  The effect of external factors $z$ on technical inefficiency ($TI$) in
stochastic frontier (SF) production models is often specified through the
variance of inefficiency term $u$. In this setup the signs of marginal effects
of $z$ on $TI$ and technical efficiency $TE$ identify how one should control
$z$ to increase $TI$ or decrease $TE$. We prove that these signs for $TI$ and
$TE$ are opposite for typical setups with normally distributed random error $v$
and exponentially or half-normally distributed $u$ for both conditional and
unconditional cases.
  On the other hand, we give an example to show that signs of the marginal
effects of $z$ on $TI$ and $TE$ may coincide, at least for some ranges of $z$.
In our example, the distribution of $u$ is a mixture of two distributions, and
the proportion of the mixture is a function of $z$. Thus if the real data comes
from this mixture distribution, and we estimate model parameters with an
exponential or half-normal distribution for $u$, the estimated efficiency and
the marginal effect of $z$ on $TE$ would be wrong. Moreover, for a misspecified
model, the rank correlations between the true and the estimated values of TE
could be small and even negative for some subsamples of data. These results are
demonstrated by simulations.
