diff: Simplifying the estimation of difference-in-differences treatment effects

In this article, I present the features of the user-written command Jiff, which estimates difference-in-differences (DID) treatment effects. Jiff simplifies the DID analysis by allowing the conventional DID setting to be combined with other nonexperimental evaluation methods. The command is equipped with an attractive set of options: the single DID with covariates, the kernel propensity-score matching DID, and the quantile DID. Specific options are included to obtain DID estimation on a repeated cross-section setting and to test the general balancing properties of the model. I illustrate the features of Jiff using a sample of the dataset from the pioneering implementation of DID by Card and Krueger (1994, American Economic Review 84: 772-793).