1. ® Designing an M&E System for Market Engagement at CARE Christian Pennotti Technical Advisor, Learning & Impact Economic Development Unit cpennotti@care.org

2. CARE’s M&E Approach – Questions for Consideration  How can we provide guidance that ensures M&E becomes an integral part of regular project processes?  How can we provide guidance that enable teams to design M&E that effectively responds to value chain dynamics?  How can we combine an increased desire to be held directly accountable to our impact group members while maintaining reasonable cost structures for M&E?

3. Core Principles 1. Base M&E on a clear and well-documented causal model 2. Support project management and accountability to target beneficiaries 3. Emphasize leading outcomes in monitoring and lagging outcomes in evaluation 4. Incorporate learning loops that leverage both tacit and explicit knowledge from multiple stakeholders 5. Be flexible and able to adapt to project evolving project priorities 6. Be feasible to implement with resources and capacity

4. Steps to Designing the M&E System

5. 1. M&E System Client Assessment Power / Relevance Mapping Relevance Application Participatory exercise Group brainstorm Small groups discuss and place stakeholders on the matrix Facilitated discussion about variance until final set of clients to be “Managed Closely” defined. Leads to discussion on client needs and expectations

6. 2. Value Chain Initiative Causal Model Application Participatory exercise Break the causal model into pieces Small groups discuss and place results on the matrix Facilitated discussion about variance until final agreement reached on the project’s fundamental logic Leads to clarity on ‘killer assumptions’ as well as indicator needs

7. 2. Example from Peru Women’s Emp.EnterpriseSector Leading Lagging Activities Impacts

8. 4. Indicator Selection  Two types of indicators  Those tracked through routine measurement – traditional M&E  Those tracked through routine observation – leveraging staff observation

9. 4. Indicator Selection – Identifying Killer Assumptions Application Participatory exercise Pull out the ‘lines’ connecting causal pathways in the causal model Blind ranking of assumptions with the greatest risk and greatest potential reward Definition of ‘indicators’ to monitor validity of prioritized ‘killer assumptions’ via observation

10. Step 5: Select Tools  Indicators to be tracked through routine measurement  Traditional M&E tools (FGDs, surveys, etc)  Indicators to be tracked through routine observation  KM-oriented tools (After Action Reviews, routine meetings, common learning agenda, observation cards, etc)

11. Step 6: Designing Data Analysis & Use  Indicators for Routine Measurement  Highly related to traditional practices but with greater focus on leveraging data for multiple M&E System Clients  Indicators for Routine Observation  Focus on KM tools, reflective practice and increased opportunities for simple ranking and benchmarking performance across the project

12. Step 7: Ensure Incentive Alignment  To be developed  Focus on ensuring data quality and ability to improve decision making  Leverage examples raised in the e- consultation like HR integration, monetary incentives, peer pressure  Need to define limitations – not everything can be solved by the M&E system!

13. Illustrations of Routine Observation Integration  Zambia ADAPT Project  Staff-defined monthly learning themes, consolidated summaries  Observation-based agro-dealer categorization tool  Bangladesh SDVC Project  Routine, focused meetings at all levels of the project  Participant-reporting categorization tool  Regional performance scorecards for field managers  Ethiopia PSNP Plus  Initiating geographically rotating technical working group meetings

14. Moving Ahead  Developing data analysis / use guidelines  Developing incentives module  Target guide release – Fall, 2011  Ongoing application, revision and building use case library  Collaboration via GROOVE & MaFI to refine, apply, advance