Dear Statalist,

I'm having trouble to derive marginal effects after computing a Generalized Ordered Logit (correcting by the parallel-lines assumption violated using the gologit2 command).

I have three levels (1, 2 and 3) of my response variable, then, after running the gologit2 command with the autofit lrforce option, the output gives me the estimates of the cumulative effects (1 vs 2 and 3; and 1 and 2 vs 3).

This is a sample of how the output looks like:

HTML Code:
gologit2  response pop_density pp_college pp_no_citizen pp_no_car income_Gini, autofit lrforce

------------------------------------------------------------------------------
Testing parallel lines assumption using the .05 level of significance...

Step  1:  Constraints for parallel lines imposed for pp_no_citizen (P Value = 0.8867)
Step  2:  Constraints for parallel lines imposed for income_Gini (P Value = 0.7025)
Step  3:  Constraints for parallel lines are not imposed for
          pop_density (P Value = 0.04162)
          pp_college (P Value = 0.00746)
          pp_no_car (P Value = 0.02939)

Wald test of parallel lines assumption for the final model:

 ( 1)  [1]pp_no_citizen - [2]pp_no_citizen = 0
 ( 2)  [1]income_Gini - [2]income_Gini = 0

           chi2(  2) =    0.17
         Prob > chi2 =    0.9203

An insignificant test statistic indicates that the final model
does not violate the proportional odds/ parallel lines assumption

If you re-estimate this exact same model with gologit2, instead
of autofit you can save time by using the parameter

pl(pp_no_citizen income_Gini)

------------------------------------------------------------------------------

Generalized Ordered Logit Estimates               Number of obs   =       1182
                                                  LR chi2(8)      =     223.80
                                                  Prob > chi2     =     0.0000
Log likelihood =  -1056.791                       Pseudo R2       =     0.0957

 ( 1)  [1]pp_no_citizen - [2]pp_no_citizen = 0
 ( 2)  [1]income_Gini - [2]income_Gini = 0
-------------------------------------------------------------------------------
     response |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
1             |
  pop_density |  -.0005147   .0002311    -2.23   0.026    -.0009676   -.0000619
   pp_college |  -.0821576   .0159082    -5.16   0.000    -.1133371   -.0509781
pp_no_citizen |   .0412044   .0049467     8.33   0.000      .031509    .0508997
    pp_no_car |   .0312774   .0307727     1.02   0.309    -.0290359    .0915907
  income_Gini |   2.607472   2.304695     1.13   0.258    -1.909648    7.124592
        _cons |  -2.476667   .8911029    -2.78   0.005    -4.223196   -.7301372
--------------+----------------------------------------------------------------
2             |
  pop_density |  -.0033817   .0014051    -2.41   0.016    -.0061356   -.0006278
   pp_college |  -.1726428   .0338568    -5.10   0.000     -.239001   -.1062847
pp_no_citizen |   .0412044   .0049467     8.33   0.000      .031509    .0508997
    pp_no_car |  -.0691822   .0464367    -1.49   0.136    -.1601966    .0218321
  income_Gini |   2.607472   2.304695     1.13   0.258    -1.909648    7.124592
        _cons |  -3.368429   .9342685    -3.61   0.000    -5.199562   -1.537297
-------------------------------------------------------------------------------


Finally, to derive the marginal effects, I'm using the margins command as follows:

HTML Code:
margins, dydx(*) predict(xb)

Average marginal effects                          Number of obs   =       1182
Model VCE    : OIM

Expression   : Linear prediction, response==1, predict(xb)
dy/dx w.r.t. : pop_density pp_college pp_no_citizen pp_no_car income_Gini

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  pop_density |  -.0005147   .0002311    -2.23   0.026    -.0009676   -.0000619
   pp_college |  -.0821576   .0159082    -5.16   0.000    -.1133371   -.0509781
pp_no_citizen |   .0412044   .0049467     8.33   0.000      .031509    .0508997
    pp_no_car |   .0312774   .0307727     1.02   0.309    -.0290359    .0915907
  income_Gini |   2.607472   2.304695     1.13   0.258    -1.909648    7.124592
-------------------------------------------------------------------------------

My question is: How can I derive the corresponding marginal effects of the Generalized Ordered Logit model in the first table, e.g., two different sets marginal per explanatory variable (1 vs 2 and 3; and 1 and 2 vs 3)? As far as I can see the margins command (second table) is computing average values which are the same for every level of the response variable.

Thanks in advance.