Given the following linear model $Y=int+aX1+bX2+c(X1*X2)+e$, where $X1$ and $X2$ are the main variables, ($X1*X2$) is the interaction term, and $a$, $b$, and $c$ are the corresponding coefficients.
Question 1: Is there is a way to determine whether the interaction effect $c$ reflects whether the effect of $X1$ on $Y$ varies by $X2$, or if instead the effect of $X2$ on $Y$ varies by $X1$?
Question 2: If this were a multi-level model (with observations across time nested within country) where $X1$ is a country-level, time-invariant variable, and $X2$ is a country-level variable that varies with time (country-year companion), then the interaction will then be country-year too. In this case, does that mean $c$ can only be added to $b$ as $X2$ and $(X1*X2)$ are both time-variant while $X1$ is not? Is it possible that significant $c$ actually means that the effect of $X1$ on $Y$ varies by $X2$?
Regarding to question 2, sorry I might be a bit confusing here. Let me put it in this way. As an example, there are two countries and we look at the effect of the legal origin (X1) on its stock market performance (Y). We also try to test the moderator effect from the country's GDP (X2) to see whether GDP would alter the effect of X1 on Y. Now, the legal origin is time-invariant (i.e. it will always be common law system), while GDP is different every year. When we interact legal origin with GDP, the interaction term will become time-variant, just like GDP. My question is, it seems like that we cannot add the interaction term to X1 due to different data structure. How could we interpret the GDP as a moderator effect of X1 and Y then?