Does the expected closeness increase the turnout? Evidence from a prediction market on
Swiss popular votes
Oliver Strijbis
Universität Hamburg
and
Wissenschaftszentrum Berlin (WZB)
Email: [email protected]
Sveinung Arnesen
UNI Rokkan Centre for Social Research
and
University of Bergen
Email: [email protected]
Laurent Bernhard
University of Zurich
and
University of Berne
Email: [email protected]
Abstract
This article analyzes the effect of the expected closeness on turnout for 40 national and
cantonal Swiss direct democratic votes. The study makes three main contributions. First, it is
the first study to measure the expected closeness by using data obtained from prediction
markets. Second, it empirically clarifies the relation between the expected closeness and the
levels of turnout in direct democratic votes. Third, it is the first study to investigate the
interrelationships between the expected closeness and turnout at different levels within a
country. We test two hypotheses, which we derive from a bounded rationality model. While
the first hypothesis assumes that the expected closeness of the outcome directly impacts on
the decision to vote (decision hypothesis), the second hypothesis states that the expected
closeness motivates political elites to mobilize more intensively, thus reducing the voters’ cost
of voting (mobilization hypothesis). Our results lend support to the decision hypothesis: It is
shown that the average closeness of national ballots has a considerable effect on turnout for
all proposals of the same date be they at the national or the subnational level.
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Introduction
Direct democratic decision making is on the rise around the world (Altman 2010; LeDuc
2003). According to many scholars, this is a very promising trend as direct democracy has
been shown to have positive effects on policy representation, economic performance, and
even life satisfaction (for reviews see Budge 2006; Lupia and Matsusaka 2004; Maduz 2010;
Papadopoulos 2001). One of the few major normative problems with direct democracy,
however, is that it is often associated with low turnout (e.g. Freitag and Stadelmann-Steffen
2010; Kriesi 2005). Hence, the question on what impacts on turnout in popular votes is of
major importance (Linder 2010; Lupia and Matsusaka 2004; Mendelsohn and Parkin 2001).
The aim of this article is to test whether the expected closeness of the outcome of the vote
triggers turnout.
From an individual voter’s perspective, the benefit of voting increases when the vote is
expected to be close, since the voter’s probability of being pivotal rises in such as situation. In
addition, competing political actors are expected to spend more resources on mobilizing the
population when engaged in a close ballot election. They thereby reduce the voters’ costs of
acquiring the knowledge they need to make an informed decision in the voting booth. Hence,
turnout should be higher the closer the expected outcome because both the benefits for the
voters are increased and the costs are reduced. In the context of elections the empirical
support for this argument is solid. According to an extensive literature review by Blais (2006),
it is one of the most consistent findings in cross-national electoral research that close elections
have a positive effect on turnout numbers (also Geys 2006).
So far, little research on direct democracy has been devoted to the question of whether the
expected closeness of popular votes increases turnout. Furthermore, the few studies that have
tried to answer this question have come to competing conclusions. An important reason for
the lack of cumulative findings lies in the difficulty to measure the expected closeness of the
outcomes. So far, scholars of direct democracy have relied on the actual outcome, while
electoral research has started to make use of polls. In this article we go beyond both types of
approaches by employing unique data obtained from prediction markets.
Prediction market data are the most valid data to use as measure for expected closeness. In
contrast to polls and actual outcomes, prediction markets directly measure the anticipated
closeness. Data from the actual outcome, in contrast, are of little use since the result is not
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known at the time voters need to make a decision on whether to participate in the ballot at
stake. Polls do only measure the current public opinion at a given time point ahead of the
vote, and are per se not making any predictions. This point is especially important in the
context of direct democratic choices, since opinions form relatively late in direct democratic
campaigns.
The article offers three main contributions. First, we contribute to the large literature on the
relationship between expected closeness and turnout by operationalizing for the first time
closeness with data from prediction markets. Second, we clarify the relationship between the
expected closeness and turnout in popular votes. Third, we are the first to investigate the
relationship between expected closeness and turnout at different levels within a country. Our
results suggest that the average closeness of the national proposals has a considerable effect
on turnout for all proposals of the same date at the national and the subnational level.
The article is structured as follows. In the first section, we summarize the current theoretical
literature on the expected closeness of election outcomes on turnout and derive our
hypotheses. In the second section, we focus more closely on the literature on turnout in
popular votes and contextualize our hypotheses accordingly. This section is followed by a
discussion of the comparative advantages of using prediction market data. We then turn to the
operationalization of the remaining variables and present our data. In the fifth section we
discuss our results. In the final section we summarize our results and point to future avenues
of research.
Direct and indirect effects of expected closeness on turnout
Why people use their right to vote is a central and persistent question in political science. In
the vast literature on electoral turnout, there are many empirically and theoretically founded
determinants of turnout. One of particular theoretical importance is that competitive elections
increase turnout. The empirical support for the positive relationship between the expected
closeness of the outcome and turnout is solid. Indeed, one of the most consistent findings in
cross-national electoral research is that close elections have a positive effect on turnout levels
(Blais 2006).
On the first sight this finding resonates well with standard rational choice theory. Rational
choice models view the individual voter as a utility maximizing individual that calculates the
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costs and the benefits of voting, before making the decision to vote or not. When the benefits
outweigh the costs, the rational voter will go to the polling station. The individual decision
hypothesis states that from the individual voter’s perspective, the benefit of voting increases
when the election is expected to be tight, since the voter’s probability of being pivotal then
rises (Downs 1957; Ledyard 1984; Palfrey and Rosenthal 1983). Consequently, an increasing
turnout with higher levels of expected closeness seems to directly confirm rational choice
assumptions.
The elite mobilization hypothesis argues that, in the context of a close election, the competing
political actors will spend more resources on convincing the population (Key 1949). They
thereby reduce the voters’ costs of acquiring the knowledge they need in order to make an
informed decision in the voting booth. Hence, also from this perspective turnout should be
higher when the race is expected to be tight than when the outcome is expected to be clear.
Instead of the decision hypothesis the mobilization hypothesis does not argue from the benefit
but from the cost side.
The paradox of voting (Downs 1957) is one of the most important challenges to rational
choice theory. In practice, the probability that one single vote will be pivotal is microscopic
(Funk 2010; Matsusaka and Palda 1993), which leads to the question why anyone would want
to vote at all. Since the direct costs seem to always outweigh the benefits of voting,
participating in elections seems to be irrational from this perspective (but see Medina 2011).
There are many different solutions to the paradox of voting (see the reviews by Blais 2000;
Dhillon and Peralta 2002; Feddersen 2004; Medina 2013) among which the conceptualization
of voting as an expressive act (Riker and Ordeshook 1968) is most famous. We will apply an
alternative solution to the paradox of voting, which is to assume bounded instead of full
rationality (Simon 1959). More specifically, while we do assume that rational voters are
motivated by a cost/benefit calculation to turn out by their perception of the probability that
one single vote will be pivotal, voters are not able to correctly estimate this probability.
Instead we assume that voters are cognitively biased in ways that are well studied by social
psychologists (Kahneman and Tversky 1973; Tversky and Kahneman 1974).
First, voters underestimate extreme probabilities. Hence, the voter systematically overstates
the probability that one single vote will be pivotal. Second, voters underestimate the effect of
the base-rate on probabilities. In our case this means that voters underestimate the effect of
the size of the electorate on the probability that their vote will be pivotal. Third, voters
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disregard the impact of turnout on the closeness of the outcome. A serious attempt to correctly
predict the closeness of an election or popular vote would imply that voters calculate the
closeness as the difference of votes between the competing camps not only in absolute
numbers, but also as the share of the electorate that is expected to participate. This assumption
is too strong because it would mean that voters have the knowledge on the absolute size of the
electorate as well as reliable information about both turnout and the share of the votes the
different camps will obtain.
To summarize, we assume that bounded rational voters make a cost/benefit calculation in
which they overestimate the probability that their vote is pivotal, underestimate the effect of
the size of the electorate on this propensity, and disregard the effect of the turnout in their
calculation. Under these assumptions both the decision and the mobilization hypothesis are
reasonable. The individual decision hypothesis becomes plausible because the benefits of
voting are strongly overestimated. The elite mobilization hypothesis is now conceivable
because the reduction of costs due to campaigns varies at levels that are relevant for the
decision whether to turn out.
State of the art
While the finding that the expected closeness of the outcome triggers turnout is established in
electoral research, this is not the case for research on turnout in popular votes. In this context
it remains contested whether the expected closeness of the outcome has a relevant impact on
turnout and the empirical evidence is contradicting. In one of the first and still the largest
study of the effect of expected closeness on turnout Matsusaka (1993) investigates this
relationship for 885 Californian ballot propositions from 1912 to 1990. He fails to find any
systematic link although he suggests that ballot propositions should be a most likely situation
for such a relationship to be established.
Kirchgässner and Schulz (2005) test the effect of the closeness of the outcome of popular
votes and – as a mediating variable – the campaign expenditure on the turnout in Swiss
national ballots. They use data on 142 Swiss referenda and initiatives from 1981 to 1999.
Their empirical results suggest that the closeness of the outcome has some impact on the
(financial) campaign intensity, which itself strongly triggers turnout. The closeness of the
outcome, however, has no direct effect on turnout. This lends support to the mobilization but
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not for the decision hypothesis (Kirchgässner and Schulz 2005, 32-33). Their finding that the
campaign intensity is central to the turnout is in line with recent research that emphasizes the
relevance of direct democratic campaigns for the activation of voters (Bernhard 2012; Kriesi
2012; Nai 2013).!
The result by Kirchgässner and Schulz contrast with the analysis of Søberg and Tangerås
(2007), which analyze voter turnout using data from 309 local Norwegian language
referendums carried out from 1965 to 2005. They find that turnout is positively related with
the closeness of the outcome. Since campaign intensity in these local referenda can be
assumed to be very modest their result seems to corroborate evidence for a direct link between
the closeness and turnout. Aguiar-Conraria and Magalhães (2010) als find in the context of
referenda on the accession to European treaties a positive effect of closeness of the outcome
on turnout. Since they do not control for campaign intensity, it is not possible to judge
whether their result confirms the decision hypothesis, mobilization hypothesis or both. As we
will discuss below, the contradicting evidence between the four studies cited above could be
caused by poor operationalizations, as they all relied on actual outcome data.
A blank spot in all studies of the relationship between expected closeness and turnout in
popular votes refers to the interaction between different territorial levels. In direct
democracies several popular votes are typically conducted at the same date and it is common
that voters decide on proposals from different territorial levels at the same time.# Electoral
scholars distinguish in this context between "first order" and "second order" elections (Reif
and Schmitt 1980). As the term suggests this distinction refers to how much is at stake at a
given election. Similarly, we might distinguish between "first order votes" that take place at
the national level and "second order votes" taking place at the regional/local level. This
distinction is relevant for us, since it impacts on when we can assume cost/benefit calculations
to be of relevance. This is because only if there is something at stake the benefits of voting
can be large enough in order for utility maximizing voters to make their decision to go to the
polls dependent upon the expected closeness. Hence, we hypothesize that only at the level of
first order popular votes the expected closeness impacts on turnout.
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!
And less so for their conversion.
#
In Germany popular votes sometimes take place on the same date as elections. The decision whether or not to
schedule the vote on the same date can be pivotal.
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The fact that in many cases several proposals are voted at the same day raises another
important question. As Kirchgässner and Schulz (2005) point out, the costs to vote for a
second proposal once a voter has voted on a first proposal are extremely small. Hence, we can
assume that voters base their decision to turn out primarily on the basis of the proposal that is
most important to them. With relation to expected closeness two assumptions are plausible.
According to what we call the "salience hypothesis", what matters is if there is one proposal
that heavily mobilizes the voters, thus leading to diffusion effects for all other ballots of the
same day (Joye and Papadopoulos 1994). In this case we would assume that only the expected
closeness for the most salient proposal has an impact on turnout. According to the "issue
public hypothesis" we assume that the importance of a vote varies for different segments of
the electorate, i.e. issue publics. In this case, we expect that the closeness of each first order
vote matters to some degree, because each vote is relevant to a different segment of the
electorate.
To summarize, both the decision and the mobilization hypothesis predict that the expected
closeness of the outcome increases turnout. Since the decision hypothesis assumes a direct
link between expected closeness and the voter's decision to turn out this correlation should
hold independently of controls for other variables. From the perspective of the mobilization
hypothesis, in contrast, the closeness of the outcome should only trigger turnout in contexts
where campaigns are intense as a result of the increased perceived closeness. Furthermore,
since the benefit of voting can only exceed its costs if there is something important at stake,
we expect that only the closeness of first order popular votes matters for turnout.
Additionally, we distinguish between the motor proposal and the issue public hypothesis. The
former states that turnout for proposals that take place at the same date are determined by the
expected closeness of the most salient proposition. The latter hypothesis assumes that the
expected closeness of a combination of all propositions positively influences turnout levels.
Measuring expected closeness with prediction markets
In the previous section, we have shown that the empirical evidence on expected closeness and
turnout in popular votes is contradicting. In addition, while the empirical relationship between
the closeness of the outcome and turnout is well established, the magnitude of the impact is
often shown to be small. According to Blais (2006) a reason might be that the variable is not
adequately measured. The fundamental problem of indicators calculated from the closeness of
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the outcome is that they estimate the perceived narrowness by using ex-post indicator (for an
early review see Matsusaka and Palda 1993; also Ashworth, Geys, and Heyndels 2006;
Kirchgässner and Schimmelpfennig 1992; Endersby, Galatas, and Rackaway 2002). The
problem is that the voters might not correctly perceive the narrowness of an election. For
instance, under the impression of 'horse race' media coverage voters might overestimate the
narrowness of the vote (Broh 1980). As a response to this problem, scholars have considered
several attempts to arrive at better indicators for the voters’ perception of the closeness.
Most importantly, research on election turnout has made use of poll results (Kunce 2001;
Shachar and Nalebuff 1999). The use of polls in order to operationalize the expected outcome
is a major advancement, it is however not a perfect solution.$ This is because polls only
measure the current public opinion at a given time point ahead of the referendum, and are per
se not making any future predictions.% Moreover, voters are often aware that the polls are
subject to change, and thus not totally trustworthy as an indicator of expected closeness. This
point is especially important in the context of direct democratic choices, since opinions are
sometimes subject to spectacular reversals that can form relatively at a late stage in direct
democratic campaigns (Magleby 1984). In Switzerland, polls that are made one month before
voting day are generally not interpreted as predictions, since the share of undecided voters is
often huge. But also polls closer to voting day are not considered to be predictions. The
reason is that they must be interpreted in the light of the dynamics of the campaign.
Undecided voters do not adopt yes- and no-preferences on an equal share until voting day, but
can swing heavily to one side. Media and the experts are aware hereof and try to anticipate
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$
"An alternative operationalization of the expected outcome is to take the first round result of
an election in two round ballots. Fauvelle-Aymar and François (2006) for French and
Simonovits (2012) for Hungarian two-round elections show that the closeness of the first
round has a positive effect on turnout in the second round. Obviously, this result is restricted
to the context of this particular electoral law and is not applicable to popular votes.
%
"The fact that polls do not measure expectations in advance of elections can be partly
overcome when one election does not take place during the same time frame in all
constituencies (Morton, Muller, Page, and Torgler 2013; Sudman 1986). Though an intriguing
way to test the effect of opinion poll information on turnout this indicator does still not
measure the expected closeness and is not possible to generalize for other cases.
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this opinion formation process. Their interpretations of the polls strongly impact on the
general perception of whether a vote is close or not.&
Instead of opinion polls, we propose to measure the expected closeness as the predicted
outcome from prediction markets. Prediction markets as defined by Berg, Nelson, and Rietz
(2003) are markets run with the purpose of using the information content in market values to
make predictions about specific future events. They are also known as information or decision
markets. The intention of these markets is to show the traders’ best collective expectation
about future events such as election results, stock prices, and movie revenues. In these
markets, values of traded contracts depend directly on future outcomes and, hence, prices give
reliable information about what traders believe will be the most probable outcomes (Berg et
al. 2003:79). Today there are a number of prediction markets, but perhaps the best known
prediction market is the Iowa Electronic Markets (IEM), which was first established at Iowa
University for experimental purposes in connection with the 1988 US presidential election.
Since then, it is generally established that prediction markets have predicted election
outcomes more accurately than comparable polls, both in the US and elsewhere (Arnesen
2011b; Berg, Nelson, and Rietz 2008; Berg, Forsythe, Nelson, and Rietz 2008).'
Prediction market data have previously been used to study partisan impacts on the economy
(Snowberg, Wolfers, and Zitzewitz 2007), to study the effect of campaign events on election
outcomes (Arnesen 2011a), and has been suggested to be used as information for political
decision makers on their likely consequences of their decisions (Berg, Nelson, and Rietz
2003; Wolfers and Zitzewitz 2009). These kinds of data have never been used to study the
effect of expected closeness, however, even though they have a natural place in that literature.
If structured correctly, the prices should reflect the expected future outcome, given the
information available at the time:
Though simple in concept, such markets act as complex, dynamic, interactive systems
that incorporate information in new ways. Through the action of traders, prediction
markets aggregate information from individuals, incorporate polls and other sources of
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&
For instance the race on the initiative "Against Mass Immigration" was by the media and the experts correctly
expected to be very tight although the last poll ten days before the vote revealed a relative Yes-majority of 50%
against 43% No and 7% undecided."
'
Note that Erikson and Wlezien (2008, 2012) have challenged the prediction markets’ superiority over polls.
*"
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information and weight all of this information through the price formation process.
(Berg, Nelson, and Rietz 2003:3)
When measuring closeness and turnout, it is the voters’ perceptions of how close a directdemocratic outcome will be that matters. The question to consider here, then, is not whether
the prediction markets accurately predict the outcome, but rather whether their expectations
are in line with the voters’ own expectations. To be sure, it is plausible that there are
discrepancies between these two expectations, so that in some instances the prediction market
expects a close election while the voters in general do not, and vice versa. Nevertheless,
prediction markets should be better synchronized with the general voters’ expectations than
the polls are. It is common knowledge that for popular votes, the early polls are highly
uncertain. When evaluating whether or not it will be a close referendum, potential voters will
look at the polls, and then take supplementary factors into account. For example what they
hear friends and colleagues talk about, what they gather from the media, and knowledge about
topics that previously have shown to split the citizenry, will give them a gut feeling about
whether or not the referendum could be narrow. This kind of unstructured, decentralized
information is exactly the kind of information that the prediction market participants add to
their evaluation when they make predictions about the upcoming referendums and elections.
These politically well-informed individuals take not only the current sentiments into
consideration, but add their knowledge of previous referendums and elections campaigns, as
well as other information from the media, friends, and colleagues that they find relevant to
form expectations about the upcoming referendum. The market traders do not represent the
voters as respondents in a survey do, but they interpret the voters and anticipate their
behavior, much like a voter would need to do when she anticipates her fellow citizens’ voting
behavior. Hence, the prediction markets should be more in tune with the voters’ expectations
about the closeness of an election than what early polls of vote intentions are.
As goes for the elite mobilization hypothesis, the question is not whether the market
predictions are representative of the voters’ predictions, but whether they are in line with the
expectations of the elites that put resources into the referendums that they believe to be close.
The argument would still be the same, namely that the information that is incorporated in
market predictions is richer than what polls can provide on their own, and likely to overlap
!+"
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with the extra information that the elites make use of. For these reasons, we argue that market
predictions give a better impression of the perceived closeness of an upcoming referendum.
Data and method
In order to make turnout rates comparable across levels, our dependent variable consists of the
turnout for both national and cantonal proposals among the electorate of the Canton of Zurich.
As Kirchgässner and Schulz (2005) propose, turnout is measured as the number of yes- and
no-votes divided by the people entitled to vote. Such an operationalization can better capture
turnout than the official numbers which include blank votes since it is less of an effort to vote
blank than not to vote for a specific proposal if one votes for at least one alternative proposal.(
The prediction market data have been gathered from our own prediction market software.)
This political market consists of electronic real-time exchanges where traders buy and sell
futures contracts. The trading procedure is conducted using an automated market maker."
An automated market maker is an algorithm that automatically offers a new price for the
contract after a trade has been made. The organizer then ensures there is a price offer for any
trader to take at any time. The obvious benefit is that the liquidity in the market is infinite.
This ensures that it is possible to make a trade at any given time, thus overcoming the
problems of illiquidity in thin markets. When using an automated market maker, it has been
established that small markets are well calibrated even with the number of active traders being
around 15-20 persons (Christiansen 2007).
The logarithmic market scoring rule (LMSR) invented by Robin Hanson (2007) has become
the most popular automated market maker among prediction market organizers. The reason
for this is its simple yet proper design. Sliding on a logarithmic scale between 0 and 100,
Hanson´s scoring rule works like a two-sided market maker, thus allowing traders to both sell
and buy contracts at will. The traders must depart from the current price, and how far they can
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(
This is because all proposals at one level (national, cantonal, local) are tied together on one piece of paper that
must be divided into parts if one decides not to vote on some proposals but wants to cast the vote on others.
)
Practically, establishing and running prediction markets in Switzerland has been organized by
politikprognosen.ch, in which two authors of this paper are involved. Around 100 individuals were registered
during each of the three prediction market cycles, of which on average 57 actively participated. All active
participants received a fixed amount of 20 or 30 (depending on the time point) Swiss Francs for participation and
30 Swiss Francs to play with. Payouts varied depending on performance between 29 and 154 Swiss Francs.
!!"
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push the price in either direction depends on what level the price is at, how much money she
has, and how much the organizer has decided that one unit of money should affect the price at
any given level.
Payoffs are based on the ultimate vote outcomes. Because real money is used, traders are
subject to the monetary risks and returns that result from their trading behaviour. The data
utilized for the analysis are from vote-share markets. The predictions from vote-share markets
may be read as a direct prediction of the share of yes-votes the referendum in question is
estimated to receive.
We calculate several indicators from our prediction market data. In order to test the
relationship between expected closeness and turnout at the level of each proposal we simply
take the absolute deviation from 50% yes-votes as an indicator. In order to test the effect of
expected closeness according to the salience hypothesis we take for each voting day the
expected closeness of the proposal which the media covered the most extensively as the
indicator (see below). In order to test the issue public hypothesis we calculate the average of
the expected closeness of all proposals at the same voting day.* Finally, since we expect that
only the closeness of first order votes impacts on the decision of voters whether to turn out,
we measure this indicator at the level of the national proposals.
In order to measure the campaign intensity, we have counted the number of newspaper
articles in the two largest quality newspapers of the German speaking part of Switzerland
(Neue Zürcher Zeitung an Tages Anzeiger). These two outlets are based in Zurich and include
a regional part covering news related to the Canton of Zurich. To measure campaign intensity
in Swiss direct-democratic campaigns, some scholars have used the number of newspaper ads
(Kirchgässner and Schulz 2005; Kriesi 2005). Given that this indicator pertains to the
commercial domain, it rather reflects the money spent by the political actors than the intensity
of the overall public debate in a given campaign. For this reason, we decided to rely on the
number articles published in the editorial part of the two newspapers of interest.
We include five control variables. First, we add an indicator for the degree of conflict among
the elites on the various proposals included. Following Kirchgässner and Schulz (2005), we
measure this degree of conflict as the difference between the share of the largest eight parties
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*
Ideally, the average would be weighted according to the sizes of the issue publics that are activated by each
proposal."
!#"
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that are in favour vs. opposed to the proposal. This number is then weighted by the vote
shares of the parties in the last national election in the Canton of Zurich. The smaller the
number the stronger the conflict, since the more similar the size of the two opposing camps
prior to the vote. Second, we include a dummy for whether a proposal is cantonal or national.
This dummy allows us to distinguish between first and second order votes. Consequently this
variable catches to an important degree how much is at stake. The operationalization is not
perfect, of course, because even at the same territorial level the implications of proposals can
be more or less far-reaching.!+ Third, we include a dummy on whether a vote was (linked to) a
counter proposal. In the case of counter proposals the complexity of voting is strongly
increased because voters do not only have to decide whether to accept or decline a proposal,
but also whether to accept or decline a counter proposal and which of the two (proposal or
counter proposal) they prefer in the case that both are accepted by a majority of the voters.
Finally, we also include the number of proposals per voting day both at the national and
regional level as well as on the national level only.
[Table 1 about here]
Table 1 lists the voting dates, the names of the various proposals, and the level at which the
vote has taken place. The names of the proposals have been shortened for the sake of
simplicity. The data are available for 40 proposals on which the electorate of Zurich had the
opportunity to vote during between 2012 and February 2014. Since typically Swiss citizens
vote four times per year on national and regional issues, the data is clustered into nine voting
days.!! Among the 40 proposals covered, 26 referred to the national and 14 to the regional
level.
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!+
The only alternative measure used so far has based its estimation on the amount of money at stake
(Kirchgässner and Schulz 2005). Such an operationalization, however, is not able to grasp the importance of
some more symbolic proposals and is based on often-contested assumptions concerning the potential effects of a
proposal.
!!
Data is missing for seven cantonal proposals.
!$"
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Results
Figure 1 shows the bivariate distribution of the expected closeness of the outcome and the
turnout levels for each proposal. While turnout varies between 32% and 57%, the expected
closeness of the outcome varies between 0.2% and 28.9%. The large variation in expected
closeness might be surprising, since we would assume that elites do not engage in a campaign
if there is low uncertainty about the outcome of the vote. However, there are two reasons for
why expected closeness might be far from 50%. First, in the case of changes in the
constitution referenda are mandatory. In these cases proposals are often unchallenged and
hence unequivocal results are expected. A second reason is that while provoking a popular
vote the goal of elites is not always to win the vote (Bernhard 2012). This is particularly true
in the case of initiatives. Instead, the goal is often in setting certain topics on the political
agenda. The goals of the initiative backers are sometimes even achieved before their proposal
is voted on. This is the case when the opponents to the initiative change legislation in the
direction of the initiative in order to reduce the probability of its success (Kriesi 2005;
Papadopoulos 2001). This can explain why in Switzerland the organization of initiatives is a
popular tool although only very few of them are approved by the voters.!#
[Figure 1 about here]
In order to test our hypotheses we regress turnout on the average expected closeness of the
national proposals (Table 2). Since we expect the turnout rates of the proposals at the same
voting day not to be independent from each other, we make use of OLS regressions with
clustered standard errors. The reason to look at the effect of the average closeness per voting
day is that according to the issue public hypothesis it should be the combined closeness of the
proposals of the same day that impact on turnout. And due to the fact that at the national level
there is more at stake, this can be restricted to these first order votes. As Table 2 reveals, the
average expected closeness at the national level is related to turnout both without controlling
for other variables (model 1) and with controls (model 2) in a significant way. This can be
considered evidence for the issue public hypothesis, because the expected closeness of several
"""""""""""""""""""""""""""""" """""""""""""""""""""""""""""
!#
The rate of approval at the national level is less than 10%.
!%"
"
votes seems to have an impact on turnout. In contrast, we do not find evidence for the salience
hypothesis: the relationship between turnout and the expected closeness of the most important
proposals remains insignificant.
The effect of the average expected closeness of the outcomes of national proposals is
substantial and direct. With a one percent increase in expected deviation from 50% turnout is
reduced by 0.5% (model 1) to 0.9% (model 3). The fact that the relationship is direct can be
deduced from the fact that the size of the coefficient does not decrease when we control for
campaign intensity (model 3). The number of newspaper articles – our measure for campaign
intensity – seems to have an additional instead of a mediating effect on turnout. This can be
deduced from the fact that the number of newspaper articles does correlate with turnout but
has no negative impact on the correlation between the latter and the average expected
closeness of national proposals.
[Table 2 about here]
Summary
This paper contributes to the low number of studies that have analyzed the impact of expected
closeness of outcomes from voting decisions on turnout. While this relationship has been
found to be solid with regards to elections, it has remained debated for popular votes. Using
prediction market data, this paper has argued that this indicator is a more valid measure of
expected closeness than previously used indicators. In the future it might also be used to
analyze the relation between the expected closeness of the outcome and turnout in elections.
The analysis of 40 ballot propositions from 2012 to 2014 indicates that the average expected
closeness of national votes triggers turnout in the context of Swiss direct democracy. While
the evidence on a positive relationship between the expected closeness of the outcome and
turnout in Swiss popular votes is in line with previous research by Kirchgässner and Schulz
(2005), our findings deviate in several important ways. First, instead of finding evidence for a
relationship between a single national vote and turnout for all other (national) votes at the
same voting day, we find evidence for an effect of the average closeness of the expected
closeness of the outcomes of national ballots and turnout for all other proposals. Second,
while Kirchgässner and Schulz (2005) find evidence only for an indirect effect of expected
!&"
"
closeness mediated by campaign intensity, we find evidence for a direct effect of expected
closeness of the outcome and turnout.
Some of these deviating results may be due to the fact that we have been able to make use of a
particularly accurate measure of expected closeness. However, this does not mean that the
debate on expected closeness of the outcome and turnout in popular votes is settled. Future
research might want to make use of a larger number of cases when making use of prediction
market data. In addition, better data is needed in order to control for other important triggers
of turnout such as the importance of the proposal and its complexity.
!'"
"
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"
!*"
"
Figure 1: Scatterplot of the expected closeness of the outcome and turnout (N=40)
#+"
"
Table 1: Overview of the proposals included in the dataset
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"
"
#!"
"
Table 2: OLS regressions with standard errors clustered by voting day
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