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As 1muflon1 mentioned, there are two main advantages of adding interaction variables over subsampling are: (1) having higher sample size, leading to higher precision and (2) higher degree of freedom.

Nornal regression equation:

Dependent_variables= pt + Independent_variables + fixed effects + error term (1)

Adding interaction variable equation:

Dependent_variables= pt + developed_dummy*pt + Independent_variables + fixed effects + error term (2)

I am wondering what is the advantage of the explanation of adding interaction variables over subsampling (if having)?

Or what are the difference between explaining the results from these two regressions?

For an example:

When running the regression (1) on each subsample (divide to developed and developing countries) on dependent variable Y1, I have

enter image description here

When running the equation (2) on the whole sample for Y1, I have:

enter image description here

pt_original is pt for the whole sample when regressing equation (1) for Y1

developedpt=developed*pt (developed is a dummy equalling to 1 if firms in developed countries and 0 otherwise)

I know how to explain the second regression result: enter image description here

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