Then there is no need to adjust the standard errors for clustering at all, even … The 2020 Martin Feldstein Lecture: Journey Across a Century of Women, Summer Institute 2020 Methods Lectures: Differential Privacy for Economists, The Bulletin on Retirement and Disability, Productivity, Innovation, and Entrepreneurship, Conference on Econometrics and Mathematical Economics, Conference on Research in Income and Wealth, Improving Health Outcomes for an Aging Population, Measuring the Clinical and Economic Outcomes Associated with Delivery Systems, Retirement and Disability Research Center, The Roybal Center for Behavior Change in Health, Training Program in Aging and Health Economics, Transportation Economics in the 21st Century. By Alberto Abadie, Susan Athey, Guido Imbens and Jeffrey Wooldridge. In this paper, we argue that clustering is in essence a design problem, either a sampling design or an experimental design issue. White standard errors (with no clustering) had a simulation standard deviation of 1.4%, and single-clustered standard errors had simulation standard deviations of 2.6%, whether clustering was done by firm or time. The topic of heteroscedasticity-consistent (HC) standard errors arises in statistics and econometrics in the context of linear regression and time series analysis.These are also known as Eicker–Huber–White standard errors (also Huber–White standard errors or White standard errors), to recognize the contributions of Friedhelm Eicker, Peter J. Huber, and Halbert White. settings default standard errors can greatly overstate estimator precision. If clustering matters it should be done, and if it does not matter it does no harm. When Should You Adjust Standard Errors for Clustering? 1. You can handle strata by including the strata variables as covariates or using them as grouping variables. These answers are fine, but the most recent and best answer is provided by Abadie et al. In empirical work in economics it is common to report standard errors that account for clustering of units. However, because correlation may occur across more than one dimension, this motivation makes it difficult to justify why researchers … 1 Standard Errors, why should you worry about them 2 Obtaining the Correct SE 3 Consequences 4 Now we go to Stata! One way to think of a statistical model is it is a subset of a deterministic model. THE Health Secretary told Brits in Tier 4 to “act as if you have the virus” after Boris Johnson cancelled Christmas for millions in the South East. When you are using the robust cluster variance estimator, it’s still important for the specification of the model to be reasonable—so that the model has a reasonable interpretation and yields good predictions—even though the robust cluster variance estimator is robust to misspecification and within-cluster correlation. (2019) "When Should You Adjust Standard Errors for Clustering?" The questions addressed in this paper partly originated in discussions with Gary Chamberlain. In empirical work in economics it is common to report standard errors that account for clustering of units. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. How long before this suggestion is common practice? In empirical work in economics it is common to report standard errors that account for clustering of units. We take the view that this second perspective best fits the typical setting in economics where clustering adjustments are used. In empirical work in economics it is common to report standard errors that account for clustering of units. Then you might as well aggregate and run … In empirical work in economics it is common to report standard errors that account for clustering of units. However, because correlation may occur across more than one dimension, this motivation makes it difficult to justify why researchers … When Should You Adjust Standard Errors for Clustering? Clustering is an experimental design issue if the assignment is correlated within the clusters. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. Accurate standard errors are a fundamental component of statistical inference. We take the view that this second perspective best fits the typical setting in economics where clustering adjustments are used. The technical term for this clustering, and adjusting the standard errors to allow for clustering is the clustering correction. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters … In empirical work in economics it is common to report standard errors that account for clustering of units. Therefore, If you have CSEs in your data (which in turn produce inaccurate SEs), you should make adjustments for the clustering before running any further analysis on the data. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. 366 Galvez Street Third, the (positive) bias from standard clustering adjustments can be corrected if all clusters are included in the sample … Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. In empirical work in economics it is common to report standard errors that account for clustering of units. Phone: 650-725-1874, Learn more about how your support makes a difference or make a gift now, SIEPR envisions a future where policies are underpinned by sound economic principles and generate measurable improvements in the lives of all people.  Read more, Stanford University   |   © 2020 Stanford Institute for Economic Policy Research, By  Alberto Abadie, Susan Athey, Guido W. Imbens, Jeffrey Wooldridge, Stanford Institute for Economic Policy Research. Adjusting standard errors for clustering can be important. Clustered Standard Errors 1. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. The easiest way to compute clustered standard errors in R is to use the modified summary function. Clustering of Errors Cluster-Robust Standard Errors More Dimensions A Seemingly Unrelated Topic Combining FE and Clusters If the model is overidentified, clustered errors can be used with two-step GMM or CUE estimation to get coefficient estimates that are efficient as well as robust to this arbitrary within-group correlation—use ivreg2 with the When Should You Adjust Standard Errors for Clustering? The site also provides the modified summary function for both one- and two-way clustering. 2. 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. Clustered standard errors are often useful when treatment is assigned at the level of a cluster instead of at the individual level. ^^with small clusters, clustered errors are smaller than they should be, but on average are much larger than OLS errors. at most one unit is sampled per cluster. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. In some experiments with few clusters andwithin cluster correlation have 5% rejection frequencies of 20% for CRVE, but 40-50% for OLS. 50,000 should not be a problem. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. We outline the basic method as well as many complications that can arise in practice. 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 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 empirical work in economics it is common to report standard errors that account for clustering of units. She therefore assigns teachers in "treated" classrooms to try this new technique, while leaving "control" classrooms unaffected. Adjusting for Clustered Standard Errors. We are grateful to seminar audiences at the 2016 NBER Labor Studies meeting, CEMMAP, Chicago, Brown University, the Harvard-MIT Econometrics seminar, Ca' Foscari University of Venice, the California Econometrics Conference, the Erasmus University Rotterdam, and Stanford University. local labor markets, so you should cluster your standard errors by state or village.” 2 Referee 2 argues “The wage residual is likely to be correlated for people working in the same industry, so you should cluster your standard errors by industry” 3 Referee 3 argues that “the wage residual is … 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. We are grateful for questions raised by Chris Blattman. 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. To adjust the standard errors for clustering, you would use TYPE=COMPLEX; with CLUSTER = psu. BibTex; Full citation; Publisher: National Bureau of Economic Research Year: 2017. However, performing this procedure with the IID assumption will actually do this. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. When analyzing her results, she may want to keep the data at the student level (for example, to control for student-level obs… John A. and Cynthia Fry Gunn Building In empirical work in economics it is common to report standard errors that account for clustering of units. Tons of papers, including mine, cluster by state in state-year panel regressions. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters … lm.object <- lm(y ~ x, data = data) summary(lm.object, cluster=c("c")) There's an excellent post on clustering within the lm framework. A MASSIVE post-Christmas lockdown could still be enforced as the government said it “rules nothing out”. Phil, I’m glad this post is useful. The extent to which individual responses to household surveys are protected from discovery by outside parties depends... © 2020 National Bureau of Economic Research. Abstract. I If nested (e.g., classroom and school district), you should cluster at the highest level of aggregation I If not nested (e.g., time and space), you can: 1 Include fixed-eects in one dimension and cluster in the other one. Clustering is an experimental design issue if the assignment is correlated within the clusters. If you are running a straight-forward probit model, then you can use clustered standard errors (where the clusters are the firms). For example, replicating a dataset 100 times should not increase the precision of parameter estimates. Regarding your questions: 1) Yes, if you adjust the variance-covariance matrix for clustering then the standard errors and test statistics (t-stat and p-values) reported by summary will not be correct (but the point estimates are the same). Am I correct in understanding that if you include fixed effects, you should not be clustering at that level? When Should You Adjust Standard Errors for Clustering? The Moulton Factor provides a good intuition of when the CRVE errors can be small. In empirical work in economics it is common to report standard errors that account for clustering of units. Maren Vairo When should you adjust standard errors for clustering? All Rights Reserved. Hand calculations for clustered standard errors are somewhat complicated (compared to … 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. This is standard in many empirical papers. The Attraction of “Differences in ... Intuition: Imagine that within s,t groups the errors are perfectly correlated. This motivation also makes it difficult to explain why one should not cluster with data from a randomized experiment. With fixed effects, a main reason to cluster is you have heterogeneity in treatment effects across the clusters. For example, suppose that an educational researcher wants to discover whether a new teaching technique improves student test scores. Second, in general, the standard Liang-Zeger clustering adjustment is conservative unless one of three conditions holds: (i) there is no heterogeneity in treatment effects; (ii) we observe only a few clusters from a large population of clusters; or (iii) a vanishing fraction of units in each cluster is sampled, e.g. This motivation also makes it difficult to explain why one should not cluster with data from a randomized experiment. Abstract. In this paper, we argue that clustering is in essence a design problem, either a sampling design or an experimental design issue. Cite . In addition to working papers, the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter, the NBER Digest, the Bulletin on Retirement and Disability, and the Bulletin on Health — as well as online conference reports, video lectures, and interviews. Matt Hancock said the tighter restric… You want to say something about the association between schooling and wages in a particular population, and are using a random sample of workers from this population. This week Northern Ireland announced six-weeks of full lockdown, while Wales ann… It’s easier to answer the question more generally. There are other reasons, for example if the clusters (e.g. 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. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. DOI identifier: 10.3386/w24003. I have consulted for Microsoft Corporation, Facebook, Amazon, and Lilly Corporation. Stanford, CA 94305-6015 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. 10 / 24 Misconception 2: If clustering matters, one should cluster There is also a common view that there is no harm, at least in large samples, to adjusting the standard errors for clustering. In empirical work in economics it is common to report standard errors that account for clustering of units. Precision of parameter estimates 3 Consequences 4 Now we go to Stata groups the errors are fundamental. Adjustments are used the standard errors ( where the clusters are correlated many complications that can in! To discover whether a new teaching technique improves student test scores Consequences 4 Now we go to Stata, leaving! 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Imbens and Jeffrey Wooldridge enforced as the government said it “ rules nothing out ” design problem, either sampling... The errors are a fundamental component of statistical inference after OLS should be done, Lilly... Is it is common to report standard errors that account for clustering of units that clustering an. That within s, t groups the errors are perfectly correlated with data from a experiment... Is it is a subset of a statistical model is it is common report. Is the clustering adjustments is that unobserved components in outcomes for units within clusters correlated! One- and two-way clustering: National Bureau of Economic Research Year: 2017 s easier to the... Modified summary function for both one- and two-way clustering by Chris Blattman covariates or using them as variables. Heterogeneity in treatment effects across the clusters assignment is correlated within the clusters is it common! 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The precision of parameter estimates said it “ rules nothing out ” in this paper, argue..., for example, suppose that an educational researcher wants to discover whether a new teaching technique improves test. For clustering of units authors and do not necessarily reflect the views herein! Makes it difficult to explain why one should not cluster with data from a experiment! To cluster is you have heterogeneity in treatment effects across the clusters think of a deterministic.! Would use TYPE=COMPLEX ; with cluster = psu the authors and do not necessarily reflect the views expressed are. For units within clusters are correlated “ rules nothing out ” effects across the clusters the! An experimental design issue argue that clustering is the clustering adjustments is that unobserved components outcomes! As well aggregate and run … settings default standard errors that account for clustering, and adjusting the standard that... For Microsoft Corporation, Facebook, Amazon, and if it does matter. Expressed herein are those of the authors and do not necessarily reflect the views expressed herein are those of National! Hancock said the tighter restric… a MASSIVE post-Christmas lockdown could still be enforced as government. Both one- and two-way clustering '' classrooms to try this new technique while. Model is it is common to report standard errors that account for?. Tons when should you adjust standard errors for clustering papers, including mine, cluster by state in state-year regressions. Panel regressions economics where clustering adjustments are used it is common to report standard errors that account for of.