Hate in Context

Week 2: Demographic Change and Hate Crime

Violeta Haas

Department of Political Science

School of Social Sciences and Philosophy

Trinity College Dublin

This Week

  1. Theoretical backdrop — contact & group threat
  2. Part I — Hangartner et al. (2019)
  3. Part II — Cikara et al. (2022)
  4. Part III — Dancygier et al. (2022)
  5. Putting the readings together
  6. Policy implications

Theoretical backdrop

Group discussion (≈8 min)

In groups of 3–4:

  1. What is contact theory? What does it predict when minority presence grows and under what conditions?
  2. What is group threat theory? What does it predict and what kinds of “threat” might it include?

Contact theory

When minority presence grows…

… contact reduces prejudice, if people meet under the right conditions (Allport, 1954):

  • Equal status
  • Common goals
  • Cooperative environment (not pure competition)
  • Institutional support (set of norms/regulations)

Group threat theory

When minority presence grows…

… out-group presence raises perceived threat to the majority’s position, leading to more prejudice (Blalock, 1967).

Threat comes in two main forms:

Realistic threat

Competition over scarce resources

→ Jobs, housing, welfare, public services

Symbolic threat

Threat to values, norms, identity

→ Culture, religion, language

Part I — Hangartner et al. (2019)

Most or least likely case?

Hangartner et al. study Greek islands during the 2015–16 refugee crisis.

For each theory, is that setting a most likely or least likely case for the mechanism to be activated and why?

Hangartner et al. (2019): the setting

What happened:

  • Sudden, large refugee arrivals on islands close to Turkey
  • Almost everyone left within about a day for Athens and beyond
  • Little lasting settlemen + chaotic local management

So, most or least likely?

→ Contact: least likely (proximity ≠ contact, fleeting, not cooperative)

→ Realistic/symbolic threat: also weak (no lasting jobs, welfare, or identity competition)

Identification strategy

X exposure Y hostility U island traits Z distance

Selection problem: Regions with more refugee arrivals often already differ:

Self-selection

  • Refugees might choose places with jobs, co-ethnic networks, or a reputation for being welcoming

Administrative selection

  • Authorities may send refugees to some municipalities and not others (e.g. available housing, reception capacity, or political considerations)

→ X and Y share many causes! Naive X–Y comparison mixes exposure with pre-existing differences. Confounding (U)!

Identification strategy: the instrument

Hangartner et al. Figure 3: Map of the Aegean Sea showing refugee arrivals per capita by island

Z distance X exposure Y hostility U island traits

Instrument Z: distance to Turkey.

  • Geography pushes boats to closer islands, not (self-)selection: welcomingness or admin readiness.
  • Two-stage least squares (2SLS): (Z → Y) / (Z → X)
    • First stage (Z → X): how distance changes exposure
    • Reduced form (Z → Y): how distance changes hostility
  • Must hold: distance only affects hostility through exposure.

→ Exclusion restriction: fails if being close to Turkey changes hostility for any reason other than more arrivals.

2 min. discussion with neighbour

Suggest an instrument Z (shifts X & affects Y only through X).

Z seismic risk X nuclear plant Y Green support U confounder

Valentim, A., Klüver, H., & Erfort, C. (2025). How nuclear power hurts the Greens: Evidence from German nuclear power plants. Electoral Studies, 96, 102959.

Z: seismic risk → plants are rarely sited in earthquake-prone areas

Disclaimer

IV solves confounding in theory; in practice the fights are about weak Z, who the effect is for (LATE), and whether exclusion is believable.

Additional resource: Scott Cunningham, Causal Inference: The Mixtape (IV chapters).

Findings

Hangartner et al. Figure 4: 2SLS estimates of refugee exposure on hostility, policy preferences, and behaviour

Findings: islands closer to Turkey (more exposure) show higher:

  • exclusionary attitudes/hostility
  • anti-asylum & anti-immigrant policy preferences
  • behaviour (petition/donate against refugees)

Puzzle: contact and group threat were both weak here. Yet, hostility still rose. What’s left is mere exposure: even a brief, chaotic presence of arrivals (with little lasting settlement) can harden attitudes → situational social threat.

Part II — Cikara et al. (2022)

Ebbinghaus illusion

Which hollow circle is bigger? Why might they look different even if they aren’t?

Ebbinghaus illusion: two equal hollow circles surrounded by differently sized dots

→ The size of the solid gray circles affects the apparent size of the hollow circles, even though they are the same size.

→ The same-sized group may be encoded very differently depending on the size of all other groups surrounding it.

Group reference dependence

RQ: How does demographic change affect local intergroup dynamics?

Argument: Group reference dependence hypothesis

Violence and negative attitudes toward each minoritized group depend on the number and size of other minoritized groups in a community, i.e., their relative ranks in size.

2 min. discussion with neighbour

  1. What role do stereotypes or essentialized properties (e.g., unchangeable group characteristics) play in this framework?

    → None. Something about group-size rank is unassociated with the threat-inducing characteristics of any particular minority group.

  2. What psychological finding about how people judge group sizes do they build on to focus on ranks rather than absolute size?

    → People are mostly inaccurate about absolute size and proportions (metric knowledge), but quite accurate about relative sizes (mapping knowledge).

Let’s try it out!

Part 1: Rank by size (≈3 min)

Rank Dublin City’s largest immigrant groups by size (largest → smallest).

QR code for POU44653 Week 2 Mapping Knowledge form

  1. Open Blackboard → Week 02 → POU44653 Week 2: Mapping Knowledge or Scan QR-code
  2. Drag the ten citizenships into order
  3. Do not consult others
  4. Anonymous

Part 2: Mapping vs. Metric Knowledge (≈5 min)

Dublin City had 580,237 residents in Census 2022. For each citizenship below, guess (1) how many people and (2) what share of the city that is.

Citizenship Absolute number % of total population
Italy
China
United States
Romania
Poland
France
Brazil
Spain
India
United Kingdom

Solution

Let’s look at your ranking!

Dublin City, Census 2022 (580,237):

Citizenship Absolute number % of total population
Brazil 11,186 1.93%
India 10,318 1.78%
Romania 10,049 1.73%
Poland 7,233 1.25%
Italy 6,945 1.20%
United Kingdom 6,917 1.19%
Spain 6,285 1.08%
France 4,613 0.80%
China 3,671 0.63%
United States 3,560 0.61%

CSO Census 2022 (F5001), usually resident population by citizenship, Dublin City Council.

Findings

Cikara et al. finding on size-based rank and hate crime

Findings: higher size-based rank → more hate crime victimization…

  • Strongest for the largest local minority (1st), smaller for 2nd and 3rd
  • Holds when ranks change over time (blue estimate)

→ Attitudes and behaviours are not only about stereotypes or groups’ essentialized properties, but relative size rank matters.

Findings

Cikara et al. regional rank encoding

Scales (small → large): LSOA → MSOA → LAD

→ Rank is most likely encoded via local experience, not only national demography (effect stronger for smaller regional units).

Open questions

If people learn relative group size from everyday life, what would they actually notice?

  • Face-to-face interactions
  • Media attention to demographic change
  • (Dis)appearance of (cultural) institutions (e.g., religious buildings, public holidays, LGBTQ+ venues)

Example: public holidays as cues

NBC News: NYC makes Diwali an official school holiday

→ NYC adds Diwali to the school calendar in recognition of the city’s growing South Asian and Indo-Caribbean communities.

Part III — Dancygier et al. (2022)

Who commits hate crime?

Perpetrators are overwhelmingly male.

  • Germany (right-wing extremist hate crime): roughly 85–97% men (cited in Dancygier 2023)
  • London (hate-crime causes, 2014–16): 78% men (Walters & Krasodomski-Jones 2018)

→ Hate crime perpetrators ≈ mostly men (often young / mid-life). Contextual explanations of hate crime are mostly general, Dancygier et al. (2022) look at profile-specific drivers.

Mating competition

Central concept: {.fragment} Mating competition

  • Function of the sex ratio (e.g., 1:1 → little competition)

Assumptions

  • Local mating pools with disproportionate numbers of men make it harder for heterosexual men to find female partners
  • Native heterosexual men perceive refugees as competitors on the “mating market”

Finding 1

Dancygier et al. finding 1: sex ratio and anti-refugee hate crime

→ Areas where men significantly outnumber women witness higher probabilities (and levels) of anti-refugee hate crime.

Finding 2

Dancygier et al. finding 2: perceived mate competition

→ Perceptions of native–refugee mate competition rise in areas with excess males — especially among men in the most mating-active age range.

Finding 3

Dancygier et al. finding 3: mate competition predicts hate crime support

→ Concerns over mate competition are a robust predictor of hate-crime support.

Group discussion: current refugee inflows

Groups of ~5 / ≈20 min.

Gender composition of Ukrainian refugee inflows

What can we expect for current gender imbalances in refugee inflows — for mate competition and violence against refugees?

  • (a) Social Dominance Theory perspective
  • (b) Gender Socialization Theory perspective

Thinking beyond heterosexual mating markets

NBC News: Nearly 30% of Gen Z women identify as LGBTQ, Gallup survey finds

If Dancygier’s mechanism runs through heterosexual mating markets, how might rising LGBTQ identification among young women change mate competition — and who gets blamed?

Putting it together

From size to composition

Demographic change raises hate crime risk — but how differs once we leave the simple threat-vs-contact contrast:

  1. Hangartner et al. (2019) — Sudden local exposure to refugee arrivals hardens hostility, exclusionary preferences, and far-right support. Mere passage can suffice; lasting economic or cultural contact is not required.

  2. Cikara et al. (2022) — Which minoritized group is targeted depends on its relative size rank among other minorities in the locality — not only absolute size or group stereotypes (group reference dependence).

  3. Dancygier et al. (2022) — Who arrives matters: a male-skewed inflow can activate mate competition among native men, raising anti-refugee hate crime where local sex ratios are already unbalanced.

Map: exposure/size threat (Hangartner) → targeting follows relative ranks (Cikara) → gender composition can switch on mating-market threat (Dancygier).

Policy Implications

Refugee allocation

What policy implications for refugee allocation follow from this week’s readings?

  • Absolute inflows vs. relative ranks across minority groups
  • Local exposure shocks vs. gradual change
  • Sex ratios and composition of inflows — not only totals

Outlook

Next week: economic causes

Week 3 (Mon 28 / Wed 30 Sep) — economic causes and hate crime.

Required readings

  • Riaz, S. (2023). Does inequality foster xenophobia? Evidence from the German refugee crisis. Journal of Ethnic and Migration Studies, 50(2), 359–378.
  • Bestenbostel, A., & Peralta, A. (2026). WARNings: The impact of negative economic news on racial animus. Journal of Economic Behavior & Organization, 243, Article 107399.

Weekly comments due Sunday 27 Sep, 11:59 pm.

Questions?

Goodbye!

See you next week.