I am researching the funding amounts of start-ups. I am calculating two models:
Model a) Funding Chances (binary dependent variable funding_yesno) on full sample Model b) Log(Funding Amount) on subsample of *funded* start-ups
The IV/CV on both models are identical.
The classic example is estimating "wage offer" and subsampling on labor market participation. In that classic example the "wage offer" can not be observed when not participating in the labor market. However, in our example, we know that the funding amount actually is 0 if a start-up is not funded.
Therefore, I am wondering, is Model B a case of endogenous sample selection, what induces a sample selection bias I need to correct for? Or does the evaluation of such depend on my research question / hypothesis? So, could a hypothesis concerning the funding amount simply state "among funded start-ups" to overcome a potential bias?
Futhermore, Wooldridge (2016, p. 556) states on Heckman correction: "Intuitively, if we do not have a variable that affects selection but not y, it is extremely difficult, if not impossible, to distinguish sample selection from a misspecified functional form". Therefore, I am wondering: can't I simply apply the Heckit approach, with the same IV/CV for both the Model A (Selection Model) and Model B?
Thanks a lot for your most valued opinions on those questions!