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Targeting is a core element of anti-poverty program design, with benefits typically targeted to those most "deprived" in some sense (e.g., consumption, wealth). A large literature in economics examines how to best identify these households feasibly at scale, usually via proxy means tests (PMTs). We ask a different question, namely, whether targeting the most deprived has the greatest social welfare benefit: in particular, are the most deprived those with the largest treatment effects or do the "poorest of the poor" sometimes lack the circumstances and complementary inputs or skills to take full advantage of assistance? We explore this potential trade-off in the context of an NGO cash transfer program in Kenya, utilizing recent advances in machine learning (ML) methods (specifically, generalized random forests) to learn PMTs that target both a) deprivation and b) high conditional average treatment effects across several policy-relevant outcomes. We find that targeting solely on the basis of deprivation is generally not attractive in a social welfare sense, even when the social planner's preferences are highly redistributive. We show that a planner using simpler prediction models, based on OLS or less sophisticated ML approaches, could reach divergent conclusions. We discuss implications for the design of real-world anti-poverty programs at scale.

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This study exploits a randomized school health intervention that provided deworming treatment to Kenyan children and utilizes longitudinal data to estimate impacts on economic outcomes up to 20 years later. The effective respondent tracking rate was 84%. Individuals who received 2 to 3 additional years of childhood deworming experience an increase of 14% in consumption expenditure, 13% in hourly earnings, 9% in non-agricultural work hours, and are 9% more likely to live in urban areas. Most effects are concentrated among males and older individuals. Given deworming's low cost, a conservative annualized social internal rate of return estimate is 37%.

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We assess the impacts of a randomized school-based deworming intervention in Kenya on the mortality of recipients' children using a 23-year longitudinal data set of over 6,500 original participants and their offspring. The under-5 mortality rate fell by 22% (17 deaths per 1000 live births) for children of treatment group individuals. We find that a combination of improved health, education and living standards, increased urban residence, delayed fertility, and greater use of health care in the parent generation contributed to the reduction. The results provide evidence for meaningful intergenerational benefits of child health investments.