Recall from Week 1 — when preparing a paper, identify:
Research question
What is explained (Y)
Main explanatory factor (X)
Theoretical argument/central concepts
Methods/research design
Main findings
Real-world implications
Contribution to the literature
Critique and limitations
1. Research question
Does inequality foster xenophobia?
Puzzle: Immigration triggers xenophobic backlash in some places but not others. Why?
Riaz’s (2023) answer: local economic inequality intensifies backlash against immigration.
2–3. Y and X
Y (outcomes)
[discussed in class]
X (Treatment)
[discussed in class]
Moderator
[discussed in class]
Wait… what’s a moderator?
Moderator vs mediator
Moderator: a factor that changes the strength (or direction) of the relationship between X and Y.
Here: refugee inflow → hate crime is stronger where inequality is high.
Mediator: a factor on the causal path from X to Y: X affects Y (partly) through it.
Example: parental education → child’s education → child’s income
4. Theoretical considerations
Causal chain
Step 1: Inequality → relative deprivation
Local income inequality increases perceptions of relative deprivation (Walker & Pettigrew 1984):
[discussed in class]
Step 2: Relative deprivation → nationalism
Relative deprivation increases incentives for national (vs. class) identification (Shayo 2009).
“The ordinary English worker hates the Irish worker as a competitor who lowers his standard of life. In relation to the Irish worker he regards himself as a member of the ruling nation and consequently he becomes a tool of the English aristocrats and capitalists against Ireland, thus strengthening their domination over himself. He cherishes religious, social, and national prejudices against the Irish worker. His attitude towards him is much the same as that of the ‘poor whites’ to [Black people] in the former slave states of the U.S.A. […] This antagonism is the secret of the impotence of the English working class […] It is the secret by which the capitalist class maintains its power.”
Karl Marx, letter to Sigfrid Meyer and August Vogt, 9 April 1870
Why switch from class to nation?
The question is which group provides higher status and feels more cohesive
[discussed in class]
Step 3: Nationalism → stronger backlash
Nationalism and xenophobia in high-inequality places translate into more hate-crime:
[discussed in class]
5. Methods/Research design
(Self-)selection and confounding
Places 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, or political considerations)
→ Hangartner et al. (2019) use geographic distance to Turkey as an instrument to guard against confounding introduced by (self-)selection.
How does Riaz deal with this?
[discussed in class]
Local inequality
5. Difference-in-differences
In the graph
In Riaz (2023)
Outcome
xenophobic hate crimes
Pre/post
before vs after 2015 refugee inflow
“Intervention” line (blue)
onset of the large-scale inflow (2015)
Intervention group (red)
high-inequality municipalities
Comparison group (green)
low-inequality municipalities
Counterfactual (dotted)
without-inflow path for high-inequality places (assumed)
Intervention effect
extra post-2015 rise in high- vs low-inequality places
Parallel trends assumption
Key assumption:
absent the refugee inflow, hate-crime trajectories in high- and low-inequality places would have moved in parallel.
In the figure: the “constant difference” and the dotted counterfactual
Not testable! We cannot observe the counterfactual
Partial check: do the two groups move in parallel before the inflow?
→ If parallel trends fail, the intervention effect could just be divergent pre-trends, not inequality moderating backlash.
Figure 3: Hate crimes over time
Does the partial check speak for parallel trends?
[discussed in class]
6. Findings
Post-2015 hate-crime rise was significantly stronger in high-inequality municipalities
Gap is clear for 2015–2018 and closes by 2019 as refugee salience fades
Survey evidence: nationalism ~4pp higher in high- vs low-inequality places
7–8. Implications & contributions
Implications
Redistribution isn’t only about fairness in market incomes but may also be a tool against xenophobia
Within-group relations (local inequality among natives) shape between-group conflict
Contributions
Complements hate-crime literature on absolute hardship (unemployment, downturns) by showing relative standing also matters
Adds xenophobic backlash to the broader literature on adverse political effects of inequality, i.e., (dis)engagement, etc.
9. Critique (≈5 min)
In groups of 3–4: - collect your critiques of Riaz (2023). Each group gives me your strongest point.
Official totals mix true incidence with who reports and what police record (true for most hate crime statistics).
Underreporting
Victims (or witnesses) never tell the police / survey / platform
Fear of retaliation
Lack of trust in law enforcement
Perceived ineffectiveness of reporting
Limited awareness
Inadequate reporting infrastructures
Concerns about privacy
Underrecording
The event is known, but not flagged as hate crime
Inconsistent definitions
Inadequate recording infrastructure
Lack of training
Biases & stereotyping
→ Rising (or falling) UCR series can reflect reporting/recording change,not only more (or less) violence.
But there is more problems…
Two-step process: the first officer can flag a case if there is any sign of bias. Only a second, trained reviewer, can send it to the FBI → cases can be missed at either step
Agency capacity varies: one trained reviewer vs a specialised unit → uneven review quality
Motivation is subjective: needs “sufficient objective facts” that a “reasonable person” would see bias
Voluntary participation → incomplete coverage (especially early years)
Let’s all move to Miami?
ADL compilation of FBI Hate Crime Statistics (cities reporting zero or DNR).
Policy advice (10-15min)
Scenario. The government has funding for precautionary measures to avoid spikes in hate crime. They have become aware of research linking economic conditions to bias-motivated crime and they are worried about AI-driven job replacement.
You are one of three advocacy organisations competing for the contract:
Institute for Intergroup Safety (IIS)
Alliance Against Bias Crime (AABC)
Coalition for Hate Crime Prevention (CHCP)
Your task (3 Groups)
Design a short policy brief: what to worry about, what to fund, and why it should work. Draw on our readings.
Each group decide on one representative to pitch your brief (2 minutes).
Vote on who gets the job!
A policy brief usually has…
Title
Executive summary / recommendation
Problem / context
Evidence
Options
Preferred option
Implementation
Risks & caveats
Next steps
Pitch
Your turn!
Who gets the job?
Institute for Intergroup Safety (IIS)
Alliance Against Bias Crime (AABC)
Coalition for Hate Crime Prevention (CHCP)
They can vote here:
Outlook
Next week: (social) media
Week 4 (Mon 5 / Wed 7 Oct): (social) media and hate crime.
Required readings
Siegel, A. A. (2020). Online hate speech. In N. Persily & J. A. Tucker (Eds.), Social media and democracy (pp. 56–88). Cambridge University Press.
Müller, K., & Schwarz, C. (2023). From hashtag to hate crime: Twitter and antiminority sentiment. American Economic Journal: Applied Economics, 15(3), 270–312.
