Non-research projects

Risk-sharing

• Estimation of a risk-sharing model with limited commitment: I created a website showcasing the structural estimation procedure of a risk-sharing model with limited commitment (the codes are written with R)

• Numerical solution of a risk-sharing model with storage under limited commitment: I created a website showing the procedure for numerically solving a risk-sharing model with storage under limited commitment, largely based on Ábrahám and Laczó (2018), “Efficient Risk Sharing with Limited Commitment and Storage” (the codes are written with R)

• Julia replication of Munshi and Rosenzweig (2016): I reproduced the baseline result of the structural estimation in Munshi and Rosenzweig (2016), “Networks and Misallocation: Insurance, Migration, and the Rural-Urban Wage Gap”

Other development economics

• R replication of Banerjee et al. (2013): I replicated the structural estimation of a network model in Banerjee et al. (2013), “The Diffusion of Microfinance”
  (with Kazuki Motohashi and Sakina Shibuya)

• Julia numerical exercise on the dynamic model in Duflo et al. (2012): I computationally solved the dynamic model in Duflo, Hanna, and Ryan (2012), “Incentives Work: Getting Teachers to Come to School”, which can be directly used for the structural estimation of the model, which can be directly used for the structural estimation of the model

• Python numerical exercise on the dynamic model in Fafchanps (1993): I computationally solved the dynamic model in Fafchamps (1993), “Sequential Labor Decisions Under Uncertainty: An Estimable Household Model of West-African Farmers”

• Derivations of equations in Foster and Rosenzweig (1995): I show how to derive equations in Foster and Rosenzweig (1995), “Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture”

• Relational contract model with learning: Largely based on Song and Ma (2021), “Relational Contracts in Well-Functioning Markets: Evidence from China’s Vegetable Wholesale”, I explain the relational contract model with learning

• Review of MATLAB codes in Kaboski and Townsend (2011): I reviewed the MATLAB codes and summarized the structural estimation procedure in Kaboski and Townsend (2011), “A Structural Evaluation of a Large-Scale Quasi-Experimental Microfinance Initiative”

Other economics

• Julia linear programming formulation for two-sided matching: I solved the stable matching problem with the linear programming formulation

• Julia implementation of a recursive algorithm for kidney exchanges: I implemented a recursive algorithm to solve an optimization problem for kidney exchanges.

• Julia implementation of the stochastic optimal growth model with the collocation method + Gaussian quadrature approximation: I implemented the collocation method for functional approximation and Gaussian quadrature method for the approximation of expectation

• Memos on Berry (1992): For deeper understandings on Berry (1992), “Estimation of a Model of Entry in the Airline Industry”, I answered the following two questions in the document: (1) How complicated will it be to compute the distribution of equilibrium numbers of entrants? (2) How can we obtain the identities of entrants under the assumed decision order?

Non-economics

• R Econ Visual Library: I was the main contributor of creating the website which showcases R visualization focusing on impact evaluation. I worked on this while I was working for DIME Analytics as a short-term consultant.

• Stata linter: I was one of the main contributors of creating the linting tool for Stata. I worked on this while I was working for DIME Analytics as a short-term consultant.

• GitHub repo for a ML competition, “Open AI Caribbean Challenge: Mapping Disaster Risk from Aerial Imagery”: This repository stores files used for a competition Open AI Caribbean Challenge: Mapping Disaster Risk from Aerial Imagery, which resulted in 20th / 1425 competitors. In this competition, we are given images of each city in Colombia, Guatemala, and St. Lucia, coordinates of buildings, and for training buildings, their roof materials (cement, healthy metal, incomplete, irregular metal, or other). Using the information, we predict the roof materials of test buildings.

• Curated list of resources: I created a curated list of resources for… anything, basically, from data, programming, economics, to good sushi restaurants in Tokyo.