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What should I cluster at in my regression?
There’s no formal test that will tell you at which level to cluster. If you think that the regressors or the errors are likely to be uncorrelated within a potential group, then there is no need to cluster within that group. Larger and fewer clusters have less bias, but they have more variability, so there’s a kind of a trade-off there.
Is it possible to cluster in two dimensions?
Depending on the structure of your dataset, it might even be possible to cluster in two dimensions, i.e. house and firm level. It depends on whether the house and firm level are nested or not.
Is there a trade-off between bias and clusters?
Larger and fewer clusters have less bias, but they have more variability, so there’s a kind of a trade-off there. To be conservative and avoid bias, use bigger and more aggregate clusters when possible, up to and including the point at which there is concern about having too few clusters.
When do you not need to cluster standard errors?
You want to say something about the association between schooling and wages in a particular population, and are using a random sample of workers from this population. Then there is no need to adjust the standard errors for clustering at all, even if clustering would change the standard errors.
When does clustering matter, one should cluster?
They note there is a misconception that if clustering matters, one should cluster. Instead, under the sampling perspective, what matters for clustering is how the sample was selected and whether there are clusters in the population of interest that are not represented in the sample. So, we can imagine different scenarios here:
When do you cluster differences at the state level?
Then cluster by village. This is also why you want to cluster difference-in-differences at the state-level when you have a source of variation that comes from differences across states, and why a “treatment” like being on one side of a border vs the other is problematic (because you have only 2 clusters).