Making the Market Work for the Poor: Case of STARS Program

Blog by  Dada Gueye, Monitoring and Evaluation and Learning Advisor for ICCO in West Africa.

The approach uses analysis and problem solving to diagnose and address the constraints that poor people face in improving their position within markets. ICCO’s program STARS facilitates the access of 210,000 smallholder farmers to financial markets and agricultural services in rural Burkina Faso, Ethiopia, Rwanda and Senegal. To reach this objective the program uses the M4P approach.

One of the key principles of M4P is ‘Understanding how any given market system works – diagnosing how and why it fails to serve the poor – prior to intervening in it’. Implementing STARS in different countries gave us different results, in certain cases we succeeded, in others we learned what to do differently or not do at all. I would like to share some of our experiences.

What worked

Information is key!

In market development programs, access to the right information is capital for a good identification of key actors of the market. At the beginning of STARS, the identification phase was a challenging exercise. But once the main partners were identified, they played an important role in reaching the farmers.

Rwanda: access to inputs and equipments

Access to inputs and equipments was a challenge for rice Producer Organizations (POs), they had to rely on getting loans from financial institutions. Based on a thorough value chain analysis STARS designed business models which facilitate the access to inputs and post-harvest equipments for farmers.  MoU’s were signed between the federation of rice farmers and processors as well as with equipments manufacturers and suppliers.

The business model on “access to input” stipulates that processors provide input loans to farmers, and farmers pay back after harvesting and selling their production. The business model on “access to post-harvest equipments” gives farmers the opportunity to get equipment through leasing and asset finance in collaboration with selected manufacturers, while processors provide a guarantee for farmers. These models are sustainable as they link various actors in the value chain. It is a win-win situation for all that could be designed only after a good analysis of market constraints.

Senegal: an agri-group loan product for women

Women smallholders face difficulties in getting access to finance. In general, women in Senegal face low level of schooling, cultural constraints, unsuitable means of transport and lack of land ownership. An analysis of all these factors together made clear that women really have difficulties in accessing finance and needed a targeted product. STARS therefore developed an agri-group loan product (based on solidarity guarantee) which focuses on women to access finance from Microfinance Institutions.

Lessons learned

If obtaining good information allows to better design interventions in programs, the lack of or wrong information can make interventions unsuccessful. Following are some learning lessons from the activities that did not reach the expected results in STARS.

Senegal: doing better analysis in identifying partners

To improve the onion value chain, STARS designed an intervention for the processing and transformation of onions into onion powder with 4 PO’s and a processing company. The initiative was innovative and ambitious, providing a way to longer conserve onions. However, after 8 months of meeting and planning, it was discovered that the company did not process onions, in fact, it imported dried onion and onion powder already processed from Morocco, and was only doing the packaging. We learned that we should have done deeper research and analysis in identifying the company.

Rwanda: consider local context

STARS wanted to introduce Business Development Services to farmers which are based on the ‘train the trainers’ concept and fee-based. Trainees had to pay a small contribution, which ensures the sustainability of the services also after the program ends. Unfortunately, the approach did not succeed, because in the context of the country, farmers are not used and not willing to pay for such services as they are provided by many development organizations, free of charge. From this we learned to always consider the local context.

The above cases show that the results of M4P interventions always depend on at what analysis-level the market diagnose was made. Research and baseline information, the location and context of the program all influence the success (or failure) of an M4P intervention.