st: Problem with margins after logit on a person period data

[Urmi Bhattacharya <ub3@indiana.edu>]

Dear Statalisters,

I am running the following logit on a person-period data

logit school_left childage i.childfemale i.urban i.scstobc
i.casteother i.dadp i.dadm i.momp i.momm wagep wage5 wage8 w
> age9 distp distm disth percapcons durat1 durat2 durat3 durat4 durat5 durat6 durat7 durat8 durat9 durat10 durat11, nocon
> s nolog

Logistic regression                               Number of obs   =      47569
                                                  Wald chi2(28)   =   14601.42
Log likelihood = -14502.393                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
 school_left |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    childage |  -.1413654    .006069   -23.29   0.000    -.1532605   -.1294703
1.childfem~e |   .0212387   .0306462     0.69   0.488    -.0388266    .0813041
     1.urban |   .0124911   .0368072     0.34   0.734    -.0596497    .0846319
   1.scstobc |   1.972676   .1234074    15.99   0.000     1.730802     2.21455
1.casteother |   1.883233    .125722    14.98   0.000     1.636822    2.129643
      1.dadp |    .680585    .044327    15.35   0.000     .5937057    .7674643
      1.dadm |   .3872552   .0506982     7.64   0.000     .2878886    .4866217
      1.momp |   1.351053   .0835161    16.18   0.000     1.187364    1.514741
      1.momm |   .9494066   .0918146    10.34   0.000     .7694532     1.12936
       wagep |  -.0281962   .0061845    -4.56   0.000    -.0403176   -.0160748
       wage5 |  -.0040992   .0045604    -0.90   0.369    -.0130373     .004839
       wage8 |    .021027   .0043869     4.79   0.000     .0124289    .0296251
       wage9 |   .0159427   .0022335     7.14   0.000     .0115652    .0203203
       distp |  -.0355589   .0162329    -2.19   0.028    -.0673748   -.0037431
       distm |  -.0024083   .0112658    -0.21   0.831    -.0244888    .0196722
       disth |   .0186932   .0039384     4.75   0.000      .010974    .0264124
  percapcons |  -.0001459   .0000278    -5.26   0.000    -.0002003   -.0000915
      durat1 |  -5.771466   .1406919   -41.02   0.000    -6.047217   -5.495715
      durat2 |  -4.947835   .1222363   -40.48   0.000    -5.187414   -4.708256
      durat3 |  -4.690019   .1193602   -39.29   0.000    -4.923961   -4.456078
      durat4 |  -4.054464   .1132586   -35.80   0.000    -4.276447   -3.832481
      durat5 |  -3.055883   .1082449   -28.23   0.000    -3.268039   -2.843727
      durat6 |  -3.560284   .1130896   -31.48   0.000    -3.781936   -3.338633
      durat7 |  -2.825943   .1099477   -25.70   0.000    -3.041436   -2.610449
      durat8 |  -2.238741   .1094063   -20.46   0.000    -2.453173   -2.024308
      durat9 |  -1.427979   .1099217   -12.99   0.000    -1.643421   -1.212536
     durat10 |  -.3967904   .1152271    -3.44   0.001    -.6226313   -.1709495
     durat11 |  -2.168164   .1486318   -14.59   0.000    -2.459477   -1.876851
------------------------------------------------------------------------------

.
end of do-file

Since I am interested in the marginal effects of the variables on the
probability of hazard,

I do

margins,dydx(*)

But this gives me the following output

 margins,dydx(*)

Average marginal effects                          Number of obs   =      47569
Model VCE    : OIM

Expression   : Pr(school_left), predict()
dy/dx w.r.t. : childage 1.childfemale 1.urban 1.scstobc 1.casteother
1.dadp 1.dadm 1.momp 1.momm wagep wage5 wage8
               wage9 distp distm disth percapcons durat1 durat2 durat3
durat4 durat5 durat6 durat7 durat8 durat9
               durat10 durat11

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    childage |  (not estimable)
1.childfem~e |  (not estimable)
     1.urban |  (not estimable)
   1.scstobc |  (not estimable)
1.casteother |  (not estimable)
      1.dadp |  (not estimable)
      1.dadm |  (not estimable)
      1.momp |  (not estimable)
      1.momm |  (not estimable)
       wagep |  (not estimable)
       wage5 |  (not estimable)
       wage8 |  (not estimable)
       wage9 |  (not estimable)
       distp |  (not estimable)
       distm |  (not estimable)
       disth |  (not estimable)
  percapcons |  (not estimable)
      durat1 |  (not estimable)
      durat2 |  (not estimable)
      durat3 |  (not estimable)
      durat4 |  (not estimable)
      durat5 |  (not estimable)
      durat6 |  (not estimable)
      durat7 |  (not estimable)
      durat8 |  (not estimable)
      durat9 |  (not estimable)
     durat10 |  (not estimable)
     durat11 |  (not estimable)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Can someone explain what am I doing wrong? How do I get the marginal
effects after running the logit?

Best

Urmi
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