Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach

Abstract

The present paper introduces a novel method for the construction of Socioeconomic Status (SES) indices that are specific to a target variable of interest. It is based on the Sufficient Dimension Reduction (SDR) paradigm and uses a factorized model‐based approach to simultaneously deal with predictor variables of mixed nature (i.e. quantitative, binary, and ordinal), which are usual in microeconomic data. These SES indices also identify relevant predictor variables using a two‐step regularized matrix factorization approach. Using data from household surveys for Argentina (Encuesta Permanente de Hogares‐EPH), the proposed method is compared with other existing dimension reduction algorithms such as standard Principal Component Analysis (PCA) and its version for mixed variables, regression on the full set of variables and Least Absolute Shrinkage and Selection Operator regression (LASSO).

Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making

“…Furthermore, they also included control variables (age and household size) as if they were additional indicators which is methodologically questionable. Duarte, Forzani, García Arancibia, Llop, and Tomassi (2021), on the other hand, used the Ising model to represent socioeconomic binary variables and combine them with other types of variables, not to investigate conditional dependencies between those variables, but rather to develop a framework for supervised dimension reduction for predictive indices.…”