Small area estimation for composite indicators: the case of multidimensional poverty incidence

TL;DR

This paper introduces a new methodology for estimating multidimensional poverty indicators in small areas, addressing the complexity of composite indicators and providing uncertainty measures through bootstrap techniques.

Contribution

It develops a novel approach for small area estimation of composite indicators, incorporating multiple data sources and uncertainty quantification.

Findings

Successful estimation of multidimensional poverty incidence at municipality level in Colombia.

Provision of uncertainty measures using a parametric bootstrap algorithm.

Demonstration of the methodology's applicability to complex composite indicators.

Abstract

This paper proposes a methodology to obtain estimates in small domains when the target is a composite indicator. These indicators are of utmost importance for studying multidimensional phenomena, but little research has been done on how to obtain estimates of these indicators under the small area context. Composite indicators are particularly complex for this purpose since their construction requires different data sources, aggregation procedures, and weighting which makes challenging not only the estimation for small domains but also obtaining uncertainty measures. As case study of our proposal, we estimate the incidence of multidimensional poverty at the municipality level in Colombia. Furthermore, we provide uncertainty measures based on a parametric bootstrap algorithm.