Triple Difference-in-differences I am currently trying to run a regression working out the change in the mental well-being of ethnic individuals in a set of countries after a migration policy was imposed. I have two waves of data, one before the policy and one after the policy. Essentially the control group are non-ethnic individuals in the countries while the treatment group are the ethnic individuals in the country. The model is:

Where,
Country - is the name of the country analysed
Ethnic - is a dummy variable for whether the individual is an ethnic =1 or non-ethnic=0
Post policy - Is for the individuals interviewed in the second period, after the policy had been placed. 1 if in second wave and 0 otherwise.
Given this the coefficient of interest is  δ_6 as it shows the difference in the well-being of ethnic and non-ethnic individuals after the policy had been imposed. It captures the difference-in-difference indifference.
I wondered if my model makes sense and whether the regression is correct.


I wondered if my model makes sense and whether the regression is correct.

Seems okay, but you're missing one of the double interactions.
Assuming the policy is uniformly adopted, then the model requires the standard triple interaction term, and all double interactions. The ethnic dummy must also be multiplied with the post-policy indicator. For brevity, I re-specified your model below using abbreviated variable names:
$$
MW_{ict} = \alpha + \delta_ 1E_i + \delta_ 2C_c + \delta_3 P_t + \delta_4 (E_i \times P_t) + \delta_5 (C_c \times P_t) + \delta_6 (C_c \times E_i) + \delta_7 (E_i \times C_c \times P_t) + \epsilon_{ict}.
$$
If executed properly, software should return eight coefficients, including the regression constant.
