In the Streets with a Degree: How Political Generations, Educational Attainment and Student
Status Affect Engagement in Protest Politics
Cristiana Olcese and Clare Saunders. A later version of this paper has been published in
International Sociology, 29(6): 525-545.
Introduction
This article explores the relationship between higher education and pathways to protest. This topic
has seen little light in recent decades under the assumption that students are relatively a-politicised,
apathetic, or failing to engage with a political agenda that overlooks their interests (Pirie and
Worcester 1998; Henn et al., 2002; Norris 2004; Henn et al. 2005; Sloam 2007). Recent student
protests across Europe due to outrage at public spending cuts have forced a reassessment of
students’ political engagement. Generally, there is now a different feeling about students and
political engagement (Hundal 2011).
Though the revival of student protests is important, this article is not concerned with student
demonstrations alone, but students’ and former students’ participation in demonstrations. Survey
data from 52 major street demonstrations across five European countries during 2009-2012i reveal
that a substantial proportion of protest participants are at or have been to university. Although only
four of the surveyed demonstrations were student demonstrations (two in the UK and two in the
Netherlands), an average of 8.9% of protest participants were full-time university students and, even
more remarkably, 51.6% of participants had a university degree. With 60.5% of protest participants
either attending university or having done so at some point in the past, these data suggest that
higher education, at least in some European countries, might foster protest participation.
Consequently, this article aims to explore how higher education might influence protest
engagement.
Higher Education and Protest Participation
There are three main theories deployed to explain student activism: what we call generational gap
theory, combining ideas about ‘political generations’ and theories discussing the role of youth and
generations in rebellion and change in political attitudes (Mannheim 1923/1952; Musgrove 1974;
Habermas 1987, Feuer 1969, Wood 1974), liberal education theory (Rootes 1986, 1995, 1980), and
the most recent critical network theory (Crossley 2008).
Generational Gap
1
One way to interpret students’ protests in the 1960s was as intergenerational conflicts. Students
were said to rebel against the control and authority of elders. Sometimes, this explanation assumed
psychoanalytic terms: i.e. rooted in developmental dilemmas (Feuer 1969). The psychoanalysis of
collective behaviour has been criticized widely and dismissed by social movement scholars for
decades (Crossley 2002). However, there is another approach to generational conflict, which
interprets generations as age cohorts born in specific socio-political contexts. Thus, the experience
of people belonging to a specific generation results from broader societal factors associated with
being young at certain times, rather than being young per se (Mannheim 1923/1952). The
generational gap thesis holds that youths’ experience during their formative years will manifest in
their future political engagement. For example, it has been shown that individuals coming of age
during periods of pronounced stress, epochal events, social unrests, or rapid socioeconomic change
tend to be politically united, hence such labels as the ‘protest generation’ or ‘silent generation’
(Inglehart 1977, 1981; Jennings 1987). According to Jennings, the generational experience often
persists because elites of that generation continue to represent their orientations even when these
are no longer widely shared by mass publics. Therefore, according to the generational gap thesis, we
would expect protesters who were in their formative years (16-25 years old) when a big protest
wave and/or rapid socioeconomic change erupted, to possess a specific and shared approach to
protest politics. This notion of generation helps understand why students of the 1960s/1970s were
more sensitive to social questions than previous generations (Searle 1972; Edmunds and Turner
2002; Hanna 2008) and more distrustful of older generations (Siegfried 2006), as well as why
students during the wave of protests associated with the Global Justice Movement (GJM) might have
been shaped by the emergence of new media and protest cultures (Della Porta and Diani 1999;
Bennett and Segerberg 2012). It could also explain why, after years of relative abeyance, students
across Europe are protesting again. They seem to perceive austerity measures as bearing particularly
hard on their generation, thus marking a shift from post-material values (Inglehart 1977; 1981) to
more material concerns (Howker and Malik 2010).
Generational gap theory has long stressed the importance of emotions in protest. Despite heavy
criticisms of the frustration-aggression theory from democracy scholars (Norris et al. 2005;
Thomassen 1990), grievances and emotions such as anger and frustration are considered to play an
important role in motivating protest participants (Jasper 1997; Aminzade et al. 2001; Goodwin et al.
2001; Van Stekelenburg and Klandermans 2007). According to Goodwin et al. (2001), emotions that
are politically relevant are highly socially constructed experiences. This explains why some
generations express certain emotions more than others (e.g. anger versus apathy). According to
Howker and Malik (2010), the post-79 generation have valid reasons to be angry in comparison to
members of older generations (i.e. the baby-boomers and the Generation X). Many lack stable jobs
2
and homes, and high debts make it challenging for them to build adult lives. Sennett (1998) suggests
that the short-term nature of contemporary society might be responsible for difficulties
encountered by young people in building stable identities, durable relationships, and long-term
goals. The ‘protest generation’ – also called the baby boomers generation – has been accused of
stealing the future of their children (Willetts 2010) by enjoying considerable ‘privileges’ such as free
education, stable jobs, and low house prices no longer available to younger generations (Howker
and Malik 2010). Perhaps because of that, even among those who participated in the protests in the
1960s and 1970s, Jennings (1987) has pointed out that only some political features distinguishing the
‘protest generation’ in the US in 1960-70s have survived overtime among a critical group of this
generation. This debate raises the question about the extent to which anger is higher among the
youngest political generation of protesters (the ‘anti-austerity generation’) compared to other
generations. Thus, in our analysis, we explore the extent to which the anti-austerity political
generation are more likely to be angrier about the issues on which they protest than those who are
either a) part of a different political generation or b) not part of a political generation at all.
Young people, regardless of their experience of higher education, because of their age, will be
disproportionately represented in the anti-austerity political generation. Thus, we test to see
whether it is political generation rather than student status which predicts anger amongst
participants in European demonstrations. We must state here that we are aware of important
differences between young and old protesters in relation to the process of politicisation. Older
participants are more likely to be veterans of demonstrations. Consequently, they will have
developed a sense of protester identity (emotions, values and so on), which younger protesters will
have been ill-afforded opportunities to develop.
Liberal Education
Liberal education theory explains students’ politicization by focusing on the values disseminated by
universities as liberal institutions (Rootes 1980; 1986; 1995). The main reason behind student
radicalism is to be found in the transformation which occurred in higher education during the 1970s.
It was the introduction of Marxist theory and political concepts such as class and social justice into
the university curriculum that had the effect of neutralizing much of the hostility bourgeois
socialization has toward political ideas, and to create the demand for a less constrained political
debate (Rootes 1980).
Liberal education theory addresses why students at university, rather than youth and secondary
students in general, are numerous in protests (an aspect overlooked by generational gap theory). It
3
is youth combined with exposure to political concepts at university that tips the balance: students,
especially in the social sciences and humanities, become more liberal and critical through studying.
Marsh’s (1977) comparison of students’ views and activities at different points in their university
education supports this theory. The politicization taking place at universities seems to have longterm effects. Sherkat and Blocker (1997) compared activists in the 1960s and early 1970s with their
non-activist counterparts at two points in time and found that former protesters, besides being
more highly educated, tend to be (and remain over time) more liberal than non-protesters. Doherty
et al. found that among the 100 individuals involved in British direct action networks who they
interviewed “the great majority, as might be expected, had been high educational achievers and had
gone on to higher education or planned to do so” (2003: 677). Although these studies do not provide
any evidence of the long-term impact of higher education, they imply a sustained correlation
between higher education, liberal values, and radical political participation.
However, knowledge and critical thinking skills do not necessarily lead to ‘politicisation’, let alone
getting involved in social movements. Students sometimes rebel in non-liberal contexts or in support
of non-liberal agendas. For example, in Iran students have been at the forefront of pro-democracy
protests, and they emerged in an illiberal educational context (though, in relative terms, universities
are more liberal than other contexts in the Iranian society). Conversely, Islamic extremist groups in
Western campuses can be considered to embody non-liberal values. According to Munson (2010),
university campuses in the US have played a key role in the rise of conservative movements over the
last 40 years.
Liberal education theory can be supplemented with political engagement theory. This argues that
without political interest and/or information, individuals will not participate in political activity
(Schussman and Soule 2005; Verba, Schlozman and Brady 1995; Putnam 2000) and those with liberal
or progressive political views tend to disproportionately protest (Dalton 2002). Since the 1990s
direct action is considered not only a radical tactic used at protests but a defining feature of a social
movement (Doherty 2003). It is radical because it might involve illegal and confrontational actions
leading to arrests – such as spilling fake blood in the cashiers’ of banks involved in arms trade,
slowing– but direct violence is rarely used. The emphasis is on creativity, symbol, and humour.
However, this association between left-wing values/tactics and protest might be due to a general
left-wing bias in the selection of protests (Corrigall-Brown 2011) and it should be acknowledged that,
while political interest and knowledge alone do not translate into politicization and mobilization, the
reverse can be true (Downton and Wehr 1997; McAdam 1986, 1988). Many people, for example,
become committed activists after attending a protest by chance.
4
Since universities are still liberal and reflexive institutions (Delanty 2001), with social sciences and
sociology in particular largely remaining critical disciplines (Bourdieu 1993) at the service of the
public (Burawoy 2005), if liberal education plays a role in student radicalism, we should still find
evidence today. Consequently, we would expect protesters who have completed a university
education to be more radical than those who have not completed a university education.
Critical Network
Crossley’s (2008) critical network theory combines ideas of critical mass and social networks to
explain campus politicization. Campuses are conducive environments for politicization because of a
small but critical number of political entrepreneurs or ‘agitators’, who come to university already
politicised, and have many opportunities to meet and bond with people in networking places such as
student unions. Thus, recruitment of other students by critical networks of agitators is favoured by
the structure of university campuses. Students, therefore, especially full-time students – as parttime students tend to be employed, mature, and non-traditional in their background (Schuetze and
Slowey 2002) – can be said to be structurally available, that is, compared with the general public,
they seem to be remarkably free of personal constraints that might inhibit participation (McAdam
1986) and have “… interpersonal networks which facilitate recruitment to activism” (Schussman and
Soule 2005: 1086). The importance of any interpersonal networks for recruitment, is confirmed by
the finding that being directly asked to attend a protest is an effective predictor of participation in
protest (Schussman and Soule 2005; Klandermans 1997; Verba, Schlozman and Brady 1995).
Organizational memberships are key in fostering the development of such interpersonal ties
(Gerlach and Hine 1970; Klandermans 1997; McAdam 1982; Snow et al. 1980, Verhulst and Van Laer
2008) and for receiving information about protests via channels that are closed to non-members
(Verhulst and Walgrave 2009). The mobilizing potential is even more pronounced if people are
members of multiple organizations (McAdam and Paulsen 1993; McAdam 1986; Oberschall 1973;
Gould 1991). It follows that students who are full-time and members of the student union or other
university societies are fully embedded in campus life and therefore more structurally available.
Also, according to Crossley (2008) these critical networks reproduce themselves over time as older
cohorts of students mobilize younger ones, ensuring that this process of recruitment, mobilisation
and politicisation continues. However, this theory does not explain why there are times when the
agitators are a small minority, and why, at other times, they are numerous and find a receptive
audience. The concept of political generation seems complimentary in this respect.
Method
5
We used standardized protest survey methodology. Approximately one thousand mail-back surveys
were randomly distributed at each demonstration, using a ‘pointer’ to select respondents (Walgrave
and Verhulst 2011). 20% of these surveys were accompanied by a matched numbered face-to-face
interview. Since refusal rates for face-to-face interviews are usually less than 10%, we can gauge the
types of people that do not respond, allowing for broad assessment of the data’s representatively.
There are only minor differences between those who responded to the face-to-face survey and
those who responded to both.
Kendall’s tau-b, reveals no significant differences between respondents in these two sub-samples in
the following variables: ‘when protesters made a firm decision to participate in the protest’ and
‘whether they are a member of an organisation staging the demonstration’. Chi squared finds no
significant difference in gender distribution, and the T-Test result comparing protesters’ ages is
insignificant. However, there are significant differences in the extent to which those in the two subsamples express political interest and are qualified. Those who responded to the face-to-face
interview but did not return the mail back questionnaire are slightly less interested in politics. 16.5%
of them claim to be either not at all or not very interested, compared to 12.8% of those who were
interviewed face-to-face and returned the postal questionnaire (kendall’s tau-b = 0.030**). Although
there are significant differences in the highest educational qualification of participants in the two
sub-samples (kendall’s tau = 0.078***), differences are small. Those responding to both surveys are
slightly more highly educated (59.7% vs. 53.3%). These significant differences between the samples
with respect to political interest and educational attainment are unsurprising but they do suggest
some caution in the interpretation of our results. In particular, although it is clear that the highly
educated disproportionately participate in protest, our data slightly exaggerates their rate of
participation.
Variables
We deploy one or two key dependent variables for each of the three theories explaining student
activism: anger for generational gap theory; left-right (LR) leaning, as well as participation in direct
action (to gauge radicalism) for liberal education theory; and being asked by (as indicator of social
embeddedness) for critical network theory. Anger was operationalised by asking ‘thinking about
[demonstration issue] makes me feel …’. LR leaning was measured by asking ‘in politics people
sometimes talk of “left” and “right”. Where would you place yourself on this scale, where 0 means
the left and 10 means the right?’ Participation in direct action in the past 12 months has a yes/no
response. The dependent variable 'being asked by' totals the number of positive responses to the
question ‘which of the following people specifically asked you to take part in the demonstration?’
6
We used several independent, individual level variables to see if they predict our key dependents:
political generation for testing the generational gap theory; educational attainment for liberal
education theory; and full-time student for critical network theory. We operationalised political
generation as such: 0=not a member of a political generation; 1=part of the 1960s political
generation [aged 16-25 in 1968-1975]; 2=part of the anti-globalisation generation [aged 16-25 in
1999-2001]; 3 =part of the anti-austerity generation [aged 16-25 in 2010-2012]. We looked at the
effect of educational attainment by having 0=no university education; 1=undergraduate university
education; 2=postgraduate university education]. Being a full-time student was a dichotomous [yes
or no] variable.
We use two-level multi-level models to explore the data, with individuals (Level 1) nested in
demonstrations (Level 2). We use linear multi-level modelling, except for predicting direct action,
where we employed a binary logistic multi-level model. Multi-level models controlling for
demonstration effects are necessary because of statistically significant variation between
demonstrations (Figures 1-3). The extent of the variability is measured using the Variance Partition
Co-efficient. We opt to include the issue of the demonstrations as a fixed effect because the issue of
demonstrations is not strictly random. Country is also a fixed effect, because we have data from only
five countries. We add political generation, educational status and full-time student in all our
models, regardless of which is the hypothesised key predictors. Additional controls are added after
inclusion of the hypothesised key predictor.
Data
Protest survey data is drawn from five countries: UK, Netherlands, Spain, Switzerland, and Belgium.
The distribution of respondents across the countries is shown in Table 1 below. This is followed by
Table 2, showing the issues of street demonstrations surveyed across countries, and Table 3 which
shows distribution of key predictor variables across countries.
<Table 1 about here >
<Table 2 about here>
<Table 3 about here>
Distribution of key dependent variables across demonstrations
Figure 1 plots residuals from the multi-level modelling for the anger variable across the 52
demonstrations, with 95% confidence intervals (see also Table 4). The mean of each of these
7
specifies the intercept in the regression models for each demonstration. Protesters were least angry
at the Pride Zurich demonstration. Closest to the overall mean for anger was the May Day
demonstration in Vigo (0.02 higher than overall mean). The four student demonstrations in the
sample are ranked 12th (Fund Our Future, London), 15th (Second National Student Demonstration,
London), 25th (First Student Demonstration, Amsterdam) and 28th (Second Student Demonstration,
Hague) for anger. There are no obviously discernable patterns in variation of the extent of anger
across countries. Although the angriest demonstrators were at the Self Determination is Democracy
demonstration in Barcelona, Spain also had the fourth least angry set of demonstrators (Real
Democracy Now, Madrid). Instead, issues seem to matter more in determining the extent of anger:
minorities, Labour, women’s and regional demonstrations contained angrier demonstrators.
< Figure 1 about here>
Figure 2 plots residuals and 95% confidence intervals for self-placement on the left-right scale across
demonstrations (see Table 5 for the multi-level model for predicting this variable). A higher score
indicates being more right-wing. Two demonstrations are considerably more right-wing: the
Retirement Demonstration (Rotterdam), with a mean left-right score 3.10 points above the mean,
and the Second National Student Demonstration in London, with a mean score as much as 4.19
points above sample mean (see the plot point and confidence interval for the last ranked
demonstration in Figure 2). The First Student Demonstration in Amsterdam was also more right-wing
than the overall sample, ranking 50th. But Fund Our Future in London and the Second Student
Demonstration in the Hague have a lower ranking, 17th and 24th, respectively. Overall, student
demonstrations were among the demonstration issues attracting right-wing demonstrators,
alongside anti-austerity, democracy, lesbian/gay, women’s and military demonstrations, with no
major differences across countries.
< Figure 2 about here>
Figure 3 plots the variability in ‘asked by’ scores across the 52 demonstrations (see Table 6). The
lowest ranking demonstration is Take Back Parliament, London (0.34 lower than sample mean).
Student demonstrations are in the lower two quartiles of the sample, ranked 5th (Fund Our Future,
London), 15th (Second National Student Demonstration, London), 21st (First Student Demonstration,
Amsterdam) and 28th (Second Student Demonstration, The Hague). Overall, participants in student
demonstrations were most likely to be asked by a higher proportion of others to attend the
demonstration.
8
< Figure 3 about here>
There is also considerable variability in the extent of the use of direct action by protesters: it varies
across the four student demonstrations, being neither exceptionally high nor low. Anti-austerity and
Labour marches attracted the highest proportion of direct activists of all the demonstrations
analysed, followed by peace, lesbian/gay and anti-austerity demonstrations.
Multi-level models
Table 4 shows the multi-level regression model predicting anger. The null model finds significant
variance between demonstrations, accounting for 5% of the variation in anger across the sample.
When we add ‘country’ to the model, we find that demonstrators who participate in demonstrations
in Belgium and the Netherlands are significantly less angry than those from the UK. Adding
demonstration issue to the model makes protesters who demonstrate in Spain appear significantly
less likely to be angry than those from the UK. In all demonstrations, except for LGBT, there is
significantly more anger when compared to climate change demonstrations. Next, we added our
main predictor variable at the individual level – political generation. Recall that, building on the
generational gap theory, we expected that members of the anti-austerity generation would be more
likely to be the angriest. The results contradict this: while protesters belonging to both the 1960s
and the GJM political generations are angrier than those who are not part of a political generation,
there are no significant differences between those who are not part of a political generation and
those in the anti-austerity generation. Indeed, as we learned from Figure 1, anti-austerity does not
generate as much anger – as an issue – compared to women’s issues and regional autonomy. But do
these variables remain significant when we add our control variables? Educational attainment and
being a full-time student are not significant predictors of anger, but being female is. Adding these
final control variables indicates that being part of the anti-austerity political generation predicts
being less angry, not more. In the final model with all independent variables included, the VPC score
is 0.04, which suggests that 4% of the variance is due to demonstration effects once all other
variables are controlled for. Model fit, as expected, improves as more variables are added to the
model.
<Table 4 about here>
Table 5 presents multi-level regression model results predicting left-right position. There is much
more variance at the demonstration level for this variable compared to anger. The null model
suggests that 26% of the variance is due to demonstration effects (see Figure 2). This reduces to 15%
when other variables are added. Upon adding ‘country’ to the null model, we find that only in the
9
Netherlands are demonstrators significantly more right-wing than in the UK. The model suggests
that LGBT, military, and women’s demonstrators are significantly more right-wing than anti-nuclear
demonstrators. Adding the issue variable makes Belgian protesters significantly more likely to be
right-wing compared to British protesters. The addition of the main predictor variables (educational
attainment at under- and post-graduate levels) does not alter existing significant variables, but is
itself significant. Those with a university education are more likely to be left-wing than those
without, and this effect remains significant when we add controls. This is significant despite the
perhaps surprising right-wing bent of participants in student demonstrations (see Figure 2), but it
should be notice that most students did not yet complete a university education. Those who are part
of the 1960s and GJM political generations are significantly more likely to be left-wing than those
who are not part of a political generation, as are full-time students. Being male predicts being more
right-wing.
< Table 5 about here>
The binary logistic multi-level model predicting participation in direct action in the past 12 months is
shown in Table 6. Demonstrators in the Netherlands appear to be less likely to have engaged in
direct action. Those in anti-austerity, May Day and student demonstrations are more likely than
those on climate change marches to be direct activists. However, the significance of being on a
student demonstration disappears when we control for political generation, being a full time student
and gender – each of which are significant. The effect of university education on predicting
participation in direct action in the past 12 months is insignificant in the full model. Members of any
political generation are likelier than those who are not part of a political generation to be direct
activists, and those in the anti-austerity generation are over twice as likely to be so. Males and full
time students are also more likely to be direct activists.
< Table 6 about here>
Our final multi-level model predicts the number of different types of people who protesters were
asked by to attend the demonstration (Table 7). The demonstration level accounts for 6% of variance
in a null model, but reduces to 2% with controls added. At first sight, those who protest in Spain
appear to be asked to participate by fewer types of people than those in the UK – that is the only
significant country effect in the country-only model. However, when we add demonstration issues,
this loses significance. Demonstrations on particular issues are significantly more likely to spur a
wider array of invitations for participation: anti-austerity, student, LGBT, military and women’s rights
(see also Figure 3). When we add our key predictor variable – being in full-time education – Spain
10
becomes significant again, women’s rights is no longer significant, and climate change and culture
demonstrations become significant with positive coefficients. Full-time educational status is
significant, even when we add political generation, educational attainment and gender. In the final
model, those who protest in Belgium appear more likely to be asked to participate by a variety of
people. Those who are part of any political generation are more likely to be widely asked to attend a
demonstration than protesters who are not part of any political generation (with those in the antiausterity generation five times more likely to be asked). Undergraduate education is not a significant
predictor, but those with a post-graduate education are more likely to be widely asked (although
this is only just significant). Finally, females are more likely to be widely asked to attend the
demonstration at which they were surveyed than males.
< Table 7 about here>
Discussion & Conclusion
This article explores the impact of higher education among protesters. Are there any differences, in
terms of motivations and pathways to protest, between those who went/go to university and those
who did/do not? We found little support for generational gap theory, partial support for liberal
education theory and full support for critical network theory. Being a full-time student is significant
in predicting structural availability for protest participation. Those with university education
(undergraduate or postgraduate) are more left-wing than those without.
However, our results also suggest the importance of political generations, though not quite in the
way predicted by the literature (i.e. the youngest generation was not angrier). We found that, in
general, protesters who are part of a political generation are significantly different from people who
are not. Protesters belonging to the 1960s and GJM political generations appear angrier and more
left-wing. Also, members of any political generation are more likely to do direct action and be
structurally available. These findings resonate with studies suggesting that ‘protest generations’
have lasting effects. Crossley (2003) and precedents (McAdam 1988; Fendrich and Lovoy 1988;
Jennings 1987; Sherkat and Blocker 1997) argued that participation in the 1960s’ protests produced
a ‘radical habitus’. To the list of long-term effects of protesting in the 1960s, we could add “being
angry”. The GJM generation seems similar.
All three theories contribute to a better understanding of how higher education affects protest
participation. Recall that the 1960s and GJM political generations are significantly more likely to be
left-wing than those who are not part of a political generation, while there are no significant
differences between the anti-austerity generation and protesters who are not part of a political
11
generation. This suggests a conservative shift in the youngest political generation. Thus, political
attitudes are influenced by political generations and liberal education.
Also, the fact that full-time students are more left-wing than part-time students implies the
importance of campus effects (which increase the chances of contact with critical networks of
agitators) to develop radical political attitudes. Thus, it is being at university (for its liberal education
and critical networks) during specific turbulent socio-political times (with the 1960s and the 1990s
being more liberal years than the recent years) that shapes political attitudes among students and
former students. Secondly, recall that university education does not predict participation in direct
action, but being part of a political generation does. Thus, membership of a political generation
influences engagement with a radical tactic such as direct action, regardless of educational
attainment. Moreover, the chance of being asked by a variety of people to attend a demonstration
significantly increases not only when engaged in full-time education (campus effect) but also as a
result of being part of any political generation. These results, taken together, seem to suggest that
structural availability is bolstered by the campus effect, but educational attainment as well as being
part of a political generation play a role.
The illustration of differences among protesters across European countries due to higher education
is a strength of the article. This is the first time that such questions have been explored on such a
large-scale. The article also provides insights on the ways higher education might affect engagement
in protest in the short- and long-term. However, we must also be candid about limitations. We are
unable to test the impact of higher education on protest participation per se. This is because whilst
we have detailed information about protest participants across countries, we lack similar
information about non-participants. However, we have been able to shed light on the character of
contemporary protest. Higher education appears to play a significant role in pathways to protest,
which implies that the demise of free/affordable university education poses worrying implications
for the future of protest politics.
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16
Table 1. Distribution of protesters across countries
Frequency
%
Netherlands
2584
20.3
Spain
2375
21.8
England
2211
20.3
Switzerland
1861
17.1
Belgium
1849
17.0
Total
10880
100.0
Table 2. Distribution of protesters across issues by country
Belgium
Netherlands
Spain
Switzerland
UK
Total
Anti-austerity
28.9
29.0
32.6
0.0
9.5
100.0
May day / labour
16.9
23.1
19.3
26.7
14.0
100.0
Nuclear
12.0
28.6
0.0
59.4
0.0
100.0
Climate
27.6
22.5
0.0
0.0
49.8
100.0
Culture
0.0
100.0
0.0
0.0
0.0
100.0
Regional autonomy
0.0
0.0
100.0
0.0
0.0
100.0
Students
0.0
64.6
0.0
0.0
35.4
100.0
LGBT
0.0
15.7
0.0
54.2
30.1
100.0
Military demo
0.0
100.0
0.0
0.0
0.0
100.0
Women
0.0
0.0
36.1
42.6
21.3
100.0
Democracy
33.1
0.0
38.0
0.0
28.9
100.0
Minorities
0.0
25.6
0.0
0.0
74.4
100.0
1945
2879
2553
1978
2465
11820
n
Note: Row percentages are
Table 3. Distribution of key predictor variables across countries
Belgium
18.5
Netherlands
28.2
Spain
19.0
Switzerland
16.2
UK
18.1
Total
100.0
GJM generation
14.7
16.8
27.5
17.8
23.1
100.0
Anti-austerity generation
10.6
31.6
14.9
20.1
22.7
100.0
N
1865
2632
2440
1897
2195
11029
7.6
34.5
15.8
15.0
27.2
100.0
1960s political generation
Undergraduate qualification
Postgraduate qualification
14.8
15.1
30.0
16.8
23.3
100.0
N
1884
2646
2432
1925
2282
11169
17
Full-time student
N
7.9
34.5
17.2
15.1
25.4
100.0
1933
2689
2553
1978
2454
11607
Note: Row percentages
Figure 1. ‘Anger’ scores across demonstrations with 95% confidence intervals plotted
.
18
Figure 2. Left-right scores across demonstrations with 95% confidence levels plotted
19
Figure 3. Number of types of people asked by across demonstrations with 95% confidence intervals plotted
20
Table 4. Multi-level model predicting anger
Null
With country
With issue
With main predictor
Full model
2*log likelihood (IGLS Deviance)
31142.42
3064.12
30612.04
28534.45
27083.53
Between demonstration variance
Between protesters within
demonstration variance
0.06 (0.02)
0.08 (0.02)
0.04 (0.01)
0.03 (0.01)
0.03 (0.01)
1.00(0.01)
0.95(0.013)
0.95 (0.01)
0.94 (0.01)
0.93 (0.01)
0.05
0.08
0.04
0.03
0.04
VPC
β
SE
z
β
SE
z
β
SE
z
Belgium (UK=reference category)
-0.16***
0.13
-1.21
-0.26***
0.10
Netherlands
-0.48***
0.12
-3.91
-0.53***
0.09
Spain
0.01
0.13
0.05
-0.24**
Switzerland
-0.09
0.14
-0.69
β
SE
z
-2.62
-0.25**
0.10
-5.80
-0.52***
0.09
-2.63
-0.25**
0.10
-2.56
-5.81
-0.52***
0.09
-5.61
0.10
-2.38
-0.22*
0.10
-2.21
-0.21*
0.10
-2.03
-0.12
0.11
-1.10
-0.11
0.11
-1.05
-0.11
0.11
-0.96
Anti-austerity (climate change = reference category)
0.66***
0.12
5.43
0.64***
0.12
5.43
0.65***
0.12
5.40
May Day / Labour
0.72***
0.13
5.49
0.69***
0.13
5.41
0.70***
0.13
5.42
Anti-nuclear
0.37**
0.15
2.47
0.37*
0.14
2.57
0.37**
0.15
2.49
Culture
0.74***
0.18
4.02
0.73***
0.18
4.08
0.73***
0.19
3.88
Regional autonomy
0.84***
0.19
4.43
0.82***
0.18
4.44
0.83***
0.19
4.40
Students
0.38***
0.14
2.63
0.46***
0.14
3.18
0.50***
0.15
3.35
0.09
0.15
0.60
0.10
0.15
0.71
0.11
0.15
0.71
Military
0.65***
0.23
2.80
0.64***
0.22
2.86
0.69***
0.23
3.00
Women
0.71***
0.15
4.68
0.69***
0.15
4.73
0.67***
0.15
4.45
Democracy
0.58***
0.13
4.38
0.57***
0.13
4.45
0.59***
0.13
4.53
(Ethnic) minorities
0.62***
0.16
4.00
0.64***
0.15
4.24
0.66***
0.15
4.25
1960s political generation
0.072***
0.02
3.00
0.08***
0.02
3.33
GJM political generation
-0.086***
0.03
-3.31
-0.09***
0.03
-3.44
-0.088
0.04
-2.38
-0.08*
0.04
-1.91
Undergraduate qualification (no university qualifications = reference category
-0.03
0.03
-1.19
Postgraduate qualification
-0.03
0.03
-1.28
-0.05
0.05
-1.15
-0.13***
0.02
-6.24
LGBT
Hypothesised key predictor (not part of a political generation = reference category)
AA political generation
Full-time student
Male
21
Table 5. Multi-level model predicting L-R position
Null
With country
With issue
With main predictor
Full model
2*log likelihood (IGLS Deviance)
36742.389
36736.678
36708.817
34790.234
32670.287
Between demonstration variance
Between protesters within
demonstration variance
0.97 (0.19)
0.87 (0.17)
0.50 (0.10)
0.51 (0.10)
0.48 (0.10)
2.75 (0.04)
2.74 (0.04)
2.74 (0.04)
2.71 (0.04)
2.68 (0.04)
0.26
0.24
0.15
0.16
0.15
VPC
β
SE
Z
β
SE
z
β
SE
z
β
SE
z
Belgium (UK=reference category)
0.521
0.42
1.26
0.76*
0.36
2.15
0.68*
0.36
1.91
0.68*
0.35
1.94
Netherlands
0.66*
0.38
1.72
0.61*
0.33
1.84
0.60*
0.33
1.80
0.65*
0.33
1.98
Spain
0.162
0.38
0.42
0.34
0.36
0.94
0.36
0.36
0.98
0.37
0.36
1.04
Switzerland
-0.23
0.43
-0.53
-0.40
0.39
-1.01
-0.39
0.40
-0.99
-0.36
0.39
-0.93
Anti-austerity (anti-nuclear = reference category)
0.33
0.46
0.71
0.30
0.46
0.65
0.25
0.45
0.56
-0.12
0.46
-0.26
-0.17
0.47
-0.36
-0.21
0.46
-0.47
Climate change
0.37
0.54
0.68
0.44
0.55
0.80
0.44
0.53
0.82
Culture
0.05
0.67
0.08
0.16
0.68
0.24
0.16
0.66
0.25
Regional autonomy
0.20
0.70
0.28
0.22
0.70
0.31
0.18
0.69
0.27
May Day / Labour
Students
0.47
0.55
0.86
0.50
0.56
0.89
0.71
0.55
1.28
LGBT
1.51***
0.52
2.93
1.51***
0.52
2.91
1.52***
0.51
2.99
Military
3.30***
0.84
3.93
3.22***
0.85
3.81
3.02***
0.83
3.66
Women
1.42***
0.52
2.72
1.41***
0.52
2.69
1.47***
0.51
2.87
Democracy
0.52
0.51
1.02
0.58
0.51
1.13
0.57
0.50
1.15
(Ethnic) minorities
0.26
0.60
0.43
0.28
0.60
0.47
0.25
0.59
0.43
Undergraduate qualification
-0.35***
0.05
-7.59
-0.35***
0.05
-7.35
Postgraduate qualification
-0.41***
0.04
-9.44
-0.44***
0.05
-9.63
1960s political generation (not part of a political generation = reference category)
-0.13***
0.04
-2.98
GJM political generation
-0.13***
0.05
-2.59
Hypothesised key predictor (no university education = reference category
AA political generation
Full-time student
Male
22
-0.05
0.08
-0.65
-0.29***
0.09
-3.18
0.23***
0.04
6.32
Table 6. Multi-level model predicting engagement in direct action in past 12 months
Between
demonstration
variance
Null model
With country
0.69 (0.41)
0.20 (0.08)
With issue
With main predictor
0.28 (0.06)
Full model
0.27 (0.06)
0.23 (0.06)
-0.08
0.30
-0.27
Exp
β
0.92
-1.17***
0.28
-4.13
0.31
Spain
0.32
0.28
1.12
1.37
0.25
0.28
0.90
1.29
0.25
0.28
0.90
1.28
0.20
0.26
0.77
1.22
Switzerland
-0.29
0.31
-0.92
0.75
-0.10
0.31
-0.33
0.90
-0.17
0.31
-0.56
0.84
-0.26
0.29
-0.89
0.77
Anti-austerity (climate change = reference category)
0.73*
0.35
2.08
2.07
0.68*
0.34
1.99
1.97
0.76**
0.33
2.33
2.13
May Day / labour
0.66*
0.37
1.76
1.93
0.64*
0.37
1.76
1.90
0.76*
0.35
2.18
2.14
Nuclear
0.80
0.43
0.89
1.46
0.37
0.42
0.88
1.44
0.44
0.40
1.10
1.54
Culture
-0.41
0.58
-0.70
0.66
-0.46
0.61
-0.76
0.63
-0.38
0.59
-0.65
0.68
Regional autonomy
-0.10
0.54
-0.18
0.91
-0.10
0.52
-0.19
0.90
-0.02
0.50
-0.05
0.98
Students
0.87*
0.41
2.12
2.38
0.85**
0.40
2.12
2.35
0.35
0.40
0.89
1.42
LGBT
-0.58
0.45
-1.29
0.56
-0.56
0.44
-1.28
0.57
-0.72*
0.43
-1.66
0.49
Military
-0.48
0.74
-0.66
0.62
-0.56
0.72
-0.77
0.57
-0.90
0.74
-1.23
0.40
Women
0.21
0.43
0.48
1.23
0.24
0.42
0.57
1.27
0.36
0.40
0.89
1.43
Democracy
0.28
0.38
0.74
1.32
0.23
0.37
0.62
1.26
0.17
0.35
0.48
1.18
Minorities
0.62
0.44
1.40
1.85
0.59
0.43
1.36
1.80
0.57
0.41
1.38
1.76
-0.12*
0.07
-1.80
0.88
-0.09
0.07
-1.15
0.92
-0.20***
0.06
-3.14
0.82
-0.10
0.07
-1.45
0.90
1960s political generation (not part of a political generation = reference category)
-0.21***
0.07
-2.93
0.81
GJM political generation
0.31***
0.07
4.24
1.37
AA political generation
0.70***
0.11
6.32
2.02
0.23*
0.12
1.93
1.26
0.21***
0.06
3.74
1.24
Belgium (UK= reference category)
Netherlands
β
SE
z
β
SE
z
-0.70
Exp
β
0.82
-0.23
0.27
-4.20
0.33
-1.13***
0.26
β
SE
z
-0.20
0.28
-1.12***
0.27
β
SE
z
-0.85
Exp
β
0.79
-0.18
0.26
-0.67
Exp
β
0.84
-4.32
0.32
-1.03***
0.25
-4.08
0.36
Hypothesised key predictor (no university education = reference category)
Undergraduate qualification
Postgraduate qualification
Full-time student
Male
23
Table 7. Multi-level model predicting the number of channels ‘asked by’ to attend demonstration
Null
Country
Issue
Main ind
Full
2*log likelihood (IGLS Deviance)
29194.69
29188.54
29131.66
28989.61
25658.75
Between demonstration variance
Between protesters within
demonstration variance
0.05 (0.01)
0.04 (0.00)
0.01 (0.00)
0.01 (0.00)
0.01 (0.00)
0.73 (0.01)
0.73 (0.01)
0.73 (0.01)
0.72 (0.01)
0.68 (0.01)
VPC
0.06
β
SE
0.05
z
Belgium (UK=reference category)
Netherlands
Spain
β
SE
-0.01
0.09
0.01
z
0.02
0.02
β
SE
z
β
SE
z
β
SE
z
-0.05
0.09
0.06
1.58
0.10
0.06
1.64
0.13*
0.06
2.11
0.02
0.09
0.18
-0.03
0.06
-0.45
-0.03
0.06
-0.58
0.01
0.06
0.17
-0.18*
0.09
-2.05
-0.10
0.06
-1.57
-0.10*
0.06
-1.70
-0.10
0.06
-1.59
-0.08
0.10
-0.82
0.03
0.07
0.45
0.01
0.07
0.18
0.00
0.07
0.00
Switzerland
Anti-austerity (democracy = reference category)
0.22***
0.06
3.61
0.25***
0.06
4.02
0.28***
0.06
4.42
May Day / Labour
0.05
0.07
0.77
0.08
0.07
1.09
0.12*
0.07
1.72
Anti-nuclear
0.01
0.08
0.16
0.03
0.08
0.35
0.07
0.09
0.84
Climate change
0.13
0.08
1.68
0.14*
0.08
1.79
0.14*
0.08
1.75
Culture
0.19
0.12
1.64
0.20*
0.12
1.74
0.17
0.12
1.40
Regional autonomy
0.15
0.10
1.47
0.16
0.10
1.57
0.18*
0.10
1.70
0.75***
0.09
8.73
0.58***
0.09
6.61
0.48***
0.10
5.04
0.18*
0.09
2.11
0.21***
0.09
2.36
0.20*
0.09
2.15
Military
0.34**
0.14
2.46
0.38***
0.14
2.77
0.40***
0.14
2.76
Women
0.19**
0.08
2.35
0.20
0.08
2.49
0.24***
0.09
2.81
0.13
0.09
1.37
0.14
0.09
1.48
0.18*
0.10
1.93
0.33***
0.03
11.68
0.13***
0.04
3.15
Students
LGBT
(Ethnic) minorities
Hypothesised key predictor (no university education = reference category
Full-time student
1960s political generation (not part of a political generation = reference category)
-0.05***
0.02
-2.30
GJM political generation
0.12***
0.02
5.35
AA political generation
0.28***
0.04
7.51
Undergraduate qualification (no university education = reference category)
Postgraduate qualification
Male
24
0.01
0.02
0.59
0.04*
0.02
2.10
-0.03*
0.02
-2.00
i
Data was collected within the framework of the *** project. The dataset includes data from Sweden, which
we were unable to include because highest educational qualification was coded differently there.
25