Data Science

by Skyler Chen. This post discusses how my training in data science changed the way I think about behavioral research. I share how simply exploring everyday datasets and noticing small, unexpected patterns can spark new research questions, and how archival data and experiments each offer distinct yet complementary insights into how people make judgments and decisions. I also highlight the growing set of tools that help us understand behavior in richer ways.

by Jiayu Lai. Shift-share instruments are among the most widely used tools in applied economics, appearing in labor, trade, immigration, and policy evaluation research. But despite their popularity, many researchers still use them as black boxes — and risk invalid instruments as a result. In this blog post, I unpack how shift-share IVs actually work, why their validity depends on both the “shifts” and the “shares,” and what practical steps researchers should take to check assumptions. I also walk through how I used the Borusyak–Hull–Jaravel (2022, 2025) framework to reproduce the seminal Autor, Dorn, and Hanson (2013) China shock analysis.

by Sarah Daniel. Researchers increasingly recognize that neighborhoods profoundly shape life outcomes, yet measuring them remains challenging. A common approach uses administrative boundaries, such as census tracts, as proxies for neighborhoods, but this method presents three key challenges. First, administrative boundaries may fail to capture residents’ lived experiences, a limitation that is particularly concerning in marginalized communities; second, they can misrepresent contextual effects; and third, they may produce inconsistent findings. To address these issues, I advocate for the use of self-defined neighborhood boundaries as an alternative measure. I compare GIS- and non-GIS-based methods and propose that GIS-based methods offer the strongest potential for more valid measurement.