Hi all,

I'm working on nested structure data with individuals nested in countries (I may also define a more detailed structure with individuals nested in country-years nested in countries).
To analyze these data, I started with a complete pooled-OLS, simply clustering the standard errors at the country (or country-wave) level.

Code:
 reg isei_r c.maxisei_cntr##ib2.ineq_group i.gender age i.emp_status ib2.gdp_group beta_coeffadjMax [pw=dweight] , vce(clu cwave)  
*Same if I cluster for country only
However, when I turn to the partial-pooling to take into account both within and between variation, I lost significance on mine main predictors.

Code:
 mixed isei_r c.maxisei_cntr##ib2.ineq_group i.gender age i.emp_status ib2.gdp_group beta_coeffadjMax [pw=dweight] || country : maxisei_cntr, cov(unstr)
What could be the reason for this change in significance? Following Gelman & Hill (2007) I know that when the standard deviation of the group-level tends to 0, the model gains very little from the between-effect. In my case the ICC is also very low (0.012) and the std deviations at the individual level are 0.049 for the slope and 1.7 for the intercept, while the residual std deviation is 15.45.
Furthermore, comparing the residual variance of the multilevel with the residual variance of the complete-pooled they are very close. Is this suggests that the hierarchical model does not add basically anything to the complete pooled OLS?

Thanks for the support