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Re: st: RE: Cluster Robust Standard Errors for Cross Country Data

From   Steve Samuels <[email protected]>
To   [email protected]
Subject   Re: st: RE: Cluster Robust Standard Errors for Cross Country Data
Date   Wed, 4 Jul 2012 23:06:58 -0500

In response to my request to see the codebook that advised against using
weights in the Demographic and Health Surveys, Abekah Nkrumah privately sent me
a document:

Shea Oscar Rutstein, Ph.D. Guillermo Rojas, M.C.S., M.A.
Demographic and Health Surveys ORC Macro Calverton, Maryland
September 2006

which states on page 14 (in reverse order):

"5. Use of sample weights biases estimates of confidence intervals in most
statistical packages since the number of weighted cases is taken to produce the
confidence interval instead of the true number of observations. For oversampled
areas or groups, use of the sample weights will drastically overestimate
sampling variances and confidence intervals for those groups."

My response: This paragraph refers to the fact that some statistical packages
are not survey-aware and so treat all weights as frequency weights. It is not
an argument against probability weighting.

"4. Use of sample weights is inappropriate for estimating relationships, such as
regression and correlation coefficients."

My response:

I'm not surprised that the authors gave no justification for their assertion.
It's not true in general (see any advanced text) and I see no reason why it
would apply to the DHS without qualification. There are certainly some
situations where an unweighted analysis is preferable. Abekah should review at a
minimum the downloadable abstract of Windship and Radbill (1994) and the
downloadable reference by DuMouchel and Duncan (1983). Groves (1989) presents
an interesting example and argument. (I am traveling and so do not have a
page reference.) To sum up: Unless Abekah can provide substantive justification 
for doing otherwise, he should use the weights.


W DuMouchel & G Duncan (1983) “Using Sample Survey Weights in Multiple
Regression Analysis.” Journal of the American Statistical Association
78(383):535-543. Download at:

Groves, R. M. (1989). Survey errors and survey costs, New York: Wiley.

Winship, C., & Radbill, L. (1994). Sampling Weights and Regression Analysis.
Sociological Methods & Research, 23(2), 230-257. Abstract at:

[email protected]

You are welcome, Gordon. Could you please post a link to the study  and to the codebook that advises that weights are not necessary?



On Jul 3, 2012, at 5:34 AM, Abekah Nkrumah wrote:

Dear Steve,

Thank you for the response. In response to your question; YES the data
has within country sample weights and strata. The strata is the
cluster_var. Each country is divided into clusters and from within
each cluster households are sampled for interviews. So the strata
variable is the same as the cluster variable. That being the case,
what will then constitute cluster_var in the survey command that you

Secondly I have already done some estimations at the country level
without using the survey command but correcting for possible
intra-cluster correlations using the cluster variable. So for
consistency I would want to continue the cross country without survey
commands. I did not use the survey commands for simplicity and
secondly the data code book advices that it is not necessary to
includes sample weights in estimations. The issue then is just
correcting the intra-cluster correlations arising from the within
country cluster correlations at a cross country level.

Nonetheless, I will appreciate your answer to the first question as
well so I can try the two and see what differences there might be.



On Mon, Jul 2, 2012 at 10:56 PM, Steve Samuels <[email protected]> wrote:
> It's quite all right to combine surveys.
> Some questions for you:
> Are sampling weights provided?  I'll assume
> so below. If not, what do you know about the sample weighting?
> Are sampling strata within  countries identified?
> I suggest that you -svyset- the data
> ***************************
> svyset cluster_var  [pw = sampling_weight ] , strata(country)
> **************************
> If there were within-country strata, then define
> ***********************************************************
> egen super_strat = group(country stratum_var)
> ******************************************************
> and substitute "strata(super_strat)" in the -svyset- statement.
> Then use  commands that take a -svy- prefix. To see Stata's official survey-aware
> commands type "help svy_estimation"
> Steve
> On Jul 2, 2012, at 5:35 PM, Abekah Nkrumah wrote:
> Dear Mark,
> Thank you very much for the response. Reading your response I was
> wondering what the difference will be if I decide to cluster on the
> cluster id instead of the household id. As I indicated in my earlier
> mail, there is actually a cluster variable for each country. This
> cluster variable contains the different clusters for each country from
> which households were sampled. in my dataset the country with the
> lowest number of clusters is about 412.
> Thank you very much
> On Mon, Jul 2, 2012 at 4:08 PM, Schaffer, Mark E <[email protected]> wrote:
>> Gordon,
>>> -----Original Message-----
>>> From: [email protected]
>>> [mailto:[email protected]] On Behalf Of
>>> Abekah Nkrumah
>>> Sent: 02 July 2012 10:32
>>> To: [email protected]
>>> Subject: st: Cluster Robust Standard Errors for Cross Country Data
>>> Dear Stata List,
>>> I have pooled cross-section household datasets from 20
>>> countries. For each of these countries, the data was
>>> collected via cluster sampling meaning there will be
>>> intra-cluster correlations which will affect the validity of
>>> the standard errors. If I were carrying out my estimations on
>>> a single country I know that I could correct for the possible
>>> bias in the standard errors by using the variable containing
>>> the cluster ids to estimate cluster robust standard errors.
>>> In the present case where I have pooled (i.e appended as in
>>> stata) the household cross-section data from 20 different
>>> countries, will it be right to still use the variable
>>> containing the cluster ids to estimate the cluster robust
>>> standard errors? Note that now the cluster ids will be for
>>> all 20 countries.
>> This is problematic.  The consistency of the cluster-robust covariance
>> estimator is asymptotic in the number of clusters, and 20 isn't very far
>> on the way to infinity.  Clustering on country is probably not a great
>> idea.
>> An alternative is to cluster on household ID and to use country dummies
>> when you pool the data.  This would allow for arbitrary within-household
>> correlation (via clustering on household ID) and invariant
>> within-country correlation (via the country dummies).
>> HTH,
>> Mark
>>> I will appreciate your help.
>>> Thank you very much
>>> Gordon
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