Ethiopia Women Agribusiness Leaders Network Impact Evaluation : Baseline Survey Report

The World Bank’s Africa region gender innovation lab (GIL) conducted a randomized controlled trial (RCT) impact evaluation of the women in agribusiness leadership network (WALN), a transformational project implemented in Ethiopia by agricultural co...

Full description

Bibliographic Details
Main Authors: Ketema, Tigist, Bastian, Gautam, Gras, Ombeline, Abro, Zewdu, Manchester, Katherine, Carranza, Eliana
Format: Report
Language:English
Published: World Bank, Washington, DC 2018
Subjects:
Online Access:http://documents.worldbank.org/curated/en/630891468189570160/Ethiopia-Women-agribusiness-leaders-network-impact-evaluation-baseline-survey-report
http://hdl.handle.net/10986/30314
Description
Summary:The World Bank’s Africa region gender innovation lab (GIL) conducted a randomized controlled trial (RCT) impact evaluation of the women in agribusiness leadership network (WALN), a transformational project implemented in Ethiopia by agricultural cooperative development international (ACDI) and volunteers in overseas cooperative assistance (VOCA), and supported by United States Agency for International Development (USAID). WALN, as a project, aims to increase participants’ business skills and self-confidence, enabling them to be community leaders and change makers. WALN also seeks to improve agribusiness outcomes by addressing gender differences in productivity, profitability, participation, and leadership in the sector. The ongoing impact evaluation is assessing the impact of participating in WALN activities on the overall performance of the selected high-potential women leaders in the agribusiness sector. In order to assess and interpret the impact evaluation outcomes, it is important to understand the specific business environment, as well as the needs and constraints faced by local entrepreneurs. Outcome data for this impact evaluation is being collected through survey instruments, administrative data, intensive qualitative interviews, and implicit association tests (IATs).