My chapter on difference-in-differences just published in the Handbook of Labor, Human Resources and Population Economics.
The chapter follows pretty closely what I teach about DID in my Ph.D. econometrics course at UGA. It’s probably less of a “practitioner’s guide” and more of an introduction to the literature for an econometrics student. And, in particular, the chapter includes:
1) Proofs of main results in the literature (e.g., Goodman-Bacon (2021), Callaway and Sant’Anna (2021), Sun and Abraham (2021)) in a unified notation.
2) A careful comparison of alternative estimation strategies that have recently been proposed in order circumvent the issues with two-way fixed effects regressions. In the chapter, I emphasize the conceptual similarities between different estimation strategies, but also try to point out differences between estimation strategies (and distinguish between fundamental differences and differences due to implementation choices made in different papers).
3) A discussion of realistic issues that show up in empirical applications such as including covariates in the parallel trends assumption and dealing with violations of the parallel trends assumption.
4) An extended application about minimum wage policies. My goal for the application was to (i) demonstrate different estimation strategies, and (ii) introduce open source code that is available for implementing new DID estimation strategies. The complete code/data that I used in the application is available here.