I successfully ran the below model and created a nice a chart showing trends in gender over time for people in a specific department and position in my data. Gender is the dependent variable, and time (year) is the independent variable.

Code:
   mlogit gender_n year, vce(cluster person)
  margins, at(year = (2008(1)2013))
  marginsplot


For a specific department and position, there are NO duplicate people in a given year. (although can be duplicate people across the years). Data sample:
Department A, Position H
person year gender
2 2009 M
2 2010 M
3 2010 F
3 2011 F
I would like to produce the same chart for EVERY department and position in my data. However, on the full dataset, there are duplicate people in a given YEAR, as the same person can be associated with more than one position and more than one department. Data sample:
All Data
person year position department gender
1 2009 H A M
1 2009 H B M
1 2009 L C M
1 2009 L B M
Is this a case where it is justifiable to run separate regression models for each combination of department and position? Or is it better to do a model on all data combined, including department and position and potentially their interaction as independent variables?

Any help is much appreciated. (Note, using Stata 15)