Studying inclusive innovation with the right data: An empirical illustration from Ethiopia
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...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/145067 |
| _version_ | 1855526895296184320 |
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| author | Alemu, Solomon Kosmowski, Frederic Stevenson, James R. Mallia, Paola Taye, Lemi Macours, Karen |
| author_browse | Alemu, Solomon Kosmowski, Frederic Macours, Karen Mallia, Paola Stevenson, James R. Taye, Lemi |
| author_facet | Alemu, Solomon Kosmowski, Frederic Stevenson, James R. Mallia, Paola Taye, Lemi Macours, Karen |
| author_sort | Alemu, Solomon |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace145067 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1450672025-10-26T12:51:41Z Studying inclusive innovation with the right data: An empirical illustration from Ethiopia Alemu, Solomon Kosmowski, Frederic Stevenson, James R. Mallia, Paola Taye, Lemi Macours, Karen agriculture innovation data farmers 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. 2024-08 2024-06-07T14:41:45Z 2024-06-07T14:41:45Z Journal Article https://hdl.handle.net/10568/145067 en Open Access Elsevier Alemu, Solomon; Kosmowski, Frederic; Stevenson, James R.; Mallia, Paola; Taye, Lemi; and Macours, Karen. 2024. Studying inclusive innovation with the right data: An empirical illustration from Ethiopia. Agricultural Systems 219(August 2024): 103988. https://doi.org/10.1016/j.agsy.2024.103988 |
| spellingShingle | agriculture innovation data farmers Alemu, Solomon Kosmowski, Frederic Stevenson, James R. Mallia, Paola Taye, Lemi Macours, Karen Studying inclusive innovation with the right data: An empirical illustration from Ethiopia |
| title | Studying inclusive innovation with the right data: An empirical illustration from Ethiopia |
| title_full | Studying inclusive innovation with the right data: An empirical illustration from Ethiopia |
| title_fullStr | Studying inclusive innovation with the right data: An empirical illustration from Ethiopia |
| title_full_unstemmed | Studying inclusive innovation with the right data: An empirical illustration from Ethiopia |
| title_short | Studying inclusive innovation with the right data: An empirical illustration from Ethiopia |
| title_sort | studying inclusive innovation with the right data an empirical illustration from ethiopia |
| topic | agriculture innovation data farmers |
| url | https://hdl.handle.net/10568/145067 |
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