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Using sparse categorical principal components to estimate asset indices: new methods with an application to rural southeast asia

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  • Giovanni Maria Merola
  • Bob Baulch

Abstract

Asset indices have been used since the late 1990s to measure wealth in developing countries. We extend the standard methodology for estimating asset indices using principal component analysis in two ways: by introducing constraints that force the indices to have increasing value as the number of assets owned increases, and by estimating sparse indices with a few key assets. This is achieved by combining categorical and sparse principal component analysis. We also apply this methodology to the estimation of per capita level asset indices. Using household survey data from northwest Vietnam and northeast Laos, we show that the resulting asset indices improve the prediction and ranking of income both at household and per capita level.

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  • Giovanni Maria Merola & Bob Baulch, 2019. "Using sparse categorical principal components to estimate asset indices: new methods with an application to rural southeast asia," Review of Development Economics, Wiley Blackwell, vol. 23(2), pages 640-662, May.
    • Handle: RePEc:bla:rdevec:v:23:y:2019:i:2:p:640-662
    DOI: 10.1111/rode.12568
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    Cited by:

    1. Sabrina Duarte & Liliana Forzani & Pamela Llop & Rodrigo García Arancibia & Diego Tomassi, 2023. "Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 318-346, June.

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