Multidimensional and monetary poverty in Peru: Evidence of their evolution, determinants, and the impact of the pandemic

Authors

  • Dante Urbina Universidad de Lima image/svg+xml
  • Nicole Ariana Caceres Bautista Banco de Crédito del Perú

DOI:

https://doi.org/10.4067/S0718-88702025000100003

Keywords:

Multidimensional poverty, Weights, Poverty cutoffs, COVID-19, Peru

Abstract

The COVID-19 outbreak has left a profound economic aftermath, disrupting decades of consistent progress in poverty reduction in several countries. In this context, it is relevant to study the Peruvian economy since it experienced one of the highest COVID-19 mortality rates globally, its GDP declined by 11% and its monetary poverty rate rose to 30%, a situation not observed since 2010. Therefore, to better understand Peru’s socioeconomic landscape after the pandemic, we construct a Multidimensional Poverty Index during the period 2019-2022 and compare it with monetary poverty using data sourced from the National Household Survey. We find that the latter was more volatile than multidimensional poverty. Employing logistic panel regressions, consistent patterns emerge in terms of the signs and statistical significance of the marginal effects for both poverty measurements across various weighting structures and poverty cutoffs. For instance, certain demographic groups, including rural women, those with only primary education, the unemployed and informal workers, exhibit a significantly heightened probability of experiencing both monetary and multidimensional poverty. The pandemic also increased the likelihood of experiencing both types of poverty across all scenarios considered. Conversely, individuals of mestizo ethnicity and those with higher education levels demonstrate a reduced likelihood of experiencing both types of poverty

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Published

2025-04-09

How to Cite

Urbina, D., & Caceres Bautista, N. A. (2025). Multidimensional and monetary poverty in Peru: Evidence of their evolution, determinants, and the impact of the pandemic. Economic Analysis Review, 40(1), 3–32. https://doi.org/10.4067/S0718-88702025000100003

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