Erik H. Wang

PhD Candidate in Political Science at Princeton


Works in Progress



About Me

Welcome! I am a PhD candidate in the Department of Politics at Princeton University and a fellow of the Program for Quantitative and Analytical Political Science (Q-APS). My interests cover comparative political economy, politics of finance, and Chinese politics. I also do research on statistical methods of causal inference. My work is forthcoming in Journal of Politics.

In 2015, a paper I coauthored won the Malcolm Jewell Award for the best graduate student paper presented at the Southern Political Science Association Annual Meeting. In 2017, my colleagues and I were awarded for the best statistical prediction of material hardship among disadvantaged children in the Fragile Families Challenge.

At Princeton, I have had the joy of teaching both substantive and methodological courses at various levels. I have taught courses in comparative politics and international relations to undergraduates. I have also taught the third course in my department’s quantitative methods sequence to PhD students, as well as programming language and research design to entering undergraduates via the Freshman Scholars Institute.


Pollution Lowers Support For China's Regime: Quasi-experimental Evidence from Beijing. Forthcoming. Journal of Politics (with Meir Alkon)
(PDF, Data)

Selected Works in Progress

Political Backlash to State Intervention: Experimental and Observational Evidence from Chinese Stock Investors. (with Xiao Ma & Jason Q. Guo)
(Poster presented at 2017 Society of Political Methodology)

Awakening Leviathan: Effect of Democracy on State Capacity, 1960-2009 (with Yiqing Xu)
-- Awarded the 2015 Malcolm Jewell Award for the best graduate student paper presented at the SPSA annual meeting. (PDF)

Authoritarian Power-Sharing through Informal Institutions


Quantitative Analysis III

PhD-level course, Princeton, NJ, Fall 2016
Preceptor for Prof. Kosuke Imai

Third course in the Politics department’s graduate quantitative methods sequence, covering discrete choice models, machine learning via EM algorithm and variational inference, models for time-series cross-section data, and event history analysis, all taught with both econometrics perspectives and causal inference perspectives.
(Student evaluations: 4.6/5)

International Relations

Undergraduate-level course, Princeton, NJ, Spring 2017
Preceptor for Prof. Andrew Moravcsik

This course is an introduction to the causes and nature of international conflict and cooperation. We critically examine various theories of international politics by drawing on examples drawn from international security, economic and legal affairs across different historical eras from 10,000 BC to the present. Topics include the causes of war, the pursuit of economic prosperity, the sources of international order and its breakdown, and the rise of challenges to national sovereignty, and such contemporary issues as international environmental politics, human rights promotion, global terrorism, and the future of US foreign policy.
(Student evaluations: 4.1/5)

Visualizing Data

Undergraduate-level course, Princeton, NJ, Summer 2017
Preceptor for Prof. Will Lowe

An introduction course to statistics and programming for newly admitted undergrads at Princeton, covering experimental deisgn, predictive modelling, as well as elementary techniques for analysis of network, text and spatial data.
(Student evaluations: 4.8/5)

Chinese Politics (currently teaching)

Undergraduate-level course, Princeton, NJ, Fall 2017
Preceptor for Prof. Rory Truex

This course provides an overview of China's political system. We will begin with a brief historical overview of China's political development from 1949 to the present. The remainder of the course will examine the key challenges facing the current generation of CCP leadership, focusing on prospects for democratization and political reform. Among other topics, we will examine: factionalism and political purges; corruption; avenues for political participation; village elections; public opinion; protest movements and dissidents; co-optation of the business class; and media and internet control.

Statistical Programming Camp (upcoming)

PhD-level course, Princeton, NJ, Winter 2018
Course Instructor

This camp will prepare students for POL 572 and other quantitative analysis courses offered in the Politics department and elsewhere. Although participation in this camp is completely voluntary, the materials covered in this camp are a pre-requisite for POL 572. Students will learn the basics of statistical programming using R, an open-source computing environment. Using data from published journal articles, students will learn how to manipulate data, create graphs and tables, and conduct basic statistical analysis. This camp assumes knowledge of probability and statistics as covered in POL 571.


Coming soon