Interpretation of multinominal logit regression (Stata)
You have that:
$$
\begin{align*}
\ln\left(\frac{\Pr(D|X = 1)}{Pr(A|X = 1)}\right) &amp;= \ln\left(\frac{\Pr(D|X = 0)}{\Pr(A|X= 0)}\right) -1.264 + 0.627,\\
&amp;\le \ln\left(\frac{\Pr(D|X = 0)}{\Pr(A|X= 0)}\right)
\end{align*}
$$
So taking exponents, we have that:
$$
\frac{\Pr(D|X = 1)}{\Pr(D| X = 0)} \le \frac{\Pr(A|X = 1)}{\Pr(A|X = 0)}. 
$$
What you find is that:
$$
|\Pr(A|X=1)- \Pr(A|X = 0)| &gt; |\Pr(D|X = 1) -\Pr(D|X = 0)|
$$
These two are not necessarily in conflict.
For example:
$$
\Pr(D|X= 1) = 0.1\\
\Pr(D|X = 0) = 0.3\\
\Pr(A|X = 1) = 0.4\\
\Pr(A|X = 0) = 0.677
$$