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

[Abekah Nkrumah <ankrumah@gmail.com>]

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
>>
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