I have a very simple situation. I am estimating the mean of a reasonably normal variable, Rto, where each sampled unit unit contributes two measurements, one from each of two locations. The errors are cluster-robust.How can I get a robust standard deviation from the Robust SE? I want to pull the "meat" out of the sandwich. -
mean Rto, vce(cluster CaseNo)
Mean estimation Number of obs = 4,962
(Std. err. adjusted for 2,481 clusters in CaseNo)
--------------------------------------------------------------
| Robust
| Mean std. err. [95% conf. interval]
-------------+------------------------------------------------
Rto | 4.064 .0236 4.018 4.111
--------------------------------------------------------------

mean Rto, vce(cluster CaseNo)
Mean estimation Number of obs = 4,962
(Std. err. adjusted for 2,481 clusters in CaseNo)
--------------------------------------------------------------
| Robust
| Mean std. err. [95% conf. interval]
-------------+------------------------------------------------
Rto | 4.064 .0236 4.018 4.111
--------------------------------------------------------------
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