Income Distribution and Economic Development: Insights from Machine Learning

49 Pages Posted: 11 Dec 2015 Last revised: 11 Aug 2020

See all articles by Pushan Dutt

Pushan Dutt

INSEAD - Economics and Political Sciences

Ilia Tsetlin


Date Written: March 31, 2020


We draw upon recent advances that combine causal inferences with machine learning, to show that poverty is the key income distribution measure that matters for development outcomes. In a predictive framework, we first show that LASSO chooses only the headcount measure of poverty from 37 income distribution measures in predicting schooling, institutional quality, and per capita income. Next, causal inferences with post-LASSO models indicate that poverty matters more strongly for development outcomes than does the Gini coefficient. Finally, instrumental variable estimates in conjunction with post-LASSO models show that compared to Gini, poverty is more strongly causally associated with schooling and per capita income, but not institutional quality. Our results question the literature’s overwhelming focus on the Gini coefficient. At the least, our results imply that the causal link from inequality (as measured by Gini) to development outcomes is tenuous.

Keywords: Poverty, Inequality, Income Distribution, Economic Development

JEL Classification: D31, I32, O10

Suggested Citation

Dutt, Pushan and Tsetlin, Ilia, Income Distribution and Economic Development: Insights from Machine Learning (March 31, 2020). INSEAD Working Paper No. 2019/39/EPS/DSC, Available at SSRN: or

Pushan Dutt (Contact Author)

INSEAD - Economics and Political Sciences ( email )

1 Ayer Rajah Avenue
Singapore, 138676
65-6799-5498 (Phone)


Ilia Tsetlin

INSEAD ( email )

Boulevard de Constance
77305 Fontainebleau Cedex

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