In the streets with a degree: How political generations,educational attainment and student status affect engagement in protest politics

Abstract

Spanish

French

Using survey data collected at 52 major street demonstrations across five European countries during 2009–2012, this article contributes to the debate on the (contentious) politics of the highly educated in Europe. In particular, it explores which of the theories explaining student activism better capture differences in motivations and ways of engaging in protests between protesters who have a university education and those who do not. The findings build on the literature explaining student participation in protest in terms of campus-based politicization. Some support for the liberal education theory comes from the finding that protesters with a university degree are more likely to be left-wing than those without a university education. The article also provides some insights on the importance of political generations.

Keywords

Europe higher education social movements youth

Introduction

This article explores the relationship between higher education and pathways to protest in Europe. This topic has seen little light in recent decades under the assumption that students are relatively a-political, apathetic, or failing to engage with a political agenda that overlooks their interests (Henn et al., 2002, 2005; Norris, 2004; Pirie and Worcester, 1998; 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 2009–2012¹ 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 (Feuer, 1969; Habermas, 1987; Mannheim, 1952 [1923]; Musgrove, 1974; Wood, 1974), liberal education theory (Rootes, 1980, 1986, 1995) and the most recent critical network theory (Crossley, 2008).

Generational gap

One way to interpret students’ protests in the 1960s was as intergenerational conflict. 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 sociopolitical 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, 1952 [1923]). 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 (Edmunds and Turner, 2002; Hanna, 2008; Searle, 1972) 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 (Bennett and Segerberg, 2012; Della Porta and Diani, 1999). 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 (Aminzade et al., 2001; Goodwin et al., 2001; Jasper, 1997; 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-1979 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 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 1960–1970s 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 among participants in European demonstrations. We are aware that older participants are more likely to be veterans of demonstrations than younger participants. Consequently, the former 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 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 long-term 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. The great majority of activists involved in British direct action networks interviewed by Doherty et al. (2003) were ‘high educational achievers’. 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 ‘politicization’, 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 (Putnam, 2000; Schussman and Soule, 2005; Verba et al., 1995) and those with liberal or progressive political views tend to disproportionately protest (Dalton, 2002). Since the 1990s, direct action has been considered not only a radical tactic used at protests, but sometimes even a defining feature of a social movement (Doherty et al., 2003). It is radical because it often involves illegal and confrontational actions leading to arrests – such as spilling fake blood in contested banks – though physically harming violence is rarely used. The emphasis is on creativity, symbol and humour. The 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 this should be acknowledged. 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.

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 politicized, 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 part-time 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 (Klandermans, 1997; Schussman and Soule, 2005; Verba et al., 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 (Gould, 1991; McAdam, 1986; McAdam and Paulsen, 1993; Oberschall, 1973). 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, mobilization and politicization 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 complementary in this respect.

Method

We used standardized protest survey methodology. Approximately 1000 mail-back surveys were randomly distributed at each demonstration, using a ‘pointer’ to select respondents (Walgrave and Verhulst, 2011). We accompanied 20% of these surveys with 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 organization staging the demonstration’. A chi square test 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 sub-samples 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 protests, our data slightly exaggerate 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 operationalized 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?’

We used several independent, individual level variables to see if they predict our key dependent variables: political generation for testing the generational gap theory; educational attainment for liberal education theory; and full-time student for critical network theory. We operationalized 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-globalization 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 coefficient. 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 hypothesized key predictor. Additional controls are added after inclusion of the hypothesized key predictor.

Figure 1.

‘Anger’ scores across demonstrations with 95% confidence intervals plotted.

Figure 2.

Left–right scores across demonstrations with 95% confidence levels plotted.

Figure 3.

Number of types of people ‘asked by’ across demonstrations with 95% confidence intervals plotted.

Data

Protest survey data are drawn from five countries: the UK, the Netherlands, Spain, Switzerland and Belgium. The distribution of respondents across the countries is shown in Table 1. 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.

Distribution of protesters across countries.

	Frequency	%
Netherlands	2584	20.3
Spain	2375	21.8
UK	2211	20.3
Switzerland	1861	17.1
Belgium	1849	17.0
Total	10880	100.0

Table 2.

Distribution of protesters across issues by country (in percentages).

	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
n	1945	2879	2553	1978	2465	11,820

Table 3.

Distribution of key predictor variables across countries (in percentages).

	Belgium	Netherlands	Spain	Switzerland	UK	Total
1960s political generation	18.5	28.2	19.0	16.2	18.1	100.0
GJM generation	14.7	16.8	27.5	17.8	23.1	100.0
AA generation	10.6	31.6	14.9	20.1	22.7	100.0
n	1865	2632	2440	1897	2195	11,029
Undergraduate qualification	7.6	34.5	15.8	15.0	27.2	100.0
Postgraduate qualification	14.8	15.1	30.0	16.8	23.3	100.0
n	1884	2646	2432	1925	2282	11,169
Full-time student	7.9	34.5	17.2	15.1	25.4	100.0
n	1933	2689	2553	1978	2454	11,607

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 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, The 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.

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	0.06 (0.02)	0.08 (0.02)			0.04 (0.01)			0.03 (0.01)			0.03 (0.01)
Between protesters within demonstration variance	1.00(0.01)	0.95(0.013)			0.95 (0.01)			0.94 (0.01)			0.93 (0.01)
VPC	0.05	0.08			0.04			0.03			0.04

		β	SE	z	β	SE	z	β	SE	z	β	SE	z

Belgium (UK = ref.)		−0.16***	0.13	−1.21	−0.26***	0.10	−2.62	−0.25**	0.10	−2.63	−0.25**	0.10	−2.56
Netherlands		−0.48***	0.12	−3.91	−0.53***	0.09	−5.80	−0.52***	0.09	−5.81	−0.52***	0.09	−5.61
Spain		0.01	0.13	0.05	−0.24**	0.10	−2.38	−0.22*	0.10	−2.21	−0.21*	0.10	−2.03
Switzerland		−0.09	0.14	−0.69	−0.12	0.11	−1.10	−0.11	0.11	−1.05	−0.11	0.11	−0.96
Anti-austerity (climate change = ref.)					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
LGBT					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
Hypothesized key predictor (not part of a political generation = ref. )
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
AA political generation								−0.088	0.04	−2.38	−0.08*	0.04	−1.91
Undergraduate qualification (no university qualifications = ref.)											−0.03	0.03	−1.19
Postgraduate qualification											−0.03	0.03	−1.28
Full-time student											−0.05	0.05	−1.15
Male											−0.13***	0.02	−6.24

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 less left-wing. Two demonstrations are considerably less left-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 less left-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, pro-lesbian/gay/bi-/transsexual (LGBT), women’s and anti-military demonstrations, with no major differences across countries.

Table 5.

Multi-level model predicting left–right 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	0.97 (0.19)	0.87 (0.17)			0.50 (0.10)			0.51 (0.10)			0.48 (0.10)
Between protesters within demonstration variance	2.75 (0.04)	2.74 (0.04)			2.74 (0.04)			2.71 (0.04)			2.68 (0.04)
VPC	0.26	0.24			0.15			0.16			0.15
		β	SE	z	β	SE	z	β	SE	z	β	SE	z
Belgium (UK = ref.)		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 = ref.)					0.33	0.46	0.71	0.30	0.46	0.65	0.25	0.45	0.56
May Day / Labour					−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
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
Hypothesized key predictor (no university education = ref.)
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 = ref.)											−0.13***	0.04	−2.98
GJM political generation											−0.13***	0.05	−2.59
AA political generation											−0.05	0.08	−0.65
Full-time student											−0.29***	0.09	−3.18
Male											0.23***	0.04	6.32

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.

Table 6.

Multi-level model predicting engagement in direct action in past 12 months.

	Null model	With country				With issue				With main predictor				Full model
Between demonstration variance	0.69 (0.41)	0.20 (0.08)				0.28 (0.06)				0.27 (0.06)				0.23 (0.06)
		B	SE	z	Exp β	β	SE	z	Exp β	β	SE	z	Exp β	β	SE	z	Exp β
Belgium (UK = ref.)		−0.08	0.30	−0.27	0.92	−0.20	0.28	−0.70	0.82	−0.23	0.27	−0.85	0.79	−0.18	0.26	−0.67	0.84
Netherlands		−1.17***	0.28	−4.13	0.31	−1.12***	0.27	−4.20	0.33	−1.13***	0.26	−4.32	0.32	−1.03***	0.25	−4.08	0.36
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 = ref.)						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
Hypothesized key predictor (no university education = ref.)
Undergraduate qualification										−0.12*	0.07	−1.80	0.88	−0.09	0.07	−1.15	0.92
Postgraduate qualification										−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 = ref.)														−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
Full-time student														0.23*	0.12	1.93	1.26
Male														0.21***	0.06	3.74	1.24

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 (AA) 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 variance partitioning coefficient (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 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 Netherlands are demonstrators significantly less left-wing than in the UK. The model suggests that LGBT, military and women’s demonstrators are significantly less left-wing than anti-nuclear demonstrators. Adding the issue variable makes Belgian protesters significantly more likely to be less left-wing compared to British protesters. The addition of the main predictor variables (educational attainment at under- and postgraduate 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 noticed that most students had not yet completed 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 less left-wing.

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.

Our final multi-level model predicts the number of different types of people by whom protesters were asked 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 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 anti-austerity generation five times more likely to be asked). Undergraduate education is not a significant predictor, but those with a postgraduate 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.

Multi-level model predicting the number of channels ‘asked by’ to attend demonstration.

	Null			Country			Issue			Main predictor			Full
2*log likelihood (IGLS deviance)	29194.69			29188.54			29131.66			28989.61			25658.75
Between demonstration variance	0.05 (0.01)			0.04 (0.00)			0.01 (0.00)			0.01 (0.00)			0.01 (0.00)
Between protesters within demonstration variance	0.73 (0.01)			0.73 (0.01)			0.73 (0.01)			0.72 (0.01)			0.68 (0.01)
VPC	0.06			0.05			0.01			0.02			0.02
	β	SE	z	β	SE	z	β	SE	z	β	SE	z	β	SE	z
Belgium (UK = ref.)				−0.01	0.09	−0.05	0.09	0.06	1.58	0.10	0.06	1.64	0.13*	0.06	2.11
Netherlands				0.02	0.09	0.18	−0.03	0.06	−0.45	−0.03	0.06	−0.58	0.01	0.06	0.17
Spain				−0.18*	0.09	−2.05	−0.10	0.06	−1.57	−0.10*	0.06	−1.70	−0.10	0.06	−1.59
Switzerland				−0.08	0.10	−0.82	0.03	0.07	0.45	0.01	0.07	0.18	0.00	0.07	0.00
Anti-austerity (democracy = ref.)							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
Students							0.75***	0.09	8.73	0.58***	0.09	6.61	0.48***	0.10	5.04
LGBT							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
(Ethnic) minorities							0.13	0.09	1.37	0.14	0.09	1.48	0.18*	0.10	1.93
Hypothesized key predictor (no university education = ref.)
Full-time student										0.33***	0.03	11.68	0.13***	0.04	3.15
1960s political generation (not part of a political generation = ref.)													−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 = ref.)													0.01	0.02	0.59
Postgraduate qualification													0.04*	0.02	2.10
Male													−0.03*	0.02	−2.00

Discussion and 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 (Fendrich and Lovoy, 1988; Jennings, 1987; McAdam, 1988; 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 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 sociopolitical times (with the 1960s and the 1990s being more liberal years than the recent years) that shapes political attitudes among students and former students. Second, 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 into the ways higher education might affect engagement in protest in the short- and long-term. However, we must also be candid about its limitations. We are unable to test the impact of higher education on protest participation per se. This is because while 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.

Footnotes

Funding

This research received funding from the ESRC (RES-062-23-1565).

Notes

Author biographies

Cristiana Olcese is LSE Fellow in the Department of Sociology at the London School of Economics and Political Science.

Clare Saunders is Associate Professor in Environmental Politics in the Environment and Sustainability Institute, University of Exeter. Her work is published in major journals and she is author of Environmental Networks and Social Movement Theory (Bloomsbury Academic).

Nikos Tzavidis is Associate Professor of Statistics and head of the Department of Social Statistics and Demography at the University of Southampton. He specializes in small area estimation, quantile regression and in robust estimation and applications of multi-level models.

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