Open lorenzoffner opened 2 months ago
Hello @lorenzoffner,
did
separately for each of those subgroups. Hope this helps, Brant
Hi @bcallaway11!
Thanks for your swift response, I deeply appreciate it!
I hope I could explain my issue more thoroughly now. Just let me know if you need further elaboration from my side. Thank you very much, Lorenz
- In the meantime, I'd recommend doing something slightly different from what you mention here. My suggestion is to create subgroups based on the treatment intensity and run the existing code in
did
separately for each of those subgroups.
Hi @bcallaway11, do you happen to know a vignette or example that illustrates how this would work using did
(in particular how to partition the data and which groups to compare specifically)?
Hi! Thanks for the awesome package!
I am doing a research project and try to assess the impact of Renewable Portfolio Standards on green innovation (in form of patent data) in US states. In my case, the treatments do not only occur in staggered form, but are also continuous and thus vary for pretty much every unit. That means that, for example, California has a RPS of roughly 40% in 2023, whereas states like Ohio require only 6,5% of their energy to be renewable. Still, with your package and the underlying logic of how the data needs to be structured, both states count as treated.
I have already seen that you and Goodman-Bacon are working on a paper dealing with this precise issue (10.3386/w32117). I have also done a fair bit of browsing online, but have not yet discovered a package in R that can deal with a continuous and staggered treatment. Thus, I reverted to use the did package for my issue and have the following questions:
Are you aware of any package that could work with a staggered and continuous treatment? Is there any chance that this function will be added some day to the did package?
Would it make sense to include my continuous treatment variable as covariates? Or is there any other way in the did package to control for the varying treatment intensities?
Finally, in my case, RPS range far into the future (i.e. 2050) and consequently impact the treated units already today. Do you have ideas on how to capture this as well? My only idea (again) would have been to somehow codify these values as covariates.
I hope I could make my issue clear enough for you. Any help would be greatly appreciated!
Best Lorenz