I am analyzing some data using an OLS model. Data represent managers working for US cities. Within each city, we surveyed more than one manager (max 5). Multiple cities per state were surveyed.
I'd like to use clustered standard errors to account for possible clusters at the city or at the state level (state policies might be relevant in our study). Some researchers (e.g., Cameron & Miller, 2015) suggest clustering at the highest level (the state level, in my case). As a check, I tried to estimate clustered SE at both levels and:
- When I use clustered SE at the city level, standard errors become slightly larger, but overall they are very similar to OLS results.
- When I use clustered SE at the state level, standard errors become much smaller, leading to quite different results than the OLS model.
What are the possible reasons for such differences? What estimates should I consider? The fact that the standard errors change more when I look at the state-level, would this be a suggestion that state-level clusterization is more important?
My sample contains 2250 observations, grouped in 487 cities (average cluster size = 4, but some cluster size = 1) and 49 states (average cluster size = 30).
Best Answer
What you observe can be explained by the correlations in the measurements within the clusters. Namely, when you select an analysis, such as OLS that does not account for these correlations, you expect that standard errors of within clusters effects to be overestimated, and standard errors of between clusters effects to be underestimated.