📈 Optimized messaging (tailored by municipality) can increase female vote shares by ~1.5 percentage points — at low costs. 20% municipios experience more than 2 percentage point increase.

Superb out-of-sample fit for
1) Full-Brazil without RCT municipios
2) RCT municipios' neighbors
3) 80-20 split out-of-sample validation

📊 Key Finding #6:
👉 Instead, underrepresentation is driven by statistical discrimination:
🔹 118% of the gap is explained by voters’ biased beliefs (about ability and especially policy positions)
🔹 –18% is attributed to taste-based factors

📊 Key Finding #5:
👉 Gender identity is very salient in voting decisions. Yet it does not explain women's underrepresentation. Why?
Because both men and women exhibit in-group preferences, and the net effect cancels out.

📊 Key Finding #3:
👉 Informative policy messages helped realign these beliefs and increased female vote shares.

📊 Key Finding #2:
👉 Many female voters believe male candidates are closer to their policy preferences than female ones — reflecting a disconnect between descriptive and substantive representation.

🎯 RCT Design:
Large-scale digital campaign via Instagram, randomizing municipalities into seven groups:
Info Ability messages
Uninfo Ability messages
Info Policy messages
Uninfo Policy messages
Gender Identity messages targeted to men
Gender Identity messages targeted to women
Control

Thank you Thank you #DelhiSchoolofEconomics, #Center_Dev_Eco, and the #EconometricSociety
for organizing the #DSEWinterSchool and awarding my paper on political rallies the best paper award in Microeconomics.