Hello,

In case of multilevel data, it is obvious to use for example - mixed -, but using - mixed - it is still possible to use - vce(cluster clustervar) - to cluster standard errors at the higher level: is this in some way useful (since - mixed - already takes the multilvel structure into account)? Why (not)?
Would an ordinary linear regression - regress - with standard errors clustered at the higher level be a valid alternative? I'm just wondering about the differences between these alternatives and reasons to prefer one or the other ...

I have a small example below:
Code:
webuse nlswork
eststo clear
* (1) Multilevel regression
mixed ln_w grade age ttl_exp tenure || id:
eststo M_lev
* (2) Multilevel regression with clustered standard errors
mixed ln_w grade age ttl_exp tenure || id:, vce(cluster id)
eststo M_lev_clus
* (3) Single level regression with clustered standard errors
reg ln_w grade age ttl_exp tenure, vce(cluster id)
eststo S_lev_clus
* (4) Single level regression (just for comparison)
reg ln_w grade age ttl_exp tenure
eststo S_lev
esttab, mtit
And these are the results:

Code:
----------------------------------------------------------------------------
                      (1)             (2)             (3)             (4)   
                    M_lev      M_lev_clus      S_lev_clus           S_lev   
----------------------------------------------------------------------------
main                                                                        
grade              0.0741***       0.0741***       0.0744***       0.0744***
                  (41.23)         (35.87)         (34.43)         (71.46)   

age              -0.00449***     -0.00449***     -0.00526***     -0.00526***
                  (-6.78)         (-5.00)         (-5.57)         (-9.70)   

ttl_exp            0.0306***       0.0306***       0.0296***       0.0296***
                  (26.92)         (18.05)         (16.08)         (30.94)   

tenure             0.0136***       0.0136***       0.0195***       0.0195***
                  (15.96)         (10.18)         (11.96)         (22.72)   

_cons               0.635***        0.635***        0.652***        0.652***
                  (23.20)         (19.37)         (19.30)         (37.12)   
----------------------------------------------------------------------------
lns1_1_1                                                                    
_cons              -1.319***       -1.319***                                
                 (-96.79)        (-80.67)                                   
----------------------------------------------------------------------------
lnsig_e                                                                     
_cons              -1.216***       -1.216***                                
                (-262.93)        (-98.91)                                   
----------------------------------------------------------------------------
N                   28099           28099           28099           28099   
----------------------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
Thank you,
Mike