Dear all,
I am analysing what are the determinants for the adoption of bonus compensation for a firm (i) headquartered in a country (j). I have more than 700 firms headquartered in 16 countries; my dependent variables is a dichotomous indicating whether bonus compensation is adopted (1) or not (0), and my key independent variables are the first two in the output of the model displayed below.

The first one indicates whether the country in which the firm is headquartered discourage (-1), encourage (1) or does not have a clear position on bonus compensation (0). The second one indicates whether the firm is listed in US.

As you can see, these two variables are measured on two different levels. Therefore, similarly to what happens with continuous dependent variable with mixed command, I'd want to understand how much of my variance is explained by firm- and country-level variables.


Code:
melogit  FIRM_INCENTIVE_POLICY CodesIncentivesRECOMM2 USListing  *** all remaining indep vars *** || CountryC:, or

Fitting fixed-effects model:

Iteration 0:   log likelihood = -163.67629  
Iteration 1:   log likelihood = -136.00745  
Iteration 2:   log likelihood = -135.00212  
Iteration 3:   log likelihood = -134.96207  
Iteration 4:   log likelihood =   -134.962  
Iteration 5:   log likelihood =   -134.962  

Refining starting values:

Grid node 0:   log likelihood = -139.52018

Fitting full model:

Iteration 0:   log likelihood = -139.52018  (not concave)
******I omitted all iteration for the sake of brevity******
Iteration 54:  log likelihood =  -134.9648  
Iteration 55:  log likelihood = -134.96271  
Iteration 56:  log likelihood = -134.96218  
Iteration 57:  log likelihood =   -134.962  
Iteration 58:  log likelihood =   -134.962  


Mixed-effects logistic regression               Number of obs     =        727
Group variable:    CountryCODED                 Number of groups  =         16

                                                Obs per group:
                                                              min =          3
                                                              avg =       45.4
                                                              max =        365

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(23)     =     200.88
Log likelihood =   -134.962                     Prob > chi2       =     0.0000
----------------------------------------------------------------------------------------------
       FIRM_INCENTIVE_POLICY | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
      CodesIncentivesRECOMM2 |   9.843344   3.403505     6.61   0.000     4.998332    19.38475
                   USListing |   10.54212    6.53276     3.80   0.000     3.129346     35.5142
                      FAMILY |   2.478101   1.416788     1.59   0.112     .8081043    7.599248
                Outsider5_LN |   1.005343   .1950676     0.03   0.978     .6873139     1.47053
                 MEETINGS_LN |   .4335576   .2472911    -1.47   0.143     .1417559    1.326027
              RepRisk_1Y_AVG |   1.003941   .0181223     0.22   0.827     .9690433    1.040096
                    BETA9802 |   .7564532   .3403387    -0.62   0.535      .313195    1.827045
                ASSETSUSD_LN |   1.074118    .225251     0.34   0.733     .7121118    1.620151
       TOTALDEBTTOTALASSETS8 |   2.979734   3.803166     0.86   0.392     .2442032     36.3583
                         TSR |   1.001388   .0037582     0.37   0.712      .994049    1.008781
          RETURNONASSETS8326 |   .3602494   1.129024    -0.33   0.745     .0007743    167.5997
                             |
               SectorENCODED |
     Consumer Discretionary  |   2.762074   1.600434     1.75   0.080     .8872009    8.599013
           Consumer Staples  |   2.734421   1.905945     1.44   0.149     .6975269    10.71938
                     Energy  |   2.054449   1.460637     1.01   0.311     .5099447    8.276902
                Health Care  |   1.901764   1.115238     1.10   0.273     .6025489    6.002343
     Information Technology  |   7.974622   5.946515     2.78   0.005      1.84921    34.39014
                  Materials  |   1.237006   .7601254     0.35   0.729     .3709527     4.12501
 Telecommunication Services  |   2.581441   2.960618     0.83   0.408     .2726662    24.43954
                  Utilities  |   1.341628    1.07053     0.37   0.713     .2808211    6.409653
                             |
Prot_min_inv_Ext_Dir_Liab_LN |   12.63175   17.98257     1.78   0.075     .7756964    205.7003
             SayONPaySwitzNB |   .9826529   .9250025    -0.02   0.985     .1552877    6.218179
                   RuleOfLaw |   3.383666   5.149616     0.80   0.423     .1713722    66.80896
           CountryMKTCap2GDP |   1.004822   .0069745     0.69   0.488     .9912444    1.018585
                       _cons |   .0000205   .0001294    -1.71   0.087     8.75e-11     4.81151
-----------------------------+----------------------------------------------------------------
CountryCODED                 |
                   var(_cons)|   1.37e-34   5.45e-18                             .           .
----------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline odds (conditional on zero random effects).
LR test vs. logistic model: chi2(0) = 5.7e-14             Prob > chi2 =      .

Note: LR test is conservative and provided only for reference.

Thank you for your time,
Luigi