Replication Data for 'First-Time Voter Boost of Turnout: New Identification Strategy' by Kentaro Fukumoto provides data for a causal study on electoral participation. The research exploits a natural experiment in Japan, using municipal-level variation in the timing of local elections relative to national elections. The dataset was last updated in April 2026.
Use Cases
- Estimate the causal effect of being a first-time voter on turnout using municipal-level election timing data.
- Analyze the relationship between the proportion of first-time voters in a municipality and turnout rates for the youngest generation.
- Replicate the study's regression model showing a first-time voter boost of 10.9 percentage points.
- Investigate the interaction between local and national election cycles as a source of exogenous variation.
Strengths
- Data supports a published causal identification strategy using a natural experiment.
- Study quantifies a specific first-time voter boost effect of 10.9 percentage points.
Limitations
- Specific row count, column names, and sample data are unavailable for assessment.
- Geographic scope is limited to Japan, limiting generalizability to other political systems.
Provenance
- Source
- Kentaro Fukumoto Dataverse.
- Collection Method
- Exploits a natural experiment using municipal-level variation in election timing.
- Time Range
- null
- Freshness
- Dataset was last updated in April 2026.
- Geography
- Japan (municipal-level).