Predictive power of composite socioeconomic indices for targeted programs: principal components and partial least squares

Stefanía D’Iorio (), Liliana Forzani (), Rodrigo García Arancibia and Ignacio Girela ()
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Stefanía D’Iorio: Universidad Nacional del Litoral
Liliana Forzani: Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
Ignacio Girela: Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

Quality & Quantity: International Journal of Methodology, 2024, vol. 58, issue 4, No 20, 3497-3534

Abstract: Abstract Principal components analysis (PCA) and partial least squares (PLS) have been used for the construction of socioeconomic status (SES) indices to use as a predictor of the well-being status in targeted programs. Generally, these indicators are constructed as a linear combination of the first component. Due to the characteristics of the socioeconomic data, different extensions of PCA and PLS for non-metric variables have been proposed for these applications. In this paper, we compare the predictive performance of SES indices constructed using more than one component. Additionally, for the inclusion of non-metric variables, a variant of the normal mean coding is proposed that takes into account the multivariate nature of the variables, which we call multivariate normal mean coding (MNMC). Using simulations and real data, we found that PLS using MNMC as well as the classical dummy encoding method give the best predictive results with a more parsimonious SES index, both in regression and classification problems.

Keywords: Dimension reduction; Categorical predictors; SES; Proxy mean test; Classification; Regression (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11135-023-01811-8

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