Targeting social safety net programs on human capabilities

Conventional approaches to targeting social safety net programs select beneficiaries on the basis of income or expenditure levels. We argue that these approaches neglect human diversity and agency, which can lead to counterintuitive targeting outcomes and thus a misallocation of benefits. In light of these issues, we develop an alternative method for targeting that is based on the capabilities approach, which we claim provides a more rigorous normative framework for targeting that respects both human diversity and agency. In particular, we adapt Bayesian additive regression trees for the estimation of human capabilities and demonstrate how the resulting estimates can be used to target social safety net programs. We examine the targeting implications of our method through a variety of simulation exercises and also with real data from a field experiment conducted in Indonesia. Relative to more traditional approaches – including not only the full and proxy means test, but also community-based targeting – we find that our method identifies a fundamentally different and arguably more disadvantaged group of beneficiaries.