Abadie, 2017 have a [great paper][1] co-authored with Wooldridge about *when we should cluster*. And this paper has been summarized by McKenzie [here][2].

I used the paper of [Dasgupta,2019][3] 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 examine the impact of such law implementation on ***firms***' asset growth. 

First of all, from the definition from [Wing, 2018][3], 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?**


  [1]: https://www.nber.org/papers/w24003
  [2]: https://blogs.worldbank.org/impactevaluations/when-should-you-cluster-standard-errors-new-wisdom-econometrics-oracle
  [3]: https://academic.oup.com/rfs/article/32/7/2587/5079300
  [4]: https://stats.stackexchange.com/questions/531782/when-we-can-ignore-clustering