Class 5: Parties

voters

Opening notes

Presentation groups

Topics to me as soon as possible

May
June
July

Presentations line-up
Date	Presenters	Method
15 May:	Idil M., Zeynep P., Liesl W., Selin K., Chiara W.	logistic regression
22 May:	Gabriel W., Lina M., Florian S., Julian B.	discourse analysis
29 May:	NO CLASS MEETING

no more regression presentations—see syllabus for some of the other options

Date	Presenters	Method
5 June:	Rasmus B., Andre D., Josefine E., Ioanna L., Santiago C.	regression
12 June:	Omar B., Lela E., Niclas W.	TBD
19 June:	NO CLASS MEETING
26 June:	Colombe I., Konstantin S., Jakob W., Veronika L.	TBD
26 June:	Maksim K., Felix S., Jon L.D., Damir S., Korbinian M.	case study

Date	Presenters	Method
3 July:	Alexander V., Luis G., Oscar O., Mia C.	TBD
10 July:	Lina S., Stephen W., Philomena B., Aarón Z.	TBD
17 July:	Corinna Z., Eva M., and Rostislav N.	TBD
24 July:	Sebastian K., Thomas R., Emilia Z., Florian P.	TBD
24 July:	Lorenz F., Daniel B., Fiona W., Medina H.	quant. text analysis

Overview of voting for far-right parties

demographically
issue-based
further explanatory factors
- place
- deprivation

Voting for far-right parties: demographically

generally, voting for far-right parties (univariate predictors, e.g., in 11 European countries, Lucassen and Lubbers 2012; also Bornschier et al. 2021; Schäfer 2022) …
- gender: men more likely than women
- age: younger people more likely
- location: rural residents more likely
- education: less educated more likely
- ethnicity: migration background less likely
- others?

BUT it is more complicated in bivariate/multivariate perspectives

Voting for far-right parties: issue-based

generally, most important issues for contemporary far-right party voters:
- immigration: security (threat perception), cultural change, welfare chauvinism
- economic: deprived economic prospects (de-industrialisation), inequality grievances, globalisation’s ‘losers’
- anti-elitism: distrust of establishment institutions (media, longstanding parties, etc.), perceived disconnect and lack of substantive representation by mainstream politicians
- gender politics: preference for traditional patterns of social relations, only two genders (i.e., biological sex) and ‘natural’ gender roles
- others?

Voting for far-right parties: further explanatory factors

rising local rental prices increase probability of support (AfD) among renters with lower household income (Abou-Chadi, Cohen, and Kurer 2024)

Voting for far-right parties: further explanatory factors

public service deprivation predicts more far-right support (Cremaschi et al. 2024)
place resentment: spatial clustering perception of one’s region as excluded produces similar clustering of populist radical right attitudes (Arzheimer and Bernemann 2023)
immigrant presence predicts more populist radical right support—mediated by education levels (Arzheimer et al. 2024)
presence of stronger dialect predicts more far-right (AfD) voting (Ziblatt, Hilbig, and Bischof 2024)

Voting for far-right parties: further explanatory factors

_
contact & threat hypotheses

(intercultural) contact hypothesis versus threat hypothesis
likelihood of Front National increases in polling stations up to intermediate distances from mosques and then decreases (Dazey and Gay 2024) — halo theory

(intercultural) contact hypothesis - direct contact between members of different groups can reduce prejudice and improve intergroup relations
- breaking down stereotypes and fostering mutual understanding
threat hypothesis - people can perceive their group (e.g., native populations) as being threatened by another group (e.g., immigrants)
- resources, culture, identity, or power are at risk
- more likely to develop negative attitudes toward out-group members

Far-right voting among the youth

(video from just before 2024 EU elections)

Far-right voting among the youth

Observations:

pluralities of young men voting far right
- some major young women support too (e.g., AfD)
youth more left wing in Anglophone countries
young Europeans more ‘right-wing’ than US/UK youth on cultural issues like immigration and welfare

Theses:

Demographic, attitudinal factors:
- educational gender gap
- declining religious salience for women voters
- polarisation from ‘culture war’ issues
two- vs. multi-party systems
underlying anti-establishment attitudes

Poll: motivations and tendencies in voting for far-right parties

A QR code for the survey.

Take the survey at https://forms.gle/im5PVyqQeGNw8FWg8

Rational choice? Misinformed, manipulated?
protest vote or genuine support?
economic insecurity or cultural/identity-based concerns?
actual immigration vs. perception of immigration?

Voting for far-right parties - hunches

md`## Poll results (Respondents: ${respondentCount})`

import { liveGoogleSheet } from "@jimjamslam/live-google-sheet";
import { aq, op } from "@uwdata/arquero";

// UPDATE THE LINK FOR A NEW POLL
surveyResults = liveGoogleSheet(
  "https://docs.google.com/spreadsheets/d/e/" +
    "2PACX-1vQI6wQF677rVAuYzA5e5tADcPH6YonPxUDzJLyiJudY31kToPsM3-u4lglGpgRXOUDTza3wTpPh9qW8/" +
    "pub?gid=1789095757&single=true&output=csv",
  10000, 1, 6); // adjust the last number to select all relevant columns 

respondentCount = surveyResults.length;

Rational choice?

rationalCounts = aq.from(surveyResults)
  .select("rational")
  .groupby("rational")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
rational_maxCountRE = Math.max(...rationalCounts.objects().map(d => d.count));

plot_rational = Plot.plot({
  marks: [
    Plot.barY(rationalCounts, {
      x: "rational",
      y: "count",
      fill: "rational",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Yes",
      "No"
    ],
    range: [
      "forestgreen",
      "darkred"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Yes", "No"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(rationalCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, rational_maxCountRE]
  },
  facet: { data: rationalCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Misinformed, manipulated?

misinformedCounts = aq.from(surveyResults)
  .select("misinformed")
  .groupby("misinformed")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
misinformed_maxCountRE = Math.max(...misinformedCounts.objects().map(d => d.count));

plot_misinformed = Plot.plot({
  marks: [
    Plot.barY(misinformedCounts, {
      x: "misinformed",
      y: "count",
      fill: "misinformed",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Yes",
      "No"
    ],
    range: [
      "forestgreen",
      "darkred"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Yes", "No"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(misinformedCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, misinformed_maxCountRE]
  },
  facet: { data: misinformedCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Voting for far-right parties - hunches

voting behaviour as informed and strategic or emotional and reactionary?

Rational choice?

plot_rational1 = Plot.plot({
  marks: [
    Plot.barY(rationalCounts, {
      x: "rational",
      y: "count",
      fill: "rational",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Yes",
      "No"
    ],
    range: [
      "forestgreen",
      "darkred"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Yes", "No"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(rationalCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, rational_maxCountRE]
  },
  facet: { data: rationalCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Misinformed, manipulated?

plot_misinformed1 = Plot.plot({
  marks: [
    Plot.barY(misinformedCounts, {
      x: "misinformed",
      y: "count",
      fill: "misinformed",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Yes",
      "No"
    ],
    range: [
      "forestgreen",
      "darkred"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Yes", "No"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(misinformedCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, misinformed_maxCountRE]
  },
  facet: { data: misinformedCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Voting for far-right parties - hunches

supportCounts = aq.from(surveyResults)
  .select("support")
  .groupby("support")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
support_maxCountRE = Math.max(...supportCounts.objects().map(d => d.count));

plot_support = Plot.plot({
  marks: [
    Plot.barY(supportCounts, {
      x: "support",
      y: "count",
      fill: "support",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Protest",
      "Genuine support"
    ],
    range: [
      "darkorchid",
      "gold"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Protest", "Genuine support"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(supportCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, support_maxCountRE]
  },
  facet: { data: supportCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

motivatedCounts = aq.from(surveyResults)
  .select("motivated")
  .groupby("motivated")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
motivated_maxCountRE = Math.max(...motivatedCounts.objects().map(d => d.count));

plot_motivated = Plot.plot({
  marks: [
    Plot.barY(motivatedCounts, {
      x: "motivated",
      y: "count",
      fill: "motivated",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Economic insecurity",
      "Cultural/identity concerns"
    ],
    range: [
      "dodgerblue",
      "firebrick"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Economic insecurity", "Cultural/identity concerns"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(motivatedCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, motivated_maxCountRE]
  },
  facet: { data: motivatedCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

immigrationCounts = aq.from(surveyResults)
  .select("immigration")
  .groupby("immigration")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
immigration_maxCountRE = Math.max(...immigrationCounts.objects().map(d => d.count));

plot_immigration = Plot.plot({
  marks: [
    Plot.barY(immigrationCounts, {
      x: "immigration",
      y: "count",
      fill: "immigration",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Actual immigration",
      "Perception of immigration"
    ],
    range: [
      "darkslategray",
      "paleturquoise"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Actual immigration", "Perception of immigration"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(immigrationCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, immigration_maxCountRE]
  },
  facet: { data: immigrationCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Voting for far-right parties - hunches

momentary rebuke or long-term voter re-alignment?
deindustrialisation, job loss, precarity vs. national identity and traditional values
‘contact hypothesis’, ‘threat hypothesis’; media influence

plot_support1 = Plot.plot({
  marks: [
    Plot.barY(supportCounts, {
      x: "support",
      y: "count",
      fill: "support",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Protest",
      "Genuine support"
    ],
    range: [
      "darkorchid",
      "gold"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Protest", "Genuine support"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(supportCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, support_maxCountRE]
  },
  facet: { data: supportCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

plot_motivated1 = Plot.plot({
  marks: [
    Plot.barY(motivatedCounts, {
      x: "motivated",
      y: "count",
      fill: "motivated",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Economic insecurity",
      "Cultural/identity concerns"
    ],
    range: [
      "dodgerblue",
      "firebrick"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Economic insecurity", "Cultural/identity concerns"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(motivatedCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, motivated_maxCountRE]
  },
  facet: { data: motivatedCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

plot_immigration1 = Plot.plot({
  marks: [
    Plot.barY(immigrationCounts, {
      x: "immigration",
      y: "count",
      fill: "immigration",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Actual immigration",
      "Perception of immigration"
    ],
    range: [
      "darkslategray",
      "paleturquoise"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -25,
    domain: ["Actual immigration", "Perception of immigration"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(immigrationCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, immigration_maxCountRE]
  },
  facet: { data: immigrationCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Voting explanations: values & attitudes

institutional contexts
demand side
supply side
silent revolution?
- cultural backlash theory?

Choices Sesamo GIFfrom Choices GIFs

It’s all about…

Values

Attitudes

It’s all about…

Attitudes

Values

broad, deep-seated beliefs about what is important in life
stable over time, typically long-term and more abstract
influenced by socialisation (e.g., family, culture, education)
e.g., societies of survival vs. self-expression

It’s all about…

Values

Attitudes

It’s all about…

Values

Attitudes

specific and short-term predispositions or opinions that individuals hold toward specific objects, issues, or policies
situational, influenced by context (e.g., economic conditions, political events) and personal experiences

It’s all about…

Values

Attitudes

values shape attitudes

Voter choice, electoral behaviour frameworks

institutional contexts
- especially electoral and party systems
  - how FR parties emerge in two-party vs. multi-party systems
demand side: major values and attitudes among electorate
- what do voting constituencies want?
- who (groups? from where?) are ‘buying’ what parties ‘supply’
supply side: elite competition and cooperation
- what do parties offer?
  - ideology and policy (party platform)
  - leaders and representatives

An opening salvo from Pippa Norris

(a full-length lecture: https://www.youtube.com/watch?v=8Qw8TtzjtL4. strongly recommended for those interested in party politics and/or doing their data report on a far-right party)

values & attitudes in brief (Inglehart, Norris, Welzel)

Inglehart magnifies Maslow’s hierarchy of needs to societal (macro-) level of analysis → aligns with groups and their socio-political values and attitudes

basic material needs satisfied enables seeking non-material needs

values & attitudes in brief (Inglehart, Norris, Welzel)

materialist values
- economic growth (maintaining stability and order)
- security and material needs safeguarded
- traditional morality
post-materialist values
- freedoms, liberties, rights — autonomy and expression
- gender and racial equality
- environmental protection
societal groups (existing cleavages) show tendencies towards these values groups: generationally, regionally, class-based, religiously

values & attitudes in brief (Inglehart, Norris, Welzel)

silent revolution: a values shift from materialist to post-materialist
- new cleavage silent counter-revolution

cultural backlash theory (Inglehart, Norris, Welzel, Dalton)

different generations, different experiences, different values
- Interwar generation (born 1900–45); Baby Boomers (1946–64); Generation X (1965–79); Millennials (1980–96)
since 1950s/1960s, national identity, nuclear family, religion → no longer majority values
liberalism expands and traditional values groups feel threatened
pushback against liberalism: parties supply what is demanded by ‘disoriented voters’ (Norris), many of whom have weaker partisan identification (Dalton)
- turn to ‘authoritarian populist’ parties
- parties and leaders are appealing for transgressive behaviour (attention economy)

cultural backlash theory (Inglehart, Norris)

Stage 1: Values

Stage 2: Votes

Stage 3: Impacts

flowchart LR
  A[Structural<br/>change:<br/>generations,<br/>education,<br/>gender,<br/>diversity,<br/>urban]:::greenBox --> B[Silent<br/>revolution<br/>in socially-<br/>liberal<br/>values]:::greenBox
  C[Immigration<br/>& diversity] --> E{Tipping point:<br/>cultural backlash &<br/>authoritarian<br/>reflex}:::redBox
  B --> E
  D[Economic<br/>grievances] --> E
  F(electoral<br/>rules):::yellowBox --> H{Votes for<br/>A-P parties}:::yellowBox
  E --> H
  G(party<br/>competition):::yellowBox --> H
  H --> I[civic culture,<br/>policy agenda,<br/>& liberal democracy]:::blueBox

  %% Style definitions - classDef blueBox fill:#d0e6ff,stroke:#003366,stroke-width:2px;
  classDef blueBox fill:#d0e6ff;
  classDef greenBox fill:#d4edda;
  classDef greyBox fill:#e2e3e5;
  classDef redBox fill:#f8d7da;
  classDef yellowBox fill:#fff3cd;

cultural backlash theory conclusions

age, education, urbanisation, cultural values predict voting for authoritarian-populist parties in Europe

older, non-college educated, rural areas with most authoritarian values drive voting for authoritarian-populist parties
- youth votes ‘authoritarian-populist parties’ (cleavage forming?)

Tipping point thesis: increasing social liberalism threatens socially conservative authoritarian voters (faith, family, nation identities)

Effects of economic conditions and social diversity reinforces these threat perceptions

Parties/leaders reinforce threat perceptions to mobilise support

Critiquing cultural backlash theory - Schäfer (2022)

Different age groups have very similar cultural attitudes; few signs of polarisation among old and young cohorts
On most topics, different cohorts agree in principle, even though they might differ in degree
No positive link between authoritarian values and populist attitudes (measured as political trust); if using a populism scale, cohorts hardly differ
Older slightly more likely to vote for authoritarian parties, less likely to vote for populists
Younger more likely to vote for authoritarian-populist parties defined in a more concise way
Interwar generation is the cohort least likely to vote for authoritarian-populist parties

far-right parties’ electoral performance - discussion

under what conditions do far-right parties perform better at elections?

Electoral system and party competition

electoral system: proportional representation, low thresholds → easier to gain seats
party competition dynamics (e.g., Carter 2005): generally, far-right parties can benefit from …
- ideological distance from nearest right-wing/centre-right competitor (‘political space’)
- being or at least appearing more moderate (contra: AfD, Trump Republicans)
- ideological convergence between mainstream parties
- hotly debated: accommodation, adopting far-right rhetoric/frames, exclusion of far right

Natural ceiling of support? Situational opportunities

far-right parties rely on voters with nativist, perhaps populist attitudes
↳ surveys suggest these attitudes hover around 20%
- is this a natural ceiling on far-right party electoral strength?

Opportunities:

crisis (security/terrorism, economy, immigration) often triggers (psychological phenomenon) ‘authoritarian response’
similarly, high issue salience of far-right ‘owned’ issues
frustration with mainstream can boost protest votes

factors/combinations enable far-right parties to surpass 20% ceiling

responses and counter-strategies (lead-in to next week)

responses and counter-strategies
- what types of responses?
- by which actors?
- what possible/likely effects?

Any questions, concerns, feedback for this class?

Anonymous feedback here: https://forms.gle/pisUmtmWdE13zMD58

Alternatively, send me an email: m.zeller@lmu.de

References

Abou-Chadi, Tarik, Denis Cohen, and Thomas Kurer. 2024. “Rental Market Risk and Radical Right Support.” Comparative Political Studies, December, 00104140241306963. https://doi.org/10.1177/00104140241306963.

Arzheimer, Kai, and Theresa Bernemann. 2023. “‘Place’ Does Matter for Populist Radical Right Sentiment, but How? Evidence from Germany.” European Political Science Review, September, 1–20. https://doi.org/10.1017/S1755773923000279.

Arzheimer, Kai, Carl Berning, Sarah De Lange, Jerome Dutozia, Jocelyn Evans, Myles Gould, Eelco Harteveld, et al. 2024. “How Local Context Affects Populist Radical Right Support: A Cross-National Investigation Into Mediated and Moderated Relationships.” British Journal of Political Science 54 (4): 1133–58. https://doi.org/10.1017/S0007123424000085.

Bornschier, Simon, Silja Häusermann, Delia Zollinger, and Céline Colombo. 2021. “How ‘Us’ and ‘Them’ Relates to Voting Behavior—Social Structure, Social Identities, and Electoral Choice.” Comparative Political Studies 0 (0): 1–36. https://doi.org/10.1177/0010414021997504.

Carter, Elisabeth. 2005. “Party Competition.” In The Extreme Right in Western Europe: Success or Failure?, 102–45. Manchester: Manchester University Press.

Cremaschi, Simone, Paula Rettl, Marco Cappelluti, and Catherine E. De Vries. 2024. “Geographies of Discontent: Public Service Deprivation and the Rise of the Far Right in Italy.” American Journal of Political Science, December, ajps.12936. https://doi.org/10.1111/ajps.12936.

Dazey, Margot, and Victor Gay. 2024. “The Mosque Nearby: Visible Minorities and Far-Right Support in France.” Comparative Political Studies, October, 00104140241283015. https://doi.org/10.1177/00104140241283015.

Lucassen, Geertje, and Marcel Lubbers. 2012. “Who Fears What? Explaining Far-Right-Wing Preference in Europe by Distinguishing Perceived Cultural and Economic Ethnic Threats.” Comparative Political Studies 45 (5): 547–74. https://doi.org/10.1177/0010414011427851.

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