I had wanted to ask this community for some advice on some potential methodologies I am developing.
Context: I want to evaluate the effectiveness of a component of this scholarship program, which involves visiting schools and talk about opportunities at universities, qualms and prejudices, and ways to finance studies through their scholarships - unfortunately, school visits are not done evenly across schools.
Primary research questions : Do school visits influence the socio-economic or gender composition of recommended students?
Data I have:
Panel dataset between of 11 admission cycles (2009-2020) and containing a bunch of SSE and financial variables linked to each school.
Dummy variable for if a school visit happened
Dummy if school only recommended one student
Dummy if school recommended as many as allowed
SES info of students recommended from school
For my primary research question, I was curious what model you would suggest? I was thinking about an IV approach, given my dataset suggests a drop-off in recommendations between the years due to a quota implementation - I was thinking that this could be a good IV variable but curious to hear thoughts!