Explore California ballot measures from 1911 to the present.
Filter by region, topic, year, or status. Click any measure for details, AI-generated summaries, and related measures.
—Measures
—Pass Rate
—Avg. Win Margin
—Avg. Turnout
—Year Range
Filter & Explore
Filter by statewide propositions or local/county measures
🏛️Statewide(6)
📍Local(10,855)
Click to select one or more regions (click again to deselect)
Click to select one or more topics (click again to deselect)
Click to select one or more years (click again to deselect)
Filter by measure outcome
✓Passed(4,626)
✗Failed(2,168)
⏳Pending/Unknown(4,067)
Filter by measure type (GO Bond, Property Tax, Sales Tax, etc.)
0measures found
🗳️ Upcoming 2026 Ballot Measures
Get informed about California's upcoming ballot measures before you vote.
📋 Full official details will be available as the election approaches.
🎯
Ballot Measure Trivia
Loading question...
Question 1 of 15
About This Project
CalBallot is a tool for exploring over 12,000 ballot measures
from across California, spanning local school bonds to statewide propositions.
Background
This project grew out of the UCLA Voter Guide Project,
a volunteer-driven initiative I started to research and publish ballot measure summaries for California voters
in areas with limited local news coverage. While that project focused on writing new summaries with student volunteers,
I wanted to build something that could make historical ballot measure data more accessible and explorable.
Features
Filter by region, topic, year, and outcome
AI-generated plain-language summaries
Related measures recommendations using semantic similarity
Vote results and historical trends
Data Pipeline
Building this database required substantial data engineering work:
Data collection: Aggregated records from multiple sources including the CA Secretary of State,
NCSL, ICPSR, and CEDA research databases
Deduplication: Merged overlapping records using fingerprinting algorithms to identify
the same measure across different sources
Standardization: Normalized county names, vote percentages, and date formats
across inconsistent source data
Topic classification: Used K-means clustering on sentence embeddings to automatically
categorize measures into ~20 topic clusters
AI summaries: Generated plain-language summaries using LLMs for measures
with sufficient ballot text
Similarity matching: Computed semantic embeddings (all-MiniLM-L6-v2) to find
related measures based on content similarity
Author
Built by Igor Geyn, a data scientist and researcher
based in the Bay Area. My background is in political economy and causal inference, with a PhD from UCLA.