[Aayush Bakshi]

Dear Akanksha,

I know this is an older post but I was hoping you could provide an insight into what you did as this is very similar to an analysis I am running where I am looking to run a staggered DiD on the impact of uber (as a treatment) on individual incomes but the treatment happened at state level in different years between 2009 and 2016( For example state 1 in 2011, state 2 in 2015 etc). Some states did not get treated at all.

You mentioned having 2 types of models in staggered DiD, a static and dynamic:

1) Y_ist = α + βT_st + sX_i + a_s + θ_t + ε_ist,

i – individual, s – state, t – year
T_st – Whether state s had the treatment by year t
X_i - vector with individual characteristics

2) Y_ist = α +Ʃ^T_t=1 β_t Years_of_Treatment_t + a_s + θ_t + ε_ist,

i – individual, s – state, t – year
Years_of_Treatment_t – Represents a dummy for t years of treatment
T is the maximum no of years any state has been treated

I would like to run the same for my analysis and have a few questions:

a) What command in stata did you use to run these regressions?
b) Did you control for state and year level effects? If so, how? did you enter state and year data for each individual?

Thanks and regards,
Aayush Bakshi

[Lara Fernandez]

Good afternoon,
I'm doing my thesis on the CAPs (agricultural eu policy) on TFP.

I'm doing a DID model with eu15 countries and USA, Australia and Canada as control group. I then have different dummies for the countries and years they enter the eu and therefore the policy.
I think this is considered a staggered DID. (these dummies basically overlap each other once the country starts being part fo the treated group).

However my dependent variable, TFP, is the rate of change of productivity, all the variables in the model are also on ROC. this means that I can't graph the 2 groups to check what was going on before 1992, I basically get white noise since is the variation rate. Should I compare them before doing the variation? Can I compare the mean and variance of the ROC?

Sorry to bother and thanks!