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.

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收入分配与经济发展：机器学习的见解

我们利用因果推理与机器学习相结合的最新进展，表明贫困是对发展成果至关重要的关键收入分配指标。在一个预测框架中，我们首先表明LASSO在预测教育程度，机构质量和人均收入方面，仅从37种收入分配指标中选择了贫困人数。其次，对LASSO后模型的因果推论表明，贫困对发展成果的影响比基尼系数更大。最后，工具变量估计与后LASSO模型相结合表明，与基尼相比，贫困与受教育程度和人均收入之间的因果关系更大，而与机构质量无关。我们的结果质疑文献对基尼系数的压倒性关注。至少