I am a fourth-year Ph.D. student in the Department of Economics at Columbia University. My research interests include Spatial Economics, Economic Growth, Development and Economic History.
RESEARCH
Work in Progress
2025
Rural Convergence, Structural Transformation, and Immigration: Evidence from the Post-WWII Japanese Repatriation
2025
Martyrs, Morale, and Militarism: The Political Impact of Devastation and Slaughter
with David Weinstein and Atsushi Yamagishi
Presentations:
BACKGROUND
2021—Present
Columbia University
Ph.D. in Economics
2019—2021
Research Assistant - Yale University
2019
Columbia University
B.A. in Economics
PROJECTS
Quantitative Spatial Model (QSM) for Japan
An implementation of the Allen and Arkolakis (2014) Quantitative Spatial Model for analyzing spatial economic patterns across Japanese cities from 1975 to 2010. This project examines population dynamics, wage patterns, and the spatial distribution of economic activity in Japan.
Key Features
- Implemented the Allen-Arkolakis model for Japanese city-level data
- Calibrated model parameters using historical economic data
- Visualized spatial economic patterns and their evolution over time
- Conducted counterfactual analyses to assess policy impacts
Methodology
Based on the equilibrium conditions from Allen and Arkolakis (2014), this model processes city-level data to calculate productivity and amenity values. The analysis provides insights into spatial economic patterns and their changes over time in Japan.
Japanese Occupation Sector Classification
A machine learning system that classifies Japanese occupation titles into economic sectors (Primary, Secondary, Tertiary) using a combination of pattern matching and BERT models. This tool helps researchers standardize occupational data for economic analysis.
Key Features
- Multi-level classification into main sectors, subcategories, and detailed occupation groups
- Specialized handling of Japanese characters and text normalization
- Pattern-based initial classification with hierarchical category assignment
- BERT model fine-tuning with cross-validation and ensemble learning
- Confidence scoring and unknown category handling
Methodology
The classification system uses a two-stage approach: first applying pattern matching for initial categorization, then employing a fine-tuned Japanese BERT model (cl-tohoku/bert-base-japanese-v3) with 5-fold cross-validation for more nuanced classification. Confidence scores are calculated through ensemble averaging across folds.
Japanese Administrative Boundary Crosswalk
A comprehensive dataset and tools for tracking changes in Japanese administrative boundaries over time. This project creates geographic crosswalks between different years of Japanese administrative boundaries
Key Features
- Creates crosswalks between different years of administrative boundaries
- Calculates area-based weights for accurate data transformation
- Handles boundary changes and mergers systematically
Methodology
Following Eckert et al. (2020), the tool creates weights based on geographic area overlap between historical and reference boundaries. For each historical year, weights are calculated as the ratio of intersection area to total historical city area. These weights enable accurate transformation of historical data to modern administrative units.