I would like to ask a question regarding prediction based on logistic regression model. 

At my work we are trying to predict if an employee is going to leave in the next period. We want to utilize logistic regression for this. Let’s assume we have developed a model based on both continuous (e.g. salary, age etc ) and categorical (e.g. city, department etc – using dummies) independent variables. Let’s assume the model has good adjusted R squared and does not suffer from multicollinearity . For the sake of simplicity let’s assume we have developed regression formula where probability is a function of city (categorical) and salary (continuous): p=f(const + b1\*city\_1 + b2\*city\_2 + … + bn\*salary ) 

My specific question is this. How can I decide which of the factor has the biggest contribution to the fact, that an employee's risk of leaving is high? Is it in this case a city or the salary for the particular employee? And what about if the model is more complex than this containing both categorical and continuous variables and I want to list maybe three the most important factors?
I fear it is not possible to only compare b1\*city\_1 whith b2\*city\_2 and bn\*salary because of what happens if the value for the given employee is city\_3 which is represented by the constant due to the k-1 rule for dummies?

I know my question might sound a bit not structured. Thanks a lot for you advice and effort in advance.