Demographic Disparities Using Ranked-Choice Voting? Ranking Difficulty, Under-Voting, and the 2020 Democratic Primary

Abstract

Ranked choice voting (RCV) has become increasingly popular in recent years, as more jurisdictions in the US adopt the voting system for local, state, and federal elections. Though previous studies have found potential benefits of RCV, some evidence suggests ranking multiple candidates instead of choosing one most preferred candidate may be difficult, with potential demographic disparities linked to age, gender, or racial or ethnic identity. Further, these difficulties have been assumed to cause individuals to improperly fill out RCV ballots, such as ranking too many or not enough candidates. This study seeks to answer three interrelated questions: 1) Which demographic groups find it difficult to rank candidates in RCV elections? 2) Who is more likely to cast under-voted ballots (not ranking all candidates)? 3) Is there a relationship between finding RCV voting difficult and the likelihood of casting an under-voted ballot? Using unique national survey data of 2020 Democratic primary candidate preferences, the results indicate most respondents find ranking candidates easy, but older, less interested, and more ideologically conservative individuals find it more difficult. In a hypothetical ranking of primary candidates, 12% of respondents under-voted (did not rank all options). Despite their perceived increased difficulty, older individuals were less likely to under-vote their ballot. No other demographic groups consistently experienced systematic differences in ranking difficulty or under-voting across a series of model specifications. These findings support previous evidence of older voters having increased difficulty, but challenge research assuming difficulty leads to under-voting, and that racial and ethnic groups are disadvantaged by RCV.

Full text

Politics and Governance (ISSN: 2183–2463)
2021, Volume 9, Issue 2, Pages X–X
DOI: 10.17645/pag.v9i2.3913
Article
Demographic Disparities Using Ranked‐Choice Voting? Ranking Difficulty,
Under‐Voting, and the 2020 Democratic Primary
Joseph A. Coll
Department of Political Science, University of Iowa, Iowa City, IA 52246, USA; E‐Mail: joseph‐coll@uiowa.edu
Submitted: 12 December 2020 | Accepted: 1 March 2021 | Published: in press
Abstract
Ranked choice voting (RCV) has become increasingly popular in recent years, as more jurisdictions in the US adopt the
voting system for local, state, and federal elections. Though previous studies have found potential benefits of RCV, some
evidence suggests ranking multiple candidates instead of choosing one most preferred candidate may be difficult, with
potential demographic disparities linked to age, gender, or racial or ethnic identity. Further, these difficulties have been
assumed to cause individuals to improperly fill out RCV ballots, such as ranking too many or not enough candidates. This
study seeks to answer three interrelated questions: 1) Which demographic groups find it difficult to rank candidates in RCV
elections? 2) Who is more likely to cast under‐voted ballots (not ranking all candidates)? 3) Is there a relationship between
finding RCV voting difficult and the likelihood of casting an under‐voted ballot? Using unique national survey data of 2020
Democratic primary candidate preferences, the results indicate most respondents find ranking candidates easy, but older,
less interested, and more ideologically conservative individuals find it more difficult. In a hypothetical ranking of primary
candidates, 12% of respondents under‐voted (did not rank all options). Despite their perceived increased difficulty, older
individuals were less likely to under‐vote their ballot. No other demographic groups consistently experienced systematic
differences in ranking difficulty or under‐voting across a series of model specifications. These findings support previous
evidence of older voters having increased difficulty, but challenge research assuming difficulty leads to under‐voting, and
that racial and ethnic groups are disadvantaged by RCV.
Keywords
Democratic primaries; elections; electoral systems; ethnic; race; ranked choice voting; United States of America
Issue
This article is part of the issue “The Politics, Promise and Peril of Ranked Choice Voting” edited by Caroline Tolbert
(University of Iowa, USA).
© 2021 by the author; licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribu‐
tion 4.0 International License (CC BY).
1. Introduction
Ranked choice voting (RCV) has become increasingly pop‐
ular in the US over the last two decades, as more cities
and states adopt the preferential voting method into
their election systems (Fortin, 2020). In 2018, Maine
used RCV for all state and federal primary elections,
as well as for Congressional general elections. Two
years later, four states—Alaska, Hawaii, Kansas, and
Wyoming—selected their 2020 presidential Democratic
nominee using RCV. As of 2020, more than fifteen cities
utilized RCV for local elections, including large popula‐
tion centers such as San Francisco, California and New
York City, New York (FairVote, 2020).
Despite the spread of RCV, most US elections oper‐
ate under plurality rules with no vote thresholds where
candidates can win with less than a majority the of votes
so long as they have the most (but see, for example, the
2020 Senate election in Georgia). Unlike plurality elec‐
tions, RCV elections require winners to obtain a majority
of the vote of the ballots cast to be crowned victor. RCV
allows respondents to rank all candidate preferences at
one time without requiring a second election be held
should no majority be reached in the first round. RCV
elections provide the opportunity for voters to rank the
candidates from most to least preferred, and if the vot‐
ers’ most preferred candidate receives the least votes,
that candidate is removed, and all votes cast for them
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 1

go to the voters’ respective second choices (cf. Grofman
& Feld, 2004). Eligible ballots are recounted until one
candidate receives a majority of the votes cast in a sin‐
gle round.
RCV elections have been found to have several bene‐
fits, from incentivizing less negative and more civil cam‐
paign environments (Donovan, Tolbert, & Gracey, 2016),
to increasing mobilization efforts (Bowler, Donovan,
& Brockington, 2003) and levels of voter satisfaction
(Donovan et al., 2016; Farrell & McAllister, 2006).
Research also suggest RCV elections increase the like‐
lihood of the winner being the most preferred or less
extreme candidate (Grofman & Feld, 2004; Horowitz,
2000). At the same time, however, some scholars argue
that having to rank multiple candidates may be more cog‐
nitively and time demanding than simple, single‐choice
plurality elections, potentially resulting in increased
rates of voters incorrectly filling out their ballot (e.g.,
Burnett & Kogan, 2014; Neely & Cook, 2008; Sinclair &
Alvarez, 2004) or abstaining altogether (e.g., McDaniel,
2016). Further, because of the uneven distribution of
political resources and knowledge in the electorate
across demographic groups (e.g., Delli Carpini & Keeter,
1996; Verba, Schlozman, & Brady, 1995), scholars often
assume that different patterns in under‐/over‐voting
arise from difficulties in voting in RCV elections (Neely
& Cook, 2008; Sinclair & Alvarez, 2004). Yet, most stud‐
ies regarding under‐/over‐voting rely on inferences from
aggregate data (e.g., Burnett & Kogan, 2014; Neely &
Cook, 2008), and none have directly tested the link
between demographic groups, ranking difficulty, and
tendencies to under‐vote using individual level data.
Few published studies have even directly measured
which demographic groups find RCV voting challenging
(e.g., Donovan et al., 2019; Kimball & Kropf, 2016).
This study seeks to answer three interrelated ques‐
tions: 1) Which voters find it difficult to rank candidates
in RCV elections?; 2) Who is most likely to cast an under‐
voted ballot (not ranking all candidates)?; 3) Is there a
relationship between finding RCV voting difficult and the
likelihood of casting an under‐voted ballot? Using a 1,000
people, nationally representative sample of likely 2020
Democratic primary voters, this study finds that 80% of
respondents had no difficulty ranking candidates, with
51% saying the method was very easy. However, nearly
1/5 of respondents said ranking candidates was some‐
what or very hard, with more difficulty ranking linked
to older age, lower political interest, and possibly more
conservative ideologies. Additional analyses find that dif‐
ferences are most pronounced regarding the extent to
which voters found ranking to be easy, not difficult.
Just about 12% of respondents asked to rank a hypo‐
thetical ballot of 2020 primary candidates under‐voted.
Surprisingly, despite their increased difficulty ranking
candidates, older respondents were actually less likely
to under‐vote than were younger individuals. This rela‐
tionship remains even after controlling for difficulty rank‐
ing, which does little to affect the relationship between
age and under‐voting. No significant relationships regard‐
ing under‐voting were uncovered comparing racial and
ethnic groups and only weak evidence linking socioeco‐
nomic status to under‐voting. This suggests under‐voting
may be a choice, not the result of difficulty in casting
a ballot.
These findings support earlier studies finding old vot‐
ers face more challenges ranking candidates (Donovan
et al., 2019) and lower under‐vote rates (Neely & Cook,
2008), as well as provide some evidence that ranking dif‐
ficulty contributes to the tendency to cast incomplete
ballots (Burnett & Kogan, 2014). At the same time, they
challenge those who suggest RCV disadvantages racial
and ethnic minorities (e.g., McDaniel, 2016), women
(e.g., Sinclair & Alvarez, 2004), or those of lower socioe‐
conomic status (e.g., Neely & Cook, 2008).
The remainder of this article is as follows. The next
section outlines the literature related to RCV’s effects
on difficulty voting and how that translates into under‐
voted ballots. It is in this section that hypotheses are for‐
mulated. Following this, the article estimates and ana‐
lyzes how difficult RCV ranking is and who finds it difficult.
The rate at which under‐voted ballots were cast is then
examined, focusing on who is more likely to cast them
and the role ranking difficulty plays in casting under‐
voted ballots. The article then closes with a summary of
the findings and suggestions for future work.
2. Ranked‐Choice Voting
This once popular progressive‐era reform has seen a
resurgence in support as of late (Amy, 1996; Fortin, 2020;
Santucci, 2017). In 2008, five US cities used RCV for local
elections. As of the 2018 midterm election, 15 cities
and the state of Maine had incorporated RCV into their
election systems. In 2020, four states went so far as to
use RCV for determining the winner of their respective
Democratic primaries. That same year, ballot measures
in Alaska and Massachusetts proposed statewide use of
RCV for state and federal elections, passing in Alaska but
failing in Massachusetts. Beginning in 2021, the largest
city in the US, New York City, will start using RCV for all
city primary and special elections. According to FairVote
(2020), there have been nearly 400 RCV elections in the
US since 2004 and over 10 million adults live in jurisdic‐
tions that use or recently implemented RCV for some
elections. Given growing popularity of RCV in the US, it is
becoming more imperative that scholars and policy mak‐
ers understand the consequences of replacing plurality
or majority systems with preferential voting.
On the one hand, previous literature has docu‐
mented the positive effects of RCV elections on cam‐
paigns and voters. Because elections can be decided
based on a voter’s second, third, or subsequent choices,
candidates in RCV elections have an incentive to behave
more civilly or risk offending other candidates’ bases and
losing prospective second and third place rankings. This
incentive to campaign civilly has led candidates in RCV
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 2

elections to behave less negatively. Using text analysis,
McGinn (2020) finds that candidates use less negative
wording in their speeches when campaigning in RCV elec‐
tions compared to those in plurality systems. RCV elec‐
tions may be more civil, as voters in preferential election
jurisdictions are more likely to perceive campaigns as
less negative, perceive less candidate‐to‐candidate criti‐
cism, and be more satisfied with the campaigns than are
those living in cities using plurality elections (Donovan
et al., 2016).
It is not enough to just not offend your opponent’s
supporters; candidates must actively try to court them.
This need to expand your base past core/likely support‐
ers results in increased mobilization efforts in RCV elec‐
tions (Bowler et al., 2003), though with mixed evidence
of increased turnout effects (Kimball & Anthony, 2016;
McDaniel, 2016; McGinn, 2020). RCV elections have also
been found to be more likely to result in the most pre‐
ferred candidate being named the winner (Grofman &
Feld, 2004; Horowitz, 2000).
Notwithstanding the potential benefits of RCV, some
scholars have also uncovered negative effects, primar‐
ily stemming from RCV’s arguably increased difficulty
compared to plurality or non‐instant runoff methods.
In non‐preferential elections, voters only mark a single
candidate. In preferential elections like RCV, voters are
asked to rank several. Not only must voters possess
knowledge about more candidates, but they must also
be able to navigate more complex RCV ballots. Ranking
multiple candidates using more complex ballots, espe‐
cially in local or primary elections with less informa‐
tional cues, may be taxing for American voters (Lau
& Redlawsk, 2006), potentially resulting in voters not
ranking enough candidates (under‐voting) or ranking too
many (over‐voting).
One of the few studies to directly measure vot‐
ers understanding of different election systems in the
US indicates RCV elections may be more difficult than
plurality elections, but not by large margins. Donovan
et al. (2019) find that 87% of voters thought RCV elec‐
tions were somewhat or very easy, significantly but only
slightly lower than in plurality cities (93%). Other studies
document similarly high rates of RCV comprehension or
voting ease (Brischetto & Engstrom, 1997; Cole, Taebel,
& Engstrom, 1990; Kimball & Kropf, 2016). However,
there is some evidence to suggest that issues with voting
may differ by demographic group. Donovan et al. (2019)
find that older individuals are more likely to report dif‐
ficulty voting but did not find differences based on gen‐
der or race/ethnicity. In contrast, Neely, Blash, and Cook
(2006) find that African American and Latino individuals
reported lower understanding of RCV instructions (but
see Kimball & Kropf, 2016; Neely, Blash & Cook, 2006).
Ballot complexity or difficulty ranking candidates has
been assumed to be the cause of voters incorrectly fill‐
ing out their ballots by either not marking enough candi‐
dates (under‐voting) or marking too many (over‐voting).
Looking at rates of under‐voting in four San Francisco
elections where voters could rank up to three candi‐
dates, Burnett and Kogan (2014) find that 27%–48% of
ballots cast did not have three unique candidates marked
(i.e., under‐voted), with 5%–12% of ballots having incor‐
rectly marked the same candidate more than once (see
also Neely & McDaniel, 2015). The authors remark: “This
likely reflects, at least in part, the reality that few voters
possess enough information to rank more than a few of
the candidates running, regardless of how many they are
allowed to select” (Burnett & Kogan, 2014, p. 48). Citing
differences in political knowledge between men and
women, Sinclair and Alvarez (2004) find that Los Angeles
precincts with greater proportions of women see more
under‐ and over‐votes. Again, drawing on a case study
of San Francisco, Neely and Cook (2008) and Neely and
McDaniel (2015) find more erroneous ballots in neigh‐
borhoods that were disproportionality older, arguing dif‐
ficulties that come with old age hinder properly filling
out the ballot. Neely and Cook (2008) also find that
precincts with larger Black and Latino populations had
greater rates of over‐votes (i.e., more ballots cast with
too many candidates) and lower rates of under‐votes
(i.e., fewer ballots cast that did not rank all options);
though, some evidence suggests differences in racial and
ethnic voting may be partially attributable to different
election technologies (e.g., Knack & Kropf, 2003; Tomz &
Van Houweling, 2003).
These studies have made significant advances in doc‐
umenting the effects of RCV elections; however, there
still exist gaps in the literature regarding RCV difficulty,
under‐/over‐voting, and demographic disparities. First,
most previous studies focus on one or a handful of elec‐
tion jurisdictions. As such, scholars know less about vot‐
ing in RCV elections on a national scale. Second, more
evidence and individual level data (as opposed to aggre‐
gate election results) is needed to link RCV difficulty to
particular demographic groups. Few studies have docu‐
mented significant differences in RCV difficulty among
different demographic groups (e.g., Donovan et al., 2019;
Kimball & Kropf, 2016) and none have directly linked
increased difficulty ranking choices with an increased
likelihood of under‐ or over‐voting. Previous studies
often rely on aggregate data to make inferences about
individual voting behavior, assuming that the relation‐
ship between greater proportions of some demographic
in a precinct being correlated with more under‐/over‐
votes reflects increased difficulty voting among that
demographic. Ecological fallacies and other issues sug‐
gest there is reason to believe under‐votes are not cast
out of ignorance or difficulty.
Though under‐voting is often attributed to voter
fatigue (Bullock & Dunn, 1996), ballot confusion
(Kimball & Kropf, 2005), or voter ignorance (Wattenberg,
McAllister, & Salvanto, 2000), under‐voting can also
reflect the true preferences of the voter, not any difficul‐
ties they may have encountered. For example, Alvarez,
Hall, and Levin, (2018) find that under‐voting rates were
nearly identical between partisan RCV elections and
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 3

non‐partisan ones. If RCV makes voting harder, then
removing party labels should exacerbate that difficulty.
The finding that voters were nearly as likely to under‐
vote with or without labels suggests that under‐voting
may not be as strongly tied to ranking difficulty as past
scholars have assumed.
This study seeks to expand on previous works by
examining whether and which voters find RCV elections
difficult, who is likely to cast an under‐voted ballot, and
whether RCV difficulty contributes to the likelihood of
casting such a ballot. In doing so, this study tests the
often‐made assumption that certain groups experience
greater difficulty in RCV elections, and these difficulty dis‐
parities lead to greater rates of erroneous ballots.
Building on previous work, this study tests the follow‐
ing hypotheses:
H1: RCV difficulty and demographic hypotheses:
Older, Black, Hispanic, and female respondents will
have greater difficulty using RCV.
H2: RCV exhausted ballot hypothesis: Those who
have greater difficulty with RCV will be more likely to
cast under‐voted ballots.
H3: RCV difficulty and demographic hypotheses:
Older, Black, Hispanic, and female voters will be more
likely to cast under‐voted ballots.
3. Data
Data for this study are from a 1,000 people, nation‐
ally representative Internet survey of likely Democratic
primary voters conducted approximately three months
before primary elections began (November 2019). The
survey was administered by YouGov, an internationally
recognized survey firm that has frequently conducted
political surveys (e.g., the Cooperative Congressional
Election Studies). YouGov recruits respondents through
their online, opt‐in survey process that pays respon‐
dents for their time. The purpose of the study was to
gauge likely Democratic primary voters’ candidate pref‐
erences, their respective rankings, and their views on
RCV. As such, the survey screened out respondents who
were unlikely to vote in the primaries and any respon‐
dents who did not identify as Democrat or Independent.
Census data is used to weight respondents so they rep‐
resent the national electorate. Summary statistics for all
variables used in this study can be found in Table A1 in
Supplementary File A.
Two specific questions were asked in the survey.
The first asks: “Imagine that the Democratic primary
election were held in your state today and the candi‐
dates were only [randomized: Joe Biden, Pete Buttigieg,
Kamala Harris, Bernie Sanders, and Elizabeth Warren].
How would you rank these candidates? Please drag your
1st‐choice candidate into the box labeled Number 1, your
2nd choice in the box labeled Number 2, and so on”
(see Table 3). The respondents were then presented with
a randomized list of candidates where they would click
and drag the candidate names to different rankings. This
question is used to explore the rates of under‐voting with
RCV. The second question immediately follows: “How
hard or easy was it to rank more than one choice in the
previous question?” with responses from very easy to
rank more than one choice, somewhat easy, neither hard
nor easy, somewhat hard, and very hard to rank more
than one choice (see Table 1). This question is used to
measure how difficult respondents found ranking to be,
coded so that 1 represents those answering very easy,
to 5 for those answering very difficult.
This data provides several advantages to studying
RCV. First, respondents were asked to rank the 2020
Democratic candidates in what was essentially an online
RCV ballot, then immediately asked how difficult they
found the process. As such, this study measures how dif‐
ficult respondents found the actual process of ranking
candidate using an RCV ballot, not more general ques‐
tions about whether voters understood the system in
their area (e.g., Donovan et al., 2019). Second, using RCV
in federal general elections would usually involve rank‐
ing partisan/ideologically opposed candidates, present‐
ing clearer options through the use of shortcuts (Lau &
Redlawsk, 2006). Ranking candidates without partisan
labels effectively renders partisanship a non‐heuristic, as
respondents cannot use partisan labels to differentiate
candidates. Investigating difficulties with ranking more
ideologically similar candidates with the same party pro‐
vides a more restrictive test as partisan cues are absent.
At the same time, using this data has some limita‐
tions. First, likely primary voters vary from the general
electorate in that they tend to be more interested, knowl‐
edgeable, and more partisan (Karpowitz & Pope, 2015;
Redlawsk, Bowen, & Tolbert, 2008; see also Abramowitz,
2008). Second, the analyses only pertain to Democratic
primary voters (i.e., no Republicans). Thus, while the
results reported here are theoretically interesting, it is
worth considering the extent to which the relationships
uncovered can be generalized to the US population.
4. Difficulty of Ranked‐Choice Voting
Table 1 displays the difficulty of ranking candidates,
where voters were asked how hard or easy it was to
rank more than one choice, with options ranging from
very easy (1) to very hard (5). 68% of respondents said
ranking candidates was easy or very easy, with nearly
2/3 of those citing ‘very easy.’ In contrast, just under
20% found ranking to be hard or very hard, with only
1/3 citing ranking difficult as very hard. The remaining
12% of respondents found ranking neither hard nor easy.
Including those who said neither hard nor easy with the
68% that reported ranking as very/somewhat easy sug‐
gests 80% of respondents found RCV to not be difficult
to use, comparable to previous studies (Donovan et al.,
2019; Kimball & Kropf, 2016).
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 4

Table 1. Difficulty ranking candidates.
Difficulty ranking candidates No. %
Very easy to rank more than one choice 412 41.2
Somewhat easy to rank more than one choice 272 27.2
Neither hard nor easy to rank more than one choice 119 11.9
Somewhat hard to rank more than one choice 132 13.2
Very hard to rank more than one choice 65 6.5
Total 1,000 100.0
To examine the extent to which difficulty differs by
demographic or political characteristics, Table 2 reports
an ordered logistic regression model where the depen‐
dent variable is how difficult respondents found rank‐
ing candidates (1 =very easy, 5 =very hard). This
study includes a continuous measure of Age (19–88), a
variable denoting whether the respondent is a Female
(1 =Female, 0 =Male); two variables for race, whether
the respondent is Black (1 =Black, non‐Hispanic, 0 =Not
Black) or is another race besides white, non‐Hispanic
(Other: 1 =Other race, 0 =Not another race). White,
non‐Hispanic respondents are the reference (left out)
group. Also included are two measures of socioeconomic
status: Income (1 =Less than $10,000, 16 =Greater
than $500,000) and Education (1 =High School graduate
or less, 5 =Post‐Graduate Degree). A variable denotes
whether the respondent identifies as a Moderate
Democrat (1 =Moderate Democrat, 0 =other) or Strong
Democrat (1 =Strong Democrat, 0 =other) is included
to control for partisan strength. Being as the sample only
includes Democrats and Independents, the reference cat‐
egory consists of Independent‐identifying respondents.
A measure of Liberalism (1 =Conservative, 4 =Very
Liberal) is included, as is a measure of Political Interest
(1 =High Interest, 0 =Low Interest) and Importance of
Religion (1 =Not at all important, 4 =Very important).
Though almost every variable had 100% response rates,
responses for Income, Ideology (Liberalism), and Political
Interest dipped just slightly (89.4%, 96.2%, and 99.4%,
respectively). The reported analyses code missing to the
respective mean or median values to maintain statistical
precision. Results are robust their exclusion unless oth‐
erwise noted (see Tables C3 and C4 in Supplementary
File C). A breakdown of each variable by what percent‐
age answered different rankings of RCV difficulty can
be found in Table D1 in Supplementary File D. Lastly,
to deal with heterogeneity and spatial dependence, the
estimations are computed with robust standard errors
clustered by state, but results are robust to the inclusion
of state fixed effects (available at request).
Preliminary model checks indicated that the assump‐
tion of parallel odds may be violated. Analyses were re‐
estimated using multinomial regression and the only dif‐
ference of note being female respondents report greater
difficulty. However, this finding may not be unique to RCV
elections and may also be evident in plurality elections
(Donovan et al., 2019). Given results are nearly identi‐
cal and that ordered logistic regression models are more
straightforward, this study reports the ordered logistic
model in the main text and the multinomial estimation in
Table C1 of Supplementary File C. Results are also robust
to collapsing the dependent variable into a three cat‐
egorical variable of very/somewhat easy, neither, and
very/somewhat hard, regardless of estimation strategy
(see Table C2 and C7 in Supplementary File C). As an addi‐
tional robustness check, Model 1 is re‐estimated using
ordinary least square regression and is presented in col‐
umn 2 of Table 2. These results are also robust to separat‐
ing the models in Table 2 and 3 so that each estimation
strategy has one model with only socio‐demographic
factors followed by a second with socio‐demographic
and political factors. Results reported in Tables C5–C6 in
Supplementary File C to save space. For interpretability
of the ordered logistic coefficients and comparability to
the ordinary least squares model, both models in Table 2
report odd‐ratios, where ratios greater than one sug‐
gest greater odds of encountering difficulty (positive rela‐
tionship) and those below one suggest lower odds (neg‐
ative relationship). The values reported in Table 2 are
odd‐ratios, not unstandardized regression coefficients.
As can be seen in Table 2, there does exist some dif‐
ferences in who perceives RCV and ranking to be more or
less difficult. In both models, older, less politically inter‐
ested, and more ideologically conservative respondents
are more likely to report greater difficulty voting, with
similar odds‐ratios across models.
To more clearly depict the relationships at hand,
Figures 1 and 2 plot the predicted probability of answer‐
ing ranking was very easy, easy, neither, hard, or very
hard across these demographic and political character‐
istics. Figure 1 show older respondents are more likely
to report difficulties ranking, in line with previous work
(Donovan et al., 2019) and supporting the assumption
that increased difficulty may cause greater voting errors
in older communities (e.g., Neely & Cook, 2008; Neely
& McDaniel, 2015). Respondents one standard devia‐
tion above the mean (49 years) are 15% less likely to
report ranking being very easy than those one standard
deviation below the mean (44% younger, 29% older).
If the range is extended to two standard deviations
above/below the mean, the youngest voters are nearly
twice as likely to report ranking being very easy com‐
pared to the oldest (48% younger, 25% older). Younger
and older respondents are no more or less likely to rank
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 5

Table 2. Who finds ranking difficult? (Odd‐ratios).
(1) (2)
Ordered Logistic Regression Ordinary Least Squares Regression
b/se b/se
main
Age 1.017*** 1.010***
(0.004) (0.003)
Female 1.184 1.120
(0.159) (0.099)
Black 1.136 1.122
(0.270) (0.185)
Hispanic 1.004 1.023
(0.192) (0.142)
Other 1.429 1.223
(0.323) (0.202)
Income 1.020 1.019
(0.025) (0.016)
Education 0.991 0.975
(0.050) (0.035)
Moderate Democrat 1.138 1.060
(0.224) (0.153)
Strong Democrat 0.912 0.912
(0.124) (0.074)
Liberalism 0.795** 0.876*
(0.077) (0.059)
Political Interest 0.577*** 0.709***
(0.055) (0.044)
Importance of Religion 0.981 0.994
(0.067) (0.045)
Observations 1,000 1,000
Notes: Coefficients converted into odds‐ratios for comparability and interpretability. See Supplementary File C for models reporting
coefficients. Both models estimated with robust and clustered(state) standard errors. * =0.1; ** =0.05; *** =0.01
voting as somewhat easy, while older voters are 5%
more likely to report ranking as neither hard nor easy
(10% younger, 15% older). Looking at how difficult they
find the process, older voters are also twice as likely
to say that ranking candidates was somewhat difficult
(10% younger, 20% older) or very difficult (4% younger,
10% older), further emphasizing the differences across
age groups.
At the same time, these results show the largest dif‐
ferences among age do not reflect difference in how
difficult respondents find ranking to be, but the extent
to which they find it easy. The largest differences in
Figure 1 occur when comparing whether voters find vot‐
ing very easy, with smaller differences for other diffi‐
culties. Further, looking at the bottom right panel in
Figure 1, which predicts the level of difficulty (1 =very
easy, 5 =very hard) across a range of ages using ordi‐
nary least squares, the results suggest that young respon‐
dents tend to find ranking very/somewhat easy (1.63),
while older respondent find ranking to be somewhat
easy (2.22), with a difference of roughly .60 (just over
half a ranking level). This finding suggests that, though
there are differences in ranking difficulty, they may not
be drastic. Yet, it is worth re‐mentioning that this sur‐
vey was conducted over the internet where respondents
ranked candidates using an online survey tool where they
dragged and dropped candidate names into boxes repre‐
senting their preferences, a process different than filling
in bubbles in standard RCV ballots. Given the relationship
between age and computer literacy, future researchers
should consider the extent to which survey format may
be inducing this relationship.
Perhaps due to the linkage between political interest
and knowledge (Delli Carpini & Keeter, 1996), the most
interested find RCV to be easier than the least interested
(Figure 2). Those with high political interest are over ten
percentage points more likely to find ranking very easy
(24% low interest, 36% high). Only small and potentially
indistinguishable differences arise when comparing the
likelihood of answering somewhat easy or not easy nor
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 6

19
Probability of Answering
.6
.2
0
49
Age
Very Easy
79
.4
59 693929 19
Probability of Answering
.6
.2
0
49
Age
Somewhat Easy
79
.4
59 693929 19
Probability of Answering
.6
.2
0
49
Age
Neither
79
.4
59 693929
19
Probability of Answering
.6
.2
0
49
Age
Somewhat Difficult
79
.4
59 693929 19
Probability of Answering
.6
.2
0
49
Age
Very Difficult
79
.4
59 693929 19
Ranking Difficilty
2.5
1.75
1.5
49
Age
OLS
79
2
2.25
59 693929
Figure 1. Effects of age in difficulty with ranking candidates. Notes: N=1,000. OLS =Ordinary Least Squares regression.
All other panels derived from ordered logistic regression estimates. Estimation of all coefficients calculated with all other
variables held at their mean or respective values. Robust and clustered(state) standard errors employed. Source: Author’s
survey using the YouGov platform, in November 2019.
hard. There is some evidence to suggest less interested
individuals also find ranking more difficult. Less inter‐
ested respondents are 6% more likely to say ranking was
somewhat easy (14% low interest, 20% high) and four
percentage points more likely to say ranking was very dif‐
ficult (7% low interest, 11% high).
Again, the biggest differences arose regarding who is
more likely to cite ranking as very easy, with only slight
differences in who finds rankings somewhat easy, nei‐
ther, or very difficult, and moderate differences in find‐
ing RCV to be somewhat hard. Looking at the ordinary
least squares results in the bottom right panel, the least
interested are estimated to report ranking as somewhat
easy/neither her nor easy (2.37), while the more inter‐
ested are predicted to say it is somewhat easy (1.92).
Once again, these results suggest that different groups
may find ranking more difficult, but the biggest differ‐
ences may lie in the extent to which respondents view
RCV as easy. With that being said, these findings may
not be unique to RCV elections. Donovan et al. (2019)
also find that those with greater interest report better
understanding of RCV elections. However, as did those
in plurality and two‐top primary elections. The authors
did not report any differences in the effects of interest
across election environment.
Interestingly, even after controlling for a host of
other influences, ideological differences in ranking dif‐
ficult are apparent (Figure D1 in Supplementary File D).
Specifically, the most liberal respondents are 15% more
likely to say ranking was very easy than were more
conservative Democrats or Independents (41% very lib‐
eral, 26% conservative). Liberal respondents are also
less likely to rank voting as somewhat or very hard,
and only slight differences were uncovered in ranking
somewhat easy or neither. Using ordinary least squares
regression, the least liberal respondents are predicted
to report ranking be somewhat easy (2.19), while the
most liberal are more likely to report ranking being
very/somewhat easy (1.79). It could be that more conser‐
vative Democrats and Independents have less familiarity
with the progressive reform of RCV. However, it is proba‐
bly more likely that the lack of partisan heuristics among
(mostly) Democratic candidates forced respondents to
rely on other candidate information (e.g., candidate posi‐
tions [Abrajano, Nagler, & Alvarez, 2005]) when making
their choices (Alvarez et al., 2018). Such information may
be less readily available in the minds of more conserva‐
tive respondents who may have less familiarity with the
Democratic candidates. Again, the evidence presented
here suggests the greatest differences occur when decid‐
ing whether ranking was very easy.
Taken together, these results challenge the assump‐
tion that higher rates of under‐/over‐voting among spe‐
cific demographic groups (other than age) is attributable
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 7

Low
Probability of Answering
.6
.2
0
Polical Interest
Very Easy
High
.4
Low
Probability of Answering
.6
.2
0
Polical Interest
Somewhat Easy
High
.4
Low
Probability of Answering
.6
.2
0
Polical Interest
Neither
High
.4
Low
Probability of Answering
.6
.2
0
Polical Interest
Somewhat Difficult
High
.4
Low
Probability of Answering
.6
.2
0
Polical Interest
Very Difficult
High
.4
Low
Ranking Difficult
2.6
2
1.8
Polical Interest
OLS
High
2.2
2.4
Figure 2. Effects of political interest on difficulty with ranking candidates. Notes: N=1,000. OLS =Ordinary Least Squares
regression. All other panels derived from ordered logistic regression estimates. Estimation of all coefficients calculated
with all other variables held at their mean or respective values. Robust and clustered(state) standard errors employed.
Source: Author’s survey using the YouGov platform, in November 2019.
to increased difficulty they face in RCV elections.
To more fully understand who casts under‐voted bal‐
lots and whether difficulty plays a role in casting such
ballots, the next section examines under‐votes in a
(semi‐)hypothetical Democratic primary race.
5. Under‐Voted Ballots
Under‐voted ballots occur when voters do not rank as
many candidates as there are rankings available. Under‐
voting has been found to be undergirded by voter fatigue
(Bullock & Dunn, 1996), ballot confusion (Kimball & Kropf,
2005), and voter ignorance (Wattenberg et al., 2000).
However, a voter may also under‐vote because they
would rather not vote than have their vote cast for a
unpreferred candidate.
To examine the extent of under‐voting and the role
RCV difficulty plays across demographic groups, this
study uses rankings from a truncated five‐candidate
race of 2020 Democratic primary candidates. The survey
asked respondents to rank the following candidates from
first(one) to last(five): Joe Biden, Pete Buttigieg, Kamala
Harris, Bernie Sanders, and Elizabeth Warren (see Data
section or Supplementary File B for question wording).
The ballot allows up to five candidates and there are
five candidates in the race, meaning that if any voter
did not rank all five candidates, their ballot is under‐
voted. Table 3 shows each candidate and the number of
votes cast for them by order of preference, as well as
the number who did not rank the candidate when given
the option. Those that skipped a candidate are essen‐
tially creating under‐voted ballots. Because voters can
Table 3. Candidate rankings.
Ranking Joe Biden Pete Buttigieg Kamala Harris Bernie Sanders Elizabeth Warren
1 311 73 96 190 305
2 166 103 189 224 273
3 117 233 213 165 189
4 149 241 252 129 119
5 189 255 155 227 44
Did not rank candidate 52 79 79 49 54
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 8

choose not to rank multiple candidates, the total num‐
ber of exhaustible ballots is not simply the sum of all
those who skipped. After removing double counts due to
respondents skipping multiple candidates, the total num‐
ber of ballots cast that did not fill all ranking options is
117, or 12% of the votes cast in the election—a rate sim‐
ilar to those found by Burnett and Kogan (2014) when
voters only had to rank three candidates.
To determine who is more likely to cast under‐voted
ballots and whether difficulty ranking plays a key role,
Table 4 displays two standard logistic regression results
using the same model specification (covariates) as dis‐
cussed above. Results are robust to alternative specifica‐
tions except those relating to income and gender when
omitting those who did not answer income or liberalism
(see Supplementary File C). Model 1 regresses whether
someone cast an under‐voted ballot on a host of demo‐
graphic and political variables. Model 2 repeats this pro‐
cess but includes how difficult the respondent found
ranking to be to see if difficulty ranking mediates any rela‐
tionships found in Model 1. Though not a perfect esti‐
mation strategy, if a coefficient is significant in Model 1,
but not Model 2, this may suggest that systematic dif‐
ferences in difficulty ranking candidates may be influ‐
encing the relationship. Though, results are robust to
the use of alternative mediating strategies, such as the
causal step approach of Baron and Kenny (1986) or the
non‐parametric approach devised by Imai, Keele, Tingley,
and Yamamoto (2011), available at request. Again, odds‐
ratios are reported in the table to allow for better
comparison across models and covariates. Odds ratios
above 1 denote a positive relationship (greater under‐
voting likelihood) and below 1 a negative relationship
(lower under‐voting likelihood).
Who is more likely to under‐vote? Contrary to expec‐
tations given the results uncovered in the previous
Table 4. Who under‐votes? (Odds‐ratios).
(1) (2)
Without Difficulty With Difficulty
b/se b/se
Under‐voted difficulty ranking candidates 1.543***
(0.115)
Age 0.986** 0.980***
(0.006) (0.007)
Female 1.380* 1.313
(0.261) (0.237)
Black 0.751 0.690
(0.250) (0.224)
Hispanic 0.754 0.782
(0.206) (0.210)
Other 1.133 1.053
(0.350) (0.313)
Income 1.065** 1.057**
(0.029) (0.029)
Education 0.919 0.936
(0.085) (0.087)
Moderate Democrat 1.760* 1.773*
(0.530) (0.559)
Strong Democrat 1.041 1.108
(0.279) (0.290)
Liberalism 0.830 0.861
(0.120) (0.134)
Political Interest 0.719*** 0.820
(0.091) (0.102)
Importance of Religion 1.057 1.055
(0.143) (0.138)
Observations 1,000 1,000
Notes: Under‐voting occurs when a respondent did not rank all candidate options available on the survey ballot. Logistic regression
estimated with robust and clustered(state) standard errors. Odds‐ratios shown for comparability across models. * =0.1; ** =0.05;
*** =0.01
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 9

section, Model 1 suggests that older individuals are less
likely to under‐vote, in line with previous work (Neely &
Cook, 2008). Additionally, Model 1 suggests that more
interested individuals are less likely to under‐vote, while
female (see also, Neely & Cook, 2008; Sinclair & Alvarez,
2004), more affluent and moderate partisans are more
likely to not rank all the candidates. Moderate partisans
may under‐vote out of dislike for one of the more liberal
candidates running in the election, not because of cogni‐
tive difficulties ranking. Additionally, it should be noted
that gender, political interest, and income fail to reach
statistical significance in several alternative specifica‐
tions using multinominal regression (see Supplementary
File C).
The left panel of Figure 3 plots the predicted proba‐
bility of not ranking all candidates across different values
of age (without controlling for ranking difficulty). As can
be seen, older respondents are 11% less likely to cast
an under‐voted ballot than are the youngest voters (21%
younger, 10% older), in line with aggregate analyses that
have found lower under‐vote rates in older precincts
(e.g., Neely & Cook, 2018). Further, this relationship
holds when controlling for difficulty (Model 2, right panel
in Figure 3) and doing so only slightly affects the substan‐
tive relationship between age and casting under‐votes
(20% younger, 7% older). The evidence that controlling
for difficulty has little effect on the relationship suggests
that difficulty is not what is causing younger voters to
not rank all candidates. Instead, it could be abstention
related to candidate preferences (e.g., ‘Bernie or Bust’).
Considering the other significant findings, female
respondents are 4% more likely to under‐vote than male
respondents (11% male, 15% female). This finding is elim‐
inated after controlling for difficulty. More interested
individuals are 4% less likely to under‐vote (19% low
interest, 15% high), while more affluent individuals are
6% more likely to under‐vote, and moderate democrats
are 7% more likely. The results for income and moder‐
ate democrat remain after controlling for difficulty rank‐
ing, while those for gender and interest are rendered
insignificant. Considering these results, more interested
individuals, as well as moderate Democrats, are unlikely
to suffer from a lack of political knowledge regarding
the 2020 Democratic candidates, leading to lower under‐
vote rates. Rather, it is likely a choice not to rank all the
candidates. Finally, more affluent individuals may simply
possess greater resources or a greater ‘stake in the game’
given the emphasis on taxing the rich among the 2020
Democratic primary candidates, leading to a choice to
under‐vote.
An important finding is that difficulty ranking is
strongly linked to casting under‐voted ballots (Model 2).
Those who had the greatest difficulty ranking are nearly
four times as likely to not fill out all rankings than
were those who faced the least difficulty (Figure 4).
Specifically, the likelihood of under‐voting a ballot
increases from 8% for those who had the least difficulty
to, to 18% for those who found ranking neither hard nor
easy, to nearly 34% for those who experienced the most
difficulty. These results suggest that, for the 20% of the
19
Probability of Casng Under-Voted Ballot
.3
.2
.1
0
49
Age
Not Controlling for Ranking Difficulty
79
59 693929 19
Probability of Casng Under-Voted Ballot
.3
.2
.1
0
49
Age
Controlling for Ranking Difficulty
79
59 693929
Figure 3. Effects of age on casting an under‐voted ballot. Notes: N=1,000. Under‐voted ballots occur when a voter does
not select a candidate for each ranking available. Estimation of logistic regression coefficients calculated with all other
variables held at their mean or respective values. Robust and clustered(state) standard errors employed. Source: Author’s
survey using the YouGov platform, in November 2019.
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 10

Very Easy
Probability of Casng Under-Voted Ballot
.5
.4
.3
.2
.1
0
Easy Hard Very HardNeither Difficulty
Ranking
Figure 4. Effect of difficulty ranking candidates on casting an under‐voted ballot. Notes: N=1,000. Under‐voted ballots
occur when a voter does not select a candidate for each ranking available. Estimation of logistic regression coefficients
calculated with all other variables held at their mean or respective values. Robust and clustered(state) standard errors
employed. Source: Author’s survey using the YouGov platform, in November 2019.
sample who had difficulty ranking candidates, that diffi‐
culty may have been a serious impediment potentially
resulting in greater likelihood of not ranking all candidate
options. Though not as stinging a rebuke as Burnett and
Kogan (2014, p. 48), these results provide support for the
assumption that high under‐voting rates may be linked to
greater difficulty ranking candidates.
6. Summary and Conclusion
This study tests the assumptions that certain demo‐
graphic groups experience greater difficulty with RCV,
that under‐voting is a result of voters experiencing
greater difficulty, and that patterns of under‐voting
reflect differences in how difficult voters find RCV to
be across demographic groups. Using a nationally rep‐
resentative sample of likely Democratic primary voters
(YouGov, N =1,000), this article finds that a large major‐
ity of respondents found ranking to be easy. Greater dif‐
ficulty ranking was found among older voters (also see
Donovan et al., 2019), with some additional evidence
that the less interested and more conservative may have
also encountered greater difficulty. Where differences in
difficulty were uncovered, evidence suggests they reflect
differences in the extent to which voters found RCV to be
easy, not hard, further suggesting that most voters find
RCV easy. Additionally, little to no evidence of differences
in difficulty were found among racial, ethnic, or socioeco‐
nomic groups, contrary to arguments made elsewhere.
Looking at under‐voting (when a voter does not fill
out all the rankings provided), this study finds that only
12% of voters under‐voted, a rate similar to those uncov‐
ered in a previous study using ballots cast in an actual
election (Burnett & Kogan, 2014). Contrary to expecta‐
tions, the results show only mixed evidence of socioeco‐
nomic factors influencing under‐voting, and no evidence
of racial or ethnic differences. Only age and difficulty
ranking candidates are significant predictors of under‐
voting across all model specifications, with younger
respondents and those who experience greater difficulty
being more likely to under‐vote. Though younger voters
were found to be more likely to under‐vote, the lack of
greater difficulty ranking for young people coupled with
the inability of RCV difficulty to affect this relationship
suggests youth under‐voting may be caused by some‐
thing other than the ranking process.
Taken together, these findings challenge the assump‐
tion that difficulty with RCV differs by demographic
group (other than age) and that these differences in
difficulty are the cause of different under‐voting rates.
Instead, the results suggest that difficulty is a contribut‐
ing factor to under‐voting but does not unduly burden
voters based on most demographic characteristics, and
that, for many voters, under‐voting may be a choice.
Still, questions remain, and future studies should con‐
sider exploring more thoroughly the relationships uncov‐
ered here.
First, a strength and limitation of this study is that
the respondents are only likely Democratic primary vot‐
ers. On the one hand, this provides a stronger test of
the degree of difficulty voters find RCV to be by forcing
them to rank candidates in an election without the use
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 11

of party labels as a heuristic. On the other hand, likely
Democratic primary voters are unlikely to be representa‐
tive of the average voter, limiting the generalizability of
the study. Future works should consider ways to expand
this study to the general electorate. Second, future stud‐
ies should distinguish reasons for increased difficulty. It is
likely less interested and more conservative individuals
faced greater difficulty due to less knowledge regarding
the many Democratic candidates. For older individuals,
did age‐related ailments make navigating the ballot more
difficult or was something else at play? It is worth noting
that additional analyses were conducted using genera‐
tional cutoffs to predict ranking difficulty (available upon
request). Some significant differences were uncovered
depending on model specification, with the youngest
generations seeing less difficulty, little differences uncov‐
ered for those in the middle, and the oldest generation
seeing increased difficulty. Third, future studies should
examine why the individual‐level under‐voting results
reported here differ from aggregate results uncovered
elsewhere. For example, this study finds no relationship
between racial and ethnic minorities and under‐voting
and a positive relationship (greater under‐voting) among
richer respondents. At the same time, other work has
found lower under‐voting rates for precincts with greater
proportions of non‐white voters and lower under‐voting
rates in precincts with higher median income (e.g.,
Neely & Cook, 2008). Is it something about the area
under study (i.e., usually San Francisco, CA), or per‐
haps these differences are accounted for by differences
in voting technology across jurisdictions (e.g., Knack &
Kropf, 2003)?
Acknowledgments
The author would like to thank Scott LaCombe for review‐
ing an earlier draft of this work. The author would also
like to thank the editors and reviewers for their construc‐
tive feedback.
Conflict of Interests
The author declares no conflict of interests.
Supplementary Material
Supplementary material for this article is available online
in the format provided by the author (unedited).
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About the Author
Joseph A. Coll is a Doctoral Candidate and Instructor in the Political Science Department at the
University of Iowa. His research focuses on how election rules and administration shape the elec‐
toral behavior and representation of voters, primarily defined along age, ethnic, racial, and socioe‐
conomic lines. Research interests also include political methodology, public policy, and public opinion.
His work has recently been published in American Politics Research, with works currently under‐review
elsewhere.
Politics and Governance, 2021, Volume 9, Issue 2, Pages X–X 13