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The Relationship Between Democratic Values and The Decision to Vote:

An Examination of the Strategic Manipulation of Democratic Values in the Short Run

Anton Freeman

Department of Political Science, University of Colorado Boulder

Honors Thesis

April 1, 2021

Thesis Advisor: Kenneth Bickers, Political Science

Honors Council Representative: Janet Donavan, Political Science

Other Committee Member: Leaf Van Boven, Psychology

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Abstract

I examine the potential existence of a relationship between the democratic values of an

individual and his/her decision to vote. I observe that the current scholarly literature on voter

behavior lacks substantial research on this relationship. I argue that this gap is significant

because the D- term, a variable which measures democratic values, has, theoretically,

considerable influence over vote-choice in models of voter behavior. To contribute to filling this

significant gap, I conduct an experiment. The data collected from the experiment suggest that the

democratic values of the subjects could not be manipulated in order to influence their decision to

vote. This has three important implications: 1) the decision to ignore democratic values in the

development of the model of voter behavior is supported, to a limited degree, by the data

gathered in this experiment, 2) the concept of quantifying a democratic value is difficult, and by

isolating democratic values to produce a quantifiable metric, a variable is produced that is too

limited to convince potential voters of a greater or lesser need to vote and, 3) voter’s democratic

values are relatively stable in the long run and are not easily shifted within the short run.

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Introduction

In recent years, the use of rhetoric that demonizes political opponents as threatening to

democracy has become increasingly salient. Throughout the 2020 presidential election, both

primary candidates accused the other of abusing the United States’ democratic systems. The

validity of these attacks is not the matter of this paper. Instead, I focus on what effect an attack

that invokes the democratic values of the electorate can have on each individual’s vote choice.

The concept of what constitutes a ‘democratic value’ is explored extensively throughout

this paper. The term ‘democratic value’ refers to the sense of satisfaction voters get from the

action of voting, independent of the outcome of the election. Often, democratic values can be

characterized as a sense of duty associated with voting or participation in democratic systems

To examine the relationship between democratic values and the decision to vote, I

examine the scholarly literature that focuses on using Rational Choice Theory. Having reviewed

the literature, I argue there is a substantial gap in this field. To date, scholars have often

neglected the potential effect that voter’s democratic values may have on vote choice. In order to

contribute to the literature and begin to fill this gap, I design a web experiment to test for a

potential relationship between the decision to vote and potential voter’s democratic values. I

hypothesize that politicians or other agents acting in a political capacity have the ability to

manipulate potential voter’s democratic values in order to strategically affect voter turnout. The

experiment provides evidence that the hypothesis is not supported.

This finding has three important implications that contribute to the scholarly

understanding of voter behavior. First, it supports the lack of analysis into the relationship

between democratic values and vote choice by demonstrating that, within the subject pool, there

was no causal relationship between democratic values and voter turnout. Second, it helps explain

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why democratic values have little influence over voter turnout: the concept of quantifying a

democratic value is problematic, and by isolating democratic values to produce a quantifiable

metric, a variable is produced that is too limited to convince potential voters of a greater or lesser

need to vote. Finally, the work demonstrates that voter’s democratic values are relatively stable

in the long run and are not easily shifted in the short run. This makes it difficult for politicians or

other agents to strategically manipulate an individual’s democratic values in order to influence a

particular election.

Literature Review

In this section, I review the literature surrounding the use of Rational Choice Theory

(RCT) in explaining voter behavior. Few scholars have conducted research analyzing how a

potential voter’s democratic values may impact their decision to vote. In this section, I argue that

this presents a significant gap in our understanding of voter behavior.

Rational Choice Theory and Modeling the Decision to Vote.

American economist Anthony Downs was one of the first researchers to popularize the

use of RCT to analyze voter behavior. In his 1957 book, An Economic Theory of Democracy,

Downs developed a basic model that explains the decision to vote as a rational choice. Downs

considered the rational voter and reasoned that his/her rationale for voting or not voting could be

explained by three variables (1957):

1. B reflects the added benefit the voter would receive if his/her preferred candidate

were to win the election.

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2. P reflects the probability that the voter’s individual vote will affect the outcome of

the election

3. C reflects any cost associated with the action of voting.

The decision to vote, R, can be determined by the equation: R= P(B)- C. An individual will only

vote if the value of R is positive; if P(B)>C. Rationally, this model appears to be logically sound.

If you multiply the added value a voter will receive if their preferred candidate wins by the

likelihood that that individual's vote will cause their preferred candidate to win, we can

determine the expected benefit of the decision to vote without the consideration of costs. Simply

subtracting costs from that value, gives us the expected benefit of an individual's decision to

vote.

There is a substantial flaw with Downs’ model, however. Given the size of the voter

population in a country like the US, the likelihood that an individual vote would have an impact

on the outcome of a national election is so small that the value of P would be close to 0 (Downs,

1957). Thus, if a voter has any cost associated with voting, the rational decision should be to

abstain from voting. Ultimately, Downs' model predicts that only under exceptionally rare

circumstances, is the decision to vote rational.

The missing variable: The D- term

Downs prediction of voter turnout is unrealistically low and not demonstrated by the data.

This suggests one of two things: 1) the majority of voters are irrational, or 2) there is a problem

with Downs’ model. Political Scientists William Riker and Peter Ordeshook, argue that there is a

problem with Downs’ model. They write that Downs’ theory is “non-explanatory (Riker &

Ordeshook, 1968, p.25).” They conduct an empirical investigation of the predictive capacity of

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Downs’ model and observe that levels of voter participation in the 1964 election were far higher

than Downs’ model would predict (p.26). Moreover, they observed substantial evidence that

voter participation varied greatly depending on race, age and other classes of voters (p.26).

Downs’ model lacked the capacity to explain this variation.

Despite their assertion that Downs’ theory is non-explanatory and non predictive, Riker

and Ordeshook still see value in the theory that RCT can be used to model voter behavior. In

their 1968 article, A Theory of the Calculus of Voting, the authors’ reason that the core flaw of

Downs’ model could be overcome by adding an additional term to the model: D. The authors

enumerate 5 potential elements the value of D reflects (p.28):

1. “The satisfaction from compliance with the ethic of voting.”

2. “The satisfaction from affirming allegiance to the political system.”

3. “The satisfaction from affirming a partisan preference”

4. “The satisfaction of deciding, going to the polls”

5. “The satisfaction of affirming one's efficacy in the political system”

The authors go into more detail explaining the rationale for why an individual may gain

satisfaction from each of these actions (p.28). Importantly, they also note this list is not

exhaustive and that it is “doubtless there are other satisfactions (p.28).” The D- term is,

essentially, a reflection of any satisfaction an individual may derive from the self-contained

action of voting.

Riker and Ordeshook go on to develop their own model, which I refer to as the rational

actor model of voting. This model is reflected by the equation: R=P(B)-C+D (p.28). The

addition of the D- term changes the calculus of the equation so that a rational actor will make the

decision to vote if P(B)+D > C (p.41). Though this change may seem insignificant, it is

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fundamental in improving the model’s predictive capacity. To verify this, the authors test the

model using survey and election data and determine the model demonstrates substantial

predictive capacity (p.34;p.41).

Criticism of the model

Despite the model’s substantial improvements in predictive and explanatory capacity,

there has still been some scholarly criticism of Riker and Ordeshook’s theory. Karl-Dieter Opp,

argues that the D- term is “exogenous to the model (2001, p.375).” The meaning of this is that

the D- term is generated by the act of voting itself, independently of the outcome of the election

or the value of any other variable. This observation does not have implications regarding the

predictive value of the rational actor model, however, it does suggest some questions regarding

the function of the D that were unaddressed by Riker and Ordeshook. Wanpat Youngmevittaya,

similarly questions if the exogenous nature of the D- term makes the model incompatible with

rational choice theory (2016, p.68). He argues that it is because the D is a reflection of tastes,

meaning it cannot be measured as “substantive and objectively rational” variable (p80). I find

this categorization to be semantic and, again, the paper does not address the predictive capacity

of the model.

In a related way, Brian Barry, John Ferejohn, Morris Fiorina, Shaun Bowler, and Todd

Donovan share a critique of Riker and Ordeshook’s work. It is, perhaps, best articulated by Brian

Barry, who argues that the nature of the D- term limits the explanatory value of Riker and

Ordeshook’s theory because although the model may predict behavior it struggles to explain the

behavior (Barry; 1970). In other words, if Riker and Ordeshook’s model predicts that a rational

actor will choose to vote because they have a high D value, the question remains: why do they

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have a high D value? In a joint paper, Ferejohn and Fiorina build on this, arguing that Riker and

Ordeshook’s reasoning is circular (1970, p.225). They assert that the D- term is a “catch-all”

variable that reflects any reason a person may vote that does not depend on the election result

(p.535). They characterize Riker and Ordeshook’s conclusion as people vote because they like to

vote, meaning that Riker and Ordeshook have contributed little explanatory value to the

question: why do people vote? Bowler and Donovan offer a similar argument asserting that

“[Riker and Ordeshook’s] model has simply no explanatory power (Bowler & Donovan, 2013,

sec. 2).”

The common theme in each of these critiques is while the authors attack the ability of the

rational actors model to explain why people vote they do not attack the model’s ability to predict

voter behavior. Unintentionally, the evidence offered by each of these authors serves to

underscore the importance of the D- term, rather than discredit it. In an effort to illustrate that the

D- term is a “catch-all,” Ferejohn and Fiorina demonstrate that when applying data to the model

“most of the action” occurs on the D- term (p.525). While this may influence our semantic

understanding and potential categorization of the rational actor model of voting as rational

choice theory, the key takeaway from this, in respect of this paper, is that the D- term and what it

represents is highly influential over an individual’s decision to vote. Barry admits that Riker and

Ordeshook’s conclusions “may very well be true (Barry; 1970).” Bowler and Donovan

characterize the nature of these critiques well, writing:

“Although the addition of ‘a sense of duty’ to models of turnout may not mesh well

with narrow definitions of rationality, it is nevertheless empirically very powerful. In

any model of turnout, a sense of ‘duty’ to vote is typically a strong predictor of

behaviour (sect.2).”

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Without analyzing the validity of these criticisms, it can be determined that given their

acknowledgments of the predictive capacity of the rational actor model of voting, they do not

invalidate Riker and Ordeshook’s theory in a manner that is relevant to the core questions of

this paper. In fact, they demonstrate the model’s predictive capacity and highlight the

importance of the D- term, serving as evidence of the argument central to this paper.

Later in this paper, I establish that Riker and Ordeshook’s model serves as the

foundation in developing my research methodology. Thus, it is important to acknowledge the

specific criticisms of these authors in my research design, as some of their assertions may

pertain to what specific implications can and cannot be drawn from the individual variables

of the model, in terms of explanatory capacity.

Sectional Takeaways

While Downs’ model of voting as a rational choice provides the framework for

modeling voter behavior under rational choice theory, the model is seriously flawed in its

lack of capacity to generate predictions that reflect real-world data. Riker and Ordeshook’s

rational actor model of voting is able to generate more accurate predictions by adding a D-

term to Downs’ basic model. The D term reflects the satisfaction an individual may receive

from the action of voting itself. Several scholars have criticized the nature of the D- term and

its substantial influence over the model’s outcomes. While these criticisms raise questions

about the model’s classification under the framework of rational choice theory, they validate

the model’s strong predictive capacity. More importantly, they emphasize the importance of

the D- term. The critical takeaway from the literature reviewed in this section is that the

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rational actor model of voting has a strong predictive capacity and that the D- term is the

singular most important variable in explaining the model’s predictions.

The relationship between the D- term and voter turnout: a gap in scholarly literature

Given the previous section’s emphasis on the importance of the D- term to the

rational actor model of voting, it may be surprising that there is a lack of research analyzing

the D- term’s role directly in affecting turnout. In the half-century since Riker and

Ordeshook’s publication, their work has been used as the foundational theory by a significant

number of scholars researching voter behavior and turnout. Interestingly, however, much of

this research focuses on the P, B and C terms of the model. This is curious given that without

the addition of the D- term, the model lacks the capacity to generate accurate predictions.

In his 1993 paper, Rational Choice and Turnout, John Aldrich notes this gap in the

literature. Aldrich argues that due to the concerns enumerated in the previous section, many

researchers do not find the D- term to be relevant (p.257). Further, Aldrich illustrates that

“unless D > C,” the inclusion of a D- term does not fundamentally alter the outcome of the

equation (p.252). The fact that the without the inclusion of the D- term the model lacks

predictive capacity, but with the D- term the model is “a strong predictor of behaviour,”

coupled the fact that the D- term variable has greater influence over the outcome of the

equation (compared to any other included variable), it is reasonable to conclude that

instances where D > C occur at a significant rate.

Nevertheless, twenty years after Aldrich’s publication, this gap in the scholarly literature

remained. In an article published in 2013, David Campbell writes, “that neither rational choice

nor behavioral scholars paid much attention to [civic duty], considering it more a nuisance than a

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concept worthy of greater study (p.43).” This reflects the greater problem regarding the use of

Riker and Ordeshook’s theory. The model was generated under the framework of rational choice

theory, and although the model’s critics acknowledge the models capacity to predict behavior

they are, perhaps, warranted in their assertion that the D- term is not compatible with this field of

research. This creates a complication in their research, often persuading researchers to ignore the

variable entirely.

Ultimately, the D- term is an elusive term to conceptualize. The factors that determine its

value are, quite possibly, infinite. Moreover, the schools of thought and areas of study necessary

to analyze the underlying components of the variable range widely. Greater analysis of the D-

term, could be the key to unlocking a better understanding of voter behavior.

Paths to the D- term

There is one area of study in which the analysis of the D- term has been a substantial

component in analyzing voter behavior and that is the study of political socialization. Essentially,

there are two ways a researcher can attempt to analyze the function of the D- term. The first is

political socialization. The complexity of assigning a value to the D- term is highlighted in the

previous subsection, however, research under the school of thought of political socialization

provides one mechanism for estimating the value of D. One example of this is a 2006 study

which revealed that students exposed to civic education are more likely to engage in political

action, like voting (Pasek et al.). Researchers are able to build on knowledge like this and make

predictions within the rational actor model of voting. However, this research is limited in its

capacity to solve the problem that is central to this paper. Ultimately, political socialization at

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this point generally considers the value of D to be a constant (at an individual level) in the long

run. Thus, it cannot effectively explain variation between elections.

The other way researchers can examine the D- term is by analyzing how politicians may

work strategically (or accidentally) to affect it. This means that the D- term of an individual

might vary from election to election, even when holding the election-type constant. This is where

there is the lack of analysis in this field of study.

Summary of Literature Review

Through my examination of the literature, I have determined that Riker and Ordeshook’s

rational actor model of voting has a strong predictive capacity as a direct result of its inclusion of

a D- term. I discovered, however, that there is a substantial gap in the scholarly research of this

field in that it ignores an important question: can politicians act strategically to affect voter

turnout by influencing the D- term? In the following section, I build on the research contained

herein. I develop a research strategy that is designed to answer this question.

Methodology

Theory and Hypothesis

Existing literature suggests that the D- term in Riker and Ordeshook’s rational actor

model of voting has a significant impact on an individual's decision to vote. In the previous

section, I argue that there is little research that explores how the D- term can be manipulated in

the short run in order to influence voter behavior. This is particularly noteworthy given the

amount of research that has been dedicated to analyzing how the other variables may be

manipulated in this way. In this section, I analyze how agents may act strategically to influence

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the D- term in order to shape a more preferable electorate. I build on this analysis to develop a

methodology to test the effect rhetoric aimed at manipulating the D- term may have on voter

turnout.

The core question I pose in this paper asks: in the short-run, can agents strategically

manipulate an individual’s ‘D’ term value in order to affect his/her decision to vote? Given the

importance of the D- term in determining an individual's decision to vote, I anticipate the answer

to this question will be yes. Thus, this paper’s core hypothesis, H1, is: In the short-run, agents

can strategically manipulate an individual’s ‘D’ term value in order to affect his/her decision to

vote.

I test this hypothesis by conducting a web experiment. Subjects are randomly divided into

3 groups. Each group is given the same information about a hypothetical election. Then, group A

is exposed to rhetoric that aims to increase the subjects D- term value. Group B is exposed to

rhetoric that aims to decrease the subjects D- term value. Group C is the control group and is not

exposed to any additional rhetoric. The purpose of the inclusion of a control group is to develop

a baseline to understand how likely subjects who are exposed to neither positive nor negative

rhetoric are to participate in the election. Each subject is then asked, on a scale of 1 to 5, how

likely they are to participate in the election. All subjects, then, complete a questionnaire1

following the experiment to establish information that will be important to understanding the

impact of the rhetoric and analyzing the data.

1 The complete questionnaire is provided in Appendix A section 1,

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Research Design

Definitions

To test this paper’s core hypothesis (H1) it is important to understand the definitions of

some of the terminology it includes. I hypothesize that in the short-run, agents can strategically

manipulate an individual’s D- term value in order to affect his/her decision to vote. First, when

referring to the “short-run,” I mean to say that within a given election cycle an agent can

manipulate an individual’s D- term value. Second, by ‘agent’ I am referring to any party

attempting to influence voter behavior. I often refer to these parties in a specific way such as a

politician or a campaign. However, bi-partisans, special interests organizations, and other groups

may behave as ‘agents’ in this way. Third, I used the term ‘strategically’ to refer to an intentional

action with the goal of achieving a specific result. I do not mean to imply that when an agent acts

strategically they understand the mechanisms by which their action will produce the intended

result. Finally, although the D- term and its properties were explored extensively in the previous

section, it is important to reiterate its key features and note the variable’s limitations.

Riker and Ordeshook offer a definition of the D- term that is opaque. It can be described

generally, however, as a variable that captures any satisfaction a voter may receive from the

action of voting itself. The authors enumerate several possible examples to establish “the nature

of D,” however, they do acknowledge that their list is non-exhaustive and that is “doubtless,”

that there are alternative satisfactions an individual may receive from the action of voting itself

(Riker & Ordeshook, 1968, p. 28). Though this definition leaves the D- term relatively open in

terms of what satisfactions it may reflect, the scope of the term is not unlimited. The satisfaction

must be derived from the action of voting itself, independent of the potential effect the decision

to vote may have on the outcome of the election. The fact that the satisfaction must come from

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the action of voting itself is the key feature that distinguishes the rhetoric of interest from

rhetoric that may influence other variables for a similar purpose. In the later subsection,

‘Rhetoric that Affects the D- term, this distinction becomes quite important.

Why a web experiment?

The decision to use a web experiment as the methodology to test the hypothesis is

influenced by a study conducted by Ansolabehere and his research team. The core question (and

title) of their 1994 paper on the study is: Does Attack Advertising Demobilize the Electorate?

Although in similar research it is common to use surveys when analyzing voter behavior, the

authors choose, instead, to conduct an experiment. Their rationale is that surveys are not able to

determine what specific campaign advertising a respondent has been exposed to and thus, are

unable to measure the specific impact a certain type of advertising may have (Ansalobehre et al,

1994, p.830). Although the focus of this paper is not specifically campaign advertising, the direct

impact specific rhetoric is under consideration, thus, surveys are not an ideal method of analysis.

Moreover, the core question of the Ansolabehere study is essentially: Can agents use

attack advertising to strategically manipulate an individual’s B- term value in order to affect

his/her decision to vote? This question is quite similar to that posed in this paper. Thus, their

experimental design provides an excellent framework for my own research. There are, however,

several key features that distinguish their work from mine. In the following subsections, I

summarize their experimental design and explain how I have adapted it to test H1.

The Use of a Hypothetical Election

In Ansolabehere et al’s study, the researchers chose to use real candidates across, “a

variety of campaigns,” for different positions (Governor, U.S. Senator, and Mayor), different

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parties, in different years and at different levels of salience (1994, pp.830-831). The variation

was key to ensuring that the subject’s responses were a direct result of the stimuli they were

given rather than a result of an unknown factor related to a specific campaign. In analyzing the

D- term, however, producing this same level of variation is more complicated. The level of

satisfaction a potential voter receives from the action of voting itself is likely to vary as a direct

result of the variation in campaigns. It is likely that turnout in presidential elections is higher

than in midterm elections as a result of the fact that voters find it to be more important to

participate in presidential elections compared to lower-level elections. The same can be said

about more or less salient elections.

Ultimately, given the open nature of what the D- term represents, it is best to hold as

many variables as constant as possible, meaning examining voter behavior in a single election is

the best approach. Given that the question addressed in this paper arose in response to the

prevalence of anti-democratic rhetoric in the 2020 US presidential election, I considered using

this election as the basis of the experiment. Using a real election, however, creates a greater

possibility that a subject’s response to the experiment is affected by outside variables. This is

particularly true given the high salience and polarizing nature of this specific election.

Consequently, I chose to use a hypothetical election. Using a hypothetical election allows

for greater control of potential confounding variables and increases the internal validity of the

experiment. The details of the hypothetical election presented to research subjects reads as

follows:

The 2020 presidential election is finally behind us, but election season is not over. The

new Biden administration is likely to appoint a number of Senators, Congressmen and

other government officials to his cabinet. That means there could be a special election

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coming up in your state. Although the details of this special election are not yet

determined, there are some details we do know:

1. This election is not likely to be close.

- Biden is unlikely to select cabinet members from closely contested seats because

he would not want to risk a sitting Democrat being replaced by a Republican.

2. The two main candidates in this election will be a Democrat and a Republican.

-The outcome of this election could determine the balance of the Senate, Congress

or statewide/ local politics.

-a Republican win could ensure a Republican majority in the Senate.

-a win for the Democrats could shift the balance in favor of the Democratic

party.

The inclusion of each detail presented in the description of the hypothetical election was

intentional. The first paragraph explaining the general nature of the election is intended to root

the election in reality. The purpose of this is to encourage subjects to respond as if it was a real

election, rather than a hypothetical one. Although at no point is it stated that the election will

definitely occur, it is also not specified that the election is entirely fictional.

The purpose of including the information that the election will not be close is intended to

lower subjects’ P value. Since P represents the probability that a voter’s individual vote will

have an impact on an election, the closer the election the higher the P value. By minimizing the

value of P across all groups, variability in the dependent variable caused by P should also be

minimized.

The final detail, the election could determine the partisan balance of politics, is intended

to maximize the B- term. Given that the B- term reflects the added benefit of the preferred

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candidate winning, by emphasizing the potentially large implications the election could hold the

B value should increase. By presenting all subjects with the same messaging, the B term should

be constant across treatment groups.

The information establishing the nature of the hypothetical election to the subjects was

designed intentionally to hold as many variables in the rational actor model of voting constant, in

order to allow for analysis of variation for the D- term across treatment groups.

Rhetoric that affects the D- term

In Ansolabehere et al’s study, the researchers were interested in how attack advertising

affected voter turnout (1994, p.828). To do so, they categorized campaign advertising into a

dichotomy. Advertisements were either “positive” (in support of a particular candidate) or

“negative” (against or an attack on a particular candidate [p.830]). The researchers were then

able to expose one group to the “positive” advertising and one group to the “negative”

advertising (p.831). This allowed the researchers to isolate the specific impact each type of

advertising had on turnout.

In this paper, I am interested in examining how agents may manipulate the D- term in

order to affect voter turnout. Thus, I dichotomize “D- term rhetoric” into two categories: rhetoric

aimed at increasing an individual's D- term value (“positive”) and rhetoric aimed at decreasing

an individual’s D- term value (“negative”). In order to do so, it is important to consider the

nature of the D- term as explored in the previous subsection, ‘Definitions.’ If an agent is acting

strategically to mobilize or demobilize voters by using rhetoric that affects the D- term; the agent

must aim to influence the level of satisfaction a voter receives from the act of voting itself.

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For example, an agent may attempt to mobilize voters by stressing the importance of a

specific election. Whether or not this strategy affects the D- term, depends on the nature of the

rhetoric itself. If the agent is arguing the election is particularly important because one candidate

is much better than the other, then this rhetoric affects the B- term of the model rather than the D-

term. If the actor, however, argues that this election is particularly important because democracy

is in jeopardy and voting is the most fundamental form of democratic action, then the rhetoric

affects the D- term.

Therefore, it is important that “positive” D- term rhetoric convinces voters that they will

gain specific satisfaction by deciding to vote. The following quotation is the ‘positive D- term

rhetoric’ that is given to subjects in the A group:

The future of America depends on your vote. It is important to vote in this

election. Regardless of the outcome, to vote is to make a statement. Not voting is

giving up your voice. The future of this country is in your hands. Do not give up

your voice: make a statement. It is your duty as an American to vote.

Ansolabehere et al were able to modify only a handful of words to distinguish their “positive”

advertisement from their “negative” advertisement. Creating ‘negative D- term rhetoric’ is a bit

more tricky. There is a degree of danger in telling subjects that they should not vote with the

intention to decrease their D- term value. This is because by telling an individual voter they

should not vote there is a risk that an unintended consequence of this rhetoric may actually cause

the subject to infer the election is important, thereby raising their D- term value. Thus, the

following quotation aims to minimize the importance of the action of voting, by emphasizing

that voting is a choice and the choice to not vote is equally valid.

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Voting is a choice. It is okay to make the choice to not vote. In fact, in some elections

more than half of Americans choose not to vote. These days it seems like some people

are saying you have to vote. But why? If for any reason you do not want to vote, then do

not. It's as simple as that. Voting is a choice and not voting is one way to exercise your

rights as a free American.

Questionnaire

In Ansolabehere et al’s study (1994), the researchers presented subjects “a lengthy

posttest questionnaire tapping their beliefs and opinions on a wide range of campaign issues,"

and about their intention to vote (p.831). Similarly, following the subject's participation in this

experiment they are asked a range of questions helping to determine if the D- term rhetoric

affected their decision and why or why not. The questionnaire was intentionally kept short in

order to hold the attention of respondents. In the following section, Data and Analysis, I analyze

several of the questions and their importance in a great degree of detail. The complete

questionnaire, written as presented to subjects, is provided in appendix A section 1, as well as a

full explanation for the purpose of inclusion for each question in appendix A section 2.

Additional detail

The experiment was hosted on Qualtrics. Qualtrics was chosen because it allows for good

design on the front end and provides the tools to code an experiment with three randomly

generated treatment groups. Subjects were recruited through Amazon’s Mechanical Turk

crowdsourcing website. The purpose of Mechanical Turk (MTurk) is to help people

(participants) find paid tasks (Litman, n.d.).” Dr. Leib Litman, a researcher familiar with the

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Mturk service provides an explanation of the recruitment process provided by Mturk (Litman

n.d.):

“MTurk enables researchers to recruit participants to perform tasks such as filling out

surveys, opinion polls, cognitive psychological studies, and many others. Researchers

advertise their studies on MTurk, and participants choose only those studies that interest

them. Participants are paid for completing the studies. Payment is transferred directly to

the participants’ credit cards immediately after the completion of a study.”

Subjects were paid at a rate equivalent to $15/hr. Given that the estimated time for

completion of the experiment was between 4 to 5 minutes, subjects were paid a standard $1.2

dollars for completing the experiment.

Demographic information is not collected by MTurk. The only information collected on

subjects came from their answers to the questionnaire. I chose not to ask subjects any

demographic or identifying information other than their self-described party identification.

The Institutional Review Board (IRB) determined that the study was exempt from need

for approval due to minimal risk posed to subjects on 02/05/2021. The experiment was published

to MTurk on 03/01/2021 and the total number of subjects was reached that same day.

Data and Analysis

Descriptive Statistics

In this section I summarize the statistics that describe the data collected during the

experiment. I also examine the internal and external validity of the experiment, in order to later

make determinations about my conclusions and gauge the generalizability of the study.

22

Internal Validity

A total of 607 respondents consented to participate in the experiment. I took several steps

to ensure the internal validity of the study. First, I added an attention check question to the

questionnaire.2 The purpose of the inclusion of this question was to ensure that subjects were

paying a reasonable degree of attention while responding to the questionnaire. 73 respondents

failed to answer the attention check question and their responses were not recorded.

Of the 534 remaining respondents, the average time of completion for the experiment was

3 minutes. 119 respondents completed the experiment in less than 60 seconds. I removed these

respondent’s data from the data set. The rationale for this decision is it seemed unreasonable that

a respondent could read the information presented in treatment and complete the survey in under

one minute.

The remaining 419 respondents were randomly assigned to one of three treatment groups.

144 were assigned to treatment group A to receive the ‘positive D- term rhetoric’; 133 were

assigned to treatment group B to receive the ‘negative D- term rhetoric’; and 142 were assigned

to the control group C.

In trying to keep the questionnaire short, and respondent attention high, I collected only

one piece of data that was strictly independent of the treatment assignment: party identification.

To ensure that members of each party were evenly dispersed across treatment groups during

random assignment, I created the following table:

Figure 1

Democrat Republican Independent Minor Party

Treatment A 84 (58%) 42 (29%) 16 (11%) 2 (1%)

2 This question is shown in appendix A section 1, Q8.

23

Treatment B 66 (49.6%) 43 (32%) 24 (18%) 0

Control C 69 (47.9%) 42 (29%) 30 (20.8%) 1 (<1%)

The above, figure 1, shows that the makeup of treatment group B and the control group is nearly

identical. Treatment group A, however, has more Democrats and fewer independents than the

other two groups. This provides a potential threat to the internal validity of the experiment. In

order to account for this inter-group party discrepancy, in a later section, I add party variables to

my regression analysis in order to control for any potentially confounding effects.

External Validity

To determine whether or not this study’s findings are generalizable, I included several

relevant ANES questions in order to gauge how similar these respondents are to ANES

respondents. The first question again deals with party identification. I used identical wording in

question Q9 to the 2020 ANES survey: “Generally speaking, do you think of yourself as a

Republican, a Democrat, an Independent, or what?.” The following table compares the responses

from this survey to that of ANES:

Figure 2

Democrat Republican Independent Minor Party

FreemanExperiment

219 (52%) 127 (30%) 70 (16%) 3 (<1%)

ANES 2020(V201228)

2864 (34%) 2564 (31%) 2527 (30.7%) 269 (3%)

The above, figure 2, shows that respondents to the ANES survey were evenly divided between

Democrats, Republicans and Independents. In comparison, subjects in the Freeman survey were

24

significantly more likely to be Democrats, about half as likely to be an Independent and roughly

equally likely to be Republican. This disparity demonstrates that the subject pool is not

representative (in terms of political ideology) of the ANES sample nor the larger voting

population.

The questionnaire also included another question, Q6, taken from the ANES survey:

“Different people feel differently about voting. For some, voting is a duty - they feel they

should vote in every election no matter how they feel about the candidates and parties.

For others voting is a choice - they feel free to vote or not to vote, depending on how they

feel about the candidates and parties. For you personally, is voting mainly a duty, mainly

a choice, or neither a duty nor a choice?”

The following figure compares the responses from this survey to that of ANES:

Figure 3

Choice Duty Neither

Freeman Experiment3 183 (43.6%) 198 (47%) 35 (8.3%)

Freeman Experiment(Control Group)

54 (38%) 78 (54%) 10 (7%)

ANES 2020(V201221)

1462 (35%) 2323 (55.6%) 387 (9.2%)

The purpose of the inclusion of this question in the questionnaire is it serves as a proxy metric

for an individual’s D- term value. The selection of ‘choice’ indicates that the respondent has

lower D- term value, while the selection of ‘duty’ indicates that the respondent has a higher D-

term value. The above table, figure 3, demonstrates that ANES respondents have, on average,

higher D- term values compared to subjects in this study. Looking only at the control group,

3 Q6 also included an option “I don’t know.” 0.7% of respondents selected this option.

25

however, shows that respondents from both studies have roughly equivalent distribution of D-

term values. This is because, as is later shown, exposure to treatment in both group A and group

B resulted in lower D- term values.

Data Summary

This paper’s core hypothesis, H1, states that in the short-run, agents can strategically

manipulate an individual’s D- term value in order to affect his/her decision to vote. The

dependent variable to this hypothesis is the potential voter's decision to vote. This is measured by

subjects' responses to the question (Q1): Based on the information you have been provided, how

likely are you to participate in a potential special election?

The following table, figure 4, shows the responses of each treatment group to the question:

Figure 4

TreatmentGroup

Extremelyunlikely

Somewhatunlikely

Neither likelynor unlikely

Somewhatlikely

Extremelylikely

I do not haveenough

informationto decide.

A 9 4 6 40 83 2

B 6 6 7 44 66 4

C 9 6 7 33 81 6

Total(ABC)

24 16 20 117 230 12

I recoded the likert scale to reflect numerical values where “Extremely unlikely” received a value

of 1 and “Extremely likely” received a value of 5. After removing all respondents who answer, “I

do not have enough information to decide,” the mean value of responses by treatment group is:

- Treatment group A: 4.3

26

- Treatment group B: 4.2

- Treatment group C: 4.3

Across all three treatment groups, both the median and the mode value of responses to Q1 was 5.

55% of respondents indicated that they were “extremely likely” to participate in the hypothetical

election. In response to Q1, variation on the likert scale was quite limited. This indicates a

potentially important finding in this paper and feature of the D- term: it can be difficult to

manipulate. I examine this finding in more depth in the following Discussion Section.

Data Analysis

In order to determine if there is a statistically significant relationship between the rhetoric

a subject received and their indication about their likelihood of voting, I used Ordinary Least

Squares (OLS) regression analysis.4 I combined the responses to question Q1 from all treatment

groups into a new variable called TreatmentABC. The mean value of TreatmentABC is 4.3. I

created a dummy variable, ProD, which assigns respondents a value of 1 if they were a part of

Treatment group A and a value of 0 if they were a part of Treatment group B or C. I created a

dummy variable AntiD which assigns a value of 1 if they were a part of Treatment group B and a

value of 0 otherwise. Then I ran a multivariate regression with Treatment ABC as the dependent

variable and ProD and Anti as the independent variables. The regression is shown in following

Figure 5.

Figure 5

===============================================Dependent variable:

---------------------------TreatmentABC

-----------------------------------------------

4 An explanation for my decision to utilize this method of analysis can be found in Appendix section 2.

27

ProD 0.038(0.134)

AntiD -0.033(0.137)

Constant 4.257***(0.096)

-----------------------------------------------Observations 407

R2 0.001Adjusted R2 -0.004

Residual Std. Error 1.115 (df = 404)F Statistic 0.138 (df = 2; 404)

===============================================Note: *p<0.1; **p<0.05; ***p<0.01

In terms of directionality, the coefficients for the ProD and AntiD variables support H1.

Subjects in treatment group A were more likely to indicate that they would participate in the

election, compared to the control group. There is no statistically significant relationship,

however, between placement in a particular treatment group and indicated desire to participate in

the election. The P value for both variables was >0.1.

R2 values are difficult to interpret when using ordinal data. This is because it is

impossible to know the actual value separating each point on the scale. This is particularly true

when the responses are crowded on a given point within the scale; which is the case with this

data with the overwhelming majority5 of values registering between 4 and 5. However, the fact

that the R2 value is only -0.004 means that the model is not likely explaining the variation in the

dependent variable.

Given that the model establishes no relationship between treatment group and dependent

variable, it is important to understand why. This can be achieved by looking at the underlying

5 82.8% of responses indicated either a 4 or 5 across treatment groups.

28

causal mechanism that would explain the relationship, if one were to exist. If H1 were to be true,

for the reasons outlined in the previous sections of this paper then two underlying assumption

would need to be true: (1) placement in treatment group A would increase likelihood of voting

because it would increase an individual’s D value; and (2) placement in treatment group B would

decrease likelihood of voting because it would decrease an individual’s D value.

In order to test these assumptions, I can look at if placement in either treatment group

increases or decreases an individual’s D value. The questionnaire included a proxy metric for D

value in Q8, which asked: For you personally, is voting mainly a duty, mainly a choice, or

neither a duty nor a choice?6 Respondents who indicated ‘duty’ are assumed to have higher D

values, while respondents who indicated ‘choice’ are assumed to have lower D values. I created

two dummy variables: ‘choice’ where respondents receive a value for 1 if they indicated choice

and ‘duty’ where respondents receive a value of 1 if they indicated choice. I added these

variables as the dependent variable to the two following regressions, figure 6 and figure 7:

Figure 6===============================================

Dependent variable:---------------------------

choice-----------------------------------------------

ProD -0.131**(0.059)

AntiD -0.091(0.060)

Constant 0.549***(0.042)

-----------------------------------------------Observations 416

R2 0.012Adjusted R2 0.007

Residual Std. Error 0.498 (df = 413)F Statistic 2.559* (df = 2; 413)

===============================================Note: *p<0.1; **p<0.05; ***p<0.01

6 Respondents also had the opportunity to respond “I don’t know.”

29

Figure 7===============================================

Dependent variable:---------------------------

duty-----------------------------------------------

ProD 0.015(0.033)

AntiD 0.027(0.034)

Constant 0.070***(0.023)

-----------------------------------------------Observations 416

R2 0.002Adjusted R2 -0.003

Residual Std. Error 0.278 (df = 413)F Statistic 0.332 (df = 2; 413)

===============================================Note: *p<0.1; **p<0.05; ***p<0.01

Theoretically, the ‘positive D- term rhetoric’ should have decreased choice and increased

duty. And, the ‘negative D- term rhetoric’ should have increased choice and decreased duty. Only

one of these relationships, however, was shown to be statistically significant by the data:

‘positive D- term rhetoric’ decreased the choice variable value. These models illustrate two

interesting findings. First, they demonstrate the difficulty of manipulating an isolated D- term,

strategically. The rhetoric presented was too limited to convince voters to change their voting

positions, and more expansive rhetoric would violate the definition of the D- term. Second, the

value of the estimated D- term of subjects in treatment group A increased, yet, as shown in figure

5, indicated voter turnout did not increase for group A. This provides further evidence to reject

H1, as the underlying theory holds true in this direction and there is still no relationship between

treatment group and the dependent variable.

Finally, I added dummy variables that represent the two major parties to test if the

disparity in party identification between the groups was potentially impacting the result of the

experiment. The following regression, figure 8, shows that there is a statistically significant

30

relationship between identification with either party and intention to participate in the

hypothetical election. By adding these variables to the model, I controlled for the effect party ID

was playing. The model, however, still demonstrated no statistically significant relationship

between treatment group placement and the dependent variable.

Figure 8===============================================

Dependent variable:---------------------------

TreatmentABC-----------------------------------------------

AntiD -0.049(0.131)

ProD -0.037(0.128)

Republican 0.823***(0.164)

Democrat 0.952***(0.152)

Constant 3.526***(0.149)

-----------------------------------------------Observations 407

R2 0.090Adjusted R2 0.081

Residual Std. Error 1.066 (df = 402)F Statistic 9.990*** (df = 4; 402)

===============================================Note: *p<0.1; **p<0.05; ***p<0.01

Discussion

The experiment demonstrated no statistically significant relationship between the D- term

and voter turnout within the subject pool. It did, however, provide evidence for several important

non-findings. First, within the subject group, H1 can be rejected. This rejection supports the

literature presented in the literature review. This research lacks the generalizability to say

conclusively that scholars can safely ignore the D- term in the construction of their voter models.

It does, however, provide the important foundation necessary to begin to fill the above-defined

gap in the literature.

31

Second, the experiment may demonstrate some evidence for why the D- term might have

little practical application in predicting voter turnout. To quote Ferejohn and Fiorina (1974), the

D- term is a “catch-all” variable (p.535). The nature of the D-term means that its value is heavily

intertwined with the value of the other variables in the model. As was demonstrated by the

experimental design, isolating the D- term presents a difficult task. And, as was shown by the

experiment, an isolated D- variable is 1) hard to influence and 2) has little effect on voter turnout.

Finally, as is demonstrated in figure 3, the rhetoric had an effect on estimated D- term

values as estimated by Q6. Figure 6 and figure 7, however, show that this relationship is not

statistically significant except in the case of placement in treatment group A resulting in higher

estimated D value. This demonstrates the difficulty of agents to strategically manipulate the D-

term in isolation. This is, perhaps, related to the political socialization theory explained in the

review of the literature. Individual’s are ‘trained’ to develop democratic values that are constant

overtime. This is shown in the experiment by the lack of variation in responses to Q1. As

explained above, the TreatmentABC variable was affected by a ceiling effect. 82.8% of

responses indicated either a 4 or 5 across treatment groups, with roughly 55% of respondents

indicating a 5. It is common to see overestimates of voter turnout in experimental and survey

settings. In the ANES pre-election survey 87% of respondents indicated that they intend to vote.

This demonstrates why it can be so difficult to work with the D- term. In a manner that is related

to the political socialization theory, individuals know that there is a societal expectation that D

values should be high. Essentially, individuals have been trained to indicate values that are not

suggestive of their behavior.

The findings from this experiment are, of course, limited as far as the degree to which

they can be generalized. The subject pool is not representative of the general public. The internal

32

validity of the experiment, however, is strong. Thus, the predictions generated may be important

in furthering the scholarly understanding of the D- term and its properties. Despite the

experiment's limitations, this research can provide an important foundation to future research on

the D- term and its relationship with voter turnout.

Conclusion

This paper identified three important implications that contribute to the scholarly

understanding of voter behavior. These three implications are: 1) the decision to ignore

democratic values in the development of the model of voter behavior is supported, to a limited

degree, by the data gathered in this experiment, 2) the concept of quantifying a democratic value

is difficult, and by isolating democratic values to produce a quantifiable metric, a variable is

produced that is too limited to convince potential voters of a greater or lesser need to vote and, 3)

voter’s democratic values are relatively stable in the long run and are not easily shifted within the

short run. In the following paragraphs, I will more generally describe the nature of these

implications. I, also, suggest potential paths for future research that can build on this work and

contribute to filling the gap in the scholarly literature.

The primary critique of the D- term by scholars, as presented in the literature review, is

that the D- term can be ignored in evaluating voter behavior because it lacks explanatory

capacity and is exogenous to voter models. The research contained in this paper provides the

missing foundation for this approach by demonstrating that, within the subject pool, the D- term

did not affect voter behavior. Though this research is not generalizable enough to conclude

definitively that democratic values can safely be ignored in all models of voter behavior, it does

33

provide some evidence that may be the case. Furthermore, it lays the groundwork for future

research on the subject.

The experimental design process outlined how difficult it can be to quantify a value that

represents the D- term. This is because the variable is so heavily correlated with other variables

within the Rational Actor Model of Voting. The concept of democratic values, as explored in this

paper, is vast and difficult to conceptualize. When it is isolated from the other variables in the

model, however, its definition becomes much more specific. As a result, the variable’s

theoretically strong influence over the predictive capacity of the model is muted. This makes

measuring the D- term in isolation quite difficult. Future studies may be able to build off the

work contained in this paper to develop ways to quantify democratic values that are more

expansive.

Finally, the data showed that the political socialization process described in the literature

review creates a learned behavior where respondents are likely to answer questions that identify

their democratic values in the way that is encouraged by society. Although the experiment was

able to manipulate the estimated D- term value of subjects in the A group, the effect was limited,

and there was no capacity to influence D- term values of other subjects. This demonstrates the

stability of the D- term in the long run. Future researches may consider an approach that

accounts for the development of long-term democratic values in research that analyzes how the

D- term can be manipulated in the short run.

References

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Appendix A

1. Complete questionnaire

Q1: Based on the information you have been provided, how likely are you to participate

in a potential special election?

➔ 1 (Very unlikely)

➔ 2

36

➔ 3

➔ 4

➔ 5 (Very likely)

➔ Unsure

➔ I do not have enough information to decide

Q2: How important do you think it is to vote in this election?

➔ Not important

➔ Somewhat not important

➔ Neither important nor not important

➔ Somewhat important

➔ Very important

Q3: Did you vote in the 2020 Presidential election?

➔ No, I did not vote

➔ Yes, I voted for Joe Biden

➔ Yes, I voted for Donald Trump

➔ Yes I voted for a third party candidate/ write-in

Q4: In general, what best describes your voting behavior?

➔ I vote in every election.

➔ I only vote in presidential elections.

➔ I only vote in midterm elections.

➔ I vote only in elections that I think are important.

➔ I do not vote in any elections.

➔ None of these options describe my voting behavior.

37

Q5: Even if an election is not close, or your favored candidate does not win how much

satisfaction do you get from the action of voting itself?

➔ I get no satisfaction from voting if the election is not close or my favored

candidate does not win.

➔ I get little satisfaction from voting if the election is not close or my favored

candidate does not win.

➔ I get some satisfaction from voting if the election is not close or my favored

candidate does not win.

➔ I am satisfied from voting regardless of if the election is close or my favored

candidate does not win.

➔ I do not understand the question.

Q6: Different people feel differently about voting. For some, voting is a duty - they feel

they should vote in every election no matter how they feel about the candidates and

parties. For others voting is a choice - they feel free to vote or not to vote, depending on

how they feel about the candidates and parties. For you personally, is voting mainly a

duty, mainly a choice, or neither a duty nor a choice?

➔ Mainly a duty

➔ Mainly a choice

➔ Neither a duty nor a choice

➔ I don’t know

38

Q7: Some people say that no MATTER who people vote for, IT WON'T MAKE ANY

DIFFERENCE to what happens. Others say that who people vote for CAN MAKE A

BIG DIFFERENCE to what happens. (ONE means that VOTING won't make any

difference to what happens and FIVE means that VOTING can make a big difference),

where would you place yourself?

➔ 1 (voting won’t make any difference to what happens)

➔ 2

➔ 3

➔ 4

➔ 5 (voting can make a big difference)

Q8: For the following question, please select the answer that reads “I am ready to

complete this questionnaire and provide useful feedback.”

➔ I prefer the oceans to the mountains

➔ I prefer the mountains to the ocean

➔ I enjoy spending time with my friends and my family

➔ I am ready to complete this questionnaire and provide useful feedback

➔ I do not regularly watch the news

➔ I prefer eating meals at home rather than at restaurants

Q9: Generally speaking, do you think of yourself as a Republican, a Democrat, an

39

Independent, or what?

➔ Democrat

➔ Republican

➔ Independent

➔ Other minor party

Q10: Would you call yourself a strong Democrat/Republican or a not very strong

Democrat/Republican? OR Do you think of yourself as closer to the Republican Party or

to the Democratic party?

➔ Strong Democrat

➔ Strong Republican

➔ Not Very Strong Democrat

➔ Not Very Strong Republican

➔ Neither a Democrat nor Republican

2. Explanation for inclusion of each question

Question Purpose

1 This is the primary dependent variable. Forthe neutral C group the purpose is to developa baseline of voter turnout for participants.For the positive/negative A and B groups thepurpose is to measure their stated likelihoodof participation, compared to the level of theC group will allow for determinations to be

40

made about how the experimental rhetoricmay affect turnout.

2 This question is intended to establish whetheror not the subject finds the act of voting in theelection to be important. The purpose of thisis to understand whether or not the D term isresponsible for any action on the answerindicated in question a.

3 and 4 These questions are intended to gain anunderstanding of subjects' voting history. Thepurpose of their inclusion is to understandwhether or not some information provided inthe activity is responsible for influencing theirdecision to vote or their perception of thiselection as particularly important. This isimportant in answering the research question,specifically do these perceptions change orare they stable over time.

5 This question is meant to isolate whether ornot the D- term is responsible for any actionon questions A and B. Since the D termreflects only satisfaction gained from the actof voting independent of the outcome, thisquestion is necessary in isolating thatinformation.

6 and 7 These questions are taken directly fromANES question V1622250 and V162282. Thepurpose of their inclusion is to allow forcomparison to data collected in ANESstudies. Differences between each study groupmay also reflect the impacts that the activity’srhetoric had on subjects’ perceptions.

8 This is an attention check question to ensurethat participants are examining the content ofeach question, rather than clicking through orrandomly selecting answers in thequestionnaire. The purpose of its inclusion isto ensure the validity of the answers.

9 and 10 These questions measure subjects' partyidentifications. Their wording is taken directly

41

from ANES questions V000523 andV023037. They are included in order toidentify if party ID is responsible for theanswers provided throughout thequestionnaire.

Appendix B

1. Rationale for selection of method of analysis:

There are several potential strategies I could employ to determine whether or not there is

a statistically significant relationship between the rhetoric a subject received and their indication

about their likelihood of voting. The two most relevant strategies are Multivariate Analysis of

Variation (MANOVA) and Ordinary Least Squares (OLS) regression analysis. I chose to, first,

conduct OLS regression analysis because it is more sensitive to variation within variables.

MANOVA provides more conservative estimates. It is highly unlikely that I find no relationship

using OLS, while finding a relationship using MANOVA. Given that I found no relationship in

OLS, I did not conduct any further MANOVA analysis.