Grow the pie, or have it? Using machine learning to impact heterogeneity in the Ultra-poor graduation model

The ‘Ultra-poor Graduation’ model, though highly effective in poverty alleviation, costs substantially more than alternative poverty alleviation approaches. One possible way of improving the cost-effectiveness of the model is to analyse the treatment effect heterogeneity and identify the participants who do not gain much from the programme and better customise the interventions to their needs. Applying recently developed machine learning methods on a large-scale RCT dataset from Bangladesh, we identify and characterise the program participants who benefit and who do not. We find significant variation in impact on assets where the top quintile gainers experience asset growth of 337% while asset growth is only 189% for the bottom quintile. Heterogeneity in impact on household expenditures is found to be present but of lower magnitude than that of assets. Importantly, the machine learning techniques we apply reveal contrasts in characteristics of beneficiaries who made the most in assets vs. consumption. The most benefitted households in per-capita wealth outcome were relatively older, were more dependent on wage income, had less involvement in self-employment activities, and had lower participation in household decision-making at baseline. In contrast, the top quintile gainers of household expenditure are younger, earn less from wages, depend more on self-employment income, and have higher participation in household decision-making. The results identify beneficiary characteristics that can be used in targeting households either to maximise impact on the desired dimension and/or to customise interventions for balancing the asset and consumption trade-off.