Hate in Context

Week 6: Political Elites and Hate Crime · LENS preview

Violeta Haas

Department of Political Science

School of Social Sciences and Philosophy

Trinity College Dublin

Updates

Strike

  • Industrial action ongoing, but Wednesday’s class takes place
  • Fairness cut stays: Dancygier (2023) no longer required
  • If time allows, I’ll show some of the Dancygier findings

Position paper

  • Apologies for the delay in returning these
  • First batch has been released
  • Second batch coming today
  • Next batch by Wednesday

This Week

  1. Catch-up: Riaz, Bischof & Wagner (2024)
  2. Theoretical backdrop
  3. Zonszein & Grossman (2025)
  4. Romarri (2020)
  5. Mid-course synthesis (group task)
  6. Outlook

Riaz, Bischof & Wagner (2024)

Overview

  • Research question: Do local immigrant-attributed crimes raise hate crimes against refugees?
  • Y: daily probability/count of hate crimes against refugees
  • X: immigrant-attributed crime event
  • Theory: vicarious retribution, i.e., natives retaliate against uninvolved out-group members after a triggering crime
  • Design: regression discontinuity in time (RDiT) around local crime events (Germany, 2015–2019)

Wait… RDiT? That sounds familiar!

Regression Discontinuity in Time (RDiT) design

Idea: a sudden local shock is a cutoff in time. Compare hate crimes from the before period vs the after period.

Comparison around local crime events

Same county, days close to the event

RDiT design: compare hate crimes before vs after immigrant-attributed crime, excluding day 0

Scope conditions

[discussed in class]

How do natives (including potential hate crime perpetrators) hear about immigrant-attributed crime in their locality?

[discussed in class]

[discussed in class]

Findings: daily hate-crime rate around events

Riaz et al. 2024 Figure 2: Daily rate of hate crimes in the two weeks before and after an immigrant crime event

Hate crimes surge locally right after immigrant-attributed crimes but spike fades within ~4 days, consistent with an emotional trigger (affect).

Findings: where the backlash is stronger

Riaz et al. 2024 Figure 3: Heterogeneous LATE estimates by AfD strength and foreign-population context

Backlash is stronger where far-right mobilization potential is high and where the foreign population has recently increased, not simply where many foreigners already live.

Anything else that didn’t convince you?

Theoretical backdrop

Discussion: social identity theory

In groups of 3-4 (≈5 min):

  1. What does social identity theory say?
  2. Social identity theory has three components: categorization, identification, and comparison. What does each mean?
  3. When people belong to a devalued/low-status group, what coping strategies can they use?

Social identity theory

[discussed in class]

Coping with low group status

[discussed in class]

Group discussion

In groups of 3-4:

  1. What is the difference between descriptive and substantive representation?
  2. What are ways to increase descriptive and substantive representation?
  3. Can a politician descriptively represent a group without substantively representing it (or vice versa)? Give an example.

Descriptive vs substantive representation

[discussed in class]

Example

[discussed in class]

Zonszein & Grossman (2025)

Overview

How do natives react when immigrant-origin minorities win political office?

What informational cues are natives supposed to receive when a minority candidate is elected?

[discussed in class]

Which actors’ reactions are Zonszein & Grossman (2024) looking at and which outcome corresponds to which actor?

[discussed in class]

Design: another regression discontinuity

Setting: UK general elections, 2010–2019 (England & Wales)

Idea: near a razor-thin election, who wins is as-good-as-random. Compare places just above vs just below the win cutoff.

Schematic of close-election regression discontinuity: jump in outcome at victory margin zero

What we estimate is a LATE (local average treatment effect): the average causal effect at the cutoff, i.e., for places with close races ≠ effect for all elections, i.e., landslide elections.

Findings on hate crimes (perpetrators)

Zonszein and Grossman 2025 Figure 1: minority victory effects on hate crime

≈ +67% hate crimes (3 months post-election) relative to barely-losing minority constituencies.

Findings on attitudes (mass public)

Zonszein and Grossman Figure 2: minority victory and exclusionary attitudes

≈ +66% more exclusionary immigration attitudes

Why would the media report negatively about minorities?

Two reasons

[discussed in class]

Findings on media coverage (media/elites)

Zonszein and Grossman Figure 3: minority victory and negative media coverage

≈ +110% more negative coverage of the relevant immigrant group

Concentrated in right-wing and smaller-circulation papers

Neighbor discussion (≈3–4 min)

Turn to a neighbor:

  1. Does this mean descriptive representation produces backlash?
  2. Why is the alternative explanation of reporting increases particularly important to rule out here?
  3. Do natives respond to all winning minority candidates the same way?

[discussed in class]

[discussed in class]

[discussed in class]

Implication: the paradox of political integration

Like Dancygier et al. (2022), who show that social integration into dating/marriage markets triggered hostility, Zonszein & Grossman (2025) show that integrating into political institutions can produce backlash.

Integration success ≠ automatically less hate crime. Sometimes the success is the trigger.

But: only first-time minority wins

Zonszein and Grossman Appendix Figure G.10b: hate-crime effects shrink when controlling for prior minority MP history

Effect concentrated in constituencies electing a minority MP for the first time

Critique?

Romarri (2020)

Overview

  • Research question: Do far-right mayors raise the probability of hate crimes against immigrants?
  • Setting: Italian municipal mayoral elections, 2008–2018
  • Y: yearly indicator that at least one hate crime is recorded (Lunaria)
  • X/treatment: far-right mayor in office
  • Design: close-election RD (far-right margin of victory)

Main RD result

Romarri 2020 Figure 1: Effect of far-right mayors on hate crimes, RD plot

Just-winning far-right mayors → +5 pp hate-crime probability

Also for severe acts (assaults / damage); strongest early in the mandate and for mayors who are not re-electable

Mechanisms

1. Erosion of social norms

Far-right wins can make people feel less alone in their prejudice → more willing to act (Bursztyn et al., 2020).

Results driven by first-time far-right mayors + spillovers into neighbouring municipalities.

2. Internet/broadband

Effect concentrated where broadband exposure is high, and fades where coverage is low (links to Week 4).

But not necessarily via easier group coordination. No differential effect on group-committed hate crimes by internet coverage.

Two takeaways

Zonszein & Grossman

A minority win can make an unsatisfactory status comparison salient → backlash to restore positive distinctiveness.

Romarri

A far-right win can signal that the in-group’s exclusionary stance is more widely shared/more legitimate → less restraint on acting against out-groups.

Who wins office can shape identity comparisons and social norms, activating hostile attitudes and behavior.

Causes Weeks 2–6 Demographic change
Economic conditions
(Social) media
Threatening events
Political elites
Consequences Week 8 Community impacts
Responses Weeks 9–10 Contact
Persuasion
Guest lecture Week 11 Inclusionary policy
Exclusionary policy
Your choice Week 12 Propose topic by
Fri 23 Oct, 11:59 pm

Group task (20–25 min)

POU44653: Determinants of Hate Crime (Weeks 1–6) — fill your group’s shared Word doc (link on Blackboard).

If time allows: short group spotlights — each group presents one part of what you put together.

Groups

A · Weeks 1–2
definition + demography

B · Weeks 3–4
economy + media

C · Weeks 5–6
threatening events + pol. elites

What to cover

  1. Core concepts, theories and definitions
  2. Theoretical argument and mechanism
  3. Actors and outcomes
  4. Research designs
  5. Data sources
  6. Policy implications
  7. Open questions

You’ll be able to draw on this document when developing your Research Proposal.

Outlook

Next up

Reading Week (26 Oct–1 Nov): no class.

After Reading Week:

  • Week 8 (Mon 2/Wed 4 Nov): community impacts of hate crime
  • Research-proposal topic meetings (Weeks 8–9): book a 15-minute slot when the booking link goes up

Questions?

Goodbye!