The document analyzes the relationship between political polarization, wealth inequality, voter turnout laws, and voter turnout. Regression models found that political polarization and voter ID laws did not significantly impact turnout, but wealth inequality did have a significant negative effect on turnout. While the study has limitations, it provides initial evidence that increasing wealth inequality, rather than polarization alone, may contribute to decreasing voter participation. Further research is needed to more fully understand the impacts of polarization and inequality on political engagement.

1. Voter Turnout, Wealth Inequality, and the Red Herring called Political Polarization
Jordan Summers Chapman
December 8, 2014
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2. Introduction
Voter turnout during a the 2014 midterm election was reported to be the lowest midterm
election turnout since 1942. The percentage of young people who voted in this election was also
the lowest in recent history. The proposed cause for this phenomena is hyper polarization.
Political elites have become increasingly extreme and many Americans have become frustrated
with a lack of moderate candidates. The argument of this paper is that an increase in political
polarization leads to a decrease in traditional citizen engagement in the political system.
Traditional citizen engagement includes volunteering for campaigns, donating to
candidates and PACs, calling and writing representatives, and voting. The first three of these
forms of engagement could potentially be measured. After collecting data, each criteria of
engagement would have to be combined to create some sort of overall engagement indicator.
Doing so would require more time and resources. Because of this, the hypothesis will be limited
to voter turnout as data is easily accessible. Previous literature has concluded that political
polarization is much more pronounced amongst political elites. The hypothesis tested in this
paper will reflect that and be limited to representatives. The testable hypothesis proposed is that
higher levels of political polarization in state legislatures will cause lower levels in voter turnout
in the state.
Background
Previous literature has determined that political polarization, particularly of individuals
who are highly engaged in the political process, has increased. Several different theories about
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3. the causes and effects of this polarization have been provided. Baldsarri and Gelman (2008)
conducted a comprehensive analysis on the causes of political polarization. In their paper,
“Partisans without Constrain: Political Polarization and Trends in American Public Opinion,”
they explain that the increase in political polarization started in the seventies and point to an
increase in activism as one indicator. However, they point out that an increase in electorate
polarization is illusory. They concluded that elite polarization has increased, and that any
increases in the polarization of the public at large is simply an after effect. In this paper, political
polarization data will be based on members of state legislatures who can safely be considered to
be political elites. Baldsarri and Gelman (2008) also point out that political polarization of elites
has been shown to highly correlate with wealth inequality. They point to wealth inequality as a
potential cause for political polarization. Because of this strong correlation, any model
attempting to understand the effects of political polarization on its own must include wealth
inequality.
Ambramowitz and Sauders (2008) also conclude that political polarization in America
has risen. In their paper, “Is Polarization a Myth?,” they go one step further and conclude that
political polarization mobilizes political action. Pointing to the 2004 presidential election as
their prime example, they explain that highly polarized candidates cause the perceived stakes of
the election to be higher. A more extreme candidate would cause more potential voters to be
motivated enough to go to the polls. However, they do not systematically study the effect of
polarized elites on voters. Instead, this conclusion is based on the individual voter’s polarization.
They found that the more extreme an individual’s feelings towards Bush were, whether negative
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4. or positive, the more likely they were to vote. This anecdote may apply when at least one of the
candidates’ policy positions are somewhat moderate. Their conclusion also does not, however,
attempt to predict or explain the behavior of individuals who are not extreme in their views.
James Adams, Jay Dow, and Samuel Merrill (2006) created a unified model of voter
abstention. In their paper, “The Political Consequences of Alienation-Based and Indifference-
Based Voter Abstention: Applications to Presidential Elections,” they explain that lots of
literature confirms that individuals who are more radical are more likely to vote but point out that
very little is known about why that phenomena exists. They attempt to explain the mechanism
by distinguishing between two reasons for voter abstention. One reason people don’t vote is
apathy, or when an individual simply does not care about the election. The second reason is
alienation from the candidate. This alienation occurs when a candidate exceeds a potential
voter’s tolerance threshold. That is to say, when a candidate’s platform is too far from the voter’s
ideal platform. They conclude that elections with two highly ideologically polarized candidates
alienate the most potential voters. This distinction from apathy is vital to predict voter turnout, as
apathetic voters will not vote regardless of the candidates extremity. The natural conclusion
from this is that voter turnout rates will be lower when candidates are greatly polarized.
The language used by Adams, Dow, and Merrill (2006) articulates the reasoning behind
my theory. Increases in elite polarization will likely decrease voter turnout because more
individuals will be alienated. As politicians become increasingly extreme more people will
decide to not vote, not because they are uninformed or apathetic, but because none of the options
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5. are appealing to the voter. This theory makes several other assumptions that will not be tested in
this paper, but are interesting to consider. Younger voters will be more likely to feel alienated
and choose not to vote. This may be because they are less likely to have a background with
either political party. There is no established history between younger voters and the parties.
Older voters will be more willing to ignore the increased polarization of elites. Increases in elite
polarization may also impact the type of people who run for office. Moderates who might have
political aspirations may choose not to run because they do not feel like they would not get
anything accomplished if the had to work with other representatives who likely will not
compromise. The political radicalization of the parties could turn some citizens off on the idea
of working for the government in any capacity. Although these questions will not be answered in
this paper, they highlight the importance of studying the effects of political polarization.
The consensus in previous literature is that political polarization has risen in America.
Two major points motivated this paper. First, the electorate as a whole is not becoming more
polarized while political elites are. Second, alienation causes potential voters to stay away from
the polls. These observations lead to hypothesis that increases in political polarization amongst
political elites will lead to decreases in voter turnout.
Research Design
In order to test whether or not political polarization has an effect on voter turnout rates,
the Ordinal Least Squares method will be used. This involves estimating the effects of
independent variables on a dependent variables within a model. The unit of analysis will be
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6. states in the United States in the year 2010. This year was primarily chosen out of convenience.
It is the most recent year with readily available data for each variable being tested. This does
limit any findings based on this research to the United States, but because the United States is
unique in having only two major parties, any findings political polarization would be difficult to
compare to different nations regardless. The sample size is twenty four, as not every state had
data available for all three variables in the year 2010.
The model proposed to test this hypothesis includes four variables: voter turnout, political
polarization amongst elites, the strictness of voting identification laws, and wealth inequality. A
bivariate linear regression model was constructed for each independent variable. A fourth
unrestricted multivariate model including all three independent variables was also constructed.
This process allows the individual effects of each independent variable on voter turnout to be
compared to the combined effect
Figure 1:
Voter turnout rates were taken from the United States Elections Project (2012). The
percentage of the voting-eligible population who voted for the highest office. Because the year
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Model 1: T = β0 + β1P + u
Model 2: T = β0 + β1G + u
Model 3: T = β0 + β1V + u
Model 4: T = β0 + β1P + β2G + β3V + u

7. chosen is 2010, the highest office for each state is likely to either be state Governor or United
States Senator. The voting eligible population is determined by applying some constraints to the
voting age population. Some of these constraints include the percentage of the population that is
composed of non-citizens, the number of ineligible felons, and the number of eligible overseas
citizens. This percentage will be used as the dependent variable and is represented in the model
as “T”. Theoretically, voter turnout could not possibly cause wealth inequality. It could possibly
impact the voter identification laws that are passed. Low voter turnout might also encourage
candidates to become more extreme as they appeal to smaller party bases that tend to be more
polarized than the general public on key issues. This is not accounted for in this model, but is
important to consider.
Political polarization will be the first independent variable in the model. As mentioned in
the background section, the polarization measured will be of elites. Data on political polarization
is taken from research conducted by Boris Shor and Nolan McCarty (2014). Polarization is
determined by calculating the difference in the ideological means of each party. The
measurement is based upon roll call votes in state legislatures and reoccurring polls of
representatives. Each representative is assigned a ideological score based on how they voted and
answered questions on key party issues. These might include healthcare, abortion, and
government spending. The scores are then averaged for each party and the difference between
the two is calculated. Polarization is represented in the model by “P.” There were many missing
values on polarization for some states. For instance, Nebraska didn’t have any data available for
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8. any year. This may cause an incomplete picture to be drawn. Nonetheless, this is the data that is
available and used.
Several studies, including Baldsarri and Gelman (2008) and McCarty, Poole, and
Rosenthal (2003), conclude that wealth inequality correlates strongly with polarization. This
variable is being included in the model largely to rule out any potential parsimony. There are,
however, some theoretical reasons wealth inequality could itself lead to varying turnout rates.
Wealth inequality shrinks the middle class, leaving many in lower income brackets which are
associated with lower voting rates. Higher wealth inequality could lead many to believe that
only the interests of the affluent and rich are represented in government, another reason
individuals might become frustrated with the system and opt out. The combined effect with voter
identification laws could also be important. With greater portions of the population earning
lower levels of income less people may be willing to get the proper identification to vote.
Wealth inequality can be measured a number of ways. For this paper the Gini-coefficient will be
used. The coefficient ranges from zero, complete inequality, to one, complete equality. The
index is based on the Lorenz curve, a graphical representation of the distribution of income
(Beggs). After the index is calculated, it is divided by one hundred to attain a coefficient. This
set of Gini-coefficient values was taken from census.gov. At 0.0004364235, the variance for the
Gini-coefficient amongst the states in 2010 appears to be really low. Wealth inequality is
indicated by “G.” I do not expect the wealth inequality to substantially impact voter turn out
because there is very low variance across units.
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9. Voter identification laws began to sweep across the nation after the Help America Vote
Act passed in 2002. The law did not require photo identification. It only required states to verify
the identity of first time voters who register by mail. Nonetheless, the bill brought national
attention to the idea of voter identification laws and inspired many state legislatures to start
implementing them. The legitimacy of voter identification laws has been widely debated.
Defenders of the laws claim they prevent fraudulent voting while critics argue that they do not
deter fraudulent voting and place unnecessary burdens on voters. In the year 2010, eighteen
states had some sort of voter identification law. The ease of voting likely affects voter turnout.
The probability of an individual voting has long been thought to be the expected pay-off of
voting minus the cost of voting. As identification laws increase the potential cost of voting more
individuals may choose not to. To account for this, a variable will be included in the model and is
notated by “V”. Meyer (2013) compiled a list of each state, what type of identification laws they
have, and when they were enacted. In this model, states were assigned a number 0 - 4. Table 1
below shows what each value represents. The categories are based on the language Meyer
(2013) provides. Ideally other indicators of the ease of voting would be included as well. The
inclusion of the ratings of other voting regulations such as when voters have to register by,
whether or not early voting is available during non-business hours, and whether communities
provide transportation to polling places for voters who may be in need.
Ideally, the political competitiveness of each state would be included in the model.
Acquiring such data in an absolute form and not a ranking proved to be difficult. Ballotpedia
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10. Table 1:
outlines a method of using three criteria to calculate scores, but only released how each state
stacked up against each other. Another possible measure of competitiveness is the Raney Index.
Holbrook, Thomas, & Van Dunk (1993) explain that, “The Ranney index takes into account the
proportion of seats won in the state House and Senate election, the Democratic percentage in the
gubernatorial election, and the percentage of the time the governorship and state legislature were
controlled by the Democratic party,” and averages the factors over a period of time (Holbrook,
Thomas, & Van Dunk, 955-954). The index ranges from zero, total Republican control, to one,
total Democratic control. This is likely to be the best available indicator of political
competitiveness by state. However, I could not find available data sets with such information
that was specific for one year. Moving averages were available from 1924 to 2014. Future
research might calculate the Ranney index and include it in a model. The demography of a state
might also affect voter turnout. States with a higher percentage of high income and highly
educated citizens might see bigger turnout rates. Further research is needed to determine if these
two variables mitigate the affect of the variables included in the model.
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Voter ID Variable Description
0 No Voter ID Law
1 Non-Strict, Non-Photo ID Law
2 Strict Non-Photo ID Law
3 Photo ID Law
4 Strict Photo ID Law

11. Empirical Results
The results of each linear regression are listed in Table 2 below. Models 1 through 3
showed polarization, wealth inequality, and voter identification laws all as having no significant
effect. Model 4 showed that political polarization and voter identification laws had no
significant effect on voter turnout. This has interesting implications on the legitimacy of voter
identification laws. If they do not keep people from voting The Gini-coefficient does however
have a significant negative impact on the percentage of eligible voters who turned out to vote.
As wealth inequality increases, the voter turnout rate decreases.
Table 2:
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Model 1 Model 2 Model 3 Model 4
Political
Polarization
1.864
(3.989)
-
-
-
-
0.711
(3.857)
Gini (Wealth
Inequality)
-
-
-36.54
(41.20)
-
-
-129.73*
(62.855)
Voter ID Law -
-
-
-
-0.465
(0.740)
0.2881
(1.106)
*Statistically Significant

12. Political polarization in elites might simply be a reflection of wealth inequality. The
mechanism linking wealth inequality to turnout rates is still unclear and ought to be looked into
further.
Conclusion
Political polarization did not have a significant effect on voter turnout in this study.
Perhaps this is truly the case, but it is possible this study did not adequately capture the
relationship. Two other types of analysis could provide insight. Public opinion data could be
collected to determine the reasons people choose to either vote or not to vote. A national study
comparing polarization in the federal legislature over time might also reveal trends not captured
in this study. A study utilizing panel data of each state over time might also reveal some
connections. All of these other approaches might also reconfirm that political polarization in
elites has no impact on voter turnout. Individuals who are more likely to be alienated by
candidates might be less likely to vote under any condition. This comports with Ambramowitz
and Sauders (2008) conclusion that states that individuals who are more extreme in political
views are more likely to vote in general.
Including demography data and political competitiveness might mitigate the impact of
wealth inequality on voter turnout, or could very well produce a greater combined effect. Studies
that include these variables might produce more insight.
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13. This analysis also only includes a single point in time. There might have been a
historical event that had nationwide impacts that could impact some of the variables in the
model. A study that is conducted over a number of years will be necessary to confirm the results
from the regressions.
Further research is needed to fully understand the effects of both wealth inequality and
political polarization. Both of these topics have important implications for government. If the
moderate citizen is alienated by either the political climate or wealth inequality, the types of
people who choose public service might become more extreme. More research is also necessary
to confirm that wealth inequality lowers voter turnout.
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14. Sources
Adams, Jay, & Merrill. 2006. “The Political Consequences of Alienation-Based and
Indifference-Based Voter Abstention.” Political Behavior 28 (March): 65-86.
Baldassarri, Delia, and Andrew Gelman. 2008. ”Partisans without Constraint: Political
Polarization and Trends in American Public Opinion." American Journal of Sociology
114.2: 408-446.
Ballotpedia, An Interactive Almanac of U.S. Politics. “A ‘Competitiveness Index’ for Capturing
Competitiveness in State Legislative Election.” _Index%22_for_capturing_competitiven
ess_in_state_legislative_elections (December 6, 2014).
Holbrook, Thomas, & Van Dunk, Emily. 1993. “Electoral Competition in the American States.”
American Political Science Review 87 (December): 955-962.
McCarty, Nolan, Keith T. Poole, and Howard Rosenthal. "Political polarization and income
inequality." Unpublished paper (2003).
Measuring American Legislatures. (2014). “Aggregate Data.” http://americanlegislatures.com/
data/ (December 4, 2014).
Meyer, Elizabeth. 2013. “Show Me Your ID: A Look at Voter ID Laws and the Effect on Voter
Turnout.” Working Paper Hartwick College.
Rappeport, Alan. 2014. “Midterm Turnout Lowest Since 1942.” The New York Times,
November 6. http://www.nytimes.com/politics/first-draft/2014/11/06/?
entry=5253&_php=true&_type=blogs (November 6, 2014).
United States Census Bureau. 2010. “Gini Index of Income Inequality.” http://factfinder2.
census.gov/faces/tableservices/jsf/pages/productview.xhtmlpid=ACS_10_1YR_B1908
3&prodType=table (December 4, 2014).
United States Election Project. 2012. “2010 November General Election Turnout Rates.” http://
www.electproject.org/2010g (December 4, 2014).
World Bank, The. 2011. “Measuring Inequality.” http://web.worldbank.org/WBSITE/
EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20238991~menuPK:
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