I believe a similar situations applies to surveys, at least in some settings. That is, paradata might be more valuable than sampling frame data. Of course, many of the surveys that I work on have very weak data on the sampling frame.

In any event, I fit random intercept logistic regression models predicting contact that include some sampling frame data from an RDD survey. The sampling frame data are generally neighborhood characteristics. I recently made this chart, which shows the predicted vs observed contact rates for households in a particular time slot (call window). The dark circles are the predictions by observed values (household contact rates) for the multi-level model. I also fit a marginal logistic regression model. The light gray squares are the predicted by observed values from this model.

It's pretty clear that the sampling frame information does not help differentiate cases in terms of contactibility. That is, the light gray squares are barely differentiated. Cases with

__high__observed contact rates have about the same predicted contact rate as cases with

__low__observed contact rates.

But the random intercept model has much better fit. That is, cases with high observed values (contact rates) also have high predicted values. This graph includes many cases that have fewer than 5 calls on which to base these predictions. It does even better when we have large samples for each household.

To me, in this case, the call records are much more valuable than the sampling frame data.