Dear All,

I am working with a cross-sectional pooled dataset with 8,586 individual-level observations, covering 15 countries, between 2004-2016 (stata v14). I am trying to estimate the effect of a policy change on the probability of individuals exhibiting a specific labour market outcome.

I have a treated group of individuals, and the policy is implemented in 13 countries. The data captures information for the pre-treatment period (2004-2007), during the treatment period (2007-2014) and post-treatment period (2014-2016). The treatment period starts in 2007 for all 13 countries, but ends at different times (i.e 2009, 2011, 2012 and 2014 the latest year). I find this latter aspect particularly difficult to reflect in the model.

I also have individual level variables, country level variables and year level variables. I would like to capture the effect of the policy on individuals within the same country, but also variation between countries. What i am trying to do, is use a probit diff-in-diff estimator in a multilevel modelling setting that nests individuals -> country-years, country-years -> countries, and countries -> years.

Given all these parameters, does the below model reflect what i am trying to obtain? Is such a model feasible? Am i overlooking something?

Code:
 meprobit outcome i.year i.country pre##treated during##treated post##treated covariates || _all: R.year || country: || country#year:

Thank you very much for your help, it is most appreciated!
Magda