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Machine Learning Model Debunks 'Poverty Line' Concept

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Mathematicians have used machine learning to develop a new consumption-based model for measuring poverty in different countries that junks old notions of a fixed "poverty line."

"A Social Engineering Model for Poverty Alleviation," published in the journal Nature Communications, suggests mainstream thinking around poverty is outdated because it places too much emphasis on subjective notions of basic needs and fails to capture the full complexity of how people use their incomes.

The researchers say their new model — which uses computer algorithms to synthesize vast amounts of spending and economic data — could help policymakers worldwide predict future poverty levels and plan interventions to alleviate the problem.

"No one has ever used machine learning to decode multidimensional poverty before," says lead researcher Amit Chattopadhyay of Aston University's College of Engineering and Physical Sciences. "This completely changes the way people should look at poverty."

In the new study, the researchers analyzed 30 years' worth of data from India. They combined datasets on incomes, asset, and commodity markets from the World Bank and other sources to produce a mathematical model that was able not only to accurately predict past poverty levels in both India and the United States, but also to predict future levels based on certain economic assumptions. 

The model revises the number of people traditionally deemed "poor" into a more practical "middle class."

"With this model, we finally have a multi-dimensional poverty index that reflects the real-world experience of people wherever they live and largely independent of the social class they are deemed to belong to," Chattopadhyay said.

From Aston University
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