Marginal Effects at the Mean vs Average Marginal Effects
The first is that in the past when studying the implications from nonlinear (i.e. logit) models, many people including me used to analyse “marginal effects at the margin”. In short, this boils down to holding most independent vars constant at their grand means/modes while plugging a range of hopefully relevant values for one or two focal variables into the equation. This approach, which is known as analysing marginal effects at the mean, is easier to understand than to explain but can result in highly unrealistic scenarios if your independent variables are highly correlated (think of holding age constant while varying pensioner/non-pensioner status).
Therefore, looking at average marginal effects might make more sense. These are calculated by varying the focal variable while holding everything else at their variables. This is was the margins command does by default. Michael Norman Mitchell has a post that clearly illustrates the differences between the two approaches to the estimation of margins. Moreover, there is an older article by Tamás Bartus on his margeff command that is also quite instructive.
Dubious Confidence Intervals
But one problem remains: margins uses a normal approximation for calculating confidence intervals. As a result, after estimating a model for categorical dependent variables, you might end up with a CI for your margins that includes zero, which obviously does not make much sense. Roger Newson seems to know how to get around this issue, but I haven’t tested this approach yet.