Evidence Generation
Asia
Evidence Generation
Asia
Types of RFE services: Learning System Diagnostic, Monitoring Technical Assistance, Cost-effectiveness analysis
Ultra-poor graduation programs have proven effective in developing countries, but organizations now face growing pressure to deliver these results in a cost-effective way at scale. Village Enterprise has over 30 years of experience implementing the Ultra Poor Graduation (UPG) model in East Africa. The organization supports people in building sustainable businesses and moving out of extreme poverty, with rigorous evidence demonstrating program impact. Following rapid growth and a strategic shift toward scaling through government technical assistance, while continuing to innovate through direct implementation, Village Enterprise identified the need to refine its MEL system to better support real-time learning and decision making.
Much of Village Enterprise’s MEL expenditure was driven by lengthy and expensive data collection for final outcomes through consumption and expenditure, despite existing causal evidence on these outcomes. While Village Enterprise’s commitment to being a learning organization meant that they were unable to completely forgo measuring final outcomes through MEL, they were limited in their ability to act on early signals and more strategic, easier to collect early outcomes. Additionally, Village Enterprise also sought to strengthen cost-effectiveness analysis not only to demonstrate value to partners, but to actively guide program adaptations and assess scalability. Finally, while core program monitoring was strong, the organization did not yet have structured learning frameworks to rigorously support innovation and digital adaptations.
In response to these needs, IPA’s Right-fit Evidence Unit first conducted a rapid MEL diagnostic and a literature review on early outcomes for graduation programs. Then, IPA facilitated workshops to refine Village Enterprise’s theory of change and learning priorities, providing recommendations to measure early outcomes such as knowledge, attitudes, confidence, and intentions related to savings, business skills, and financial management. IPA also provided guidance on evidence-based indicators aligned with Village Enterprise’s strategic pillars, including gender empowerment, climate resilience, nutrition, and food security.
Beyond core MEL refinement, IPA delivered expert technical assistance to design a learning framework for innovations and digital adaptations of the graduation program. This included mapping user journeys for digital tools, identifying key learning questions and right fit learning approaches, and building internal capacity around innovation-focused learning approaches, such as A/B testing.
Finally, IPA developed a cost-effectiveness framework specifying key parameters and modeling choices for rigorous cost-effectiveness analysis. Using this framework, IPA provided tailored recommendations to strengthen Village Enterprise’s cost-effectiveness model, supporting both external benchmarking and internal decision making around scalability and program adaptation.
Through the partnership, Village Enterprise fully refined and streamlined its theory of change, clarifying causal pathways and integrating critical early outcomes that previously were missing. They are in the process of translating these updates into a MEL system that is significantly more streamlined, with a reduced and more strategic set of indicators poised to drive decision making. Importantly, Village Enterprise developed a plan to embed early outcome measures into ongoing randomized evaluations, enabling the organization to rigorously identify which early signals predict long-term poverty impacts and to shift toward lower-cost, faster, and more actionable monitoring for adaptive management during implementation.
Beyond technical system improvements, IPA’s RFE Unit served as a core thought partner in navigating evidence tradeoffs, helping Village Enterprise move from “more data” to the “right data” for a learning-driven organization. Rather than simply introducing new tools, the engagement supported intentional choices about rigor, cost, speed, and decision relevance aligned with Village Enterprise’s dual mandate of learning and scale.
The partnership established a robust, best-practice cost-effectiveness framework that strengthened the credibility and comparability of Village Enterprise’s cost-effectiveness analysis. More importantly, it laid the analytical foundation for a strategic initiative to intentionally iterate and adapt the graduation model to double cost-effectiveness over time, positioning evidence as a central driver of scalability, innovation, and impact.
