I have the following regression
$children = \beta_0 + \beta_1 \log(earnings) + \beta_2 grandparents + \epsilon$
and $\beta_1>0$ with $p$=0.01 and $\beta_2>0$ with $p$=0.01, and N is large (N>10.000) and grandparents takes values 0,1,2,3,4.
Then I add the interaction term ($\log(earnings)*grandparents$) to equation 1, such that:
$children = \beta_0 + \beta_1 \log( earnings) + \beta_2 grandparents+ \beta_3 \log( earnings)*grandparents + \epsilon$
and $\beta_1>0$ with $p$=0.01, $\beta_2$ is no longer statistically significant and also $\beta_3$ is not statistically significant.
I do not understand how to interpret the results and if the interaction term wipes out the direct effect of grandparents since $\log(earnings)$ is always different from 0.
There is a way to test the stat. sign. of the effect of Grandparents in the interacted model? (Thanks Maarten for your previous answer)