AppAppendix References

.tocmtchapter mtchaptersection mtappendixnone

# Did Private Election Administration Funding Advantage Democrats in
2020?††thanks: For excellent research assistance, the authors thank Jessica
Persano. For helpful discussion and comments, the authors thank Avi Acharya,
Avi Feller, Anthony Fowler, Justin Grimmer, Christian Grose, Jens Hainmueller,
Eitan Hersh, Guido Imbens, Ethan Kaplan, Lynn Vavreck, and Stefan Wager,
seminar participants at Dartmouth, UCLA, and Stanford, and attendees of the
2023 Election Science, Administration, and Reform Conference and 2023 Meeting
of the Society of Political Methodology. Daniel Thompson also thanks the UCLA
Academic Senate’s Faculty Research Grant program for generously funding this
research.

Apoorva Lal111Independent Researcher<EMAIL_ADDRESS>https://apoorvalal.github.io
Daniel M. Thompson222Assistant Professor, Department of Political Science.
<EMAIL_ADDRESS>http://www.danmthompson.com.

###### Abstract

Private donors contributed more than $350 million to local election officials
to support the administration of the 2020 election. Supporters argue these
grants were neutral and necessary to maintain normal election operations
during the pandemic, while critics worry these grants mostly went to
Democratic strongholds and tilted election outcomes. These concerns have led
twenty-four states to restrict private election grants. How much did these
grants shape the 2020 presidential election? To answer this question, we
collect administrative data on private election administration grants and
election outcomes. We then use new advances in synthetic control methods to
compare presidential election results and turnout in counties that received
grants to counties with identical average presidential election results and
turnout before 2020. While counties that favor Democrats were much more likely
to apply for a grant, we find that the grants did not have a noticeable effect
on the presidential election. Our estimates of the average effect of receiving
a grant on Democratic vote share range from 0.02 percentage points to 0.36
percentage points. Our estimates of the average effect of receiving a grant on
turnout range from -0.03 percentage points to 0.13 percentage points. Across
specifications, our 95% confidence intervals typically include negative
effects, and our confidence intervals from all specifications fail to include
effects on Democratic vote share larger than 0.58 percentage points and
effects on turnout larger than 0.40 percentage points. We characterize the
magnitude of our effects by asking how large they are compared to the margin
by which Biden won the 2020 election. In simple bench-marking exercises, we
find that the effects of the grants were likely too small to have changed the
outcome of the 2020 presidential election.

##  1 Introduction

Private donors contributed more than $350 million to local election officials
to support the administration of the presidential election in 2020.333See
https://www.techandciviclife.org/our-work/election-officials/grants/ and
https://pollingaccessgrants.org/ This nearly matches the supplemental funding
Congress appropriated to support local election administration in
2020444https://www.eac.gov/payments-and-grants/CARES and is a substantial
share of the $2 to $3 billion spent in a typical national election (Mohr et
al. 2018).555For a review of recent estimates of the cost of elections, see
https://electionlab.mit.edu/sites/default/files/2022-05/TheCostofConductingElections-2022.pdf.
The private donors and their supporters argue this money was necessary to
ensure all eligible citizens had a chance to vote amid the disruptions caused
by the COVID-19 pandemic.666https://www.techandciviclife.org/2020covidsupport/
Many local election officials echo this view with one anonymous official
saying they used the funding to “alleviate choke points and barriers to
voting.”777https://www.techandciviclife.org/election-officials-made-democracy-
happen-in-2020/ Favoring these arguments, Michigan voters approved an
amendment to their state’s constitution in 2022 that protects the right of
local governments to receive private funding for election administration.

Critics of these donations argue that private donors overwhelmingly favored
Democratic-leaning counties and municipalities and that these grants have the
potential to tilt elections in favor of one party by increasing the
participation of some citizens more than others.888See, e.g.,
https://thefga.org/briefs/show-me-the-zuckerbucks-outside-money-infiltrated-
missouris-2020-election/ In one complaint filed before the Federal Election
Commission, the plaintiff points out that a large share of the funds donated
in 2020 went to Democratic-leaning parts of the country and alleges that the
donors have a “hidden motive to increase Joe Biden’s statewide
vote.”999https://eqs.fec.gov/eqsdocsMUR/7854_01.pdf These concerns led twenty-
four states to adopt laws banning or limiting private donations to local
election officials.101010https://www.ncsl.org/elections-and-
campaigns/prohibiting-private-funding-of-elections How much did private
election administration grants tilt the 2020 election?

We address this question by combining county-level administrative data on
turnout, presidential voting, election spending, and election administration
with records of which county governments received a private donation from the
largest private election administration donor in 2020, the Center for Tech and
Civic Life (CTCL). We document that counties that support Democrats were more
likely to apply for private election funding in 2020, which is consistent with
critics’ allegations. Since Democratic-leaning counties were much more likely
to apply, and every eligible applicant received a grant, a simple comparison
of turnout and presidential vote shares in counties that did or did not
receive a grant fails to reveal the grant’s impact. We mitigate this bias by
comparing grant-funded counties to those without funding but with similar
pre-2020 turnout and voting trends using recent advancements in synthetic
control methods (Arkhangelsky et al. 2021).

We find that, despite the scale of the CTCL grant program in 2020 and the
tendency of the money to go to Democratic-leaning counties, private funding
did not noticeably advantage Joe Biden in the 2020 presidential election. We
estimate that receiving a grant increased support for Democrats by between
0.02 and 0.36 percentage points and increased turnout by less than 0.13
percentage points. We validate our estimates using alternative machine
learning and econometric approaches to estimating the effects, and these
approaches produce similar estimates. Since large counties and counties in
battleground states have a larger effect on the aggregate election outcome, we
also estimate the effects in these counties separately. We find that grants
had a similar effect in large counties and counties in battleground states. We
also present evidence that the small average effects are not masking large
effects for the small number of counties that received relatively large
grants.

To characterize the magnitude of the effects, we compare our estimates to the
state-by-state margins in the 2020 presidential election. We conduct a simple
analysis in which we remove the average effect on turnout and Democratic vote
share from every grant-receiving county’s vote total. Despite the razor-thin
margins in 2020, we find that the turnout and Democratic vote share effects
are not large enough to have swung the election to Donald Trump.

Beyond the ongoing policy debate about private election funding, this paper
contributes to a growing social scientific literature on the effects of local
election administration. Democratic and Republican officials often disagree
over how much to spend on elections and how they should be funded (Hasen 2012;
Mohr et al. 2019). This leads to a conventional wisdom that spending more or
less will have substantial effects on the outcomes of elections. Yet, while
some changes in state and local election administration can affect
participation and alter the composition of the electorate, the turnout effects
tend to be modest, and the compositional effects are often hard to predict
(Cantoni and Pons 2021; Clinton et al. 2020; Gerber, Huber, and Hill 2013;
Gronke et al. 2008; Kaplan and Yuan 2020; Tomkins et al. 2023; Thompson et al.
2020; Yoder et al. 2021). Further, despite large differences between the
Democratic and Republican positions on how much money to spend on elections,
Democratic local officials do not produce more turnout or higher Democratic
vote shares than do Republican local officials (Ferrer, Geyn, and Thompson
2023). This paper advances this literature by evaluating a large increase in
resources rather than a single policy change. If local election officials are
motivated to increase participation and are well informed about what
administrative changes will be most effective, providing them resources should
have a larger effect on turnout than any individual policy change. Our
findings suggest that there may be less room for increasing turnout with local
administrative changes than previously expected.

This paper also contributes to the large literature on the role of money in
American politics. A vast literature studies the effects of campaign finance
and the influence of special interests (see, e.g., Ansolabehere, De
Figueiredo, and Snyder Jr 2003; Levitt 1994). This project is especially
closely related to a small but growing literature on the influence of business
leaders in politics (Hersh 2023). Many reforms have sought to limit the
influence of money in politics by changing who can spend how much money on
which races (see, e.g., Fouirnaies and Fowler 2022; Hall 2016; Kilborn and
Vishwanath 2022; Yorgason 2021). While many public officials and members of
the public are concerned that donations to support local election
administration offer a new, previously untapped way to influence election
results without giving directly to candidates, our results suggest this may
not be a substantial risk.

When considering the implications of our results, it is important to note that
our paper reports estimates of the average effect of additional election funds
at the current margin. If, for example, election officials had much smaller
budgets than in 2020, grants may help to maintain the most basic election
functions and thereby have substantial effects on participation and election
outcomes. More resources may also help local governments maintain better
security measures or make voting more convenient. While we cannot measure this
directly, many of the recipients said the grants helped them make their local
election more secure.111111For example, one election official told the CTCL:
“We are a small community struggling to find ways to handle unfunded mandates,
especially during a pandemic. It means a lot to us to ensure our election
process is done in all the right ways.”
https://www.techandciviclife.org/grant-update-october/

##  2 Private Election Administration Grants in 2020

###  2.1 Center for Tech and Civic Life’s 2020 Grant Program

In fall 2020, Mark Zuckerberg and Priscilla Chan donated approximately $350
million to the Center for Tech and Civic Life (CTCL), a Chicago-based
nonprofit, to administer a grant program for local election administration.
The CTCL invited all local governments responsible for administering elections
to apply for funding and gave the funding to every eligible government that
applied.121212Unless cited to a different source, we rely on the CTCL’s
website for details about the grants. https://www.techandciviclife.org/our-
work/election-officials/grants/ The grants were intended to offset election
administration expenses incurred from June 2020 until December 2020. The CTCL
determined the maximum amount of each grant based on the eligible voting
population of each jurisdiction as well as other demographics. According to
our calculations, CTCL gave the median grant-receiving county approximately
$0.81 per voting-age resident. The typical local government spends
approximately $8 per eligible citizen on elections (Mohr et al. 2018), making
this a roughly 10% increase in the typical recipient’s election funding in a
normal year.

According to reports submitted to the CTCL, election officials intended to use
the grants in a variety of ways to make election day run smoother, offer
alternative ways to vote, and reduce COVID
transmission.131313https://www.techandciviclife.org/grant-update-november/
Officials reported using the money to hire poll workers and other temporary
staff, purchase mail balloting equipment and supplies, obtain protective
equipment such as masks, and purchase other standard election equipment. One
election administrator told the CTCL that “this unprecedented voter
participation simply would have crippled the administration of our elections
with devastating effects if we were left with the limited available municipal
funds.”141414https://www.techandciviclife.org/election-officials-made-
democracy-happen-in-2020/

###  2.2 Grant and Election Data

Figure 1: Geographic Distribution of Grants

We draw on grant and election data to study the effect of private election
administration grants in the 2020 election. We build our dataset of grant
recipients using the CTCL’s 2020 tax filing which contains a list of every
grant made under this program. We digitized this tax document and extracted
all of the recipient names and grant amounts. We also compiled and cleaned
county-level presidential election results from 1992 to 2020. These results
were collected from secretaries of state and reported in Dave Leip’s Atlas of
US Presidential Elections.151515Alaska’s secretary of state reports election
results at the election district level rather than the borough level, which is
the equivalent of counties in Alaska and the level at which CTCL made grants
in Alaska. We exclude Alaska from our data.

The official responsible for running elections varies across states and even,
occasionally, within states (Kimball and Kropf 2006). In ten mostly New
England and Midwestern states, election administration is largely handled by
municipal governments.161616These states are Connecticut, Maine,
Massachusetts, Michigan, Minnesota, New Hampshire, Rhode Island, Virginia,
Vermont, and Wisconsin. In these states, we cannot distinguish between
counties that did and did not receive grants because there are often many
municipalities within a given county. To avoid incorrectly labeling these
counties as receiving a grant when only a small portion of the county received
a grant, we withhold these states from our analysis. We also withhold five
counties with municipal election administrators in states where elections are
typically run by county officials.171717We exclude Cook, St. Clair, Vermilion,
and Winnebago counties in Illinois and Jackson County in Missouri. Finally, we
exclude 59 counties that either have fewer than 1,000 residents or have
changing borders during our analysis period given the challenges associated
with estimating turnout when the population estimate in the denominator of
turnout will be noisy.

###  2.3 Reasoning About the Effects of Election Administration Grants

Should we expect grants like this to affect turnout and election results? One
way to reason about this is to consider each of the changes in election
administration that the grant money facilitates and evaluate who the change
targets, how much it will increase participation in the targeted group, and
how different the targeted and non-targeted groups are in terms of expected
partisan voting.181818This section draws heavily from Grimmer and Hersh (2023)
and private conversation with the authors.

Consider a grant-receiving county that spends the money on additional poll
workers—more counties mentioned spending grant money on temporary staff or
poll workers than on any other spending
category.191919https://www.techandciviclife.org/grant-update-november/ Staff
may make the process of registering or requesting an absentee ballot easier,
activities that are necessary for well-run elections but are unlikely to have
substantial effects on participation. They may also help keep lines shorter.
Suppose the additional staff reduce the average wait time by 10 minutes, a
quite large effect. Pettigrew (2021) finds that wait times decrease future
turnout by approximately 1 percentage point for every hour a person waits. If
this effect is linear and applies to people deciding whether to stay or leave
based on line length, not just in future years, then reducing wait times by 10
minutes for the average voter increases participation by 0.17 percentage
points. Imagine there are two polling places in a county with equal numbers of
voters using each in a typical year. The local election official sends the new
staff to only one location. Suppose further, as an extreme example, that 75%
of voters in the precinct that received the extra staff typically vote for
Democrats while only 25% of voters in the other precinct vote for Democrats.
In this extreme example, overall turnout increases by 0.085 percentage points
and Democratic vote share increases by an even smaller 0.021 percentage
points. Similar exercises using a similar approach for other common
interventions—like adding polling places (Clinton et al. 2020; Tomkins et al.
2023; Yoder 2018), adding ballot drop boxes, and expanding early voting hours
(Gronke et al. 2008; Kaplan and Yuan 2020)—lead to the same conclusions. A
large share of counties also reported spending money in ways that should have
even smaller effects on participation and partisan balance like purchasing
personal protective equipment for poll workers or purchasing new election
equipment.

Even after combining many small effects, this exercise leads us to expect that
the effect of this grant money on turnout and Democratic vote share is quite
small if there is any detectable effect at all.

##  3 Democratic-Leaning Counties Were More Likely to Apply for and Receive a
Grant

One of the key concerns among critics of the private election administration
grants is that Democratic-leaning counties were more likely to receive them.
The CTCL gave a grant to every eligible county and municipality that
applied,202020The FEC held that this fact was uncontested in MUR 7854.
https://www.fec.gov/files/legal/murs/7854/7854_25.pdf but every county and
municipality did not apply for the funding. If Democratic counties are more
likely to apply and receive the money, and the money leads to higher turnout,
the money can advantage Democrats when the state adds up the county totals.

Figure 2 captures how much more likely Democratic-leaning counties were to
apply for a grant than Republican-leaning counties. We use Democratic vote
share in the 2016 presidential election as a measure of partisan lean. We find
that the CTCL gave grants to about 30% of counties where Donald Trump received
approximately 75% of the two-party presidential vote in 2016. Meanwhile, the
CTCL gave to 65% of counties where Hilary Clinton received 75% of the two-
party vote in 2016.

Figure 2: Democratic-Leaning Counties More Likely to Apply for and Receive
Election Assistance Grants. Each dot represents the average of 144 counties
binned based on two-party Democratic presidential vote share from 2016. The
regression line is fit to the underlying county-level data.

###  3.1 Why Did Democratic-Leaning Counties Apply at Higher Rates?

Democratic-leaning counties differ from Republican-leaning counties in a large
number of ways that could plausibly make them more likely to apply for a
private election grant: they have larger populations, they are more densely
populated, they are more racially and ethnically diverse, and they were more
exposed to COVID-19 before the grants were announced on average. Might these
characteristics account for the tendency of Democratic counties to apply for
the private election grants at higher rates than Republican counties?

Table 1: Unpacking the Relationship Between Democratic Vote Share and Grant Application. | Applied for and Received Grant
---|---
| (1) | (2) | (3) | (4) | (5) | (6)
Lag Dem Vote Share | 0.69 | 0.55 | 0.31 | 0.44 | 0.42 | 0.42
| (0.06) | (0.07) | (0.07) | (0.10) | (0.10) | (0.10)
Log(Population) |  |  | 0.06 | 0.06 | 0.06 | 0.06
|  |  | (0.01) | (0.01) | (0.01) | (0.01)
Log(Median Income) |  |  | 0.10 | 0.06 | 0.06 | 0.06
|  |  | (0.05) | (0.05) | (0.05) | (0.05)
Metro |  |  |  | 0.03 | 0.03 | 0.03
|  |  |  | (0.02) | (0.02) | (0.02)
Non-Hisp White Share |  |  |  | 0.16 | 0.13 | 0.13
|  |  |  | (0.09) | (0.09) | (0.09)
COVID Death Rate |  |  |  |  | -0.00 | -0.00
|  |  |  |  | (0.02) | (0.02)
Social Distancing Share |  |  |  |  | -0.02 | -0.02
|  |  |  |  | (0.03) | (0.03)
NACo |  |  |  |  |  | 0.01
|  |  |  |  |  | (0.02)
Constant | 0.14 |  |  |  |  |
| (0.02) |  |  |  |  |
Observations | 2594 | 2594 | 2593 | 2592 | 2537 | 2537
State FEs | No | Yes | Yes | Yes | Yes | Yes
Robust standard reported in parentheses. Population is the voting-age
population. Median Income is median household income measured with the 5-year
ACS ending in 2019. Metro is an indicator for urban and suburban counties
based on the Census nine-value urban-rural continuum. Non-Hisp White Share is
the share of residents who are classified as non-Hispanic white in the 2020
census. Covid death rate is the number of deaths per 1,000 residents prior to
September 1, 2020. Social Distancing Share is the share of Nationscape
respondents in the county who report always complying with recommended social
distancing in the early fall of 2020. NACo is an indicator for county
membership in the National Association of Counties.

In Table 1, we document some simple descriptive patterns to help us understand
how plausible are different explanations for the Democratic-Republican gap in
private election grant applications. Each column presents coefficient
estimates from a linear regression of grant receipt on 2016 Democratic
presidential vote share and, in some cases, additional factors that might
correlate with Democratic vote share and applying for a private election
grant. We do not intend to estimate the causal effect of changing the share of
Democrats in a county on the probability of applying for a grant. Instead, we
use these regressions to evaluate plausible explanations for the Democratic-
Republican gap in grant application rates.

As a point of reference, column 1 of Table 1 reports the coefficient on 2016
Democratic vote share from a simple bivariate regression. The coefficient
implies that the probability of applying for and receiving a grant is 6.8
percentage points higher on average in a county with a 10-percentage-point
higher Democratic vote share.

Some states were more favorable to private election grants while others
actively discouraged counties from applying. For example, 23 states applied
for private election grants from a similar grant program for state-level
election officials.212121For a discussion of these grants, see
https://electioninnovation.org/research/ceir-2020-voter-education-grant-
program/. Some state officials also reached out to local officials encouraging
them to apply while others threatened to sue local officials that
applied.222222See the case of Louisiana in which the Secretary of State
encouraged applications while the Attorney General threatened to sue parishes
that applied.
https://www.theadvocate.com/baton_rouge/news/politics/elections/mark-
zuckerberg-funded-free-election-grants-draw-ire-of-jeff-landry-who-files-
suit/article_e59425a0-08a2-11eb-9757-cba83bb12048.html In column 2 of Table 1,
we include state fixed effects and find that the coefficient on 2016
Democratic vote share is approximately 19% lower after accounting for the
tendency of Democratic counties to be in states where counties applied for the
CTCL grant at a higher rate.

Among counties in the same state, Democratic counties tend to have more
people, be more urban and racially diverse, be members of a national
organization that informed counties about the grants, and have had worse
experiences with COVID-19 in the spring and summer of 2020. We evaluate
whether these differences between Democratic and Republican counties can
account for the pattern of Democratic counties applying for the private
election grants at higher rates. In column 3 of Table 1, we find that counties
were 3.1 percentage points more likely to apply than other counties in the
same state with the same populations and median incomes that voted 10
percentage points more for Republicans in 2016. In column 4, we add to the
regression in column 3, adjusting for the non-Hispanic-white share of the
population and whether the county is urban or rural. After accounting for
population, income, and the state a county is in, urban counties with a
smaller share of non-Hispanic white residents vote more for Democrats and
applied for the election administration grant at a lower rate. Accordingly,
adjusting for the fact that Democratic counties are more urban and diverse, we
find that the coefficient on 2016 Democratic vote share increases to 0.44. In
columns 5 and 6 we make further adjustments to account for COVID-19 deaths and
social distancing preferences prior to the 2020 election as well as membership
in the National Association of Counties (NACo) which notified members of the
grants. Accounting for these factors does not substantially change the
coefficient on 2016 Democratic vote share.

In Section A.2 in the online appendix, we present an additional analysis in
which we compare the relationship between 2016 Democratic vote share and
applying for a grant in states that dramatically expanded mail balloting in
2020 compared to states that severely restricted mail balloting or already
mailed every registrant a ballot. The estimates are noisy and sensitive to
which covariates we include in the regression, but the point estimates are
consistent with some election officials in Democratic counties anticipating an
increase in their costs associated with a transition to mail balloting, and
applying for a grant to cover those costs.

##  4 Grants Did Not Substantially Increase Turnout or Democratic Vote Share

In this section, we detail our finding that the private election
administration grants did not substantially increase turnout or Democratic
vote share, and we explain how we estimate the effects of the grants. This
section has five parts: First, we describe our estimation strategy and why it
is appropriate for this setting. Second, we present graphical evidence that
grants did not substantially increase turnout or Democratic vote share. Third,
we report estimates of the effect of grants on turnout and Democratic vote
share. Fourth, we document our independent analysis of grants to
municipalities in Wisconsin producing similar results as our main estimates.
Fifth, we discuss alternative estimation strategies and document how all of
these strategies yield similar results. Sixth, we present evidence that the
effects are similar in more and less competitive states and more and less
populous counties. Finally, we document that the effects are less positive in
counties that received larger grants, suggesting that funding is not
substantially affecting turnout or the composition of the electorate at the
current margin.

###  4.1 Estimating the Effect of Private Grants in the 2020 Election

Our goal in this section is to estimate the average effect CTCL grants had on
turnout and Democratic vote share. As we document in Figure 2, grant-receiving
counties favored Democrats in 2016, four years before the grants were made.
Given the tendency of counties to continue voting for the same party from one
election to the next, we would expect grant-receiving counties to favor
Democrats in the 2020 election more than counties that did not receive grants
even if the grants had no effect on Democratic vote share or turnout. This
type of selection can be accounted for using a difference-in-differences
strategy, where we compare the difference between 2020 and pre-2020 turnout
and vote share for Democrats in treated counties with the analogous difference
in untreated counties. This approach would yield valid causal estimates under
the assumption that turnout and Democratic vote share would have increased by
the same amount in 2020 in treatment and control counties in the absence of
the grants. Since we have measures of county-level turnout and Democratic vote
share for many elections prior to 2020, we evaluate the plausibility of this
assumption in Figure 3. We find that Democratic vote share is decreasing
slower in treated counties than in control counties. We also find that turnout
is increasing faster in treated counties. These differences in treated and
control trajectories suggest that simple difference-in-differences estimates
will dramatically overstate the effect of grants on Democratic vote share and
turnout.

Figure 3: Democratic Vote Share Was Declining Slower and Turnout Was
Increasing Faster in Grant-Receiving Counties Long Before 2020.

To address the shortcomings with the standard difference-in-differences design
in our setting, we follow Arkhangelsky et al. (2021) in reweighting our
difference-in-differences regressions with weights ensure the treatment and
control units are on similar trajectories prior to the treatment and make the
pre-treatment period as similar as possible to the post-treatment.232323This
approach, which they call synthetic difference-in-differences, builds on the
synthetic control method and other related approaches (Athey et al. 2021;
Abadie, Diamond, and Hainmueller 2010; Ben-Michael, Feller, and Rothstein
2021; Xu 2017) This approach has three steps: 1) compute county weights that
make the trend in the control units approximately equal to the trend in the
treated units, 2) compute election weights that make the pre-treatment period
as similar as possible to the post-treatment period among control units, and
3) estimate a reweighted difference-in-differences regression weighting by the
product of the county and election weights.

Formally, we compute county weights $\omega_{i}$ that minimize the expression

$\text{argmin}_{\omega_{0}\in\mathbb{R}_{+},\omega\in\Omega}\sum_{t=1}^{T_{pre}}(\omega_{0}+\sum_{i=1}^{N_{co}}\omega_{i}Y_{it}-\frac{1}{N_{tr}}\sum_{i=N_{co}+1}^{N}Y_{it})^{2}+\zeta^{2}T_{pre}||\omega||_{2}^{2}$

where $Y_{it}$ is the outcome in county $i$ and election $t$, $T_{pre}$ is the
number of pre-treatment periods, $N$ is the number of control and treated
units, $N_{co}$ is the number of control units, $\Omega$ is the set containing
all $\omega$ in which all $\omega_{i}$ fall between between 0 and 1 inclusive
and $\omega$ sums to one, and $\zeta$ is a regularization parameter proposed
in Arkhangelsky et al. (2021). Since $\omega_{0}$ is not regularized,
$\omega_{0}$ represents the average pre-treatment difference between the
treated and control units and means that the county weights produce a weighted
control mean that follows the same trajectory as the treatment mean but may
not be at the same level.

We then compute time weights $\lambda_{t}$ that minimize an expression nearly
identical to the expression for the unit weights:

$\text{argmin}_{\lambda_{0}\in\mathbb{R}_{+},\lambda\in\Lambda}\sum_{i=1}^{N_{co}}(\lambda_{0}+\sum_{t=1}^{T_{pre}}\lambda_{t}Y_{it}-\frac{1}{T_{post}}\sum_{t=T_{pre}+1}^{T}Y_{it})^{2}$

We use the product of these weights as the weights in a weighted difference-
in-differences least squares regression

$\text{argmin}_{\tau,\alpha,\beta}\sum_{i=1}^{N}\sum_{t=1}^{T}(Y_{it}-\alpha_{i}-\beta_{t}-W_{it}\tau)^{2}\hat{\omega_{i}}\hat{\lambda}_{t}$

where $W_{it}$ is an indicator for the treatment, $\alpha_{i}$ is a county
fixed effect, $\beta_{t}$ is an election fixed effect, and $\tau$ is the
treatment effect estimate. As with the classical synthetic control method
(Abadie, Diamond, and Hainmueller 2010) and generalized synthetic control
method (Xu 2017), the weighted regression coefficient $\hat{\tau}$ from the
synthetic difference in differences method yields consistent estimates for the
average treatment effect on the treated (ATT) under a low-rank approximation
for the untreated potential outcome $Y^{0}$, which requires that election
outcomes in the absence of the grants can be approximated by county
intercepts, election year shocks, and low-rank time varying slopes at the
county level. This design assumption is strictly weaker than the parallel
trends assumption required by difference in difference methods, which we see
is implausible from figure 3.

To validate the weighted difference-in-differences estimator, we study the
2016 presidential election as a placebo case. We do this by deleting 2020 from
our data and pretending that the grants were handed out in 2016. We then rerun
the weighted difference-in-difference procedure to produce estimates of the
placebo effect. Since the grants were not handed out until 2020, an unbiased
estimator will find placebo effects that are close to zero. We present these
estimates in Section A.2 in the online appendix. Consistent with the goal of
the estimator, we confirm that the weighted difference-in-differences approach
fails to estimate a statistically significant placebo effect.

###  4.2 Graphical Evidence That Grants Had Minimal Effect on Democratic Vote
Share and Turnout

Figure 4: Trends in Democratic vote share and turnout in treated and synth
difference-in-differences counterfactual over time

(a) Trends in Democratic Vote Share

(b) Trends in Turnout

First, we present graphical evidence that CTCL grants did not substantially
increase turnout or vote share for Democrats. Figure 4 compares Democratic
vote share and turnout for the average grant recipient over time to the
counterfactual implied by synthetic difference-in-differences. Across both the
Democratic vote share panel on the left and the turnout panel on the right, we
see the synthetic difference-in-differences procedure produces a
counterfactual that almost perfectly matches the average trajectory for grant
recipients. The exceptions to this perfect match are in 1992 where
counterfactual Democratic vote share is slightly higher than observed
Democratic vote share and 2004 and 2008 where counterfactual turnout is
slightly lower than observed turnout. In all three cases, the differences are
small with gaps of less than 0.25 percentage points.

Figure 4 also makes clear that any average effect of the grants is so small as
to not be visible in the Democratic vote share or turnout plots. If there were
a visible effect in either plot, it would appear as a difference between the
grant recipient and counterfactual lines in 2020. Instead, these lines
continue to perfectly overlap in 2020 just like they did prior to 2020,
implying that the average grant did not substantially advantage either party
or noticeably increase turnout.

###  4.3 Estimates of Effect of Grants on Democratic Vote Share and Turnout

Table 2: Election Administration Grants Did Not Noticeably Advantage Democrats or Increase Turnout in 2020. | Dem Vote Share (%) | Turnout (%)
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
Grant Recipient in 2020 | 3.24 | 0.36 | 0.10 | 0.02 | 0.24 | -0.03 | 0.13 | 0.03
| (0.34) | (0.11) | (0.12) | (0.11) | (0.21) | (0.15) | (0.14) | (0.14)
Num Grant Recipients | 924 | 924 | 924 | 924 | 924 | 924 | 924 | 924
Num Counties | 2,597 | 2,597 | 2,597 | 2,597 | 2,597 | 2,597 | 2,597 | 2,597
Observations | 20,776 | 20,776 | 20,776 | 20,776 | 20,776 | 20,776 | 20,776 | 20,776
County FEs | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Year FEs | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
County Weights | No | No | Yes | Yes | No | No | Yes | Yes
Year Weights | No | Yes | No | Yes | No | Yes | No | Yes
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data is a balanced panel of counties in the 8 presidential
elections from 1992 to 2020.

Next, we present our estimates of the effect of receiving a grant on turnout
and Democratic vote share. Table 2 reports our estimates using a variety of
estimation approaches. Column 1 is the simple difference-in-differences
regression estimate of the effect of grant receipt on Democratic vote share.
As we establish in Section 4.1, this is a dramatic overestimate of the effect
because, even before 2020, Democrats were increasingly performing better in
counties that received a grant than in counties that did not. In column 2, we
present estimates from difference-in-differences regressions including time
weights but not including county weights. The 2020 election result is much
more similar to the 2016 result than any other previous period in our data, so
the weight selection procedure places all of the mass on 2016 in the pre-
treatment period. This means that column 2 is equivalent to a two-group, two-
period difference-in-differences design using only 2016 and 2020. With this
estimator, we find that grants increase Democratic vote share by 0.36
percentage points. In column 3, we present our estimate from a difference-in-
differences regression with county weights but no time weights. We find that,
compared to counties that did not receive a grant but were on a similar
average presidential voting trajectory prior to 2020, and after accounting for
remaining pre-2020 average differences in Democratic vote share, grant
recipients had only a 0.1-percentage-point higher Democratic vote share in
2020. Column 4 presents our difference-in-differences estimate using both time
and county weights. Using this specification, we estimate that grants caused
an average increase in Democratic vote share of 0.02 percentage points. In
summary, once we compare grant recipients to more similar counties, we find
that these grants did not substantially increase Democratic vote share in
receiving counties.

In columns 5 through 8, we present estimates of the effect of a grant on
turnout using the same estimation strategies as in columns 1 through 4. Grant
recipients and non-recipients are on more similar turnout trajectories than
Democratic vote trajectories prior to 2020. Accordingly, the four estimation
strategies produce more similar results. In column 5, we present a likely
upwardly biased 0.24-percentage-point difference-in-differences estimate of
the effect of the grants on turnout. In columns 6 through 8 we find that, when
we use time weights, county weights, or both, our estimates range from -0.03
percentage points to 0.13 percentage points.

Focusing on our preferred specification in columns 4 and 8 where we use both
time and county weights in a difference-in-differences regression, we find
that the grants did not substantially increase Democratic vote share or
turnout. Our estimates are highly precise on both outcomes. The standard error
we report in column 4 is 0.11 percent, meaning that we could reject a
hypothetical effect of the grants on Democratic vote share that is greater
than 0.25 percentage points. Similarly, the standard error we report in column
8 is 0.14 meaning that we could reject a hypothetical effect of 0.30
percentage points.

###  4.4 Estimated Effects Similar or Less Favorable to Democrats in Wisconsin

In this section, we supplement our main finding with new municipality-level
election data from Wisconsin. We find that the grants did not substantially
increase Democratic vote share and or turnout.

As we discuss in Section 4.1, our main analyses study places where elections
are administered at the county level because most parts of the country
administer elections at that level, presidential election data is widely
available at the county level, the population denominator is noisily measured
in the many small municipalities, and changing municipal boundaries add
potentially systematic noise to the election results data. Wisconsin is one of
the states we leave out of this analysis because elections are primarily
conducted at the municipal level (Herron 2023). To validate our main findings,
we constructed a separate dataset of municipal-level election results and
grant receipt in Wisconsin. We provide more details on this dataset in Section
A.5 in the online appendix.

Using the same estimation strategies we use in Table 2, we report estimates of
the effect of private election administration grants in Wisconsin in Table A.5
in the online appendix. We estimate very similar effects of the grants on
Democratic vote share in Wisconsin as in our nationwide county-level analysis.
Our preferred synthetic difference-in-differences estimator with county and
year weights in column 4 estimates an effect of 0.11 percentage point. Across
all three of our synthetic difference-in-differences estimates, our point
estimates range from 0.11 to 0.31 percentage points, and we cannot reject the
null hypothesis that the grants had no effect on Democratic vote share. The
upper bound of the 95% confidence interval on our preferred estimate is an
effect of 0.48 percentage points.

In columns 6 through 8 of Table A.5, we report our estimates of the effect of
grants on turnout in Wisconsin. Contrary to the expectation that grants
increased turnout in Democratic strongholds and thereby advantaged Democrats,
we find that municipalities that received grants had a modest but
statistically significant drop in turnout of approximately 0.7 percentage
points. We interpret this result as evidence against grants improving turnout
in Democratic strongholds rather than strong evidence that the grants caused
lower turnout in grant-receiving Wisconsin municipalities.

###  4.5 Estimated Effects Not Sensitive to Estimation Strategy

We examine the robustness of our main estimates in Table 2 using five
alternative estimation strategies. Across all five approaches, we find
similar, substantively small effects of the grants on turnout and Democratic
vote share. Our estimates of the average effect on Democratic vote share,
reported in Table A.3 in the online appendix, range from 0.15 percentage
points to 0.40 percentage points. Our estimates of the average effect on
turnout, reported in Table A.4 also in the online appendix, range from -0.02
percentage points to 0.47 percentage points. These estimates come from five
different strategies: 1) weighted regressions like synthetic difference-in-
differences but without county-specific intercepts so the weights attempt to
balance treatment and control outcomes on levels rather than trends, 2)
regularized synthetic control (Doudchenko and Imbens 2016), 3) weighting the
control units such that pre-2020 control-group outcome means exactly match
treatment means while deviating as little as possible from uniform weights
(Hainmueller 2012), 4) predicting the outcome and treatment propensity using
random forests and using these estimates for augmented inverse propensity
weighting (Athey, Tibshirani, and Wager 2019), and 5) predicting the outcome
and treatment propensity using ensemble learners—pooling generalized additive
models, boosting, regression trees, splines, and elastic nets—and using these
predictions for augmented inverse propensity weighting.

###  4.6 Estimated Effect Similar in Battleground States

One concern is that, while the grants had a small effect on average, they may
have had a larger effect in the closest states. We evaluate this claim by
estimating the average effect of the grants on Democratic vote share and
turnout in three sets of the most competitive states—states decided by less
than 5 percentage points, states that the Cook Political Report identified as
battleground states prior to election day, and Wisconsin alone.

In Table A.6 in the online appendix, we document that, even in the most
competitive states, the effect of the grants was small. In column 1, we find
that the effect of grants on Democratic vote share in close states was 0.54
percentage points. While this point estimate is larger than the effect we
estimate using the full sample, the smaller sample size also means the
subgroup estimates are substantially noisier, and we are unable to reject the
hypothesis that the grants had no effect. When we extend our analysis to the
battleground states according to the Cook Political Report, our estimate is
more precise, and the estimated effect on Democratic vote share is nearly
identical to our estimates from Table 2 using all counties.

In columns 3 and 4, we find that the effect of the grants on turnout in states
decided by less than 5 percentage points and those the Cook Political Report
labeled as battlegrounds was approximately the same size as we estimate using
the full sample. In both cases, we cannot reject the hypothesis that the
grants failed to increase turnout. We can also rule out positive effects on
turnout of greater than 0.75 percentage points in the closest states.

Returning to the evidence we presented in Section 4.4, when we study the
battleground state of Wisconsin independently, we find roughly the same
pattern of small average effects, no average effects, or even negative average
effects of the grants on Democratic vote share and turnout.

###  4.7 Estimated Effect Similar in Populous Counties

Private election funding would have a larger effect on the aggregate election
outcome if it was most effective in counties with more voters. Are the effects
of grants larger in populous counties? We evaluate this possibility by
splitting our sample into terciles by voting-age population and estimating
effects for each subgroup separately. We present our results in Table A.7 in
the online appendix. We find that the effects on turnout and Democratic vote
share are no larger in more and less populous counties.

###  4.8 Effects Not Larger for Counties Receiving Larger Grants

If additional spending on local election administration increases turnout or
Democratic vote share, this would most likely happen because local officials
use the money to make it easier for citizens to participate. This implies that
the effects of money should increase as they get larger or, at the very least,
be unrelated to grant size. While CTCL reports using a formula to decide the
maximum amount each county was eligible to receive, the amounts that CTCL
distributed to counties ranged from $0.63 per voting-age resident at the 25th
percentile to $1.38 per voting-age resident at the 75th percentile. Might our
small effect estimates mask an effect in counties that receive larger grants?

To answer this question, we split treated counties into three groups based on
the amount of grant money going to the county per voting-age resident and
produce separate synthetic difference-in-differences estimates of the effect
of small, medium, and large grants. We present the results of these analyses
in Table A.8 in the online appendix. Contrary to the expectation that the
effect may be limited to places receiving the largest grants, we find that
small grants increased Democratic vote share and turnout by more than large
grants. We estimate that small grants increased Democratic vote share by 0.62
percentage points and turnout by 0.32 percentage points while large grants
decreased Democratic vote share by 0.61 percentage points and turnout by 0.27
percentage points. Given that, unlike our other analyses, the synthetic
difference-in-differences weights do a poor job of matching pre-2020 treated
and control trajectories for these subgroups, we also present estimates using
entropy balancing to match pre-2020 outcome means for control counties to the
treated county means. Using entropy balancing, we no longer find negative
effects for the largest recipients—the effects are almost exactly zero. We
also find slightly smaller effects in places that received smaller grants.
These effects average out to approximately the average effect estimates we
present in sections 4.3 and 4.5.

##  5 Characterizing the Magnitude of the Effects

How large are the effects of private election administration funding? In this
section, we benchmark the magnitude of our effect estimates against the
remarkably tight margin of the 2020 presidential election. The 2020
presidential election turned on four states decided by margins of 1.16
percentage points or less: Georgia, Arizona, Wisconsin, and Pennsylvania. The
margins in these states were widely understood to be very tight. Are the
effects of the private election administration grants as small or smaller than
these margins?

One simple way to interpret the effect size is to compare the effect of
private election funding on Democratic vote share to the margin in these four
close states. Of our three main estimates reported in Table 2, two of them are
too small to have changed the outcome in any state, including Georgia,
Arizona, Wisconsin, and Pennsylvania. Our third estimate is about as large as
the margin in Wisconsin but still smaller than the margin in Pennsylvania.
This is not to say that grant funding was sufficient to change the outcome in
any of these states—only a subset of counties received the money, so county-
level effects that are roughly the same magnitude as the margin in the state
are not large enough to have swung the statewide outcome.

How large are the effects on turnout? One way to interpret these effects is to
compare them to the effects of other election administration changes. The
effect of the grants on turnout was less than half of the effect of an extra
day of early voting (Kaplan and Yuan 2020), roughly half of the effect of a
mailer encouraging citizens to vote by mail (Hopkins et al. 2021), less than
one-tenth of the effect of universal vote by mail (Gerber, Huber, and Hill
2013; Thompson et al. 2020), and less than one-twentieth of the effect of
mobile voting (Fowler 2020). While the estimated effects of polling place
locations on turnout are typically noised than our estimates of the effects of
grants, our estimates of the effects of grants tend to be smaller than the
effect of having your polling place moved further away (Clinton et al. 2020;
Tomkins et al. 2023; Yoder 2018). Our estimates of the effects of grants on
turnout are small compared to all of these administrative changes.

Given the strong tendency of the grants to go to Democratic-leaning counties,
the grants could advantage Democrats more than is implied by the Democratic
vote share effect alone. On the other hand, many counties did not receive
grants, so the effect of the grants on statewide totals is substantially
smaller than the effect in the average county. To account for these concerns,
we conducted a simple simulation study. In our simulation, we remove the
average effect on turnout and Democratic vote share from all of the treated
counties and assume untreated counties remain unchanged.242424We also include
municipal-level data from Wisconsin in our simulation analysis. In states
where we do not have grant data, we impute the probability that a county
received a grant based on 2016 Democratic vote share and sample 1,000 random
possible treatment assignments in those states. We then handle the counties we
randomly assigned to treatment in that simulation as we handle the truly
treated counties, removing the average effect of the grants on turnout and
vote share.

Based on this simple simulation, we find that the estimates from two of our
three of our weighted difference-in-differences estimation strategies imply
that the grants were too small to swing the outcome of any statewide election.
The estimates from our third weighted difference-in-differences strategy are
large enough to have changed the outcome of the election in Georgia and
Arizona but inconsistent with changing the outcome of the election in
Pennsylvania or Wisconsin. Put together, this suggests that, even compared to
the margin in very close elections recent elections, the effects of the grants
were quite small.

It is important to note that, while these simulations help us understand the
magnitude of the effects, we do not intend them as a reflection of what would
have happened in the 2020 election had CTCL not made any grants. Our
simulations do not account for the general equilibrium effects of the grants,
such as changes in partisan spending, get-out-the-vote operations, or other
government spending on election administration. Instead, we view our
simulations as consistent with our interpretation that the grants had minimal
effects.

##  6 Discussion

The large influx of private funding for election administration in 2020, and
the fact that Democratic counties were more likely to receive it, has led many
politicians, journalists, and pundits to speculate that the funding advantaged
Democrats. Despite these widespread concerns, we present evidence that these
grants did not substantially increase turnout or Democratic vote share. Our
results answer one of the key questions at the center of the debate over
private funding of election administration, suggesting that it does not always
clearly and substantially favor one party.

Still, our findings leave unanswered two important questions: First, while we
find that private funding did not increase turnout, it may have improved the
election on other important dimensions. Many of the local officials who
received the money said that they would have had trouble reporting their
election results on time without the grants. Others said that the money
allowed them to hire more staff which may have made the election run more
smoothly, made voting more convenient, or improved election security and the
accuracy of the count. We cannot observe these effects of the money, but they
are important when deciding whether grant programs like these are effective.
Second, the large backlash suggests that the grants may have led some citizens
to doubt the outcome of the election. If that is the case, it is a potential
cost worth considering in future attempts to shore up local election funding.

While our results suggest these grants did not substantially alter the outcome
of the 2020 presidential elections, our results do not imply that a grant
program like this cannot change election outcomes in the future. We understand
our results to be only one input into a broader policy discussion about the
appropriateness of grant programs like this given the potential positive
effects grants could have on voter experience and security, the potentially
harmful effects grants could have on citizen trust in elections, and the risk
that future funders could alter election outcomes.

## References

  * (1)
  * Abadie, Diamond, and Hainmueller (2010) Abadie, Alberto, Alexis Diamond, and Jens Hainmueller. 2010.  “Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program.” Journal of the American statistical Association 105(490): 493–505.
  * Ansolabehere, De Figueiredo, and Snyder Jr (2003) Ansolabehere, Stephen, John M De Figueiredo, and James M Snyder Jr. 2003.  “Why Is There So Little Money in US Politics?” Journal of Economic Perspectives 17(1): 105–130.
  * Arkhangelsky et al. (2021) Arkhangelsky, Dmitry, Susan Athey, David A Hirshberg, Guido W Imbens, and Stefan Wager. 2021.  “Synthetic difference-in-differences.” American Economic Review 111(12): 4088–4118.
  * Athey, Tibshirani, and Wager (2019) Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019.  “Generalized random forests.” The Annals of Statistics 47(2): 1148 – 1178.
  * Athey et al. (2021) Athey, Susan, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. 2021.  “Matrix Completion Methods for Causal Panel Data Models.” Journal of the American Statistical Association 116(536): 1716–1730.
  * Ben-Michael, Feller, and Rothstein (2021) Ben-Michael, Eli, Avi Feller, and Jesse Rothstein. 2021.  “The Augmented Synthetic Control Method.” Journal of the American Statistical Association 116(536): 1789–1803.
  * Cantoni and Pons (2021) Cantoni, Enrico, and Vincent Pons. 2021.  “Strict ID Laws Don’t Stop Voters: Evidence from a US Nationwide Panel, 2008–2018.” The Quarterly Journal of Economics 136(4): 2615–2660.
  * Clinton et al. (2020) Clinton, Joshua D., Nick Eubank, Adriane Fresh, and Michael E. Shepherd. 2020.  “Polling Place Changes and Political Participation: Evidence from North Carolina Presidential Elections, 2008–2016.” Political Science Research and Methods pp. 1–18.
  * Doudchenko and Imbens (2016) Doudchenko, Nikolay, and Guido W Imbens. 2016.  “Balancing, Regression, Difference-in-Differences and Synthetic Control Methods: A Synthesis.” NBER Working Paper. https://www.nber.org/papers/w22791.
  * Ferrer, Geyn, and Thompson (2023) Ferrer, Joshua, Igor Geyn, and Daniel M. Thompson. 2023.  “How Partisan Is Local Election Administration?” Working Paper. https://dthompson.scholar.ss.ucla.edu/wp-content/uploads/sites/19/2022/09/Ferrer_et_al_Election_Admin.pdf.
  * Fouirnaies and Fowler (2022) Fouirnaies, Alexander, and Anthony Fowler. 2022.  “Do Campaign Contributions Buy Favorable Policies? Evidence from the Insurance Industry.” Political Science Research and Methods 10(1): 18–32.
  * Fowler (2020) Fowler, Anthony. 2020.  “Promises and Perils of Mobile Voting.” Election Law Journal: Rules, Politics, and Policy 19(3): 418–431.
  * Gerber, Huber, and Hill (2013) Gerber, Alan S., Gregory A. Huber, and Seth J. Hill. 2013.  “Identifying the Effect of All-Mail Elections on Turnout: Staggered Reform in the Evergreen State.” Political Science Research and Methods 1(1): 91–116.
  * Grimmer and Hersh (2023) Grimmer, Justin, and Eitan Hersh. 2023.  “How Election Rules Affect Who Wins.” Working Paper. https://www.eitanhersh.com/uploads/7/9/7/5/7975685/effectslaws_062923.pdf.
  * Gronke et al. (2008) Gronke, Paul, Eva Galanes-Rosenbaum, Peter A Miller, and Daniel Toffey. 2008.  “Convenience Voting.” Annual Review of Political Science 11: 437–455.
  * Hainmueller (2012) Hainmueller, Jens. 2012.  “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies.” Political Analysis 20(1): 25–46.
  * Hall (2016) Hall, Andrew B. 2016.  “Systemic Effects of Campaign Spending: Evidence from Corporate Contribution Bans in US State Legislatures.” Political Science Research and Methods 4(2): 343–359.
  * Hasen (2012) Hasen, Richard L. 2012.  The Voting Wars: From Florida 2000 to the Next Election Meltdown.  Yale University Press.
  * Herron (2023) Herron, Michael C. 2023.  “Allegations Made against Dominion Voting Systems and the 2020 Presidential Election in Wisconsin.” Election Law Journal: Rules, Politics, and Policy .
  * Hersh (2023) Hersh, Eitan. 2023.  “The Political Role of Business Leaders.” Annual Review of Political Science 26: 97–115.
  * Hopkins et al. (2021) Hopkins, Daniel J, Marc Meredith, Anjali Chainani, Nathaniel Olin, and Tiffany Tse. 2021.  “Results from a 2020 Field Experiment Encouraging Voting by Mail.” Proceedings of the National Academy of Sciences 118(4): e2021022118.
  * Kaplan and Yuan (2020) Kaplan, Ethan, and Haishan Yuan. 2020.  “Early Voting Laws, Voter Turnout, and Partisan Vote Composition: Evidence from Ohio.” American Economic Journal: Applied Economics 12(1): 32–60.
  * Kilborn and Vishwanath (2022) Kilborn, Mitchell, and Arjun Vishwanath. 2022.  “Public Money Talks Too: How Public Campaign Financing Degrades Representation.” American Journal of Political Science 66(3): 730–744.
  * Kimball and Kropf (2006) Kimball, David C., and Martha Kropf. 2006.  “The Street-Level Bureaucrats of Elections: Selection Methods for Local Election Officials.” Review of Policy Research 23(6): 1257–1268.
  * Levitt (1994) Levitt, Steven D. 1994.  “Using Repeat Challengers to Estimate the Effect of Campaign Spending on Election Outcomes in the US House.” Journal of Political Economy 102(4): 777–798.
  * Mohr et al. (2019) Mohr, Zachary, JoEllen V. Pope, Martha E. Kropf, and Mary Jo Shepherd. 2019.  “Strategic Spending: Does Politics Influence Election Administration Expenditure?” American Journal of Political Science 63(2): 427–438.
  * Mohr et al. (2018) Mohr, Zachary, Martha Kropf, JoEllen Pope, Mary Jo Shepherd, and Madison Esterle. 2018.  “Election Administration Spending in Local Election Jurisdictions: Results from a Nationwide Data Collection Project.” Working Paper. https://esra.wisc.edu/wp-content/uploads/sites/1556/2020/11/mohr.pdf.
  * Pettigrew (2021) Pettigrew, Stephen. 2021.  “The Downstream Consequences of Long Waits: How Lines at the Precinct Depress Future Turnout.” Electoral Studies 71: 102188.
  * Thompson et al. (2020) Thompson, Daniel M., Jennifer A. Wu, Jesse Yoder, and Andrew B. Hall. 2020.  “Universal Vote-by-Mail Has No Impact on Partisan Turnout or Vote Share.” Proceedings of the National Academy of Sciences 117(25): 14052–14056.
  * Tomkins et al. (2023) Tomkins, Sabina, Keniel Yao, Johann Gaebler, Tobias Konitzer, David Rothschild, Marc Meredith, and Sharad Goel. 2023.  “Blocks as Geographic Discontinuities: The Effect of Polling-Place Assignment on Voting.” Political Analysis 31(2): 165–180.
  * Xu (2017) Xu, Yiqing. 2017.  “Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models.” Political Analysis 25(1): 57–76.
  * Yoder (2018) Yoder, Jesse. 2018.  “How Polling Place Changes Reduce Turnout: Evidence from Administrative Data in North Carolina.” Working Paper. http://stanford.edu/~yoderj/pollingplaces.pdf.
  * Yoder et al. (2021) Yoder, Jesse, Cassandra Handan-Nader, Andrew Myers, Tobias Nowacki, Daniel M Thompson, Jennifer A Wu, Chenoa Yorgason, and Andrew B Hall. 2021.  “How Did Absentee Voting Affect the 2020 US Election?” Science Advances 7(52): eabk1755.
  * Yorgason (2021) Yorgason, Chenoa. 2021.  “Campaign Finance Vouchers Do Not Reduce Donor Inequality.” Working Paper. https://osf.io/preprints/socarxiv/76mjp/.

## Online Appendix

Intended for online publication only.

.tocmtappendix mtchapternone mtappendixsubsection

###### Contents

  1. 1 Introduction
  2. 2 Private Election Administration Grants in 2020
    1. 2.1 Center for Tech and Civic Life’s 2020 Grant Program
    2. 2.2 Grant and Election Data
    3. 2.3 Reasoning About the Effects of Election Administration Grants
  3. 3 Democratic-Leaning Counties Were More Likely to Apply for and Receive a Grant
    1. 3.1 Why Did Democratic-Leaning Counties Apply at Higher Rates?
  4. 4 Grants Did Not Substantially Increase Turnout or Democratic Vote Share
    1. 4.1 Estimating the Effect of Private Grants in the 2020 Election
    2. 4.2 Graphical Evidence That Grants Had Minimal Effect on Democratic Vote Share and Turnout
    3. 4.3 Estimates of Effect of Grants on Democratic Vote Share and Turnout
    4. 4.4 Estimated Effects Similar or Less Favorable to Democrats in Wisconsin
    5. 4.5 Estimated Effects Not Sensitive to Estimation Strategy
    6. 4.6 Estimated Effect Similar in Battleground States
    7. 4.7 Estimated Effect Similar in Populous Counties
    8. 4.8 Effects Not Larger for Counties Receiving Larger Grants
  5. 5 Characterizing the Magnitude of the Effects
  6. 6 Discussion
  7. A.1 New Laws Limiting Private Election Administration Grants
  8. A.2 Decomposing Grant Selection
  9. A.3 Validating Weighted Diff-in-Diff Using 2016 as a Placebo Treatment Period
  10. A.4 Alternative Strategies for Estimating the Effect of Grants
  11. A.5 Estimating the Effect of Grants in Wisconsin
  12. A.6 Investigating How Grant Effects Vary Across Counties
    1. A.6.1 Effect of Grants in Battleground States
    2. A.6.2 Effect of Grants by County Population Tercile
    3. A.6.3 Effect of Grants by Grant Size Tercile

##  A.1 New Laws Limiting Private Election Administration Grants

Figure A.1: Twenty-Four States Have Passed Laws Limiting Private Election
Administration Grants Since 2020.

As we discuss in Section 1, twenty-four states have passed laws banning or
substantially limiting private donations that support local election
administration. Figure A.1 maps these states. Nearly every Southern state has
passed a ban or limit on private funding. The only two Southern states that
have not passed such a limit, Louisiana and North Carolina, have Democratic
governors who vetoed legislative bills that introduced limits.

##  A.2 Decomposing Grant Selection

In Section 3 in the main text, we compare the types of counties that applied
for funding to counties that did not apply. In this section, we explore the
connection between mail balloting and applying for grant funding.

During the spring and summer of 2020, COVID killed many more people in
Democratic-leaning counties than in Republican-leaning counties. Figure A.2
captures this pattern. Possibly for this reason as well as other
psychological, sociological, political, and economic reasons, Democrats in the
public were more eager to vote by mail in the 2020 election than were
Republicans. Could this Democratic-Republican gap in demand for mail balloting
in 2020 lead to different costs in Democratic and Republican counties and
ultimately local officials to expect new costs and apply for a grant to cover
them?

Figure A.2: COVID Cases Primarily Concentrated in Clinton Counties Prior to 2020 Election. Table A.1: In Democratic-Leaning Counties, More People Intended to Vote-by-Mail and Officials Were More Likely to Apply for CTCL Grant. | Mail Voting Share | Received Grant
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
Lag Dem Vote Share | 0.23 | 0.33 | 0.09 | 0.43 | 0.15 | 0.44
| (0.04) | (0.06) | (0.07) | (0.11) | (0.13) | (0.15)
Lag Dem Vote Share $\times$ |  |  |  | 0.54 | 0.35 | 0.26
Mail Voting Expansion |  |  |  | (0.18) | (0.18) | (0.17)
Mail Voting Expansion |  |  |  | -0.10 | -0.01 |
|  |  |  | (0.06) | (0.07) |
Observations | 2356 | 2355 | 2355 | 1238 | 1237 | 1237
Controls | No | Yes | Yes | No | Yes | Yes
State FEs | No | No | Yes | No | No | Yes
Robust standard reported in parentheses. Mail voting share is the weighted
share pre-election NationScape respondents in a county who said they intended
to vote by mail. No-excuse expansion codes states that enacted no-excuse
absentee voting for the first time in 2020 as 1. States that already had
universal mail ballot delivery or offered absentee voting only with an excuse
in 2020 are coded as 0. All other states are held out of this analysis. The
controls are the log of voting age population, the log of median household
income, and the non-Hispanic white share of the population.

In columns 1 through 3 of Table A.1, we present suggestive evidence that more
citizens in Democratic-leaning counties were eager to vote by mail. We
estimate the share of county registrants who intend to vote by mail using UCLA
Nationscape survey questions about vote mode linked to the respondent’s
county. We find that counties that voted more Clinton in 2016 had
substantially more residents saying they intended to vote by mail. This
relationship shrinks considerably when we include state fixed effects that
account different state election laws and is no longer statistically
distinguishable from zero at conventional levels, but the point estimate is
still positive.

In columns 4 through 6 of Table A.1, we present suggestive evidence that
election officials in Democratic counties were more likely to apply for grants
in part because they expected increased costs associated with expanding mail
voting. We define three groups of states: those that only allow people to vote
by mail with a special excuse, those that send every citizen a mail ballot,
and those that removed the need for an excuse to vote by mail in 2020. If
officials in Democratic counties were more likely to apply for a grant in part
because they anticipated increased mail voting costs, this relationship
between Democratic vote share and applying for a grant should be larger in
states that expanded mail voting than in states that maintained substantial
restrictions on it or already mailed all registered voters a ballot. In column
4 we document that the relationship between Democratic vote share and grant
receipt is substantially larger in states that expanded mail voting. In
columns 5 and 6, we adjust for additional features of the counties and states.
With these adjustments, the relationship between lagged Democratic vote share
and grant receipt is more similar in states that expanded mail voting and
those that did not, and the difference is no longer statistically
distinguishable from zero at conventional levels, but we the point estimate is
still positive and substantively large.

##  A.3 Validating Weighted Diff-in-Diff Using 2016 as a Placebo Treatment
Period

Table A.2: Weighted Difference-in-Differences Approach Balances Dem Vote Share and Turnout in 2016. | Dem Vote Share (%) | Turnout (%)
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
Grant Recipient in 2016 | 3.38 | 0.81 | 0.48 | 0.38 | 0.12 | -0.14 | -0.20 | -0.17
| (0.36) | (0.22) | (0.23) | (0.23) | (0.17) | (0.10) | (0.10) | (0.10)
Num Grant Recipients | 924 | 924 | 924 | 924 | 924 | 924 | 924 | 924
Num Counties | 2,594 | 2,594 | 2,594 | 2,594 | 2,594 | 2,594 | 2,594 | 2,594
Observations | 18,158 | 18,158 | 18,158 | 18,158 | 18,158 | 18,158 | 18,158 | 18,158
County FEs | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Year FEs | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
County Weights | No | No | Yes | Yes | No | No | Yes | Yes
Year Weights | No | Yes | No | Yes | No | Yes | No | Yes
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data is a balanced panel of counties in the 7 presidential
elections from 1992 to 2016.

##  A.4 Alternative Strategies for Estimating the Effect of Grants

Table A.3: Election Administration Grants Did Not Noticeably Advantage Democrats in 2020, Alternative Estimators. | Dem Vote Share (%)
---|---
| (1) | (2) | (3) | (4) | (5)
Grant Recipient in 2020 | 0.28 | 0.40 | 0.23 | 0.25 | 0.15
| (0.13) | (0.77) | (0.11) | (0.11) | (0.07)
Num Grant Recipients | 924 | 924 | 924 | 924 | 924
Num Counties | 2,597 | 2,597 | 2,597 | 2,597 | 2,597
Observations | 20,776 | 20,776 | 2,597 | 2,597 | 2,597
Estimator | SDID Without | Synthetic | Entropy | Causal | Super
| Intercept | Control | Balancing | Forest | Learner
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data for columns 1, 2, 6, and 7 is a balanced panel of
counties in the 8 presidential elections from 1992 to 2020. Data for columns
3, 4, 5, 8, 9, and 10 is wide with 7 lags of the dependent variable included.
Synthetic control is a regularized synthetic control. SDID without intercept
is synthetic difference-in-differences without county fixed effects. Entropy
balancing is maximum entropy reweighting to balance grant recipients and non-
recipients on the average of each lag of the outcome. Causal forest is double
machine learning using random forests for both the outcome and propensity
models. Super learner is double machine learning using an ensemble learner for
both the outcome and propensity models.
Table A.4: Election Administration Grants Did Not Noticeably Increase Turnout in 2020, Alternative Estimators. | Turnout (%)
---|---
| (1) | (2) | (3) | (4) | (5)
Grant Recipient in 2020 | 0.03 | -0.02 | 0.47 | 0.00 | 0.04
| (0.14) | (0.39) | (0.39) | (0.14) | (0.12)
Num Grant Recipients | 924 | 924 | 924 | 924 | 924
Num Counties | 2,597 | 2,597 | 2,597 | 2,597 | 2,597
Observations | 20,776 | 20,776 | 2,597 | 2,597 | 2,597
Estimator | SDID Without | Synthetic | Entropy | Causal | Super
| Intercept | Control | Balancing | Forest | Learner
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data for columns 1, 2, 6, and 7 is a balanced panel of
counties in the 8 presidential elections from 1992 to 2020. Data for columns
3, 4, 5, 8, 9, and 10 is wide with 7 lags of the dependent variable included.
Synthetic control is a regularized synthetic control. SDID without intercept
is synthetic difference-in-differences without county fixed effects. Entropy
balancing is maximum entropy reweighting to balance grant recipients and non-
recipients on the average of each lag of the outcome. Causal forest is double
machine learning using random forests for both the outcome and propensity
models. Super learner is double machine learning using an ensemble learner for
both the outcome and propensity models.

##  A.5 Estimating the Effect of Grants in Wisconsin

We supplement our main county-level analysis with a municipality-level
analysis in Wisconsin. We built our main municipality-level analysis dataset
from four sources: Wisconsin’s Legislative Technology Services Bureau provided
election results from 1990 to 2020 at the 2020 municipal ward level. We
aggregate this data to the municipal level and link it to a list of all
Wisconsin municipalities from Wisconsin’s Department of Administration. We add
grant amounts by hand to the list of municipalities with geocodes. Finally, we
join this data with estimates of the voting age population in each
municipality by mapping 2000 and 2010 Census block population statistics into
2020 Census blocks and aggregating to the municipal level. Given that we do
not have visibility into how the Legislative Technology Services Bureau
computed ward-level election results, we also replicate their work, collecting
the original ward-level election results from 2004 to 2020. Our two election
datasets are highly correlated and, in many cases, show the exact same number
of votes for each party in the same municipality and year.

As we discuss in 4.4, Table A.5 captures our finding that the grants did not
substantially increase Democratic vote share and, if anything, reduced
turnout.

Table A.5: Election Administration Grants Did Not Noticeably Advantage Democrats or Increase Turnout in Wisconsin in 2020. | Dem Vote Share (%) | Turnout (%)
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
Grant Recipient in 2020 | 1.33 | 0.31 | 0.24 | 0.11 | -0.64 | -0.71 | -0.70 | -0.69
| (0.58) | (0.20) | (0.19) | (0.19) | (0.40) | (0.28) | (0.28) | (0.29)
Num Grant Recipients | 206 | 206 | 206 | 206 | 206 | 206 | 206 | 206
Num Counties | 1,843 | 1,843 | 1,843 | 1,843 | 1,843 | 1,843 | 1,843 | 1,843
Observations | 12,901 | 12,901 | 12,901 | 12,901 | 12,901 | 12,901 | 12,901 | 12,901
County FEs | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Year FEs | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
County Weights | No | No | Yes | Yes | No | No | Yes | Yes
Year Weights | No | Yes | No | Yes | No | Yes | No | Yes
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data is a balanced panel of counties in the 8 presidential
elections from 1992 to 2020.

##  A.6 Investigating How Grant Effects Vary Across Counties

###  A.6.1 Effect of Grants in Battleground States

Table A.6: Election Administration Grants Did Not Noticeably Advantage Democrats or Increase Turnout in 2020, Battleground States. | Dem Vote Share (%) | Turnout (%)
---|---|---
| Close | Cook | Close | Cook
| States | Battlegrounds | States | Battlegrounds
Grant Recipient in 2020 | 0.54 | 0.04 | 0.10 | -0.46
| (0.29) | (0.21) | (0.32) | (0.22)
Num Grant Recipients | 119 | 326 | 119 | 326
Num Counties | 421 | 906 | 421 | 906
Observations | 3,368 | 7,248 | 3,368 | 7,248
County FEs | Yes | Yes | Yes | Yes
Year FEs | Yes | Yes | Yes | Yes
County Weights | Yes | Yes | Yes | Yes
Year Weights | Yes | Yes | Yes | Yes
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data is a balanced panel of counties in the 8 presidential
elections from 1992 to 2020. Close states are those where the winner was
decided by fewer than 5 percentage points. Cook battleground states are those
that the Cook Political Report identified as battlegrounds prior to election
day.

###  A.6.2 Effect of Grants by County Population Tercile

Table A.7: Election Administration Grants Did Not Noticeably Advantage Democrats or Increase Turnout in 2020, Voting Age Population Tercile. | Dem Vote Share (%) | Turnout (%)
---|---|---
| Small | Medium | Large | Small | Medium | Large
| Population | Population | Population | Population | Population | Population
Grant Recipient in 2020 | -0.24 | 0.15 | -0.42 | -0.71 | 0.08 | -0.14
| (0.17) | (0.13) | (0.21) | (0.24) | (0.23) | (0.23)
Num Grant Recipients | 256 | 253 | 415 | 256 | 253 | 415
Num Counties | 866 | 865 | 865 | 866 | 865 | 865
Observations | 6,928 | 6,920 | 6,920 | 6,928 | 6,920 | 6,920
County FEs | Yes | Yes | Yes | Yes | Yes | Yes
Year FEs | Yes | Yes | Yes | Yes | Yes | Yes
County Weights | Yes | Yes | Yes | Yes | Yes | Yes
Year Weights | Yes | Yes | Yes | Yes | Yes | Yes
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data is a balanced panel of counties in the 8 presidential
elections from 1992 to 2020. Small, medium, and large population counties are
defined by terciles of voting age population.

###  A.6.3 Effect of Grants by Grant Size Tercile

Table A.8: Election Administration Grants Did Not Noticeably Advantage Democrats or Increase Turnout in 2020, Grant Size Tercile. | Dem Vote Share (%) | Turnout (%)
---|---|---
| Small | Medium | Large | Small | Medium | Large
| Grant | Grant | Grant | Grant | Grant | Grant
Grant Recipient in 2020 | 0.62 | 0.08 | -0.61 | 0.32 | 0.07 | -0.27
| (0.12) | (0.14) | (0.18) | (0.21) | (0.21) | (0.22)
Num Grant Recipients | 305 | 304 | 315 | 305 | 304 | 315
Num Counties | 1,978 | 1,977 | 1,988 | 1,978 | 1,977 | 1,988
Observations | 15,824 | 15,816 | 15,904 | 15,824 | 15,816 | 15,904
County FEs | Yes | Yes | Yes | Yes | Yes | Yes
Year FEs | Yes | Yes | Yes | Yes | Yes | Yes
County Weights | Yes | Yes | Yes | Yes | Yes | Yes
Year Weights | Yes | Yes | Yes | Yes | Yes | Yes
Standard errors estimated with 1,000 county block bootstrap samples reported
in parentheses. Data is a balanced panel of counties in the 8 presidential
elections from 1992 to 2020. Grant sizes are determined by tercile of grant
size per voting age resident among recipients. Small is the smallest tercile,
and large is the largest tercile.