I'm wondering how an instrumental variable addresses selection bias in regression.
Here's the example I'm chewing on: In Mostly Harmless Econometrics, the authors discuss an IV regression relating to military service and earnings later in life. The question is, "Does serving in the military increase or decrease future earnings?" They investigate this question in the context of the Vietnam war. I understand that military service cannot be randomly assigned, and that this is a problem for causal inference.
To address this issue, the researcher uses draft eligibility (as in "your draft number is called") as an instrument for actual military service. That makes sense: the Vietnam draft randomly assigned young American men to the military (in theory--whether the draftees actually served touches on my question). Our other IV condition seems solid: draft eligibility and actual military service are strongly, positively correlated.
Here's my question. It seems like you'd get self-selection bias: maybe richer kids can get out of serving in Vietnam, even if their draft numbers are called. (If that wasn't actually the case, let's pretend for the sake of my question). If this self-selection creates systemic bias within our sample, how does our instrumental variable address this bias? Must we narrow our scope of inference to "the types of people who couldn't escape the draft?" Or does the IV somehow salvage that part of our inference? If anybody could explain how this works, I'd be very grateful.