Exploring the Association between Multidimensional Poverty and Depression Using Structural Equation Models

Abstract

Using data from the 2022 Peruvian Demographic and Health Survey, we estimated structural equation models to explore the association between two unobservable latent variables: multidimensional poverty and major depressive disorder. We estimated the former using the ten indicators of non-income deprivation included in the global Multidimensional Poverty Index, whereas we used the items of the Patient Health Questionnaire-9 to estimate the onset of major depression. We found that living in multidimensional poverty was positively and significantly associated with experiencing symptoms of major depression. This result held valid after controlling for other variables such as gender, ethnicity, and area of residence. Our study contributes to the relatively scarce yet growing literature that uses structural equation modeling to explore the association between multidimensional poverty and variables related to mental health in low- and middle-income countries. Overall, our findings provide relevant insights for policymakers in Peru and other similar low- and middle-income countries that could be useful in developing interventions to enhance the mental health of people living in multidimensional poverty.

Citar como:

Clausen, J., Barrantes, N., Caballero, Guillen, H. (2023) Exploring the Association between Multidimensional Poverty and Depression Using Structural Equation Models. Applied Research Quality Life 19, 727–747 (2024). https://doi.org/10.1007/s11482-023-10262-0