Public Perception and Segmentation of Agricultural Protests in Germany

Public Perception and Segmentation of Agricultural Protests in Germany | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Public Perception and Segmentation of Agricultural Protests in Germany Marius Michels, Thore-Maximilian Biß, Sven Grüner, Oliver Mußhoff This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7394023/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Using Germany as a case study, this paper examines public perceptions of agricultural protests across different societal segments. Based on quota-representative survey data from 637 adult individuals collected in 2024, we employ factor and cluster analyses to identify distinct attitude patterns. Our findings reveal four population segments: agricultural skeptics, reform-oriented supporters, critical supporters, and moderate reformers. While farmers' protests generally enjoy high legitimacy compared to other social movements, acceptance varies statistically significantly with protest tactics. Support patterns are primarily shaped by proximity to agriculture and political orientation rather than traditional socio-demographic factors. Agricultural Economics & Policy Agricultural Protests Cluster Analysis Germany Public Perception Figures Figure 1 Figure 2 1 Introduction In early 2024, German farmers launched widespread protests in response to proposed cuts to agricultural diesel tax rebates and vehicle tax exemptions, sparking intense public debate about agricultural policy reform. These protests emerged as part of a broader European movement where farmers pushed back against sweeping changes to farming systems (Budjdei-Tebeica, 2024; Matthews, 2024b). The economic implications extend beyond direct subsidy adjustments to fundamental questions of farm viability, climate change mitigation, and structural transformation (Pe'er et al., 2019; Cuadros-Casanova et al., 2023). Measures targeting greenhouse gas emissions, nitrogen pollution, and biodiversity conservation have intensified pressure on farming communities across the continent (Finger et al., 2024; Mc Loughlin, 2024). As a result, farmers across Europe have mobilized against various policy changes affecting their operations. For instance in the Netherlands, nitrogen reduction targets have sparked massive demonstrations, with farmers blocking highways and agricultural centers to protest proposed livestock reduction measures (Mc Loughlin, 2024). Spanish farmers protested against water use restrictions implemented due to drought conditions. Italian farmers expressed opposition to plans for removing their traditional income tax exemption. In Ukraine's neighboring countries, farmers called for limiting Ukrainian agricultural imports, arguing these were depressing local market prices (Matthews, 2024b). Matthews (2024a) identifies both country-specific triggers and common themes across protests, including increasing regulation, cheap imports, high production costs, declining social status, and subsidy cuts. McLoughlin (2024) frames recent European farmer protests as reflecting an identity crisis, as farmers navigate between traditional roles and new environmental, economic, and societal demands (Finger et al., 2024; Matthews, 2024a; Mazzocchi et al. 2024). Research on agricultural issues has extensively examined public perceptions across several domains, including agricultural digitalization (e.g. Zeddies et al., 2024), animal farming practices (e.g. Busch et al., 2022), and policy frameworks (Vainio et al., 2021; El Benni et al., 2024). Furthermore, Mazzocchi et al. (2024) showed via a sentiment analysis of social networks that overall a positive feeling was conveyed regarding posts about farmers’ protests in Italy. However, three critical knowledge gaps persist in our understanding of public perception of agricultural protests. First, recent research has explored why farmers are protesting (Matthews, 2024b), and polls show that many people support these protests (Forschungsgruppe Wahlen, 2024; YouGov, 2024; Civey, 2024). However, while some studies have examined how people view agricultural issues in general (e.g. Vainio et al., 2021), we lack comprehensive insight into how consumers perceive and evaluate agricultural protests. Moreover, our understanding of how different societal segments view these protests remains limited. This gap is particularly noteworthy given that protest effectiveness is fundamentally tied to public legitimacy (Kleinschmit and Feindt, 2004). Second, we have limited understanding of how various agricultural protest tactics are linked to these perception patterns across different social groups. This gap is particular noteworthy since multiple factors influence public support, including protest size, tactics employed, underlying attitudes toward the cause, protesters' political alignment, and media coverage (Gutting, 2020; Brown and Mourão, 2022; Rogers, 2023). Research on social movements demonstrates that radical factions using extreme tactics can paradoxically benefit the moderate factions through a contrast effect, making the latter appear more reasonable and increasing public support for their positions (Simpson et al., 2022). Furthermore, analysis of Extinction Rebellion's 2019 UK protests showed reduced opposition to pro-environmental policies but limited impact on personal environmental attitudes or consumer behavior (Kountouris and Williams, 2023). The Fridays for Future climate protests in Germany during 2019 led to increased Green Party votes, despite protesters being predominantly below voting age. This effect stemmed from intergenerational transmission of environmental attitudes, enhanced social media engagement by Green Party politicians, and increased local media coverage of climate issues (Waldinger et al., 2023). Third, not all responses to the protests were positive. Budjdei-Tebeica (2024) argue that radical right parties strategically frame environmental issues as threats to national sovereignty, economic growth, and cultural identity. Likewise, accusations that some of the current protests have also been infiltrated by far-right populist groups and that farmers are spreading radical positions have been the subject of controversial public debate in several countries (DW, 2024; Euronews, 2024; Monbiot, 2024; Parker and Robinson, 2024). Similar claims were made about past farmers’ protests in other European countries, describing the farmers’ demands as agrarian populism (Van Der Ploeg, 2020; Bosma and Peeren, 2021; Monbiot, 2024), yet their impact on public perception remains poorly understood. Based on quota-representative survey data from 637 individuals living in Germany collected in 2024 and through the lens of legitimacy theory (Suchman, 1995), this study pursues three objectives: To identify and characterize distinct segments of public perception toward agricultural protests, examining how different societal groups evaluate farmers' demands based on their proximity to agriculture, political orientation, and socio-demographic characteristics To analyze the acceptance of various protest tactics across these societal segments, with focus on the range between peaceful demonstrations (such as tractor parades) and more disruptive actions (such as blockades of policymakers) To investigate how concerns about political extremism among farmers influence public support for agricultural protests across different population segments Germany presents an ideal case study for exploring these dynamics as it is one of Europe's largest agricultural producer, accounting for 14% of EU agricultural output (eurostat, 2023), with ambitious climate goals (OECD, 2022). The early months of 2024 saw widespread farmer protests in response to proposed subsidy reductions, particularly regarding agricultural diesel tax rebates and vehicle tax exemptions. These protests, which included tractor demonstrations in major cities and blockades of infrastructure as well as politicians similar to other countries (Budjdei-Tebeica, 2024; Matthews, 2024b), sparked intense public debate about agricultural policy and protests. The combination of strong farmer mobilization, broad media coverage, and emerging discourses on agrarian populism provides a particularly rich analytical context for understanding how different segments of society perceive and evaluate agricultural protests. Through factor and cluster analysis, we reveal four distinct population segments with varying attitudes towards agricultural protests and reforms. Our findings demonstrate that while farmers' protests generally enjoy higher legitimacy compared to other social movements, acceptance varies statistically significantly with protest tactics. Support patterns are primarily shaped by proximity to agriculture and political orientation rather than traditional socio-demographic factors (e.g. gender), with peaceful demonstrations receiving broader support across all segments. This study makes several important contributions to both research and practice. First, it provides the first systematic segmentation of public attitudes toward agricultural protests, moving beyond simplistic support-opposition dichotomies to reveal distinct perception patterns. Second, it advances our understanding of how different protest tactics affect public support across societal segments. Third, it delivers novel insights into growing debates about the relationship between agricultural protests and populist movements which is interest for policy makers and protest organizers alike. Finally, it provides practical implications for protest organizers aiming to maintain public support while advocating for their interests. 2 Material and Methods 2.1 Sampling Procedure We conducted an Internet-based survey from April 26 to May 10, 2024, using the professional survey company Bilendi to obtain a quota-representative sample mirroring Germany's adult population structure in terms of age, gender, and education. The survey was part of a larger consumer study, with participants remaining anonymous to ensure data security and enhance response credibility. The study received IRB approval from the German Association for Experimental Economic Research e.V. (Certificate No. PWrdXhPf). 2.2 Questionnaire Design The questionnaire on farmers’ protests was part of a larger consumer study and comprised several distinct sections. In the first section, participants were asked to evaluate the justification of various current protests and strikes. For this purpose, participants had to allocate 100 points, reflecting their perception of justification, with more points indicating higher perceived legitimacy. The second section assessed participants' proximity to agriculture through five standardized statements. In the third part, the participants were asked to evaluate several statements on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree), which were used for the subsequent cluster analysis. For the development of the statements, this study adopts legitimacy theory as its conceptual framework. According to Suchman (1995: 574), "legitimacy is a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions." This definition provides a valuable lens for analyzing public perceptions of agricultural protests and reform attitudes, as it encompasses both general support for agriculture and specific evaluations of protest actions. Following Suchman's (1995) conceptualization, we distinguish between three dimensions of legitimacy: pragmatic, moral, and cognitive legitimacy. Pragmatic legitimacy rests on stakeholders' self-interested calculations about direct benefits or consequences. In the agricultural protest context, this dimension concerns how policy changes and reforms affect stakeholders' immediate interests, particularly regarding economic implications of agricultural policies and subsidies. We operationalized this through statements about specific policy measures being contested: The agricultural diesel tax rebate should be abolished. The vehicle tax exemption for agricultural vehicles should be abolished. Moral legitimacy reflects normative evaluations of whether activities align with societal values, moving beyond individual benefits to ask whether actions are "the right thing to do." For agriculture, this encompasses broader judgments about the sector's societal contributions. We captured this through statements about agriculture's multiple roles: German agriculture is of great importance for food security. German farmers are a vital part of the German economy German farmers meaningfully contribute to fostering community and social cohesion in rural areas. German farmers make a positive contribution to preserving biodiversity. Cognitive legitimacy concerns whether activities are comprehensible and align with existing cultural frameworks. In the protest context, this relates to whether farmers' actions make sense within broader societal narratives and expectations. Given current debates about political instrumentalization, we measured this through statements about protest comprehensibility and political dimensions: Farmers are part of the mainstream of society. I can understand the reasons behind the farmers' protest. The farmers' protests were exploited by a small minority/peripheral group for extreme positions. The majority of German farmers hold politically extreme/right-wing extremist and/or right-wing populist positions. Right-wing extremist and/or right-wing populist positions overshadow the true meaning and purpose of the farmers' protests 2.3 Factor and Cluster Analysis Our analysis followed a three-stage process combining factor and cluster analyses to identify and characterize distinct population segments. First, we conducted exploratory factor analysis using principal component analysis with Varimax rotation. We verified sampling adequacy using the Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity (Hair et al., 2018). Factor retention decisions were based on multiple criteria: eigenvalues > 1, parallel analysis, and scree plot inspection. We assessed factor reliability using both Guttman's lambda-2 (λ-2) and Cronbach's alpha (α), considering values above 0.70 satisfactory (Backhaus et al., 2015; Hair et al., 2018). Second, we determined the optimal number of clusters using three complementary criteria: Calinski-Harabasz pseudo-F statistic (Calinski & Harabasz, 1974) to evaluate between-cluster versus within-cluster variation Duda-Hart index (Duda & Hart, 1973) to assess cluster homogeneity improvements Omega criterion (Milligan & Cooper, 1985) to measure relative changes in within-cluster dispersion Finally, we employed a two-stage clustering approach combining hierarchical and non-hierarchical methods. We initially used Ward's hierarchical clustering with squared Euclidean distances to identify preliminary groupings, followed by k-means clustering (MacQueen, 1967) to refine cluster assignments. We validated the final solution using Kruskal-Wallis tests with post-hoc Dunn tests (Bonferroni-adjusted) and Chi-squared tests to confirm statistically significant differences between clusters. 3 Results and Discussion 3.1 Sample Characteristics Table 1 presents the socio-demographic characteristics of our sample (N = 637) compared to German population averages. The mean age of respondents is 48.45 years, slightly higher than the German average of 44.6 years. The age distribution shows some variations from national averages, with participants under 20 years old underrepresented and older participants overrepresented as only the adult individuals were interviewed and children excluded in our survey. Our sample largely reflects national demographics, with only minor deviations. The gender ratio (49% male) and migration background (31%) closely match national averages. Educational levels broadly align, though high school graduates are overrepresented (11% vs. 6% nationally) and university graduates underrepresented (21% vs. 27%). Household composition shows more single-person (29% vs. 20%) and two-person households (39% vs. 33%) than the national average. The geographic distribution across settlement sizes generally mirrors national patterns, with accurate representation in large cities (17%) but some overrepresentation in small communities (10% vs. 5%). Regional distribution follows national patterns, with North Rhine-Westphalia comprising the largest share (21%) (Appendix Figure A1). Regarding agricultural connections, most respondents (53%) have minimal direct contact, while 22% live in rural areas with regular agricultural exposure, and 17% actively seek agricultural information. Only 6% have direct agricultural ties through work or family. Overall, the sample demonstrates good representativeness across key socio-demographic characteristics. While showing exact matches in gender distribution (49% male) and migration background (31%), minor deviations exist in age structure (mean 48.45 years vs. 44.6 nationally), household size, and income distribution (Appendix Figure A2). The geographic distribution across federal states and educational attainment levels broadly reflects national patterns, despite some slight variations. The political attitudes of respondents were measured on an 11-point Likert scale ranging from left-wing (0) to right-wing (10). Figure 1 shows a mean score of 4.94, with 43% of respondents centering in the political spectrum. Moderate left (positions 3 and 4) had slightly higher proportions (11% and 12%) than moderate right (positions 6 and 7, 11% and 9%). Table 1 Socio-demographic characteristics of survey respondents (N = 637) compared to German population averages Variable Description M SD Min Max German Avg. age Age of the respondents in years 48.45 16.23 18 80 44.6 a under 20 years 0.02 - 0 1 0.19 b 20 to 39 years 0.32 - 0 1 0.25 b 40 to 59 years 0.38 - 0 1 0.27 b 60 to 79 years 0.27 - 0 1 0.23 b 80 to 99 years < 0.01 - 0 1 0.07 b 100 years and older 0.00 - 0 1 < 0.01 b gender 1, if the respondent is male; 0, otherwise 0.49 - 0 1 0.49 c education No qualification < 0.01 - 0 1 0.02 d Secondary school qualification 0.32 - 0 1 0.29 d Intermediate school qualification 0.34 - 0 1 0.35 d High school diploma 0.11 - 0 1 0.06 d University degree (BA) 0.09 - 0 1 0.12 d University degree (MA) 0.12 - 0 1 0.15 d Doctorate < 0.01 - 0 1 0.01 d household Number of people live in same household including the participant 2.33 1.11 1 6 2.03 e 1 0.29 - 0 1 0.20 e 2 0.39 - 0 1 0.33 e 3 0.18 - 0 1 0.18 e 4 0.10 - 0 1 0.19 e 5 or more 0.04 - 0 1 0.10 e migration 1, if the respondent has a migration background; 0, otherwise 0.31 - 0 1 0.30 f inhabitants Number of inhabitants in the primary place of residence Less than 2,000 inhabitants 0.10 - 0 1 0.05 g 2,000 to less than 5,000 inhabitants 0.10 - 0 1 0.08 g 5,000 to less than 20,000 inhabitants 0.21 - 0 1 0.27 g 20,000 to less than 50,000 inhabitants 0.14 - 0 1 0.19 g 50,000 to less than 100,000 inhabitants 0.12 - 0 1 0.09 g 100,000 to less than 500,000 inhabitants 0.14 - 0 1 0.15 g 500,000 inhabitants and more 0.17 - 0 1 0.17 g agri I work directly in agriculture, and it shapes my daily life. < 0.01 - 0 1 n.a. Agriculture is an integral part of my family and/or social environment, even though I do not work directly in it. 0.05 - 0 1 n.a. I come from a rural area and therefore regularly encounter agriculture, even without direct family or professional ties. 0.23 - 0 1 n.a. I am interested in agricultural topics and actively seek information, but I am not directly involved in agriculture and have no connections through family or friends. 0.18 - 0 1 n.a. I have little to no direct connection to agriculture. 0.54 - 0 1 n.a. M = Mean; SD = Standard deviation; Avg = average a Statistisches Bundesamt (2024) b Destatis (2024a) c Destatis (2024b) d Destatis (2024c) e Destatis (2024d) f Destatis (2024e) g Statistisches Bundesamt (2024b) 3.2 Descriptive Results on Public Perceptions Table 2 presents respondents' agreement with statements measuring different dimensions of agricultural legitimacy. Statements reflecting pragmatic legitimacy show moderate support for maintaining existing policies: Respondents tend to disagree with abolishing both the diesel tax rebate (M = 2.73, SD = 1.30) and vehicle tax exemption (M = 2.56, SD = 1.38), indicating support for maintaining these agricultural subsidies. Moral legitimacy indicators reveal consistently positive evaluations across different aspects. Respondents strongly endorse agriculture's importance for food security (M = 4.42, SD = 0.84) and the economy (M = 4.37, SD = 0.85). They also acknowledge farmers' contributions to rural communities (M = 3.97, SD = 1.01) and biodiversity preservation (M = 3.89, SD = 1.03). The results resemble Salazar-Ordonez et al. (2013), who showed that Spanish citizens also value agricultural economic, environmental and social functions. Regarding cognitive legitimacy, respondents demonstrate mixed views. While they strongly understand the reasons behind the farmers' protests (M = 4.01, SD = 1.09) and moderately view farmers as part of mainstream society (M = 3.37, SD = 1.07), there are some concerns about extremist influences: Respondents reject the notion that most farmers hold extreme right-wing positions (M = 1.95, SD = 1.11), but moderately agree that protests were exploited by extreme groups (M = 2.72, SD = 1.27) and that extremist positions overshadow the protests' original purpose (M = 2.86, SD = 1.36). Table 2 Public perceptions of agricultural legitimacy dimensions (N = 637) Variable Description M SD Pragmatic legitimacy protest_diesel The agricultural diesel tax rebate should be abolished. 2.73 1.30 protest_vehicle The vehicle tax exemption for agricultural vehicles should be abolished. 2.56 1.38 Moral legitimacy agri_food German agriculture is of great importance for food security. 4.42 0.84 agri_econ German farmers are a vital part of the German economy. 4.37 0.85 agri_social German farmers meaningfully contribute to fostering community and social cohesion in rural areas. 3.97 1.01 agri_bio German farmers make a positive contribution to preserving biodiversity. 3.89 1.03 Cognitive legitimacy farmers_main Farmers are part of the mainstream of society. 3.37 1.07 protest_reasons I can understand the reasons behind the farmers' protests. 4.01 1.09 farmers_mino The farmers' protests were exploited by a small minority/peripheral group for extreme positions. 2.72 1.27 farmers_right The majority of German farmers hold politically extreme/right-wing extremist and/or right-wing populist positions. 1.95 1.11 farmers_extrem Right-wing extremist and/or right-wing populist positions overshadow the true meaning and purpose of the farmers' protests. 2.86 1.36 M = Mean; SD = Standard deviation Responses are measured on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) across three dimensions: pragmatic legitimacy (policy support), moral legitimacy (societal value), and cognitive legitimacy (protest comprehension and political concerns). Results in Table 3 show clear differences in the perceived legitimacy of different protest tactics. Tractor parades in cities received the highest acceptance (M = 3.60, SD = 1.28), followed by protest bonfires (M = 3.14, SD = 1.41). These more symbolic or traditional protest forms garnered moderate support. In contrast, more disruptive protest forms received lower acceptance rates which is in line with the results of Simpsons et al. (2022). Blocking distribution centers (M = 2.90, SD = 1.31), streets in cities (M = 2.75, SD = 1.36), and highway entrances (M = 2.73, SD = 1.39) were viewed as less justified. The lowest acceptance was recorded for dumping manure and slurry (M = 2.29, SD = 1.41), suggesting public disapproval of potentially destructive protest forms. The standard deviations (ranging from 1.28 to 1.51) indicate considerable variation in individual responses, with the tactic of blocking politicians showing the highest variability in public opinion (SD = 1.51), but also a moderate average approval (M = 3.17). Table 3 Public acceptance of different farmers' protest forms (N = 637) Variables Description M SD grocery Blocking distribution centers of grocery retailers with tractors. 2.90 1.31 highway Blocking highway entrances with tractors. 2.73 1.39 manure Dumping manure and slurry on streets/in front of buildings. 2.29 1.41 streets Blocking intersections/streets in cities with tractors. 2.75 1.36 parades Tractor parades in cities. 3.60 1.28 bonfires Protest bonfires. 3.14 1.41 way Blocking the way of politicians. 3.17 1.51 M = Mean; SD = Standard deviation Responses are measured on a 5-point Likert scale (1 = completely unacceptable to 5 = completely acceptable) Respondents distributed 100 points among ten contemporary protests to assess perceived legitimacy, with higher points indicating greater legitimacy (Fig. 2 ). Farmers' protests ranked highest (22 points), followed by anti-right-wing demonstrations (18 points) and doctors' strikes (16 points). Union strikes received moderate support (12 points), while labor-related protests like train drivers' and Lufthansa strikes scored lower (7 and 6 points, respectively). Climate protests varied, with Fridays for Future receiving 7 points and The Last Generation’s disruptive tactics the lowest (3 points). Hollywood writers’ (5 points) and Pro-Palestine demonstrations (4 points) also garnered low legitimacy. These results highlight a hierarchy with professionally motivated protests and domestic societal issues receiving more support, while climate activism and international demonstrations are less favored.[1] 3.3 Results of the Factor and Cluster analysis Exploratory Factor Analysis Initial factor analysis included all items measuring agricultural legitimacy (Table 2 ). The item farmers_main was excluded due to low communality (0.33), indicating that less than one-third of its variance was explained by the common factors. The suitability of data for the final factor analysis was confirmed by the Kaiser-Meyer-Olkin measure (overall KMO = 0.81) and Bartlett's test of sphericity (χ² = 2689.47, df = 45, p < 0.001). Principal component analysis with varimax rotation yielded a three-factor solution explaining 69.55% of the total variance. The number of factors was determined using multiple criteria including eigenvalues > 1, scree plot inspection, and parallel analysis[2] . Factor 1, labeled "Agricultural support" (33.48% of variance), primarily captures aspects of moral legitimacy as conceptualized by Suchman (1995). The high loadings of items related to agriculture's importance for food security (0.86), economic contribution (0.82), and social cohesion (0.82) reflect the normative evaluations of agriculture's broader societal role. Interestingly, the item protest_reasons (0.69) also loaded on this factor, suggesting that understanding of protests is closely tied to recognition of agriculture's societal value. Factor 2, "Extremism concerns" (19.09% of variance), emerged as a specific aspect of cognitive legitimacy in the protest context. While Suchman's (1995) cognitive legitimacy focuses on general comprehensibility and taken-for-grantedness, our findings suggest that in the context of agricultural protests, this dimension is particularly shaped by concerns about political instrumentalization ( farmers_mino : 0.84, farmers_right : 0.85, farmers_extrem : 0.58). Factor 3, "Reform attitudes" (16.98% of variance), aligns with Suchman's (1995) concept of pragmatic legitimacy, focusing on concrete policy measures affecting stakeholders' interests. This factor specifically captures attitudes toward the proposed changes to agricultural tax benefits, with high loadings for items related to diesel tax rebates ( protest_diesel : 0.88) and vehicle tax exemptions ( protest_vehicle : 0.87). All factors show satisfactory reliability as indicated by both λ-2 and α coefficients with values above 0.7 (Table 4 ). The emergence of these three distinct factors not only validates Suchman's (1995) theoretical framework but also reveals how legitimacy dimensions manifest specifically in the context of agricultural protests and reform debates. Table 4 Factor analysis results identifying key dimensions of public perception toward agricultural protests (N = 637) Factor name Variable Factor 1 Factor 2 Factor 3 Eigenvalue Variance explained Agricultural support (λ-2 = 0.87, α = 0.87) 3.35 33.48% agri_food 0.86 -0.09 -0.06 agri_econ 0.82 -0.16 -0.04 agri_social 0.82 -0.05 -0.14 agri_bio 0.80 -0.08 -0.16 protest_reasons 0.69 -0.28 -0.21 Extremism concerns (λ-2 = 0.71, α = 0.70) 1.91 19.09% farmers_mino -0.06 0.84 0.04 farmers_right -0.09 0.85 0.09 farmers_extrem -0.30 0.58 0.26 Reform attitudes (λ-2 = 0.77, α = 0.77) 1.70 16.98% protest_diesel -0.14 0.12 0.88 protest_vehicle -0.15 0.13 0.87 KMO = 0.81; Bartlett's test of sphericity: χ² = 2689.47, df = 45, p < 0.001; n = 637; highest factor loadings in bold Cluster Analysis To determine the optimal number of clusters, we employed three stopping rules. The Calinski-Harabasz pseudo- F statistic reached its highest value (202.22) for the four-cluster solution. The Duda-Hart criterion also supported this solution, with the pseudo T -squared statistic peaking at 202.60 before a substantial drop. Similarly, the omega criterion favored the four-cluster solution, with the minimum value of -34.463[3] . Validation included cross-tabulation of hierarchical (Ward's method) and non-hierarchical (k-means) clustering, showing 70% overall agreement and 88% for Cluster 2 (110 out of 125 cases). Sensitivity analysis with k-means clustering (k = 2 to 10) confirmed the four-cluster solution as optimal, with the highest Calinski-Harabasz value (258.72) at k = 4, followed by a consistent decline for higher k values. These findings reveal distinct attitudinal patterns based on factor scores (Table 5 ). Table 5 Cluster analysis results revealing four distinct population segments (N = 637) Cluster Agricultural support Extremism concerns Reform attitudes Agricultural skeptics*** (N = 148) -1.36 (0.72) a 0.35 (0.70) a 0.16 (0.78) a Reform-oriented supporters*** (N = 134) 0.38 (0.75) b -0.78 (0.69) b 1.08 (0.65) b Critical supporters*** (N = 165) 0.59 (0.56) b 1.07 (0.62) c 0.02 (0.93) a Moderate reformers*** (N = 190) 0.28 (0.59) c -0.65 (0.60) d 0.90 (0.42) c ***p < 0.001; **p < 0.01; *p < 0.05 Values represent means of factor scores with standard deviations in parentheses. Different lowercase letters indicate statistically significant differences between clusters. For example, if two clusters share the same letter, their means are not statistically significantly different. Cluster 1: "Agricultural skeptics" (N = 148) shows strong skepticism toward agriculture (M = -1.36), along with moderate concerns about extremism (M = 0.35), and limited support for reforms (M = 0.16). This cluster differs statistically significantly from all others in agricultural support, showing the most negative stance. Cluster 2: "Reform-oriented supporters" (N = 134) combines moderate support for agriculture (M = 0.38, not statistically significantly different from Cluster 3) with a strong pro-reform attitude (M = 1.08) and low concerns about extremism (M = -0.78, not statistically significantly different from Cluster 4). This group differs statistically significantly from others particularly in reform attitudes, showing the highest support. Cluster 3: "Critical supporters" (N = 165) strongly supports agriculture (M = 0.59, not statistically significantly different from Cluster 2) and has the highest level of concern about extremism (M = 1.07), while showing neutral attitudes toward reform (M = 0.02), not statistically significantly different from Cluster 1). This group differs statistically significantly from all others in extremism concerns, showing the highest value. Cluster 4: "Moderate reformers " (N = 190) expresses moderate agricultural support (M = 0.28, statistically significantly different from Clusters 2 and 3) along with pro-reform inclination (M = 0.90) and minimal extremism concerns (M = -0.65, not statistically significantly different from Cluster 2). It shows a balanced but reform-supportive stance, with statistically significant differences from other clusters in most dimensions. Our findings demonstrate how Suchman's (1995) three dimensions of legitimacy - pragmatic, moral, and cognitive - manifest differently across societal segments in the context of agricultural protests. The agricultural skeptics (23.2%) primarily evaluate protests through moral legitimacy, prioritizing environmental considerations over immediate policy impacts. Reform-oriented supporters (21.0%) balance pragmatic legitimacy (accepting the need for subsidy reforms) with moral legitimacy (recognizing agriculture's societal importance). Critical supporters (25.9%) emphasize cognitive legitimacy, showing strong concern about the comprehensibility of protests within existing social frameworks, particularly regarding political instrumentalization. Moderate reformers (29.8%) demonstrate the strongest integration of all three legitimacy dimensions, combining pragmatic acceptance of reforms with moral support for agricultural traditions and cognitive understanding of protest motivations. Tables 6 , 7 and 8 provide further insights. Agricultural skeptics (23.2%, N = 148), predominantly younger (M = 42.79 years) and urban-dwelling (41% in cities > 100,000 inhabitants), consistently show low acceptance of farmers' protest tactics. With the highest proportion of university degrees (29%) and weakest agricultural connections (65% reporting no direct connection), they demonstrate strong support for climate-related protests and anti-right-wing demonstrations while maintaining a left-leaning political orientation. Reform-oriented supporters (21.0%, N = 134) exhibit balanced evaluation patterns. They strongly support farmers' protests while favoring peaceful demonstrations, particularly tractor parades and protest bonfires. With moderate rural connections and educational levels, their political orientation (M = 5.03) and demographic characteristics reflect mainstream societal positions. Critical supporters (25.9%, N = 165) present notable contrasts. Despite having the strongest direct agricultural connections, they express more moderate support for farmers' protests compared to other pro-agriculture groups. They show substantial support for anti-right-wing demonstrations while opposing disruptive protest tactics. Their mature age profile and centrist political orientation suggest a balanced perspective. Moderate reformers (29.8%, N = 190), the largest cluster, demonstrate the highest acceptance across all farmers' protest forms. With strong rural connections (51% having agricultural contact) and right-leaning tendencies, they show the strongest support for farmers' protests while firmly rejecting climate protests. This oldest group (M = 50.70 years) is predominantly rural-based with the highest proportion of migration background (34%). Across all clusters, protest acceptance follows a clear pattern: peaceful demonstrations like tractor parades receive consistently higher support than disruptive tactics like blockades or manure dumping which is in line with Simpson et al. (2022), who found that concerns about radical tactics in environmental protests reduced support for their broader cause. While traditional labor protests (train drivers, Lufthansa staff, doctors, unions) show relatively consistent support across clusters, statistically significant differences emerge in the evaluation of politically charged protests (farmers', climate, and anti-right-wing demonstrations). Educational background, gender, migration status, and household income show no statistically significant differences between clusters, suggesting that attitudes toward agricultural protests and reforms are more strongly influenced by political orientation and connection to agriculture than by other socio-demographic factors. One particular challenge in making comparisons to the literature is that previous studies have primarily focused on agricultural policy and environmental schemes rather than agricultural protest movements. Despite this difference, our results reveal notable contrasts with broader research on agricultural policy perceptions. For example, Salazar-Ordóñez et al.'s (2013) finding that Spanish citizens valued economic, environmental and social functions of agriculture equally is reflected in our moderate reformers cluster. However, our other clusters, particularly the agricultural skeptics (23.2%), demonstrate that this balanced view is not universal. This suggests regional differences in how agricultural multifunctionality is perceived across Europe. Traditional socio-demographic variables like age, education, and gender have been influential in shaping attitudes toward agricultural policies, such as the Common Agricultural Policy’s Greening (Beer and Heise, 2020) and multifunctional agriculture (Hyytiä and Kola, 2006). However, our findings indicate that these factors are less important in the context of agricultural protests. Among them, only age, alongside political orientation and agricultural connection, showed statistically significant impacts. The persistent meaning of agricultural connection across different contexts - from Hyytiä and Kola's (2006) study of multifunctional agriculture perceptions, through our analysis of protest attitudes, to Mata and Dos-Santos' (2024) examination of CAP support - reinforces the fundamental importance of agricultural proximity in shaping public attitudes toward agricultural issues. However, our findings diverge from Vainio et al. (2021), who reported no statistically significant effect of living in agricultural environments on perceptions of agri-environmental schemes. Furthermore, our finding that this factor operates independently of other socio-demographic variables represents a notable evolution from earlier studies where agricultural knowledge and interest were more closely aligned with education levels and urban-rural divides. Regarding the urban-rural divide, Mittenzwei et al. (2023) found a rural-urban gradient in preferences for climate policy. El Benni et al. (2024) challenged this traditional notion in agricultural policy contexts, identifying no statistically significant rural-urban differences in the prioritization of conflicting agricultural policy goals. Our findings align more closely with El Benni et al.'s (2024) work, suggesting that societal divides in support of agricultural protest may not necessarily align with spatial or demographic divides but rather with political orientation. Earlier studies, such as Hyytiä and Kola (2006), identified political orientation as a secondary factor. Nevertheless, our study highlights its prominence, with a clear left-right divide emerging in protest support. This reflects the growing politicization of agricultural issues and suggests that protest movements are increasingly intertwined with broader ideological narratives. Table 6 Mean values of protest form acceptance and perceived legitimacy of different protests by cluster segment (N = 637) Variable Cluster Agricultural skeptics (N = 148) Reform-oriented supporters (N = 134) Critical supporters (N = 165) Moderate reformers (N = 190) Protest forms grocery*** 2.30 a 3.03 b 2.73 c 3.42 d highway*** 2.16 a 2.94 b 2.52 b 3.18 c manure*** 2.09 a 2.36 b 2.04 b 2.59 c streets*** 2.21 a 2.91 b 2.55 b 3.23 c parades*** 2.66 a 3.88 b 3.54 c 4.18 d bonfires*** 2.54 a 3.31 b 3.04 c 3.54 d way*** 2.37 a 3.55 b 2.88 c 3.76 d Perceived legitimacy of different protest and strikes Fridays for Future*** 9.96 a 5.89 b 6.66 a 4.61 c The Last Generation*** 4.60 a 2.93 b 4.19 a 1.93 c Farmers' protest*** 10.70 a 25.03 b 20.29 c 28.86 d Train drivers' strike (GDL) 6.41 a 6.94 a 7.95 a 8.07 a Lufthansa staff strike 7.95 a 6.29 a 5.25 a 6.13 a Pro-Palestine demonstration 5.33 a 4.35 a 3.77 a 3.90 a Anti-right-wing demonstration*** 22.45 a 13.66 b 20.84 c 14.02 d Hollywood writers' strike 6.01 a 4.49 a 4.81 a 3.73 a Doctors' strike at university hospitals*** 13.99 a 16.52 b 15.97 c 16.62 d Union strike (e.g., Verdi) 12.56 a 13.85 a 10.32 a 12.10 a ***p < 0.001; **p < 0.01; *p < 0.05; Kruskall-Wallis test with post-hoc Dunn test (Bonferroni correction) Protest form acceptance measured on 5-point scale. Protest legitimacy measured through 100-point allocation task. Different lowercase letters indicate statistically significant differences between clusters. For example, if two clusters share the same letter, their means are not statistically significantly different. Table 7 Socio-demographic characteristics and political attitudes across cluster segments (N = 637) Variable Cluster Agricultural skeptics (N = 148) Reform-oriented supporters (N = 134) Critical supporters (N = 165) Moderate reformers (N = 190) age*** 42.70 a 49.94 b 49.83 b 50.70 b under 20 years 0.03 0.01 0.02 0.01 20 to 39 years 0.46 0.29 0.27 0.25 40 to 59 years 0.38 0.37 0.38 0.38 60 to 79 years 0.12 0.32 0.33 0.34 80 to 99 years 0.06 - - < 0.01 χ² (Chi-squared test) 32.97 (p = 0.001) gender 0.52 0.45 0.49 0.50 χ² (Chi-squared test) 1.27 (p = 0.734) education No qualification 0.01 - 0.01 0.01 Secondary school qualification 0.27 0.36 0.28 0.36 Intermediate school qualification 0.32 0.31 0.40 0.34 High school diploma 0.11 0.12 0.08 0.12 University degree (BA) 0.11 0.06 0.10 0.08 University degree (MA) 0.18 0.15 0.10 0.09 Doctorate - - 0.02 - χ² (Chi-squared test) 25.98 (p = 0.100) migration 0.27 0.32 0.29 0.34 χ² (Chi-squared test) 2.64 (p = 0.449) household 2.31 2.26 2.24 2.13 inhabitants Less than 2,000 inhabitants 0.06 0.11 0.11 0.14 2,000 to less than 5,000 inhabitants 0.09 0.11 0.10 0.12 5,000 to less than 20,000 inhabitants 0.18 0.21 0.22 0.24 20,000 to less than 50,000 inhabitants 0.15 0.12 0.16 0.14 50,000 to less than 100,000 inhabitants 0.11 0.10 0.16 0.11 100,000 to less than 500,000 inhabitants 0.20 0.16 0.13 0.08 500,000 inhabitants and more 0.21 0.18 0.13 0.18 χ² (Chi-squared test) 21.62 (p = 0.249) political attitude*** 4.47 a 5.03 b 4.79 c 5.35 d income Less than €,1000 0.07 0.07 0.05 0.12 €1,000 – €1,499 0.15 0.17 0.15 0.12 €1,500 – €1,999 0.11 0.16 0.14 0.13 €2,000 – €2,499 0.12 0.10 0.13 0.09 €2,500 – €2,999 0.11 0.09 0.12 0.79 €3,000 – €3,499 0.05 0.09 0.16 0.13 €3,500 – €3,999 0.11 0.11 0.05 0.07 €4,000 – €4,499 0.10 0.07 0.06 0.08 €4,500 – €4,999 0.07 0.02 0.05 0.09 More than €4,999 0.11 0.12 0.08 0.10 χ² (Chi-squared test) 35.12 (p = 0.136) ***p < 0.001; **p < 0.01; *p < 0.05; Kruskall-Wallis test with post-hoc Dunn test (Bonferroni correction) Different lowercase letters indicate statistically significant differences between clusters. For example, if two clusters share the same letter, their means are not statistically significantly different. Table 8 Distribution of agricultural connections across identified cluster segments (N = 637) Variable Cluster Agricultural skeptics (N = 148) Reform-oriented supporters (N = 134) Critical supporters (N = 165) Moderate reformers (N = 190) I work directly in agriculture, and it shapes my daily life. - - 0.02 - Agriculture is an integral part of my family and/or social environment, even though I do not work directly in it. 0.03 0.02 0.08 0.07 I come from a rural area and therefore regularly encounter agriculture, even without direct family or professional ties. 0.16 0.25 0.23 0.26 I am interested in agricultural topics and actively seek information, but I am not directly involved in agriculture and have no connections through family or friends. 0.16 0.21 0.17 0.18 I have little to no direct connection to agriculture. 0.65 0.52 0.50 0.49 χ² (Chi-squared test) 28.82 (p = 0.004) Participants had to choose one statements that fits them most. No multiple answers. 4 Conclusions and Implications Based on a quota-representative survey data from 637 individuals living in Germany, this study examined societal perceptions of agricultural protests using legitimacy theory. Factor and cluster analyses identified four population segments: agricultural skeptics (23.2%), reform-oriented supporters (21.0%), critical supporters (25.9%), and moderate reformers (29.8%). These findings reveal systematic variations in legitimacy evaluation influenced by political position and agricultural proximity. Three key factors emerge as central to understanding these patterns. First, agricultural proximity rather than socio-demographic factors strongly shapes protest perceptions and determines support levels. This is evidenced by the consistent influence of agricultural connections across clusters, from the predominantly urban agricultural skeptics to the rurally-connected moderate reformers. Second, political orientation systematically affects how protests are evaluated, with clear differences in protest support between left-leaning segments and more right-leaning groups. Third, attitudes toward different protest tactics show remarkable consistency across segments: peaceful demonstrations like tractor parades receive substantially higher support than disruptive actions. These patterns demonstrate how protest perception is filtered through specific societal and experiential lenses, with agricultural proximity and political orientation emerging as key determinants. Thus, our findings have important implications for both policymakers and protest organizers. First, the identification of four distinct population segments reveals nuanced patterns in how protest support varies across society. Each segment shows distinct combinations of agricultural support, extremism concerns, and views on protest tactics, moving beyond simplistic support-opposition dichotomies. For protest organizers, this segmentation provides crucial insights into how different groups perceive and evaluate their actions. For policymakers and stakeholders in public discourse, understanding these distinct perception patterns is essential when engaging with protest movements and their concerns. Second, our results regarding protest tactics have direct implications for protest organization and effectiveness. The clear preference for peaceful demonstrations (such as tractor parades) across all population segments, compared to lower acceptance of disruptive actions (such as blockades of highway entrances), suggests that protest organizers should carefully consider their tactical choices. While disruptive tactics might generate immediate media attention, they risk eroding broader public support, particularly among segments already skeptical of agricultural demands. Third, the role of proximity to agriculture in shaping support patterns, rather than traditional socio-demographic factors (e.g. gender), reveals important insights about protest perception. The strong influence of agricultural proximity suggests that existing ties to agriculture shape how protests are perceived and evaluated. This finding highlights the importance of understanding the divide in protest perception, as agricultural connection, rather than traditional demographic characteristics, emerges as a key factor in determining protest support. It can be concluded that creating opportunities for direct engagement between farming communities, agricultural stakeholder and urban populations might help build mutual understanding. Fourth, concerns about political extremism and agrarian populism impact public perception, particularly among critical supporters. This suggests that protest organizers need to clearly distance themselves from extreme positions to maintain broad societal support. Similarly, policymakers should address legitimate agricultural concerns while being careful not to inadvertently amplify populist narratives in public discourse. These findings emerge at a crucial time when many European countries are attempting to implement agricultural reforms while maintaining social cohesion. While our study focuses on Germany, several factors suggest broader applicability of our findings. The identified perception patterns may be relevant for other European countries experiencing similar agricultural protests, particularly given comparable policy pressures and protest tactics observed in countries like Belgium, France and the Netherlands, (Matthews, 2024b). 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Public support for different types of protest movements can fluctuate based on the prevailing social discourse and current events Figures A3 and A4 in the Appendix present the scree plot of eigenvalues and parallel analysis results, which support the three-factor solution. The negative omega value (-34.463) indicates optimal cluster separation while acknowledging some remaining within-cluster heterogeneity. Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7394023","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501548308,"identity":"8391a2b6-783b-4292-8fe8-81e56faa8409","order_by":0,"name":"Marius Michels","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4391-4457","institution":"Georg-August-Universität Göttingen","correspondingAuthor":true,"prefix":"","firstName":"Marius","middleName":"","lastName":"Michels","suffix":""},{"id":501548309,"identity":"475e06c2-6f2a-4aa4-9c62-f1cfcce53013","order_by":1,"name":"Thore-Maximilian Biß","email":"","orcid":"","institution":"Georg-August-Universität Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Thore-Maximilian","middleName":"","lastName":"Biß","suffix":""},{"id":501548310,"identity":"23ec91ef-07ce-4299-8489-0cdda11a49ae","order_by":2,"name":"Sven Grüner","email":"","orcid":"","institution":"Georg-August-Universität Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Sven","middleName":"","lastName":"Grüner","suffix":""},{"id":501548311,"identity":"d57d0878-2ca3-4e8c-89a0-3d3a016f313f","order_by":3,"name":"Oliver Mußhoff","email":"","orcid":"","institution":"Georg-August-Universität Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Mußhoff","suffix":""}],"badges":[],"createdAt":"2025-08-17 18:52:11","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7394023/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7394023/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89388560,"identity":"885cd08b-442b-4002-8e16-db8f2dee593b","added_by":"auto","created_at":"2025-08-19 12:42:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15625,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSample distribution by political attitude (N = 637) \u003cbr\u003e\n \u003c/strong\u003eMeasured on an 11-point scale from left-wing (0) to right-wing (10).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7394023/v1/e608abe6391a5f6d77e42b16.png"},{"id":89388561,"identity":"02d0251f-e1fa-46f3-ba53-e08369d518fb","added_by":"auto","created_at":"2025-08-19 12:42:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerceived legitimacy of different protests and strikes (N = 637). \u003cbr\u003e\n \u003c/strong\u003eBased on a 100-point allocation task where respondents distributed points according to perceived legitimacy. Results show the average number of points each protest received.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7394023/v1/a33dd1329d0961b0ad542d99.png"},{"id":89391289,"identity":"155aa6b9-4ca7-457e-b6e1-39f09747fd06","added_by":"auto","created_at":"2025-08-19 13:06:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1654768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7394023/v1/b59938c1-17cd-491f-be88-ef5f37e0c64c.pdf"},{"id":89389682,"identity":"a7c0331d-9719-4f62-bdc7-7702e3a44c0f","added_by":"auto","created_at":"2025-08-19 12:50:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3925977,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7394023/v1/9945cfc2e4803a89519292b3.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePublic Perception and Segmentation of Agricultural Protests in Germany\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn early 2024, German farmers launched widespread protests in response to proposed cuts to agricultural diesel tax rebates and vehicle tax exemptions, sparking intense public debate about agricultural policy reform. These protests emerged as part of a broader European movement where farmers pushed back against sweeping changes to farming systems (Budjdei-Tebeica, 2024; Matthews, 2024b). The economic implications extend beyond direct subsidy adjustments to fundamental questions of farm viability, climate change mitigation, and structural transformation (Pe'er et al., 2019; Cuadros-Casanova et al., 2023). Measures targeting greenhouse gas emissions, nitrogen pollution, and biodiversity conservation have intensified pressure on farming communities across the continent (Finger et al., 2024; Mc Loughlin, 2024).\u003c/p\u003e\u003cp\u003eAs a result, farmers across Europe have mobilized against various policy changes affecting their operations. For instance in the Netherlands, nitrogen reduction targets have sparked massive demonstrations, with farmers blocking highways and agricultural centers to protest proposed livestock reduction measures (Mc Loughlin, 2024). Spanish farmers protested against water use restrictions implemented due to drought conditions. Italian farmers expressed opposition to plans for removing their traditional income tax exemption. In Ukraine's neighboring countries, farmers called for limiting Ukrainian agricultural imports, arguing these were depressing local market prices (Matthews, 2024b). Matthews (2024a) identifies both country-specific triggers and common themes across protests, including increasing regulation, cheap imports, high production costs, declining social status, and subsidy cuts. McLoughlin (2024) frames recent European farmer protests as reflecting an identity crisis, as farmers navigate between traditional roles and new environmental, economic, and societal demands (Finger et al., 2024; Matthews, 2024a; Mazzocchi et al. 2024).\u003c/p\u003e\u003cp\u003eResearch on agricultural issues has extensively examined public perceptions across several domains, including agricultural digitalization (e.g. Zeddies et al., 2024), animal farming practices (e.g. Busch et al., 2022), and policy frameworks (Vainio et al., 2021; El Benni et al., 2024). Furthermore, Mazzocchi et al. (2024) showed via a sentiment analysis of social networks that overall a positive feeling was conveyed regarding posts about farmers\u0026rsquo; protests in Italy. However, three critical knowledge gaps persist in our understanding of public perception of agricultural protests. First, recent research has explored why farmers are protesting (Matthews, 2024b), and polls show that many people support these protests (Forschungsgruppe Wahlen, 2024; YouGov, 2024; Civey, 2024). However, while some studies have examined how people view agricultural issues in general (e.g. Vainio et al., 2021), we lack comprehensive insight into how consumers perceive and evaluate agricultural protests. Moreover, our understanding of how different societal segments view these protests remains limited. This gap is particularly noteworthy given that protest effectiveness is fundamentally tied to public legitimacy (Kleinschmit and Feindt, 2004).\u003c/p\u003e\u003cp\u003eSecond, we have limited understanding of how various agricultural protest tactics are linked to these perception patterns across different social groups. This gap is particular noteworthy since multiple factors influence public support, including protest size, tactics employed, underlying attitudes toward the cause, protesters' political alignment, and media coverage (Gutting, 2020; Brown and Mour\u0026atilde;o, 2022; Rogers, 2023). Research on social movements demonstrates that radical factions using extreme tactics can paradoxically benefit the moderate factions through a contrast effect, making the latter appear more reasonable and increasing public support for their positions (Simpson et al., 2022). Furthermore, analysis of Extinction Rebellion's 2019 UK protests showed reduced opposition to pro-environmental policies but limited impact on personal environmental attitudes or consumer behavior (Kountouris and Williams, 2023). The Fridays for Future climate protests in Germany during 2019 led to increased Green Party votes, despite protesters being predominantly below voting age. This effect stemmed from intergenerational transmission of environmental attitudes, enhanced social media engagement by Green Party politicians, and increased local media coverage of climate issues (Waldinger et al., 2023).\u003c/p\u003e\u003cp\u003eThird, not all responses to the protests were positive. Budjdei-Tebeica (2024) argue that radical right parties strategically frame environmental issues as threats to national sovereignty, economic growth, and cultural identity. Likewise, accusations that some of the current protests have also been infiltrated by far-right populist groups and that farmers are spreading radical positions have been the subject of controversial public debate in several countries (DW, 2024; Euronews, 2024; Monbiot, 2024; Parker and Robinson, 2024). Similar claims were made about past farmers\u0026rsquo; protests in other European countries, describing the farmers\u0026rsquo; demands as agrarian populism (Van Der Ploeg, 2020; Bosma and Peeren, 2021; Monbiot, 2024), yet their impact on public perception remains poorly understood.\u003c/p\u003e\u003cp\u003eBased on quota-representative survey data from 637 individuals living in Germany collected in 2024 and through the lens of legitimacy theory (Suchman, 1995), this study pursues three objectives:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo identify and characterize distinct segments of public perception toward agricultural protests, examining how different societal groups evaluate farmers' demands based on their proximity to agriculture, political orientation, and socio-demographic characteristics\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo analyze the acceptance of various protest tactics across these societal segments, with focus on the range between peaceful demonstrations (such as tractor parades) and more disruptive actions (such as blockades of policymakers)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo investigate how concerns about political extremism among farmers influence public support for agricultural protests across different population segments\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eGermany presents an ideal case study for exploring these dynamics as it is one of Europe's largest agricultural producer, accounting for 14% of EU agricultural output (eurostat, 2023), with ambitious climate goals (OECD, 2022). The early months of 2024 saw widespread farmer protests in response to proposed subsidy reductions, particularly regarding agricultural diesel tax rebates and vehicle tax exemptions. These protests, which included tractor demonstrations in major cities and blockades of infrastructure as well as politicians similar to other countries (Budjdei-Tebeica, 2024; Matthews, 2024b), sparked intense public debate about agricultural policy and protests. The combination of strong farmer mobilization, broad media coverage, and emerging discourses on agrarian populism provides a particularly rich analytical context for understanding how different segments of society perceive and evaluate agricultural protests.\u003c/p\u003e\u003cp\u003eThrough factor and cluster analysis, we reveal four distinct population segments with varying attitudes towards agricultural protests and reforms. Our findings demonstrate that while farmers' protests generally enjoy higher legitimacy compared to other social movements, acceptance varies statistically significantly with protest tactics. Support patterns are primarily shaped by proximity to agriculture and political orientation rather than traditional socio-demographic factors (e.g. gender), with peaceful demonstrations receiving broader support across all segments.\u003c/p\u003e\u003cp\u003eThis study makes several important contributions to both research and practice. First, it provides the first systematic segmentation of public attitudes toward agricultural protests, moving beyond simplistic support-opposition dichotomies to reveal distinct perception patterns. Second, it advances our understanding of how different protest tactics affect public support across societal segments. Third, it delivers novel insights into growing debates about the relationship between agricultural protests and populist movements which is interest for policy makers and protest organizers alike. Finally, it provides practical implications for protest organizers aiming to maintain public support while advocating for their interests.\u003c/p\u003e"},{"header":"2 Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sampling Procedure\u003c/h2\u003e\u003cp\u003eWe conducted an Internet-based survey from April 26 to May 10, 2024, using the professional survey company Bilendi to obtain a quota-representative sample mirroring Germany's adult population structure in terms of age, gender, and education. The survey was part of a larger consumer study, with participants remaining anonymous to ensure data security and enhance response credibility. The study received IRB approval from the German Association for Experimental Economic Research e.V. (Certificate No. PWrdXhPf).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Questionnaire Design\u003c/h2\u003e\u003cp\u003eThe questionnaire on farmers\u0026rsquo; protests was part of a larger consumer study and comprised several distinct sections. In the first section, participants were asked to evaluate the justification of various current protests and strikes. For this purpose, participants had to allocate 100 points, reflecting their perception of justification, with more points indicating higher perceived legitimacy. The second section assessed participants' proximity to agriculture through five standardized statements.\u003c/p\u003e\u003cp\u003eIn the third part, the participants were asked to evaluate several statements on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree), which were used for the subsequent cluster analysis. For the development of the statements, this study adopts legitimacy theory as its conceptual framework. According to Suchman (1995: 574), \"legitimacy is a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions.\" This definition provides a valuable lens for analyzing public perceptions of agricultural protests and reform attitudes, as it encompasses both general support for agriculture and specific evaluations of protest actions. Following Suchman's (1995) conceptualization, we distinguish between three dimensions of legitimacy: pragmatic, moral, and cognitive legitimacy. Pragmatic legitimacy rests on stakeholders' self-interested calculations about direct benefits or consequences. In the agricultural protest context, this dimension concerns how policy changes and reforms affect stakeholders' immediate interests, particularly regarding economic implications of agricultural policies and subsidies. We operationalized this through statements about specific policy measures being contested:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eThe agricultural diesel tax rebate should be abolished.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eThe vehicle tax exemption for agricultural vehicles should be abolished.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eMoral legitimacy reflects normative evaluations of whether activities align with societal values, moving beyond individual benefits to ask whether actions are \"the right thing to do.\" For agriculture, this encompasses broader judgments about the sector's societal contributions. We captured this through statements about agriculture's multiple roles:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eGerman agriculture is of great importance for food security.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eGerman farmers are a vital part of the German economy\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eGerman farmers meaningfully contribute to fostering community and social cohesion in rural areas.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eGerman farmers make a positive contribution to preserving biodiversity.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCognitive legitimacy concerns whether activities are comprehensible and align with existing cultural frameworks. In the protest context, this relates to whether farmers' actions make sense within broader societal narratives and expectations. Given current debates about political instrumentalization, we measured this through statements about protest comprehensibility and political dimensions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eFarmers are part of the mainstream of society.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eI can understand the reasons behind the farmers' protest.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eThe farmers' protests were exploited by a small minority/peripheral group for extreme positions.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eThe majority of German farmers hold politically extreme/right-wing extremist and/or right-wing populist positions.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eRight-wing extremist and/or right-wing populist positions overshadow the true meaning and purpose of the farmers' protests\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Factor and Cluster Analysis\u003c/h2\u003e\u003cp\u003eOur analysis followed a three-stage process combining factor and cluster analyses to identify and characterize distinct population segments. First, we conducted exploratory factor analysis using principal component analysis with Varimax rotation. We verified sampling adequacy using the Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity (Hair et al., 2018). Factor retention decisions were based on multiple criteria: eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, parallel analysis, and scree plot inspection. We assessed factor reliability using both Guttman's lambda-2 (λ-2) and Cronbach's alpha (α), considering values above 0.70 satisfactory (Backhaus et al., 2015; Hair et al., 2018).\u003c/p\u003e\u003cp\u003eSecond, we determined the optimal number of clusters using three complementary criteria:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCalinski-Harabasz pseudo-F statistic (Calinski \u0026amp; Harabasz, 1974) to evaluate between-cluster versus within-cluster variation\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDuda-Hart index (Duda \u0026amp; Hart, 1973) to assess cluster homogeneity improvements\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOmega criterion (Milligan \u0026amp; Cooper, 1985) to measure relative changes in within-cluster dispersion\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFinally, we employed a two-stage clustering approach combining hierarchical and non-hierarchical methods. We initially used Ward's hierarchical clustering with squared Euclidean distances to identify preliminary groupings, followed by k-means clustering (MacQueen, 1967) to refine cluster assignments. We validated the final solution using Kruskal-Wallis tests with post-hoc Dunn tests (Bonferroni-adjusted) and Chi-squared tests to confirm statistically significant differences between clusters.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Sample Characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the socio-demographic characteristics of our sample (N\u0026thinsp;=\u0026thinsp;637) compared to German population averages. The mean age of respondents is 48.45 years, slightly higher than the German average of 44.6 years. The age distribution shows some variations from national averages, with participants under 20 years old underrepresented and older participants overrepresented as only the adult individuals were interviewed and children excluded in our survey.\u003c/p\u003e\u003cp\u003eOur sample largely reflects national demographics, with only minor deviations. The gender ratio (49% male) and migration background (31%) closely match national averages. Educational levels broadly align, though high school graduates are overrepresented (11% vs. 6% nationally) and university graduates underrepresented (21% vs. 27%). Household composition shows more single-person (29% vs. 20%) and two-person households (39% vs. 33%) than the national average. The geographic distribution across settlement sizes generally mirrors national patterns, with accurate representation in large cities (17%) but some overrepresentation in small communities (10% vs. 5%). Regional distribution follows national patterns, with North Rhine-Westphalia comprising the largest share (21%) (Appendix Figure A1). Regarding agricultural connections, most respondents (53%) have minimal direct contact, while 22% live in rural areas with regular agricultural exposure, and 17% actively seek agricultural information. Only 6% have direct agricultural ties through work or family.\u003c/p\u003e\u003cp\u003eOverall, the sample demonstrates good representativeness across key socio-demographic characteristics. While showing exact matches in gender distribution (49% male) and migration background (31%), minor deviations exist in age structure (mean 48.45 years vs. 44.6 nationally), household size, and income distribution (Appendix Figure A2). The geographic distribution across federal states and educational attainment levels broadly reflects national patterns, despite some slight variations.\u003c/p\u003e\u003cp\u003eThe political attitudes of respondents were measured on an 11-point Likert scale ranging from left-wing (0) to right-wing (10). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a mean score of 4.94, with 43% of respondents centering in the political spectrum. Moderate left (positions 3 and 4) had slightly higher proportions (11% and 12%) than moderate right (positions 6 and 7, 11% and 9%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-demographic characteristics of survey respondents (N\u0026thinsp;=\u0026thinsp;637) compared to German population averages\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGerman Avg.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eage\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge of the respondents in years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunder 20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 to 39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 to 59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 to 79 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 to 99 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100 years and older\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egender\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1, if the respondent is male; 0, otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.49\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eeducation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo qualification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary school qualification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate school qualification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity degree (BA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity degree (MA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoctorate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ehousehold\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of people live in same household including the participant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.03\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003emigration\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1, if the respondent has a migration background; 0, otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003einhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of inhabitants in the primary place of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 2,000 inhabitants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,000 to less than 5,000 inhabitants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,000 to less than 20,000 inhabitants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20,000 to less than 50,000 inhabitants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50,000 to less than 100,000 inhabitants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100,000 to less than 500,000 inhabitants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500,000 inhabitants and more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eagri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI work directly in agriculture, and it shapes my daily life.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgriculture is an integral part of my family and/or social environment, even though I do not work directly in it.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI come from a rural area and therefore regularly encounter agriculture, even without direct family or professional ties.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI am interested in agricultural topics and actively seek information, but I am not directly involved in agriculture and have no connections through family or friends.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI have little to no direct connection to agriculture.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eM\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard deviation; Avg\u0026thinsp;=\u0026thinsp;average\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e Statistisches Bundesamt (2024)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003e Destatis (2024a)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003e Destatis (2024b)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ed\u003c/sup\u003e Destatis (2024c)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ee\u003c/sup\u003e Destatis (2024d)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ef\u003c/sup\u003e Destatis (2024e)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eg\u003c/sup\u003e Statistisches Bundesamt (2024b)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Descriptive Results on Public Perceptions\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents respondents' agreement with statements measuring different dimensions of agricultural legitimacy. Statements reflecting pragmatic legitimacy show moderate support for maintaining existing policies: Respondents tend to disagree with abolishing both the diesel tax rebate (M\u0026thinsp;=\u0026thinsp;2.73, SD\u0026thinsp;=\u0026thinsp;1.30) and vehicle tax exemption (M\u0026thinsp;=\u0026thinsp;2.56, SD\u0026thinsp;=\u0026thinsp;1.38), indicating support for maintaining these agricultural subsidies.\u003c/p\u003e\u003cp\u003eMoral legitimacy indicators reveal consistently positive evaluations across different aspects. Respondents strongly endorse agriculture's importance for food security (M\u0026thinsp;=\u0026thinsp;4.42, SD\u0026thinsp;=\u0026thinsp;0.84) and the economy (M\u0026thinsp;=\u0026thinsp;4.37, SD\u0026thinsp;=\u0026thinsp;0.85). They also acknowledge farmers' contributions to rural communities (M\u0026thinsp;=\u0026thinsp;3.97, SD\u0026thinsp;=\u0026thinsp;1.01) and biodiversity preservation (M\u0026thinsp;=\u0026thinsp;3.89, SD\u0026thinsp;=\u0026thinsp;1.03). The results resemble Salazar-Ordonez et al. (2013), who showed that Spanish citizens also value agricultural economic, environmental and social functions.\u003c/p\u003e\u003cp\u003eRegarding cognitive legitimacy, respondents demonstrate mixed views. While they strongly understand the reasons behind the farmers' protests (M\u0026thinsp;=\u0026thinsp;4.01, SD\u0026thinsp;=\u0026thinsp;1.09) and moderately view farmers as part of mainstream society (M\u0026thinsp;=\u0026thinsp;3.37, SD\u0026thinsp;=\u0026thinsp;1.07), there are some concerns about extremist influences: Respondents reject the notion that most farmers hold extreme right-wing positions (M\u0026thinsp;=\u0026thinsp;1.95, SD\u0026thinsp;=\u0026thinsp;1.11), but moderately agree that protests were exploited by extreme groups (M\u0026thinsp;=\u0026thinsp;2.72, SD\u0026thinsp;=\u0026thinsp;1.27) and that extremist positions overshadow the protests' original purpose (M\u0026thinsp;=\u0026thinsp;2.86, SD\u0026thinsp;=\u0026thinsp;1.36).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePublic perceptions of agricultural legitimacy dimensions (N\u0026thinsp;=\u0026thinsp;637)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePragmatic legitimacy\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprotest_diesel\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe agricultural diesel tax rebate should be abolished.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprotest_vehicle\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe vehicle tax exemption for agricultural vehicles should be abolished.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMoral legitimacy\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eagri_food\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGerman agriculture is of great importance for food security.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eagri_econ\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGerman farmers are a vital part of the German economy.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eagri_social\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGerman farmers meaningfully contribute to fostering community and social cohesion in rural areas.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eagri_bio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGerman farmers make a positive contribution to preserving biodiversity.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCognitive legitimacy\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efarmers_main\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmers are part of the mainstream of society.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprotest_reasons\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI can understand the reasons behind the farmers' protests.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efarmers_mino\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe farmers' protests were exploited by a small minority/peripheral group for extreme positions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efarmers_right\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe majority of German farmers hold politically extreme/right-wing extremist and/or right-wing populist positions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efarmers_extrem\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRight-wing extremist and/or right-wing populist positions overshadow the true meaning and purpose of the farmers' protests.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eM\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eResponses are measured on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree) across three dimensions: pragmatic legitimacy (policy support), moral legitimacy (societal value), and cognitive legitimacy (protest comprehension and political concerns).\u003c/p\u003e\u003cp\u003eResults in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show clear differences in the perceived legitimacy of different protest tactics. Tractor parades in cities received the highest acceptance (M\u0026thinsp;=\u0026thinsp;3.60, SD\u0026thinsp;=\u0026thinsp;1.28), followed by protest bonfires (M\u0026thinsp;=\u0026thinsp;3.14, SD\u0026thinsp;=\u0026thinsp;1.41). These more symbolic or traditional protest forms garnered moderate support. In contrast, more disruptive protest forms received lower acceptance rates which is in line with the results of Simpsons et al. (2022). Blocking distribution centers (M\u0026thinsp;=\u0026thinsp;2.90, SD\u0026thinsp;=\u0026thinsp;1.31), streets in cities (M\u0026thinsp;=\u0026thinsp;2.75, SD\u0026thinsp;=\u0026thinsp;1.36), and highway entrances (M\u0026thinsp;=\u0026thinsp;2.73, SD\u0026thinsp;=\u0026thinsp;1.39) were viewed as less justified. The lowest acceptance was recorded for dumping manure and slurry (M\u0026thinsp;=\u0026thinsp;2.29, SD\u0026thinsp;=\u0026thinsp;1.41), suggesting public disapproval of potentially destructive protest forms. The standard deviations (ranging from 1.28 to 1.51) indicate considerable variation in individual responses, with the tactic of blocking politicians showing the highest variability in public opinion (SD\u0026thinsp;=\u0026thinsp;1.51), but also a moderate average approval (M\u0026thinsp;=\u0026thinsp;3.17).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePublic acceptance of different farmers' protest forms (N\u0026thinsp;=\u0026thinsp;637)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egrocery\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlocking distribution centers of grocery retailers with tractors.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ehighway\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlocking highway entrances with tractors.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003emanure\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDumping manure and slurry on streets/in front of buildings.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003estreets\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlocking intersections/streets in cities with tractors.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eparades\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTractor parades in cities.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ebonfires\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtest bonfires.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eway\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlocking the way of politicians.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eM\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eResponses are measured on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;completely unacceptable to 5\u0026thinsp;=\u0026thinsp;completely acceptable)\u003c/p\u003e\u003cp\u003eRespondents distributed 100 points among ten contemporary protests to assess perceived legitimacy, with higher points indicating greater legitimacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Farmers' protests ranked highest (22 points), followed by anti-right-wing demonstrations (18 points) and doctors' strikes (16 points). Union strikes received moderate support (12 points), while labor-related protests like train drivers' and Lufthansa strikes scored lower (7 and 6 points, respectively). Climate protests varied, with Fridays for Future receiving 7 points and The Last Generation\u0026rsquo;s disruptive tactics the lowest (3 points). Hollywood writers\u0026rsquo; (5 points) and Pro-Palestine demonstrations (4 points) also garnered low legitimacy. These results highlight a hierarchy with professionally motivated protests and domestic societal issues receiving more support, while climate activism and international demonstrations are less favored.[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Results of the Factor and Cluster analysis\u003c/h2\u003e\u003cp\u003e\u003cem\u003eExploratory Factor Analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eInitial factor analysis included all items measuring agricultural legitimacy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The item \u003cem\u003efarmers_main\u003c/em\u003e was excluded due to low communality (0.33), indicating that less than one-third of its variance was explained by the common factors. The suitability of data for the final factor analysis was confirmed by the Kaiser-Meyer-Olkin measure (overall KMO\u0026thinsp;=\u0026thinsp;0.81) and Bartlett's test of sphericity (χ\u0026sup2; = 2689.47, df\u0026thinsp;=\u0026thinsp;45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Principal component analysis with varimax rotation yielded a three-factor solution explaining 69.55% of the total variance. The number of factors was determined using multiple criteria including eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, scree plot inspection, and parallel analysis[2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eFactor 1, labeled \"Agricultural support\" (33.48% of variance), primarily captures aspects of moral legitimacy as conceptualized by Suchman (1995). The high loadings of items related to agriculture's importance for food security (0.86), economic contribution (0.82), and social cohesion (0.82) reflect the normative evaluations of agriculture's broader societal role. Interestingly, the item \u003cem\u003eprotest_reasons\u003c/em\u003e (0.69) also loaded on this factor, suggesting that understanding of protests is closely tied to recognition of agriculture's societal value.\u003c/p\u003e\u003cp\u003eFactor 2, \"Extremism concerns\" (19.09% of variance), emerged as a specific aspect of cognitive legitimacy in the protest context. While Suchman's (1995) cognitive legitimacy focuses on general comprehensibility and taken-for-grantedness, our findings suggest that in the context of agricultural protests, this dimension is particularly shaped by concerns about political instrumentalization (\u003cem\u003efarmers_mino\u003c/em\u003e: 0.84, \u003cem\u003efarmers_right\u003c/em\u003e: 0.85, \u003cem\u003efarmers_extrem\u003c/em\u003e: 0.58).\u003c/p\u003e\u003cp\u003eFactor 3, \"Reform attitudes\" (16.98% of variance), aligns with Suchman's (1995) concept of pragmatic legitimacy, focusing on concrete policy measures affecting stakeholders' interests. This factor specifically captures attitudes toward the proposed changes to agricultural tax benefits, with high loadings for items related to diesel tax rebates (\u003cem\u003eprotest_diesel\u003c/em\u003e: 0.88) and vehicle tax exemptions (\u003cem\u003eprotest_vehicle\u003c/em\u003e: 0.87).\u003c/p\u003e\u003cp\u003eAll factors show satisfactory reliability as indicated by both λ-2 and α coefficients with values above 0.7 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The emergence of these three distinct factors not only validates Suchman's (1995) theoretical framework but also reveals how legitimacy dimensions manifest specifically in the context of agricultural protests and reform debates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor analysis results identifying key dimensions of public perception toward agricultural protests (N\u0026thinsp;=\u0026thinsp;637)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFactor 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFactor 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEigenvalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVariance explained\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAgricultural support (λ-2\u0026thinsp;=\u0026thinsp;0.87, α\u0026thinsp;=\u0026thinsp;0.87)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33.48%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eagri_food\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eagri_econ\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eagri_social\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eagri_bio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eprotest_reasons\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExtremism concerns (λ-2\u0026thinsp;=\u0026thinsp;0.71, α\u0026thinsp;=\u0026thinsp;0.70)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.09%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003efarmers_mino\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003efarmers_right\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.85\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003efarmers_extrem\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReform attitudes (λ-2\u0026thinsp;=\u0026thinsp;0.77, α\u0026thinsp;=\u0026thinsp;0.77)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.98%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eprotest_diesel\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eprotest_vehicle\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eKMO\u0026thinsp;=\u0026thinsp;0.81; Bartlett's test of sphericity: χ\u0026sup2; = 2689.47, df\u0026thinsp;=\u0026thinsp;45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; n\u0026thinsp;=\u0026thinsp;637; highest factor loadings in bold\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCluster Analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo determine the optimal number of clusters, we employed three stopping rules. The Calinski-Harabasz pseudo-\u003cem\u003eF\u003c/em\u003e statistic reached its highest value (202.22) for the four-cluster solution. The Duda-Hart criterion also supported this solution, with the pseudo \u003cem\u003eT\u003c/em\u003e-squared statistic peaking at 202.60 before a substantial drop. Similarly, the omega criterion favored the four-cluster solution, with the minimum value of -34.463[3]\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e. Validation included cross-tabulation of hierarchical (Ward's method) and non-hierarchical (k-means) clustering, showing 70% overall agreement and 88% for Cluster 2 (110 out of 125 cases). Sensitivity analysis with k-means clustering (k\u0026thinsp;=\u0026thinsp;2 to 10) confirmed the four-cluster solution as optimal, with the highest Calinski-Harabasz value (258.72) at k\u0026thinsp;=\u0026thinsp;4, followed by a consistent decline for higher k values. These findings reveal distinct attitudinal patterns based on factor scores (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCluster analysis results revealing four distinct population segments (N\u0026thinsp;=\u0026thinsp;637)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural support\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExtremism concerns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReform attitudes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural skeptics*** (N\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.36 (0.72) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35 (0.70) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16 (0.78) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReform-oriented supporters*** \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.38 (0.75) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.78 (0.69) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08 (0.65) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCritical supporters*** (N\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59 (0.56) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07 (0.62) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02 (0.93) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate reformers*** (N\u0026thinsp;=\u0026thinsp;190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28 (0.59) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.65 (0.60) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.42) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eValues represent means of factor scores with standard deviations in parentheses. Different lowercase letters indicate statistically significant differences between clusters. For example, if two clusters share the same letter, their means are not statistically significantly different.\u003c/p\u003e\u003cp\u003eCluster 1: \"Agricultural skeptics\" (N\u0026thinsp;=\u0026thinsp;148) shows strong skepticism toward agriculture (M = -1.36), along with moderate concerns about extremism (M\u0026thinsp;=\u0026thinsp;0.35), and limited support for reforms (M\u0026thinsp;=\u0026thinsp;0.16). This cluster differs statistically significantly from all others in agricultural support, showing the most negative stance.\u003c/p\u003e\u003cp\u003eCluster 2: \"Reform-oriented supporters\" (N\u0026thinsp;=\u0026thinsp;134) combines moderate support for agriculture (M\u0026thinsp;=\u0026thinsp;0.38, not statistically significantly different from Cluster 3) with a strong pro-reform attitude (M\u0026thinsp;=\u0026thinsp;1.08) and low concerns about extremism (M = -0.78, not statistically significantly different from Cluster 4). This group differs statistically significantly from others particularly in reform attitudes, showing the highest support.\u003c/p\u003e\u003cp\u003eCluster 3: \"Critical supporters\" (N\u0026thinsp;=\u0026thinsp;165) strongly supports agriculture (M\u0026thinsp;=\u0026thinsp;0.59, not statistically significantly different from Cluster 2) and has the highest level of concern about extremism (M\u0026thinsp;=\u0026thinsp;1.07), while showing neutral attitudes toward reform (M\u0026thinsp;=\u0026thinsp;0.02), not statistically significantly different from Cluster 1). This group differs statistically significantly from all others in extremism concerns, showing the highest value.\u003c/p\u003e\u003cp\u003eCluster 4: \"Moderate reformers\u003cb\u003e\"\u003c/b\u003e (N\u0026thinsp;=\u0026thinsp;190) expresses moderate agricultural support (M\u0026thinsp;=\u0026thinsp;0.28, statistically significantly different from Clusters 2 and 3) along with pro-reform inclination (M\u0026thinsp;=\u0026thinsp;0.90) and minimal extremism concerns (M = -0.65, not statistically significantly different from Cluster 2). It shows a balanced but reform-supportive stance, with statistically significant differences from other clusters in most dimensions.\u003c/p\u003e\u003cp\u003eOur findings demonstrate how Suchman's (1995) three dimensions of legitimacy - pragmatic, moral, and cognitive - manifest differently across societal segments in the context of agricultural protests. The agricultural skeptics (23.2%) primarily evaluate protests through moral legitimacy, prioritizing environmental considerations over immediate policy impacts. Reform-oriented supporters (21.0%) balance pragmatic legitimacy (accepting the need for subsidy reforms) with moral legitimacy (recognizing agriculture's societal importance). Critical supporters (25.9%) emphasize cognitive legitimacy, showing strong concern about the comprehensibility of protests within existing social frameworks, particularly regarding political instrumentalization. Moderate reformers (29.8%) demonstrate the strongest integration of all three legitimacy dimensions, combining pragmatic acceptance of reforms with moral support for agricultural traditions and cognitive understanding of protest motivations.\u003c/p\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e provide further insights. Agricultural skeptics (23.2%, N\u0026thinsp;=\u0026thinsp;148), predominantly younger (M\u0026thinsp;=\u0026thinsp;42.79 years) and urban-dwelling (41% in cities\u0026thinsp;\u0026gt;\u0026thinsp;100,000 inhabitants), consistently show low acceptance of farmers' protest tactics. With the highest proportion of university degrees (29%) and weakest agricultural connections (65% reporting no direct connection), they demonstrate strong support for climate-related protests and anti-right-wing demonstrations while maintaining a left-leaning political orientation. Reform-oriented supporters (21.0%, N\u0026thinsp;=\u0026thinsp;134) exhibit balanced evaluation patterns. They strongly support farmers' protests while favoring peaceful demonstrations, particularly tractor parades and protest bonfires. With moderate rural connections and educational levels, their political orientation (M\u0026thinsp;=\u0026thinsp;5.03) and demographic characteristics reflect mainstream societal positions. Critical supporters (25.9%, N\u0026thinsp;=\u0026thinsp;165) present notable contrasts. Despite having the strongest direct agricultural connections, they express more moderate support for farmers' protests compared to other pro-agriculture groups. They show substantial support for anti-right-wing demonstrations while opposing disruptive protest tactics. Their mature age profile and centrist political orientation suggest a balanced perspective. Moderate reformers (29.8%, N\u0026thinsp;=\u0026thinsp;190), the largest cluster, demonstrate the highest acceptance across all farmers' protest forms. With strong rural connections (51% having agricultural contact) and right-leaning tendencies, they show the strongest support for farmers' protests while firmly rejecting climate protests. This oldest group (M\u0026thinsp;=\u0026thinsp;50.70 years) is predominantly rural-based with the highest proportion of migration background (34%). Across all clusters, protest acceptance follows a clear pattern: peaceful demonstrations like tractor parades receive consistently higher support than disruptive tactics like blockades or manure dumping which is in line with Simpson et al. (2022), who found that concerns about radical tactics in environmental protests reduced support for their broader cause. While traditional labor protests (train drivers, Lufthansa staff, doctors, unions) show relatively consistent support across clusters, statistically significant differences emerge in the evaluation of politically charged protests (farmers', climate, and anti-right-wing demonstrations). Educational background, gender, migration status, and household income show no statistically significant differences between clusters, suggesting that attitudes toward agricultural protests and reforms are more strongly influenced by political orientation and connection to agriculture than by other socio-demographic factors.\u003c/p\u003e\u003cp\u003eOne particular challenge in making comparisons to the literature is that previous studies have primarily focused on agricultural policy and environmental schemes rather than agricultural protest movements. Despite this difference, our results reveal notable contrasts with broader research on agricultural policy perceptions. For example, Salazar-Ord\u0026oacute;\u0026ntilde;ez et al.'s (2013) finding that Spanish citizens valued economic, environmental and social functions of agriculture equally is reflected in our moderate reformers cluster. However, our other clusters, particularly the agricultural skeptics (23.2%), demonstrate that this balanced view is not universal. This suggests regional differences in how agricultural multifunctionality is perceived across Europe. Traditional socio-demographic variables like age, education, and gender have been influential in shaping attitudes toward agricultural policies, such as the Common Agricultural Policy\u0026rsquo;s Greening (Beer and Heise, 2020) and multifunctional agriculture (Hyyti\u0026auml; and Kola, 2006). However, our findings indicate that these factors are less important in the context of agricultural protests. Among them, only age, alongside political orientation and agricultural connection, showed statistically significant impacts.\u003c/p\u003e\u003cp\u003eThe persistent meaning of agricultural connection across different contexts - from Hyyti\u0026auml; and Kola's (2006) study of multifunctional agriculture perceptions, through our analysis of protest attitudes, to Mata and Dos-Santos' (2024) examination of CAP support - reinforces the fundamental importance of agricultural proximity in shaping public attitudes toward agricultural issues. However, our findings diverge from Vainio et al. (2021), who reported no statistically significant effect of living in agricultural environments on perceptions of agri-environmental schemes. Furthermore, our finding that this factor operates independently of other socio-demographic variables represents a notable evolution from earlier studies where agricultural knowledge and interest were more closely aligned with education levels and urban-rural divides. Regarding the urban-rural divide, Mittenzwei et al. (2023) found a rural-urban gradient in preferences for climate policy. El Benni et al. (2024) challenged this traditional notion in agricultural policy contexts, identifying no statistically significant rural-urban differences in the prioritization of conflicting agricultural policy goals. Our findings align more closely with El Benni et al.'s (2024) work, suggesting that societal divides in support of agricultural protest may not necessarily align with spatial or demographic divides but rather with political orientation. Earlier studies, such as Hyyti\u0026auml; and Kola (2006), identified political orientation as a secondary factor. Nevertheless, our study highlights its prominence, with a clear left-right divide emerging in protest support. This reflects the growing politicization of agricultural issues and suggests that protest movements are increasingly intertwined with broader ideological narratives.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean values of protest form acceptance and perceived legitimacy of different protests by cluster segment (N\u0026thinsp;=\u0026thinsp;637)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCluster\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural skeptics (N\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReform-oriented supporters (N\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCritical supporters \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate reformers (N\u0026thinsp;=\u0026thinsp;190)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProtest forms\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egrocery***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.03 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.73 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.42 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ehighway***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.16 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.94 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.52 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.18 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003emanure***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.09 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.36 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.59 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003estreets***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.21 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.91 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.55 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.23 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eparades***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.66 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.88 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.54 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.18 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ebonfires***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.54 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.31 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.04 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.54 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eway***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.37 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.88 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.76 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerceived legitimacy of different protest and strikes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFridays for Future***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.96 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.89 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.66 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.61 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eThe Last Generation***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.60 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.93 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.19 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.93 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFarmers' protest***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.70 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.03 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.29 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.86 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTrain drivers' strike (GDL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.41 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.94 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.95 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.07 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLufthansa staff strike\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.95 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.29 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.25 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.13 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePro-Palestine demonstration\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.33 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.35 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.77 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.90 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnti-right-wing demonstration***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.45 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.66 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.84 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.02 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHollywood writers' strike\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.01 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.49 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.81 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.73 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDoctors' strike at university hospitals***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.99 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.52 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.97 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.62 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnion strike (e.g., Verdi)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.56 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.85 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.32 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.10 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kruskall-Wallis test with post-hoc Dunn test (Bonferroni correction)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eProtest form acceptance measured on 5-point scale. Protest legitimacy measured through 100-point allocation task.\u003c/p\u003e\u003cp\u003eDifferent lowercase letters indicate statistically significant differences between clusters. For example, if two clusters share the same letter, their means are not statistically significantly different.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-demographic characteristics and political attitudes across cluster segments (N\u0026thinsp;=\u0026thinsp;637)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCluster\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural skeptics \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReform-oriented supporters \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCritical \u003c/p\u003e\u003cp\u003esupporters \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate \u003c/p\u003e\u003cp\u003ereformers \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;190)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eage***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.70 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.94 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.83 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.70 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eunder 20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20 to 39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40 to 59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 to 79 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e80 to 99 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (Chi-squared test)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.97 (p\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egender\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (Chi-squared test)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.27 (p\u0026thinsp;=\u0026thinsp;0.734)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eeducation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo qualification\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSecondary school qualification\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIntermediate school qualification\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh school diploma\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUniversity degree (BA)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUniversity degree (MA)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDoctorate\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (Chi-squared test)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.98 (p\u0026thinsp;=\u0026thinsp;0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003emigration\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (Chi-squared test)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.64 (p\u0026thinsp;=\u0026thinsp;0.449)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ehousehold\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003einhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLess than 2,000 inhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e2,000 to less than 5,000 inhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e5,000 to less than 20,000 inhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e20,000 to less than 50,000 inhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e50,000 to less than 100,000 inhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e100,000 to less than 500,000 inhabitants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e500,000 inhabitants and more\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (Chi-squared test)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.62 (p\u0026thinsp;=\u0026thinsp;0.249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003epolitical attitude***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.47 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.03 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.79 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.35 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eincome\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLess than \u0026euro;,1000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;1,000 \u0026ndash; \u0026euro;1,499\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;1,500 \u0026ndash; \u0026euro;1,999\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;2,000 \u0026ndash; \u0026euro;2,499\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;2,500 \u0026ndash; \u0026euro;2,999\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;3,000 \u0026ndash; \u0026euro;3,499\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;3,500 \u0026ndash; \u0026euro;3,999\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;4,000 \u0026ndash; \u0026euro;4,499\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026euro;4,500 \u0026ndash; \u0026euro;4,999\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMore than \u0026euro;4,999\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (Chi-squared test)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.12 (p\u0026thinsp;=\u0026thinsp;0.136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kruskall-Wallis test with post-hoc Dunn test (Bonferroni correction)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDifferent lowercase letters indicate statistically significant differences between clusters. For example, if two clusters share the same letter, their means are not statistically significantly different.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of agricultural connections across identified cluster segments (N\u0026thinsp;=\u0026thinsp;637)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCluster\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural skeptics \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReform-oriented supporters \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCritical supporters \u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate reformers (N\u0026thinsp;=\u0026thinsp;190)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI work directly in agriculture, and it shapes my daily life.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgriculture is an integral part of my family and/or social environment, even though I do not work directly in it.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI come from a rural area and therefore regularly encounter agriculture, even without direct family or professional ties.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI am interested in agricultural topics and actively seek information, but I am not directly involved in agriculture and have no connections through family or friends.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI have little to no direct connection to agriculture.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (Chi-squared test)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e28.82 (p\u0026thinsp;=\u0026thinsp;0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eParticipants had to choose one statements that fits them most. No multiple answers.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Conclusions and Implications","content":"\u003cp\u003eBased on a quota-representative survey data from 637 individuals living in Germany, this study examined societal perceptions of agricultural protests using legitimacy theory. Factor and cluster analyses identified four population segments: agricultural skeptics (23.2%), reform-oriented supporters (21.0%), critical supporters (25.9%), and moderate reformers (29.8%). These findings reveal systematic variations in legitimacy evaluation influenced by political position and agricultural proximity.\u003c/p\u003e\u003cp\u003eThree key factors emerge as central to understanding these patterns. First, agricultural proximity rather than socio-demographic factors strongly shapes protest perceptions and determines support levels. This is evidenced by the consistent influence of agricultural connections across clusters, from the predominantly urban agricultural skeptics to the rurally-connected moderate reformers. Second, political orientation systematically affects how protests are evaluated, with clear differences in protest support between left-leaning segments and more right-leaning groups. Third, attitudes toward different protest tactics show remarkable consistency across segments: peaceful demonstrations like tractor parades receive substantially higher support than disruptive actions. These patterns demonstrate how protest perception is filtered through specific societal and experiential lenses, with agricultural proximity and political orientation emerging as key determinants.\u003c/p\u003e\u003cp\u003eThus, our findings have important implications for both policymakers and protest organizers. First, the identification of four distinct population segments reveals nuanced patterns in how protest support varies across society. Each segment shows distinct combinations of agricultural support, extremism concerns, and views on protest tactics, moving beyond simplistic support-opposition dichotomies. For protest organizers, this segmentation provides crucial insights into how different groups perceive and evaluate their actions. For policymakers and stakeholders in public discourse, understanding these distinct perception patterns is essential when engaging with protest movements and their concerns.\u003c/p\u003e\u003cp\u003eSecond, our results regarding protest tactics have direct implications for protest organization and effectiveness. The clear preference for peaceful demonstrations (such as tractor parades) across all population segments, compared to lower acceptance of disruptive actions (such as blockades of highway entrances), suggests that protest organizers should carefully consider their tactical choices. While disruptive tactics might generate immediate media attention, they risk eroding broader public support, particularly among segments already skeptical of agricultural demands.\u003c/p\u003e\u003cp\u003eThird, the role of proximity to agriculture in shaping support patterns, rather than traditional socio-demographic factors (e.g. gender), reveals important insights about protest perception. The strong influence of agricultural proximity suggests that existing ties to agriculture shape how protests are perceived and evaluated. This finding highlights the importance of understanding the divide in protest perception, as agricultural connection, rather than traditional demographic characteristics, emerges as a key factor in determining protest support. It can be concluded that creating opportunities for direct engagement between farming communities, agricultural stakeholder and urban populations might help build mutual understanding.\u003c/p\u003e\u003cp\u003eFourth, concerns about political extremism and agrarian populism impact public perception, particularly among critical supporters. This suggests that protest organizers need to clearly distance themselves from extreme positions to maintain broad societal support. Similarly, policymakers should address legitimate agricultural concerns while being careful not to inadvertently amplify populist narratives in public discourse.\u003c/p\u003e\u003cp\u003eThese findings emerge at a crucial time when many European countries are attempting to implement agricultural reforms while maintaining social cohesion. While our study focuses on Germany, several factors suggest broader applicability of our findings. The identified perception patterns may be relevant for other European countries experiencing similar agricultural protests, particularly given comparable policy pressures and protest tactics observed in countries like Belgium, France and the Netherlands, (Matthews, 2024b). Still, future research should test the robustness of our segmentation approach in different national contexts to validate the generalizability of these protest perception patterns. Likewise, it could be of interest to investigate panel data to analyze potential changes over time.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBackhaus, K., Erichson, B., \u0026amp; Weiber, R. (2015). \u003cem\u003eFortgeschrittene multivariate Analysemethoden: eine anwendungsorientierte Einf\u0026uuml;hrung\u003c/em\u003e. Springer-Verlag.\u003c/li\u003e\n\u003cli\u003eBeer, L., \u0026amp; Heise, H. (2020). Einstellungen der Bev\u0026ouml;lkerung zum Greening der Gemeinsamen Agrarpolitik: Ergebnisse einer Panel-Befragung. \u003cem\u003eGerman Journal of Agricultural Economics\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(3), 173-182. http://dx.doi.org/10.22004/ag.econ.334281\u003c/li\u003e\n\u003cli\u003eBosma, A. \u0026amp; Peeren, E. (2021): #Proudofthefarmer. In Pospech, P., Fuglestad, E. M., \u0026amp; Figueiredo, E. (eds). \u003cem\u003ePolitics and policies of rural authenticity\u003c/em\u003e. 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The legitimacy of result-oriented and action-oriented agri-environmental schemes: A comparison of farmers\u0026rsquo; and citizens\u0026rsquo; perceptions. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e, 104358. https://doi.org/10.1016/j.landusepol.2019.104358\u003c/li\u003e\n\u003cli\u003eMittenzwei, K., Gustavsen, G. W., Grimsrud, K., Lindhjem, H., \u0026amp; Bj\u0026oslash;rkhaug, H. (2023). Perceived effects of climate policy on rural areas and agriculture: A rural-urban-divide. \u003cem\u003eJournal of Rural Studies\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e, 103001. https://doi.org/10.1016/j.jrurstud.2023.03.009\u003c/li\u003e\n\u003cli\u003eWaldinger, M., Rainer, H., Ludwig, M., Fl\u0026uuml;ckiger, M., Wichert, S., \u0026amp; Fabel, M. (2023). The Power of Youth: The Impact of the\u0026quot; Fridays for Future\u0026quot; Climate Movement on Voters, Politicians, and the Media. Pre-Print. https://doi.org/10.21203/rs.3.rs-3199060/v1\u003c/li\u003e\n\u003cli\u003eYouGov. (2024): Mehrheit der Deutschen hat laut Umfrage Verst\u0026auml;ndnis f\u0026uuml;r die Bauernproteste. Online available at: https://yougov.de/topics/society/survey-results/daily/2024/01/10/10794/1\u003c/li\u003e\n\u003cli\u003eZeddies, H. H., Busch, G., \u0026amp; Qaim, M. (2024). Positive public attitudes towards agricultural robots. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 15607. https://doi.org/10.1038/s41598-024-66198-4\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e It is important to note that these findings represent a snapshot in time and should be interpreted with caution. Public support for different types of protest movements can fluctuate based on the prevailing social discourse and current events\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Figures \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003eA3\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eA4\u003c/span\u003e in the Appendix present the scree plot of eigenvalues and parallel analysis results, which support the three-factor solution.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The negative omega value (-34.463) indicates optimal cluster separation while acknowledging some remaining within-cluster heterogeneity.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agricultural Protests, Cluster Analysis, Germany, Public Perception","lastPublishedDoi":"10.21203/rs.3.rs-7394023/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7394023/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing Germany as a case study, this paper examines public perceptions of agricultural protests across different societal segments. Based on quota-representative survey data from 637 adult individuals collected in 2024, we employ factor and cluster analyses to identify distinct attitude patterns. Our findings reveal four population segments: agricultural skeptics, reform-oriented supporters, critical supporters, and moderate reformers. While farmers' protests generally enjoy high legitimacy compared to other social movements, acceptance varies statistically significantly with protest tactics. Support patterns are primarily shaped by proximity to agriculture and political orientation rather than traditional socio-demographic factors.\u003c/p\u003e","manuscriptTitle":"Public Perception and Segmentation of Agricultural Protests in Germany","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:42:00","doi":"10.21203/rs.3.rs-7394023/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4df35fa5-9c0c-406f-b491-e1db362fba24","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53267642,"name":"Agricultural Economics \u0026 Policy"}],"tags":[],"updatedAt":"2025-08-19T12:42:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:42:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7394023","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7394023","identity":"rs-7394023","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}