Unit of observation is the individual. Treatment (binary) is assigned on city level. Every state contains 4 cities, 2 get randomly chosen for treatment, 2 control. There are only few (e.g. 5+) states (strata). The outcome of interest is likely to be regionally clustered. I only observe one wave of outcomes, not a panel. (Side remark: For other reasons it is desirable to use state fixed effects.)
How to cluster standard errors for treatment effect inference?
Cameron and Miller (2014) state that
[If] either the regressors or the errors are likely to be uncorrelated within a potential group, then there is no need to cluster within that group [...] If a key regressor is randomly assigned within clusters [...] then the within-cluster correlation of the regressor is likely to be zero. Thus there is no need to cluster standard errors, even if the model’s errors are clustered.
Following this logic, it would not be necessary to cluster at the state level, as city-treatment is random within state. However, varying city sizes introduce within state correlation of treatment. Yet, the small number of states makes it less attractive to cluster at the state level.
I think, because the exact character of the within cluster correlation of treatment is known (city size), there must be a more efficient way to correct for this, i.e. to cluster at the city level and cope in some other way with the ex post within-state correlation of treatment.
A. Colin Cameron and Douglas L. Miller (2014), A Practitioner’s Guide to Cluster-Robust Inference, Journal of Human Resources: http://www.econ.ucdavis.edu/faculty/cameron/research/Cameron_Miller_JHR_2014_July_09.pdf