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?

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:

$$Treatment -> Countries -> Firms$$

Abadie, 2017 have a great paper co-authored with Wooldridge 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 examine 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?

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?