
tication program when their response to the interrogatory indi-
cated that they contacted the voter directly (13 counties), posted a
list of voters with ballot issues online (3) or in a public place (1), or
shared such a list with the political parties (4).
We also rely on additional sources that documented the number
of rejected mail ballots by rejection reason in several counties.
ese sources include Board of Election meeting minutes or tran-
scripts, media reports, and direct communications from counties
Board of Elections.
Supplementary Materials
This PDF le includes:
Supplementary Text
Tables S1 to S9
References
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Acknowledgments: We thank participants in the Shambaugh Conference at the University of
Iowa, the Election Science, Reform, and Administration Conference at the University of
Southern California, and the University of Pennsylvania American Politics working group, as
well as A. Lang at the Penn Carey Law library, for helpful comments and suggestions. We also
thank the numerous election administrators who assisted in fullling the public record
requests necessary to conduct this analysis. Funding: Funding for part of this project was
provided by the Penn Program on Opinion Research and Election Studies to M. Me. and M. Mo.
Author contributions: M.Me. and M.Mo. contributed equally to all aspects of the paper,
including ideas, design, data collection, analysis, visualization, presentation, writing, and
revision. A.M. and K.S. contributed to data analysis and visualization. Competing interests:
The authors declare that they have no competing interests. Data and materials availability:
All data needed to evaluate the conclusions in the paper are present in the paper, the
Supplementary Materials, or the replication data posted at http://www.sas.upenn.
edu/~marcmere/replicationdata/PA%20Lost%20Mail%20Votes%20Replication%20Data.zip.
Submitted 20 June 2024
Accepted 15 November 2024
Published 20 December 2024
10.1126/sciadv.adr2225
ResearchGate has not been able to resolve any citations for this publication.
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