Campaigning in Direct Democracies:
Initiative Petition Signing, Voter Turnout,
and Acceptance
Katharina E. Jaronicki
November 2013 Discussion Paper no. 2013-33
School of Economics and Political Science,
Department of Economics
University of St. Gallen
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Email
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School of Economics and Political Science
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Campaigning in Direct Democracies: Initiative Petition Signing, Voter
Turnout, and Acceptance 1
Katharina E. Jaronicki
Author’s address:
1
Katharina E. Jaronicki
Swiss Institute for Empirical Economic Research (SEW-HSG)
Varnbüelstrasse 14
CH-9000 St.Gallen
Phone +41 71 224 3459
Fax
+41 71 224 2302
Email
katharina.jaronicki@unisg.ch
For valuable comments, I thank Monika Bütler, Patricia Funk, Andreas Steinmayr, and Christian Marti, as well
as participants at the Sinergia Workshop of the Swiss National Science Foundation (February 2013), and the
Ph.D. Seminar at the University of St.Gallen (May 2013).
Abstract
This paper investigates whether petition signing campaigns for popular initiatives constitute a
partisan campaigning instrument by revealing potentially relevant information to the signer
which increases the benefit from voting or reduces its cost.
The analysis is based on the complete sample of Swiss federal initiatives between 1978 and
2000 with aggregate voting data at state level. The results suggest that initiatives collecting
many signatures yield higher approval rates at the polls. Petition signing is, however, not
significantly related to turnout, and is dominated by initiative-specific characteristics. To
show that the relation between signatures collected and acceptance reflects a causal
campaigning effect, several approaches are pursued to control for voter preferences which
potentially could drive both signatures and acceptance rates.
This research relates to turnout and voting literature in general, and to campaigning and
voter motivation more specifically. Further, it extends a small stream of literature analyzing
signature collection for initiatives.
Keywords
Direct democracy, Initiatives, Signatures, Campaigning, Turnout, Voting, Information.
JEL Classification
D72, D80.
1
Introduction
The main purpose of direct democracy is to provide citizens with political powers beyond the
mere election of political representatives. The availability of initiatives and referenda serves as
a mean to correct undesirable policy outcomes, or as a threat to politicians already in the early
legislative process (Feld & Matsusaka, 2003). A second, less obvious purpose of direct democracy
is to educate voters to become active citizens (Tolbert & Smith, 2005). The possibility of shaping
and influencing policies as well as deciding about single issues should awake the interest of voters.
Active participation would then lead to better informed and interested voters who ideally become
regular voters and responsible citizens.1
A frequently used direct democracy instrument is the voter initiative which allows citizens or
political minorities to put issues on the political agenda. To qualify an initiative for ballot, the
initiating group needs to collect a legally specified amount of signatures to prove that the issue
enjoys sufficient support in the population. This qualifying stage of initiatives is at the core of my
research. In this paper, I investigate whether initiative petition signing increases the probability
to subsequently turn out on election day and accept the initiative at ballot. I hypothesize that by
signing an initiative petition the signer is exposed to campaigning and receives relevant information
about the issue at question which increases his awareness of the issue. Information enhances his
benefit from voting and facilitates his decision. The awareness created makes the issue more salient
such that signers perceive it to be relatively more relevant. Also, signing an initiative petition means
that this person should be more likely to accept the initiative because of positive motivation, or a
feeling of moral obligation.
These hypotheses are tested with aggregate data from all popular initiatives at federal level in
Switzerland qualified and voted between 1978 and 2000. The main advantage of the Swiss setting
over e.g. data from the U.S. is the availability of the exact number of signatures for all initiatives
which allows generalizable results. All data on collected signatures and voting results is at state
level2 which allows to use regional variation in the data.
The results show a positive relationship between signatures collected and the initiative’s acceptance rate at ballot: signature collection displays increasing returns to scale such that collecting an
additional signature increases voter acceptance by more than one over a relevant range of observations. Possible explanations are spillover and network effects from talking to family and friends
about the initiative. Results remain similar after the inclusion of state and initiative fixed effects,
as well as relevant political and socioeconomic controls. Signatures per capita and voter turnout
are also positively related. However, the effect becomes insignificant once initiative fixed effects
are controlled for together with state fixed effects or control variables. Possible explanations are
1
Tolbert and Smith (2005) provide an extensive overview of the development of direct democracy and its functions
in U.S. history.
2
Switzerland has a strong federal structure and is divided into 26 states - the cantons.
relatively low roll-off rates for federal ballots which means that turnout with multiple elections on
the same day is almost identical for all propositions (Schmid, 2013).
Regarding the significant relation between acceptance rates and signatures, the main empirical
challenge to campaigning research is the question of causality (Gerber & Green, 2000): if there
exists a further variable, e.g. voter preferences, which drives both the number of signatures and
acceptance rates in a state, a significantly positive coefficient would be expected in the regressions.
However, the relation between initiative petition signing and voter behavior would not be causal.
To shed light on the question of causality, I propose several extensions to the baseline model. I
develop three controls to proxy voter preferences. I firstly account for elite mobilization by political
parties (Kriesi, 1995, 2006). Voters identify with their preferred parties and look to them for voting
cues. Therefore, I control for the fraction of the population which has elected parties supporting
the initiative, i.e., issuing a positive voting recommendation. Next, I identify states which were
particularly affected by the initiative, and thus have a reason to have either positive or negative
preferences for the initiative. Last, I use voting results from thematically closely related referendum
ballots to proxy voter preferences. The baseline results regarding acceptance prove extremely robust
to the inclusion of these preference variables.
In a second approach, I exploit state variation in the introduction of postal voting to account
for the reduced possibility of collecting signatures from regular votes near places of elections. Since
regular voters are usually better informed, the introduction of postal voting increases the probability
that random citizens sign the initiative. Also, the cost of collection increases since it gets more
difficult for initiating groups to collect signatures. Indeed, with postal voting the effect of petition
signing on acceptance decreases but remains highly significant.
This paper generally relates to investigations in turnout and voting, and gives further insight
on why people vote (for overviews see for example Aldrich (1993), Matsusaka and Palda (1999),
Coate and Conlin (2004), or Feddersen (2004)). It is particularly close to models of voting including information. Also, initiative petition signing can be seen as a particular form of face-to-face
campaigning which wants to motivate signers to support the initiative. Regarding the link between
petition signing and acceptance, this paper further relates to literature about cognitive dissonance
(Festinger, 1957): should a signer reject the initiative at ballot, he would feel discomfort from two
action obviously contradicting each other. By signing the petition, he feels morally obliged to also
cast a positive ballot. Further, my research extends a small literature concerning the link between
petition signing and voter turnout by also analyzing acceptance behavior (Boehmke & Alvarez,
2012; Parry, Smith & Henry, 2012). In addition, my results are more generalizable thanks to a
larger sample size, and explicitly addressing the issue of causality.
To analyze the qualifying stage of the initiative process the use of data from Switzerland is
particularly well suited. First, comparable data are available for a long time period such that
findings are generalizable to many initiatives over several decades. In contrast to the qualifying
stage in many U.S. states, collected signatures are always fully counted and thus the collected
and valid number is known with certainty. Invalid signatures from double signers or foreigners on
average amount to 2.5 percent of the collected signatures and therefore do not pose a big problem.
4
A further advantage of the Swiss initiative process is that the data are available from all 26 Swiss
states such that regional differences in the signature collection process can be accounted for. Also
by looking exclusively at federal initiatives, I guarantee that all states are exposed to the same
institutional framework, have the same regulation regarding the initiative process, and are thus
comparable. In the U.S. such comparisons between states are virtually impossible since regulation
varies from state to state (e.g. different signature requirements). By using Swiss data I also
overcome the registration problem apparent in the U.S.: while in many U.S. states voters need to
register before they can vote and possibly even face some registration time restrictions, Swiss voters
are automatically registered. The advantage of this regulation is that registration does not require
additional effort and does not pose a hurdle to voter participation. At the same time, the initiative
process in Switzerland resembles processes in other direct democracies which makes my findings
comparable to other settings. In sum, Switzerland being one of the places with the most frequent
use of direct democracy makes it the perfect institutional setting to study my research question.
The paper begins by laying down the theoretical foundation and building the hypotheses based
thereon in section 2. In section 3, I give background information on the Swiss initiative process,
data, and descriptives. The estimation strategy is described in section 4. I present the baseline
results regarding turnout and acceptance in section 5. It is followed by attempts to establish
causality of the results, and various robustness checks. The last section gives a brief discussion of
the results and concludes.
2
Theoretical justification and hypotheses
The process of signature collection to qualify an initiative for ballot can be seen as one, albeit
unusual, form of face-to-face campaigning (Parry, Smith & Henry, 2012). Therefore this paper
directly relates to research on voter mobilization. This literature finds that turnout is significantly
and positively affected by campaigning efforts. Early advances attribute some effect to mobilization
through campaign spending (Copeland, 1983; Patterson & Caldeira, 1983; Caldeira, Patterson &
Markko, 1985). Later investigations based on field experiments note that the likelihood of voting is
increased by face-to-face contact (Gerber & Green, 2000; Green, Gerber & Nickerson, 2003; Niven,
2004), telephone calls from dedicated callers (Nickerson, 2006), and sometimes nonpersonal messages (Dale & Strauss, 2009). Smith (2001) notes that campaigning can be interpreted as increasing
civic duty from voting by creating more awareness for the ballot (Riker & Ordeshook, 1968). Since
petition signing can be viewed as a campaigning tool, it can be interpreted as increasing the benefit
from voting. Consequently, it should relate positively to voter participation and acceptance.
Generally, two forms of campaigning efforts can be distinguished: non-partisan and partisan
efforts (Parry et al., 2008). In the first form, voters are generally motivated to turn out. In contrast,
partisan campaigning tries to motivate voters to turn out for a particular candidate. My research
contributes to the latter type since citizens are expected to cast votes in favor of the initiative.
Connected to the motivation literature mentioned above, there is a small literature emerging
5
which has the motivational effect of initiative petition signing on turnout at its core, and is thus
closest to this paper.3 Parry, Smith and Henry (2012) analyze individual voting data matched with
signature records from three initiative ballots in Florida as well as Arkansas and find a significantly
positive effect of petition signing on turnout only in one of their three models. In particular,
campaigning effects are stronger for irregular voters. In a similar vain, Boehmke and Alvarez
(2012) conduct their analysis with county-level data from eight Californian initiatives. The results
show a positive and significant effect of petition signing on turnout. In addition, they find a positive
relationship between petition signing and voter registration, and a negative one between signing
and roll-off rates. However, their results are based on a small samples and might therefore not be
generalizable to other settings. Similarly, Parry, Smith and Henry (2012) find a positive effect for
only one of the three initiatives they analyze. Next, Boehmke and Alvarez (2012) do not control for
voting history in the counties. Consequently, their analysis may suffer from endogeneity problems
since voting history is an important driver of turnout. Both papers do not provide evidence about
the effect of petition signing on acceptance probabilities. For Parry Smith and Henry (2012) this
is not feasible since voting records are not publicly available. I therefore extend previous research
by addressing this issue.
2.1
Turnout
Signature collection can be interpreted as a face-to-face campaigning device which provides
potential voters with information or creates awareness of the initiative issue. Signing an initiative
petition might thus either activate the voter’s positive predisposition towards the initiative issue, or
add information to the undecided voter. Voting models predict awareness, information or factual
knowledge to positively impact the probability of turnout. Already Downs (1957) recognized that
information plays a crucial role in the participation and voting decision process of citizens. Even
though information is not explicitly included in the standard voting models, it can be interpreted
as increasing the voter’s benefit from participating (Smith, 2001). Voters understand the issue
better and can evaluate the consequences of a potential vote more precisely than before. At the
same time, information can also decrease the cost of voting (Matsusaka, 1995).
In a series of papers Feddersen and Pesendorfer develop voting models explaining abstention and
roll-off without voting cost and including the role of information (Feddersen & Pesendorfer, 1996,
1999). In their 1996 model, agents are either partisans for one of the candidates, or independents
preferring one of the candidates depending on the state of the world. Next, voters are either
informed or uninformed about the state of the world. In equilibrium, partisans support their own
3
Early advances in the analysis of initiative petition signing are scarce, mostly due to difficult data collection
work. By drawing two random samples, one from registered voters and one from registered voters who signed a
particular initiative, Neiman and Gottdiener (1982) observe that signers show more political interest and knowledge
about the initiative than non-signers. However, it is beyond the reach of their study to show a causal relationship
between signing an initiative and gaining more political knowledge through this channel. By also working with two
samples of signers and the general population, Pierce and Lovrich (1982) surprisingly find that signers significantly
underreport signing a petition when questioned about it several months after the ballot. They conclude that micro
data about petition signing from surveys might be severely biased.
6
candidates. Informed independent agents, who by definition know the state of the world, vote for
the “correct” candidate. However, uninformed independent agents have equilibriums in which they
are strictly better off abstaining. The intuition is that if they knew the state of the world, they
would vote identically as the informed independent agents. Since they lack this information, they
may be better off letting informed independent agents vote for the “right” candidate. The rationale
of this model can easily be adapted to the setting in this paper.
Voters usually have some innate predisposition when it comes to making judgements about
political issue (Copeland, 1983): they can either favor the issue, oppose it, or be indifferent. Partisan agents will vote sincerely, and if asked to sign the initiative petition, supporters will do so
while opponents will decline. Independent agents, however, need information in order to be able
to evaluate the issue at question. Being approached to sign an initiative petition can hence be
interpreted as receiving an informative message. When asked by a signature collector, potential
signers learn the initiative title and the initiative text. Though this information is not complete, it
should be enough to help agents understand the main features of the initiative, and create awareness of the issue. If the information is favorable, they will sign the initiative. Consequently, their
best strategy is also to vote in favor of the initiative. This model thus predicts that independent
agents who sign the initiative should participate in the election and vote in favor of the initiative.
Further, partisan agents should participate and vote sincerely, i.e., revealing their true preferences,
regardless of signature collection. In the light of this models, the act of signing can be interpreted
as an informative action.
Based on this understanding, the first hypothesis to be tested is the following:
Hypothesis 1 Signing an initiative petition increases the probability of turning out in the subsequent initiative ballot, everything else held constant.
2.2
Acceptance
As described above, voting models with information like in Feddersen and Pesendorfer (1996,
1999), predict that partisan as well as favorably informed independent voters should turn out more
likely. It should thus be expected that citizens with a positive predisposition about the initiative,
and previously indifferent signers should be more likely to accept it at ballot.
Alternative theoretical considerations relevant here come from psychology. The theory of cognitive dissonance predicts that actions might be initiators to form preferences (Festinger, 1957; Mills,
1958). In more detail, conducting some action might be the reason to form believes which dictate
to act accordingly to the first action in the future. This contrasts with standard economic models
in which preferences usually lead to actions. Related research based on the theory of cognitive
dissonance in the voting context is e.g. by Mullainathan and Washington (2009). They find evidence that voting for a certain candidate leads to a more favorable opinion about his policies after
the election. Related to this, Beasley and Joslyn (2001) find supporting evidence of a widening
evaluating distance between candidates after committing to one of them by voting.
7
In this paper, the act of signing an initiative petition can be interpreted as such an initiating
action. Driven by his psychological need for consistent behavior, the signer is urged to accept the
initiative at ballot. Rejecting the initiative, in contrast, would lead to a feeling of discomfort caused
by the clashing actions of signing (supporting) and voting against (rejecting) the initiative. The
theory of cognitive dissonance thus predicts that of those who participate in the election, signers
should be more likely to accept the initiative. The second hypothesis to be tested in this paper is
thus
Hypothesis 2 Signing an initiative petition increases the probability of voting in favor of the
initiative at ballot, everything else held constant.
3
Institutional background, data, and descriptives
3.1
Institutional background
Switzerland has particularly strong direct democratic institutions. At federal level, its instruments include the mandatory referendum, optional referendum, and the constitutional initiative.
Mandatory referenda take place after the parliament proposed a change to the Swiss constitution
such that voters need to agree to the change before it comes into force. For all other federal legislation which does not amend the constitution but e.g. laws, collecting signatures from at least 50.000
citizens leads to an optional referendum. If the referendum is rejected at ballot, the legislation
does not come into force. In addition, the 26 Swiss states, the cantons, which have many political
liberties have their own direct democratic institutions. In this paper, I concentrate exclusively on
the initiative at federal level.
The initiative at federal level in Switzerland was first established in 1891 and is concerned solely
with constitutional changes (Linder, 2007). At the qualifying stage, the initiating petitioners need
to collect at least the legally required 100.000 signatures within 1.5 years (50.000 signatures until
1978). No additional requirement regarding the signature distribution in Swiss states exists. Upon
successful completion of the signature collection, government and the two chambers of parliament
decide whether to issue a counter proposal or not. The counter proposal is defined as an alternative
or compromise to the initiative which would also amend the constitution.4 In case of a counter proposal, it is voted simultaneously with the initiative. Should the petitioners withdraw the initiative,
only the counter proposal is voted upon. If the initiative or the counter proposal are voted upon
individually against the status quo, the absolute majority of votes decides whether it comes into
force or not. Should both, the initiative and the counter proposal, be voted simultaneously, voters
can accept only one of the alternatives, or reject both. In 1987 this regulation changed and the
4
More precisely, this is called a direct counter proposal. Another option is to issue an indirect counter proposal
which is usually a law and thus not at the constitutional level. However, in most cases no ballot takes place after
indirect counter proposals which is why none of them is in this sample. In what follows, the term counter proposal
is used synonymously with the direct counter proposal at constitutional level which has to be voted by citizens.
8
tie-break question was introduced. This allows voters to vote both the initiative and the counter
proposal versus the status quo, and choose in the tie-break question which of the two they like best.
The tie-break question is decisive should both of the proposals receive more than 50 percent of the
votes.
In the sampling period 1978-2000, initiative and counter proposal were voted simultaneously
three times. These infrequent cases are likely to stir additional attention and to be prone to strategic
voting, i.e. inconsistent voting profiles may occur (Bochsler, 2010). They thus might have some
additional effect on turnout and acceptance. Also, voting rules regarding these cases have changed
during the sample period. For these reasons, all three initiatives are excluded from the sample.
3.2
Data and descriptives
For the estimation, I use the dataset of all Swiss federal initiatives that have started data
collection in 1978 or later, and have been voted no later than 2000. The reasons to restrict the
sample to this time period are threefold. First, the initiative threshold has doubled to 100.000
signatures in 1978 which makes initiatives before and after that date more difficult to compare.
Also, in 1979 part of the state Bern split away to create the new state Jura. Consequently, the
number of states increased in this year. Last, there are no comparable socioeconomic controls at
state level available outside the time span 1970 to 2000.5 Average income is only available since
1974.
There has been a total of 68 initiatives in the observation period. As explained above, 3 of
these are excluded from the sample because the initiative and the counter proposal were voted
simultaneously. Of the remaining 65 initiatives, 58 ballots have been about the initiative alone, i.e.,
there has been no counter proposal. In the remaining 7 cases the initiating committee withdrew the
initiative after the parliament decided to formulate a counter proposal such that only the counter
proposal was voted. A look at voting results points to the existence of differences between ballots
dealing exclusively with either initiatives or counter proposals: while 5 of 7 counter proposals were
accepted, only 4 of 58 initiatives received a majority of votes. The reason for this difference lies
in the fact that though counter proposals take up the most important issues of the initiative, they
compromise on some other points. In this way, they are accessible to a larger part of the population
than initiatives which usually address minority issues.
Switzerland comprises 26 states which vary in size and other characteristics. By far the largest
state Zurich was populated by nearly 790.000 eligibles in the year 2000 while Appenzell Innerrhoden
had only about 10.000 eligible citizens. Population-weighted average turnout for the initiative
ballots was 42.9% and varied between 13.8% and 82.4%. Not only this variation but also differences
in state mean turnouts are large: the highest average participation rate prevails in the states
Schaffhausen6 (68.14 %) and Solothurn (50.85 %), and the lowest in Vaud (35.66 %). The upper
5
Control variables are taken from Swiss censuses which are conducted every ten year. Due to a methodological
change, the 2010 census is not comparable to the prior ones.
6
Schaffhausen poses a special case since it is the only state with compulsory voting during the observation period.
9
30
20
6
10
Density
4
Density
0
2
0
.2
.4
.6
.8
0
.05
.15
.2
.3
Yes votes / eligibles
.4
.2
8
.1
Valid signatures per capita
Density
4
0
0
1
2
2
Density
3
6
4
5
Turnout
0
.2
.4
.6
Yes votes / voters
.8
1
0
.1
.5
Figure 1: Histogram of signatures per capita, turnout, acceptance rate
left panel of figure 1 shows the distribution of population size-weighted state turnout in the sample.
Average population-weighted acceptance defined as the number of yes votes divided by the total
number of votes amounts to 38.21%, and varies between 4.98% and 93.12%. Similarly to the turnout
data, notable differences between states exist: Basel City has the highest average acceptance rate
with 44.90%, while the lowest average can be found in Appenzell Innerrhoden with 29.94%. The
lower panels of figure 1 depict the distribution of the acceptance rate as usually defined (yes/voters)
in the lower left panel and defined as yes/eligibles in the lower right panel. the distribution of voter
acceptance is right skewed, which reflects that initiatives usually get rejected at ballot.
In the aggregate, between 101.337 and 390.273 valid signatures with a mean of 132.052 have been
collected for the 65 initiatives in the sample. In terms of signatures per capita (signatures/eligible
citizens), about 3.0% of the Swiss eligible population have signed the initiatives on average with
a variance of 2.2%. Variation between states is large. States with most signatures on average are
Basel City (5.7%) while the lowest mean can be observed in Appenzell Innerrhoden (1.3%). In
all estimations, I use the number of valid instead of collected signatures since this is the publicly
reported number. Also, the number of invalid signatures is small in the aggregate (mean invalid
signatures of collected signatures are 2.50% with a standard deviation of 3.49%), and can consequently be neglected. The distribution of all state values of signatures per capita is depicted in the
upper right panel of figure 1.
Descriptives of the main variables are reported in table 1. All data are at state level. Data on
signatures for initiatives qualified between 1978 and 1998 are hand-collected from the homepage
of the Swiss Federal Archive. Since 1999, the signature data are available on the homepage of
Abstainers have to pay a symbolic fine so that Schaffhausen traditionally has a more active electorate on average
(Federal Announcement, 2003). Since I include state fixed effects in the regressions, this should not constitute a
problem for the estimates.
10
the Swiss Federal Chancellery. A detailed description of the data, its sources, and how it can be
accessed is available in the appendix.
Table 1: Descriptives
Turnout (voters/eligible)
Acceptance (yes/eligible)
Acceptance (yes/voters)
Valid signatures per capita
Mean
Std. Dev.
Min
Max
0.4287
0.1599
0.3821
0.0297
0.0959
0.0732
0.1661
0.0217
0.1384
0.0178
0.0498
0.0001
0.8244
0.5774
0.9312
0.2476
Note: 1690 observations. Summary statistics are weighted according to Swiss eligible population size in the states.
4
Estimation strategy
As a first step of my estimation strategy, I test in a baseline framework whether petition signing
and turnout as well as acceptance are related. The main focus thereafter is to establish a causal
relationship between these variables.
4.1
Turnout
Denote the eligible population in state s at the point in time when the initiative i is voted
eligiblesi . Signaturessi is the number of collected signatures for initiative i in state s, and participating voters are denoted by voterssi . The main independent variable is defined as the number of valid signatures collected divided by the state eligible population (signatures p.c.si =
signaturessi /eligiblesi ), which is similar to the variable signatures per capita used by Boehmke
and Alvarez (2012). By definition, this variable is constrained to values between 0 and 1: it takes
the value 0 if no one signs the initiative, and the value 1 if the complete eligible population of a
state would sign it. Turnout for initiative i in state s is defined as the fraction of eligible citizens
who participates, i.e., turnoutsi = voterssi /eligiblesi . si denotes the error term. Equation (1)
shows the baseline linear estimation equation.
turnoutsi = β0 + β1 signatures p.c.si + si
(1)
I estimate a weighted least squares model with proportional weights according to the eligible
state population. Weights are necessary for proportional data because they correspond to many
more individual observations in large states than in small states. The coefficient of interest is β1
which is expected to be positive. In a second specification, I also add the squared value of signatures
per capita to account for nonlinear effects. In line with campaign spending literature, I expect a
negative coefficient for the quadratic term.
The analysis could potentially suffer from reverse causality. If voters who regularly participate in
elections are also more likely to sign initiatives, a positive correlation between these two variables
11
would be the consequence. But causality could not be established that way. This problem has
been widely addressed in the campaigning literature. A remedy has been to either conduct field
experiments (Gerber & Green, 2000), or to control for the voting history of those being contacted
(Parry, Smith & Henry, 2011). Since the first one is not an option, I need to control for regular
voters in the states. Generally, voter mobilization seems to have a stronger effect on non-habitual
voters because habitual voters will participate in an election regardless of whether they have been
contacted or not (Huckfeldt & Sprague, 1992). Also Parry et al. (2008) find voting history to be a
good predictor of turnout.
State voting history is accounted for by controlling for turnout in the election for national
parliament preceding the initiative ballot. This is a good approximation of voting history for
several reasons. First, it is a parliamentary election and thus signature collection cannot play a
motivating role for turning out in this election. Second, on election day the parliamentary election
is the only federal election taking place. This means that every voter deciding to participate does
so because he wants to elect his political representative. Even though there might be state-level
votes on the same day, federal parliamentary elections are likely to be more important elections.
The control for voting history is based on voting data from Swiss parliamentary elections starting
with the year 1979 and changing every four years. This voting information is available from the
Swiss Statistical Office.
4.2
Acceptance
The second hypothesis examined in this paper is that signing an initiative petition increases
the probability of subsequently accepting it at ballot. Y essi stands for the number of yes votes
initiative i receives at ballot in state s. I define the acceptance rate as the number of valid yes votes
divided by the state eligible population (acceptancesi = yessi /eligiblesi ). Acceptance thus denotes
the part of the eligible population voting in favor of the initiative. The standard way to define
acceptance would be to divide yes votes by the total number of votes. I use the number of eligibles
in the denominator instead of the valid votes because it facilitates the interpretation of the impact
signatures per capita have.7 The baseline estimation equation is stated in (2). Analogously to the
turnout estimation, I conduct a second estimation including the squared value of signatures per
capita. Again, weighted least squares are used for the estimation.
acceptancesi = β0 + β1 signatures p.c.si + si
(2)
Similarly to the turnout analysis, reverse causality is an important issue to address. If underlying
preferences for a particular initiative are high, a high number of signatures as well as high acceptance
rates might be expected. A positive effect in the estimation results would then only reflect the
underlying preferences which have led to an intensive signature collection and high acceptance
7
For robustness, I repeat all main regressions using acceptance defined in the standard way as the number of yes
votes divided by sum of yes and no votes, acceptancesi = yessi /(yessi + nosi ). The significance of the results remains
unaffected. The results are available from the author on request.
12
at the same time. While the expected coefficient would be positive, the effect would not result
from causality of more signatures on acceptance. An ideal control variable would be a preference
distribution of the state population for each initiative topic. Including this as a control would make
sure that variation explained by signatures is not predominantly due to any underlying preferences.
To account for this causality issue, I propose several extensions of the baseline specification in
section 5 with the goal to control for voter preferences.
4.3
Fixed effects and controls
A first step towards a causal analysis is to include state and initiative fixed effects.8 State
fixed effects control for time-invariant unobserved differences in the states. Prominent examples
for the Swiss states are political institutions like strong direct democratic elements, or cultural
differences between the mainly German-, French-, or Italian-speaking states (e.g., Funk, 2010;
Lüchinger, Rosinger & Stutzer, 2007). Initiative fixed effects account for unobserved differences
between initiatives where the impact is identical for all states. Among such initiative fixed effects
are the salience of an initiative issue, or campaign effort at federal level.
I further extend the baseline regression by adding political and socioeconomic controls which
are standard to use in the turnout and voting literature. The length of the political process is likely
to have an effect on turnout and acceptance. The longer the time between the qualification of the
initiative and the respective ballot, the weaker should the campaigning effect be9 : the issue might
lose its salience and citizens their interest in the topic.
The availability of initiatives or referenda at ballot is a motivating factor for voters to turn out
(Tolbert & Smith, 2005). While a small number of additional initiatives at ballot has a motivating
effect, however, too many issues to be voted upon have a much smaller effect (Bowler & Donovan,
1998; Magleby, 1984). Similarly to the study of Tolbert and Smith, I thus control for the number
of state issues voted at state level on a particular date in addition to the initiative, and its squared
term. Additional ballots at federal level are not included in this control since they are absorbed in
the initiative fixed effects. The expectation is a positive coefficient for the former and a negative
for the latter. Also, turnout research usually finds stronger campaigning effects in years without
presidential elections (e.g. Smith, 2001; Tolbert & Smith, 2005). Therefore, I introduce a dummy
taking the value one in years with federal parliamentary elections which are the most important
elections in Switzerland. The intuition is that public and media attention focuses more heavily
on the national election such that public attention is drawn away from initiative and referendum
ballots. I would consequently expect a negative coefficient.
In some cases in the sample, the initiative has been withdrawn and only the counter proposal
is voted upon. Counter proposals constitute compromises by the parliament which take up the
8
An alternative estimation strategy could use a grouped logit estimator. While it is a good approach to analyze
proportion data, it suffers from the efficiency that fixed effects cannot be included.
9
During this time, the government and the chambers of parliament discuss the initiative, and decide whether to
issue a counter proposal. The maximum duration of the process is fixed by law. However, it is possible to extend the
process by several months up to years. (Federal Act on the Federal Assembly, 2002)
13
main initiative issue but make some concessions. Hence, they are less extreme than initiatives, and
appeal to a larger public by being closer to the median voter’s desired policy. For this reasons, I use
a dummy variable taking the value 1 if the initiative was withdrawn but the counter proposal voted
upon. While the intuition for turnout is not clear-cut, a positive coefficient should be expected in
the acceptance regressions.
Further, I use socioeconomic control variables common to turnout and voting research. Income
and education are positive drivers of turnout (Wolfinger & Rosenstone, 1980). Also, education,
age, and unemployment play an important role in an individual’s ability to understand and process
information which makes them indispensable voting controls (Matsusaka, 1995). Regarding voting
propositions with financial issues at stake, income and education are relevant predictors of acceptance. For income control, I take the average taxable income at state level. Education is measured
by the fraction of population older than 15 with tertiary education. I also include the fraction of old
population in the analysis which is measured by the percentage of people 65 years old or older (e.g.,
Parry et al. (2008) find age to be the second most important driver of turnout in their analysis).
The general notion is that older people are more likely to vote. Also unemployment is often used in
turnout analysis. E.g., Rosenstone (1982) finds lower voting probabilities for unemployed or poor
people. I therefore expect unemployment to have a negative effect on turnout and measure it by
the unemployment rate of the population older that 15 years. Descriptives are in table 9 in the
appendix.
The average taxable income is from the Federal Tax Administration. The information on the
number of state propositions at ballot comes from the Centre for research on direct democracy.
The dates of initiative qualification and ballot were taken from the homepage of the Swiss Federal
Chancellery. The control for postal voting comes from Funk (2010) which was updated for the
states Ticino and Valais by calling the state administrations. All other controls (population older
than 64 years, tertiary education, unemployment) were provided by the Swiss Statistical Office and
can be found in the Swiss census. Data on average taxable income are biannual, and census data
are compiled every ten years. The relevant censuses are 1970, 1980, 1990, and 2000. To receive
yearly data, I linearly interpolate the data for the missing years.
5
Baseline results
In this section, I present the baseline results and some extensions regarding turnout, section
5.1, and acceptance, section 5.2. In section 5.3, I extend the baseline model to show the causality
of my results and present results from additional robustness checks.
5.1
Turnout
To identify the effect of initiative signing on voter turnout, I regress the turnout on signatures
per capita. In all estimations, standard errors are clustered at state level, and weights according
14
Table 2: Effect of initiative signing on voter turnout I
Signatures per
capita
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.770***
(0.185)
2.006***
(0.394)
0.721***
(0.193)
1.614***
(0.401)
0.342***
(0.115)
0.960***
(0.180)
0.125
(0.119)
0.195
(0.199)
Signatures per
capita squared
Controls
State FE
Initiative FE
Adjusted R2
Observations
-11.714***
(2.620)
no
no
no
0.030
1.690
no
no
no
0.048
1.690
-8.522***
(2.792)
no
no
yes
0.544
1.690
no
no
yes
0.553
1.690
-5.529***
(1.295)
no
yes
no
0.236
1.690
no
yes
no
0.239
1.690
-0.626
(1.218)
no
yes
yes
0.772
1.690
no
yes
yes
0.772
1.690
Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable turnout is defined as the number
of valid votes divided by the number of eligible citizens. Weighted least squares according to Swiss
eligible population size. Clustered standard errors at state level.
Table 3: Effect of initiative signing on voter turnout II
Signatures per
capita
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.480***
(0.132)
0.985***
(0.310)
0.226
(0.148)
0.249
(0.279)
0.446***
(0.128)
1.077***
(0.230)
0.112
(0.122)
0.162
(0.203)
Signatures per
capita squared
Voting
history
Controls
State FE
Initiative FE
Adjusted R2
Observations
-4.540*
(2.351)
-0.207
(1.836)
-5.616***
(1.848)
-0.439
(1.205)
0.385***
(0.129)
0.378***
(0.129)
0.417***
(0.133)
0.417***
(0.133)
-0.075
(0.138)
-0.090
(0.139)
0.106
(0.110)
0.106
(0.109)
yes
no
no
0.226
1.690
yes
no
no
0.228
1.690
yes
no
yes
0.684
1.690
yes
no
yes
0.684
1.690
yes
yes
no
0.308
1.690
yes
yes
no
0.311
1.690
yes
yes
yes
0.776
1.690
yes
yes
yes
0.775
1.690
Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable turnout is defined as the number of valid votes divided by the number of eligible citizens. Weighted least squares according to
Swiss eligible population size. Clustered standard errors at state level.
to the size of the state eligible population are applied. For the baseline estimation in the first
two columns of table 2, I only use the my measure of signatures and no control variables. In
specifications (3) to (8), I add initiative and state fixed effects one at a time and afterwards both
at the same time.
The linear effect of initiative signing on voter turnout is positive in all specifications, and the
quadratic term is negative as expected. The linear coefficient suggests that collecting signatures
from an additional percentage point of the eligible population increases turnout by 0.77 percentage
points. The effect is slightly reduced when adding initiative fixed effects, and roughly halved when
state fixed effects are included. At the same time, initiative fixed effects have a high explanatory
power for the data since they increase the adjusted R2 by more than 0.5.
At first, the effect is significant. However, the coefficient of signatures per capita becomes
15
insignificant as soon as initiative and state fixed effects are both accounted for. I repeat the above
specification this time including a control for voting history as well as political and socioeconomic
controls (cf. table 3). Results are very similar but adding initiative fixed effects alone already makes
the coefficient of the signature measure insignificant. Hence, including initiative-specific effects in
addition to either state fixed effects or controls, or both, renders the campaigning effect of petition
signing on turnout insignificant.
Several potential explanations exist for this insignificant effect. In Switzerland, typically several
federal and state issues are voted upon on the same election day. When inspecting the respective
turnout rates, it becomes evident that virtually no roll-off exists in Switzerland: turnout rates for
federal ballots on the same day are almost identical. E.g., two extremely different initiatives, one
on limiting immigration and the other on reducing the number of working hours which were both
voted upon on 4 December 1988, had federal turnout rates of 52.84% and 52.86% respectively. This
suggests that voters usually vote for most issues once they have decided to participate in the election.
Therefore, turnout for two ballot propositions on the same day is very similar. Consequently,
the number of signatures collected which varies strongly between initiatives would not be a good
predictor of turnout. In the sample, 65 are voted on 39 election days. On 18 of these more than
one initiative has been voted on the same day. In total this affects 44 initiatives in the sample.10
Initiative fixed effects account for constant initiative-specific characteristics which cannot be
controlled for. Salience of the initiative topic constitutes one such initiative fixed effect which
is an important factor strongly influencing voter turnout in Switzerland (Lüchinger, Rosinger &
Stutzer, 2007).11 E.g., a highly disputed initiative aiming at abolishing the Swiss army voted on 26
November 1989 had a turnout rate of 69.18%, while for a less salient initiative about the support
of public transport only 31.23% of eligibles turned out on 3 March 1991.12 This demonstrates the
importance of salience for initiative turnout. It may consequently be a better predictor of turnout
than the number of signatures collected and be also correlated with the latter.13 Additional evidence
comes from Schmid (2013) who finds that a mobilization measure based on petition signatures of
the most mobilizing ballot proposition on a particular ballot day has high explanatory power for
ballot-day turnout levels.
Since fixed effects are required to correctly estimate the effect of petition signing on turnout,
the hypothesis that signing an initiative provides information to voters and activates citizens to
participate in elections has to be rejected when initiative-specific effects are accounted for in combination with state-specific effects or state control variables. These results are surprising and stand
10
Ideally, regressions could be estimated for a subsample of initiatives where the initiative was the only federal
proposition at ballot. In the sample this is true for only two initiative and therefore not feasible.
11
A term indicating the importance of the election is often included in models (cf. Feddersen & Sandroni, 2006).
It is high in important elections (e.g., presidential), and low in less important ones (e.g., at local level).
12
Supporting evidence shows that an initiative also voted upon on 26 November 1989 on velocity limits also had
a turnout rate of 69.15%. However, other initiatives concerning traffic and motorways voted on 1 April 1990 had
average turnout rates around 41%. Thus, high turnout for the initiative about velocity limits was largely driven by
the other attractive initiative on the same election day, and not by the topic itself (Swiss Federal Chancellery, 2013).
13
I created a measure of initiative importance by coding a dummy with value 1 if an initiative was the most
important federal proposition on a particular ballot day. Repeating the regressions for a subsample of 24 important
initiatives did not yield significant results.
16
in contrast to the findings of Parry, Smith and Henry (2012) and especially Boehmke and Alvarez
(2012). The latter found positive significant effects for eight initiatives based on aggregate data.
The former discovered a positive effect by using individual data, but only in one of their three models. My results suggest, that on average over a longer period no correlation between the fraction of
the population that signed an initiative and subsequent turnout exists. Hence, the positive effects
in the research mentioned above might stem from initiative-specific effects and need not to hold
true in general.
5.2
Acceptance
The second hypothesis tested is that the number of valid signatures per capita has a positive
effect on the acceptance rate measured as the number of yes votes divided by the eligible population
size. Table 4 shows the results. Again, all estimates are weighted least squares, no controls
are included in the baseline regressions, and initiative as well as state fixed effects are added
alternatingly. The estimated effect of signatures per capita on acceptance rates is positive and
highly significant. Increasing the fraction of the eligible population which signed the initiative by
one percentage point, is related to additional 1.081 percentage points of the eligible population
accepting the initiative in the first specification. The linear coefficient decreases slightly when
state fixed effects are included, and more strongly once initiative fixed effects are controlled for.
When both kinds of fixed effects are accounted for, the effect decreases to 0.835 percentage points.
Similarly as in the above analysis, initiative fixed effects contribute considerably to increasing the
adjusted R2 which means that they explain a lot of observed variation in the acceptance rate.
In the quadratic specifications, the linear coefficients are positive with values larger than 1, and
squared terms display negative effects. Both are highly significant. The size of the coefficients will
be interpreted below.
I extend the analysis by repeating all estimation and adding socioeconomic as well as political
Table 4: Effect of initiative signing on acceptance I
Signatures per
capita
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1.081***
(0.078)
1.837***
(0.186)
1.107***
(0.090)
1.791***
(0.210)
0.873***
(0.073)
1.371***
(0.173)
0.835***
(0.077)
1.229***
(0.163)
Signatures per
capita squared
Controls
State FE
Initiative FE
Adjusted R2
Observations
-7.159***
(1.391)
no
no
no
0.102
1.690
no
no
no
0.114
1.690
-6.533***
(1.282)
no
no
yes
0.747
1.690
no
no
yes
0.757
1.690
-4.455***
(1.274)
no
yes
no
0.147
1.690
no
yes
no
0.151
1.690
-3.491***
(0.964)
no
yes
yes
0.804
1.690
no
yes
yes
0.806
1.690
Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable acceptance is defined as the number
of yes votes divided by the number of eligible voters. Weighted least squares according to Swiss eligible
population size. Clustered standard errors at state level.
17
Table 5: Effect of initiative signing on acceptance II
Signatures per
capita
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.936***
(0.060)
1.441***
(0.149)
0.921***
(0.100)
1.398***
(0.200)
0.804***
(0.053)
1.280***
(0.136)
0.824***
(0.078)
1.212***
(0.165)
Signatures per
capita squared
Controls
State FE
Initiative FE
Adjusted R2
Observations
-4.565***
(1.171)
yes
no
no
0.307
1.690
yes
no
no
0.311
1.690
-4.305***
(1.113)
yes
no
yes
0.768
1.690
yes
no
yes
0.772
1.690
-4.233***
(1.017)
yes
yes
no
0.360
1.690
yes
yes
no
0.363
1.690
-3.433***
(0.998)
yes
yes
yes
0.806
1.690
yes
yes
yes
0.808
1.690
Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable acceptance is defined as the number
of yes votes divided by the number of eligible voters. Weighted least squares according to Swiss eligible
population size. Clustered standard errors at state level.
controls to the estimations (cf. results in table 5). Results are robust to the inclusion of control
variables, and the coefficients of interest remain highly significant. Further, I restrict the sample to
contain only voted initiatives and drop the 7 voted counter proposals because acceptance is much
higher for counter proposals than for initiatives on average. However, results are not affected by
this manipulation (results not reported). This means that voting behavior from counter proposals
is not driving the results.
In what follows, I refer to the results in the specification including both fixed effects and control
variables in columns (7) and (8) of table 5. In this specifications, the most extensive model is
estimated and the R2 is highest which means that most variation in the data is explained. The
linear effect suggests that collecting signatures from one percentage point more of the population, is
related to 0.824 percentage points of the eligible population accepting the initiative on average. In
the quadratic estimation, the coefficient increases to 1.212, and the estimates suggest a significantly
negative quadratic effect of -3.433. This means that for all values of signatures per capita below
0.0270, signature collection exerts increasing effects to scale, i.e., the increase in population accepting the initiative goes up by a factor larger than one. Over the range [0, 0.0270], the signature of
an additional person increases acceptance by more than one yes vote in the quadratic model. The
marginal effect of signing on acceptance turns negative at the value 0.1541 signatures per capita
(cf. figure 2 for a visual representation).
When looking back at descriptive statistics, the population-size-weighted mean of signatures
per capita amounts to 0.0297, and roughly 63% of the observation lie in the interval [0, 0.0270].
Over a relevant range of this variable the marginal effect is hence positive and larger than 1. The
existence of spillover effects might be a possible explanation for this result: by talking to family
and friends who belong to the personal network, a single signer might motivate a further person to
vote in favor of the initiative. Comparing the linear with the quadratic model, results suggest that
according to the adjusted R2 , the quadratic model has a narrowly better fit, i.e. it explains more
of the variation in the data than the linear one. Hence, it should be the preferred specification.
18
-.5
0
Marginal effect
.5
1
1.5
In the next section, I extend the baseline model to tackle the issue of causality.
0
.05
.1
Signatures per capita
.15
.2
Figure 2: Marginal effect on signatures p.c. on acceptance (yes/eligible)
5.3
Extensions of the baseline model
The main concern with the significant results for the effect of petition signing on acceptance
is the issue of causality: it might well be that underlying state preferences favoring an initiative
might lead to a higher number of signatures. At the same time, more intensive preferences might
lead to a higher acceptance rate. This constellation would also bring about significantly positive
coefficients in the regressions, however, without any direct causality running from the number
of signatures to the voting outcome. A first step already undertaken to deal with this problem
is the inclusion of socioeconomic control variables like income, education, and unemployment in
the baseline specification. Especially for economically framed initiatives like about pension age,
unemployment benefits, or pensions, income and unemployment should have high explanatory
power and be able to partly explain variations in acceptance between the states.
To account for this issue, I propose several extensions to the baseline model to establish causality
between petition signing and voting of the results above. In section 5.3.1, I propose three approaches
to control for voter preferences. In section 5.3.2, I account for the randomness of the signature
collection process. Robustness checks are in section 5.3.3.
5.3.1
Elite mobilization, affected states, and voter preferences
Elite mobilization
Elite mobilization constitutes an important factor with great influence on voting results (Kriesi,
1995, 2006). Voters of a particular party look to the party for voting cues because they identify
themselves with its political agenda. Also, as elected political representatives parties should optimally reflect their voters’ preferences. For the first preference measure, I thus account for party
mobilization. In Switzerland, all parties and some main interest groups issue voting recommendations to their electorate. These recommendations are publicly communicated. I create a measure
19
of elite mobilization by grouping all parties that have issued a positive voting recommendation
together. Next, I take the fraction of the state electorate which has voted in favor of these parties
at the last election for federal parliament. E.g. if only the left party and the green party issue a
yes recommendation for initiative i, and 30% of the voters in state s supported these two parties
in the last national election, the control variable takes the value 0.3 for initiative i in state s.
Voting recommendations are taken from swissvotes, and party support in national elections is
from the Swiss Statistical Office. The elite mobilization variable is distributed between 0 and 1.
Its population-size weighted mean is 0.3278 with a standard deviation of 0.2294.
Results are reported in table 6. Including this control does not alter the significant effect of
signatures per capita on acceptance. However, the coefficient from the linear model is slightly
reduced from 0.824 to 0.763. Also, the results of the quadratic model are similar to the baseline
model. The control variable itself has a positive and strongly significant coefficient as expected. If
parties favoring the initiative are supported by 1% more of the state population, additional 0.056
percentage points of the eligible population accept the initiative.
Affected states
In a second approach, I account for states which are particularly concerned with an initiative and
therefore should be more likely to accept or reject it. To this goal, I create two dummy variables.
If a state was supposedly more likely to accept (reject) the initiative, the first (second) dummy
variable is coded with the value 1 and zero otherwise.
To create this additional control variable, I have consulted all communications of the government
available through the Federal Chancellery for each initiative in my sample individually. These
communications are prepared by the government prior to parliamentary debate about the initiative.
They contain extensive information on the initiative, its goals, political, economic, and financial
consequences. I have screened the government communications for mention of states which might
be particularly concerned with the initiative. The states were either explicitly mentioned or could
be inferred from the communication. A detailed list with explanations for each initiative how and
why these two variables were coded is available from the author on request. The same holds for
the coding of related referendum ballots in the next paragraphs.
One example of a positively affected state is the initiative demanding to allow counter proposals
not only for initiatives but also for referenda voted on 24 September 2000. Two states, namely
Bern and Nidwalden, already had similar provisions at state level. Therefore, they should be more
likely to favor such a provision. An example for a negatively affected state is an initiative asking
for the prohibition of animal trials voted on 7 March 1993. Several states like Basel Landschaft,
Basel Stadt, Vaud and Zurich have a strong pharmaceutical industry relying on animal trials such
that they should be less likely to accept the initiative. There are other initiatives like one about
the protection of tenants voted on 7 December 1986 for which no especially affected states can be
found and all states are coded with a zero.
In total, I identify 60 positively and 51 negatively affected states for all initiatives. The dummy
for positively affected states has a mean of 0.0499 while the dummy for negatively affected states
20
is 0.0493 on average with standard deviations of 0.2177 and 0.2167 respectively. Similarly as with
the control for elite mobilization, the significance of the baseline results for the effect of signatures
per capita remains unaffected. This time also the coefficient size is virtually unchanged. The
coefficient for negatively affected states is significant and as expected negative. But the coefficient
for positively affected states is insignificant.
Related referendum ballots
In my third attempt to account for voter preferences, I take an approach similar to Funk and
Gathmann (2011), and proxy preferences with old voting results on a related issues. In more detail,
I again consult the government communications which contain the information about the article or
paragraph of the Swiss constitution that is about to be altered by the initiative in question. Usually,
government communications provide information on the history of the initiative and similar ballots
concerning the same constitutional article. The important issue is that the best preference controls
are voting results of mandatory referenda. The reason is that voting results from other similar
initiatives or optional referenda have a signature collection phase preceding the ballot. Therefore,
I would expect the voting results of the two latter forms of ballots to be partly driven by their
signature collection process. Only mandatory referenda do not require a qualification stage and
can consequently serve as preference measure.
I identify mandatory referenda concerning the same constitutional article or a very similar topic
as the initiatives for 37 of the initiatives in my sample. The mandatory referenda are coded such
that they point into the same direction as the corresponding initiative (e.g., more environmental
protection, or a more generous pension system). The variable is defined analogously to the dependent variable acceptance as yes votes divided by the number of eligibles. It does not change the
results qualitatively if the standard definition of acceptance yes votes divided by voters is used.
The eligible population-weighted mean is 0.2660 with a standard deviation of 0.0932. For the regressions, I drop the observations from the other 28 initiatives for which no applicable mandatory
referendum could be found. The reasons that no referendum can be matched are the following:
first, the initiative might concern a topic which is regulated by a law and not directly by the constitution. Such issues are typically voted upon in optional referenda which have a signature collection
themselves. Next, a mandatory referendum with a similar topic might exist. However, sometimes
it has been voted too long ago in the past to assume that preferences are time constant. On average, the time difference between voting dates of the initiative and the related referendum amounts
to roughly 12 years. Last, some initiatives address issues which have never been on the political
agenda before, e.g. the introduction of a national holiday. Consequently, no similar mandatory
referendum is available.
The estimation results including the voter preference measure are provided in table 7. Since
sample size is reduced for these estimates, I repeat the baseline regressions including controls and
both fixed effects in columns (1) and (2). Reducing the sample size from 65 to the selected 37
initiatives has a strong impact on the estimation results. While the main effect remains significant,
the coefficient in the linear specification takes on a value larger than one. Further, the adjusted R2
21
drops by around a half. This suggests that 28 initiatives are excluded which systematically seem
to have particularly good explanatory power of the data variation. The most plausible explanation
boils down to the fact that the 28 excluded initiatives indeed systematically differ from the 37 left
in the sample: while related mandatory referenda exist for the latter, this is not true for the former
for the reasons explained above. In particular, the excluded initiatives are most likely to affect
issues regulated by law and not necessarily be suitable to be included in the constitution.
In columns (3) and (4) the control for voter preferences is included. Additional robustness
checks where all three control variables elite mobilization, affected states, and voter preferences are
jointly tested, are reported in columns (5) and (6). The relation between signatures per capita and
the acceptance rate is unaffected by the inclusion of the variables and is highly significant as in the
baseline model. Hence, the main results are robust to the inclusion of various measures of voter
preferences which could potentially affect the voting outcome.
The control variable for voter preferences itself is positive but not significant. As before, mobilization from political parties significantly increases acceptance, while negatively affected states
have lower acceptance rates. The coefficient of positively affected states remains insignificant.
Table 6: Effect of initiative signing on acceptance - preferences I
Signatures per
capita
(1)
(2)
(3)
(4)
(5)
(6)
0.763***
(0.079)
1.135***
(0.168)
0.832***
(0.079)
1.235***
(0.169)
0.775***
(0.079)
1.162***
(0.170)
Signatures per
capita squared
Elite mobilization
-3.278***
(0.998)
0.056***
(0.017)
-3.555***
(1.027)
0.055***
(0.017)
-3.404***
(1.012)
0.055***
(0.017)
0.054***
(0.017)
Positively affected
states
-0.005
(0.005)
-0.005
(0.005)
-0.007
(0.004)
-0.007
(0.005)
Negatively affected
states
-0.020***
(0.006)
-0.020***
(0.007)
-0.019***
(0.006)
-0.019***
(0.007)
yes
yes
yes
0.808
1’690
yes
yes
yes
0.810
1’690
yes
yes
yes
0.813
1’690
yes
yes
yes
0.815
1’690
Controls
State FE
Initiative FE
Adjusted R2
Observations
yes
yes
yes
0.811
1’690
yes
yes
yes
0.812
1’690
Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable acceptance is defined
as the number of yes votes divided by the number of eligible voters. Weighted least
squares according to Swiss eligible population size. Clustered standard errors at state
level.
22
Table 7: Effect of initiative signing on acceptance - preferences II
Signatures per
capita
(1)
(2)
(3)
(4)
(5)
(6)
1.038***
(0.088)
1.659***
(0.266)
0.933***
(0.102)
1.484***
(0.172)
0.893***
(0.114)
1.389***
(0.201)
Signatures per
capita squared
-5.982**
(2.323)
Yes votes in related
mandatory referendum
-5.339***
(1.148)
0.041
(0.037)
0.042
(0.037)
Elite mobilization
0.059***
(0.021)
0.055***
(0.021)
Positively affected
states
-0.010
(0.007)
-0.011
(0.007)
Negatively affected
states
-0.029***
(0.009)
-0.029***
(0.010)
yes
yes
yes
0.791
949
yes
yes
yes
0.794
949
Controls
State FE
Initiative FE
Adjusted R2
Number of observations
0.047
(0.039)
yes
yes
yes
0.372
949
yes
yes
yes
0.376
949
yes
yes
yes
0.779
949
0.048
(0.039)
-4.759***
(1.242)
yes
yes
yes
0.783
949
Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable acceptance is defined as
the number of yes votes divided by the number of eligible voters. Weighted least squares
according to Swiss eligible population size. Clustered standard errors at state level. Sample size is reduced from 962 (37 initiatives times 26 states) to 949 because there are no
voting results from the then non-existing state Jura before 1979.
5.3.2
Probability of signing an initiative
In this section, I account for the randomness of the signature collection process by utilizing the
state variation in the introduction of postal voting in Switzerland.
The total number of signatures collected might reflect signatures from initiative partisans signing
the initiative, as well as from previously uninformed voters who receive favorable information from
the collection campaign. Theoretical models of voting predict that partisan voters are more likely
to turn out than uninformed ones. Also, the dominant strategy for partisans is to support their own
candidate (Feddersen & Pesendorfer, 1996). From this it follows that part of the positive relation
found between signatures per capita and acceptance might stem from partisans. Only part of the
correlation, however, would reflect the campaigning effect of signing from previously uninformed
voters. In this section, I therefore control for the randomness of the signature collection process to
separate independent from partisan signers.
A typical location to gather signatures would be near a polling place such that people who have
23
just participated in an election could be asked to sign the initiative. Thus, collection near places of
election would target regular and potentially better informed voters with a higher probability than
a collection campaign near the train station or a shopping mall. Consequently, collection campaigns
near places of election are less random than at other places and thus should be controlled for. A
remedy is to exploit state variation in the introduction of postal voting in Switzerland between 1978
and 2005 (Funk, 2010). With the introduction of postal voting, the channel of collecting signatures
near places of election has been considerably reduced: voters can return their ballot papers by mail.
In Switzerland, citizens often make use of this voting model (Klaus, 2006). However, the option of
voting at the booth has not been abolished after the availability of postal voting but locations and
their opening hours have been considerably reduced (Lüchinger, Rosinger & Stutzer, 2007).
The intuition is thus, that with the availability of postal voting it becomes more difficult to
collect signatures near polling places because part of the population votes by mail. Consequently,
it becomes less likely that regular voters who, based on their political knowledge and experience,
should be more likely to have a predisposition regarding the initiative are asked to sign an initiative
petition. If the hypothesis is true that the act of signing increases a citizen’s acceptance probability,
then controlling for postal voting and its interaction term with the number of signatures should
leave the significance of the signature coefficient unaffected. For the interaction term I expect a
negative coefficient. This is because my baseline regressions probably overestimate the true effect
if part of the signers are more informed regular voters. I regress the following estimation equation.
acceptancesi = β0 + β1 signatures p.c.si + β2 postalsi ∗ signaturessi + β3 postalsi
(3)
+β4 Xsi + us + vi + si
β1 can be interpreted as the effect of petition signing on acceptance for on average more informed
citizens (postalsi = 0). The total effect β1 + β2 reflects the relationship for on average less informed
signers (postalsi = 1). I also estimate a quadratic model which includes signatures per capita
squared and its interaction with the postal voting dummy.
The results which are reported in table 8 suggest several interesting effects. First, coefficient β1
remains highly significant in the linear and in the quadratic specification. Second, as expected the
interaction term between postal voting and signatures per capita has a negative coefficient which
is only significant in the quadratic specification in column (2). This means that the relationship
between petition signing and acceptance is smaller in states with postal voting. The result suggests
that petition signing by frequent and consequently more informed voters might indeed play a role.
However, though the effect of petition signing on acceptance decreases with postal voting, the
significance of the main result remains unaffected. Third, acceptance is significantly higher in
states with the possibility of postal voting. Recall that acceptance is defined as the number of yes
votes as a fraction of the eligible population and therefore encompasses turnout and acceptance
decisions. The positive postal coefficient might partly reflect that turnout on average increased in
states with postal voting as shown by Lüchinger, Rosinger and Stutzer (2007).
24
Table 8: Effect of initiative signing on acceptance - postal
voting
Signatures per capita
(1)
(2)
0.881***
(0.000)
1.456***
(0.000)
-4.777***
(0.000)
-0.649***
(0.006)
3.792**
(0.018)
0.018***
(0.001)
yes
yes
yes
0.809
1.690
Signatures per capita squared
Signatures per capita * postal voting
-0.178
(0.173)
Signatures per capita squared * postal voting
Postal voting
0.009*
(0.092)
yes
yes
yes
0.806
1.690
Controls
State FE
Initiative FE
Adjusted R2
Number of observations
Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable
acceptance is defined as the number of yes votes divided by the
number of eligible voters. Weighted least squares according to
Swiss eligible population size. Clustered standard errors at state
level.
5.3.3
Additional robustness checks
As additional robustness checks, I conduct several placebo regression. All results are reported
in tables 10 and 11 in the appendix. The main intuition for the placebos is that I relate the measure
of signatures with voting results from some other initiative or referendum. I expect results to be
insignificant.
First, I assign to the signatures of initiatives to the voting results of a mandatory referendum
that has been held on the same day. For this, I take into account that topics should not be related.
E.g., if an initiative and a referendum about energy policy are held on the same day, it can be
expected that state outcomes are similar for both ballots. Therefore, I match initiative signatures
with referenda concerning unrelated topics, e.g. motherhood and migration policy. In total, I can
match 19 initiatives with unrelated referenda on the same ballot day. The intuition is that the
signature measure should be insignificant because signature collection is matched with the voting
results of a different proposition. Indeed, even though the coefficients have the expected sign they
are insignificant.
Second, I assign to every initiative the voting result of a thematically related mandatory referendum. I.e., I again test whether signatures are a good explanatory variable for the voting results
of a distinct but similar ballot which is touching the same subject. If my analysis truly identifies
the motivational effect of petition signing on acceptance, in contrast to signatures just reflecting
voter preferences, the estimates should be insignificant. For this, I use the same 37 initiatives as in
section 5.3.1. As expected, signature coefficients are not significantly positive. For the linear model,
the coefficient is negative and significant at the five percent level. In the quadratic specification
25
both coefficients for signatures and signatures squared are negative and insignificant. The placebos
add to the evidence that the analysis indeed reflects a causal effect of campaigning by signature
collection on initiative acceptance.
6
Concluding remarks
This paper analyzes the qualifying stage of popular initiatives. It extends previous work by
exploring the campaigning effect of signing initiative petitions on turnout and voter approval. It
also thoroughly addresses the question of causality. The main findings are twofold. First, signing
an initiative petition does not necessarily increase the probability of subsequently turning out at
the voting booth. Initiative-specific effects like e.g. proposition salience are better predictors of
turnout. Second, signing an initiative petition is associated with a higher probability of casting
approving votes at the subsequent initiative ballot. This result proves to be highly robust to the
inclusion of various preference measures, socioeconomic and political controls, as well as state and
initiative fixed effects.
In the light of my results, initiative signature collection can be interpreted as a partisan campaigning tool for the initiating group: signers receive relevant information and become aware of
the initiative issue. In terms of approving votes, it turns out to be worthwhile to run a larger
collection campaign and gather additional signatures which increases the citizens’ awareness of the
issue and gives them necessary information, especially in ranges where signatures display increasing
returns to scale. Hence, my results contribute to understanding why initiatives usually collect more
signatures than legally required to qualify initiatives for ballot - other than to insure against invalid
signatures. Though larger collection campaigns mean higher collection cost, they reap benefits in
terms of additional support from the eligible population at ballot.
26
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29
Appendix
Data used from Swiss census (1970, 1980, 1990, 2000) provided by the Swiss
Statistical Office
• Total state population
• Population 65 years old or older per state
• Unemployed population per state
• Population with tertiary education per state
Other data
• Variable “mean taxable income” is from the Eidgenössische Steuerverwaltung (Federal Tax
Administration) in Bern.
• Number of state ballots on same election day is calculated based on state voting data available
online from the Centre for research on direct democracy on www.c2d.ch.
• Turnout in election for the federal parliament (Nationalrat) are from the Swiss Statistical
Office.
• Dates of initiative qualification and ballot to calculate time between initiative qualification
and ballot are from the homepage of the Swiss Federal Chancellery.
• Introduction of postal voting is from Funk (2010). Updated for the states Ticino and Valais
by calling the state administrations.
30
Table 9: Descriptives of control variables
Turnout at last parliamentary election
Number of ballots on same day
Time between initiative qualification and ballot in days
Counter proposal
Year with federal parliamentary election
% of old (older than 64)
% of population older than 15 with tertiary education
% of population older than 15 unemployed
Average taxable income in 10.000
Mean
Std. Dev.
Min
Max
0.4517
4.33
1556.0
0.1069
0.0922
0.1473
0.1315
0.0169
5.137
0.0705
2.53
471.7
0.3091
0.2895
0.0174
0.0361
0.0076
1.044
0.1735
0
370
0
0
0.1055
0.0444
0.0051
2.877
0.7370
21
3184
1
1
0.2103
0.2485
0.0407
8.313
Note: 1690 observations. Summary statistics are weighted according to Swiss eligible
population size in the states.
Table 10: Effect of initiative signing on acceptance - placebo I
Signatures per capita
(1)
(2)
-0.240**
(0.108)
-0.325
(0.336)
0.828
(3.380)
yes
yes
yes
0.665
949
Signatures per capita squared
Controls
State fixed effect
Initiative fixed effect
Adjusted R2
Number of observations
yes
yes
yes
0.665
949
Note: *** p<0.01, ** p<0.05, * p<0.1. The
dependent variable acceptance is defined as the
number of yes votes divided by the number of
eligible voters, however, not for the initiative
ballot but of a closely related mandatory referendum ballot. Weighted least squares according to Swiss eligible population size. Clustered
standard errors at state level. Sample size is reduced from 962 (37 initiatives times 26 states)
to 949 because there are no voting results from
the then non-existing state Jura before 1979.
31
Table 11: Effect of initiative signing on acceptance - placebo II
Signatures per capita
(1)
(2)
0.036
(0.104)
0.134
(0.201)
-0.762
(1.030)
yes
yes
yes
0.784
494
Signatures per capita squared
Controls
State fixed effect
Initiative fixed effect
Adjusted R2
Number of observations
yes
yes
yes
0.784
494
Note: *** p<0.01, ** p<0.05, * p<0.1. The
dependent variable acceptance is defined as the
number of yes votes divided by the number of
eligible voters, however, not for the initiative
ballot but of a different ballot on the same election day. Weighted least squares according to
Swiss eligible population size. Clustered standard errors at state level.
32