I'd like to make sure I'm interpreting average marginal effects for categorical and continuous variables correctly (interpretation of binary variables seems straightforward).
Using Stata, I ran a logistic regression to model a binary outcome as a function of Census region (1 = Northeast, 2 = Midwest, 3 = South, and 4 = West) and age category (values 1-5; modeled as a continuous variable). Northeast is the reference category for region.
logistic outcome i.region agecategory
Categorical variable - using this Stata code...
Stata provides an average marginal effect of 0.1 for South (region = 3) vs Northeast (region = 1). Does this mean that the difference between the predicted probability of the outcome is 0.1 percentage points when assuming everyone has a value of region = 3 vs region = 1 (holding age category at its observed value)?
Using this Stata code...
Stata provides an average marginal effect is 0.5. Does this mean that the change in predicted probability of the outcome is 0.5 percentage points for all possible one-unit increments in agecategory: 2 vs 1, 3 vs 2, 4 vs 3, 5 vs 4?