# Sigma interpretation in panel probit model

I am struggling interpreting sigma in a random effect panel probit regression and why is this term strongly significant. I understand all the others results from the regressions.

I used the following fit call :

 Model<-pglm(Variable_to_explain ~ fv + context + male + context*male,
model=("random"),
effect=("twoways"),
index=c("indiv", "period"),