Correlations between explanatory variables I have a multiple regression with 7 explanatory variables. All are statistically significant and the coefficients, in general, make sense. However, I produced a correlation matrix and noticed that correlations between some explanatory variables are quite high.

Data came from different sources, so this is a 'genuine' connection between some variables. Is there a way to check if this is a problem? What level of correlation should cause concern? Thank you.

If your only objective is to predict the explanand, then this is not a problem.
However, if you want to interpret the regression coefficients, it is a problem. With these data, it is not possible to distinguish a causal role for cattle from a causal role for celt, opp, or size.
The mathematical expression of this is that you are solving a set of equations that is nearly degenerate, so the coefficients you get are unstable. You can get a feel for this by drawing some random subsets of your data and calculating a regression for each - you will see that the coefficient for cattle varies a lot.
If the contributions of these different factors matter, then I suggest making a random forest model. Its importance scores will be a bit more helpful than the regression coefficients in untangling the different contributions.
