Asking about clustering condition following Abadie, Wooldridge 2017 Abadie, 2017 have a paper about when we should cluster. And this paper has been summarized by McKenzie here.
I used the paper of Dasgupta,2019 to link to the summarized work of McKenzie. So, in Dasgupta's paper, he examines the impact of anticollusion laws of firms' asset growth in a standard Difference-in-Differences (DID) setting with multiple groups and periods. In specific, each country will pass the anticollusion laws in different years, and he examines the impact of such law implementation on firms' asset growth.
First of all, from the definition from Wing, 2018, DID is a quasi-experimental research design. So, I am wondering if I can apply the "The Experimental Design Reason for Clustering" for DID setting as above?
Second, if my first argument is correct, in the summarized work, McKenzie mentioned that

Then if the treatment is assigned at the individual level, there is no
need to cluster (*)


(*) unless you are using multiple time periods,
and then you will want to cluster by individual, since the unit of
randomization is individual, and not individual-time period.

So, is it in Dasgupta's case as above, he does not need to cluster at all based on the above judgment?
The schematic of the data collection scheme and experimental design of Dasgupta, 2019 is:
$$Treatment -> Countries -> Firms$$
 A: To briefly summarize Abadie, et al., there are two reasons to cluster standard errors:

*

*The sampled entity and the treated entity are not the same.

*The treated entity and the measured entity are not the same.

In Dasgupta, 2019, it appears that the treatment condition (leniency laws) is applied to countries, while they measure the asset growth of firms within those countries. While this is a quasi-experimental design, one can imagine that every firm in a given country experiences the same random treatment errors, such as implementation details of the law, etc. To account for these random errors, it makes sense to cluster standard errors by country.
As an aside, I think it is often useful to write out a schematic of the data collection scheme and experimental design. I may be misunderstanding what was done is Dasgupta, 2019, but it seems to be something like this:
$$Treatment -> Countries -> Firms$$
Assuming this is how the data was collected, we see that there is no need to cluster for "sampling reasons" but there is reason to cluster for "treatment assignment" reasons.
