I am using a logistic regression model with continuous independent variables and two log transformed size variables (total assets and total deposits).

My question is how to interpret the results and measure the economic impact?
The normal steps in interpretation I take is as follows: I firstly run stata test on summary statistics (sum), then a logistic regression (logit) and than the marginal effects for the economic significance (mfx).

These results can be explained by sentences as "a one standard deviation increase in $X$ (an independent variable) increases the probability of participation by $X\%$
I will multiply the $dy/dx$ of the marginal effects by the SD to get the % impact.

So far so good, but how do I interpreted the results with the two ln(size) variables?
To clarify my problem I will show you a part of the results:

```
sum stat:
obs mean stdD min max
lnTCD| 2755 -1.469624 2.992985 -15.01948 4.827369
lnTA | 2767 -1.131045 2.796627 -16.51857 6.731258
logit res:
Coeff stERR Z P>Z 95%
lnTCD| 1.088141 .1424768 0.65 0.519 .8418451 1.406494
lnTA | .7880959 .105272 -1.78 0.075 .6065653 1.023954
Margin eff:
dy/dx stERR Z P>Z 95% X
lnTCD| .0104326 .01619 0.64 0.519 -.021308 .042174 -1.09399
lnTA | -.0294112 .0165 -1.78 0.075 -.061749 .002926 -.7886
```

In a normal situation I would multiply lnTA of the marginal effects (-0.0294112) by the Standard deviation of the summary statistic (2.796627) which results in -8.22%

Although this holds for the other non log variables, intuitively this does not sound correct for these (ln) variables.