Can AI Improve Household Welfare in Poorer Countries? Evidence and Measurement Challenges

Hai-Anh Dang () and Minh N. N. Do

No 1837, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: Can artificial intelligence (AI) make scarce expertise more widely available in poorer countries and improve household welfare? Can it help measure welfare where surveys are infrequent? This structured critical review examines both questions and finds that the answers depend on the application and setting. Across nine selected evaluations, AI assistance raised output per unit of time by 29% on average, and the average remains positive in every reported sensitivity check. Eight tutoring comparisons give an average gain of 0.21 standard deviations on tests taken without the tool, including in poorer-country schools, but this result is less robust when higher-risk trials are excluded. Business and labor-market effects vary, and our search found no qualifying causal study of a generative-AI tool's effect on household consumption poverty in a poorer country. Measured use rises steeply with national income. AI-based models predict wealth levels more accurately than changes, and their value for targeting depends on the alternative and its cost. The review identifies promising applications for evaluated expansion, proposes a survey module, and sets out conditions for validating AI-based statistics and for assessing costs, persistence, and equity.

Keywords: artificial intelligence; poverty; inequality; developing countries; measurement; critical review (search for similar items in EconPapers)
JEL-codes: C81 D63 I32 J24 O33 (search for similar items in EconPapers)
Date: 2026
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Working Paper: Can AI Improve Household Welfare in Poorer Countries? Evidence and Measurement Challenges (2026)
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