st: gllamm for binary outcome - interpretation?

[Gitit Kadar Satat <G.Kadar-Satat@sms.ed.ac.uk>]

Hello Statalist helpers !
(apologies for re-posting, not sure the first time has been received)

I ran a gllamm multilevel model to explore a binary outcome. the data  
I'm using are individuals clustered in schools. The outcome of  
interest is whether or not they have passed an exam.
I started by estimating an "unconditional model" with no explanatory  
variable and then ran additional model with the explanatory variables  
included.
Based on the output (below), how can I estimate the percentage of  
variance explained at the "grouping" level (that is - schools)? Or is  
my output missing important information? In other words - I would like  
to be able to report the % variance in the outcome (whether or not  
individuals have passed the exam) that is explained by differences  
across schools. Is this possible?
Here is the main output:

*Model 1 (unconditional):
. gllamm pass_exam, i(sptn00) pweight(pwt) link(logit)  
family(binomial) nip(30)  adapt
number of level 1 units = 12552
number of level 2 units = 335
 Condition Number = 1.1434506
 gllamm model
 log likelihood = -8160.5935
 Robust standard errors
--------------------------------------------------------------------------------------
pass_exam            |      Coef.   Std. Err.      z    P>|z|     [95%  
Conf. Interval]
---------------------+----------------------------------------------------------------
               _cons |   -1.31337   .0397836   -33.01   0.000     
-1.391345   -1.235396
--------------------------------------------------------------------------------------
 Variances and covariances of random effects
------------------------------------------------------------------------------
***level 2 (sptn00)
    var(1): .2054349 (.03404487)
------------------------------------------------------------------------------

*Model 2 (explanatories included)
. xi: gllamm pass_exam i.S4_Parental_nssec0 i.S4_Parental_nvq  
i.S4_Income_quartiles ,
i(sptn00) pweight(pwt) link(logit) family(binomial)  nip(30)  adapt
number of level 1 units = 12379
number of level 2 units = 335
 Condition Number = 281.7895
 gllamm model
 log likelihood = -7870.4423
 Robust standard errors
--------------------------------------------------------------------------------------
pass_exam             |      Coef.   Std. Err.      z    P>|z|      
[95% Conf. Interval]
---------------------+----------------------------------------------------------------
       _IS4_Parent_2 |   .1579462   .1034318     1.53   0.127     
-.0447763    .3606688
       _IS4_Parent_3 |   .2136482   .0943537     2.26   0.024      
.0287183    .3985781
       _IS4_Parent_4 |    .205371   .1163226     1.77   0.077      
-.022617     .433359
        _IS4_Parenta2 |   .2970159   .2201864     1.35   0.177     
-.1345416    .7285733
       _IS4_Parenta3 |    .329872    .191429     1.72   0.085     
-.0453219    .7050659
       _IS4_Parenta4 |   .3068724   .2005867     1.53   0.126     
-.0862702     .700015
       _IS4_Income_2 |    .090987   .1359474     0.67   0.503     
-.1754649    .3574389
       _IS4_Income_3 |    .127511   .1269022     1.00   0.315     
-.1212128    .3762348
       _IS4_Income_4 |   .3252327   .1420127     2.29   0.022       
.046893    .6035724
               _cons |  -2.080896   .1809789   -11.50   0.000     
-2.435609   -1.726184
--------------------------------------------------------------------------------------
 Variances and covariances of random effects
------------------------------------------------------------------------------
***level 2 (sptn00)
    var(1): .20224494 (.03582371)
------------------------------------------------------------------------------

Thanks in advance,

Gitit

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