What determines the spatial resolution of a study?
It sounds to me like the reviewer is fixated on a 'rule' they encountered in grad school, but the rule ignores some real-world nuance (as rules do).

Quick caveat: my answer isn't definitive, it's more of an opinion, but it was too long for a comment.

It seems intuitively true that some phenomena just are smooth, so the resolution of the data is not the limiting factor in an analysis of resolution. I'm thinking of things like maps of average seasonal air temperature (say). Certainly, you can combine that with higher-resolution data and still claim to have a high-resolution analysis.

Other data may not be implicitly smooth, but it's interesting to aggregate the data for the purposes of generalization.So the quality of academic institutions obviously varies on a per-institution basis (or per teacher, if you want to get silly about it), but it would be crazy to interpolate data like that and, since it's reasonable to think there might be national-level influence (e.g. in funding for education), it only makes sense to aggregate it that way. Again, I think it's reasonable to argue that you can combine data like this with higher-resolution data and have a high-resolution analysis overall.

Still other data may simply be oversmoothed, or smoothed on the wrong basis. Temperature data by country, or geological data on a 100-km grid... Then I'd say those are limiting the resolution of any analysis you do.

In conclusion...

I can't think offhand of a way to formalize all this, and don't know of any research around it (though I'd bet on its existence). Assuming you don't want to go down the research rabbit hole, or have a protracted argument with the reviewer, my advice is to respond to the objection by using more specific language. Instead of generalizing, specify the resolutions of the various datasets involved and mention where it might be possible to improve the resolution.