Better Understanding, Better Choices, Better Impact
Last year in Kabul, a woman entrepreneur boldly offered much-needed insight into USAID programming. “We really appreciate the week-long business courses you have provided,” she said, “but what we need is someone who can help us put the lessons into practice.” For her, standard training was not enough.
But what is enough? In a world of limited resources, we’re constantly forced to make choices, and in the world of women’s economic empowerment, those choices can be critical. What makes the difference in helping women entrepreneurs succeed in growing their businesses? Which project interventions work? Which don’t?
In 2013, USAID decided to find out which kinds of interventions are most effective in moving women-owned small and medium enterprises further up the economic ladder. The resultant Women’s Leadership in Small and Medium Enterprises (WLSME) Initiative selected three grantees in disparate locations to build an evidence base:
- In India, CARE has been working among women cashew growers and processors;
- In the Kyrgyz Republic, ACDI/VOCA has been engaging women-run businesses in agribusiness, tourism, and garments; and
- In Peru, GRADE has been providing assistance to women-owned SMEs in textiles, handicrafts, and food services.
Over the next year, our robust monitoring and evaluation of these projects (to be carried out by Management Systems International) should begin to answer our questions on effectiveness. But even before the final results are in, our implementers have some valuable lessons to share, such as how to construct a randomized control trial, how to deal with attrition, and how to engage men in support of women entrepreneurs.
Over the next the next few weeks, our implementing partners will be sharing these and other insights with you. More will follow this summer, including a learning brief with the combined lessons from all three interventions. And, in the next year, we expect that our monitoring and evaluation efforts will begin to provide clear, evidence-based answers to guide future programming for greater effectiveness.