mitchell_186_final paper copy

Alec Mitchell
Poli Sci 186 – Professor Nils Ringe
12/14/13
The Real Relationship Between Weekend Voting and Turnout
At the very foundation of democracy, citizen participation is essential to ensuring a
strong civil society, effective representation, and healthy democratic ideals. Yet voter turnout,
the basic method of citizen participation, differs greatly from country to country. Among the
complex factors that determine turnout, the day of the week on which an election is held has
been theorized to have significant effects. From 2000-2012, two thirds of democratic elections
around the world were held on a Saturday or Sunday, election days generally thought to increase
voter turnout (International Institute for Democracy and Electoral Assistance 2012; hereafter
IDEA 2012). This, however, still leaves one third of all democratic elections over a 13-year
period to be held during the week, begging an answer to whether weekend voting truly does
increase voter turnout.
Most current literature on voter turnout passively addresses weekend voting, including it
in the list of explanatory variables or simply assuming that weekend voting increases turnout.
Indeed, almost all empirical studies that include weekend voting find a statistically significant
correlation between holding elections on the weekend and increased voter turnout. A 2002 study
finds a 5.6% increase in a 25-country model and a 6.8% increase in a 31-country model (Franklin
2002). A study the following year finds an even higher effect, with an 11% difference between
weekend and weekday elections in 14 countries (Mattila 2003). A 12-country model in a 1997
study finds the effect to be a 15.9% difference (Blondel, Sinnott, and Svensson 1997; hereafter
Blondel 1997). These three major studies show that while there seems to be a general consensus

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that weekend voting increases voter turnout, the actual magnitude is not truly known. Generally,
papers on voter turnout that discuss weekend voting cite the Franklin (2002) study, which
predicts a 5-6% increase; therefore, this is the consensus I will build off of.
Studies that include all democratic elections worldwide are difficult to find, as many only
focus on a particular country or region. Each of the models described above are restricted to
certain sets of countries. The two Franklin (2002) studies are the most inclusive weekend voting
models, however they still restrict many democratic elections that could potentially affect
weekend voting relationship. The Mattila (2003) and Blondel (1997) studies are even more
restricted and are both focused on European elections. This mimics the overall trend seen in
turnout models: most studies focus on Europe or select an arbitrary set of countries. In their
defense, many studies use election statistics that date back many decades, and reliable data may
not be available for certain countries. Even so, this case selection has the definite possibility of
affecting their findings.
Studies that do include weekend voting in their analysis assume that holding elections on
the weekend increases total turnout itself, failing to account for possible interactions with other
variables. Most studies say something along the lines of Mattila (2003), who hypothesizes that,
“On weekdays, people are at work, studying, or following their other daily routines, and taking
time to go to polls is more costly than during weekends.” However, no papers actually test this
hypothesis using regressions, and only one (the Blondel study), uses empirical data to try and
explain the differences in cost of voting between weekday and weekend elections. As such, the
typical weekend voting hypothesis is just that, having not been tested to see why weekend voting
supposedly increases turnout.

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Very few studies point to possible problems with the weekend voting models. One
exception is the Blondel (1997) paper, which digs the deepest into the possible effects of
weekend voting. After analyzing individual level data on why certain people abstain from voting,
he finds that Sunday elections present a much different set of abstention reasons than weekday
voting. He explains that, “…Sunday voting also brings with it its own inhibiting factors – the
probability that significant numbers of voters will be otherwise engaged or away from home for
the weekend or even just for the day and, as a result, will not be able to vote” (Blondel 1997). In
his opinion, “…the generally held belief that Sunday voting facilitates turnout while weekday
voting inhibits it is too simple” (Blondel 1997). It is both interesting and impressive that Blondel
goes into such depth for his analysis. First of all, his regressions point to an extremely significant
15.9% increase in voter turnout for weekend voting countries (Blondel 1997). He is essentially
questioning his own results, possibly because he sees the shortcomings of restricting his study to
a 12-country European model. Even so, his analysis of weekend elections digs deeper than any
other paper and uses voter interviews to bring up questions about the supposed effect of weekend
voting. Additionally, one paper finds issues with Franklin’s commonly cited weekend voting
data. While the cross-sectional data shows a 5-6% increase in voter turnout for weekend
elections, “…these same variables proved incapable of predicting changes in turnout over time”
(Blais 2006). Franklin himself discusses this problem, stating that, “Evidently countries that
move to or from Sunday voting do not thereby clearly increase or reduce their turnout, as might
have been expected from the cross-sectional findings” (Franklin 2002). However, he does not go
in depth to explain this finding, and his 5-6% weekend voting increase model remains widely
accepted.

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As a result of these differing studies, the debate over weekend voting and its effect on
voter turnout is still not well understood. Almost all papers find that weekend voting increases
voter turnout, with the consensus seeming to agree on a 5-6% increase. Some red flags have been
raised, though they have not definitively been tested. The reasons weekend voting affects voter
turnout are likewise highly speculative. I could find no interaction studies that sought to examine
why weekend voting impacts voter turnout.
Theory
As a result of certain shortcomings in previous studies, my election case selection is
widely inclusive and seeks to analyze all democratic elections instead of a select few. The
analysis focuses both on how weekend voting affects turnout and on what factors influence
weekend voting. This falls into four parts: modeling turnout based on weekend voting, finding
differences across groups, differences across democracies, and indicator interactions. Each of
these analyses will add to the bigger picture on weekend voting, which together allow me to
develop a clearer idea of why weekend voting impacts turnout in the way it does.
Analyzing the Impactof Weekend Voting
At the very basic level, I believe that weekend voting does provide a significant impact
on turnout. During the typical workweek (Monday-Friday), the majority of voting age citizens
are likely to be working the traditional 9-5 job. While some countries may offer protections for
missing work to vote, the added hassle of dealing with time off work and the difficulty of
effectively enforcing these laws increases individual level costs of voting, causing many workers
to abstain. By holding elections on the weekend, a country likely misses the primary working
hours of its voting age population. Weekend voting could have its consequences as well, with
citizens more likely to be on vacation, observing religious rest days, or simply staying home after

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a long week. With these issues in mind, weekend voting still seems to be a better option for
higher turnout, as workday complications are more likely to add to the cost of voting and deter
potential voters.
Simply looking at weekend voting versus turnout will surely cause some omitted variable
bias. The most obvious example is compulsory voting, which Fowler (2013) shows to increase
turnout by an estimated 24%, Jackman (1987) by 22.2%, Blondel (1997) by 19.2%, and Franklin
(2002) by 7.4%. A challenge with compulsory voting is that many countries have such laws on
the books, but only a fraction actually enforce them. Thankfully, pinpointing the countries that
enforce the laws is relatively easy.
Additional variables may impact voter participation. Proportional representation has been
shown to have a positive effect on turnout across multiple studies. Some papers measure this
effect by accounting for disproportionality. Franklin (2002) finds that for every percentage point
a legislature is disproportional to its true percentage of votes, turnout decreases by .57%, while
Jackman (1987) finds it a bit higher at .79%. Lijphart (1997) estimates that simply having a
proportional representation system increases turnout by around 9%.
Levels of democracy in a country can certainly affect voter’s attitudes towards
participation. While all countries included in this analysis are democratic, they do vary within
that category. Stronger democracies will, intuitively, have more reliable and competitive
elections. As such, including the polity score as an explanatory variable will likely return
significant results. While the polity score is not directly related to elections, it makes sense to see
stronger democracies return higher turnout rates.
When looking at election structure, there are major differences between the general and
legislative elections: the general decides both the head of government and legislature while the

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legislative only decides the legislature. With more at stake in general elections, it is reasonably
assumed that such elections see higher turnout rates than legislative elections. The best example
of such a situation is the United States, which sees about a 20% difference between general
elections and midterm elections (IDEA 2012). As such, it seems important to include an
executive dummy as an explanatory variable.
Effective number of legislative parties is a likely driver of turnout, based on a voter’s
chance to vote sincerely. In countries with few effective parties (ex.- Jamaica at 1.95), voters will
likely choose strategic voting over sincere voting because a vote for a minority candidate or
party would be wasted. This could deter voters from the polls if they feel little allegiance to the
dominant parties. Seemingly, countries with lower numbers of effective legislative parties should
see lower turnout rates. Lastly, the prevalence of elections will influence how excited citizens
will be to vote. Voter apathy is likely to increase when elections are held more frequently,
causing voters to abstain either because of a tiredness of voting or distrust in a legislature that
changes so frequently. As such, it would be expected that the higher the number of elections, the
lower the turnout.
Differences Across Democracies
As mentioned in the first theory section, higher polity scores are expected to increase
turnout. Their effect on weekend voting, however, is most likely different. Polity scores are
calculated with respect to the institutional characteristics of a country, not the demographical
characteristics that should influence weekend voting. When my turnout model is restricted to
certain polity scores, there should be little to no change in the weekend voting coefficient.
Additionally, an interaction term between polity score and weekend voting should return little to
no difference in weekend voting among polity scores.

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Differences Across Groups
In determining what affects weekend voting, certain groups of countries are likely to have
more impact than others. When looking at country size as a function of total population, I do not
expect to see much difference. My main argument for weekend voting’s impact focuses on the
economic and demographic aspects of each country. Population size can influence such factors,
yet there are many examples of both large and small countries that are rich or poor,
demographically heterogeneous or homogenous, and economically diverse or uniform. As such,
when restricting my turnout regression based on population size, there should be little difference
in the impact of weekend voting between small and large countries. Along the same lines,
interaction models should show little to no interaction between weekend voting and population.
Economic size of a country, however, will surely be a different story. Both total GDP and
GDP per capita are useful indicators of a country’s economic status. When restricting models by
GDP or GDP per capita, I expect to see a higher positive impact of weekend voting for richer
countries. While not always true, richer countries will tend to have a more traditional workforce
and more stable demographic statistics, both of which have been hypothesized as impacting
weekend voting. Similarly, when taking into account interactions between GDP or GDP per
capita on weekend voting, there should be a positive trend favoring richer countries.
The last useful group to analyze will be country samples used by previous research.
Earlier in this paper I mentioned four major models that include weekend voting in their analysis
of turnout. For each of these studies, the sample size is restricted to some selection of countries,
whether by region, data availability, or personal choice. By restricting my election sample to the
countries included in each of these models, I will hopefully be able to mimic their results. A
potential problem arises in the fact that each model uses elections from before 2000, while my

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elections are between 2000 and 2012. However, I expect trends in country turnout to stay
roughly the same over time, so my modern models should closely resemble the previous four.
Indicator Interactions
My last section will deal with the effects of certain labor and demographic indicator
interactions with weekend voting. I found five specific indicators with complete and reliable data
to test these interactions. Starting with labor indicators, unemployment stands out as a perfect
example for testing the theory that missing work increases the cost of voting on a weekday. The
interaction term in an unemployment model should come out as slightly negative, since higher
unemployment rates means less people in work, and a smaller average cost to voting on a
weekday. Another indicator, labor participation rate, measures a similar effect. With greater
labor participation, the cost of weekday voting for the average citizen should increase since more
people are likely to be working. As such, the interaction term for labor participation and
weekend should be positive, with higher participation rates influencing greater weekend election
turnout rates.
In addition to labor, three demographic indicators will likely have an effect on weekend
voting. Life expectancy reflects the overall health and well being of a particular country, and
higher life expectancies can be expected from richer countries that are able to better care for the
health of its citizens. I believe that the interaction model for life expectancy will reflect the GDP
per capita interaction model, with higher life expectancies returning a more positive effect of
weekend voting. Birth rates similarly indicate the demographic structure and social well being of
a country. Typically, higher birth rates correspond to poorer economies, lower life expectancies,
and inadequate health care systems. Countries with these attributes will likely have some
combination of higher unemployment, a more agricultural based workforce, or low GDP per

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capita. As such, higher birth rates should correspond to less important weekend elections. When
running interaction models with birth rate, the trend for the interaction term should be negative,
showing that higher birth rates lower the impact of weekend voting. Finally, rural population
rates will be closely related to the number of traditional workers. As rural population increases
the number of agricultural and non-traditional jobs will also increase, meaning that the prevailing
argument of weekday voting costs will likely diminish in importance. An interaction model to
test this theory should show a negative coefficient between the rural population and weekend
interaction term, as higher rural populations will lead to progressively lower turnout from
weekend voting.
Hypothesis
This paper focuses on both the impact of weekend voting and why such an impact exists.
I expect to see a positive correlation between elections held on the weekend and voter turnout
because the cost of voting during the workweek is much higher than the relative cost of voting
on the weekend. When analyzing what impacts weekend voting, countries with stronger
economies, lower unemployment, and balanced demographics should all see a greater positive
impact of weekend elections. Models that estimate the impact of democracy and population size
on weekend voting should see few significant results, since these factors are not directly related
to the implementation and success of weekend vs. weekday elections.
The Data
The research for this paper includes 27 variables on 288 elections in 96 countries from a
wide array of sources, each listed in Table 1. The first set of variables includes information
specific to the results and structure of each election. A second set of variables is made up of
dichotomous dummy variables, which split the elections into two distinct groups. Third, a

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selection of continuous variables provides democracy, economic, and labor indicators to measure
the performance of a specific country. Last, a set of interaction variables combines the weekend
voting dummy and previous variables.
My data pertains to elections in the lowest national legislative house of a country (ex.-
House of Representatives in the United States or the House of Commons in the United
Kingdom). In countries where there is more than one round of voting, the data is relevant to the
last round which includes all eligible voters. In a few cases, elections were held only months
after a previous election because a majority government failed to form. In these cases, only
elections in which a government forms (i.e.- the latest election) are included in this dataset.
For elections to be eligible, three conditions must be met. First, the election must have
occurred between 2000 and 2012 (inclusive). There are two reasons for this restriction. I want to
analyze modern elections in the age of information and the Internet, as this has surely had some
impact. Secondly, I want to include as many countries as possible in my models, and election
data becomes less available and reliable as I travel further from the new millennium.
The second required condition is that the country in which an election is held must be
rated as a democracy by the Polity IV index for that year. Most countries hold elections, but
some are merely “elections” which are rigged or restricted to one party. In measuring levels of
democracy, Polity IV stands out as one of the top indexes in political science. The purpose of
this paper is to analyze the effect weekend voting has on voter turnout in democratic elections. In
order to find this effect, I must first have a concept of what a democratic election is. The Polity
IV index provides a measure of democracy for each country containing over 500,000 people,
with -10 to -6 being an autocracy, -5 to 5 being an anacracy, and 6 to 10 being a democracy. Six
components make up the score, each to do with either executive recruitment, constraints on

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executive authority, or political competition (Polity IV Project 2013; hereafter Polity 2013). I
chose the Polity index for this dataset because it provides yearly scores, uses up-to-date current
events information to determine how political changes in a country affect its score, and is not too
inclusive or exclusive.
Lastly, there must be reliable data for the election. I was able to find enough information
for almost all of the elections eligible under the first two restrictions, but a few had no reliable
sources of turnout information. As a result, only a handful of elections are thrown out due to
insufficient data.
Election Information
For each of the 288 elections included in this analysis, reliable data is needed to
effectively analyze my research question. About half of my election-specific data comes from the
International Institute for Democracy and Electoral Assistance (IDEA). IDEA is an
intergovernmental organization with 25 member countries, aiming to support current and
emerging democracies, help in democratic transitions, provide information, and influence
democracy worldwide (International Institute for Democracy and Electoral Assistance 2013).
The IDEA voter turnout database contains official information on lower house elections
since 1945. For each election, I was able to find complete information on total turnout, which
comes straight from the elections authority in each specific country. As such, this is the origin of
my turnout variable, the dependent variable in each regression I run. The database also contains
information on compulsory voting status for each election, an undeniable driver of turnout.
However, many countries with compulsory voting laws do not actually enforce them, raising the
need for a dummy variable for countries that actually enforce a compulsory law. Defining who
does and does not enforce these laws could arguably be subject to personal bias, but for the most

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part it is easy to tell. I draw this information from the IDEA page on compulsory voting, which
outlines which countries actually enforce their laws (IDEA 2012).
I also derive my proportional representation (PR) dummy from the IDEA database.
Included on the organization’s website is a table of electoral systems worldwide which lists the
electoral type (PR, Mixed, Majority/Plurality, other) and then specifies the type of election
system (IDEA 2012). From this, I am able to create a dummy variable that specifies which
countries have purely PR systems and which do not. Lastly, I use the table of elections provided
for each country to count the number of elections held during the period of 2000-2012. This
variable counts the number of democratic lower house elections held during the 13 year period
included in this paper, so only elections during years in which the country was ranked a
democracy by polity are included in the count.
There are two reasons why this collection of data is the best for my research purposes.
First, it is undeniably reliable. The data is collected by a major cooperation of democratic
governments, and all information on elections comes from the official elections
commission/department/agency for each country. This means that any bias in election results
would come from the official election results themselves (which is minimized when only
democracies, as classified by Polity IV, are included). Second, the collection has the most
election information of any dataset available for use; only 6 qualifying elections do not have
enough official information to be included in this analysis. I could not find any reliable
information on these elections elsewhere, further proving the authenticity and reliability of this
source.
Two additional election variables are found through the International Foundation for
Electoral Systems (IFES), a non-profit organization that assists new democracies with election

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support. Voter turnout information on the IDEA website does not include specific dates, only the
year of the election. IFES has complete information on the date of elections since 2000, so I use
this to determine whether or not an election is held on a weekend. Additionally, while the voter
turnout database only includes election information for lower house elections, the IFES website
includes information on presidential elections. This allows me to create the executive election
dummy. For this variable, any election in which the head of government is chosen is coded as a
1. In PR parliamentary systems, this is always a 1 since the prime minister is decided by the
outcome of the election. In systems where there are separate elections for an executive, only
elections in which the head of government is being elected on the same day as lower house
elections are coded as 1.
One variable not found through IDEA or IFES is the effective number of legislative
parties. For this, I use information from a dataset used for another academic paper (Bormann and
Golder 2013). After the results of each election are finalized, the effective number of legislative
parties can be calculated based upon each party’s share of seats in the legislature (formula in
table 1). This number could be important in driving voter turnout, since a greater number of
parties typically means elected officials represent more ideologies and the opportunity for sincere
voting is higher.
Group Variables
My analysis includes many regressions based on status within certain groups of countries.
One such group, G-20 status, is common knowledge. For this, I create a dummy variable, with 1
corresponding to an election held in a country that is a member of the G-20, and a 0 for all
others. Two more dummy variables are based off of Mark Franklin’s (2002) country selection in
his 2002 paper models. For the Franklin31 variable, I code each country included in his 31

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variable model as a 1, and for the Franklin25 variable, I do the same. For both of these variables,
there are two countries included in Franklin’s model that are not included in mine (Malta and
Iceland).
The last set of group variables all come from the World Bank database (The World Bank
2013; hereafter World Bank 2013). An internationally recognized organization, the World Bank
includes the most complete and reliable country data that cuts across numerous topics.1 Within
the group variables, I derive my GDP and GDP per capita variables from this database. Both
variables are measured in current US dollars and both are split up into 4 dummy variables
corresponding to the top 10, 15, 20, and 30 countries in each category. For example, for an
election to be included in the top 10 GDP dummy, it must be held in one of the 10 largest
economies in my dataset, based on total GDP. The same applies to the top 10 GDP per capita
dummy, except instead of total GDP, countries are ranked by GDP per capita.
ContinuousIndicators
In addition to the GDP data, I also found six continuous indicators on the World Bank
database which are used during my analysis: total population, unemployment rate, labor
participation rate, life expectancy, birth rate, and rural population percentage. Each of these
variables is missing data for less than 10% of my 288 elections, and the missing values are not
systematic in a way that would bias my results. The total population, life expectancy, and birth
rate variables are (technically) on an infinite scale starting at 0, while the other three continuous
variables range from 0 to 100. All of the data collected is aggregated from multiple sources
including the United Nations, official country reports, and World Bank employees on the ground
in each country. As such, the data can reasonably be assumed to be accurate.
1 The World Bank does not recognize Taiwan as independent of China, so Taiwanese data
was found on indexmundi.com (Index Mundi 2012)

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Interaction Variables
The last set of variables I include in my analysis consists of interaction terms between
previously described variables and the weekend dummy. In all, nine different variables are used
to estimate the effect that each has on weekend voting. Creating the interaction variables is fairly
simple: multiply the value for the selected variable with the corresponding value in the weekend
variable. Since the weekend variable is a dummy, any election held on a weekday will see all of
its interaction variables have values of 0.
ResearchDesign
My analysis will use three different methods to examine weekend voting: multiple
regressions, restricted regressions, and interaction multiple regressions. To simply find how
weekend voting impacts turnout, a multiple regression is a necessary and useful tool that allows
me to estimate the effect of weekend voting while accounting for other independent variables.
When looking at whether specific multiple regressions are useful for my analysis, there are a few
aspects to consider. The first and most important is the coefficient of the weekend dummy. That
value must be statistically significant (to at most 5%) for it to be considered a solid estimate or
“approaching significance” (to at most 10%) to be considered for a trend. Second, the joint
significance of all variables in the model also needs to pass under the 5% significance threshold.
Some variables within specific models may not be significant on their own, but if, as a whole,
they are jointly significant, each variable should stay included. Lastly, the individual significance
of each variable is an added bonus to an effective multiple regression. Joint significance takes
precedence over individual significance of independent variables, but having each statistically
significant on their own makes for a more reliable model. Overall, my first section will find one
multiple regression which reliably predicts turnout. From that, I will be able to determine the

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general effect of weekend voting and begin to manipulate that same multiple regression through
restricted and interaction models.
Later sections of my analysis will call for these restricted and interaction models. In
restricted models, I will exclude a certain number of elections and see how the coefficient on
weekend voting differs from original and restricted models. Interaction models will tell a similar
story, but do so without omitting any elections. These interaction models will be limited to one
interaction term per model, along with the original seven independent variables and the
interacted variable. There are a mix of dichotomous and continuous interaction terms included in
my analysis. Interacting two dichotomous variables is easier to interpret than continuous and
dichotomous interactions. As a result, any interaction term which includes a continuous variable
will be analyzed both numerically and graphically to help explain the results of such interactions.
Results
At first glance, the preliminary results from my regression analysis of weekend on voter
turnout are surprising. A quick average test shows that there exists little difference between the
averages of weekend elections and weekday elections, with weekday elections actually holding a
slight advantage (66.92% weekday average versus a 66.04% weekend average). Additionally, the
weekend voting dummy returns significant negative coefficients in my multiple regressions,
effectively shattering the common belief that holding elections on a weekend increases voter
turnout.2 Further analyses show that certain interaction effects can explain why weekend voting
does not have the same positive impact across different groups of countries. However,
2 This result was so unexpected that I checked my data multiple times for possible errors. After
finding none, I decided to continue on with my original analysis to find out why such a
relationship exists.

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throughout the complete analysis, the strongly negative coefficient on weekend voting remains a
surprise.
Complete Group Multiple Regressions
The first step in my analysis consists of multiple regressions, which estimate the effects
of institutional variables on voter turnout. A basic multiple regression of turnout on enforced
compulsory voting and weekend elections returns jointly significant results. As would be
expected, compulsory voting greatly increases turnout, in this case by 21.6%, while weekend
voting interestingly decreases turnout by 4.6%; both are individually significant to 1%. While
this model is jointly significant it certainly suffers from an omitted variable bias. Attempting to
account for as much bias as possible, I test many regression models with certain combinations of
variables to find which predictors of turnout are actually significant. The final model includes
seven independent variables (Table 2). This model returns jointly significant results and
individually significant results to at most 5%. The compulsory voting and weekend variables
retain similar coefficients and are more significant than in the first model. Among the five other
variables, all but one return expected coefficients. Each additional point on the polity scale gives
a country a 2.1% boost in turnout, showing that more democratic countries can expect higher
turnout rates. As described in my theoretical section, a proportional representation electoral
system has been shown to boost voter turnout. The results of model 2 confirm this, with a
proportional representation system estimated to boost turnout by around 3.1%. Elections in
which the executive or head of government is being chosen are boosted by 6%, as there is more
at stake in a general election than a legislative election. My theory on number of elections is
confirmed as well, with each additional election held between 2000 and 2012 decreasing turnout
by an estimated 2.1%. The only variable that returns unexpected results is the effective number

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of legislative parties. As discussed earlier, more effective parties would seemingly increase
turnout because more voters would be able to vote sincerely and feel that their views are better
represented. However, the model estimates that each additional effective party actually decreases
turnout by around 1.2%. For the model as a whole, five of the variables follow their expected
trend, while weekend voting and effective number of legislative parties return surprising
negative results.
Differences Across Democracies
Next, my analysis turns to differences in weekend voting across levels of democracy. In
order to define level of democracy, I use each election’s respective polity score (ranging from 6
to 10) to equate level of democracy. The first step in analyzing this relationship is to run similar
but restricted models of different groups of democracy. Table 3 shows four such models, with
the first two models restricting elections among polity scores of 6-8 and 9-10, and the last two
models restricted among polity scores of 6-9 and 10. The differences between both divisions are
minimal for weekend voting. For the first two models, the difference is less than two percent,
with elections held in countries with a polity score of 9 or 10 seeing less of a negative effect of
weekend voting than elections with polity score of 6-8. Both coefficients fall within the 95%
confidence interval of the other, meaning that the two cannot necessarily be distinguished as
different. The difference between the last two models is even smaller. Weekend elections with a
polity score of 10 see a 5.3% decrease in turnout while weekend elections with a polity score of
6-9 see a slightly higher 5.5% decrease. From this analysis, my hypothesis that polity score
makes no difference on the impact of weekend voting seems to be confirmed.
I can also incorporate an interaction model into my democracy analysis. Table 3 shows
the polity interaction model which includes six of the original variables along with the

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polity*weekend interaction term.3 With respect to significance, the interaction term does not fall
below 5%; however, at 5.8%, this coefficient “approaches significance.” The coefficient on
polity*weekend shows that a higher polity score should trend towards increasing turnout for
weekend elections. For example, a weekend election that has a polity score of 7 should see
increased turnout compared to a weekend election with a score of 6. Graph 1 clearly shows that
the turnout effect of weekend voting increases as polity score increases. Even so, the coefficient
on weekend is still strongly negative, such that even a weekend election with a perfect polity
score of 10 is predicted to have lower turnout than a weekday election.
[INSERT GRAPH 1 HERE]
Overall, my analysis on level of democracy and weekend voting generally proves my
hypothesis that polity scores should not affect weekend voting. Among restricted models, there is
no significant difference in weekend voting between groups with lower and higher polity scores.
When taking into account the interaction between polity and weekend, it looks like higher polity
scores trend towards increasing weekend voting turnout, but the interaction cannot be
distinguished from 0 at 5% significance. From these two analyses, I have shown that being a
higher scored democracy does not necessarily have an effect on weekend voting, though being
more democratic probably would not hurt.
Differences Across Groups
Now my analytical focus turns towards testing different groups of countries. For this
section, I will split up elections based on population size, economic prowess, and previous model
restrictions. Testing for population returns results at only one size. When the groups are
restricted to populations below and above seven million, the coefficients on weekend are either
3 The variable for number of elections was omitted because it caused joint significance to
fall above the 5% level.

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significant or approaching significance. For populations below seven million, weekend voting is
estimated to decrease turnout by 8.2% (to .1% significance), while populations above trend
negative with an estimated decrease of 3.6% (to 8.6% significance) (Table 4). From these results,
it seems as if countries with higher populations would tend to be impacted less from weekend
voting. However, when testing the hypothesis that the effect is the same, the null cannot be
rejected at 5% significance. Additionally, testing a model with a population interaction returns
no significant results. Therefore, my hypothesis that there exists no difference in weekend voting
between large and small countries is not disproven.
In terms of estimating differences between economic powers, there are three subdivisions
that can be used: total GDP, GDP per capita, and G-20 membership. Looking at total GDP, two
interaction models stand out as significant. The first model includes the top 10 GDP dummy and
top10*weekend interaction dummy. From the results, I find that top 10 countries with weekend
elections see a 4.8% increase in turnout over other countries with weekend elections.4 Among
top 10 countries, weekend elections also return 5.8% more turnout than weekday elections, but
still see a decrease when compared to weekday elections in other countries. The same results are
seen for top 15 GDP countries, with weekend elections estimated to have higher turnout than
weekday elections in the top 15 and weekend elections in excluded countries. Weekday elections
in excluded countries, however, still see an increase (Table 5).
GDP per capita returns much different results, and the only model that comes out as
significant is the top 30 per capita interaction. This first model estimates that for weekend
4 This percentage is calculated by subtracting the Weekend Elections coefficient (applicable
to excluded weekend elections) from the summation of the Weekend Elections, Top 10
GDP, and Interaction coefficients (applicable to a selected country with weekend elections).
Numerically, (13.48 – 7.656 – 8.649) – (-7.656) = 4.8. This method is used for determining
all the differences in interaction models, and the results are displayed in table 6.

Mitchell 21
elections, being a top 30 country increases turnout by about 6.2%. However, weekend elections
in top 30 countries see lower turnout than all weekday elections. The results for the G-20
interaction model more closely resemble the two total GDP interactions. For G-20 countries,
holding elections on the weekend increases turnout by an estimated .5% over weekdays, while
weekend elections in G-20 countries see a 2.6% increase in turnout over weekend elections in
non-G-20 countries. Weekday elections in non-G-20 countries still see higher turnout than G-20
weekend elections. My original hypothesis that richer countries will return better weekend voting
turnout seems to be confirmed by these results; each model shows that richer countries with
weekend elections see higher turnout rates than weekend elections in poorer countries.
The last regressions testing interactions between certain groups are those mimicking
previous weekend voting studies. As mentioned in the theory section of this paper, there are four
major models that incorporate weekend voting when estimating voter turnout (two from Franklin
(2002) and one each from Blondel (1997) and Mattila (2003)). Regressions using the Blondel
(1997) and Mattila (2003) restrictions are inconclusive, but the Franklin models return
significant results. The Franklin25 and Franklin31 models include the franklin dummy that
restricts countries based upon the country selection in his two models. Both of my models return
the same results, with countries chosen by Franklin estimated to increase turnout for weekend
elections. Weekday elections in both the chosen and excluded countries see a very small .2%
increase over weekend elections in Franklin’s countries.
Table 6 more clearly lays out the results of my interaction models. Since simply looking
at regression coefficients cannot explain differences between groups, I calculated the differences
myself. There are six charts in this table, one for each interaction model explained above. Within
each interaction model, there are four groups: weekend elections in selected countries, weekday

Mitchell 22
elections in selected countries, weekend elections in excluded countries, and weekday elections
in excluded countries. Each chart shows how much higher or lower weekend elections in
selected countries are estimated, compared to the other three groups.5 For example, in the first
chart, weekend elections in a top 10 GDP country are expected to be 5.8% higher than weekday
elections in top 10 GDP countries, 4.8% higher than weekend elections in excluded countries,
and 2.8% lower than weekday elections in excluded countries. My main hypothesis predicts that
weekend elections in selected countries will be higher than weekend elections in excluded
countries, so those results are bolded in each chart.
[INSERT TABLE 5 HERE]
Overall, my testing across groups returns generally expected results. I cannot
significantly determine whether or not population affects weekend voting, although having a
higher population most likely would not hurt. However, for richer countries and those chosen by
Mark Franklin, weekend elections certainly see increased turnout compared to excluded
countries. The negative coefficient on weekend voting still heavily impacts my results. For some
of my interaction models, selected countries do see higher weekend turnouts compared to
weekday elections. However, all six models show that weekend elections in the selected
countries still see lower turnout than weekday elections in the omitted countries.
Laborand Demographic Indicator Interactions
My last analytical section looks at certain indicators and their effects on weekend voting.
The most common argument for proponents of weekend elections centers on the traditional 9-5
worker not having time to vote during the workweek; however, this hypothesis has never been
statistically tested. If this hypothesis is correct, I should see the importance of weekend voting
5 Holding all other variables constant.

Mitchell 23
diminish as the percentage of 9-5 workers in a country decreases. Unfortunately, there is not
reliable and complete data comparing “traditional” vs. “non-traditional” workers. Instead, I run a
multiple regression model using unemployment to test the interaction between employed
workers and weekend voting. The model returns expected results, with each additional
percentage in unemployment decreasing weekend turnout by around .5% compared to other
weekend elections. As such, the higher the unemployment in a country, the more that weekend
voting has a negative impact (Table 8).
[INSERT GRAPH 2 HERE]
Additional labor force interaction models are unable to produce significant results.
Taking into account the labor participation rate, the coefficient looks to be trending in the correct
direction, but the significance levels are too high. Demographic indicators fare no better, with
interaction models based on life expectancy, birth rate, and rural population percentage returning
high significance levels.
While the inability of most interaction models to produce significant results is frustrating,
it goes to show that there is no one definitive reason why weekend voting affects turnout. The
unemployment model gives some confidence to the assumption that the traditional 9-5 worker
faces higher costs to voting on weekdays than on weekends. Even so, better data collection based
on specific worker statistics would be needed to completely test this hypothesis.
Conclusion
The first and most surprising aspect of my analysis is that weekend voting is, on average,
actually detrimental to voter turnout: within the multiple regression model, the coefficient on
weekend voting was significantly negative. This directly contradicts my hypothesis and most
common assumptions about weekend voting. My own ideas on the impact of weekend voting

Mitchell 24
have been shaped from numerous studies that have shown there to be a positive correlation. So
are these studies wrong? The short answer is no, previous models on weekend voting are not
necessarily wrong. Instead, they are flawed because of their restrictions, which cherry picked
groups of countries conducive to positive results. It is true that their final conclusions on
weekend voting are incorrect by assuming that turnout increases among all countries. However,
their findings are accurate to their selections. The Franklin (2002), Blondel (1997), and Mattila
(2003) papers do not suffer from incorrect data; as I found in my analysis, selecting certain sets
of countries can return positive significant results for weekend voting. In fact, my mimic models
did not even include Iceland and Malta, two countries that hold weekend elections and have
historically high turnout.6 With these elections added in, I would expect my models to show an
even more significantly positive relationship between weekend voting and turnout among
selected countries. So, in fact, it was biased restrictions that doom previous research into making
incorrect assumptions on weekend voting.
Past the fact that weekend voting on its own decreases turnout, my predictions for
interactions are, for the most part, correct. Population does not seem to matter much, albeit
slightly when looking at populations below or above seven million. Larger economies, however,
see more positive effects from weekend voting, presumably because they are more mature and
employ a greater percentage of the traditional 9 to 5 worker. Along the same lines, higher
unemployment leads to diminished impact of weekend voting. In higher unemployment
countries, less people will be working during the week and face lower costs of voting.
Essentially, what I have found is some basis to back the common theory that weekday voting
6 Malta and Iceland have populations less than 500,000; as such, they do not have polity
scores, disqualifying them from my selection process.

Mitchell 25
involves certain costs for traditional workers. However, as I will now explain, those costs may
not be as high as previously thought.
One thing my analysis cannot definitively explain is why weekend voting actually
depresses turnout. Presumably, there is some cost to weekend voting that is not well understood.
As my analysis shows, previous theories on weekday voting costs are not necessarily wrong.
Instead, they have likely been exaggerated. So, why are the costs to weekend voting higher and
what are they? To answer this, an individual level study would provide the most valid
information. By asking individual abstaining voters why they did not vote in weekend and
weekday elections, certain costs will inevitably come out as more significant and prevalent than
others. Indeed, many studies have been conducted to examine why people do not vote. The
problem with these studies is that they either come in with the assumption that weekend voting
increases turnout, or that is not the primary research question. What is needed is an unbiased
individual level survey across a heterogeneous set of countries to analyze why voters abstain
from both weekend and weekday elections.
Certain alternative theories could possibly arise from my conclusions. First, some could
find issue with my selection of elections, arguing that some countries included in this analysis
are truly not democratic; without these countries, the true trend in weekend voting would be
positive. While Polity IV is more inclusive than the Freedom House index, it is still one of the
three most widely used indices of democracy (the Democracy Index from The Economist being
the third). From my analysis, when I restricted my regression model to countries with 9 or 10
(almost all of which are included in the other two indices’ definitions of democracies), the
weekend coefficient remained negative. The trend might not be as negative with a more
restricted set of countries, but using a different index would not change the fact that weekend

Mitchell 26
voting depresses turnout across all democracies. A second argument could come from concerns
over omitted variable bias. This argument is not without validity; however, this can be argued for
almost any multiple regression. At least four of my seven multiple regression variables
(compulsory voting, weekend voting, proportional representation, and executive elections) have
been shown to impact turnout in previous studies. These are the major explanatory variables that
almost all previous turnout studies rely on. Omitted variables would likely lead to very little bias,
unless some unknown major explanatory variable for turnout has never been found, which seems
unlikely at this point.
My overall analysis has shown that the debate on weekend versus weekday elections is
far from over. Previous restricted models consistently estimate that weekend voting increases
turnout. But these models are just that: restricted. When all democratic elections are included,
weekend elections take their true negative form. From this, it seems that there are some unknown
costs to voters on the weekend that cause them to abstain from the ballot box. Individual level
surveys and analysis will tell what these are and which costs have the greatest effect. In any case,
my analysis proves that weekend elections are still a limited good idea for richer, larger
countries. For the average country, however, weekday voting does not seem like such a bad idea.

Mitchell 27
Table 1
List of Variables
Variable Description Expected Trend Source
Polity Score
Polity score of nation for
that year; ranges from 6-
10.
Higher scores
increase turnout Polity 2013
Effective Number of
Legislative Parties
Effective number of
legislative parties;
calculated 1
∑ 𝑠𝑖
2⁄ where
𝑠𝑖 is the percent share of
seats won in the election
for each party.
Higher effective
numbers
increase turnout
Bormann and Golder
2013
Executive Being Elected
Dummy; Whether or not
an election for the head
of government was held
at the same time;
1=concurrent executive
election, 0=no concurrent
executive election
Elections in
which an
executive are
elected will
increase turnout International
Foundation of
Electoral Systems
2013
Weekend Elections
the election is held on the
weekend; 1=election held
on Saturday or Sunday,
0=election held Monday-
Friday
Weekend
elections will
increase turnout
Voter Turnout
Total voter turnout,
calculated as 100 ∗
(
𝑡𝑜𝑡𝑣𝑜𝑡𝑒
𝑟𝑒𝑔𝑖𝑠𝑡𝑒𝑟𝑒𝑑
)
-
IDEA 2012
Number of Elections
Held (2000-2012)
Counts the number of
democratic elections held
between 2000 and 2012
More elections
will lead to
decreased
turnout
Compulsory Voting,
Enforced
a country enforces its
compulsory voting
law(s); 1=enforces a
compulsory voting law,
0=does not enforce
compulsory law or does
not have compulsory law
Compulsory
voting laws will
increase turnout

Mitchell 28
Proportional
Representation
the country has a
proportional
representation election
system; 1=has a pr
system (List PR or STV),
0=does not have a pr
system
Proportional
Representation
systems will
increase turnout
IDEA 2012
Unemployment Rate
The unemployment rate
in the country in which
the election is held
-
World Bank 2013
and
Index Mundi 2012
Rural Population
Percentage
The percentage of
citizens living in rural
areas in the country in
which the election is held
-
Total Population Total population in
country
No weekend
voting difference
between
populations
Top 10 GDP Country
the election is held in a
country that is one of the
top 10 economies with
respect to total GDP;
1=is in top 10, 0=not in
top 10
-
Top 15 GDP Country
respect to total GDP;
1=is in top 15, 0=not in
top 15
-
Top 30 Per Capita
Country
respect to GDP per
capita; 1=is in top 30,
0=not in top 30
-

Mitchell 29
Franklin25
the country is included in
the case selection for
Mark Franklin’s 25
country model; 1=is
included, 0= is not
included
A country which
is included in
Franklin’s case
selection will
see higher
positive
weekend voting
returns Franklin 2002
Franklin31
the country is included in
the case selection for
Mark Franklin’s 31
country model; 1=is
included, 0= is not
included
A country which
is included in
Franklin’s case
selection will
see higher
positive
weekend voting
returns
Polity*Weekend Interaction term No effect -
Total
Population*Weekend
Interaction term No effect -
Top 10 GDP*Weekend Interaction term Positive
relationship
-
Top 15 GDP*Weekend Interaction term Positive
relationship
-
Top 30 GDP Per
Capita*Weekend
Interaction term Positive
relationship
-
G-20*Weekend Interaction term Positive
relationship
-
Franklin25*Weekend Interaction term Positive
relationship
-
Franklin31*Weekend Interaction term Positive
relationship
-
Unemployment*Weekend Interaction term Negative
relationship
-
Rural Population
Percentage*Weekend
Interaction term Negative
relationship
-
G-20
the country belongs to
the G20; 1=belongs to
G20, 0=does not belong
to G20
Being a G-20
country will
increase the
impact of
turnout
-

Mitchell 30
Table 2
Multiple Regressions
Simple Seven Variable
Compulsory Voting, 21.57*** 22.15***
Enforced (2.367) (2.278)
Weekend Elections -4.549** -5.677***
(1.608) (1.515)
Polity Score 2.055**
(0.642)
Proportional 3.069*
Representation (1.507)
Executive Being 6.006***
Elected (1.751)
Effective Number of -1.223**
Legislative Parties (0.466)
Number of Elections -2.068**
Held (2000-2012) (0.711)
Constant 66.92*** 54.65***
(1.278) (4.880)
N 288 288
F 41.67 21.32
Standard errors in parentheses
F is the F statistic for the joint significance of all the variables in the model
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Mitchell 31
Table 3
Differences Across Polity Scores
6-8 9-10 6-9 10 Polity
Interaction
Compulsory Voting, 19.05*** 24.43*** 21.13*** 24.25*** 22.063***
Enforced (4.479) (2.698) (3.328) (3.352) (2.298)
Weekend Elections -6.478* -5.044** -5.538* -5.284* -22.97*
(2.709) (1.907) (2.204) (2.239) (9.322)
Proportional 2.105 4.134* 2.622 4.815* 3.323*
Representation (2.594) (1.959) (2.086) (2.397) (1.516)
Executive Being 7.539** 6.335* 6.071** 8.980** 7.046***
Elected (2.496) (2.584) (2.033) (3.288) (1.725)
Effective Number of -0.872 -1.145+ -1.046+ -1.680* -0.937*
Legislative Parties (0.740) (0.678) (0.615) (0.832) (0.474)
Number of Elections -1.014 -1.172 -1.512 -1.995+
Held (2000-2012) (1.203) (0.848) (1.020) (1.076)
Polity Score -0.0728
(0.82)
Polity Interaction 2.018+
(Polity Score*weekend) (1.059)
Constant 66.01*** 69.34*** 67.89*** 73.29*** 63.99***
(5.193) (5.401) (4.312) (7.360) (7.125)
N 118 170 163 125 288
F 5.996 17.41 10.66 13.15 20.3
Model titles refer to Polity score group restrictions
Polity Score variable omitted for collinearity
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Mitchell 32
Table 4
Population Models
Below
7 Million
Above
7 Million
Population
Interaction
Compulsory Voting, 24.64*** 21.00*** 21.81***
Enforced (5.492) (2.909) (2.304)
Weekend Elections -8.214*** -3.550+ -6.816***
(2.315) (2.056) (1.683)
Polity Score 1.022 2.700** 2.190***
(1.134) (0.884) (0.647)
Proportional 4.477+ 1.592 3.023+
Representation (2.295) (2.081) (1.569)
Executive Being 8.004+ 5.929** 6.002***
Elected (4.207) (2.142) (1.754)
Effective Number of -2.113** -0.509 -1.211*
Legislative Elections (0.769) (0.626) (0.481)
Number of Elections -1.377 -2.298* -2.154**
Held (2000-2012) (1.447) (0.907) (0.726)
Total Population -9.45e-09
(6.80e-09)
Population Interaction 3.03e-08
(Total Pop*Weekend) (2.47e-08)
Constant 63.57*** 46.81*** 54.42***
(7.585) (6.566) (4.874)
N 117 171 288
F 8.311 13.72 16.95
First two titles refer to populations above and below 7 million
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Mitchell 33
Table 5
GDP Interaction Models
Top 10
Interaction
Top 15
Interaction
Enforced (2.248) (2.267)
Weekend Elections -7.656*** -7.783***
(1.620) (1.679)
Polity Score 2.082** 2.108**
(0.642) (0.642)
Proportional 2.790+ 3.213*
Representation (1.550) (1.573)
Executive Being 6.467*** 5.681**
Elected (1.737) (1.745)
Effective Number of -1.329** -1.222**
Legislative Parties (0.466) (0.463)
Number of Elections -1.501* -1.868*
Held (2000-2012) (0.727) (0.723)
Top 10 GDP Country -8.649**
(3.285)
Top 10 GDP Interaction 13.48**
(Top 10 GDP*Weekend) (4.319)
Top 15 GDP Country -5.713+
(2.940)
Top 15 GDP Interaction 10.21**
(Top 15 GDP*Weekend) (3.561)
Constant 54.23*** 55.13***
(4.881) (4.861)
N 288 288
F 18.18 17.88
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Mitchell 34
Table 6
Estimated Interaction Differences for Group Weekend Elections
Chart 1 Top 10 GDP w/
weekend elections
Top 10 GDP w/
weekday elections Higher by 5.8%
Excluded w/
weekend elections Higher by 4.8%
Excluded w/
weekday elections Lower by 2.8%
Chart 2 Top 15 GDP w/
weekend elections
Top 15 GDP w/
weekday elections Higher 2.4%
Excluded w/
Excluded w/
Chart 3 Top 30 GDP per capita
w/ weekend elections
Top 30 GDP per
capita w/ weekday
elections
Lower by 1.1%
Excluded w/
Excluded w/
Chart 4 G-20 w/
weekend elections
G-20 w/ weekday
elections Higher by 0.5%
Excluded w/
Excluded w/
Chart 5 Franklin 25 w/
weekend elections
Franklin 25 w/
Excluded w/
Excluded w/
Chart 6 Franklin 31 w/
weekend elections
Franklin 31 w/
weekend elections Lower by 0.2%
Excluded w/
Excluded w/

Mitchell 35
Table 7
Continuous Indicator Interaction Models
Unemployment
Interaction
Rural Population
Interaction
Enforced (2.239) (2.622)
Weekend Elections -1.574 -1.533
(2.630) (3.408)
Polity Score 1.215* 0.886
(0.599) (0.650)
Proportional 3.743* 4.197**
Representation (1.573) (1.541)
Executive Being 8.477*** 7.766***
Elected (1.732) (1.817)
Effective Number of -0.933+ -1.150*
Legislative Parties (0.476) (0.489)
Unemployment Rate 0.161
(0.186)
Unemployment Interaction -0.522*
(Unemployment*Weekend) (0.219)
Rural Population Percentage 0.0269
(0.0524)
Rural Population Interaction -0.107
(Rural Population*Weekend) (0.0763)
Constant 50.67*** 54.32***
(6.253) (7.059)
N 275 284
F 20.82 17.65
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Mitchell 36
Graph 1
Graph 2
606264666870
LinearPrediction
6 7 8 9 10
Polity Score
weekend=0 weekend=1
Polity Interaction for Weekend and Weekday Elections
4050607080
LinearPrediction
0 5 10 15 20 25 30 35 40 45 50 55 60
Unemployment Rate
weekend=0 weekend=1
Unemployment Interaction for Weekend and Weekday Elections

Mitchell 37
Works Cited
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Participation.” European Journal of Political Research. 32 (October): 243–272.
Mikko, Matilla. 2003. “Why Bother? Determinants of Turnout in the European Elections.”
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Bormann, Nils-Christian and Matt Golder. 2013. “Democratic Electoral Systems Around the
World, 1946-2011.” https://files.nyu.edu/mrg217/public/es_data-130123.zip (November
5, 2013).
Fowler, Anthony. 2013. “Electoral and Policy Consequences of Voter Turnout: Evidence from
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(June): 111–125.
Jackman, Robert. 1987. “Political Institutions and Voter Turnout in the Industrial Democracies.”
The American Political Science Review 81 (June): 405-424.
Lijphart, Arend. 1997. “Unequal Participation: Democracy’s Unsolved Dilemma.” The American
Political Science Review 91 (March): 1-14.
Franklin, Mark. 2002. “The Institutional Context: Turnout.” In Comparing Democracies 2: New
Challenges in the Study of Elections and Voting, eds Lawrence LeDuc, Richard Niemi,
and Pippa Norris. London: SAGE Publications Ltd, 148-168.
International Foundation of Electoral Systems (IFES). 2013. “Election Guide.”
http://www.electionguide.org/ (October 16, 2013).

Mitchell 38
International Institute for Democracy and Electoral Assistance (IDEA). 2012. “Voter Turnout
Database.” October 11. http://www.idea.int/vt/viewdata.cfm (October 16, 2013).
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http://www.idea.int/about/ (November 11, 2013).
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