mitchell_186_final paper copy

Alec Mitchell

Poli Sci 186 – Professor Nils Ringe

12/14/13

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 Impact of 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.

Continuous Indicators

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.

Research Design

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,

throughout the complete analysis, the strongly negative coefficient on weekend voting remains a

surprise.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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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

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

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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

polity*weekend interaction term.3 With respect to significance, the interaction term does not fall

3 The variable for number of elections was omitted because it caused joint significance to fall above the 5% level.

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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

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

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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

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,

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.

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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

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

5 Holding all other variables constant.

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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.

Labor and 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

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

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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

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

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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

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.

6 Malta and Iceland have populations less than 500,000; as such, they do not have polity scores, disqualifying them from my selection process.

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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

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

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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.

Table 1List of Variables

Variable Description Expected Trend Source

Polity ScorePolity score of nation for that year; ranges from 6-

10.

Higher scores increase turnout Polity 2013

Effective Number of

Effective number of legislative parties;

Higher effective Bormann and Golder

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Legislative Partiescalculated

1∑ si

2 where si

is the percent share of seats won in the election

for each party.

numbers increase turnout

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

Dummy; Whether or not 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 TurnoutTotal voter turnout,

calculated as

100∗( totvoteregister ed )

-

IDEA 2012

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

Dummy; Whether or not 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

Proportional Representation

Dummy; Whether or not 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

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Unemployment RateThe unemployment rate in the country in which

the election is held-

World Bank 2013and

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

Dummy; Whether or not 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


country that is one of the top 15 economies with respect to total GDP;

1=is in top 15, 0=not in top 15

-

Top 30 Per Capita Country


country that is one of the top 30 economies with

respect to GDP per capita; 1=is in top 30,

0=not in top 30

-

Franklin25

Dummy; Whether or not 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

Dummy; Whether or not the country is included in

the case selection for Mark Franklin’s 31 country model; 1=is included, 0= is not

A country which is included in

Franklin’s case selection will

see higher positive

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included weekend voting returns

Polity*Weekend Interaction term No effect -Total

Population*WeekendInteraction 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-20Dummy; Whether or not 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

-

Table 2

Multiple Regressions

Simple Seven VariableCompulsory Voting, 21.57*** 22.15***

Enforced (2.367) (2.278)

Weekend Elections -4.549** -5.677***

(1.608) (1.515)

Polity Score 2.055**

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(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 288F 41.67 21.32

Standard errors in parenthesesF 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

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)

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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 288F 5.996 17.41 10.66 13.15 20.3


Model titles refer to Polity score group restrictionsPolity Score variable omitted for collinearity

+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Table 4

Population Models

Below7 Million

Above7 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***

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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 288F 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

Table 5GDP Interaction Models

Top 10 Interaction

Top 15 Interaction

Compulsory Voting, 22.44*** 21.82***

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)

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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 288F 18.18 17.88


+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001Table 6

Estimated Interaction Differences for Group Weekend Elections

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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%



Mitchell

Chart 5 Franklin 25 w/ weekend elections

Franklin 25 w/ weekday elections Lower by 0.2%



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Chart 3 Top 30 GDP per capita w/ weekend elections

Top 30 GDP per capita w/ weekday elections

Lower by 1.1%



Chart 4 G-20 w/weekend elections

G-20 w/ weekday elections Higher by 0.5%



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Table 7Continuous Indicator Interaction Models

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Chart 6 Franklin 31 w/ weekend elections

Franklin 31 w/ weekend elections Lower by 0.2%



Mitchell

Compulsory Voting, 21.95*** 20.71***

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 284F 20.82 17.65


+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Graph 1

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Graph 2

Works Cited

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Blondel, Jean, Richard Sinnott, and Palle Svensson. 1997. “Representation and Voter

Participation.” European Journal of Political Research. 32 (October): 243–272.

Mikko, Matilla. 2003. “Why Bother? Determinants of Turnout in the European Elections.”

Electoral Studies 22: 449-468.

Polity IV Project. 2013. “Political Regime Characteristics and Transitions.” April 21.

http://www.systemicpeace.org/inscr/inscr.htm (October 11, 2013).

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

Compulsory Voting in Australia.” Quarterly Journal of Political Science 8: 159-182.

Blais, André. 2006. “What Affects Voter Turnout?.” Annual Review of Political Science 9

(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).

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International Institute for Democracy and Electoral Assistance (IDEA). 2012. “Voter Turnout

Database.” October 11. http://www.idea.int/vt/viewdata.cfm (October 16, 2013).

International Institute for Democracy and Electoral Assistance (IDEA). 2013. “About Us.”

http://www.idea.int/about/ (November 11, 2013).

The World Bank (World Bank). 2013. “Data.” http://data.worldbank.org/ (November 13, 2013).

Index Mundi. 2012. “Taiwan.” July 26. http://www.indexmundi.com/taiwan/ (October 18, 2013).

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