We know that in a logit model, the coefficient $\beta_j$ for the variable $x_j$, measures the impact of the variable on the log(odds)
In order to measure the impact on the Odds, we have to consider the $\exp(\beta_j)$
Specifically, if we have that $\exp(\beta_j)=1.16$, we say that, for one unit increase of $x_j$, the odds increases 16%.
but in this formula $[ ( \exp(\beta_j)-1 ) * 100 ]$ , where does the $-1$ come from? why do we subtract $1$ from the exponential?