Oh, that Filmer and Pritchett stuff. Take a look at our working paper
-- http://www.cpc.unc.edu/measure/publications/pdf/wp-04-85.pdf.
Typically the first component is the measure of wealth, while the next
one or two are the measures of structure of that wealth (e.g.
urban/rural difference, etc.). But in your case, you have dummy
variables produces from the same factor (floor material, wall
material, etc.), and they are negatively correlated. Rather than
picking the common variation due to underlying wealth, the PCA now has
to work on explaining those extra correlations.
On Wed, Jul 23, 2008 at 1:55 PM, Mona Mowafi <[email protected]> wrote:
> Dear Statalisters,
>
> I'm new to the list and hope you will be able to help me with an analysis problem before me.
> I have conducted a principal components analysis to identify principal components for 67
> underlying indicators or household asset.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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