# Blocks as geographic discontinuities:
The effect of polling place assignment on voting

Sabina Tomkins  Stanford University  Keniel Yao  Stanford University  Johann
Gaebler  Stanford University  Tobias Konitzer  PredictWise  David Rothschild
PredictWise  Microsoft Research  Marc Meredith  University of Pennsylvania
Sharad Goel  Stanford University

###### Abstract

A potential voter must incur a number of costs in order to successfully cast
an in-person ballot, including the costs associated with identifying and
traveling to a polling place. In order to investigate how these costs affect
voting behavior, we introduce two quasi-experimental designs that can be used
to study how the political participation of registered voters is affected by
differences in the relative distance that registrants must travel to their
assigned Election Day polling place and whether their polling place remains at
the same location as in a previous election. Our designs make comparisons of
registrants who live on the same residential block, but are assigned to vote
at different polling places. We find that living farther from a polling place
and being assigned to a new polling place reduce in-person Election Day
voting, but that registrants largely offset for this by casting more early in-
person and mail ballots.

##  1 Introduction

It has long been accepted by political scientists that the choice by a
potential voter to vote or abstain from voting is based on a assessment of
whether the benefits from voting are higher than the costs (Riker and
Ordeshook, 1968). The opportunity cost of the time spent casting a ballot is
one of the most important costs that a potential voter must incur in order to
vote (Downs, 1957). There are concerns that even small increases in
expectations about the time it takes to vote could reduce turnout,
particularly given that many potential voters receive limited expected
benefits from voting (Aldrich, 1993).

Potential voters are likely to consider the search costs and transportation
costs associated with casting an in-person ballot (Brady and McNulty, 2011).
Search costs refer to the cost of identifying where a polling place is located
and how to get there, and are thought to decrease when a potential voter
repeatedly votes at the same polling place. Transportation costs refer to the
cost of traveling to a polling place, and will increase, typically, as a
polling place moves further from a potential voter’s residence.

Political science research shows that turnout decisions are affected by
changes in search and transportation costs. Brady and McNulty (2011) find that
potential voters are two percentage points less likely to cast an in-person
ballot on Election Day when they were assigned to vote at a new polling place
that was equally far from their house. Two percentage points represents the
median estimated reduction in in-person voting on Election Day from a polling
place change in existing work, with McNulty et al. (2009) and Amos et al.
(2017) finding more than a two-percentage point decline, and Yoder (2018) and
Clinton et al. (2019) finding less. Cantoni (2020) also shows that potential
voters who live in the same neighborhood are less likely to vote when the
polling place that they are assigned to vote at on Election Day is further
from their residence.

Existing research differs in whether increasing the search and transportation
costs associated with Election Day voting primarily causes potential voters to
abstain or substitute to early in-person voting or mail balloting. Clinton et
al. (2019) show that most potential voters dissuaded from voting in-person on
Election Day by increases in search and transportation costs switched to early
in-person voting. In contrast, Brady and McNulty (2011) find that about 60
percent of the potential voters who were dissuaded from voting in-person on
Election Day because of higher search costs abstained, with the other 40
percent of shifting to mail ballots. Likewise, Amos et al. (2017) find that
about 60 percent of the potential voters who were dissuaded from voting in-
person on Election Day because of higher search costs abstained, with the
other 40 percent of shifting to early in-person voting or mail balloting.

Synthesizing the existing evidence is challenging because studies vary both in
the context that is being studied and the specific design being applied to
conduct the study. States vary in the ease with which voters can substitute
into using mail ballots or early in-person voting as the cost of voting in
person increases. Some designs focus on the impact of transportation costs
among those who are experiencing an increase in search costs, while others
focus only on cases in which there are no changes in polling locations.
Designs also differ in whether they focus exclusively on polling place changes
resulting from the consolidation of polling places, or consider the larger set
of polling place changes caused by consolidation, expansion, and movement of
polling places. These differences make it hard to infer how the results of a
given study may generalize to alternative contexts in which substitutes are
more or less accessible or there is a different cause of the differences in
voting costs.

We introduce two new quasi-experimental designs to study the consequences of
increased search and transit costs on potential voters in a wide variety of
contexts. In brief, our designs leverage cases in which registrants who live
on one side of a street are assigned to vote at a different polling place on
Election Day than registrants who live on the other side of the same street.
This allows us to make comparisons of otherwise similarly situated registrants
who travel different distances to their polling place or differ in whether
they are assigned to vote at a new polling place or not, and ascribe
differences in their turnout patterns to the differential costs associated
with finding and getting to their assigned polling place. Our primary analysis
focuses on data from ten states in which we were able to: (1) collect
information of polling place addresses in the 2016 and 2012 presidential
elections; (2) link these data with voter registration records; and (3)
discern from the voter registration record the mode in which a 2016 vote was
cast (i.e., mail-ballot, early in-person ballot, or polling place). We also
perform some additional analyses for the six of these ten states in which
there were sufficient observations to estimate the effect of being assigned to
a new polling place.

Our designs have a number of features that make it relatively easy to apply to
many states simultaneously in order to generate the most comprehensive
analysis to date of registrants’ ability to offset voting costs with
substitutes. First, these designs incorporate data from many types of
jurisdictions. Our approach for studying relative distance uses information
from any jurisdiction in which registrants on the same block are assigned to a
different polling location, which occurs frequently. Similarly, our approach
for studying shocks considers polling place changes that result from the
consolidation, the expansion, and the movement of polling places. Second,
residential address, the key variable that we exploit to generate variation in
the cost of in-person voting, is a variable that is available in any voter
registration database. Third, our approach does not require states to provide
any information beyond the mapping between precincts and polling locations and
the addresses of the polling locations. Importantly, this means we are not
limited to only studying states that make it easy to access the mostly non-
existent data files of precinct boundaries.

Our results show that the ability to substitute into other modes of voting is
key to helping mitigate consequences of increased search and transit costs on
Election Day voting. We find that a registrant who lives further from their
polling places is approximately 1.6 percentage points less likely to vote at
this polling place than a similarly situated registrant who lives closer, but
offsets for this with their increased use of mail balloting or early in-person
voting. Likewise, a registrant assigned to a different polling place in 2016
than in 2012 is about 1 percentage point less likely to vote in-person on
Election Day than a similarly situated registrant who was assigned to the same
polling place in both elections, but partially compensates for this by
substituting into mail balloting or early in-person voting. These findings
highlight the importance of making alternative voting methods available for
potential voters who find it difficult to vote at their polling place on
Election Day.

##  2 Data

Our analysis uses state-supplied polling place files, which provide
identifiers and addresses for polling places, and a national voter file, which
provides registrant characteristics, voting behavior and voting place
identifiers. Together these data allow us to infer which polling place
registrants were assigned to vote at on Election Day and to compute the
distance from their residences to this polling place. We then select blocks
comprised of similar registrants who are assigned to vote at different polling
places and use variation in distance between the block and the polling places,
as well as the stability of the location of polling places between two
elections, to estimate the effects of relative distance and changes to polling
places (henceforth shocks) on turnout decisions. The remainder of this section
provides a sketch of the process of cleaning and combining our primary data
sources, with more details provided in Section A.5.

To collect data on polling place locations, we filed public-records requests
to state-election officials in all fifty states. The full account of these
requests is included in Section A.3. We received records from 18 states which
met the basic criterion that they contain address and voting jurisdiction
descriptors for both 2012 and 2016. A number of states, including Texas,
reported that there was no statewide aggregation of polling place locations. A
subset of 254 counties in Texas responded to our public record requests with
data that met the basic criterion. Table 2 shows a stylized example of the
information these data contain.

We merge information on polling place locations to registration records in
snapshots of TargetSmart’s national voter file from November 2012 and November
2016. These data include a registrant’s voting jurisdiction (i.e., precinct),
registration address, and vote history. In some states, the vote history data
also include information on which method of voting was used.

Our design requires us to find blocks of registrants in which some registrants
on the block are assigned to vote at one polling location on Election Day,
while other registrants on the same block are assigned to vote at a different
polling location on Election Day. We create a block identifier for each
address of registration that consists of all but the final two digits of the
street number (e.g. 200 would be encoded as 2, and 2100 would be encoded as
21), the street name, the street type, and the city and state of a voter’s
residential address. For example, the two registrants in Table 2, residing at
123 Main St. and 125 Main St. in Milwaukee, Wisconsin, respectively, share a
block identifier. Table 2 shows a stylized example of these data after these
block identifiers are generated.

Place ID | Address | City | State | Precinct
---|---|---|---|---
PP-1 | 200 Main St | Milwaukee | WI | Cherry School 1
PP-2 | 1000 Third St | Milwaukee | WI | Apple School 1

Table 1:  Example polling place file.

Voter ID | Address | City | State | Block ID | Precinct
---|---|---|---|---|---
Voter-1 | 123 Main St | Milwaukee | WI | 1-Main-St-Milwaukee-WI | Cherry School 1
Voter-2 | 125 Main St | Milwaukee | WI | 1-Main-St-Milwaukee-WI | Cherry School 1
Voter-3 | 2000 Third St | Milwaukee | WI | 20-Third-St-Milwaukee-WI | Apple School 1

Table 2:  Example voter records.

To infer polling-place assignments we match registrants to polling places with
voting-jurisdiction metadata, such as precinct name and city. Consider the
three example voters in Table 2 and the two example polling places in Table 2.
Voter-1 and Voter-2 would be assigned to PP-1 while Voter-3 would be assigned
to PP-2. Section A.5 discusses how we calculate distance between a
registrant’s residential address and the assigned polling place once we
construct these data.

We apply filters to our data after matching registrants to polling places to
ensure that registrants in the same block are similar except potentially for
the location of their polling place. First, our analysis focuses on
registrants with the same registration address in the 2012 and 2016 snapshots
to ensure changes in the location of polling places result from changes in
polling place assignment rather than registrants moving. Second, we restrict
our sample to blocks in which registrants are assigned to two distinct polling
locations. We also require that at least two registrants were assigned to both
of these polling locations. Third, we discard blocks in which there are two
registrants on that block that live more than $0.3$ miles111Small alterations
to this threshold do not qualitatively change the results. apart to ensure
that a block constitutes a group of registrants who live close to each other.
Finally, many of our analyses restrict our sample to those states that provide
information on voting method in their vote history.

##  3 Methods: Measuring the effects of relative distance and shock

Polling-place assignments can dictate many aspects of registrants’ voting
experiences, including how long it takes to cast a ballot and how far one must
travel to do so. We propose identification strategies that exploit the quasi-
random assignment of registrants living on the same block to different polling
places to estimate two causal effects.

We refer to the first effect as the effect of relative distance. Here we
consider two simplified notions of distance when registrants on a block are
assigned to two polling places: farther and nearer. All registrants on a block
assigned to the nearer polling place are said to experience a nearer distance,
while those on the face with a greater average distance are said to experience
a farther distance. The effect of relative distance is the difference in the
likelihood that those registrants who experience a farther distance vote using
a given method relative to those registrants who experience a nearer distance.

We refer to the second effect as the effect of shock. A shock occurs when a
registrant, living at the same residence, is reassigned to a new polling place
in between elections. The effect of shock is the difference in the likelihood
that those registrants who experience a shock vote using a given method
relative to comparable registrants who were assigned to vote at the same
polling place in both elections. Additionally, we require that one of two
conditions holds: either that all registrants were assigned to the same
polling place in 2012, or, if registrants were assigned to two different
places that the average distance to each place be similar (that the difference
in the average distance to each polling place be less than 0.25 miles).

We are interested in the effect of relative distance and the effect of shock
on turnout, as well as voting using specific methods. Thus, we examine the
effect of both on all voting (any method), voting in person (in person), and
voting by a substitute method such as casting a mail-in or absentee ballot, or
voting early in person (substitution).

For each of the voting behaviors we examine, let

$votecast^{m}_{i}=\begin{cases}1,&\text{if voter }$i{}$\text{ cast a vote by
method}$m$,\\\ 0,&\text{otherwise}.\end{cases}$

where $m\in\\{\text{in person,\ substitution,\ any method}\\}$. We use a
linear regression to estimate Equation 1 with standard errors clustered by
household to account for autocorrelation in the unmodeled determinants of
voting behavior by registrants who reside in the same household.

Let $i$ refer to an individual voter living in household $h$,
$\mathcal{T}_{b}$ be the set of all voters on block $b$ assigned to treatment,
$\mathcal{C}_{b}$ be the set of all voters on block $b$ assigned to control,
$\gamma_{b}$ be a block fixed effect, and
$treatment_{i}=\mathbb{I}_{i\in\mathcal{T}_{b}}$ be an indicator of whether a
registrant is assigned to treatment or control.

$Y^{m}_{i}=\gamma_{b}+\theta treatment_{i}+\epsilon_{i,h}$ (1)

We use Eqn. 1 to estimate the corresponding effect of relative distance and
effect of shock.

###  3.1 Identification strategies

To estimate the effect of relative distance we identify all blocks which lie
on the boundaries of two voting jurisdictions. For example, we show a block
where one face (shown with a solid line) is assigned to a different polling
place than the face across the street (shown with a dashed line) in the top
panel of Figure 1. To compare the voting behavior of the registrants who live
farther from their polling place relative to those on the same block who live
closer, we require that a block contain registrants assigned to two different
polling places. Additionally, we require that these assignments remain
unchanged between 2012 and 2016 as this allows us to isolate the effect of
relative distance independent of effect of shock. For such blocks, registrants
assigned to the polling place with the lesser average distance to its
registrants are assigned to control, and registrants assigned to the place
with the greater average distance are assigned to treatment. This approach is
similar to one used by Middleton and Green (2008), which used variation in
canvassing activity along the boundary of a precinct to estimate the effect of
canvassing on turnout. The set of all relevant blocks defined by this
identification strategy spans ten states (Hawaii, Iowa, Indiana, Maryland,
North Carolina, Pennsylvania, Rhode Island, Texas, Utah and Wisconsin) and a
total of 249,309 registrants.

Our identification strategy for the effect of shock is illustrated in Figure
1. We locate all blocks which lie on the boundaries of two precincts, such
that a single face of this block experiences a polling-place shock between
2012 and 2016 while the other does not. Figure 1 highlights two block faces
outlined with dashed and solid borders. All registrants who reside on the face
enclosed with the dashed line experience a shock (and are assigned to
treatment) and all those residing on the side enclosed with the solid line do
not (and are assigned to control).

Note that a shock may arise for multiple reasons. First, a reduction in the
number of polling places, which is sometimes referred to as a consolidation,
causes some registrants to be assigned to a new polling place that, on-
average, will be located further away from their residence. Second, an
increase in the number of polling places causes some registrants to be
assigned to a new polling place that, on-average, will be located closer to
their residence. Finally, a polling place being moved causes some registrants
to be assigned to a new polling place without any clear expectation about how
the change will affect the average distance between the polling location and
the registrants’ residence. Here, we do not differentiate between types of
shocks; any block where one block face experiences a change and the other does
not is included in our analysis regardless of whether the change is a result
of a consolidation, addition or a movement. This contrasts our approach with
some previous studies which focus specifically on the effects of
consolidations (Brady and McNulty, 2011; Amos et al., 2017).

We use this identification strategy to select relevant blocks from the dataset
constructed in Section 2. In addition to the criteria detailed in Section A.5,
in the blocks used to estimate the effect of shock we require that each block
face on the same block be a similar distance from its assigned polling place
in 2012. In particular we require that the difference between the average
distance to the polling place across the block faces be no more than 0.25
miles. In total, the dataset used to estimate the effect of shock spans six
states (Iowa, Indiana, Maryland, North Carolina, Pennsylvania and Wisconsin)
and includes 47,456 registrants.

Our identification strategies are examples of geographic discontinuity
designs, which have been recently popularized in political science by Keele
and Titiunik (2015, 2016). Focusing only on comparisons within blocks will
cause us to ignore many potential comparisons of registrants who live in close
proximity to one another, but are assigned to vote at different polling
places. The benefit is that we expect there to be fewer differences in the
underlying propensity to vote among registrants on the same block than
registrants living on different blocks that are located in close proximity.
And even when there are differences, there is no reason to expect that
registrants with a higher propensity to vote should systematically end up
closer or further from their polling place than those with a lower propensity
to vote. Thus, focusing on registrants residing on the same block allows us to
implement a geographic discontinuity design even when we know little about the
specific registrants, and when there are too many boundaries to apply a method
like that proposed by Keele and Titiunik that empirically investigates whether
housing prices are comparable on either side of a given boundary to determine
whether we expect the underlying propensity to vote to be similar on either
side of the boundary.

Figure 1:  This map illustrates the block-randomization identification
strategy for the effect of shock with two precincts in Milwaukee, WI across
2012 and 2016. Each precinct’s color corresponds to its polling place
assignment. In 2012 all registrants in both precincts are assigned to PP-1. In
2016 all registrants of the right-most precinct experience a shock as their
polling place assignment changes from PP-1 to PP-2. To identify eligible
blocks of voters for our analysis, we identify blocks on the boundaries of the
two precincts; registrants of the block face that experiences a shock (dashed
outline) are assigned to treatment while registrants of the block face that
does not experience a shock (solid outline) are assigned to control.

Our identification strategy assumes that registrants who live on different
sides of a block would use voting methods at the same rate if not for
differences in the polling places that registrants on each side of a block
were assigned to vote at on Election Day. The figures that we present in
Section A.1 of the Appendix examine how the characteristics of registrants
assigned to different types of polling places differ. We first look at age,
gender, modeled partisanship, and modeled race, as well as indicators
representing that the total number of registrants on that side of the block
fell into a certain range and find that all absolute differences are less than
2 percentage points and that the majority are less than 1 percentage point.
For a subset of registrants for which we observe sale prices of homes, we
follow Keele and Titiunik (2015) and examine whether the housing prices are
comparable on the side of blocks that are closer and further from their
polling place. We find that housing prices are similar for each side of the
block.

Similar patterns are found when we conduct the same analysis except defining
those on the side of a block experiencing a polling place change as the
treatment group and those of the side of a block keeping the same polling
place as the control group. Unlike with our relative distance analysis, we
also expect to observe similar voting patterns in the treatment and control
groups in the previous election before the shock was realized. This is indeed
the case, as we find similar historical voting patterns in both groups (please
see Section A.1 of the Appendix).

##  4 Results

We first report the results of the effect of relative distance. The left panel
of Figure 2 shows that registrants that live relatively further from their
assigned polling places are generally less likely to vote at their assigned
polling place on Election Day than registrants that live relatively closer.
The solid orange bars show that in eight of the tens states we study,
registrants who live relatively further from their assigned polling place were
less likely to vote at that polling place on Election Day than registrants who
live relatively closer. The error bars represent 95 percent confidence
intervals for each state as well as our aggregate estimate. Pooling over all
ten states, a registrant that lives relatively further from their assigned
polling place is 1.6 ($\pm 0.4$) percentage points 222We report 95% confidence
intervals. less likely to vote at their assigned polling place on Election Day
than a registrant that lives relatively closer.

Figure 2:  Relative distance does not affect the likelihood of voting, but
does affect the method used to vote.

The remainder of Figure 2 shows that registrants that live relatively further
from their assigned polling location compensate for a lower rate of voting at
their assigned polling place with higher rates of early in-person voting or
mail-in balloting. The solid purple bars show that in eight of the tens states
we study, registrants who live relatively further from their assigned polling
place were more likely to use early in-person voting or mail-in balloting than
registrants who live relatively closer. Pooling over all ten states, a
registrant that lives relatively further from their assigned polling place is
1.7 ($\pm 0.36$) percentage points more likely to use early in-person voting
or mail-in balloting than a registrant that lives relatively further. The
right panel shows that there is almost no difference in the turnout rate of
registrants who live relatively further or closer from their assigned polling
place.

Figure 3 helps to contextualize how much further registrants who live
relatively further from their assigned polling location have to travel in
order to vote in-person on Election Day. Figure 3 shows the average additional
distance these registrants must travel as compared to registrants who live
relatively closer in a random sample of 1,000 blocks included in our analysis.
Even though 85% of all distance differences are less than one mile, this is
still sufficient to generate the effect of relative distance on both voting in
person and by substitution observed in Figure 2. Figure 3 also demonstrates
that the additional distance that registrants that live relatively further
from their assigned polling location must travel to that polling location
increases as a function of the distance that registrants that live relatively
closer to their assigned polling location must travel to that polling
location.

Figure 3: Average distance to the polling place among registrants who live
relatively closer and the average additional distance registrants who live
relatively further must travel in a sample of 1,000 blocks in our effect of
relative distance analysis.

Figure 2 shows that substitution methods appear to offer an alternative to
registrants when the cost of voting on Election Day increases. However, access
to substitutions varies between states and even within states. For example,
some states allow for both no-excuse mail ballots and early in-person voting
(e.g., North Carolina), while others allow no-excuse absentee voting only for
certain voters (e.g., Indiana). Thus we inspect how voting by substitution
varies with a state’s openness to substitution adoption. As a proxy for a
state’s openness to substitution adoption, we use the percentage of voters
that cast a mail or early in-person ballot in the 2012 presidential election.
Consistent with our expectations, Figure 4 shows that registrants who live
relatively further from their assigned polling location are least likely to
respond with substitution in states with the lowest level of adoption.
However, we cannot rule out the possibility that there may be other state-
specific factors that affect substitution patterns and the use of mail or
early in-person ballots.

Figure 4: Registrants who live relatively further from their assigned polling
location are least likely to respond with substitution in states with the
lowest usage of mail or early in-person ballots.

Next, we address the question of how a shock affects Election Day voting,
voting by substitution and voting by any method. In Figure 5 we consider the
data produced by the effect of shock, where one block face experiences a
polling place assignment shock in 2016. It shows that shock creates a shift in
how voting occurs, reducing voting in person by 1.3 ($\pm 1.00$) percentage
points and increasing voting by substitution by 0.80 ($\pm 0.73$) percentage
points. Overall, we see that as with effect of relative distance, effect of
shock is moderated by registrants voting by substitute modalities rather than
in person.

Figure 5:  While shocks reduce Election Day voting, more that half of this
reduction is compensated for by increased voting by substitution.

One complication with examining shocks is that registrants experiencing shocks
often also experience changes in the relative distance between their residence
and their polling place. Figure 6 shows that some shocks cause registrants to
reside closer to their polling place, while other shocks cause registrants to
reside further. One reason for this heterogeneity is that the shocks in our
data result from a combination of counties adding polling places,
consolidating polling places, and moving polling places. Each of these events
might influence the cost of voting differently. Consolidations may not only
increase the average distance registrants travel to their polling place, but
also increase the cost of voting via longer lines. Conversely, additions may
make Election Day voting more convenient in other ways beyond reducing the
average distance registrants travel to their polling place. Table A.1 in the
Appendix shows that more registrants who experience a shock live in a county
that added polling places than a county that subtracted polling places,
suggesting that additions may be more likely to generate the shocks in our
data than subtractions.

Figure 6: Average distance to the new polling place among registrants who
experience a polling place change in a sample of 1,000 blocks in our effect of
shock analysis.

Figure 7 examines whether the effect of shock on Election Day voting is more
pronounced when registrants must travel greater distances than they were
previously required. It inspects the difference in the voting rate between the
block side which experienced a shock and the side that did not as a function
of the difference in the distance of the block from the new polling place and
the distance of the block from the old polling place. The $x$-axis varies
which blocks are included when estimating the effect of shock based on this
difference in distance. For example, the estimate reported for “$>$ 0.5 miles”
only includes blocks that experienced a shock which resulted in the new
polling place being 0.5 miles or more further from the block than the old
polling place. The top panel shows that the relatively further the new polling
place is from the block, the greater the decline in Election Day voting among
those that experience the shock. Similarly, Figure A.8 in the Appendix shows
that the effect of relative distance on Election Day voting is larger the more
we restrict the sample to blocks where the difference in distance between the
further and closer polling place is larger.

Figure 7:  Shocks cause a greater reduction in Election Day voting when the
sample of blocks is restricted to those where difference in distance to the
polling place is greater among those who did and did not experience a shock.

The trend demonstrated in Figure 7 cannot necessarily be interpreted as the
effect of distance. The middle and bottom panels show that the underlying
population used to construct each estimate varies, with shocks causing larger
differences in distance to the polling place in shocked blocks in rural areas.
Moreover, shocks that cause registrants to reside substantially further from
their polling place are disproportionately likely to be caused by
consolidations. We leave it to future work to isolate the simultaneous effect
of a unit increase in distance on registrants experiencing polling place
assignment shocks.

##  5 Discussion

While existing designs rely on knowledge of precinct boundaries, ours instead
utilizes residential addresses and polling place addresses. This novel quasi-
experimental design allows us to utilize data from multiple states and present
the most comprehensive analysis to date of the effect of relative distance and
effect of shock. We find clear evidence that both cause registrants to cast
early in-person or mail-in ballots instead of voting at their polling place on
Election Day. The reduction in Election Day voting caused by having a longer
trip to the polls appears to be completely offset by increases in early in-
person or by mail-in voting. Likewise, our point estimates suggest that a
majority of the reduction in Election Day voting caused by shocks are offset
by increases in early in-person or by mail-in voting, and that overall
registrants who experience shocks are similarly likely to vote as those who
live on the same block who do not.

The finding of minimal effects of shocks on overall turnout contrasts with
many of the previous studies of shocks. We speculate that one reason why our
findings differ from previous work is that most previous work focuses
specifically on shocks resulting from the consolidation of polling places. In
contrast, we find that in our data registrants are more likely to experience
shocks in counties that are adding polling places than in counties that are
reducing polling places, suggesting that many of the shocks in our data are
not caused by consolidation. While all shocks impose search costs, shocks that
result from consolidation are likely to also impose other additional costs on
voting. Conversely, shocks that result from polling place exchanges, or
especially additions, may reduce the cost of voting in other ways.

Our results also contrast with Cantoni (2020), which is the only other paper
that studies the effect of relative distance independent of shocks. While
Cantoni finds a substantial decrease in overall turnout among registrants
located further from their polling place relative to similarly situated
registrants that are located closer, we find registrants who need to travel
further to their polling place compensate for their reduced Election Day
voting with greater early in-person and mail voting. There are a number of
potential explanations for why we reach different conclusions. First, the
ability for registrants to switch to early in-person or mail balloting likely
depends, in part, on the accessibility of these alternative voting methods.
Second, registrants may not respond differently to small and large differences
in relative distance. Most blocks in our sample were within a mile of their
assigned polling place and the registrants who lived further from their
polling place usually lived less than 0.5 miles further from their polling
place than the registrants who lived closer. Finally, the effect of relative
distance may depend on the characteristics of the specifics registrants and
elections being studied. Cantoni finds, for example, the effect of relative
distance is greater in areas where car ownership is lower, and is lower in the
2016 presidential election than in the other elections that he studied.

While our identification strategy can be applied broadly in settings where
registrants are assigned to polling places, it is limited in some respects.
First, it only allows us to estimate the effect of relative distance and the
effect of shock on the turnout of registrants. If the experience of greater
distance or shocks causes potential voters to not be registered, then our
design may underestimate the effect of these variables on turnout (Nyhan et
al., 2017). Moreover, our focus on registrants who lived at the same address
for four years means our sample under-represents the effect of relative
distance and the effect of shock on potential voters with less residential
stability. We also require that a registrant’s address can be reliably
geocoded. Perhaps because rural addresses might be more difficult to reliably
geocode and we require all registrants on a block to live within 0.3 miles of
each other, registrants from rural areas appear to be underrepresented in our
analysis.

Future work can build off the identification strategy we propose here to
develop more complex measures of distance. Here, we simplify all measures of
continuous distance into a simple notion of farther or closer. This
distinction allows us to compare registrants across geographies in a way which
we believe is statistically defensible. However, this means our estimated
treatment effects bundle together many comparisons of the effect of relative
distance that likely affect voting behavior in different ways. However, we do
not think it necessarily makes sense to bundle together blocks with similar
values of distance differences because a given distance difference may
correspond to a large increase in the cost of voting in a urban areas where
people walk to a polling place and a small increase in the cost of voting in a
rural area where everyone drives to the polls. We believe future work would
benefit from greater thinking about how to translate measures of relative
distance in different contexts into a measure of the time and effort that is
required to reach a polling place.

Our work is motivated by a desire to understand the magnitude of the costs of
voting on Election Day. While we caution that these are limited to the
population of registered voters, an optimistic result of our work is that
these costs are readily offset by the ability of registrants to shift into
substitute modalities. The provision of mail-in ballots and early voting
venues can have important implications beyond the ability to vote, but perhaps
on the experience of political engagement. We leave it to future work to
explore not only the ability of various voting modalities to offset costs but
the implications of this effect for all potential voters.

## References

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##  Appendix A Appendix

###  A.1 Additional Figures

Figure A.1 shows how the demographics of registrants who live on the side of
the block that is further from its polling place (i.e., treatment group)
compares to the demographics of registrants who live on the side of the block
that is closer to its polling place (i.e., control group). We inspect age,
gender, modeled partisanship, modeled race, as well as the number of
registrants on either side of the block. Each point is the difference between
the proportion of registrants with a given characteristic in treatment and the
proportion of registrants with this characteristic in control, divided by the
proportion of registrants with a given characteristic in control. The size of
each point is scaled by the number of registrants in the control group, where
larger points represent groups with more registrants.

The difference between the proportion of registrants with a given
characteristic in the treatment group relative to the control group is close
to zero for the most common characteristics. For less common characteristics
the differences can be greater. Here, we show the percent differences.
However, note that all absolute differences are less than 2 percentage points
and that the majority are less than one percentage point.

We also observe information on the sale prices of homes for about 14 percent
of registrants. Figure A.2 and Figure A.3 show that houses sell for a similar
amount in the treatment and control groups for this subset of registrants.
Home value amount is estimated based on a number of public record data, such
as documents filed at the county recorder’s office.

Next, we conduct the same analysis except defining those on the side of a
block experiencing a polling place change as the treatment group and those of
the side of a block keeping the same polling place as the control group. The
one notable difference is that, because we have substantially fewer
observations, Figure A.4 sometimes reveals larger percent differences than
were observed in Figure A.1. Once again Figure A.5 and Figure A.6 show housing
prices are comparable on the side of blocks that do and do not experience
shocks for the 12 percent of registration records for which that information
is available. Unlike with the relative distance, we also expect to observe
similar voting patterns in the treatment and control groups in the previous
election before the shock was realized, which Figure A.7 shows is the case.

Figure A.1: We see the age, gender, modeled partisanship, modeled race of
registrants, as well as indicators representing that the total number of
registrants are similar on either block face. (Distance)

Figure A.2:  We see that the home prices of registrants on either block face
are similar. (Distance).

Figure A.3:  We see that the home prices of registrants on either block face
are similar. (Distance)

Figure A.4: We see the age, gender, modeled partisanship, modeled race of
registrants, as well as indicators representing that the total number of
registrants, are similar on either block face, although the percent difference
can be larger because the sample size is smaller. (Shocks)

Figure A.5:  We see that the home prices of registrants on either block face
are similar. (Shock)

Figure A.6:  We see that the home prices of registrants on either block face
are similar. (Shock)

Figure A.7:  We see that the historical voting patterns in the 2012
presidential election of registrants on either block face are similar, with
registrants experiencing a shock being, if anything, less likely to vote by
substitution than registrants who did not. The roughly 5 percent lower usage
of substitution voting reflects that 13.1 percent of registrants who
experienced a shock voted using a substitution method in 2012 as compared to
13.8 percent of registrants who did not.

In Figure A.8 we show the effect of relative distance for all blocks where the
additional distance traveled by the treatment group ranges from 0 to more than
2 miles. When we include all voters we see that the average distance is quite
small and the effect of relative distance is around 2 percentage points. While
we see a more pronounced effected of relative distance for those voters who
live on a block with a relatively large difference between the distances
traveled by the close and far block faces, this might be attributable to other
characteristics of these voters which differentiate them from the rest. For
example, they tend to be more rural and more white.

Figure A.8: As the difference in distance between the closer and farther block
faces grows, so does the effect of distance. However, the population also
changes with distance.

###  A.2 Types of shocks

Polling-place assignment changes can occur when precinct boundaries are
redrawn or to meet the changing constraints placed on local election
officials, e.g. when new regulations render a former site unfit. Broadly,
polling places are 1) removed and consolidated within jurisdiction, 2) added
to a jurisdiction and 3) moved to different locations where the total number
within a jurisdiction is unchanged. To examine the types of shocks experienced
by voters in this analysis, we describe each county in the dataset as either
removing, adding, or not changing the total number of polling places from 2012
to 2016. Figure A.1 shows both the percentage of counties and the percentage
of voters living in those counties for each type of change to the number of
polling places at the county level. We find that in our data the majority of
voters who experience a shock live in a county where polling places were
moved. To inspect county level changes, we consider a polling place to be a
precinct name, assuming that within a county precinct names are not
duplicated. This definition however may be likely to over-estimate the number
of polling places as several precincts are often assigned to the same polling
place.

| Percent of counties | Percent of registrants
---|---|---
Polling places removed in 2016 | 14.7 | 21.5
Polling places added in 2016 | 41.1 | 55.3
Same number of polling places in 2016 and 2012 | 44.2 | 23.2

Table A.1:  Different types of shocks and the percentage of counts and voters
affected.

Each of these events might influence voting differently. For example,
consolidations leave fewer polling places for voters, increasing the distance
for some voters to reach the polling place. They might also increase the cost
of voting via longer lines and less staff members at the polling place.
Alternatively, new polling places might reduce some costs, such as distance
and time at the polling place. Analysing these distinct events would likely
uncover different effects for each.

Here, we describe shock broadly, as having one’s polling place assignment
changed. While this description does not differentiate between the nuanced
experiences of each type of change, it captures the common event of
experiencing a search cost, where a voter must now determine where to vote. We
see that this cost reduces in-person and increases substitution voting,
respectively, even when the type of shock is not considered and when roughly
55% of all voters live in counties with polling-place additions.

When voters experience polling-place assignment changes they might receive a
mailer describing the new assignment. In some cases this mailer might serve as
an advertisement for substitution voting methods, describing how voters can
pursue alternatives. The effect that we see might in part be the result of
substitution methods being advertised to voters. However, we do see a
reduction in in-person voting across states with different access to
substitutes, different forms of substitutions and likely different mailers.

###  A.3 State-Supplied Polling-Place Location Files

Public records requests were made to state election officials in all fifty
states. In response to our requests, most states ($31$) provided partial
records; the remaining states denied our request ($1$), failed to complete the
request ($10$), did not have applicable records because of the prevalence of
voting by mail ($2$), or were unable to fulfill the request because relevant
polling place location information was only available at the county level
($6$). Of the states which provided partial records 18 were complete enough to
be potentially included in the analysis, but the number of states with a
sufficient number of voters varied by experimental design. Each address in the
state-supplied files is geocoded, according to a process described in Section
A.4.

For six states it is not possible to query a single state election official as
polling-place locations are aggregated at the county level. Instead, for the
largest of these states (Texas) we filed public records requests with county
clerks in each of 254 counties. We received partial records from 142 counties,
but were only able to recover reliable polling-place location data in 106 of
these.

###  A.4 Filtering geocodes

State-supplied polling-place files describe each polling-place location with
address descriptors such as street name, city, zip code and a textual place
description. Our goal is to generate a geocode for each location, using
Google’s geocoding API
333https://developers.google.com/maps/documentation/geocoding/overview. We
form two potential addresses to geocode for each location. The first address
is the full street address which consists of a street number, street name,
street descriptor (such as st. or ave.), as well as the city and state. The
second address is the place address which consists of the place description,
city and state. For example, a polling place might be described as the Local
Elementary School and the resulting place address might be Local Elementary
School, Doeville TX.

If the full street address is found to be precise and accurate according to
the system below, its geocode is counted as valid and the associated latitude-
longitude coordinates are used. If, however, the full street address fails
either check, we instead attempt to geocode the place address. A geocode
belonging to a place address must also be precise, but it only needs to pass
the city and state accuracy tests.

#### Precision

Each geocode is accompanied with metadata about the geocoding process. Here,
we use a geocode’s location type. There are four potential location types:
rooftop, geometric center, range interpolated and approximate. If a geocode’s
location type is classified as rooftop we accept it immediately, while we
immediately reject all geocodes classified as approximate.

If a geocode is classified with a location type of geometric center or range
interpolated we accept it conditionally. We would like to accept all location
types of sufficient granularity; however some subjectivity is required to
discern between categories of geometric centers or interpolated ranges. We
take a conservative approach and retain geometric centers or interpolated
ranges which are tagged as an establishment, store, local government office or
point of interest.

#### Accuracy

Another item of metadata produced by the geocoding process is a formatted
address of the geocode. We consider a geocode to be accurate if this formatted
address matches the original address which was geocoded. To check accuracy we
consider several components of an address, the street name, the street number,
the city and the state.

  * •

City and State For each city and state we check that the formatted city and
state match the original city and state. All geocodes whose formatted address
city and state do not match the original city and state are considered
inaccurate.

  * •

Street Address To check that the street names and numbers match we first parse
a street address into components using the Python software package
USADDRESS.444https://github.com/datamade/usaddress\\#readme This package
standardizes address components so that, for example, 1st would be transformed
to first. However, upon inspection, this standardization is not universally
applied. Consequently, we performed a second hand-coded round of
standardization where all numerical street names and descriptors were encoded
in long-form (i.e. first and street or avenue), respectively. Finally, we
check that a geocode’s formatted address street number and name match the
originals.

  * •

Evaluating Place Descriptions If a street address is not considered to be
precise and accurate, we instead geocode a location’s place description. For
example, a polling place might be described as Local Elementary School,
Doeville TX. It is difficult to check the accuracy of a place description, as
it will not contain an address, and descriptors returned from the geocoder
might not match local descriptors. Instead of the full accuracy checks
described above, for place descriptions we only check that the city and state
of the place description’s geocode match the original code. Thus, we only
ensure that a geocode returned from a place description be precise, not that
it be accurate.

###  A.5 Filtering to eligible registrants for analysis

Beginning with potential voters from the TargetSmart data file we conduct a
series of filtering steps to produce the datasets used for the shock and
distance analyses. These steps are described at a high level in Table A.2 and
in more detail in the following.

Initially, we begin with a set of potential voters in valid voting
jurisdictions (counties and polling places). Within the states included in the
analysis there are some counties which hosted vote centers in either 2012 or
2016. In these counties registrants can cast a ballot at any vote center
regardless of their registration precinct. This environment is in stark
contrast to that which we assume in these analyses where voters must vote at
their assigned precinct. Consequently, we remove all counties with vote
centers in either 2012 or 2016 from the analysis.

Filtering Step | Number of potential voters remaining
---|---
| 2012 | 2016
Potential voters in valid voting jurisdictions (counties and polling places) | 80,271,123 | 83,928,328
Filter potential voters with valid address | 62,432,960 | 66,975,996
Filter potential voters with potential polling place assignment | 41,756,035 | 41,756,035
Filter to registered and plausible voters | 33,256,459 | 37,453,896
| Filter to registrants who live on a block where all pairs of registrants
live
---
within .3 miles from one another
26,133,615 | 29,273,108
Filter to registrants who live on the same block in 2012 as in 2016 | 14,486,807
Filter to registrants in Distance analysis: | 249,309
Filter to registrants in Shock analysis: | 47,456

Table A.2:  Here, we detail the data filtering steps followed to create final
data set.

Filter to potential voters with valid address As addresses are used to ensure
the quality of a polling-place assignment, and in effect of relative distance
experiment, we require that voters have valid addresses in order to computer
their distance to the polling place. Several filtering steps are performed
towards this end. We first drop all voters with missing address records. To
compute a registrant’s distance to their assigned polling place we rely on
geo-coordinates and we filter out all registrants with missing geo-
coordinates.

TargetSmart infers each voter’s current address. We drop all voters whose
current address does not match their registration address. Each geo-coordinate
is accompanied with descriptors of the precision level at which it was
recorded. For example, low precision levels are Extrapolate or Zip Code while
a higher precision level is Street. A geo-coordinate at the level of
Extrapolate refers to the closest known address to the original address and a
geo-coordinate at the level of Zip Code refers to the centroid location of the
zip code of the original address, while a geo-coordinate at the level of
Street refers to the street address of the original address. We retain only
those addresses which are geocoded at the level of Street.

Finally, each address is associated with a United States Postal Office (USPS)
dwelling code. We retain only those residential addresses with a USPS code of
High-rise, Building, or Apartment or Street Address. This removes all
residences with a USPS code corresponding to a firm record, a general delivery
area, a Post Office box or a rural route or highway.

Filter to potential registrants with potential polling place assignment To
infer polling-place assignments for each voter we utilize both of our original
data sources: the national voter file and the state-supplied polling-place
files. Consider the three example voters in Table 2 and the two example
polling places in Table 2. Voter-1 and Voter-2 would be assigned to PP-1 while
Voter-3 would be assigned to PP-2. We filter out all voters who are assigned
to a polling place which we could not geocode.

This matching process is vulnerable to errors both in the original data
sources and in the synchronization between them. We limit our analysis to
registrants whose inferred assignments are no more than 25 miles from their
home to remove egregious matching errors.

Filter to registered and plausible voters Next, we filter only to plausibly
registered voters. That is we remove all unregistered voters and any voter who
is marked as deceased. Hence, from this point on we refer to units in the
analysis as registrants.

Filter to registrants who live on a block where all pairs of registrants live
within .3 miles from one another Our analysis operates on blocks of
registrants, where each block can be divided into two faces (see Figure 1 for
an example). To identify eligible blocks of registrants for our analysis, we
create a block identifier for each voter. This consists of all but the final
two digits of the street number (e.g. 200 would be encoded as 2, 2100 would be
encoded as 21), the street name, the street type, the city and state of a
voter’s residential address. For example, the two registrants, shown in Table
2 to be residing at 123 Main St. and 125 Main St. in Milwaukee, Wisconsin,
respectively, share a block identifier.

To create a block identifier we require that an address consist only of
numeric characters and that it be at least three digits long. The rational for
the latter requirement is that in more rural locations where addresses are
shorter there are examples of addresses with the same leading digit being on
different blocks. For example, 80 Park Rd and 82 Park Rd might not be on the
same block. We further ensure that all voters assigned to the same block live
reasonably close by ensuring that no two voters with the same block identifier
live more than 0.3 miles away from each other.

Filter to registrants who live on the same block in 2012 as in 2016 Finally,
our analysis considers events that take place between the 2012 and 2016
elections. We therefore restrict our attention to registrants who resided on
the same block in 2012 as in 2016.

Filter to registrants in analysis Finally, in both analyses we restrict our
attention to blocks which meet the following eligibility criteria. In the
TargetSmart data we observe instances where registrants with the same
registration address or the same registration geocode are assigned to
different precincts. We consider this to be an administrative error. One
possible example of such an error is that a voter’s registration is out-of-
date and the record indicates a previous polling-place assignment. To prevent
these errors from effecting our analysis, we filter out all blocks where
multiple polling places are assigned to a single address or geocode.
Additionally, we restrict our attention to those blocks where each block face
has at least two voters.

Finally, for each analysis, we filter to all registrants still in the dataset
who reside on a block which is present in both the filtered 2012 and filtered
2016 data. For the shock analysis this produces 47,456 voters. For the
distance analysis this produces 249,309 voters.