Poverty prediction and targeting over time and space: Evidence from Nigeria

Marup Hossain, Lisa Jäckering, Conner Mullally and Paul Winters

Applied Economic Perspectives and Policy, 2025, vol. 47, issue 3, 1191-1208

Abstract: Understanding poverty dynamics is crucial to target and tailor economic policies in developing countries like Nigeria—a country at the risk of hosting about a quarter of all people living in poverty worldwide. To facilitate the targeting of poverty‐reducing interventions, we build a nationally representative panel dataset spanning 2011–2019 with more than a hundred covariates and apply econometric and machine learning tools to predict and examine factors associated with the static, transient, and persistent poverty status of Nigerian households. Results show that demographic factors, asset holdings, access to infrastructure, and housing indicators can accurately predict poverty in 80% of cases.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/aepp.13515

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:wly:apecpp:v:47:y:2025:i:3:p:1191-1208

Access Statistics for this article

More articles in Applied Economic Perspectives and Policy from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().