Seeing Like a District: Understanding What Close-Election Designs for Leader Characteristics Can and Cannot Tell Us
In over 100 influential articles, political scientists have used close elections to study how important outcomes vary after a certain type of candidate wins, such as a Democrat or a Republican. This politician characteristic regression discontinuity (PCRD) design offers opportunities for inferential leverage but also the potential for confusion. In this paper, we clarify what causal claims the PCRD licenses, offering three principle lessons. First, PCRDs do nothing to isolate the effect of the politician characteristic of interest as apart from other politician characteristics. Second, selection processes (regarding both "who runs" and "which elections are close") can generate and exacerbate such confounding, as noted in Marshall (2022). Third and more fortunately, this approach does make it possible to estimate the average effect of electing a leader of type A vs. B in the context of close elections, treating the units as districts, not leaders. We also suggest a set of tools that can aid in falsifying key assumptions, avoiding unwarranted claims, and surfacing mechanisms of interest. We illustrate these issues and tools through a re-analysis of an influential study about what happens when extremists win primaries Hall (2015).
Chad Hazlett completed his Ph.D. in Political Science at Massachusetts Institute of Technology in 2014. His dissertation in the subfields of Political Methodology and International Relations was entitled “Inference in Tough Places: Essays on Modeling, Matching, and Measurement with Applications to Civil Conflict.” Professor Hazlett previously served as a predoctoral fellow in the Department of Politics at Princeton University. His interests include machine learning, developing and extending approaches to causal inference, and using these tools to study civil war and mass violence.