Abstract Evaluating the targeting of large‐scale social assistance programs is crucial for improving public administration. Traditional approaches, relying solely on quantitative proxy means testing or qualitative community inputs, have limitations in capturing complex socioeconomic realities. This paper argues for employing hybrid methods that integrate quantitative and qualitative data sources to comprehensively assess targeting accuracy. Using China's Dibao program as a case study, we demonstrate that a hybrid approach reduced inclusion errors and enhanced program understanding among beneficiaries and communities. Our findings highlight the importance of collaborative evaluation mechanics that leverage localized knowledge while guarding against elite capture.
Lan et al. (Sat,) studied this question.