Question About Simultaneity Bias/Causality in Panel Regression for Master's Thesis

Hi everyone, I have a question for you.

I am currently doing my Master's thesis in Public Policy, focusing on quantitative methods. What I currently want to research for my thesis is the role of social insurance programs on human capital development. How I want to operationalize that is by looking at how in the United States the Children's Health Insurance Program (CHIP) has affected educational outcomes like test scores on a state level. I wanted to analyze panel data on a county level for one state, but someone I was just talking to said they felt that might not be appropriate for some statistical limitations.

He told me that there would be problems of endogeneity because of simultaneous causality. I'm aware that health status and education can influence each other at the same time, but I felt with the specific variables/indicators that I had chosen: enrollment rates in CHIP on a county level and county average test scores, that for a given year, the average test score wouldn't necessarily direct cause any changes in enrollment rates. It might have an indirect association, but with control variables I had felt I could control for any of those that might approximate an indirect reverse causality.

This advisor recommended trying to implement the research on an individual level, but the problem with that is I don't know if such data is available. I've been looking but with little success in finding anything relevant.

So basically my question is do you agree with the advisor and this is not a good design because of reverse causality or is it acceptable? And if it is the former, are there any ways I can limit or control for reverse causality short of instrumental variables? I had looked into used lagged dependent or independent variables, but I'm not an expert, and from a brief review of literature it seems like that doesn't really address simultaneity bias well.

Any help on the topic is very much appreciated.