Hello everybody,

I'm running a Heckman selection model based on a heckprob. I have some categorical variables such as grup_ha grupo_edad niveleducativo and the rest of them are dichotomous variables.
I can notice in my results that only approximately 15% of the sample are uncensored observations. So I'm not sure whether this is helping to explatin the whole behavior. Even more so if the model is appropiate.
Because χ 2 = 57.35, this clearly justifies the Heckman selection equation with these data, but I'm not sure yet about it and I can't find much literature about this kind of model. Could anyone tell me how to Interpret these heckprobit results? I suppose I have to interpret as a probit model.
Thank you!



Code:
. heckprob cred_o_fin_aprob i.regiones_co sexo tenenciapropia cuidadotierrayanim existeinfraestructura acceso_sistderiego accesoenergia desti
> noventa recibir_asistenciaoasesoria grup_ha niveleducativo grupo_edad, select( soli_cred_o_fin2013=grup_ha sexo grupo_edad sabeleeryesc) vc
> e(robust)

Fitting probit model:

Iteration 0:   log pseudolikelihood = -31412.985  
Iteration 1:   log pseudolikelihood = -30745.027  
Iteration 2:   log pseudolikelihood = -30740.017  
Iteration 3:   log pseudolikelihood = -30740.017  

Fitting selection model:

Iteration 0:   log pseudolikelihood = -245658.48  
Iteration 1:   log pseudolikelihood = -242017.34  
Iteration 2:   log pseudolikelihood = -241997.85  
Iteration 3:   log pseudolikelihood = -241997.85  

Fitting starting values:

Iteration 0:   log pseudolikelihood = -61035.075  
Iteration 1:   log pseudolikelihood =  -30953.04  
Iteration 2:   log pseudolikelihood = -30710.317  
Iteration 3:   log pseudolikelihood = -30709.844  
Iteration 4:   log pseudolikelihood = -30709.844  

Fitting full model:

Iteration 0:   log pseudolikelihood = -272920.09  
Iteration 1:   log pseudolikelihood = -272709.04  (not concave)
Iteration 2:   log pseudolikelihood = -272708.68  (backed up)
Iteration 3:   log pseudolikelihood = -272707.97  
Iteration 4:   log pseudolikelihood = -272707.92  
Iteration 5:   log pseudolikelihood = -272707.91  
Iteration 6:   log pseudolikelihood = -272707.91  

Probit model with sample selection              Number of obs     =    571,952
                                                Censored obs      =    483,897
                                                Uncensored obs    =     88,055

                                                Wald chi2(15)     =    1092.15
Log pseudolikelihood = -272707.9                Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------------
                            |               Robust
                            |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
cred_o_fin_aprob            |
                regiones_co |
                    Caribe  |  -.3207418   .0201058   -15.95   0.000    -.3601483   -.2813352
                  Pacifico  |    .075082   .0145369     5.16   0.000     .0465902    .1035738
                  Oriental  |   .1377078   .0150459     9.15   0.000     .1082184    .1671973
          Orinoco-Amazonia  |  -.2023915   .0241538    -8.38   0.000    -.2497322   -.1550508
                            |
                       sexo |  -.0885042   .0142334    -6.22   0.000    -.1164013   -.0606072
             tenenciapropia |   .0716722   .0120028     5.97   0.000     .0481471    .0951974
         cuidadotierrayanim |   .1055903   .0179416     5.89   0.000     .0704253    .1407552
      existeinfraestructura |   .0249654   .0102271     2.44   0.015     .0049206    .0450102
         acceso_sistderiego |   .0202652   .0134314     1.51   0.131    -.0060599    .0465903
              accesoenergia |   .0666022   .0110267     6.04   0.000     .0449903    .0882141
               destinoventa |  -.0020524     .01243    -0.17   0.869    -.0264148    .0223099
recibir_asistenciaoasesoria |    .111292   .0118058     9.43   0.000     .0881531    .1344309
                    grup_ha |  -.8661633   .8533362    -1.02   0.310    -2.538672    .8063449
             niveleducativo |  -.0214992   .0051078    -4.21   0.000    -.0315102   -.0114881
                 grupo_edad |  -.0740636   .0065678   -11.28   0.000    -.0869362    -.061191
                      _cons |   2.724741   .8553536     3.19   0.001     1.048278    4.401203
----------------------------+----------------------------------------------------------------
soli_cred_o_fin2013         |
                    grup_ha |  -.0262168   .4370274    -0.06   0.952    -.8827748    .8303412
                       sexo |   .1801206   .0046461    38.77   0.000     .1710145    .1892267
                 grupo_edad |   .0252196    .002117    11.91   0.000     .0210703    .0293689
               sabeleeryesc |   .4375166   .0060783    71.98   0.000     .4256034    .4494298
                      _cons |  -1.567543   .4371122    -3.59   0.000    -2.424268   -.7108193
----------------------------+----------------------------------------------------------------
                    /athrho |  -.4703892   .0621132    -7.57   0.000    -.5921288   -.3486496
----------------------------+----------------------------------------------------------------
                        rho |  -.4385137   .0501692                     -.5314249   -.3351774
---------------------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) =    57.35   Prob > chi2 = 0.0000
end