The coefficients are not really interpretable since they give you the marginal effect on the latent scale, where the true metric is unknown since they are identified up to scale only.
You probably want to use the margins command after you fit the
ologit, like this:
This will give you the additive marginal effect on the probability for each of the three outcomes from seeing the gain frame.
You can also calculate $\exp(\beta),$ which gives you the multiplicative marginal effect on the odds of higher outcome compared with the lower outcome. Personally, I find these harder to wrap my brain around. You can get this by adding the
or option at the end of the
ologit. In this case, $\exp(.4554101)=1.5768199$, which means that the odds of being in the top category versus the combined middle and low are about $1.6$ times higher for those who saw the gain frame compared to the other one. You can also say the same thing about top and middle compared to the bottom category.