This sample of counties was "stratified" by region, but the selection of counties was not random. The name for this kind of sample is "purposive".
Lohr (1999, p 463) has the following evaluation of purposive samples, based on an example discussed by Jerzy Neyman (1934). Neyman's paper is perhaps the foundation document of survey sampling theory.
Here is the quote from her book:
Neyman's paper pretty much finished off the idea that results from
purposive samples could be generalized to the population. He
presented an example of the purposive sample taken by Gini and Galvani
in the late 1920's. Gini and Galvani chose 29 districts that gave the
averages of all 214 districts in the 1921 Italian census, on a dozen
variables. But Neyman showed that all statistics other than the
average values of the controls showed a violent contrast between the
sample and the whole population.
In your case, I assume that the experts had some criteria on which they based their selection. Can you describe them and how they defined "typical"? Like Neyman, if you have external information on the same criteria for all counties, you can compare the distribution of the purposive sample and the entire population.
The pity is that you could have created a valid random sample based on the criteria that the experts used. It would probably have involved stratifying on their criteria in the selection of counties and of areas within counties. Tille (2006) discusses some advanced sampling algorithms that can balance a sample on multiple criteria. (These are implemented in R.) But you could have created a valid random sample counties even without using Tille's methods.
Sharon Lohr, 1999, Sampling. Design and Analysis, Duxbury.
Jerzy Neyman, 1934. On the two different methods of the representative method: The method of stratified sampling and the method of purposive selection. J. Royal Statistical Society 197: 558-60
Tillé, Yves. 2006. Sampling algorithms. New York: Springer.