Luna Yue Huang

Bio

Luna is a PhD candidate at UC Berkeley interested in development economics and data science. She is particularly interested in leveraging granular remote sensing data and state-of-the-art deep learning algorithms to improve measures of economic development in regions with poor official statistics, and evaluate policy effectiveness in international development.

Luna is currently a Ph.D. candidate in Agricultural and Resource Economics, at University of California, Berkeley, working with Ted Miguel and Marco Gonzalez-Navarro. Prior to joining UC Berkeley, she received a B.A. in Economics and a B.S. in Environmental Sciences from Peking University, China.

Research

Publication

Using RCTs to Estimate Long-Run Impacts in Development Economics, joint with Adrien Bouguen, Michael Kremer, Edward Miguel. Annual Review of Economics, Volume 11, 2019. (Published Version | NBER Working Paper Version | Vox Talks)

Working Papers

Information, Incentives and Air Quality: New Evidence from Machine Learning Predictions, joint with Minghao Qiu (Slides | Github)

Work in Progress

Beyond Nightlight: Using Daytime High-Resolution Satellite Images in Economics

Impact Evaluation with Satellite Imagery and Machine Learning: Evidence from the GiveDirectly Experiment in Kenya (Github)