Difference-in-differences (DiD) designs are typically used to identify and estimate average causal effects, even though treatment can affect the entire outcome distribution. This has led to a growing interest in distributional DiD methods. However, identifying the full counterfactual distribution requires a parallel trends assumption on the entire distributional dynamics, which can be too strong in practice. We address this problem by introducing a spectrum of identifying restrictions that lie between the average and fully distributional DiD by imposing parallel trends on $L$-moments. This corresponds to a basis expansion of the distributional dynamics. Importantly, each $L$-moment is interpretable as familiar distributional components: location, dispersion, skewness, kurtosis, etc. Researchers can pick and choose which features they want to target. For example, the second $L$-moment identifies the Gini mean difference, but does not require imposing mean-level or higher-order parallel trends. Taking a collection of $L$-moments can approximate distributional dynamics under weaker conditions since it leaves untargeted components unrestricted. We develop conditions under which higher-order $L$-moments decay, efficient estimators, and simultaneous inference across growing collections of moments. We demonstrate the benefits of our framework by exploring distributional changes to healthcare spending after the rollout of Medicare Part D.

翻译：暂无翻译