Hello,

I'm currently estimating some earning equations and controlling for unexplained earnings for public employees at State level here in Brazil. One procedure common to this approach that I found on the most recent papers about the subject is to correct the earning equations with a biprobit model, controlling for the probability of the individual to be employed and the probability of being a public employee as well.

These are the results that I have for my whole sample for the year 2015:
Code:
Survey: Bivariate probit regression

Number of strata   =       728              Number of obs     =      2,190,952
Number of PSUs     =    16,305              Population size   =  1,069,853,667
                                            Subpop. no. obs   =        180,108
                                            Subpop. size      =    103,535,992
                                            Design df         =         15,577
                                            F(  28,  15550)   =        1091.28
                                            Prob > F          =         0.0000

----------------------------------------------------------------------------------
                 |             Linearized
                 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
emprego          |
        nfilhos2 |  -.2114478   .0128748   -16.42   0.000    -.2366839   -.1862116
      nfilhos3_5 |  -.1098781   .0126787    -8.67   0.000    -.1347299   -.0850264
     nfilhos6_12 |  -.0974262   .0080992   -12.03   0.000    -.1133016   -.0815508
   nfilhos13_17h |  -.0164063   .0113128    -1.45   0.147    -.0385808    .0057682
   nfilhos13_17m |  -.0086634   .0127159    -0.68   0.496     -.033588    .0162611
           chefe |   .3851882   .0106939    36.02   0.000     .3642269    .4061496
           negro |  -.0563385   .0086413    -6.52   0.000    -.0732764   -.0394005
     renda_extra |  -.0001249   5.15e-06   -24.27   0.000     -.000135   -.0001148
          estudo |   .1492859   .0037893    39.40   0.000     .1418584    .1567133
           idade |   .1454735   .0019834    73.34   0.000     .1415857    .1493613
                 |
c.estudo#c.idade |  -.0006611   .0000822    -8.04   0.000    -.0008222      -.0005
                 |
 c.idade#c.idade |  -.0018171   .0000225   -80.87   0.000    -.0018611   -.0017731
                 |
       estudante |  -.3296371   .0129974   -25.36   0.000    -.3551135   -.3041607
         membros |   .0038305   .0034291     1.12   0.264     -.002891     .010552
           _cons |  -3.929445   .0538253   -73.00   0.000    -4.034949   -3.823942
-----------------+----------------------------------------------------------------
pub_est          |
        nfilhos2 |  -.1002891     .02476    -4.05   0.000    -.1488216   -.0517565
      nfilhos3_5 |  -.0979429   .0255955    -3.83   0.000    -.1481131   -.0477726
     nfilhos6_12 |  -.0527653   .0162168    -3.25   0.001    -.0845522   -.0209784
   nfilhos13_17h |  -.0236773   .0223264    -1.06   0.289    -.0674398    .0200851
   nfilhos13_17m |  -.0065224   .0235071    -0.28   0.781    -.0525991    .0395543
           chefe |   .1427142   .0150768     9.47   0.000     .1131619    .1722665
           negro |     .05077   .0159538     3.18   0.001     .0194987    .0820413
     renda_extra |  -.0000702   3.92e-06   -17.91   0.000    -.0000779   -.0000625
          estudo |   .2175808   .0101962    21.34   0.000     .1975952    .2375665
           idade |   .1371285   .0045807    29.94   0.000     .1281497    .1461072
                 |
c.estudo#c.idade |  -.0012952   .0001982    -6.53   0.000    -.0016837   -.0009067
                 |
 c.idade#c.idade |  -.0012525   .0000367   -34.09   0.000    -.0013245   -.0011805
                 |
       estudante |   .1469598   .0229742     6.40   0.000     .1019278    .1919918
         membros |   .0251703    .006382     3.94   0.000     .0126607    .0376799
           _cons |   -7.12937   .1658712   -42.98   0.000    -7.454497   -6.804243
-----------------+----------------------------------------------------------------
         /athrho |   1.386635   .0257015    53.95   0.000     1.336257    1.437013
-----------------+----------------------------------------------------------------
             rho |   .8824283   .0056883                      .8707703    .8930948
----------------------------------------------------------------------------------
Here I have controls for number of children, age, number of family members, race, condition on family, etc. Some of them are exclusive to the participation equations and are not included in my earning equations.

The model seems ok and now I need to obtain the Inverse Mills Ratio to plug in my earning equations to correct for selection bias. Seems like the -biprobit- command does not have a built in option to obtain that, so, any thoughts on how I cant obtain it and plug in my other equation?

Thanks,

Fernando Martins.