A Bayesian framework for estimating human capabilities

The capabilities approach provides a rich framework for welfare assessment, but its practical relevance is limited by methodological difficulties associated with the measurement of human capabilities. We argue that, unlike existing approaches to capability estimation, Bayesian stochastic frontier analysis (BSFA) is consistent with the key features of the capabilities approach and thus provides a natural framework for estimating capabilities. Using simulated data, we show that BSFA outperforms the leading alternatives (e.g., structural equation models) in comparable settings. We further show that our approach is more flexible than the alternatives: BSFA can provide cardinal representations of entire capability sets and can be used with continuous, discrete, and multivariate outcomes. Finally, we provide an empirical illustration of our estimator by examining the impact of Uganda’s Youth Opportunities Program on the educational capabilities of children in the treated households.