The approach you describe seems like the best option for identifying the most granular geography for each person in the CPS Voter Supplement. I’m finding that about 40% of respondents in the November 2022 supplement resided in one of 556 counties that are large enough to be identified on their own. Since metro areas are composed of one or more counties, there are many cases where the county of residence is not identified, but the metro area is. As an example, of those residing in unidentified counties in the November 2022 supplement, about 60% resided in one of 151 identified METFIPS metro areas.
The remainder of the population which resided in neither an identified county or metro area can be identified as residing in one of the 50 states. These respondents will typically be located in small rural counties in their state, however they can also include residents of smaller unidentified metro areas as well. You might use the variable METRO to further divide respondents within a state between metro and non-metro residents (i.e., METRO = 1 vs METRO = 2, 3, 4). Note that METRO = 0 (not identified) groups respondents both in unidentified metro areas and in non-metro areas; as a result, their metro status is indeterminate. INDIVIDCC is unlikely to give you much additional information since in most cases if a county is not identified then the individual city is also not identified.
With all of these tools in mind, you should be aware that the resulting small sample sizes for these granular geographical areas may add significant sampling error to your estimates. The Census Bureau specifically states that estimates for individual metropolitan areas “should be treated with caution. Although estimates for the larger areas such as New York, Los Angeles, and so forth, should be fairly accurate and valid for a multitude of uses, estimates for the smaller metropolitan areas (those with populations under 500,000) should be used with caution because of the relatively large sampling variability associated with these estimates.” Similar precaution should be taken with county-level estimates as well. One method you can use to be more informed about the sampling variability of your estimates is to calculate the standard error of your estimates by using the CPS supplement replicate weights. While you will need to download these separately since we currently do not provide them on IPUMS CPS, you can link the replicate weight observations with an IPUMS CPS extract using the linking keys HRHHID, HRHHID2, and LINENO (referred to as PULINENO in the CB data). Replicate weights allow a single sample to simulate a range of possible alternative samples, generating more informed standard error estimates that mimic the theoretical basis of standard errors while retaining all information about the complex sample design. See our user guide with sample code for running analyses using replicate weights.
As far as I’m aware, there are no county-level summary statistics published by the Census Bureau for the CPS Voter supplement. However, you can obtain education and income summary statistics on the county level through our spatial data project, IPUMS NHGIS. We recommend that new users of IPUMS NHGIS review the short video tutorials on the user guide as well as relevant portions of the FAQ page to familiarize themselves with the website.