I am trying to estimate the impact of a minimum wage policy in a given region and I was initially thinking of only using a DiD design. However, I came across this causal inference post on synthetic control methods SCM, and I am now considering but was curious if one method is "superior" to the other. Specifically, I want to know if I should pick my own control group based on institutional knowledge, as in the case of a DiD. Or use SCM to create an artificial control group based on a dataset of all 8 regions in X country I am studying.

I have several macro variables/indicators at the region-level on both my treated and control regions, and both before and after treatment. These variables include:

unemployment rate
Labor fore participation rate
employment rate 
Gini coefficient
share of prime-age workers
median household income
median individual income
poverty rate
% of population on food stamps
% of workforce receiving unemployment insurance