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


From   Abekah Nkrumah <ankrumah@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Cluster Robust Standard Errors for Cross Country Data
Date   Tue, 3 Jul 2012 10:34:57 +0100

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
gave?

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.

Regards

Gordon

On Mon, Jul 2, 2012 at 10:56 PM, Steve Samuels <sjsamuels@gmail.com> 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 <M.E.Schaffer@hw.ac.uk> wrote:
>> Gordon,
>>
>>> -----Original Message-----
>>> From: owner-statalist@hsphsun2.harvard.edu
>>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>>> Abekah Nkrumah
>>> Sent: 02 July 2012 10:32
>>> To: statalist@hsphsun2.harvard.edu
>>> 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
>>>
>>> --
>>> **********************************************
>>> Dept. of Public Admin & Health Serv. Mgt University of Ghana
>>> Business School P.O. Box LG 78 Legon-Accra Ghana
>>> Tel: ++ 233 21 500159 Ext. 6247
>>>       ++ 233 21 502258 Ext. 6247
>>>       ++ 233 21 502255 Ext. 6247
>>> Cell: ++233 243 198 313
>>>
>>> Email: gankrumah@ug.edu.gh
>>>           ankrumah@gmail.com
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>>
>>
>> --
>> Heriot-Watt University is the Sunday Times
>> Scottish University of the Year 2011-2012
>>
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>> registered under charity number SC000278.
>>
>>
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