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Predictive Power of Composite Socioeconomic Indices in Regression and Classification: Principal Components and Partial Least Squares

Author

Listed:
  • Stefanía D’Iorio

    (Universidad Nacional de Entre Ríos)

  • Liliana Forzani

    (Universidad Nacional del Litoral/ CONICET)

  • Rodrigo García Arancibia

    (Universidad Nacional del Litoral/ CONICET)

  • Ignacio Girela

    (Universidad Nacional de Córdoba/ CONICET)

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, that 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.

Suggested Citation

  • Stefanía D’Iorio & Liliana Forzani & Rodrigo García Arancibia & Ignacio Girela, 2023. "Predictive Power of Composite Socioeconomic Indices in Regression and Classification: Principal Components and Partial Least Squares," Working Papers 246, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:246
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    References listed on IDEAS

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