Residual variance for glmer model missing

Question

I am trying to calculate the intraclass correlation for a random intercept model fit with glmer() from the lme4 package in R.

Here is how the model looks:

model1 <- lme4::glmer(funded ~ 
                          tweeted +
                          `Project Resource Type` + 
                          `Project Subject Category` +
                          `School Poverty Level` +
                      (1 | `School State`),
                      family = "binomial",
                      data = df)

Here is the output (fixed effects omitted):

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: funded ~ tweeted + `Project Resource Type` + `Project Subject Category` +  
    `School Poverty Level` + (1 | `School State`)
   Data: df

      AIC       BIC    logLik  deviance  df.resid 
 599914.4  600091.2 -299941.2  599882.4    465409 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.2563 -1.2068  0.6538  0.7744  1.3985 

Random effects:
 Groups       Name        Variance Std.Dev.
 School State (Intercept) 0.04272  0.2067  
Number of obs: 465425, groups:  School State, 51

Beneath Random effects:, I expected there to be individual (in this case, an individual project from Donors Choose) Residual Variance and Std.Dev. estimates, as well as the group (School State) Variance and Std.Dev. estimates, but there is not.

Can these estimates be extracted from the output? Is there a reason it is not included? I recognize this may be a statistics-focused question relevant to Cross Validated, but I think because it is related to the output it is appropriate here.