Hi Stata users,
Please I'd help for this issue. I run a xtologit model where my dependent variable is exportation ranking from 1 to 3 (where 1 is the best option). The results of the odds ratio are:
HTML Code:
. xtologit RANKING_EXPORT i.CEO_WOMEN i.FEMALE_OWNER LOG_SIZE ROA , nolog or

Random-effects ordered logistic regression      Number of obs     =      2,186
Group variable: IDENT                           Number of groups  =        256

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          1
                                                              avg =        8.5
                                                              max =         12

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =     188.64
Log likelihood  = -1187.1172                    Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
RANKING_EXPORT | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
               |
   1.CEO_WOMEN |      .4384   .1975547    -1.83   0.067     .1812577    1.060339
1.FEMALE_OWNER |   .8843898   .3665757    -0.30   0.767     .3924862    1.992797
      LOG_SIZE |   .0099495   .0034347   -13.35   0.000     .0050578    .0195725
           ROA |   .9995997   .0078575    -0.05   0.959     .9843172    1.015119
---------------+----------------------------------------------------------------
         /cut1 |  -33.12666   2.379283   -13.92   0.000    -37.78997   -28.46335
         /cut2 |  -27.62037   2.273256   -12.15   0.000    -32.07587   -23.16486
---------------+----------------------------------------------------------------
     /sigma2_u |   19.18655   2.829527                      14.37031    25.61695
--------------------------------------------------------------------------------
LR test vs. ologit model: chibar2(01) = 1663.05       Prob >= chibar2 = 0.0000
The odds ratio if the CEO is woman is .438 (p=0.067), it means that if the CEO of the company is a woman (CEO_WOMAN=1), the odds of high export versus the combined 2-3 categories are (1/0,40=2,5) 2.5 smaller, given that all of the other variables in the model are held constant.
However, when I run the marginal effect, for the otcome=1, we can say that if the CEO of the company is a woman, the probability of belonging to the first group in the exportation ranking is 7.6% higher. I notice that these results are contradictories. Please, could someone help me?
Thanks!
HTML Code:
. . margins, dydx(*) predict(pu0 outcome(1))

Average marginal effects                        Number of obs     =      2,322
Model VCE    : OIM

Expression   : Predicted mean (1.RANKING_EXPORT), assuming u_i=0, predict(pu0 outcome(1))
dy/dx w.r.t. : 1.CEO_WOMEN LOG_SIZE ROA

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 1.CEO_WOMEN |   .0766569   .0399485     1.92   0.055    -.0016407    .1549545
    LOG_SIZE |   .3866462   .0248591    15.55   0.000     .3379232    .4353691
         ROA |    .000163   .0006111     0.27   0.790    -.0010347    .0013608
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. . margins, dydx(*) predict(pu0 outcome(2))

Average marginal effects                        Number of obs     =      2,322
Model VCE    : OIM

Expression   : Predicted mean (2.RANKING_EXPORT), assuming u_i=0, predict(pu0 outcome(2))
dy/dx w.r.t. : 1.CEO_WOMEN LOG_SIZE ROA

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 1.CEO_WOMEN |  -.0070803    .012385    -0.57   0.568    -.0313544    .0171938
    LOG_SIZE |   .0028512   .0474311     0.06   0.952    -.0901121    .0958146
         ROA |   1.20e-06   .0000202     0.06   0.952    -.0000384    .0000408
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. . margins, dydx(*) predict(pu0 outcome(3))

Average marginal effects                        Number of obs     =      2,322
Model VCE    : OIM

Expression   : Predicted mean (3.RANKING_EXPORT), assuming u_i=0, predict(pu0 outcome(3))
dy/dx w.r.t. : 1.CEO_WOMEN LOG_SIZE ROA

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 1.CEO_WOMEN |  -.0695766   .0319731    -2.18   0.030    -.1322426   -.0069106
    LOG_SIZE |  -.3894974   .0334593   -11.64   0.000    -.4550764   -.3239183
         ROA |  -.0001642   .0006144    -0.27   0.789    -.0013684    .0010399
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.