  The regression discontinuity (RD) design offers identification of causal
effects under weak assumptions, earning it a position as a standard method in
modern political science research. But identification does not necessarily
imply that causal effects can be estimated accurately with limited data. In
this paper, we highlight that estimation under the RD design involves serious
statistical challenges and investigate how these challenges manifest themselves
in the empirical literature in political science. We collect all RD-based
findings published in top political science journals in the period 2009-2018.
The distribution of published results exhibits pathological features; estimates
tend to bunch just above the conventional level of statistical significance. A
reanalysis of all studies with available data suggests that researcher
discretion is not a major driver of these features. However, researchers tend
to use inappropriate methods for inference, rendering standard errors
artificially small. A retrospective power analysis reveals that most of these
studies were underpowered to detect all but large effects. The issues we
uncover, combined with well-documented selection pressures in academic
publishing, cause concern that many published findings using the RD design may
be exaggerated.
