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  • Marginal effect after xtmlogit

    Hi,

    I am trying to get average marginal effect after xtmlogit.
    Here is the example of using -estatus-.
    Code:
    webuse estatus
    
    xtset id year
    
    xtmlogit estatus i.hhchild##(c.hhincome l2.c.hhincome) 
    
    margins hhchild, dydx(hhincome)
    
    ----------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
    -----------------+----------------------------------------------------------------
    hhincome         |
    _predict#hhchild |
               1#No  |   .0003457   .0004927     0.70   0.483      -.00062    .0013113
              1#Yes  |   .0005054   .0004882     1.04   0.301    -.0004514    .0014622
               2#No  |  -.0031088   .0004538    -6.85   0.000    -.0039983   -.0022194
              2#Yes  |  -.0032074   .0003835    -8.36   0.000     -.003959   -.0024558
               3#No  |   .0027631   .0005181     5.33   0.000     .0017477    .0037786
              3#Yes  |    .002702   .0004685     5.77   0.000     .0017838    .0036202
    ----------------------------------------------------------------------------------
    
    margins hhchild, dydx(hhincome L2.hhincome)
    
    ----------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
    -----------------+----------------------------------------------------------------
    hhincome         |
    _predict#hhchild |
               1#No  |   .0005094   .0027027     0.19   0.851    -.0047878    .0058066
              1#Yes  |  -.0001652   .0024298    -0.07   0.946    -.0049275    .0045971
               2#No  |  -.0021406   .0021466    -1.00   0.319    -.0063479    .0020666
              2#Yes  |  -.0020974   .0017222    -1.22   0.223    -.0054729     .001278
               3#No  |   .0016312   .0029516     0.55   0.580    -.0041539    .0074164
              3#Yes  |   .0022626   .0025153     0.90   0.368    -.0026674    .0071926
    -----------------+----------------------------------------------------------------
    L2.hhincome      |
    _predict#hhchild |
               1#No  |  -.0001637   .0027309    -0.06   0.952    -.0055161    .0051887
              1#Yes  |   .0006706   .0024668     0.27   0.786    -.0041643    .0055055
               2#No  |  -.0009682   .0021789    -0.44   0.657    -.0052387    .0033023
              2#Yes  |    -.00111   .0017582    -0.63   0.528     -.004556     .002336
               3#No  |   .0011319   .0029834     0.38   0.704    -.0047154    .0069792
              3#Yes  |   .0004394   .0025543     0.17   0.863    -.0045669    .0054458
    ----------------------------------------------------------------------------------
    Why do I get different results for hhincome?

    The way I understand is that the two commands should give the same results for hhincome, and the second command is a shorthand for getting marginal effects of multiple variables. Do I miss anything?
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