Studying inclusive innovation with the right data: An empirical illustration from Ethiopia

Solomon Alemu, Frederic Kosmowski, James Stevenson, Paola Mallia, Lemi Taye and Karen Macours
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Solomon Alemu: CGIAR Standing Panel on Impact Assessment, International Livestock Research Institute, Addis Ababa
Frederic Kosmowski: CGIAR Standing Panel on Impact Assessment, International Livestock Research Institute, Addis Ababa
James Stevenson: CGIAR Standing Panel on Impact Assessment, International Food Policy Research Institute, Washington, DC
Lemi Taye: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: CONTEXT Agricultural innovations are inclusive when they are used by any member of society who wants to use them. Conversely, agricultural innovations that can only be used by a specific privileged group within society can be characterized as "exclusive". OBJECTIVE The first objective of this paper is to examine the inclusivity of agricultural innovations in Ethiopia, using national representative data and considering a wide portfolio of innovations resulting from the collaborative research between CGIAR and its national partners. Second, we also examine how measurement error may affect how we characterize the inclusivity of agricultural innovations. METHODS We use nationally-representative survey data from Ethiopia (collected in 2018/19) in which best-practice measures of the adoption of a large number of agricultural innovations were embedded, including the adoption of CGIAR-related improved maize varieties measured using two different approaches: subjective, self-reported survey data; and objective DNA fingerprinting of crop samples taken from the same farmers' plots. A rich set of household variables is also collected in the survey, which allows characterizing the types of farmers that are adopting different innovations, and the extent to which conclusions regarding the inclusivity of innovations depends on the measurement of the latter. RESULTS AND CONCLUSIONS Many innovations are not disproportionately more likely to be adopted by male, larger, richer, or more connected farmers. When using self-reported data on adoption of improved maize varieties, adoption appears positively correlated with having larger landholdings and households with lower female participation in agriculture, and negatively correlated with poorer households (being among the bottom 40% of consumption distribution). Substituting survey responses with the results of DNA fingerprinting these correlations disappear, with farm size, gender and poverty status no longer predictive of adoption. SIGNIFICANCE The results suggest the potential value of offering a menu of innovations to farmers to increase inclusivity, as it allows each farmer to be a critical consumer of potential innovations and select those that best correspond to their own needs and constraints. We also highlight how important data quality is in ensuring we have correct information about inclusive innovation.

Keywords: Measurement error; Nationally-representative data; Portfolio of innovations; Ethiopia (search for similar items in EconPapers)
Date: 2024-08
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Published in Agricultural Systems, 2024, 219, ⟨10.1016/j.agsy.2024.103988⟩

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Journal Article: Studying inclusive innovation with the right data: An empirical illustration from Ethiopia (2024)
Working Paper: Studying inclusive innovation with the right data: An empirical illustration from Ethiopia (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:halshs-04630515

DOI: 10.1016/j.agsy.2024.103988

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