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st: Intraclass correlation coefficient biased if number of clusters is small?

From   "Tobias Pfaff" <>
To   <>
Subject   st: Intraclass correlation coefficient biased if number of clusters is small?
Date   Thu, 6 Dec 2012 09:43:29 +0100

Dear Statalisters,

Can I trust estimates of the intraclass correlation coefficient (ICC) when
the number of clusters/groups is small?

I need the answer for a setting where the adjustment of standard errors is
recommended for a regression with a dependent variable at the individual
level and the key regressor aggregated at the regional level, while the
number of clusters is small.

Regions (clusters) = 6
Observations = 80,000

-regress indiv_depvar regional_indepvar micro_indepvars-
-predict res, resid-
-loneway res region-
=> rho_e (ICC of the residuals) = 0.73837

Now with region dummies:
-regress indiv_depvar regional_indepvar micro_indepvars i.region-
=> rho_e = 0

(the same happens when I use Moulton's formula for intraclass correlation
with Steve Pischke's moulton.ado,
(and it happens with nested data as well as with non-nested data)

My number of clusters is small (=6). So I would normally assume that my
standard errors exhibit a downward bias. To correct for the downward bias I
could use a parametric correction with the Moulton factor. However, if the
ICC of the residuals is zero, the Moulton factor is 1, which means that my
standard errors are multiplied with 1, and are effectively not corrected.

THEORY: (details on the literature below)

Angrist & Pischke (2009) suggest a parametric correction with the Moulton
factor as one option if the number of clusters is small (p. 322).

However, Feng et al. (2001) say that "rho is typically estimable only poorly
in GRTs" (Feng et al., p. 169) [GRT = group-randomized trials].
Further, "(...) because in most GRTs the number of groups is relatively
small, the estimate of the between-group variance, sigma_between^2, has
small df and a large standard error. (...) ignoring the lack of precision
with which the ICC (...) is estimated can also lead to incorrect results:
underestimating rho, or using a wrong df (often too big) in testing the
intervention effect."


a) I include region dummies, rho_e = 0 tells me that there is no ICC of the
residuals, I conclude that my standard errors are not downward biased, and I
don't do any correction of the standard errors, or

b) I cannot trust the estimation of the ICC in a setting with small number
of clusters, and need to apply another adjustment method like wild
bootstrap, and

c) Does that mean that the parametric correction with the Moulton factor is
eventually not suitable for settings where the number of clusters is small,
because rho cannot be estimated correctly?

It's just strange since Angrist & Pischke (2009) suggest this method for few
cluster settings, while Angrist & Lavy (2009) warn that "parametric cluster
adjustments [are] too optimistic" (p. 1392), citing Feng et al. (2001). But
maybe I missed something.

Thanks very much for any comments.


Angrist & Lavy (2009), The effects of high stakes high school achievement
awards: evidence from a randomized trial, The American Economic Review,
99(4), 1384-1414.
Angrist & Pischke (2009), Mostly Harmless Econometrics, Princeton University
Feng, Ziding, P.  Diehr, A.  Peterson, and D.  McLerran (2001), Selected
Statistical issues in Group Randomized Trials,  Annual Review of Public
Health, 22, 167-87.

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