They will simply be sucked up into one variable. To "fix" this, you could simply drop all of Brazil's characteristics and leave a constant in: $\ln(EXP_D) = c + \beta_1 \ln(GDP_D) + \beta_2 \ln(POP_D) + \ldots$

They will simply be sucked up into one variable. To "fix" this, you could simply drop all of Brazil's characteristics and leave a constant in: $\ln(EXP_D) = c + \beta_1 \ln(GDP_D) + \beta_2 \ln(POP_D) + \ldots$

Edit

Just understood the question from the comments below.

If you have only a cross-sectional data, you cannot distinguish the effects of GDP, population, etc. of the origin country (Brazil) on its exports.

One way you could do this is to run a panel regression... then you'll have variation in GDP, population, exports, etc.

In order to do this, you need to assume that the relationship remains the same throughout your sample period. If, for example, you have a change in government, then you might need to control for that, etc.