Assignments

• Last Thursday
  • I forgot the quiz; sorry
• Today
  • Hassan et al. (2022)
  • Lebas and Young (2023) Movements and Democracy in Africa.pdf)
• Thursday (3/14)
  • Distribute Data Assignment 1
  • Two readings on Accountability and Information

Agenda

1. Garcia-Ponce and Wantchekon
2. Hassan
3. Lebas and Young
4. Final Project Essentials: Interaction terms and Fixed Effects

Why countries Democratize

• Correlation between income and democracy
  • Modernization theory: wealth, education, and urbanization are associated with democratization
• Why might this be true?

Context

• Anti-colonial movements chose between two strategies:
  • Rural insurgency
  • Urban protest
• Democratization was delayed by the Cold War

Theory

Why does opposition strategy affect democratization decades later?

Findings

Threats to inference

• Things that affect whether or not opposition uses insurgency vs protest might also affect current levels of democracy

Identification

• Covariates
• Sensitivity analysis
• Difference-in-Differences
  • Leverages changes within countries over time
  • Before the end of the cold war, groups look similar
  • After the end of the cold war, groups trend away from each other
• Instrumental Variables
  • Leverages ‘plausibly exogeneous’ variation in terrain
  • Terrain predicts strategy choice

Instrumental Variable

Mechanisms: Institutions

We do not seen an impact of Post-WWII Opposition Strategy on Post-1990 Institutions

Mechanisms: Behavior

Policy Implications

• Historical social movements help understand the link between structural conditions and democratic development
• Non-violent collective action appears critical for the establishment of lasting democracy

Political Control

Strategies of Control

• Violence
• Indoctrination
• Coercive distribution
• Infiltration

Repression and Backlash

Moments of uncertainty about political opportunity

• Citizens uncertain about relative strength of regime and opposition
  • Risk of repression (cost)
  • Size of protests (benefit)

Research Questions

• How are are individual decisions about protest affected by dissent and repression of others
• What are the mechanisms?
  • Information on costs and benefits
  • Affective polarization
  • Emotions

Context

Political moment

• Ruling party leadership transition
• Contentious election
• Economic crisis

Treatment

• Exposure to local dissent
• Exposure to local repression

Measurement

Design

• Difference-in-difference estimator can partials out time-invariant differences across participants
• Assumes units are on similar trajectories prior to the treatment
• Bias could be introduced if individual shocks co-occur with treatment

Difference-in-Differences

Year = c(0,1,2,3)
Outcome = c(NA, 1.3, 1.5, 1.7,
            1, 1.2, 1.4,1.6,
            1.1, 1.3, 1.7, 1.9)
Treatment = c("Comparison","Comparison","Comparison","Comparison",
              "Control", "Control","Control","Control",
              "Treatment", "Treatment", "Treatment", "Treatment")

dat = data.frame(Year, Outcome, Treatment)
dat$Treatment = factor(dat$Treatment, levels = c("Treatment", "Comparison", "Control"))

ggplot(data = dat, aes(x = Year, y = Outcome,  color = Treatment)) +
  geom_line(aes(linetype=Treatment),size=2) +
  geom_point(size = 6) +
  ylim(0.8, 2.2) +
  scale_linetype_manual(values=c("solid", "dotted", "solid")) +
  scale_color_manual(values = c("blue", "black", "red"  ) ) +
  theme(legend.position = c(0.8, 0.2), text = element_text(size=20),
        legend.title=element_blank())

Findings

Findings

• Exposure to dissent and repression increases willingness of non-supporters to protest
• Different mechanisms drive diffusion and backlash
  • Observing dissent affects beliefs about dissent (but not perceptions of risk)
  • Observing repression affects emotions (but also risk)
• Local events drive diffusion and backlash; media events do not

Interaction Terms

What is an interaction term?

• Simple linear models assume that the effect of predictors is independent of other factors
• Interaction terms allow us to estimate the difference in the slope of a predictor across unit characteristics

What are they used for?

• Heterogeneous effects
• Difference-in-differences

Interaction Terms

How do they do work?

\[ Y_i = \alpha + \beta_1 X_{i1} + \beta_2 X_{i2} + \beta_3 (X_{i1}*X_{i2}) + \epsilon_i \]

Example: Continuous outcome with two binary predictors

• \(\alpha\): Intercept when \(X_{i1}\) and \(X_{i2}\) are 0
• \(\beta_1\) Slope when \(X_{i2} = 0\)
• \(\beta_2\) Difference in \(\alpha\) between \(X_{i2}=0\) and \(X_{i2}=1\)
• \(\beta_3\) Difference in \(\beta_1\) between \(X_{i2}=0\) and \(X_{i2}=1\)