Work

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

Year: 2025

Type: article

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.

Source: Applied Economic Perspectives and Policy

DOI: 10.1002/aepp.13515

Authors: Marup Hossain, Lisa Jäckering, Conner Mullally, Paul Winters

Institutions: Texas Tech University, Bundesministerium für Landwirtschaft, Ernährung und Heimat, University of Florida, University of Notre Dame

Language: English

Topic: Income, Poverty, and Inequality

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDGs: No poverty, Reduced inequalities

Open Access status: green

OpenAlex ID: W4408327349

FWCI: 1.773

Cites: 34

Cited by: 1

Related to: 10