  We study the bunching identification strategy for an elasticity parameter
that summarizes agents' response to changes in slope (kink) or intercept
(notch) of a schedule of incentives. A notch identifies the elasticity but a
kink does not, when the distribution of agents is fully flexible. We propose
new non-parametric and semi-parametric identification assumptions on the
distribution of agents that are weaker than assumptions currently made in the
literature. We revisit the original empirical application of the bunching
estimator and find that our weaker identification assumptions result in
meaningfully different estimates. We provide the Stata package "bunching" to
implement our procedures.
