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Socio-Technical Innovation
Bundles for Agri-Food
Systems Transformation:
Implications for impact
assessment in One CGIAR &
country-level experience
Karen Macours
Paris School of Economics
INRAE
SPIA (Standing Panel on
Impact Assessment) CGIAR
karen.macours@psemail.eu
1
How to measure impact of AR4D with move to innovation
bundles for AFS transformation?
• Lots of Historical evidence on yield-enhancing innovations playing an
important role in reducing poverty …. and more, but with trade-offs
• Are those answers still relevant today?
– No, different innovations to respond to challenges of today’s world =>
innovation bundles (Barrett et al, 2020)
– Trade-offs and need to understand impacts in multiple domains =>
increase need for rigorous causal evidence
• New questions => Rigorous impact studies to advise on modalities for scaling
– E.g. with technology bundles, how to adapt scaling mechanisms to
characteristics of innovation (ongoing SPIA studies)
– E.g. providing menu of options rather than training on individual
technologies => ICRAF’s farmer-to-trainer cascade model in Uganda
(Behaghel, Gignoux, Macours, 2020)
– Multitude of outcomes of interest call for new metrics & measurement
innovations
– Magnitude of challenge means we need evidence of reach at scale
Shining a Brighter Light at the country level
https://cas.cgiar.org/spia 3
• Systematically and factually document CGIAR
reach across crop, livestock and natural research
management
=> necessary, if not sufficient, for impact
• Ethiopia: Almost all CGIAR centers/CRPs active
=> other countries to follow
• Stocktaking of last 2 decades of research by
CGIAR and national partners
=> 52 innovations and 26 policy influences
• Novel data protocols and methods incorporated
in Ethiopia Socioeconomic Survey (ESS)
=> representative and at scale
=> objective & independent measures
• Through partnership with CSA and World Bank
Bit.ly/SPIA-Ethiopia
Innovations from different CGIAR research domains have
scaled, but many innovations have not
https://cas.cgiar.org/spia 4
Number of
rural HHs
adopting
each
CGIAR-
related
innovation
(millions)
Based on ESS 4 only (not including wheat and haricot beans)
https://cas.cgiar.org/spia 5
• Relatively few innovations reaching large numbers of households
• Widespread adoption of recent agricultural innovations
• When considering portfolio of innovations, instead of single innovations
=> One CGIAR
• Many innovations are not disproportionately more likely to be adopted by
male, larger, richer, more educated or more connected farmers
• Good news for contributing to end rural poverty
• But certain innovations not reaching target households & regions
• Representative estimates of the “who, where and when” of diffusion can
help in process of re-evaluating Theories of Change (ToC)
• Where and for whom are scaling efforts working?
• Why? Synergies between biophysical and policy research?
• Where/for whom are the bottlenecks proving to be too constraining?
Main take-aways from national-level data in Ethiopia

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Socio-Technical Innovation Bundles for Agri-Food Systems Transformation: Implications for impact assessment in One CGIAR & country-level experience

  • 1. Socio-Technical Innovation Bundles for Agri-Food Systems Transformation: Implications for impact assessment in One CGIAR & country-level experience Karen Macours Paris School of Economics INRAE SPIA (Standing Panel on Impact Assessment) CGIAR karen.macours@psemail.eu 1
  • 2. How to measure impact of AR4D with move to innovation bundles for AFS transformation? • Lots of Historical evidence on yield-enhancing innovations playing an important role in reducing poverty …. and more, but with trade-offs • Are those answers still relevant today? – No, different innovations to respond to challenges of today’s world => innovation bundles (Barrett et al, 2020) – Trade-offs and need to understand impacts in multiple domains => increase need for rigorous causal evidence • New questions => Rigorous impact studies to advise on modalities for scaling – E.g. with technology bundles, how to adapt scaling mechanisms to characteristics of innovation (ongoing SPIA studies) – E.g. providing menu of options rather than training on individual technologies => ICRAF’s farmer-to-trainer cascade model in Uganda (Behaghel, Gignoux, Macours, 2020) – Multitude of outcomes of interest call for new metrics & measurement innovations – Magnitude of challenge means we need evidence of reach at scale
  • 3. Shining a Brighter Light at the country level https://cas.cgiar.org/spia 3 • Systematically and factually document CGIAR reach across crop, livestock and natural research management => necessary, if not sufficient, for impact • Ethiopia: Almost all CGIAR centers/CRPs active => other countries to follow • Stocktaking of last 2 decades of research by CGIAR and national partners => 52 innovations and 26 policy influences • Novel data protocols and methods incorporated in Ethiopia Socioeconomic Survey (ESS) => representative and at scale => objective & independent measures • Through partnership with CSA and World Bank Bit.ly/SPIA-Ethiopia
  • 4. Innovations from different CGIAR research domains have scaled, but many innovations have not https://cas.cgiar.org/spia 4 Number of rural HHs adopting each CGIAR- related innovation (millions) Based on ESS 4 only (not including wheat and haricot beans)
  • 5. https://cas.cgiar.org/spia 5 • Relatively few innovations reaching large numbers of households • Widespread adoption of recent agricultural innovations • When considering portfolio of innovations, instead of single innovations => One CGIAR • Many innovations are not disproportionately more likely to be adopted by male, larger, richer, more educated or more connected farmers • Good news for contributing to end rural poverty • But certain innovations not reaching target households & regions • Representative estimates of the “who, where and when” of diffusion can help in process of re-evaluating Theories of Change (ToC) • Where and for whom are scaling efforts working? • Why? Synergies between biophysical and policy research? • Where/for whom are the bottlenecks proving to be too constraining? Main take-aways from national-level data in Ethiopia