Econometric Analysis

WHAT FACTORS
CONTRIBUTE TO THE
LIKELIHOOD OF VOTER
TURNOUT IN
NETHERLANDS AND
PORTUGAL?
Matthew Bittle
13833677
Abstract
An analyse that presents research behind the determinants of voting, and relates
discovered variables to Netherlands and Portugal’s. By indicating what variables are
most relevant in causing voter turnout through research, variables from the 2012
European Social Survey data set are able to be chosen to formulate a regression.
From here, each variables affect and significance can be analysed from the
regression model. A logit model is chosen as the regression method in order to
formulate odds ratios. The model is tested for multicollinearity and heteroscedasticity
with results being justified. Conclusions find that the majority of variables follow the
same trend as the researched suggested, with those that do not being explained why.
Bias factors are highlighted and recommendations are given.

Matthew Bittle 13833677
1
TABLE OF CONTENTS
Table of Contents .....................................................................................................1
Introduction...............................................................................................................2
Literature review .......................................................................................................2
Data ...........................................................................................................................5
Methodology ..........................................................................................................10
Discussion of Empirical Results..............................................................................10
Conclusion ..............................................................................................................13
Appendix ................................................................................................................14
References ..............................................................................................................19

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INTRODUCTION
Voting rights are what some countries in the model just do not have. Often taken for granted, being
able to have a say on what passes as law and who is in charge of their countries, Netherlands and
Portugal are no exception. As a part of a developed society, Netherlands’ and Portugal’s political
system means that politicians need to promote themselves in order to gain the votes to win elections.
Because of this, one must ask, what causes people to turn out to vote? If factors can be proven to
have an impact on voter turnouts, it would be worthwhile for politicians and political parties to take into
consideration when formulating political campaigns. Knowing what factors cause people to go out and
vote means political parties can craft policies to cater to the needs of voters, resulting in increased
votes and higher probabilities of electoral wins.
To know what variables are of highest significance to voter turnout, research will first be conducted to
give an outline on what others have depicted as most significant to voter turnout. From here,
previously used variables will be used to formulate a logit model regression with the dependent
variable being whether or not the respondent voted. The model formulated will be tested for
heteroscedasticity and multicolinearity by a number of diagnostic tests. Results will then be analysed,
accompanied by an overall discussion of the regression, what tools were used to counter any
heteroscedasticity and what bias factors exist behind the model. Lastly, for the purposes of further
research into this topic, several recommendations will be provided that look into what would have
made this model better.
LITERATURE REVIEW
In order to correctly specify which explanatory variables are most likely to affect an individual’s
Propensity to vote, a review of the appropriate theoretical empirical literature must be conducted.
What this will achieve is a solid understanding of what others have done around this topic and provide
a benchmark to what variables will be included in the regression.
As reviewed by Harder and Krosnick (2008), the analysis conducted by Downs (1957) illustrated a
framework to be used in order to determine an individual’s likelihood to vote. Simply put:
R= (B)(P) – C + D
Whereby, R is the total reward gain from voting, B is benefit one receives from having their preferred
candidate win, P is an individual’s perception of the probability that their vote will matter, C is the cost
to the individual by voting and D is one’s psychic satisfaction gained from voting. We can assume
from this model that the higher the total reward gain from voting, the higher chance that that individual
will cast a vote. However, is this all that affects an individual’s likelihood to vote? Simply, no it is not.

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Although a good, rational starting framework, further literature analysis must be conducted to
strengthen the model that will be proposed further on in this report.
After depicting this model, it would be wise to illustrate the cost to the individual by voting. Therefore,
a question was asked, does an individuals’ wealth depict voting probability? As discussed by Filer,
Kenny and Morton (1993) as family income rises, voter turnout is said to fall then rise, and a negative
relationship exists between voter turnout and real wages (i.e. voter turnout increases as real wages
decrease). This is supported by Charles and Stephens (2013), as they found that “higher local wages”
lower turnouts of certain elections in the United States. Given this finding, a wealth type variable of
individuals should be included in the regression model.
From here, Matsusaka and Palda (1999) gave an overview about the main determinants they found
for voter turnout. They found that age, education and campaign expenditure were the key significant
variables attributed to voter turnout. Those who are older and have a higher level of education raise
the likelihood of voting. Also discussed were the limitations of the model and the impact of variables
that cannot be added to the model, for example the “sense of citizen duty” and “time-stationary
variables” (Matsusaka & Palda, 1999, pg 432). These issues, amongst others, are important to note
as knowing what others have identified can help structure tests and regressions. What we can draw
from this research is that there is an affect of age, education and campaign expenditure on voter
turnouts, all of which should be considered for inclusion in the regression.
Another depiction came from Filer, Kenny and Morton (1993), which stated higher turnouts of voters
will occur as education increases, elections are tight between candidates, and as taxes and literacy
tests associated with voting are removed. Much can be said for taxes associated with voting, as this
lowers the cost to the individual and as seen from the Downs (1957) model, the lower the cost, the
total reward gained from voting increases, leading to an increase in an individual’s likelihood to vote.
Given this finding, education will be a key explanatory variable to be used. For the same reason, if a
voting tax variable is available, that should also be included.
From Charles and Stephens (2013), the effect of employment on individual turnout was also
discussed. The overall impact on employment on voter turnout had a negative effect, whereby as
employment increases the voter turnout decreases (from given elections in the United States). There
are scarce articles that confirm this finding, meaning if an employment based variable were to be
included, it would need to be carefully analyzed and interpreted as there is little research to confirm
the findings of Charles and Stephens (2013). However, the work done by Charles and Stephens
(2013) does warrant the potential inclusion of an employment variable.
Furthermore, for an individual to vote, they have to be assured that politicians will stand by what they
have said and the best wishes of the individual will be recognized. Therefore, one must have a certain
level of trust in the political system. As explored by Grönlund and Setälä (2007), it is fair to say that
political trust increases voter turnout. However, trusting the political process is one thing, but voters
must also be interested in the political system. As highlighted by Henry (2003) non-voters will exhibit

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low interest and little knowledge in the political system, therefore becoming less likely to vote. Hence
why the inclusion of a political trust and interest variable should by considered.
Additionally, studies such as Plutzer and Wiefek (2006)’s identify that, in the long term, those who are
married have a higher tendency to vote than those who are single. Due to this, marital status would
be a significant variable to include in the model.
As a result of the research behind life satisfaction and voter turnout, it would be wise to include a
‘happiness’ variable into the model. Work done in the past has indicated a positive correlation
between voter turnout and life satisfaction, as stated by Flavin and Keane (2012).
Another common trend behind the analysis of voter turnout is gender. With many researches using
gender as an influencer on voter turnout, including gender in the model should be significant. With
Brooks (2010) concluding a negative relationship, with men voting less than women (because men
are more influenced by messages), we can expect that the model produced further on will align with
this conclusion. However, given the difference in countries and economic conditions, this is only a
guideline, not a proven assumption. None the less, research is strong enough to take the idea of
gender further.
Furthermore, Olsen (1972) conducted an analysis behind the correlation between individual’s
involvement in social organisation, such as religion and voting turnouts. This review concluded that
individual’s who are involved in social organisations will be more likely to turn up to vote. This aligns
with the Downs (1957) model, as it just defines one of the variables that could influence the psychic
satisfaction of having that individuals preferred candidate win. Similarly, Knack (2000) found that
“socially cooperative behaviours” heightened the likelihood of individuals to vote, these included
responding to the census and donating to charities. What can be concluded from these articles is that
the more that individuals are involved in their given communities the higher the probability that they
will vote. From these findings, a social organisation/religious involvement variable would be wise to
include in the final regression.
After analysis on the appropriate theoretical literature, a pattern has arisen surrounding the variables
that are most predominant around voting. These include, age, education, income, employment, tax,
religious status, political trust, political interest, social involvement, marital status, life satisfaction and
gender. When conducting the empirical analysis to identify the factors associated with an individual’s
likelihood to vote, these factors will need to be in the mix. However, a key observation is that some of
these variables such as age, education, income and employment, appeared more often in reviews
than others, therefore the significance of all variables, but especially those which did not appear as
often, will need to be observed and interpreted.

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DATA
After researching what others have discovered what was most relevant in depicting voter turnouts, the
analysis of what causes voter turnouts in Portugal and Netherlands can be conducted. To keep the
model as concise as possible, but yet still not too vague, a total of 12 variables were chosen.
Individual variables are as follows:
Variable (ADD
name)
Description Descriptive Statistics Motivation
Age
Age*age
Respondents who are 18
years and over.
As the respondents age
increases by one, the
impact on voting will
increase by the size of the
age coefficient.
Squared to make non-
linear.
Observations: 1959
Mean: 2919.348 (54)
Std. Dev: 2157.48
Min: 324
Max: 9409
Age was a common
characteristic of voter
turnout when examining
what others had done to
predict the determinants
of voter turnout,
especially by Matsusaka
and Palda (1999). It is
important to note that the
age variable has been
squared to become non-
linear as research has
shown elderly citizens
become unable to travel
to and from casting their
votes. Resulting in
skewed results, squaring
the residuals corrects the
issue.
Income
(Low_income)
(Med_income)
Remove those
respondents who
answered don’t know and
refusal and create 2
dummy variables.
Separate respondents
into low, medium and high
household incomes, whilst
only incorporating low and
medium type variables
into the model. Do not
include high income group
Low_income
Observations: 1965
Mean: 0.33944
Std. Dev: 0.47364
Min: 0
Max: 1
Med_income
Observations: 1965
Mean: 0.23919
Supported by Filer, Kenny
and Morton (1993), as
income rises voter turnout
is said to fall then rise.
Income was also another
common variable among
many papers predicting
voter turnout.

6
because including all
variables will result in the
variables being omitted
because of collinearity.
Low_income= 1
st
-3
rd
decile
Med_income= 4
th
-7
th
decile
The remaining deciles are
used as the reference
group (high income).
Std. Dev: 0.4267
Min: 0
Max: 1
Education
(Low_edu)
(Med_edu)
Remove those
respondents who
refusal and create 2
dummy variables.
Separate respondents
into those who have low,
medium and highlevels of
education.
Low_edu= 0-313
Med_edu= 321-520
520 and above were used
as the reference variable
(high_edu)
Low_edu
Observations: 1965
Mean: 0.68906
Std. Dev: 0.462997
Min: 0
Max: 1
Med_edu
Observations: 1965
Mean: 0.13435
Std. Dev: 0.341116
Min: 0
Max: 1
Matsusaka and Palda
(1999) provided the main
motivation to include
education into the model,
as it was found that
higher education raises
the probability to vote.
Education was also a
common variable included
in most research papers.
Employment
(unemp)
Remove those
respondents who
refusal and create a
dummy variable.
Respondents were then
grouped into those who
are employed (1) or who
have not been employed
in the last 7 days but are
Observations: 1965
Mean: 0.09262
Std. Dev: 0.28997
Min: 0
Max: 1
Motivation provided from
Charles and Stephens
(2013), where it was
found that employment
has a negative
relationship to voter
turnout (employment
increases leads to a voter
turnout decrease).
Employment was also
included because it

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actively looking for work
(0).
appeared in many papers
analyzing the effects on
voter turnout.
Religious
Status
(religious)
If the respondent is a
member or a religion or
not (dummy variable).
Again, those who
answered don’t know or
refusal were not included
in the variable.
0=not religious
1=religious
Observations: 1959
Mean: 0.56815
Std. Dev: 0.49546
Min: 0
Max: 1
Included because of the
research done by Olsen,
(1972), where religion
was found to have a
positive connotation to
voting.
Political Trust
(Trust)
Remove those
respondents who
refusal.
The variable measures
the level of trust that the
respondent has in the
political system.
Trust was set as a scale,
whereby as the variable
increases by one, the
probability of voting
increases by the
coefficient size.
Observations: 1925
Mean: 3.74026
Std. Dev: 2.5393
Min: 0
Max: 10
As discussed in the
literature review, there
appears to be positive
relationship between trust
and voter turnouts e.g.
Grönlund and Setälä
(2007) found that high
trust in the political
system leads to a higher
voter turnout.
Political
Interest
(interest)
Remove those
respondents who
refusal.
Simply those who are
interested in politics on a
scale from 1-4. 1 being
very interested and 4
being not interested at all.
Observations: 1963
Mean: 2.7733
Std. Dev: 0.96897
Min: 1
Max: 4
Stemming from political
trust, political interest was
also said to have a
positive link to voter
turnouts (Henry, 2003).
Therefore, both trust and
interest in politics were
included.

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Charity
involvement
(charity)
Remove those
respondents who
refusal.
Made up of those who
answered to the question:
“work for voluntary or
charitable organisations,
how often in the past 12
months”. The answers act
as a scale, whereby as
you increase your level of
involvement in charity
work, it raises the
probability of voting. 1
being per week- 4 being
never.
Observations: 1965
Mean: 4.9043
Std. Dev: 1.7326
Min: 1
Max: 6
Similar to those who are
involved in religious
groups, charity
involvement is also said
to have a positive link to
voter turnout (Olsen,
1972). Included as a
reinforcement to religion
to strength the overall
model.
Marital Status
(married)
Remove those
respondents who
refusal.
Create dummy variable:
1= legally married or in
legally registered civil
union
0=other (legally
separated, legally
divorced, civil union
dissolved, widowed/civil
partner died, none of
these)
Observations: 1965
Mean: 0.04936
Std. Dev: 0.21668
Min: 0
Max: 1
Included because those
who are married are said
to have a higher tendency
to vote over time (Plutzer
& Wiefek, 2006), as
discussed in the literature
review.

9
Life
satisfaction
(Life_satis)
Remove those
respondents who
refusal.
From here a scale exists
whereby as satisfaction
increases by one unit the
probability to vote
increases by the value of
the satisfaction
coefficient.
Observations: 1965
Mean: 6.53028
Std. Dev: 2.1725
Min: 0
Max: 10
Added to the model to
due to the research into
how life satisfaction has a
positive correlation to
voter turnout (Flavin &
Keane, 2012).
Gender
(Gender)
Separated gender into
two groups male and
female (dummy variable).
Male=0
Female=1
Observations: 1956
Mean: 0.61323
Std. Dev: 0.48713
Min: 0
Max: 1
Because of the occurance
in many research papers,
especially by Brooks
(2010), gender was an
obvious variable to
include into the model.
Free and Fair
elections
(Freefair)
Produced by taking the
“fairelc” variable and
removing respondents
who answered don’t know
or refused to answer.
Observations: 1930
Mean: 8.67617
Std. Dev: 1.7042
Min: 0
Max: 10
The Free and Fair
elections question was
chosen as it was the
closest match to the
research done behind
taxes of voting in the
literature review i.e. if
voting is tax free, people
are more likely to vote
(Filer, Kenny, & Morton,
1993).

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METHODOLOGY
When regressing a model that has a dependent variable of equalling 0 or 1, the probit or logit model is
used. Both these regression models are unique in the way that they assume that a dependent and
continuous variable (Z) is influenced by a number of independent variables (X1, X2, X3 etc.) and an
error term. The concept of the Z value is such that it cannot be monitored or observed, therefore a Y
value is used and is set to equal 0 or 1, resulting in a probability distribution of Z. If Y=0, then the
probability of Z will be less than or equal to 0, but if Y=1, then the probability of Z will be greater than
0.
In the case of voting, respondents either voted (Y=1) or did not vote (Y=0), and were influenced by
factors such as age, income, education etc. Therefore, a choice existed between either choosing the
logit model or the probit model to regress the variables against voting. The logit model was selected
because of the ability to produce and interpret odds ratios, which is a method of analysing the
probability of an independent variable affecting the outcome (voting).
Once the variables were regressed with the logit model, several diagnostic tests were run to ensure
its accuracy. Tests conducted were to measure correlation between independent variables,
multicollinearity and heteroscedasticity. These tests included the collinearity test, regressing squared
values and the link test. The first test that was done was a visual test of a correlation matrix. Values
were simply correlated with one another and observed. To support this was the collinearity test,
whereby the variance inflation factor of each variable was calculated. The results of which simply
suggests if variables are impacting the regression because of their correlation to other variables.
When testing for heteroscedasticity, what is being referred to is the error term not being held constant
(not homoscedastic). In the model, there is no time-series data so the only way to test for this is by
squaring the residuals in the data set and plotting them against Y. The final test conducted was the
link test where the regression was tested to indicate whether or not key independent variables had
been omitted.
DISCUSSION OF EMPIRICAL RESULTS
To begin the discussion of the test results, multicollinearity was first analysed. With a Prob>chi2
significance at the 1% level, the predicted model was significant. In order to have a deeper analysis a
correlation matrix was created. As seen in appendix (3), only a few variables were highly correlated
with one another. Trust and interest had a -0.4737 correlation, however this is to be expected as one
who is interested in politics can be expected to have a certain level of trust or distrust in politics.
Similarly, low_income and med_income had a correlation of -0.4092. This correlation can be
explained by those who are not in the low income group fall into the bracket of medium or high
income. Furthermore, the collinearity test was also used to continue the tests for multicollinearity
(appendix 4). With only low_income having a high VIF (2.02), the overall model had a good mean VIF
(1.30). Simply put, this means that the independent variables do not influence one another. Being that
a rule of thumb to dropping a variable is having a VIF between 5 and 10 and low levels of back up

11
research, a low_income VIF of 2.02 did not warrant dropping the variable entirely. This concluded the
tests for multicollinearity.
As previously mentioned, no time-series data exists here, therefore only one test was conducted for
heteroscedasticity. Residuals were squared and plotted against Y (appendix 5). Heteroscedasticity
was observed, as there was a clear trend amongst squared residuals and Y. In order to counter this
heteroscedasticity the “robust” command was added to the regression as a remedial measure
(appendix 6). Lastly, to ensure no relevant variables have been omitted, a linktest was conducted
(appendix 7), which concluded a _hat significant at the 1% level and an insignificant _hatsquared
variable. This confirms that no significant variables were omitted and the model is good.
From appendix (9) we see the regressed model’s odds ratios. Using these odds ratios, the impact of
each variable on the probability of turning out to vote is able to be analysed.
Note: Calculation, if the odds ratio is >1, subtract 1 and x100 to get percentage. If the odds ratio is <1,
take the reciprocal (1/odds ratio) subtract 1 and x100 to get percentage impact on voter turnout.
Overall, the model is significant at the 1% level, with 15.07% of Y being explained by X (appendix 2).
Looking at the impact of each variable on voter turnout, the majority of outputs conclude with the
research done (appendix 9). The only variable that did not follow the same trend as the research
suggested was marital status. Research says that married people have a higher tendency to vote
over time, however the data shows those who are married are 2.69% less likely to vote. This could be
explained by the fact that the research stated marital status “over time”. The regression model here
was only a snapshot of Portugal and Netherlands, not time series. For this reason, marital status will
remain in the model due to the existing research done to support its inclusion.
Before any further discussion, some misleading variable outcomes need to be addressed, specifically
unemployment, interest and charity. These variables do follow the same trend as the research
suggests, however, the outcome at first look does appear to contradict the research. Unemployment
seems to have a negative relationship with voter turnout. However the dummy variable was created
with employment equalling 1 and unemployment equalling 0 (charity has the same depiction).
Therefore, as people become unemployed they are more likely to vote. Interest has the same
problem, with interest appearing to have a negative relationship, it is only because the interest scale
is highest at 1 and lowest and 4. Therefore, interest does conclude with the research by Grönlund &
Setälä (2007).
By assessing the p-values, we are depicting whether or not the variables are significant. Of which
trust, interest, low_edu and age were all significant at the 1% level and life satisfaction was significant
at the 5% level (appendix 2). From here a simply question was proposed, just because the remaining
variables are insignificant, should they be removed from the model? Usually, if a variable was
insignificant it would have no impact on the dependent variable (voting). However, due to the research
done that supports the inclusion of all variables selected, no variables have been removed from the
model. To support the inclusion of all variables, the tests conducted in the methodology found that the

12
model (as a whole) was significant and had no strong multicolinearity. If the model was tested and
found that the variables were correlated to each other or contributed to a high VIF then some
variables would have had to have been analysed, and their inclusion discussed. When testing for
heteroscedasticity, it was observed that there was a clear relationship between the squared residuals
and Y, therefore the regression was rerun with the “robust” command to counter any
heteroscedasticity.
A key variable combination to note was low_edu and med_edu. This is because research analysed
stated that as education increases, the likelihood to vote also increases, of which the variables here
follow that same trend. Low_edu has an extremely high negative effect on voter turnout, however
med_edu has a lower negative effect than low_edu, suggesting that there is a diminishing effect of
education. This means that as education levels rise, the probability of voting increases (aligned with
research).
In order to ensure model fairness, any biases must be explained. Firstly, because the model includes
data from Netherlands and Portugal, one can only use the results of this regression to predict the
impacts on voter turnout in both countries together, not separately. In other words, one cannot
separate the two countries and use these results to depict how variables affect voter turnout in each
country. For individual country analysis to be conducted, a new model needs to be run. Another
problem stemming from analysing Netherlands and Portugal together, is that the economic and
political conditions of both countries are not the same. This means a bias exists where there is not a
consistent respondent base. Thirdly, data variables selected are a result of research done in other
countries. This raises queries behind whether or not the findings are able to be used in regression
models for other countries.
With regards to the regression itself, there is a potential for omitted variable bias. This is characterised
as a situation where key independent variables, which are correlated to the existing variables have
not been included into the model. This results in the error term capturing a key variable that alters the
ordinary least squares (OLS) regression. Although the link test concluded that there were no omitted
significant variables, that is only a basic test and further research could have been done to warrant
the inclusion of another variable and to ensure the best variables were included.
With regards to research done in future of this topic, several recommendations can be made. Firstly,
the need to conduct further academic research. In order to be fully confident in including variables,
one can never have enough research to back up each variable. This is because assumptions will
have to be made regarding a variables significance to the model, e.g. marital status was insignificant
but remained in the model due to research. Therefore, to increase the confidence in variable
selection, ensure model significance and avoid omitted variable bias, more research would help.
Secondly, it would have been beneficial for the model if investigation was done into census data. This
data could be used to correlate how significant the variables researched were to the respondents in
Netherlands and Portugal, further justifying the inclusion of the variables. Finally, comparing the

13
political views of voters between countries would make the model more robust by allowing for
differences in the political beliefs of respondents between the two countries.
CONCLUSION
In conclusion, the research done in the past provided the base for identifying the variables that should
be included in the regression. Variables were then selected based on this research and formatted to
fit into a regression. A logit model was chosen due to its ability to provide odds ratios that could be
interpreted. Through this logit model, most regressed variables followed the same trend that was
expected from the research. Variables religion, political trust, gender, income, life satisfaction, free
and fair elections and age all maintain positive effects on voting turnouts. Unemployment, income,
political interest, charity work and education maintaining negative effects on voter turnouts. However,
those that did not (marital status) remained in the model due to the significance of the research
behind why the variables impacts voting behaviours. Tests of the model searched for signs of
multicollinearity and heteroscedasticity. The regressed model displayed moderate levels of collinearity
but was acceptable due to rules of thumb and reasoning of why collinearity was present.
Heteroscedasticity was observed visually and corrected through a robust command.
A test for omitted variable bias was done and proved that no significant variables were left out of the
model. However, further research could have been conducted to ensure that the best variables were
included that relate specifically to Netherlands and Portugal. Another bias was identified with regards
to the research behind the data. Netherlands and Portugal are two different countries that have
varying beliefs. Being as the two countries were analysed together, there is a bias amongst the data
that produces results on the assumption that the two countries have the same beliefs. For the model
to be free of these, it is recommended that a model be rerun to correct for this, even though biases
will always be present.

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APPENDIX
1) Regression
logit voting religious unemp trust married gender low_income med_income interest charity life_satis
low_edu med_edu freefair age2
2) Regression (with odds ratio)
low_edu med_edu freefair age2, or

15
3) diagnostic test (correlate)
correlate religious unemp trust married gender low_income med_income interest charity life_satis
low_edu med_edu freefair age2
4) Collinearity test

16
5) Heteroscedasticity
6) Regression (with robust)
age2 edu low_edu med_edu, robust

17
7) Link test
8) Descriptive statistics

18
9) Odds ratio interpretation
Variable Odds Ratio Interpretation Same as research
Religious 1.160056 16% more likely to vote Yes
Unemployed 0.7517772 33.02% less likely to
vote
Yes
Trust 1.112628 11.26% more likely to
vote
Yes
Married 0.9738548 2.69% less likely to vote No
Gender 1.077658 7.77% more likely to
vote
Yes
Low_income 0.8728149 14.57% less likely to
vote
Yes
Med_income 1.176565 17.66% more likely to
vote
Yes
Interest 0.5871188 70.32% less likely to
vote
Yes
Charity 0.9400628 6.38% less likely to vote Yes
Life_satis 1.059553 5.96% more likely to
vote
Yes
Low_edu 0.4393347 127.62% less likely to
vote
Yes
Med_edu 0.7861438 27.2% less likely to vote Yes
Freefair 1.017928 1.79% more likely to
vote
Yes
Age2 1.000225 0.023% more likely to
vote
Yes

19
REFERENCES
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american elections. Politics & Gender, 6(3), 319-341.
doi:http://dx.doi.org/10.1017/S1743923X10000218
Charles, K. K., & Stephens, M. (2013). Employment, wages, and voter turnout. American
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