This is a central question in what I do and in years of looking at the literature and talking to statistical experts I have never found an answer. I often get asked which of a set of variables, some of which are dummies, has more impact on a dependent variable than the others. So if there are three predictors this has the most impact on change in the DV followed by this variable. For example, if the dependent variable is income at closure, and you have a spending predictor, and two categorical predictor, which of the three has the most impact on increasing income at closure. As I understand it slopes as normally constructed don't answer this unless they are all measured on exactly the same scale. You can use standardized coefficients, but there appears to be significant doubt in some quarters if using standardized coefficients is valid with dummy variables (many of my variables are categorical). I am surprised how little this issue is addressed in regression, one statistician told me determining relative impact is not the goal of regression.
The whole point is that we would focus on which would raise income (the dependent variable) the most. This is what I mean by relative impact - its not a theoretical issue, it is where we focus resources to make improvements, so we want to find out which variables will generate the biggest increase in a dv when the variables are measured on different scales.