Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina

Ratzanyel Rincón
Additional contact information
Ratzanyel Rincón: The University of British Columbia

Estudios Económicos, 2023, vol. 38, issue 1, 3-68

Abstract: This article addresses the lack of timely information about multidimensional poverty in Mexico. Three machine learning algorithms —the LASSO logistic regression, random forest, and support vector machines— are trained with the ENIGH to find generalizable patterns of multidimensional poverty in the raw data. The fitted models are used to classify each individual in the ENOE as poor or non-poor to obtain aggregated poverty rates on a quarterly basis. These estimates are closer to the official levels of multidimensional poverty than the labor poverty measurement and provide an accurate poverty outlook more than a year ahead of the official measure.

Keywords: multidimensional poverty; machine learning; LASSO logistic regression; random forest; support vector machines (search for similar items in EconPapers)
JEL-codes: C14 C8 D6 I32 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/435 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:emx:esteco:v:38:y:2023:i:1:p:3-68

Access Statistics for this article

More articles in Estudios Económicos from El Colegio de México, Centro de Estudios Económicos Contact information at EDIRC.
Bibliographic data for series maintained by Ximena Varela ().