When Should You Adjust Standard Errors for Clustering?

Alberto Abadie, Susan Athey, Guido Imbens and Jeffrey Wooldridge

Research Papers from Stanford University, Graduate School of Business

Abstract: In empirical work in economics it is common to report standard errors that account for clustering of units. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. However, because correlation may occur across more than one dimension, this motivation makes it difficult to justify why researchers use clustering in some dimensions, such as geographic, but not others, such as age cohorts or gender. It also makes it difficult to explain why one should not cluster with data from a randomized experiment. In this paper, we argue that clustering is in essence a design problem, either a sampling design or an experimental design issue. It is a sampling design issue if sampling follows a two stage process where in the first stage, a subset of clusters were sampled randomly from a population of clusters, while in the second stage, units were sampled randomly from the sampled clusters. In this case the clustering adjustment is justified by the fact that there are clusters in the population that we do not see in the sample. Clustering is an experimental design issue if the assignment is correlated within the clusters. We take the view that this second perspective best fits the typical setting in economics where clustering adjustments are used. This perspective allows us to shed new light on three questions: (i) when should one adjust the standard errors for clustering, (ii) when is the conventional adjustment for clustering appropriate, and (iii) when does the conventional adjustment of the standard errors matter.

JEL-codes: C14 C21 C52 (search for similar items in EconPapers)
Date: 2017-10
New Economics Papers: this item is included in nep-exp
References: Add references at CitEc
Citations: View citations in EconPapers (763)

Downloads: (external link)
https://www.gsb.stanford.edu/gsb-cmis/gsb-cmis-download-auth/442656
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Journal Article: When Should You Adjust Standard Errors for Clustering? (2023)
Working Paper: When Should You Adjust Standard Errors for Clustering? (2022)
Working Paper: When Should You Adjust Standard Errors for Clustering? (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:repec:ecl:stabus:3596

Access Statistics for this paper

More papers in Research Papers from Stanford University, Graduate School of Business Contact information at EDIRC.
Bibliographic data for series maintained by ().