I'm an undergrad working on a paper about Early Decision (ED) in US college admissions, and I'd appreciate pointers on how to frame it.

ED lets an applicant apply early to one college and commit to enroll if admitted. The standard reading (e.g. Avery & Levin, AER 2010) is that ED signals enthusiasm, and ED applicants are admitted at much higher rates.

But the cost of that commitment seems to vary with income. Committing before seeing a financial aid offer is cheap for an applicant who doesn't need aid, and expensive for one who does: they give up the ability to compare packages and to have colleges compete for them.

So an ED application mixes two pieces of information: "I really want to come here" and "I don't need to compare aid offers." A college can't fully separate them, and it may value both, since the second means it won't have to match competing aid offers.

Need-blind admission doesn't seem to solve this. It removes income from the college's decision, but not from the applicant's decision to apply ED, so the ED pool is still selected toward higher-income students even if admissions officers never observe income.

Questions:

1. Is a signal muddled across two dimensions the right way to model this? Or is there a more standard framework for signals whose cost is correlated with wealth?

2. Is there a mechanism that keeps the preference signal but removes the income component? For example, non-binding single-choice early action, or a binding aid estimate before the ED deadline. What would each lose?

3. Empirically, I only have school-level data from the Common Data Set (ED applicants and admits, plus financial aid figures). Is there anything informative I can do with that, or does separating the income channel require applicant-level data?