Dear statalist:

I have an individual-year panel, and I am estimating a fixed effect ordered logit model using xtologit. My dependent variable is happiness measured from 1 to 5. Here is my code, very simple:

Code:
 xtologit happiness hhincome_per_log rd_hhper health , vce(cluster CID)
To obtain the marginal effect at outcome(5), I run

Code:
margins, dydx(*) predict(pu0 outcome(5)) atmean post
I get the following result:

Code:
. margins, dydx(*) predict(pu0 outcome(5)) atmean post

Conditional marginal effects                      Number of obs   =       8644
Model VCE    : Robust

Expression   : Predicted mean (5.happiness), assuming u_i=0, predict(pu0 outcome(5))
dy/dx w.r.t. : hhincome_per_log rd_hhper health
at           : hhincome_p~g    =    9.837705 (mean)
               rd_hhper        =    .0920682 (mean)
               health          =    .6171911 (mean)

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
hhincome_per_log |  -.0158093   .0054077    -2.92   0.003    -.0264082   -.0052103
        rd_hhper |  -1.386275   .1765556    -7.85   0.000    -1.732318   -1.040233
          health |   .1109692   .0088198    12.58   0.000     .0936827    .1282556
----------------------------------------------------------------------------------

I don't understand why the absolute value of the coefficient of rd_hhper is larger than 1.

I run dozen of similar regressions using the same dataset, most of the results look normal. As far as I understand, the coefficients here mean how the probability of having happiness being 5 will change with the change of the explanatory variable. But maybe I am wrong. Any help is appreciated!