Innovations for Poverty Action Evaluating the Impact of Agricultural Development Programs Africa Rising 23 October 2012

Agenda Introduction: IPA and our research methods IPA Agricultural Research Projects M&E Tools The Way Forward

Introduction Introduction to IPA and IPA’s research methods

What does IPA do? Established in 2002 to measure the effects of poverty alleviation programs  Uses the randomized controlled trial (RCT) method of rigorous evaluation  Works in the areas of agriculture, health, education, microfinance, water and sanitation, and governance  Openly shares findings about what does and doesn’t work with policymakers, donors, practitioners, researchers and other critical stakeholders Established in Ghana in 2007  Ongoing national and regional projects in agriculture, education, health and microfinance

What is randomized control trial evaluation? Control Treatment Impact

Our Approach We generate insights on what works and what does not through randomized evaluations, and ensure that those findings will be useful to, and used by practitioners and policy makers InnovateEvaluateReplicate Communi -cate Scale - Understand market failures -Develop innovative solutions to poverty - Use frontier knowledge from economics, and psychology Randomized Controlled Trials -Impact evaluations - Comparing variations of an intervention - Experiment with product designs Replicate evaluations in various settings to : - Generalize research findings - Tell practitioners what works (and not), when Effectively communicate to practitioners: -Conferences -Workshops with policy makers and practitioners -Policy memos and focus notes Facilitate scale- up of effective solutions : - Active policy outreach -Practitioners’ toolkits - Hands-on technical assistance

Surprisingly little hard evidence on what works Can do more with given budget with better evidence If people knew money was going to programs that worked, could help increase pot for anti-poverty programs We should be asking: Which programs work best, why and when? How can we scale up what works? Why Impact Evaluation?

IPA Agricultural Research Projects Examining Underinvestment in Agriculture

Background: EUI ( ) Research design  EUI: Examining Underinvestment in Agriculture  Investigators: economists at Yale and Ghana Legon  Question: why do smallholders underinvest in farms?  Hypotheses: capital constraints and risk aversion  Treatments: unconditional cash grants and rainfall index insurance Findings  Investment effects: insurance significantly increased farm investment, while capital alone did not  Profitability effects: higher investment did not lead to higher farm profitability  Insurance demand: high, when priced reasonably

IPA Agricultural Research Projects Disseminating Innovative Resources and Technologies to Smallholders

EUI and DIRTS Questions that arise from EUI results:  How can we take advantage of increased investment by insured farmers?  How can farmers move away from risk-averse farming techniques, and towards profit maximization?  What influences investment decisions? Information? Access to fertilizer and seeds?

Background: DIRTS ( ) Research design  DIRTS: Disseminating Innovative Resources and Technologies to Smallholders  Investigators: economists at Yale, UDS, SARI, IFPRI  Question: building on EUI findings, why are smallholder farm productivity and profitability levels so low, even when investment increases?  Hypotheses: risk aversion, limited access to quality inputs, limited access to good information  Treatments: varying combinations of:  (1) drought index insurance  (2) access to improved-yield technologies and  (3) Community Extension Agent (CEA) program

IPA Agricultural Research Projects Community Extension Agent Pilot

CEA: Community Extension Agent program Partners: IPA, MoFA, NRGP, Grameen Foundation Objective: complement existing extension services by training community members to use Android phones to provide better information resources to farmers.  CEAs will be connected to AEAs, providing a link between AEAs and the community.  Improved information developed in content workshops with key stakeholders and experts made available through Grameen application  Messaging allows for improved communication between supervisors, CEAs, AEAs, and community farmers  Training provided through modules sent via phones  Collecting quality data through survey app Background: CEA pilot (2012)

The Way Forward Partnership Opportunities

Partnership Opportunities: What we bring Program evaluation study design Survey data collection M&E tools  Paper  Electronic  Data quality protocols

Partnership Opportunities: What we are looking for On current projects:  Integrating agricultural research findings into best practices resources  Identifying context-relevant technology input packages to test  Designing mobile extension programs  Designing community extension agent programs Future project interests:  Identifying promising solutions for increasing farmer investment and profitability for evaluation  Childhood nutrition  Other issues of importance to development partners?

Contact Information Annie Smith ◦ Senior Project Associate Elizabeth Schultz ◦ Ghana Research Cluster Manager

M&E Tools M&E Tools: Quality Data for Quality Evaluation

M&E tools: Why? Is the program being implemented?  Record keeping by implementing agents  Spot checks  Interviews with program recipients  Spending tracking Is the program influencing outcomes as measured by key indicators?  Administrative data (eg. Fertilizer sales)  Survey data (eg. Farmer-reported use of fertilizer)  Observational data (eg. Fertilizer use witnessed by enumerators)

M&E tools: How? Paper: low investment, easy to teach  Field monitoring records  Paper surveys Electronic: fast, additional data collection capabilities  Netbooks: electronic surveys  Mobile: automatic recording of database use patterns by CEAs  Mobile: collection of GPS coordinates for monitoring  Mobile: frequent labor use data collection by CEAs

M&E tools: Ensuring Quality Collect the right data Is the data source biased? Does the data source have the capacity to do quality data collection?  Time  Training  Tools  Funds