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Measuring Household Wealth with Latent Trait Modelling: An Application to Malawian DHS Data

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  • Milo Vandemoortele

Abstract

There is increasing awareness in the literature that the more commonly utilised approaches to measure wealth from assets are problematic. The theoretical foundations of sum-scores are weak and principal component analysis (PCA) is an inappropriate statistical method to apply to categorical data, akin to using a linear OLS regression with categorical data. Latent trait modelling (LTM) offers a statistically superior approach to measure household wealth. There are powerful arguments for using LTM: it takes into account the categorical nature of asset data; it is explicit about the assumptions underpinning the model, and it allows for inferences to be made about the broader population. This article applies LTM to three Malawian Demographic and Health Surveys, and compares results to those of a PCA approach. While the correlation is moderately high, indicating that a similar concept is being measured, results from LTM reflect the characteristics of the asset data and therefore represents a statistically superior measure of household wealth. Further research that draws on LTM methods to calculate wealth indices is to be encouraged. Copyright Springer Science+Business Media Dordrecht 2014

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  • Milo Vandemoortele, 2014. "Measuring Household Wealth with Latent Trait Modelling: An Application to Malawian DHS Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 877-891, September.
    • Handle: RePEc:spr:soinre:v:118:y:2014:i:2:p:877-891
    DOI: 10.1007/s11205-013-0447-z
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