Dear Statalist,

I have read about this topic in various discussions but I could not take a clear response.

I have micro cross-sectional data-base (at firm level) for 58 countries. What I am trying to do is a logit regression (my dependent variable is a binary one) controlling for each country due to heterogeneity between countries. Furthermore, I would like to add macro variables like lnGDP (logarithm of the GDP per worker). My code is the next:

logit Y X Z i.country , vce(robust) // where Y is my dependent variable, X is a vector of variables at firm level and Z a vector of variables at country level.

However, doing this, various country levels are omitted. I have read in some papers that it is not possible to include country variables when you are controlling for countries. So, then I decided to run multilevel logit model of two levels (firms and countries). My code is the next

melogit Y X Z || country: , vce(robust)

So, my question is. It is a good idea to run a multilevel model, or maybe I should run the first regression but omitting "i.country" variable and replacing vce(robust) by vce(cluster country)?

I hope you can give me some guidance.

Thanks in advanced,
Ibai