Group Lead, Local Innovation Group Massachusetts Institute of Technology (MIT), United States
When dynamic and context-responsive programs intervene into complex local systems, outcomes are often emergent and may only become clear after some time. This paper describes an approach to helping program implementers and the evaluation team identify meaningful emerging outcome trajectories at the mid-point of a five-year program aimed at transforming traditional agricultural value chains in the northern Indian state of Bihar. This approach involved combining an ex-ante theory of change with regular after-action reviews and an “outcome evidencing” process. Participants in the after-action reviews and outcome evidencing workshops described emerging outcome trajectories as stories of change, and these narratives were subsequently triangulated and substantiated through empirical research conducted by the evaluation team, which is currently ongoing. This narrative-based approach enabled program implementers and the evaluation team to jointly prioritize outcome trajectories that were most meaningful to implementers and connected to the program’s ex-ante theory of change and core evaluation questions. The approach helped to scope the evaluation's empirical research and to build buy-in from implementers and key stakeholders to the areas of "evidencing research" that were prioritized based on the outcome stories.
