Poverty Classification Using Machine Learning: The Case of Jordan

Adham Alsharkawi, Mohammad Al-Fetyani, Maha Dawas, Heba Saadeh and Musa Alyaman
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Adham Alsharkawi: Department of Mechatronics Engineering, The University of Jordan, Amman 11942, Jordan
Mohammad Al-Fetyani: Department of Data Science, Xina Technologies, Amman 11180, Jordan
Maha Dawas: Department of Poverty, Department of Statistics, Amman 11181, Jordan
Heba Saadeh: Department of Computer Science, The University of Jordan, Amman 11942, Jordan
Musa Alyaman: Department of Mechatronics Engineering, The University of Jordan, Amman 11942, Jordan

Sustainability, 2021, vol. 13, issue 3, 1-16

Abstract: The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, and expensive. Machine learning could revolutionize this process. The contribution of this work is the proposal of an original machine learning approach to assess and monitor the poverty status of Jordanian households. This approach takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. Various machine learning classification models are applied. The LightGBM algorithm has achieved the best performance with 81% F1-Score. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government.

Keywords: sustainable development goals (SDGs); poverty prediction; data preprocessing; classification algorithms; machine learning; society (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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