Does it satisfy Parallel trend assumption if coef before the shock is non-zero yet constant over time?

[James Park]

The treatment is in 2003. I want to estimate the impact of this treatment differed by race.

The estimation result from the usual test for parallel trend assumption (shown below) says the coefficient was negative already before the treatment. I(YYYY) is the usual year dummy.

Variable           Coef    p-value
I(2000)*White       -0.1     0.000
I(2001)*White       -0.1     0.000
I(2002)*White       -0.1     0.000
I(2003)*White       -0.2     0.000
I(2004)*White       -0.3     0.000
I(2005)*White       -0.4     0.000

"White" still had some impact before the shock. It's non-zero, although it's constant over time.

Can I say this result satisfies the parallel trend assumption? Even if #2 and #3 are typical DID results that pass the parallel trend assumption, #1 invalidates the parallel trend assumption?

Or perhaps, am I just estimating a different thing than typical DID?

[Clyde Schechter]

As you do not show the code you used for your DID estimation, nor shown example data, nobody can vouch for the correctness of your analysis.

Nevertheless, assuming your analysis is correct, the kind of pattern you show is precisely what you expect to see when the parallel trends assumption is met. The effect of White is constant before the treatment begins, and then changes after the onset of treatment. Parallel trends means that the difference between the treated and untreated groups (White and non-White in your situation) is constant in the absence of treatments. Think of it in geometry: the distance between two parallel lines is constant. That's what you see here.