My topic is on the Affordable Care Act's Medicaid expansion which happened in America for different years for different states, and I am seeing how this affected violent crime rates through treating for alcohol abuse. I am doing so by looking at how this impacted different races violent crime rates in different states because different races have different alcohol abuse rates.

For my model, I am doing a generalised difference in difference using panel data. I have grouped together "race" and "state" to be "race-state" to be the individual level of the regression. So my "individuals" are for example: "Asians in NY" "Asians in California" "Native Americans in NY" etc. and the time component refers to the years, ranging from 2010 to 2019.
My regression is:

Y(rs)t = Ξ΄(BingeAlcoholRate(rs)𝑑 π‘₯ Medicaid𝑠𝑑) + c(rs)+ d(t)+ controls(rs)t + Ξ΅(rs)t
Where Medicaid𝑠𝑑= {1 π‘€π‘’π‘‘π‘–π‘π‘Žπ‘–π‘‘ β„Žπ‘Žπ‘  𝑏𝑒𝑒𝑛 π‘–π‘šπ‘π‘™π‘’π‘šπ‘’π‘›π‘‘π‘’π‘‘ 𝑖𝑛 π‘‘β„Žπ‘–π‘  π‘π‘Žπ‘Ÿπ‘‘π‘–π‘π‘’π‘™π‘Žπ‘Ÿ π‘ π‘‘π‘Žπ‘‘π‘’ π‘Žπ‘‘ π‘‘π‘–π‘šπ‘’ 𝑑, 0 π‘œπ‘‘β„Žπ‘’π‘Ÿπ‘€π‘–π‘ π‘’

c(rs) refers to the unobserved race-state effects or the race-state fixed effects that are time invariant. Given that this is a difference-in-difference, do I still need this variable as surely these unobservable effects will disappear, and if I still do, how would I input this data; in particular if I would include, factors like state geography how should I input that data?

d(t) also refers to time effects that are common shocks to all units that vary through time. I am struggling to think of examples for this, could someone give me an idea of what would be included?

Also in my research I have found that no effect is exclusively time invariant for race-states or time variant but for all race-states, but vary in both to at least some degree; making them more suitable for controls. Is this common and how should I approach this?

A bit of a wordy question but than you for reading and all help is welcome!