When Should You Adjust Standard Errors for Clustering?

Bibliographic Data

ID23328786
AuthorsAlberto Abadie (National Bureau of Economic Research), Susan Athey (0000-0001-6934-562X, National Bureau of Economic Research), Guido W Imbens (0000-0002-4846-7326, National Bureau of Economic Research), Guido Imbens, Jeffrey M Wooldridge (0000-0002-1579-6406, National Bureau of Economic Research), Jeffrey Wooldridge
Year2017
Publication date2017-11-01
Peer ReviewedYes
Open AccessNo
TypeREPORT
VenueNational Bureau of Economic Research (REPOSITORY)
PublisherNational Bureau of Economic Research (PUBLISHER)
DOI10.3386/w24003
OpenAlexW2760925952
LanguageEN
Citations received278
References cited12

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.This motivation 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, and 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.

Cluster analysis · Artificial Intelligence · Computer Science · Consumer Market Behavior and Pricing · Economic and Environmental Valuation · Spatial and Panel Data Analysis

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