Estimating resilience outcomes in an impact assessment framework with high-frequency data

As part of an ex-post impact assessment of an IFAD-supported project, the Participatory Small-scale Irrigation Development Programme (PASIDP), an agricultural project aimed at improving food security and increasing income of beneficiaries by providing access to small-scale irrigation infrastructure systems in four regions of Ethiopia, we ran a lab-in-the-field experiment and collected a high-frequency dataset to investigate the relationship between farmers' resilience profiles, risk preferences and agricultural productivity and well-being outcomes. The novel high-frequency dataset consists of a baseline survey collected in November 2016, and three follow-up surveys conducted every three months until November 2017. The findings from this study offer greater insights into measurement of resilience in the context of ex-post impact assessments with quasi-experimental designs. Specifically, the robustness of resilience measurement results from different methodologies is explored extensively, especially when these results are contingent on the methodology used, as the analysis of resilience, and subsequent lessons learned from the analysis to inform future project design and implementation could differ greatly. This study aims to fill this gap in the literature, notably by measuring resilience impact reliably using a novel dataset, and second, by characterizing resilience profiles according to risk and time preferences.