Hi all,

I am estimating the effect on infant mortality (imr) of a health policy implemented on a municipal level starting in 2013, with municipalities adopting the policy in different years. I have constructed panels for each municipality with data ranging from 2010-2017. A Generalized DiD seems to be the most fitting approach.

Following advice on a previous thread, I have run the regression as follows:
Code:
xtreg imr i.treat i.active_treat i.year Z, fe vce(cluster mun)
where treat =1 if the municipality ever implemented the policy and =0 otherwise, active_treat =1 in all periods when the policy is active and =0 for all periods before policy adoption, as well as =0 in control municipalities for all periods. Z is a vector of municipal time-variant characteristics, such as population, GDP, literacy rate, etc.

I have also created a period index variable, m, taking on the value of 0 in the period where the policy was adopted and the value of negative/positive N in the N periods pre/post policy implementation.
My first question is how to incorporate the period index variable, m, (or perhaps only the post-policy m values, let's call it m_post), into the regression to allow for continued effects?
Secondly, the policy implementation differed across treated municipalities in that some received doctors of either type1 or type2, or both. How do I incorporate this into the model to evaluate the impact of a municipality receiving type2 or both as compared to just type1? Is it sufficient just to create a dummy variable and add it in as a covariate or should I restrict the sample to only type1, run the regression, and re-run on the sample with only type2 and only both?

Many thanks for the help!