Clustered Standard Errors occur when a few observations in the data set are linked to each other. The correlation happens when the trait of an individual such as the socio-economic background is similar or identical for observation within clusters.

A vital component of statistical inference is the accurate standard errors. So, if there are CSEs in the data, adjustments should be made for clustering prior to running the further analysis. Hand calculations for these errors are complicated than average statistical formulas.

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When Clustering Matters Then You Should Do

There is a common opinion that there is no harm in large samples to cluster the standard errors. Thus, one must cluster at the greatest level of the aggregation subject to the sample issues that are finite. When clustering matters, the cluster should be done. When it does not matter, cluster the standard errors because it does not harm mainly in large samples.

Depending on this perception, discussions of cluster adjustments recommend the researchers in calculating diagnostics to inform whether one should or not cluster. The diagnostics compare the standard errors without or with clustering adjustments

Whether clustering makes any difference to standard errors or not, it should not form the basis to determine whether to cluster or not. If clustering matter, then one must cluster. What actually matters in clustering is the way the sample was chosen and whether clusters are present in the population, which are not in the sample.

The Experimental Design

One of the reasons for clustering that we are familiar with is when the clusters of units instead of the individual units have been designated to a treatment. When a treatment is given at the individual, you do not have to cluster

It has been noted that there lies confusion regarding clustering using fixed effects. According to the general rule, you have to cluster when either the assignment or sampling was clustered for treatment. The cluster adjustments shall make an adjustment using fixed assets in case there lies heterogeneity in the effects of treatment.

There is a new perspective regarding clustering standard errors. There are motivations for adjustments; one is based on the clustered assignment and the other one is based on the clustered sample. When researchers search for justification behind clustering, they rely on the justifications of clustered sampling. This has resulted in new conclusions regarding when to adjust the standard errors to cluster and at the level, the adjustment should be done. The researcher must assess whether  sampling is clustered or not.

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