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How Direct Democracy Affects
Local Naturalization Rates:
Evidence from Switzerland
Tess Wise
Department of Political Science
Massachusetts Institute of Technology
A thesis submitted for the degree of
Bachelor of Science (BS)
2011 May
Acknowledgements
I would like to acknowledge the help and guidance of my thesis advisor,
Jens Hainmueller, who not only helped with this thesis, but has allowed
me to work with him on the Swiss Naturalizations Project for the past two
years and has generally been a source of inspiration and guidance during
my time at MIT. Dominik Hangartner at Berkeley has also been invovled
in this project and I own a great debt to the previous work done by these
two men.
Additionally, this project would not have been possible without the data
made available by the Swiss Government, specifically the Swiss Federal Of-
fice of Statistics. Funding for the Swiss Naturalizations Project research was
generously provided by Swiss National Science grant no. 100017-132004.
I would also like to acknowledge the helpful comments made by my second
reader, Bruno Perreau, as well feedback from Roger Petersen, who helped
guide the development of this thesis during the thesis preparation class
[17.THT].
Finally, I would like to express my gratitude to the thousands of unnamed
individuals who have coded for the LaTeX project for free. It is due to their
efforts that we can now generate professionally typeset PDFs.
Contents
1 Introduction 1
2 The Swiss Case 5
2.1 Swiss nationality: a historical perspective . . . . . . . . . . . . . . . . . 5
2.2 Right-wing opposition: the rise of the SVP . . . . . . . . . . . . . . . . 7
2.2.1 Brief overview of the Swiss political system . . . . . . . . . . . . 7
2.2.2 The emergence of the SVP as a major party . . . . . . . . . . . . 8
2.2.3 SVP ideology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Literature Review 15
3.1 Review of Helbling (2008) . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Review of Helbling (2010) . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2.1 Discussion of Helbling’s dependent variable . . . . . . . . . . . . 16
3.2.1.1 Validity issues regarding Helbling’s dependent variable 17
3.2.2 Discussion of Helbling’s independent variable . . . . . . . . . . . 18
3.2.2.1 Validity issues regarding Helbling’s independent variable 19
3.2.3 Discussion of Helbling’s confounding variables . . . . . . . . . . . 20
3.2.4 Discussion of Helbling’s results . . . . . . . . . . . . . . . . . . . 21
4 Hypotheses 23
5 Methodology 25
5.1 Why I have selected a fixed effects estimator . . . . . . . . . . . . . . . 25
5.2 The Mechanics of a fixed effects estimator . . . . . . . . . . . . . . . . . 26
5.3 My fixed effects model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.4 Discussion and operationalization of variables . . . . . . . . . . . . . . . 28
iii
CONTENTS
5.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.4.2 Indepdent Variable: Municipal Institution . . . . . . . . . . . . . 28
5.4.3 Selection Principle . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5.4.4 Dependent Variable: Municipal Naturalization Rate . . . . . . . 31
5.4.4.1 Trends in naturalizations: 1990 – 2009 . . . . . . . . . . 31
6 Data Analysis 35
6.1 Model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.2 Model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.3 Model 1: Subgroup Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.3.1 Differences by language-region . . . . . . . . . . . . . . . . . . . 40
6.3.2 Municipality Size . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6.4 Model 3: Interaction Effects . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.4.1 Influx of immigrants . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.4.2 Level of SVP support in 2003 . . . . . . . . . . . . . . . . . . . . 57
6.5 Unions of Subgroups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
7 Discussion 69
7.1 H1: Removing directly democratic institutions leads to increased natu-
ralization rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
7.2 H2: The effect does not depend on initial conditions such as size of
municipality or increases in the foreign population . . . . . . . . . . . . 70
7.3 H3: A high level of SVP support suppresses naturalization rates . . . . 71
7.4 Trends recovered from finer-grained analysis . . . . . . . . . . . . . . . . 72
8 Conclusion 75
References 77
iv
1
Introduction
Immigration is one of the biggest challenges facing the modern states. As increasing
numbers of immigrants arrive at their gates, states must decide which newcomers will
be extended the possibility of citizenship. In the modern Western European state,
citizenship implie not only the extension of legal rights, but often redistributive benefits.
As such, deciding which immigrants can become citizens is a critical decision. For
centuries many European states have relied on the principle of jus sanguinis (citizenship
by blood) as the mechanism through which citizenship was acquired. Like culture
and language, citizenship was passed down through generations from parent to child
through kinship. Foreigners who arrived in these countries were assimilated, or in the
post-war boom of the 1950s and 60s, were labeled as “guest workers” implying a level
of transience which kept them separate from the national identity. Now, instead of just
work, immigrants to Western Europe are looking for new homes for themselves and
their families. They are looking to become citizens.
Currently some of the immigrants to Western European nations look different than
past waves of immigrants. Instead of being from Portugal or Poland they are often
from the Balkans or North Africa, bringing different ethnic and religious traditions to
their new homes. Naturalizing these foreigners has raised questions of national identity
throughout Europe and rejecting their claims to citizenship, or even residence, has
become a rallying cry for right-wing anti-immigrant parties. This thesis explores only
a small part of these larger issues by studying the influence of the institutions which
govern naturalization. These institutions are not neutral actors and, as we will see, the
use of certain institutions may have a profound effect on the naturalization process.
1
1. INTRODUCTION
In Switzerland the process of naturalization in encased in a peculiar institutional
set-up. In order to become a citizen a foreign resident must have lived in Switzerland for
twelve or more years 1 and must be integrated into the Swiss way of life, familiar with
Swiss customs and traditions, comply with the Swiss rule of law, and cannot endanger
Switzerland’s internal or external security ([admin.ch, 2010]). The subjective parts of
the application are assessed “based on cantonal and communal 2 reports” ([admin.ch,
2010]). Therefore, before applying for federal citizenship, a foreigner must first gain
both municipal and cantonal citizenship. In general, aside from posing certain residency
requirements, the canton defers to the municipality. In sum, Swiss citizens are first and
foremost a citizen of their local municipality and those who wish to apply for citizenship
must do so at municipal level.
Swiss municipalities have a variety institutions which govern the naturalization pro-
cess ranging from executive committees, in which a group of either elected or appointed
officials make rulings on citizenship applications, to the use of popular referenda (direct
democracy), in which the citizens of the municipality vote yes or no on the application
of a local foreigner. The use of popular referenda in Switzerland is not unique to citi-
zenship, but is used throughout the political system on a wide variety of topics at the
municipal, cantonal and national levels.3
In 2001 the Swiss municipality of Emmen generated controversy when its use of
popular referenda to confer citizenship was said to enable racism in the naturalization
process. Emmen is a city of about 30,000 of which 30% of the population are foreign
nationals, the majority of which are from the former Yugoslavia. Using popular refer-
enda, Emmen rejected 19 out of the 23 citizenship applications in 2001 (Gross [2006],
37). Notably, all of the rejected came from ex-Yugoslavia. Some of the applicants had
been living in Switzerland for more than 20 years (Gross [2006], 37).
In the wake of this controversy and others, the Swiss Federal Court ruled in 2003
that negative citizenship decisions had to be substantiated. Using popular referenda
or other directly democratic institutions to confer citizenship did not allow the appli-
cant to learn why he or she was denied citizenship and thus these institutions became
1There are some cases in which this is relaxed (facilitated naturalizations). Additionally, yearsspent living in Switzerland between the ages of 10 and 20 count as double ([admin.ch, 2010]).
2Municipalities (also called communes) are the smallest governmental division in Switzerland. Theyrange in size from small towns to large cities and currently number around 2,500 (Feb, 2010).
3Citizens are also asked to vote on many referenda every year. For example, in Zrich, citizens couldvote on an average of 60 referenda per year between 1936 and 1995 ([Ladner, 2002], 817)
2
unconstitutional. Many municipalities were forced to change the institution they used
to confer citizenship.
This change generates variation in municipal institutions over time and because
this variation is due to a somewhat exogenous force (change in the law), it creates
a quasi-natural experiment. Using a fixed effects transformation, I can remove time-
constant confounding variables then apply a fixed effects estimator to estimate the
average treatment effect of removing direct democracy on naturalization rates. Using
these tools, I find that moving away from direct democracy to another institution, such
as an executive committee, increases naturalization rates by 0.7 percentage points.
Once I have established this trend, I will explore different subgroups within my sample
to explore the effect of removing directly democratic institutions in different types of
municipalities.
Better understanding the effect of direct democracy will make a contribution to
the scholarship in this area. If directly democratic institutions suppress naturalization
rates, it may be due to the fact that they enable a certain form of discrimination on the
part of voters. Many scholars (Gamble [1997], Frey and Goette [1998]) have theorized
that direct democracy enables discrimination against minorities, but because institu-
tions rarely change in an exogenous fashion, cross-sectional studies struggle to isolate
the institutional effect. Understanding the effect of directly democratic institutions will
help policy makers decide if they want to use them. Additionally, as we will see in this
thesis, exploring this effect will allow me to test a variety of theories presented in the
related political science literature.
3
1. INTRODUCTION
4
2
The Swiss Case
Currently one in five Swiss inhabitants is not a citizen. The number of foreign nationals
as a percent of the overall population has risen steadily over the past 40 years, going
from 16.2% in 1970 to 22.0% in 2009. Compared to other European nations, the
percentage of foreign nationals in Switzerland is very large. While the magnitude of
this number is partially due to significant immigrant inflows, a secondary reason for
the high percentage of foreigners in Switzerland is the exclusionist nature of Swiss
citizenship.
Over the past two decades, the politics of citizenship have become an increasingly
contentious political topic in Switzerland. National referenda regarding the natural-
ization process were voted on in 1983, 1994, 2004 and 2008. During this period, the
Swiss People’s Party (SVP) has come to represent the anti-immigrant position in Swiss
politics. Compared to other right-wing anti-immigrant parties in Europe, such as the
Front National in France, or the Austrian Freedom Party, the SVP enjoys widespread
popularity, and in a short period of time has emerged as the most widely supported
party in Switzerland. The uniqueness of the Swiss case in terms of both the construc-
tion of Swiss nationality and the particularly strong support for anti-immigrant parties
such as the SVP makes it a fascinating choice for the study of citizenship politics.
2.1 Swiss nationality: a historical perspective
Switzerland is an interesting case in which to consider citizenship politics due to the par-
ticular construction of the Swiss national identity. As a multi-ethnic and multi-lingual
5
2. THE SWISS CASE
country, drawing a distinction between foreigners and natives has allowed Switzerland
to reinforce a unified national identity which is “negatively constructed” (Riano and
Wastl-Walter [2006], p.1694). Where linguistic and cultural unity are not possible axes
upon which to build national solidarity, definition by opposition to “foreigners” has
sufficed.
In Riano and Wastl-Walter [2006], the authors identify four periods in Swiss his-
tory which help describe the development of an exclusionist citizenship regime. First,
after Switzerland began to industrialize in the 1850s, the Swiss government brought in
foreign labor from France, Germany, and a few other European countries. For these im-
migrants, citizenship was offered after two years of residence (Riano and Wastl-Walter
[2006], p.1696), however, by the early 1900s, a “negative reaction” to these foreigners
had developed amongst the Swiss elite (Riano and Wastl-Walter [2006], p.1697).
Second, during the inter-war period, right-wing Swiss politicians popularized the
term “uberfremdung,” referring to a foreign threat to Swiss identity, even though during
this time there were relatively few foreigners in the country(Riano and Wastl-Walter
[2006], p.1698). In 1932 the Federal Law on Settlement and Residence of Foreigners
was passed. This law meant that foreigners were no longer entitled to permanent
residence and officials granting work permits were required to consider the “intellectual
and economic interests of the country as well as the degree of uberfremdung” (quoted
in Riano and Wastl-Walter [2006], p.1698). This ideology was cemented during the
Second World War when threat from the neighboring regimes of Germany and Italy
was combatted by proclaiming support for “traditional Swiss values” leading to an
attitude known in Switzerland as the “hedgehog mentality” (Riano and Wastl-Walter
[2006], p.1698).
Third, in the post-war period, foreigners were brought to Switzerland as “guest
workers,” a policy which emphasized their transience on the national landscape and
kept them safely away from the national identity (Riano and Wastl-Walter [2006],
p.1699). Despite this, the feeling of uberfremdung was still widespread, and in 1952
the minimum residency required to apply for citizenship was elevated to twelve years
(Riano and Wastl-Walter [2006], 1699). To this day, Switzerland still requires twelve
years of residence before foreigners can apply for citizenship. The economic downturn in
the 1970s meant that Switzerland was no longer a “guest worker” destination. Despite
the decrease in foreign nationals, 85% of Swiss voters rejected an initiative proposing
6
2.2 Right-wing opposition: the rise of the SVP
solidarity with foreigners by granting automatic family reunions and the abolition of
seasonal-worker status (Riano and Wastl-Walter [2006] p.1701).
Fourth, in the post 1990s asylum seekers from ex-Yugoslavia began arriving in
Switzerland reigniting feelings of uberfremdung. In tension with this sentiment, Switzer-
land officials felt that the country needed to develop closer relationship with the Euro-
pean Union in order to “ensure its economic future” (Riano and Wastl-Walter [2006],
p. 1702). In response to these dual pressures, the Swiss government created a commis-
sion to answer the question of how it could develop an immigration policy which would
simultaneously move the country closer to the EU without increasing uberfremdung
(Riano and Wastl-Walter [2006], p.1702). The commission suggested that Switzer-
land consider immigrants as being either “culturally close” or “culturally distant” from
native Swiss citizens as determined by the immigrant’s country of origin (Riano and
Wastl-Walter [2006], p.1703). To this day, the Swiss government still categorizes non-
European non-North American foreigners as “third nation foreigners” on its statistical
documents (see, for example, PETRA [2009]).
In sum, it is interesting to note that hostility towards immigrants is not a new
development in Switzerland and that the construction of foreigners as a “outgroup”
allows a Swiss “ingroup” to form by opposition where common language and culture do
not necessarily bind the Swiss together. Despite the constancy posed by uberfremdung,
it is important to note that the interaction between feelings of threat and the Swiss
citizenship regime has evolved in an exclusionist direction and may well continue in
this manner.
2.2 Right-wing opposition: the rise of the SVP
2.2.1 Brief overview of the Swiss political system
To understand the rise of the Swiss People’s Party it is important to give a brief
overview of the Swiss political system. Switzerland is a federal republic which has a
bicameral parliament and seven-member executive. The parliament consists of a 200-
seat lower house called the National Council which represents the population as a whole
and a 46-seat upper house called the Council of States which represents the cantons
(of which there are 20 “full cantons” with two seats each and six “half cantons” which
have one seat each). Switzerland uses a PR (proportional representation) system with
7
2. THE SWISS CASE
an open list to elect the representatives to the National Council (the lower house). The
representatives in the Council of States (upper house) are elected in their individual
cantons and nearly all the cantons use a two-round majoritarian system (Dardanelli
[2008]). The parliamentary elections occur once every four years.
The seven-member executive, called the Federal Council, is elected two months after
the parliamentary elections by both chambers sitting together as the “United Federal
Assembly.” Once elected, representatives serve for a four-year term and members of
the Federal Council cannot be impeached or voted out by the parliament (Dardanelli
[2008]). Unlike unitary heads of state, the Federal Council is a non-hierarchical body in
which each of the seven members has an equal footing (Church [2004], p.117). Between
1959 and 2003 the seats in the Federal Council were distributed according to a “magic
formula” in which the Christian Democrats (CVP), the Social Democrats (SPS) and
the Free Democrats (FDP) each had two seats and the Swiss People’s Party (SVP)
had one. A fifth, party, the Green Party, often gained significant representation, but
never gained a seat. In 2003 the “magic formula” was broken with the election of
Christoph Blocher and the new repartition of the seats became 2-SVP, 2-SPS, 2-FDP
and 1-CVP. In 2007, the SVP gained an even larger percentage of the popular vote
than in pervious elections (28.9%), but due to inter-party politics one of the SVP
representatives, Eveline Widmer-Schlumpf, broke with the SVP to start her own party,
the Conservative Democratic Party of Switzerland (BDP), so the repartition of seats
became 1-SVP, 1-BDP, 2-FDP, 2-SPS, 1-CVP.
2.2.2 The emergence of the SVP as a major party
The SVP grew out of the Party of Farmers, Traders and Independents (BGB) which
changed its name to the Swiss People’s Party when it merged with the Democratic Party
in 1971. In the 1970s and 1980s the support for the SVP was around 11% (Skenderovic
[2009], p.128). The emergence of the SVP as a more powerful party is linked to the work
of Christoph Blocher who, as the president of the Zurich branch of the SVP, proposed an
increasingly radical right-wing agenda (Skenderovic [2009], p.130-131). By the 1990s
the populist and right-wing Zurich branch of the SVP emerged in conflict with the
more traditional Bern branch. During this time the SVP also doubled its number of
cantonal branches until it was represented in all cantons (Skenderovic [2009], p.133).
Radiating out from Zurich, the party steadily gained vote share during the 1990s and in
8
2.2 Right-wing opposition: the rise of the SVP
the 1999 federal election the party became the strongest in Switzerland, gaining 22.5%
of the popular vote (Skenderovic [2009], p.133). The difference between SVP support
in 1995 and 1999 was 12.5 percentage points which was the biggest increase in votes
every seen by a party in the history of Swiss politics (Skenderovic [2009], p.150). It is
interesting to note that this huge increase was seen when the cantonal branches aligned
with the agenda of radical Zurich branch of the party lead by Blocher (Skenderovic
[2009], p.151). Figure 2.1 below shows the evolution of the SVPs vote share from 1971
to 2007.
Figure 2.1
10
10
1015
15
1520
20
2025
25
2530
30
30Percent of popular vote
Perc
ent
of p
opul
ar v
ote
Percent of popular vote1970
1970
19701980
1980
19801990
1990
19902000
2000
20002010
2010
2010year
year
yearEvolution of SVP vote share 1971 - 2007
Evolution of SVP vote share 1971 - 2007
Evolution of SVP vote share 1971 - 2007
2.2.3 SVP ideology
The SVP is described by scholars as a “far-right anti-immigrant party” (FRAIP) or
simply an “anti-immigrant party” (AIP) (see Cochrane and Nevitte [2007], Dancygier
[2010]). In terms of ideology, the SVP is extreme within the Swiss political system.
Figure 2.2 below shows the relative position of the SVP compared to the four other
largest parties in the 2007 elections (CVP, FDP, SPS (shown here as simply SP) and
the Greens). Two main takeaways emerge from this graphic. First, it is clear that
SVP elites are more conservative than the rank and file SVP voters. Second, it is also
clear that the SVP is much more extreme than other three governing parties (FDP,
CVP and SPS) which are all closer to center. Another way of understanding SVP
9
2. THE SWISS CASE
Figure 2.2
ideology relating to immigrants and naturalizations is to consider the anti-immigrant
or anti-naturalization policies proposed or supported by the SVP during the 1990 –
2009 period. While immigration was not “high on the SVP’s list of priorities” in the
1980s, during the 1990s (in part due to the influence of Blocher) the party became
“increasingly preoccupied” with immigration issues (Skenderovic [2009], p.163). In
1987 the party platform completely ignored immigrant issues except to state that the
SVP would like to see the percentage of foreigners reduced (Skenderovic [2009], p.163).
In the 1991, the party platform still supported “facilitated naturalization” for second
and third generation immigrants 1 (Skenderovic [2009], p.164).
As the years progressed, the party became more exclusionist. Damir Skenderovic
cites this as one part of a three-part “winning formula” of neoliberalism, exclusionism
and nationalism which lead to the dramatic gains made by the SVP between 1999 and
2003 (Skenderovic [2009], p.170). In particular, in order to promote an exclusionist
policy, the SVP promoted the municipal right to confer citizenship through direct
1recall that Switzerland espouses jus sanguinis so second and third generation immigrants may notbe Swiss citizens even if they are born and live all their life in Switzerland
10
2.2 Right-wing opposition: the rise of the SVP
democracy even asfter this practice was declared unconstitutional by the Swiss Federal
Tribunal in July 2003 (Skenderovic [2009], p.166, also see Gross [2006]). Beginning
in the early 2000s, the party began to declare that immigrants were a threat to the
national identity and emphasized the existence of a common Swiss mentality which was
diluted by the presence of immigrants (Skenderovic [2009], p.168).
In 2004 the SVP campaigned against a referendum calling for facilitated natural-
ization for Swiss-born foreign nationals which would have also granted automatic Swiss
citizenship to persons born in Switzerland with one parent also born in Switzerland
(third generation foreign nationals). As part of this campaign, the SVP used a par-
ticularly provocative poster (Figure 2.3) which shows multi-colored hands reaching for
a pile of Swiss passports. In 2007 the party launched a campaign to amend the pe-
Figure 2.3
Poster reads ”Stop the mass naturalizations” (http: // worldradio. ch )
nal code to allow judges to directly deport foreigners who commit serious crimes and
potentially deport the entire family of the criminal if they are less than 18 years old.
Again, a series of provocative posters lead to international attention. This time, the
11
2. THE SWISS CASE
poster (shown in Figure 2.4) showed a white sheep kicking a black sheep out of Switzer-
land with the tag-line “For more security.” Apparently, this poster was mailed to every
home in Switzerland (dailymail.co.uk [2007]) and while the initiative was not successful
in 2007, it eventually passed in November 2010. The poster of the white sheep kicking
the black sheep provoked outrage within and outside of Switzerland. Bruno Walliser, a
Zurich chimney sweep who ran for Parliament on the party ticket, responded by saying:
“Our political enemies think the poster is racist, but it just gives a sim-
ple message...The black sheep is not any black sheep that doesn’t fit into
the family. It’s the foreign criminal who doesnt belong here, the one that
doesn’t obey Swiss law. We dont want him” (Sciolino [2007] - http:
//www.nytimes.com).
Figure 2.4
Poster reads ”For more security” (http: // www. dailymail. co. uk )
Finally, in 2009 an initiative proposed by the SVP to ban the building of future
minarets on Swiss mosques gained 57.5% of the votes and was passed. This initiative
was seen as using directly democratic institutions to target a particular minority group,
12
2.2 Right-wing opposition: the rise of the SVP
namely Muslim immigrants from Turkey and Kosovo. The ban, which is now in force,
seems to project an ideological rather than material victory as of the 150 mosques of
prayer rooms in Switzerland only four had minarets when the initiative was proposed
(Erlanger and Cumming-Bruce [2009] - http://www.nytimes.com). The poster used
by the SVP in support of this initiative (see Figure 2.5) portrays a woman in a full
Islamic veil (which are almost never seen in Switzerland) standing in front of the Swiss
flag which is seemingly pierced by imposing black minarets.
Figure 2.5
http: // purpler. files. wordpress. com/ 2010
In summary, the particular construction of the Swiss nationality and the presence of
a widely supported anti-immigrant political party, the SVP, make studying the politics
of naturalizations particularly interesting in the Swiss case. Now, I will discuss how
my work responds directly to previous scholarship on the interaction between direct
democracy and naturalizations in Switzerland.
13
2. THE SWISS CASE
14
3
Literature Review
Marc Helbling is the major author contributing to the recent literature on naturalization
politics in Switzerland. In this section, I will analyze his book published in 2008 and
an article published in 2010. These works pose a series of questions which will form my
hypotheses listed in the next section.
3.1 Review of Helbling (2008)
In Practising citizenship and heterogenous nationhood: naturalizations in Swiss mu-
nicipalities (2008) Helbling attempts to answer the question of whether closed ballot
decisions on naturalization are more discriminatory to applicants of Muslim origin.
Helbling makes the argument that municipalities which use closed ballot decisions (di-
rect democracy) had higher rejection rates of Muslim immigrants than other groups
(Helbling [2008], 88). He does not, however, determine conclusively if there is a causal
mechanism at work. Such a causal mechanism would link one factor, such as insti-
tutional arrangements, to naturalization outcomes. Instead Helbling shows that these
two factors both appear in some cases where there are a host of other uncontrolled
variables at play. This study, while qualitatively interesting, has a very limited sample
of only 14 municipalities (out of a total of around 2500), uses average Muslim rejection
rates (averaged over twelve years) as its dependent variable, and, does not show any
causal mechanism because its data all fall on one side of the institutional reform of
2003 so we cannot know how these municipalities would have acted in the absence of
direct democracy. I now turn to Helbling’s 2010 work, which is a large-N analysis that
more closely mirrors my own.
15
3. LITERATURE REVIEW
3.2 Review of Helbling (2010)
In “Naturalization politics in Switzerland: Explaining rejection rates at local levels,”
(2010) Helbling uses large-N analysis to analyze the connection between “local citi-
zenship politics” and the “average municipal rejection rate” using data which ranges
from 1990 to 2002. I will discuss Helbling’s choice and operationalization of both the
dependent, independent and confounding variables and his results.
3.2.1 Discussion of Helbling’s dependent variable
Helbling uses the “average municipal rejection rate” as his dependent variable. This is
given as the ratio between the number of rejected applications and the total number
of applications averaged over the 12 year period of observation (1990-2002) for each
of 106 municipalities. In collecting the average municipal rejection rate, Helbling on
self-reported data about the average municipal rejection rate from 1990-2002. Hel-
bling started by contacting 207 municipalities and requesting that their administrators
complete a questionnaire. To select this initial group of 207, Helbling first limited his
sample to the municipalities documented by Ladner and his colleagues in their Swiss
municipality surveys (because these were the only ones for which he had socio-economic
and political information). These surveys apparently covered 80% of all Swiss munici-
palities (Helbling [2010], 39). From this group, Helbling first selected all municipalities
with a population over over 10,000 (N=107). Then he supplemented this group by
randomly selecting 100 municipalities from the rest of the Ladner data (population
less than 10,000). This gave him the initial group of 207 which were contacted in the
summer of 2003.
Out of this initial group which was contacted, 74% (N=154) gave Helbling their
average municipal rejection rates for 1990-2002. Among these municipalities, Helbling
found that 48 of the 154 municipalities in his sample had seen less then ten applications
total between 1990 and 2002. These also “correspond roughly to the group of munic-
ipalities with fewer than 1,000 inhabitants” (Helbling [2010], 40). Helbling removes
these from the analysis to get a final group of 106 cases. With respect to the smaller
municipalities, Helbling then notes that he is not sure how this initial condition actu-
ally effect the results because he has only a small sample, but he poses an interesting
hypothesis that small municipalities may pursue a more generous citizenship policy:
“Surprisingly, I found that in almost all municipalities where only up to
ten applications were submitted between 1990 and 2002, none of them was
rejected (Helbling and Kriesi 2004: 46-48; see also Piguet and Wanner 2000:
16
3.2 Review of Helbling (2010)
56-58). Does this mean that small communities pursue a more generous
citizenship policy? Given the very small number of candidates, it is hard
to tell, since the acceptation rate depends on very few individual cases”
(Helbling [2010], 40).
Helbling accepts that this might not actually be the case and proposes some other
mechanism which could explain his observation. First, he proposes that perhaps in
smaller municipalities foreigners are “better integrated” due to increased contact with
the local population and therefore “meet with less resistance of the local population
when they seek to become a full member of their municipality “(Helbling [2010], 40).
Second, Helbling considers the opposing argument that small municipalities are less
generous with their citizenship policy and “considering the small size of these commu-
nities, negative decisions are anticipated more easily and potential candidates deterred
from submitting their dossiers” (Helbling [2010], 40). This dependent variable presents
certain validity issues.
3.2.1.1 Validity issues regarding Helbling’s dependent variable
First, the initial selection of municipalities is far from random and also includes selection
on the dependent variable. The first 107 municipalities are selected entirely based upon
size and out of the 100 small municipalities which were randomly selected, it seems that
only a handful made it into the final data set (N=106). Additionally, Helbling provides
no summary statistics so we do not know how the group of municipalities selected
by Helbling compare to the average Swiss municipality. Helbling concedes that given
his final sample, he cannot draw inferences about citizenship politics in very small
municipalities (Helbling [2010], 41), but it is not even clear the extent to which he can
draw inferences about Swiss municipalities more generally.
Second, as Helbling notes, the dependent variable does not account for the fact that
some candidates may be suggested to withdraw their application (this is sometimes rec-
ommended if they do not have sufficient command of a local language). Through inter-
views with local officials, Helbling was told that most applications which are originally
withdrawn are resubmitted.
Third, Helbling raises the issue that in conservative municipalities some immigrants
may not even apply (even though they would like to) because they do not expect to be
successful. Helbling notes that current ethnographic literature on Switzerland indicates
that the decision to naturalize is “not always consciously taken and cannot be explained
by clearly distinguishable factors” (Helbling [2010], 38). This may or may not mitigate
the validity issue. Helbling concludes that “one can assume that some people are
17
3. LITERATURE REVIEW
deterred from submitting an application in municipalities with a restrictive citizenship
policy” and that if anything, this probably makes the naturalization rejection ratio
artificially lower in more conservative municipalities because “more candidates would
be rejected in municipalities with a restrictive understanding of citizenship” (Helbling
[2010], 39).
In this case, the choice of rejection rates as the dependent variable confounds these
problems. By considering only those immigrants which apply, it leaves the researcher
with a lack of information about whether the institution in question acts as a deterrent
to naturalizations relative to the immigrant population of the municipiality. If Helbling
were to use the naturalization rate (calculated as the number of naturalizations divided
by the total foreign population of the municipality in the pervious year) this information
would be present.
Finally, using self-reported information from a limited number of municipalities
raises many additional validity questions. Should we assume that municipal clerks are
always honest or carefully report these statistics to researchers? In the data used in this
thesis we asked for self-reported information from municipalities and then contrasted
it with the official data received from the Swiss Federal Office of Statistics. More
general work using this data has shown there to be significant differences between the
self-reported data and the official values.
3.2.2 Discussion of Helbling’s independent variable
The independent variable used by Helbling in 2010 is “local citizenship politics.” Hel-
bling proposes “a continuous scale,” which differentiates how restrictive the understand-
ing of citizenship is for a given municipality. At one end of the scale are municipalities
with a very liberal understanding of citizenship. At the other end (though allowing for
several options between the extremes), are municipalities with very “hostile attitudes
towards immigrants” (Helbling [2010], 44).
To create this variable, Helbling uses three indicators. The first is the municipal
understanding of citizenship. This indicator is operationalized as the percentage of
yes votes on certain municipal referenda concerning immigration and naturalization.
Helbling justifies this indicator, saying that if “in a municipality laws on facilitated
naturalization are rejected and laws limiting immigration are approved, this indicates
that the majority of Swiss citizens have a restrictive understanding of citizenship”
(Helbling [2010], 44). To create this variable, Helbling carried out factor analysis with
the percentage of yes votes and used the second resulting factor because it “matched
fairly well” with his conceptualization of municipal understanding of citizenship (Hel-
18
3.2 Review of Helbling (2010)
bling [2010], 44). I am not entirely sure how this was eventually operationalized, but
it appears that a dummy-variable was assigned to some municipalities which had a
restrictive understanding of citizenship.
A second indicator is municipal ideology. Helbling uses data collected by Ladner
during three surveys in 1988, 1994 and 1998 (see citation for Ladner [1991]) which has an
indicator for “the perception of the strength of the respective parties. The municipal
secretaries were asked to indicate how important each political party is” (Helbling
[2010], 45). For the regression analysis, the party variables have been operationalized
as binary: ’important’/’unimportant’. Helbling claims that “Such an operationalisation
is better than accounting for the seats of the different parties in the local parliament or
the executive body insofar as the number of received votes does not necessarily reflect
the power of these parties. Particularly in small towns and villages, the position of
individual actors is often more important than the size of the party” (Helbling [2010]).
The final indicator is the formal institutional structure. It is important to note that
the particular types of direct democracy are coded differently and enter the analysis as
a series of dummy variables. As Helbling explains:
“In the analysis, I will make a distinction between municipalities in which
the entire population decides at closed ballot and those in which decisions
are taken during municipal assemblies. While both systems constitute di-
rect democratic institutions, let us see whether it makes a difference when
decisions are taken in complete anonymity at closed ballot or when people
have to show their hands during municipal assemblies” (Helbling [2010]).
These three indicators together comprise “local citizenship politics” which is Helbling’s
independent variable.
3.2.2.1 Validity issues regarding Helbling’s independent variable
This three-part independent variable presents a variety of validity issues. First, the
indicator for “municipal understanding of citizenship” is not clearly explained and may
simply be another form of ideology. Including both could lead to a sort of double
measuring of this aspect of local citizenship politics.
Second, the indicator for municipal ideology relies on data from Ladner’s review
of Swiss municipalities which covers the years 1988, 1994 and 1998. Given that the
SVP gained significant support in the 1990s, these may not reflect the actual political
situation in a given municipality in 2003.
19
3. LITERATURE REVIEW
Third, the indicator for decision-making structure also relies on the data collected
by Ladner and thus may be out of date. This is probably less problematic than the
ideology indicator as these institutions were (as we will see) more or less constant in
the pre-2003 period (especially before 2000).
Fourth, considering these factors as constant across time is problematic. While
certain aspects may not change, it would not be surprising if a municipality varied
ideologically or politically during 1990 – 2002. The assumption of a static set up means
that if this analysis is thought of in experimental form, the treatment and control units
are completely different municipalities. Helbling does not provide a balance table or
summary statistics, so it is not clear whether these two groups are comparable.
3.2.3 Discussion of Helbling’s confounding variables
Along with the dependent and independent variable, Helbling also includes a variety of
confounding variables in his analysis. Arguing that the “feeling of threat” (and therefore
conservativeness of a municipality) is influenced by unemployment and the number
of Muslim immigrants, Helbling proposes two indicators to capture this confounding
variable he feels might account for bias in his results. First, unemployment ratios among
foreigners in each municipality and second, the proportion of Muslim applicants. A
second control variable is the location of the municipality.
Unemployment rate of foreigners is operationalized by using data from Ladner
(1991) in which the secretaries of the local administrations were asked “to what extent
their municipalities have been affected by increasing unemployment” the options were
’very much’, ’partly’, or ’not at all’ . For regression analysis this variable was made into
a binary: ’not affected/partly or very much affected’. To get the “unemployment rate
for foreigners” Helbling simply multiplies this indicator by the proportion of foreigners
in a municipality. This does not seem particularly accurate to me, but it may be better
than not including a control for unemployment in his model.
Number of Muslim immigrants is operationalized by creating a ratio between ap-
plications from immigrants from the former Yugoslavia and Turkey and all submitted
applications. Anticipating that this is a somewhat controversial approach, Helbling
mentions in an endnote that he is “aware of the fact that not all candidates who emi-
grated from the countries of the former Yugoslavia are Muslims, nor can it be certain
whether all Muslim applicants are religious” (Helbling [2010], 53). He justifies his
approach by saying that “unfortunately, there is no data at our disposal providing in-
formation about the religious affiliation of the individual candidates. However, it is a
fact that Muslims from Kosovo constitute by far the largest immigrant group from the
20
3.2 Review of Helbling (2010)
former Yugoslavia” (Helbling [2010]).
Location of the municipality is included to investigate the potential impact of re-
gional differences in Switzerland. These are expected to be somewhat significant. Hel-
bling notes that “it is often put forward that the population of the French-speaking part
of Switzerland has a different relationship with its nation and with its foreigners than
the population of the German-speaking part” (Helbling [2010]). He does not actually
indicate exactly how this variable is operationalized for regression, but I would guess
it is simply a dummy variable for language-region.
These control variables are somewhat problematic because some of them are likely
correlated to the independent variables. For example, the location of a municipality
may be related to ideology or SVP support. Including unemployment statistics may
in fact simply be a proxy for the wealth of a municipality and as such may influence
political leanings.
3.2.4 Discussion of Helbling’s results
Helbling’s results are interesting. Table 1.1 which summarizes from his paper is included
below in Figure 3.1.
Figure 3.1: Helbling’s Table 1.1 -
We can see that some of the data was lost as the final N is only 95 which is reduced
to 75 in models 2 and 3 (because Helbling only had information on SVP strength for
21
3. LITERATURE REVIEW
this subset). As for the other 11 municipalities, they were apparently lost because
they were missing data on one or several independent variables. Since we have no
idea what the final group of 95 municipalities look like, it is hard to say anything
conclusive. It does appear, however, that direct democracy through popular referenda
at the ballot is related to higher rejection rates along with the municipal “understanding
of citizenship.” This model would argue that popular votes at the ballot increase
rejection rates by around 25%, though this seems to be a tenuous conclusion for the
reasons discussed above and additionally as we do not even know how many of the
95 municipalities actually used this method. Helbling argues that the confounding
variables (foreign residents, unemployment, number of Muslim candidates, language-
region etc...) do not influence rejection rates. However, when we consider that there
were almost no French-speaking municipalities which used popular votes at the ballot
as their mechanism, this conclusion seems dubious.
Helbling then considers a modified dependent variable: the rejection rates of can-
didates from Muslim countries and finds the same pattern, but with higher values the
coefficient on “Popular votes at ballot” (44.2, 52.0 and 54.5 for each model respec-
tively). In this analysis, other factors lose significance and it seems that institution
is the main predictor of rejection rates for candidates from Muslim countries (former
Yugoslavia and Turkey).
In these results, it is particularly curious that popular votes at the ballot had a
huge and highly significant coefficient, while “Municipal assembly” (the institution in
which referenda are voted on by raise of hand at the municipal assembly instead of
the ballot box) is not large or significant. Given this, it is hard to attribute the large
coefficient to “direct democracy” since that is occurring in both cases. Perhaps there
are few municipalities which use popular votes at the ballot and these happen to be
more extreme and thus drive the result. In any case, it is far from clear that direct
democracy is actually to blame.
While Helbling does a commendable job with the data he has, he cannot attribute
causality to the effect of directly democratic institutions because he does not have a
“before” and “after” model as his data only runs through 2002. Second, his variables
exhibit many validity concerns and the final sample is very small. In sum, Helbling’s
analysis is interesting but incomplete and raises several testable hypotheses which I
will pursue in my thesis.
22
4
Hypotheses
From Helbling’s work, several hypotheses emerge which are testable using my data.
Instead of rejection rates I use municipal naturalization rate as my dependent variable
(this choice will be discussed in the methodology section) so my hypotheses will be
stated using this as the dependent variable.
H1 Removing directly democratic institutions leads to increased naturalization rates.
H2 Language region, foreign population and size of municipality have little or no
effect on naturalization rates.
H3 The influence of the SVP has a moderate, yet significant effect which reduces
naturalization rates
23
4. HYPOTHESES
24
5
Methodology
My thesis uses a fixed effects estimator to link municipal institutions with municipal
naturalization rates. In this section I will first explain why I have selected a fixed effects
estimator and the mechanics of its operation. Second, I will explain my methodological
set-up and discuss each of my variables.
5.1 Why I have selected a fixed effects estimator
A fixed effects (FE) estimator uses a within transformation to remove the unobserved
unit-specific effects, along with any other time-constant explanatory variables before
estimation (Wooldridge [2009], 365). When selecting a FE estimator one must justify
its selection over other similar estimators such as first-differencing (FD) or random
effects (RE). I will address each of these estimators in turn.
A FE estimator allows arbitrary correlations between the unit-specific effects and
the independent variable which a RE estimator does not (Wooldridge [2009], 375).
In my case, it is clear that municipal-specific effects may be related to the choice of
institution, so the RE estimator is probably not a good choice.
The question of whether to use FE or FD is more complicated. While the FE
and FD estimators are identical for longitudinal (panel) data which only includes two
time periods, they are different for longitudinal data which covers three or more time
periods (as my data covers 20 periods, we are clearly in the second case). In Economet-
rics(Wooldridge [2009]) it is shown that both FE and FD are unbiased and consistent
under the traditional set of panel assumptions 1. This eliminates two of the traditional
1Random sample of the cross section, each explanatory variable changes over time, no perfectlinear relationships among explanatory variables, for each time-period the expected value of the time-variant-unit-specific (idiosyncratic) error given the explanatory variables in all time periods and the
25
5. METHODOLOGY
criteria which allow us to select an estimator. The main difference between these two
estimators is in their relative efficiency which depends on whether the time-variant-
unit-specific errors (idiosyncratic errors) are serially correlated, that is to say, whether
there are confounding time-variant forces which effect the outcome and are related to
each other from one period to the next.
Clustering the standard errors by municipality is used in order to break serial cor-
relation of the errors within municipalities, but the model still assumes that the errors
from different municipalities are independent. This is certainly more reasonable to
assume than a lack of serial correlation between the errors within a municipality over
time, but we can imagine situations in which this assumption might be violated. For
example, municipalities may receive canton-level information which results in similar
policies. Or, a contagion effect may occur in which municipal policies are noticed by
neighboring municipalities and spread.
If we feel comfortable assuming that the idiosyncratic errors are serially uncorre-
lated then, according to wooldridge, the FE estimator is more efficient than the FD
estimator (Wooldridge [2009], 370). Another factor which distinguishes FE and FD
models is sensitivity to violations of the strict exogeneity assumption (for each time-
period the expected value of the time-variant-unit-specific (idiosyncratic) error given
the explanatory variables in all time periods and the unit-specific effect is zero). This
assumption can be violated by feedback between the idiosyncratic errors and future
outcomes of explanatory variable or if a lagged dependent variable is included among
the regressors (Wooldridge [2009], 370).
5.2 The Mechanics of a fixed effects estimator
In this model the standard regression equation is expressed as follows (Eqn1):
yit = β1xit + ai + uit
In this notation t denotes the year (1, 2,...,T) and i denotes the specific unit (in our case,
this would be the municipality). Additionally, yit indicates the outcome in a specific
municipality during a specific time year, β1 indicates the effect of a one-unit change in
the independent variable (in a specific municipality at a specific times, as denoted by
xit). Finally, ai indicates the fixed effects which do not change over time (but might
change over location), and uit indicates the time variant effects. In my model I have
unit-specific effect is zero (Wooldridge [2009], 379)
26
5.3 My fixed effects model
twenty periods (T=20), and I use a differencing to cancel out the unit-specific “fixed
effects” (ai) because they are time invariant. The differencing involves subtracting the
average unit-specific outcome, yi, from Eqn.1, where yi is given by the formula: (Eqn2)
yi = β1xi + ai + ui
In this formula, yi = 1T
∑TT=1 yit and xi and ui are calculated in the same manner. If
we subtract Eqn.2 from Eqn.1 we get (Eqn.3)
yit = β1xit + uit
In Eqn. 3, yit = yit− y = yit− 1T
∑TT=1 yit and xit and uit are calculated accordingly. A
key assumption of this is that the idiosyncratic time-demeaned errors (uit) are uncor-
related with the independent variable (xit). This means that the independent variables
are strictly exogenous. While we might believe this to be true due to the somewhat
exogenous nature of the institutional change 2003, there are many possible stories one
could tell which could violate this assumption. For example, municipalities may de-
cide to change their institution for reasons other than the 2003 court decision. As we
will see when we look at the distribution of institutions over time, it seems as though
many municipalities preempt the court’s decisions while others lag behind. These dif-
ferences are probably not randomly distributed – one can imagine that more liberal
municipalities might change their institution endogenously in order to be more kind to
immigrants whereas more conservative municipalities might resist the change and even
defy the ruling. As it stands, however, due to the at least somewhat exogenous nature
of the change in institutions, this assumption has a much higher chance of being true
than in cross-sectional analyses.
5.3 My fixed effects model
The general FE model for my thesis is :
yit = β1xit + uit
In this model yit indicates the demeaned municipal naturalization rate (relative to the
average of that municipality over 20 years between 1990 and 2009) of a particular mu-
nicipality, i, at a particular year, t, and xit indicates the demeaned “institution score”
(relative to the average of that municipality over 20 years) of a particular municipal-
27
5. METHODOLOGY
ity, i, at a particular year, t. Therefore, indicates the effect of a one-unit change in
the institution (which represents, for example, going from “ direct democracy” to “not
direct democracy”). Finally, uit indicates the time-demeaned time-varying errors for a
particular municipality, i, in a particular year, t. An example of one such error might
be the concentration of immigrants (as this may change over time). Additionally, as
stated in above, there may be endogenous reasons why municipalities choose to change
their institution away from direct democracy. Given this set up, I will now discuss the
measurement and operationalization of each of my variables.
5.4 Discussion and operationalization of variables
5.4.1 Data
The data used in this project comes from a larger projected headed by Jens Hainmueller
at MIT and Dominik Hangartner at Berkeley which I have had the privilege to work
on. Funding for the overall project was provided by Swiss National Science grant no.
100017–132004. The independent variable, the municipal institution, was collected
using an online survey tool during the summer of 2010. The dependent variable, the
municipal naturalization rates, comes from the Swiss Federal Office of Statistics.
5.4.2 Indepdent Variable: Municipal Institution
The independent variable in my thesis is the institution governing naturalization at
the municipal level. Henceforth this will be referred to as simply “institution.” This
variable was collected through work done with Jens Hainmueller at MIT. We created
a survey which was sent to each municipality and asked the municipal clerk to classify
their municipality’s institution from 1990 to 2009 into one of ten official categories.
These are as follows:
1. The “citizen’s community”1 votes in a closed ballot
2. The “citizen’s community” votes at a municipal assembly
3. The “citizen’s counsel”
4. Swiss citizens vote in closed ballot popular vote
5. Swiss citizens vote at a municipal assembly
1The “citizen’s community” is a group of established citizens who have lived in the municipalityfor generations
28
5.4 Discussion and operationalization of variables
6. The municipal parliament (legislative)
7. The municipal counsel (executive)
8. The naturalization commission
9. The decision is made at the cantonal level (i.e. there are no requirements ofmunicipal citizenship beyond what is required at the canton level)
10. Other
While this coding captures the full universe of cases, it may be useful to code the
variable in a narrower way so that we can capture change over time. In my thesis, I
will use two alternative codings of institution which are called institution binary and
institution linear respectively. I will address each of these separately. First, consider
a two-level institution variable, institution binary, which is 0 for directly democratic
cases and 1 for not directly democratic cases (executive, legislative or canton-level
governance). This binary indicator provides the simple case in which a 1-unit change is
equivalent to moving from a directly democratic institution to a not directly democratic
institution. This option is coded as follows with respect to the original 9 categories:
institution binary :
0 = Direct democracy
Option 1) The “citizen’s community” votes in a closed ballot
Option 2) The “citizen’s community” votes at a municipal assembly
Option 4) Swiss citizens vote in closed ballot popular vote
Option 5) Swiss citizens vote at a municipal assembly
1 = Executive, Legislative or made at the cantonal level
Option 3) The “citizen’s counsel”
Option 6) The municipal parliament (legislative)
Option 7) The municipal counsel (executive)
Option 8) The naturalization commission
Option 9) The decision is made at the cantonal level (i.e. there are norequirements of municipal citizenship beyond what is required at the cantonlevel)
29
5. METHODOLOGY
Second, consider a three-level institution variable, institution linear, which breaks
the overall effect into different levels of direct democracy and non-directly democratic
institutions. In his analysis, Helbling noted that there was a large difference between
municipalities which exercised direct democracy at the ballot box (Option 4) and those
which held votes in municipal assemblies (Option 5).
institution linear :
0 = Direct democracy closed ballot
Option 4) Swiss citizens vote in closed ballot popular vote
Option 1) The “citizen’s community” votes in a closed ballot
1 = Modified direct democracy vote at municipal assembly
Option 5) Swiss citizens vote at a municipal assembly
Option 2) The “citizen’s community” votes at a municipal assembly
2 = Legislative, Executive or committee
Option 6) The municipal parliament (legislative)
Option 7) The municipal counsel (executive)
Option 8) The naturalization commission
Option 9) The decision is made at the cantonal level (i.e. there are norequirements of municipal citizenship beyond what is required at the cantonlevel)
This independent variable is a subset of Helbling’s three-part independent variable.
For each municipality included in the project (selection principle will be explained
below) there is a value for this variable for each year between 1990 and 2009. Ideally,
for each municipality that changes institution I will have data many years before and
after 2003 (sometimes municipalities didn’t change their institutions until 2004 or 2005)
to use fixed effects to their full potential.
5.4.3 Selection Principle
Ideally I would like to use every municipality (there are about 2,500) in Switzerland for
this project, but only 60% of all municipalities responded to the data collection survey.
This is still quite a substantial number, and it is important to note that the response rate
is 72% if we only consider municipalities which had 10 or more naturalizations during
the 1990 – 2009 period. Amongst this group, a small number cannot be used because
30
5.4 Discussion and operationalization of variables
their data is incomplete or incomprehensible. Another group cannot be used because
they have had no naturalizations during this period. There are 1,483 municipalities in
my final data set (approx 60%) all Swiss municipalities. While this is only 60% of total
municipalities, the non-respondents tend to be smaller municipalities, many of which
have been verified as having no naturalizations during the 1990 to 2009 period thus
they would not contribute to the analysis even if they were included.
Clearly, there are a few unavoidable problems with this selection principle. First,
there is an element of non-randomness because some municipality clerks are unlikely to
respond for non-random reasons (e.g. they live in a very small municipality, there has
been municipal restructuring etc...). As such, smaller municipalities belong dispropor-
tionately to this group. Despite this, I currently have a larger N than any other study
in this area. Additionally, since I have data points for each municipality in each year,
there is a very large response space.
5.4.4 Dependent Variable: Municipal Naturalization Rate
The dependent variable for the my thesis is the naturalization rate. This is calculated
as the number of naturalizations as a percent of the total foreign population in the year
pervious in each municipality from 1990 – 2009.
As discussed in the significance section, naturalizations rates in Switzerland are
usually very low (between 2 and 3%) mostly due to the fact that the residency require-
ment is quite high and the process is time-consuming (and, as we have seen, can end
in failure).
5.4.4.1 Trends in naturalizations: 1990 – 2009
During the time under consideration in this thesis, the two largest forces influencing
naturalization trends and naturalization policy in Switzerland are first, a desire for
rapprochement with the European Union and second, a feeling of uberfremdung due to
immigrants for ex-Yugoslavia (Riano and Wastl-Walter [2006]). In this section I will
discuss the structure of naturalizations during the 1990 – 2009 period.
Along with the war in the Balkans, a second force to note is both a relative and
absolute decrease in Italian naturalizations over this period. The two graphs below
explore the evolution of naturalizations in Switzerland between 1981 and 2009. Figure
5.1 shows the total number of naturalizations from each of the top ten countries of
origin. Figure 5.2 shows the same relationship, but the value displayed on the y-
axis is the number of naturalizations from the country of origin as a percent of total
naturalizations (from all countries) in that year.
31
5. METHODOLOGY
Figure 5.1
0
0
05000
5000
500010000
1000
0
1000015000
1500
0
15000total number of naturalizations
tota
l num
ber o
f nat
ural
izat
ions
total number of naturalizations1980
1980
19801990
1990
19902000
2000
20002010
2010
2010year
year
yearSerbia and Montenegro
Serbia and Montenegro
Serbia and MontenegroItaly
Italy
ItalyTurkey
Turkey
TurkeyGermany
Germany
GermanyBosnia and Herzegovania
Bosnia and Herzegovania
Bosnia and HerzegovaniaSri Lanka
Sri Lanka
Sri LankaMacedonia
Macedonia
MacedoniaPortugal
Portugal
PortugalEx-Yugoslavia
Ex-Yugoslavia
Ex-YugoslaviaCroatia
Croatia
CroatiaEvolution of Swiss Naturalizations from Top 10 Countries
Evolution of Swiss Naturalizations from Top 10 Countries
Evolution of Swiss Naturalizations from Top 10 Countries
Data obtained from Swiss Federal Office of Statistics(http: // www. bfs. admin. ch/ bfs/ portal/ fr/ index/ themen/ 01/ 07/ blank/ key/ 03. html )
The story shown in these graphs is clearly the huge surge in Serbian naturalizations
which jumped from around 10% (counting Ex-Yugoslavia) in 1998 to around 25% in
2006, to drop to around 20% of all naturalizations in 2009. This surge in Serbian
naturalizations is accompanied by a drop in Italian and German naturalizations (though
German naturalizations do recover slightly in the 2007 - 2009 period. Now that I have
discussed the methodological set-up, I will turn to my data analysis.
32
5.4 Discussion and operationalization of variables
Figure 5.2
0
0
0.1
.1
.1.2
.2
.2.3
.3
.3.4
.4
.4Percent of total naturalizations
Perc
ent
of t
otal
nat
ural
izat
ions
Percent of total naturalizations1980
1980
19801990
1990
19902000
2000
20002010
2010
2010year
year
yearSerbia and Montenegro
Serbia and Montenegro
Serbia and MontenegroItaly
Italy
ItalyTurkey
Turkey
TurkeyGermany
Germany
GermanyBosnia and Herzegovania
Bosnia and Herzegovania
Bosnia and HerzegovaniaSri Lanka
Sri Lanka
Sri LankaMacedonia
Macedonia
MacedoniaPortugal
Portugal
PortugalEx-Yugoslavia
Ex-Yugoslavia
Ex-YugoslaviaCroatia
Croatia
CroatiaEvolution of Swiss Naturalizations from Top 10 Countries (as % of total naturalizations)
Evolution of Swiss Naturalizations from Top 10 Countries (as % of total naturalizations)
Evolution of Swiss Naturalizations from Top 10 Countries (as % of total naturalizations)
Data obtained from Swiss Federal Office of Statistics(http: // www. bfs. admin. ch/ bfs/ portal/ fr/ index/ themen/ 01/ 07/ blank/ key/ 03. html )
33
5. METHODOLOGY
34
6
Data Analysis
In this section I will test the three hypotheses which are stated in section 4. I will do
this by developing three statistical models and running significant subgroup analysis
using one of the models. While doing this analysis, I will be sensitive to variation on
both my independent and dependent variable (necessary for a fixed effects model to
work) and will also explore issues of balance in the data between subgroups.
6.1 Model 1
Model 1 uses the the FE estimator described in the methodology section with insti-
tution binary as the independent variable and naturalizaiton rate as the dependent
variable. In this model, I only consider observations which have values for these two
variables for at least 50% of the time periods (10 or more). This reduces the sample
to a total of 1,428 municipalities. The summary statistics for these municipalities are
shown in Table 6.1 where the averages are given as the average across all time periods.
In Table 6.1, the first three variables are the dependent and independent variables
(using both specifications of the independent variable). naturalizations refers to the
raw number of naturalizations in a given municipality in a given year. Total pop ref-
eres to the total population of a given municipality in a given year. Swiss pop refers
to the number of Swiss citizens living in a municipality in a given year. Foreign pop
refers to the number of foreign nationals living in a municipality in a given year. Ra-
tio foreign swiss is calculated by dividing foreign pop by Swiss pop. Percent foreigners
is found by dividing foreign pop by Total pop . It is important to note that this is not
the same as ratio foreign swiss as the former is the percent of foreigners as compared to
the entire population. Language region is an indicator that is 1 if the language spoken
35
6. DATA ANALYSIS
Table 6.1: Summary statistics
Variable Mean Std. Dev. Min. Max. N
naturalization rate 2.126 4.499 0 200 28824institution binary 0.419 0.493 0 1 28162institution linear 1.391 0.543 0 2 28162naturalizations 14.117 87.302 0 4851 29080total pop 3584.144 12277.92 0 368677 29080swiss pop 2842.485 8722.236 0 262368 29080foreign pop 741.659 3738.594 0 112429 29080ratio foreign swiss 0.148 0.143 0 1.46 29047percent foreigners 11.815 9.112 0 59.349 29047language region 2.587 0.609 1 3 29080
in the municipality is Italian, 2 if the language spoken is French and 3 if the language
spoken is German.
From these summary statistics we can ascertain that the sample in question is
primarily German-speaking (language region = 2.6), has a relatively low naturalization
rate (2.1%), is about 12% foreigners (this is somewhat lower than the average across
Switzerland which is closer to 20%) and has an average total population of about 3,500
inhabitants.
Now, I will consider variation over time of the independent and dependent variable
for these municipalities. The distribution of institution binary over time is shown in
Figure 6.1 and the average naturalization rate over time is shown in Figure 6.2.
We can see that there is quite a bit of over-time variation on both these variables.
Now, we can use the FE estimator described above on model 1. I have clustered the
standard errors by each municipality to break potential serial correlation between the
errors within municipalities. The results from this regression are shown below in Table
6.2. For this, and all subsequent tables, the yearly effects are not included and standard
errors are clustered by municipality.
This analysis indicates that a 1-unit increase in institution binary (going from direct
democracy to not directly democracy) leads to a 0.76 percentage point increase in
naturalization rate. This is a significant increase, especially considering that the average
naturalization rate for this model is around 2.1%. Thus, at least initially, it appears
that we can conclude that direct democracy suppresses naturalization rates. Now, I will
consider the same regression using institution linear to try to disaggregate the effect of
direct democracy at the ballot box from other forms of direct democracy.
36
6.2 Model 2
Figure 6.1: Variation of insitution binary over time -
Table 6.2: Model 1
Variable Coefficient(Cl Std. Err.)
institution binary 0.760∗∗
(0.141)
Intercept 1.243∗∗
(0.146)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
6.2 Model 2
Model 2 follows the same specification as Model 1 except it considers institution linear
instead of institution binary in order to test whether considering direct democracy at
the ballot box separately from direct democracy in other forms gives different effects.
As we can see in Figure 6.3, direct democracy at the ballot box only occurs in a
small percent of municipalities, but it does have some variation (directly post 2003).
37
6. DATA ANALYSIS
Figure 6.2: Variation of naturalization rate over time -
Model 2 considers the same municipalities as Model 1 and because institution linear is
already considered in the pervious balance table, and the distribution of the dependent
variable is the same, I will not repeat those graphs here.
Now we can consider running the FE regression on Model 2. The results are dis-
played below in Table 6.3.
Table 6.3: Model 2
Variable Coefficient(Cl Std. Err.)
institution linear 0.567∗∗
(0.118)
Intercept 0.766∗∗
(0.204)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
The significant effect which is found when considering ballot box institutions at a
38
6.3 Model 1: Subgroup Analysis
Figure 6.3: Variation of insitution linear over time -
different level is interesting. It seems that even a 1-unit movements in institution linear
generates significant changes in the naturalization rate. This indicates that direct
democracy at the ballot box has an independent effect compared to other forms of
what we might call “moderated direct democracy.” Now that I have established the
general effect of direct democracy on naturalization rates, I will used Model 1 to explore
subgroups within the response space to check for heterogeneity in responses.
6.3 Model 1: Subgroup Analysis
Now I will consider Model 1 as specified above, but I will limit my analysis to include
certain groups of municipalities. Because language-region and other municipal charac-
teristics are constant over time, they are differenced out in our original model. Knowing
the general effect for all 1,438 municipalities in my data set is a good first step, but
we may also be curious if this effect is different for different subgroups. First, I will
consider different language-regions. Second, I will consider municipalities of different
39
6. DATA ANALYSIS
sizes. Third, I will consider municipalities which experienced an influx of immigrants.
Fourth, I will consider municipalities which had high SVP support in 2003.
6.3.1 Differences by language-region
In this section, I subset my data by language-regions. Depending on the majority lan-
guage spoken in the municipality, municipalities are coded as either French, German or
Italian (there are not enough Romansch-speaking municipalities to constitute a group).
I will first consider only German-speaking municipalities as they constitute the largest
proportion of my data set (941 municipalities). Running the same regression above on
only these municipalities gives the following results which are displayed in Table 6.4.
Table 6.4: Model 1: German Speaking Municipalities (N=941)
Variable Coefficient(Cl Std. Err.)
institution binary 0.754∗∗
(0.157)
Intercept 1.243∗∗
(0.153)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
It is interesting to note that this looks almost exactly the same as the previous
regression. This indicates that perhaps the French-speaking and Italian-speaking re-
gions are not contributing to the result. That is to say, French-speaking and Italian-
speaking municipalities do not have significant variation on the independent variable
of institutions (we can conclude this because recall that in a fixed effects estimator the
independent variables is the change in institution relative to the demeaned value for
an individual municipality). To check this, we can graph the types of institutions over
time in the French-speaking and Italian-speaking regions Figure 6.4 displays the distri-
bution of institutions over time in the French-speaking regions as they were distributed
before the re-coding (10 different institutional options) and Figure 6.5 displays the dis-
tribution of institutions over time in the Italian-speaking regions, again with original
coding.
As we can see there is almost no variation at all in the Italian-speaking region.
This means we cannot do differencing analysis on this region (because there is nothing
to difference) and henceforth it will be excluded from data analysis. In the French-
speaking region we observe significant variation in institutions, however, most of the
changes are municipalities which move from legislative to executive institutions. In the
40
6.3 Model 1: Subgroup Analysis
Figure 6.4: Variation of all institutions over time: French-speaking municipal-ities (N = 400) -
binary institution variable used for the previous analysis I only differentiate between
directly democratic institutions and all other institutions, where the second category
includes both legislative and executive options. In this model, the French-speaking
region has hardly any variation on the independent variable as we can see from Figure
6.6 which shows the distribution of institutions in the French-speaking region as it enters
our analysis above. We can compare this to Figure 6.7 which shows the distribution of
the same institutions in the German-speaking region.
Given the almost complete lack of variation in Figure 6.6 compared to Figure 6.7,
it is not surprising that the French-speaking regions do not contribute to our result
from Table 1. Looking back at Figure 6.6, however, we can see that there was signifi-
cant variation in French-speaking regions between legislative and executive institutions.
Looking at variation between these two institutions does not allow us to make the same
assumptions of exogeneity of institutional change which were justified in the previous
models due to the fact that changes away from direct democracy were known to be
caused (at least in the large part) by the 2003 court decision. In this case we are sim-
41
6. DATA ANALYSIS
Figure 6.5: Variation of all institutions over time: Italian-speaking municipal-ities (N = 87) -
ply exploring a correlative relationship between these institutions and naturalization
rates. To explore this relationship, I recode the institutions in the French-speaking
regions to capture the relevant change in institution. I propose simply looking at the
difference between legislative and executive institutions because these make up the
majority of the changes (the bright red and pale blue lines from figure 6.6). In this
regression, I make the new variable, french exec, which is 1 if the municipality uses
an executive committee as an institution and 0 if the municipality uses the municipal
legislature as an institution (and is in the French-speaking region). Running a fixed
effects regression of french exec on naturalizaiton rate allows us to see the relationship
between of moving from 0 (legislative institution) to 1 (executive institution) on the
demeaned naturalization rate of a French-speaking municipality which had either leg-
islative or executive institutions between 1990 and 2009 (N= 300). The results of this
analysis are shown in Table 6.5 below.
Table 6.5 indicates that going from a legislative institution to an executive in-
stitution in French-speaking Switzerland leads to a 0.35 percentage point increase in
42
6.3 Model 1: Subgroup Analysis
Figure 6.6: Variation of institution binary over time: French-speaking munic-ipalities (N = 400) -
Table 6.5: Model 1: Correlative Results (N=300).
Variable Coefficient(Std. Err.)
french exec 0.350(0.224)
Intercept 1.316∗∗
(0.203)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
naturalization rates. This effect is not statistically significant and the 95% confidence
interval goes from -0.09 to 0.79 (crossing 0) thus we are not even completely sure if
this effect is positive. This indicates that going from a legislative to executive insti-
tution might have a small positive effect but doesn’t have a significant impact on the
naturalization rates in a given French-speaking municipality. This is not particularly
surprising given that we believe that both legislative and executive institutions require
43
6. DATA ANALYSIS
Figure 6.7: Variation of institution binary over time: German-speaking mu-nicipalities (N = 941) -
substantiation of naturalization decisions and therefore changing from one to the other
should not significantly change the naturalization process.
By observing the effect of institution change on naturalization rates in different
language regions we can assert that changes away from directly democratic institu-
tions mostly occurred in German-speaking Switzerland and therefore our conclusions
regarding the first hypothesis (moving away from directly democratic institutions leads
to increases in municipal naturalization rates) only really apply to German-speaking
Switzerland because observations from French-speaking and Italian-speaking Switzer-
land drop out of our analysis due to lack of variation on institution binary.
6.3.2 Municipality Size
In this section I will subset my data by size of the municipality. While some Swiss
municipalities are large cities, the majority are small towns and villages with relatively
small populations. Figure 6.8 shows the distribution of the total population of German-
44
6.3 Model 1: Subgroup Analysis
speaking municipalities in 2003. In 2003 there were 17 German-speaking municipalities
with populations over 20,000 which are not shown in this graph because they made it
impossible to see the distribution of the majority of the municipalities.1
Figure 6.8: Distribution of Municipal Population in German-speaking munici-palities in 2003 -
If we consider only municipalities which had a population of greater than 6,000 in
2003 (the top 15% of municipalities in my sample of 941 German-speaking municipali-
ties). We can assess the effect of institutional change on German-speaking municipali-
ties which large populations. First, we must check if there is adequate variation on the
independent variable. Figure 6.9 shows the distribution of institution binary in these
municipalities.
From Figure 6.9 we can see that there is significant variation on the independent
variable. We can now run the fixed effects regression only on these municipalities. The
results from this regression are displayed in Table 6.6.
1These are: Dubendorf, Uster, Winterthur, Dietikon, Zurich, Bern, Koniz, Thun, Emmen, Kriens,Baar, Zug, Riehen, St. Gallen, Rapperswil-Jona, Chur and Frauenfeld
45
6. DATA ANALYSIS
Figure 6.9: Distribution of institution binary in large German municipalities -
Table 6.6: Model 1: High Pop (N=142)
Variable Coefficient(Cl Std. Err.)
institution binary 0.452∗∗
(0.154)
Intercept 0.692∗∗
(0.089)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
From Table 6.6, we can see that in these municipalities the effect of changing insti-
tutions away from direct democracy increases the demeaned naturalization rate by 0.45
percentage points. This is significantly lower than the original effect for all German-
speaking municipalities (0.74). We can now compare this to small municipalities with
populations of less than 2,500 in 2003. For these municipalities (64% of our sample
N=595) the distribution of institution binary over time is shown in Figure 6.10 and not
46
6.3 Model 1: Subgroup Analysis
surprisingly looks very similar to the distribution of institutions for the whole.
Figure 6.10: Distribution institution binary in small German-speaking munic-ipalities -
Running the regression to find the relationship between institution and naturaliza-
tion rate in these municipalities gives the following results which are displayed in Table
6.7.
Table 6.7: Model 1: Low-Pop (N=595)
Variable Coefficient(Std. Err.)
institution binary 0.627∗∗
(0.237)
Intercept 1.547∗∗
(0.244)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Again, we observe that the effect of institutional change on naturalization rate is
47
6. DATA ANALYSIS
lower for these municipalities than the whole. As a comparison, I also investigated
the effect for mid-sized municipalities (population between 2,500 and 6,000 in 2003)
which constitute 21% (N=204)of the sample. For these municipalities the distribution
of institution binary is shown in Figure 6.11
Figure 6.11: Distribution institution binary in mid-sized German-speaking mu-nicipalities -
The results from the regression on this group reveals a surprising result. The effect
of institutional change on naturalization rates is much higher. The results from this
regression are shown below in Table 6.8.
As we can see, for these 204 municipalities, the effect of moving away from directly
democratic institutions increases the demeaned naturalization rate by 1.22 percentage
points. This effect is nearly twice as large as the effects in the high-population or low-
population groups. This indicates that municipality size leads to heterogeneous effects
of institution change with both small and large municipalities seeing a smaller effect
whereas mid-sized municipalities see a much larger effect. Of course, we can’t read
too far into these effects because of the limits of statistics. One way to get some more
48
6.4 Model 3: Interaction Effects
Table 6.8: Model 1: Mid-pop (N=204)
Variable Coefficient(Cl Std. Err.)
institution binary 1.224∗∗
(0.198)
Intercept 0.865∗∗
(0.084)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
traction on this question is to consider a third model which includes interaction effects
which allows us to actually test if the three different sizes constitute separate effects.
6.4 Model 3: Interaction Effects
The model with interaction effects is very similar to the standard FE model used in
this analysis, but it includes dummy variables. Model 3, in general, looks like this:
yit = aβ1xti + bβ2xit + cβ3xit + uit
In this model a, b and c are a dummy variables which indiciate whether the mu-
nicipality is in the high, medium or low population group. Running the fixed effects
estimator on the model above I will again limit myself to German-speaking municipal-
ities (N=941). The F-test statistic produced by this analysis (Table 6.9) is 29.30 and
allows us to reject the null hypothesis that these three groups have the same effect.
Finally, we might consider how these three groups compare to each other on sum-
mary statistics (see Tables 6.10, 6.11, 6.12)
49
6. DATA ANALYSIS
Table 6.9: Model 3: Interactions (N=941)
Variable Coefficient(Cl Std. Err.)
interaction high 0.452∗∗
(0.154)
interaction mid 1.224∗∗
(0.197)
interaction low 0.627∗∗
(0.237)
Intercept 1.268∗∗
(0.156)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.10: Summary statistics: High Population
Variable Mean Std. Dev. Min. Max. N
naturalization rate 1.995 1.392 0 11.551 2880institution binary 0.44 0.496 0 1 2791institution linear 1.391 0.579 0 2 2791naturalizations 68.84 220.793 0 4851 2880total pop 15991 30802.129 4658 368677 2880swiss pop 12598.766 22496.797 3635 262368 2880foreign pop 3392.234 8475.901 141 112429 2880ratio foreign swiss 0.252 0.141 0.024 1.013 2880percent foreigners 19.239 8.093 2.39 50.319 2880language region 3 0 3 3 2880
Table 6.11: Summary statistics
Variable Mean Std. Dev. Min. Max. N
naturalization rate 1.927 1.822 0 17.742 4088institution binary 0.186 0.389 0 1 4045institution linear 1.142 0.459 0 2 4045naturalizations 9.856 10.389 0 93 4100total pop 3778.682 987.518 0 6617 4100swiss pop 3220.908 832.029 0 5801 4100foreign pop 557.774 355.897 0 2448 4100ratio foreign swiss 0.179 0.122 0.011 0.84 4090percent foreigners 14.361 7.858 1.08 45.664 4090language region 3 0 3 3 4100
50
6.4 Model 3: Interaction Effects
Table 6.12: Summary statistics: Low Population
Variable Mean Std. Dev. Min. Max. N
naturalization rate 2.171 5.513 0 200 11762institution binary 0.199 0.399 0 1 11813institution linear 1.163 0.455 0 2 11813naturalizations 1.552 3.002 0 54 11960total pop 1002.481 639.77 0 3151 11960swiss pop 918.963 573.134 0 2984 11960foreign pop 83.518 100.298 0 780 11960ratio foreign swiss 0.079 0.076 0 0.722 11937percent foreigners 6.951 5.701 0 41.944 11937language region 3 0 3 3 11960
51
6. DATA ANALYSIS
6.4.1 Influx of immigrants
Another factor we may expect to lead to a heterogeneous effect is whether a particu-
lar municipality experienced an influx of immigrants in the pre-treatment (pre-2003)
period. To do this, I will create a variable, delta percent foreigners which measures
the difference between the ratio of foreigners to Swiss citizens (ratio foreign swiss·100)
in 2003 and in 1990. In this section I will subset the German-speaking municipalities
into three groups. First, I will consider those which saw a large influx. Second, I will
consider those which saw either no influx or a very small influx. Third, I will consider
municipalities which saw a decrease (negative influx) in the percent of foreigners in their
populations. Again, for each group I will check and see if there is adequate variation
on the independent variable.
Figure 6.12 shows the general distribution of changes in the foreign population
between 1990 and 2003 for all German-speaking municipalities in my sample (N=941).
Figure 6.12: Distribution of delta percent foreigners in German-speaking mu-nicipalities -
As we can see, the majority of municipalities did not see a huge change in the
52
6.4 Model 3: Interaction Effects
percentage of their population that are foreigners during this period. In order to have
adequate observations to garner significant results, I was not able to subset my data as
much as I would have liked to on this variable. First, I will consider municipalities with
an influx of more than 2.5% (57% of the sample) in their foreign population between
1990 and 2003. The distribution of institution binary in these municipalities is shown
in Figure 6.13.
Figure 6.13: Distribution of institution binary in German-speaking municipal-ities which saw a high influx of immigrants (N=540) -
Running the regression on this subsample gives the result shown in Table 6.13.
For these 540 municipalities. moving away from direct democracy leads to a 0.63
percentage point increase in naturalization rate. This is slightly lower than the result
for all German-speaking municipalities, but is fairly comparable. It would be very
interesting to look at the effect of larger increases (5% or 10%) but these results are
inconclusive because there is not enough observations at those levels.
Now we can consider municipalities which saw a positive, but low influx of foreigners
between 1990 and 2003. For these municipalities (30% of the sample) the distribution
53
6. DATA ANALYSIS
Table 6.13: Model 1: High Influx of Immigrants (N=540)
Variable Coefficient(Cl Std. Err.)
institution binary 0.631∗∗
(0.160)
Intercept 1.137∗∗
(0.137)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
of institution binary over the period 1990 2009 is shown below in Figure ??.
Figure 6.14: Distribution of institution binary in German-speaking municipal-ities which saw a low influx of immigrants (N=285) -
There appears to be significant variation in institutions amongst these municipalities
and the results of running the regression on them are given in Table 6.14.
For these 285 municipalities the effect of moving away from direct democracy leads
to an increase of 0.87 percentage points in naturalization rate. This is slightly higher
54
6.4 Model 3: Interaction Effects
Table 6.14: Model 1: Low Influx Immigrants (N=285)
Variable Coefficient(Cl Std. Err.)
institution binary 0.877∗∗
(0.288)
Intercept 1.709∗∗
(0.426)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
than overall increase (0.74 percentage points). Some of the municipalities (12% of the
sample) experienced decreases in the percentage of foreigners living in them between
1990 2003. It would be interesting to consider these as a separate subgroup. Un-
fortunately there are not enough observations in this group to generate statistically
significant point estimates of the effect. Instead I will consider the effect of a 2% influx
of less (including negative values) 37% of the sample. The distribution of institu-
tion binary for these municipalities is shown in Figure 6.15 below.
Given that there is adequate variation in institutions for these municipalities, I then
run the regression subset on them. The results are shown in Table 6.15.
Table 6.15: Model 1: Low to negative influx of immigrants (N=341)
Variable Coefficient(Cl Std. Err.)
institution binary 0.897∗∗
(0.314)
Intercept 1.060∗∗
(0.215)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
As we can observe, the results are very similar to the ones shown in Table 9 (coef-
ficient = 0.90). This seems to indicate that a decrease in the foreign population does
not constitute a hugely different effect. Overall, these results indicate that a moderate
influx (2.5% or more) dilutes the effect of moving away from direct democracy whereas
a low influx slightly increases it, however, we cannot read too far into these results
because of the limits of statistical significance. To gain better traction on this effect
I will again consider an interaction model. The model is the same as the model 3
except there are only two dummy variables, a and b and instead of referring to the
municipality size, they refer to either a high, low/negative influx. The results from this
55
6. DATA ANALYSIS
Figure 6.15: Distribution of institution binary in German-speaking municipal-ities which saw a low or negative influx of immigrants (N=341) -
model are shown below in Table 6.16. The F-statistic of 18.76 allows us to again reject
the null that these two groups are the same (though not as strongly as with regard to
population).
Table 6.16: Model 3: Interactions with influxes of immigrants (N=941)
Variable Coefficient(Cl Std. Err.)
interaction high influx 0.631∗∗
(0.160)
interaction low influx 0.897∗∗
(0.313)
Intercept 1.247∗∗
(0.151)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
56
6.4 Model 3: Interaction Effects
Finally, we might consider how these two groups compare to each other on summary
statistics which are shown in Table 6.17 and Table 6.18.
Table 6.17: Summary statistics: High influx of immigrants
Variable Mean Std. Dev. Min. Max. N
naturalization rate 1.893 3.527 0 200 10808institution binary 0.229 0.42 0 1 10699institution linear 1.187 0.486 0 2 10699naturalizations 21.019 116.991 0 4851 10880total pop 5510.668 17021.322 0 368677 10880swiss pop 4425.777 12543.185 0 262368 10880foreign pop 1084.891 4574.272 0 112429 10880ratio foreign swiss 0.172 0.13 0 1.013 10847percent foreigners 13.739 8.441 0 50.319 10847language region 3 0 3 3 10880
Table 6.18: Summary statistics: Low to negative influx of immigrants
Variable Mean Std. Dev. Min. Max. N
naturalization rate 2.384 5.681 0 100 6685institution binary 0.237 0.425 0 1 6767institution linear 1.201 0.483 0 2 6767naturalizations 3.393 11.331 0 357 6820total pop 1665.416 2285.254 49 31954 6820swiss pop 1511.384 1924.78 48 25027 6820foreign pop 154.032 407.976 0 6927 6820ratio foreign swiss 0.067 0.065 0 0.463 6820percent foreigners 5.924 5.159 0 31.649 6820language region 3 0 3 3 6820
In general, we can conclude that for municipalities which experience a low to nega-
tive influx of immigrants, the effect of institutional change away from directly democ-
racy is higher than in those municipalities in which there is a high influx of immigrants.
Now I will consider a third source of potential heterogeneity between municipalities:
SVP vote share in 2003.
6.4.2 Level of SVP support in 2003
Another factor which may distinguish municipalities is their support for the SVP. As
mentioned above, the SVP is the right-wing anti-immigrant party. In this section I use
SVP support in 2003 to subset my data into two groups. This additional data reduces
57
6. DATA ANALYSIS
my data set to 912 municipalities because a certain number either did no stage a SVP
candidate in 2003 or were not in one my data sets. The distribution of SVP support
in 2003 amongst my sample is shown in Figure 6.16
0
0
02
2
24
4
46
6
68
8
8percent
perc
ent
percent0
0
020
20
2040
40
4060
60
6080
80
80SVP vote share in 2003
SVP vote share in 2003
SVP vote share in 2003Distribution of 2003 SVP vote share in German-speaking Municipalities
Distribution of 2003 SVP vote share in German-speaking Municipalities
Distribution of 2003 SVP vote share in German-speaking Municipalities
Figure 6.16: Distribution of institution binary in German-speaking municipal-ities which saw a low or negative influx of immigrants (N=341) -
First, I will consider municipalities which have a SVP vote share in 2003 of less
than 35%. These municipalities constitute 46% of my current data set. Given this
large percent I will assume that there is enough variation on the independent variable.
The results of running my regression on this data are displayed below in Table 6.19.
From Table 6.19 we can see that municipalities with low SVP support in 2003 have
a somewhat lower value for increase in naturalization rate relative to the German-
speaking region as a whole. While this point estimate seems rather low, the 95%
confidence interval on this estimate is quite large (0.21 to 0.99). Now we can compare
the complementary municipalities which had a SVP vote share of 35% or more in
2003. These municipalities constitute 54% of the sample. The results from running the
regression on these municipalities are shown in Table 6.20.
58
6.4 Model 3: Interaction Effects
Table 6.19: Model 1: Low SVP (N=417)
Variable Coefficient(Cl Std. Err.)
institution binary 0.598∗∗
(0.198)
Intercept 1.249∗∗
(0.188)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.20: Model 1: High SVP (N=495)
Variable Coefficient(Cl Std. Err.)
institution binary 0.834∗∗
(0.241)
Intercept 1.273∗∗
(0.244)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.20 shows that moving away from direct democracy leads to an increase
of 0.83 percentage points in naturalization rate for the municipalities which had SVP
support of 35% or more in 2003. This result is the opposite of what we would predict
from ECT and PPH. Instead of more anti-immigrant areas having lower naturalization
rates, they seem to have higher ones (however, keep in mind that the 95% confidence
intervals overlap significantly).
Again, I will estimate a version of Model 3 using the same logic as perviously. The
F-statistic generated from this model is 20.53 which indicates that these are statistically
different subgroups (see Table 6.21)
Finally, we may want to consider the summary statistics for these two groups which
as displayed in Table 6.22 and Table 6.23.
In general, areas with higher SVP support in 2003 (ostensibly more anti-immigrant)
see a bigger increase in naturalization rates after moving away from direct democracy.
Now I will consider subgroups formed by the union of the subgroups discussed so far.
59
6. DATA ANALYSIS
Table 6.21: Model 3: Interactions SVP (N= 912)
Variable Coefficient(Cl Std. Err.)
interaction high svp 0.834∗∗
(0.241)
interaction low svp 0.598∗∗
(0.198)
Intercept 1.262∗∗
(0.157)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.22: Summary statistics: Low SVP
Variable Mean Std. Dev. Min. Max. N
naturalization rate 2.018 3.34 0 100 8364institution binary 0.254 0.435 0 1 8257institution linear 1.256 0.511 0 3 8257naturalizations 21.333 131.722 0 4851 8420total pop 5716.359 19196.16 0 368677 8420swiss pop 4644.373 14143.19 0 262368 8420foreign pop 1071.986 5153.217 0 112429 8420ratio foreign swiss 0.144 0.121 0 0.959 8417percent foreigners 11.692 8.231 0 48.962 8417
Table 6.23: Summary statistics: High SVP
Variable Mean Std. Dev. Min. Max. N
naturalization rate 2.172 5.436 0 200 9722institution binary 0.212 0.409 0 1 9820institution linear 1.187 0.492 0 3 9820naturalizations 7.303 20.622 0 439 9940total pop 2358.071 3124.547 0 24414 9940swiss pop 1977.156 2385.856 0 17059 9940foreign pop 380.915 826.644 0 9246 9940ratio foreign swiss 0.114 0.119 0 1.013 9910percent foreigners 9.369 8.097 0 50.319 9910
6.5 Unions of Subgroups
In this section I will consider how my most heterogeneous subgroups (population and
influxes of immigrants and, SVP support in 2003) interact. I will show the results from
60
6.5 Unions of Subgroups
these interactions when they are statistically significant at the 10% level or lower. First
I will consider the subgroups formed by union between my population indicators and
the influx of immigrant indicators. These are shown in Tables 6.24, 6.25, 6.26, 6.27 and
6.28.
Table 6.24: Model 1: Mid Pop + Low immigrant influx (N=53)
Variable Coefficient(Cl Std. Err.)
institution binary 1.632∗∗
(0.361)
Intercept 0.812∗∗
(0.152)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.25: Model 1: High Population + Low influx of immigrants (N=11)
Variable Coefficient(Cl Std. Err.)
institution binary 1.303∗∗
(0.397)
Intercept 0.503(0.318)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.26: Model 1: Low Pop + High influx of immigrants (N=270)
Variable Coefficient(Cl Std. Err.)
institution binary 0.711∗
(0.299)
Intercept 1.497∗∗
(0.265)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
61
6. DATA ANALYSIS
Table 6.27: Model 1: Mid pop + High influx of immigrants (N=142)
Variable Coefficient(Std. Err.)
institution binary 0.741∗∗
(0.181)
Intercept 0.935∗∗
(0.094)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.28: Model 1: High pop + High influx of immigrants (N=128)
Variable Coefficient(Std. Err.)
institution binary 0.371∗
(0.164)
Intercept 0.714∗∗
(0.095)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
62
6.5 Unions of Subgroups
It is interesting to note that the largest change occurs in medium-sized municipali-
ties (population between 2,500 and 6,000 in 2003) which also saw a low increase (2% or
less) or a decrease in their foreign population (as a percent of the municipal population)
between 1990 and 2003. In contrast, the smallest change occurs in large municipalities
(population greater than 6,000 in 2003) which also see a moderate (to large) influx of
immigrants (2.5% or more). Of course, in this case, it is important to note that there is
a somewhat large confidence interval on this effect. Next, I will analyze the interaction
between SVP support in 2003 and population. The statistically significant (at the 10%
level or lower) results are shown in Tables 6.29, 6.30, 6.31 and 6.32.
Table 6.29: Model 1: Mid Pop + Low SVP support (N=100)
Variable Coefficient(Cl Std. Err.)
institution binary 1.179∗∗
(0.277)
Intercept 1.040∗∗
(0.111)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.30: Model 1: Low Pop + High SVP support (N=352)
Variable Coefficient(Cl Std. Err.)
institution binary 0.710∗
(0.333)
Intercept 1.553∗∗
(0.345)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
63
6. DATA ANALYSIS
Table 6.31: Model 1: Mid Pop + High SVP support (N=96)
Variable Coefficient(Cl Std. Err.)
institution binary 1.295∗∗
(0.285)
Intercept 0.643∗∗
(0.121)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.32: Model 1: High pop + High SVP support (N=47)
Variable Coefficient(Cl Std. Err.)
institution binary 0.612∗∗
(0.214)
Intercept 0.608∗∗
(0.133)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
64
6.5 Unions of Subgroups
Again, it seems that municipalities with a medium population (2,500 to 6,000 in-
habitants in 2003) see larger effects. Now I will consider the interaction between SVP
support in 2003 and influx of immigrants between 1990 and 2003. The statistically
significant (at the 10% level or lower) results are shown in Tables 6.33, 6.34 and 6.35
Table 6.33: Model 1: High SVP + High influx of immigrants (N=250)
Variable Coefficient(Cl Std. Err.)
institution binary 0.927∗∗
(0.240)
Intercept 1.062∗∗
(0.163)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.34: Model 1: Low SVP + High influx of immigrants (N = 212)
Variable Coefficient(Cl Std. Err.)
institution binary 0.717†
(0.432)
Intercept 0.913∗∗
(0.238)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.35: Model 1: Low SVP + Low influx of immigrants (N=124)
Variable Coefficient(Cl Std. Err.)
institution binary 1.110∗
(0.437)
Intercept 1.211∗∗
(0.404)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
65
6. DATA ANALYSIS
Now, I will consider the full cross product of these interactions. The results are
only listed if they are significant at the 10% level or lower. Additionally, it is important
to note that the numbers of municipalities in these groups is somewhat lower. These
results are shown at the end of the section.
Table 6.36: Model 1: Low Pop + High SVP + High Influx of Immigrants (N=151)
Variable Coefficient(Std. Err.)
institution binary 1.221∗∗
(0.386)
Intercept 1.360∗∗
(0.260)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.37: Mode1: Mid pop + Low SVP + Low influx of immigrants (N=16)
Variable Coefficient(Std. Err.)
institution binary 1.662∗
(0.773)
Intercept 0.926∗∗
(0.247)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.38: Model 1: Mid pop + Low SVP + High Influx of immigrants (N=80)
Variable Coefficient(Std. Err.)
institution binary 0.868∗∗
(0.237)
Intercept 1.058∗∗
(0.124)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
66
6.5 Unions of Subgroups
Table 6.39: Model 1: Mid-pop + High SVP + Low influx of immigrants (N=35)
Variable Coefficient(Std. Err.)
institution binary 1.782∗∗
(0.393)
Intercept 0.673∗∗
(0.160)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.40: Model 1: Mid pop + High SVP + High influx of immigrants (N=56)
Variable Coefficient(Std. Err.)
institution binary 0.462†
(0.266)
Intercept 0.764∗∗
(0.152)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
Table 6.41: High Pop + High SVP + High influx of immigrants (N=43)
Variable Coefficient(Std. Err.)
institution binary 0.578∗∗
(0.210)
Intercept 0.573∗∗
(0.144)
Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
67
6. DATA ANALYSIS
68
7
Discussion
The data analysis reveals some interesting results. To interpret these results, recall
that the coefficients recovered by this analysis represent the change in naturalization
rates due to the removal of direct democracy. Larger numbers indicate that the effect
of direct democracy was more deleterious in those areas because, for whatever reason,
it suppressed naturalization rates compared to their values after its removal. I will
present each of my hypotheses from section 4 and show how my work response to their
claims.
7.1 H1: Removing directly democratic institutions leads
to increased naturalization rates
Removing directly democratic institutions in favor of legislative, executive or other in-
stitutions which require the substantiation of opinions, does indeed lead to increased
naturalization rates. I find this effect to be an increase in the naturalization rate of 0.7
percentage points, which considering that naturalization rates are usually between 2 and
3%, constitutes a significant increase. Having leveraged both the quasi-experimental
nature of my data and a fixed effects transformation to remove time-constant confound-
ing variables, I am prepared to defend this as a causal effect, however, this conclusion
is still subject to time-variant confounders and relies on a few key assumptions which
I believe are plausible in this case, but which could potentially be violated.
Further work indicates that this conclusion is mainly valid for the German-speaking
municipalities in Switzerland because the majority of Italian-speaking and French-
speaking municipalities drop out of the analysis due to inadequate variation on the
independent variable.
69
7. DISCUSSION
7.2 H2: The effect does not depend on initial conditions
such as size of municipality or increases in the foreign
population
I find that, contrary to what Helbling predicts, the effect of moving away from direct
democracy is heterogeneous across different categories of municipalities. Most of all,
I find that having a mid-sized population of between 2,500 and 6,000 inhabitants in
2003 leads to a much stronger effect of 1.2 percentage points. When we consider this in
relation to the usual naturalization rate of 2 to 3% we can see that even in municipalities
which have higher naturalization rates, this constitutes around a around a 40% increase.
I use a model with interaction effects (Model 3) to test if these three-levels of population
constitute statistically different effects and I find that they do.
This is an interesting effect. It means that in these municipalities, putting citizen-
ship decisions in the hands of the citizenry suppressed naturalization rates much more
than in small or large municipalities. Why would we see this effect? I believe that
the answer lies in the literature on ethnic threat (see, for example, Schneider [2007]).
In small municipalities (which are predominately rural) there are probably fewer im-
migrants relative to the population so the threat posed by their presence is less. In
large municipalities, while there might be large groups of immigrants, their presence
is probably less surprising in urban areas. We may also think that urban areas are
correlated with higher education which has been shown in many studies to improve
attitudes towards immigrants (Hainmueller and Hiscox [2007, 2010]).
In mid-sized municipalities, immigrant groups are likely to visible to the native
citizens. Literature on ethnic threat predicts the the relative size and visibility of
the immigrant outgroup is likely to determine the level of tension between the two
groups (Schneider [2007], Quillian [1995], Forbes [1997]). While some attribute this
tension to economic competition (Quillian [1995]) more recent works (Hainmueller and
Hiscox [2010], Schneider [2007]) have shown that self-interested theories of immigrant
threat are not backed up by the data and that immigrants, especially those of different
ethnicities (Schneider [2007]) seem to pose a primarily cultural threat. While I cannot
test this directly with my data, the replacement of Italian immigrants by immigrants
from ex-Yugoslavia during this period (see the discussion of naturalization trends in
the methodology section) seems to lend credence to this theory.
Second, I find that having a low influx of foreigners in the pre-treatment period
(1990 –2003) (2% or less) leads to a larger increase in naturalization rates once directly
democratic institutions are removed. The effect for this group is around 0.9 percentage
70
7.3 H3: A high level of SVP support suppresses naturalization rates
points, as compared with their high-influx compatriots which see only a 0.6 percentage
point increase. Using an interaction effects model (Model 3) to test if these two effects
are statistically different, I find that they are.
The reason for this effect is probably related to the previous observation regarding
the size of municipalities. When we observe the summary statistics, we see that areas
with a high influx of immigrants had an average population of 5,511 whereas are areas
with a low influx of immigrants had an average population of 1,665. The average number
of naturalizations over the entire 1990 - 2009 period for the low-influx municipalities
was 3.4 compared to the high influx municipalities which averaged 21.0. This may
indicate that in areas where there are larger groups of immigrants and naturalizations
are more common, putting the decisions in the hands of the citizens is not as deleterious
to outcomes as in small communities in which there are very few naturalizations.
7.3 H3: A high level of SVP support suppresses natural-
ization rates
I find that having a high level of SVP support in 2003 (35% or more of the municipal
vote share) leads to relatively larger increase (0.8 percentage points) in naturaliza-
tion rate after direct democracy is removed when compared with municipalities with
lower SVP support which only saw increases of 0.6 percentage points. This indicates
that in the municipalities which saw high SVP support, the effect of directly demo-
cratic institutions was more deleterious to immigrants wishing to naturalize. Given
the anti-immigrant and explicitly anti-naturalization stance supported by the SVP it is
not surprising that removing the naturalization decision from the hands of right-wing
supporters lead to a larger increase in naturalization rates than for their less radical
counterparts. The effect, however, is not huge compared to the one observed for mid
sized municipalities.
It is interesting to note that municipalities with a low level of SVP support in 2003
tended to have higher populations (average population of 5,716) compared to munic-
ipalities with high SVP support which had an average population of 2,358. Between
these two groups, however, the ratio of foreigners to Swiss citizens (relative size of the
outgroup) is very similar (0.14 for low SVP areas and 0.11 for high SVP areas). This
means that given similarly sized outgroups, SVP support seems to deter immigrants
from naturalizing when the institution is directly democratic.
71
7. DISCUSSION
7.4 Trends recovered from finer-grained analysis
In my more fine grained analysis, I recover a few other trends. The conditions which
seem to mitigate the effect of direct democracy, (that is to say, lead to lower coefficients
that are statistically significant) are:
1. High population, High influx of immigrants (coefficient 0.37*)
2. Mid sized population, High SVP, High influx of immigrants (coefficient 0.46†)
3. High population, High SVP, High influx of immigrants (coefficient 0.58**)
On the other hand, the conditions in which direct democracy suppresses naturaliza-
tion rates much more strongly than average (that is to say, there are high coefficients
which are statistically significant) are:
1. Mid sized population, Low influx of immigrants (coefficient 1.63**)
2. High population, Low influx of immigrants (coefficient 1.30**)
3. Mid sized population, Low SVP support (coefficient 1.18**)
4. Mid sized population, High SVP support (coefficient 1.30**)
5. High population, High SVP support (coefficient 0.93**)
6. Low population, Low influx of immigrants (coefficient 1.11*)
7. Mid sized population, High SVP, Low influx of immigrants (coefficient 1.78**)
8. Mid sized population, Low SVP support, Low influx of immigrants (coefficient
1.66*)
9. Low population, High SVP, High influx of immigrants (coefficient 1.22**)
On the whole, it seems that high influxes of immigrants tend to be the most deter-
minate in dampening the effect of directly democratic institutions, whereas low influx
of immigrants lead to a suppression in naturalization rates. This is not surprising given
that in areas where there are more immigrants there is a larger group of potential
applicants.
On the other hand, in municipalities with mid sized populations (especially when
combined with low influxes of immigrants) we see much more deleterious effects from
the presence of directly democratic institutions. This seems to reify the theories of
ethnic threat discussed above.
72
7.4 Trends recovered from finer-grained analysis
A final interesting detail is the 9th observation in the last list (low population,
high SVP support and a high influx of immigrants). This seems to indicate that in
small, conservative, areas a large influx in the number of immigrants leads to some sort
of potential backlash through the suppression of naturalization rates. Following the
theories of ethnic threat, we could speculate that large numbers of immigrants moving
to these areas would be very visible and would increase the level of ethnic tension.
73
7. DISCUSSION
74
8
Conclusion
In this thesis I have explored the effect of direct democracy on local naturalization rates
in Switzerland. I have found that conferring citizenship through direct democracy has
a deleterious effect on naturalization rates, and moving away from directly democratic
institutions leads to a significant increase in local naturalization rates. Additionally, I
explored the heterogeneity of this effect and identified a series of conditions which lead
certain municipalities to see a much greater effect than their compatriots. Somewhat
surprisingly, the most prominent of these was simply being a mid-size municipality.
This thesis contributes to the literature on citizenship, naturalization and immigra-
tion in Switzerland where these issues are highly contentious. Most importantly, how-
ever, this thesis makes a more generalizable contribution to the literature on the effects
of direct democracy because it is one of the few studies to leverage a quasi-experimental
set-up allowing me to make causal claims about the effect of directly democratic insti-
tutions. Given this evidence, policy makers may want to consider moving away from
direct democracy in order to protect minority rights. Lyndon B. Johnson, once said
that “the vote is the most powerful instrument ever devised by man for breaking down
injustice and destroying the terrible walls which imprison men because they are differ-
ent from other men,” however, in this case, I find that allowing citizens to vote on the
rights of their neighbors leads to one such injustice.
75
8. CONCLUSION
76
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