Evaluating poverty in all its forms and dimensions : monetary, multidimensional, and subjective poverty in Peru

• Autores: Clausen Lizarraga, Jhonatan A., Barrantes Gamba, Nicolás, Trivelli, Carolina, Salas, Fabio
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/204093
• Enlace del recurso: http://hdl.handle.net/20.500.14657/204093; https://doi.org/10.1007/s11205-025-03641-7
• Nivel de acceso: acceso abierto
• Materia: Pobreza--Perú; Pobreza urbana--Perú; Pobreza rural—Perú; https://purl.org/pe-repo/ocde/ford#5.06.01

Studies on the simultaneous relationships between monetary, multidimensional, and subjective poverty in low- and middle-income countries remain scarce and face critical limitations, including reliance on inaccurate monetary data, narrow non-monetary indicators, unclear poverty identification criteria, and limited focus on overlaps and joint incidences of poverty forms. Using data from the 2022 Peruvian National Household Survey, we address these gaps in five ways. First, we estimate monetary poverty using detailed household consumption data. Second, we measure multidimensional poverty across a comprehensive set of dimensions. Third, we establish clear identification criteria to determine who is poor according to the monetary, multidimensional, and subjective measures. Fourth, we analyze overlapping poverty patterns to identify subgroups experiencing multiple poverty forms. Fifth, we introduce the Multi-Spatial Poverty Index (MSPI), drawing on Amartya Sen's concept of evaluative space and the Alkire-Foster method. The MSPI integrates three evaluative spaces-monetary resources, capabilities and functionings, and subjective wellbeing-identifying individuals as multi-spatially poor if they face at least two poverty forms. Our results reveal positive associations among poverty forms, although they do not consistently identify the same individuals. We find that 39% of Peru's population is multispatially poor, with multidimensional poverty contributing the most to the MSPI (40%), followed by subjective and monetary poverty (35% and 24.9%, respectively). Subnational disparities are stark, with the highest incidence of multi-spatial poverty observed in the rural highlands (76.7%) and rural Amazonia (71.1%). Overall, our findings highlight the urgent need for integrated policy interventions targeting regions and populations experiencing simultaneous poverty forms.