I am a Ph.D. Candidate in Politics at Princeton University. I am also a graduate affiliate with both the Niehaus Center for Globalization and Governance and the the Princeton Sovereign Finance Lab. You can find a copy of my CV here.

I am broadly interested in the politics of government finances. My dissertation research explores the politics underlying adoption of national fiscal rules. My interests also include the politics of sovereign finance, natural resource politics, and the political economy of climate change.

Previously, I was a Junior Data Analyst for the AidData Research Lab at William & Mary, where I conducted geospatial impact evaluations of development projects. I also managed the AidData Geocoding team. I earned a A.B. in Economics from Washington University in St. Louis and an M.A. in Politics from Princeton University.

Journal Publications

Highway to the Forest? Land Governance and the Siting and Environmental Impacts of Chinese Government-Funded Road Building in Cambodia
Christian Baehr, Ariel BenYishay, and Brad Parks
Journal of Environmental Economics and Management, Vol 122(1).

Linking Local Infrastructure Development and Deforestation: Evidence from Satellites and Ad- ministrative Data
Christian Baehr, Ariel BenYishay, and Brad Parks
Journal of the Association of Environmental and Resource Economists, Vol 8(2).

Working Papers

Climate Exposure Drives Firm Political Behavior: Evidence From Earnings Calls and Lobbying Data
With Fiona Bare and Vincent Heddesheimer.

Landmine Clearance and Economic Development
With Ariel BenYishay, Rachel Sayers, Kunwar Singh, and Madeleine Walker.

Teaching Experience

POL 396: International Organizations (Spring 2024)

This introductory course surveys the network of major international organizations and introduces undergraduate students to the political factors driving participation in, and behavior of, international organizations.

POL 504: Text As Data (Fall 2023)

This graduate-level methods course trains students to be practitioners of textual methods, spanning from the foundations of text-as-data to cutting-edge machine learning methods for analyzing text.