MA in Economics

John Wong, 2025

I use econometric and computational methods to understand complex patterns in the economy. I worked at research organizations such as the Mercatus Center at George Mason University, Progressive Policy Institute, and PolicyEngine.

What was your specific area of study and how did you choose it?

I have worked on several projects during my time at George Mason that I really like. I used computational modeling to solve a large problem of many agents exchanging many goods. I have a recent working paper where I discuss a common misspecification in many highly influential economic development papers. I am also trying to quantify tax code complexity, a phenomenon that increases both cost and frustration for individuals and families. Finally, I published a working paper that examined the impact of regulation on economic growth in U.S. states. Using machine learning, we were able to quantify complex regulatory codes into usable data that we combined with causal inference strategies to obtain credible estimates. We found that the amount of regulation explains a nontrivial amount of differences in growth performance. 

How did your academic experiences in the College of Humanities and Social Sciences impact you? 

I have been able to take classes from leading scholars in fields such as agent-based modeling, structural econometrics, and industrial organization. In general, I am now far better at stating problems in terms of formal models. This helps justify empirical strategies and makes results more interpretable. 

Which accomplishments during your time at George Mason are you most proud of? 

I participated in the Peter G. Peterson Foundation’s fiscal internship program last summer. It opened the door to a field where practitioners are actively applying cutting-edge economic tools. I also saw how my education at George Mason allows me to uniquely contribute to the work of policy researchers in D.C. 

I’ve also become a far better general-purpose programmer—writing scripts, reusable functions, and creating Github issues. I have Rob Axtell and PolicyEngine to thank for this. 

Are there faculty or staff members who made a difference during your George Mason career? 

I would like to thank four faculty members in detail: Rob Axtell, Tim Groseclose, Garett Jones, and Alex Tabarrok. Professors Axtell and Groseclose managed to design courses, respectively on agent-based and discrete choice models, that made an extremely-useful-but-otherwise-unapproachable set of tools very easy to understand and learn. Professors Jones and Tabarrok taught classes that distilled significant research from several subfields and respectively changed how I think about currency and auctions, and they have both been incredibly supportive in terms of professional development and research opportunities. Finally, I would like to acknowledge Vincent Geloso, Johanna Mollerstrom, Carlos Ramirez, and Jessica Carges. 

What advice would you give to an incoming cohort of graduate students? 

One piece of advice I've personally taken to heart is to “Always Do the Reading” (attributable to Ezra Klein, I believe). First, I have found that being able to thoroughly understand a topic makes all the difference in a competitive environment. Second, as someone who was once an underperforming student in college, I can attest that it's never easy to read into a difficult topic like linear algebra. But I can also promise that with time and practice, everything eventually gets easier. Finally, the more you're able to internalize the material, the more it can inspire original research or improve the solutions you come up with for future problems. 

What are your current career plans following graduation? What are your long-term career goals? 

I will be applying agent-based methods I learned at George Mason to maintain and contribute to the microsimulation models at the Urban-Brookings Tax Policy Center.