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It also suggests the importance of endowments generally, which is partly captured in the non-livestock asset variable.
From these data 5 sub-indicators were constructed within which each asset variable was equally weighted.
Variable reduction techniques combined assets and household characteristics into a single asset variable [ 28].
From these data five sub-indicators were constructed within which each asset variable was weighted equally.
The asset index was then constructed using principal components analysis (PCA) to assign weights for asset variable.
Using SPSS for computing, the assigned indicator weights were multiplied by the asset variable, and then the resultant score was obtained.
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This is because of the low variability of the asset variables and the resultant clumping and clustering, which were solved by the addition of per capita food expenditure to the index.
Hence, adding the per capita food expenditure as a continuous variable that would help to better capture inequality between households (especially given the low variability of the some of the asset variables) helped to solve the problem of truncation.
This is based on 40 asset variables and assigned score for each [ 7, 8].
We created an index of SES using a set of household asset variables and principal components analysis (PCA).
Multiple correspondence analysis was applied on household asset variables to create an index of assets which was used as a proxy for household SES.
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