A network-driven data collection approach for agri-food value chains
A key challenge in systematically collecting data on intermediary agri-food value chain actors is that value chains take the form of a network, with actors linked by a series of transactions. Moreover, we have limited ex ante knowledge about the structure or scale of these networks, which complicate...
| Autores principales: | , , , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/144207 |
| _version_ | 1855529764452827136 |
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| author | Ambler, Kate Bloem, Jeffrey R. de Brauw, Alan Herskowitz, Sylvan Wagner, Julia |
| author_browse | Ambler, Kate Bloem, Jeffrey R. Herskowitz, Sylvan Wagner, Julia de Brauw, Alan |
| author_facet | Ambler, Kate Bloem, Jeffrey R. de Brauw, Alan Herskowitz, Sylvan Wagner, Julia |
| author_sort | Ambler, Kate |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | A key challenge in systematically collecting data on intermediary agri-food value chain actors is that value chains take the form of a network, with actors linked by a series of transactions. Moreover, we have limited ex ante knowledge about the structure or scale of these networks, which complicates the construction of valid sampling frames and limits traditional random sampling approaches to collect data. To address these challenges, we adapt the respondent-driven sampling approach to collect data on intermediary agri-food value chain actors within their transaction-linked network and implement this approach in the arabica coffee and soybean value chains in Uganda and the rice and potato value chains in Bangladesh. We observe meaningful heterogeneity in the structure and scale of agri-food value chains across commodities and countries. Focusing on traders, we show that the respondent-driven sampling approach generates a larger sample of traders who differ in observable characteristics (i.e., value added, enterprise scale, and financial access) compared to a sub-sample of traders generated in a way that mimics traditional random sampling approaches used to study traders. We conclude by discussing how this respondent-driven sampling approach, applied within transaction-linked networks, can provide a useful data collection method for studying intermediary agri-food value chain actors. |
| format | Artículo preliminar |
| id | CGSpace144207 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1442072025-11-06T06:41:13Z A network-driven data collection approach for agri-food value chains Ambler, Kate Bloem, Jeffrey R. de Brauw, Alan Herskowitz, Sylvan Wagner, Julia data agrifood systems value chains networks arabica coffee soybeans rice potatoes A key challenge in systematically collecting data on intermediary agri-food value chain actors is that value chains take the form of a network, with actors linked by a series of transactions. Moreover, we have limited ex ante knowledge about the structure or scale of these networks, which complicates the construction of valid sampling frames and limits traditional random sampling approaches to collect data. To address these challenges, we adapt the respondent-driven sampling approach to collect data on intermediary agri-food value chain actors within their transaction-linked network and implement this approach in the arabica coffee and soybean value chains in Uganda and the rice and potato value chains in Bangladesh. We observe meaningful heterogeneity in the structure and scale of agri-food value chains across commodities and countries. Focusing on traders, we show that the respondent-driven sampling approach generates a larger sample of traders who differ in observable characteristics (i.e., value added, enterprise scale, and financial access) compared to a sub-sample of traders generated in a way that mimics traditional random sampling approaches used to study traders. We conclude by discussing how this respondent-driven sampling approach, applied within transaction-linked networks, can provide a useful data collection method for studying intermediary agri-food value chain actors. 2024-05-31 2024-05-31T15:46:23Z 2024-05-31T15:46:23Z Working Paper https://hdl.handle.net/10568/144207 en https://doi.org/10.2499/p15738coll2.137050 https://doi.org/10.2499/p15738coll2.136944 https://hdl.handle.net/10568/126921 Open Access application/pdf International Food Policy Research Institute Ambler, Kate; Bloem, Jeffrey R.; de Brauw, Alan; Herskowitz, Sylvan; and Wagner, Julia. 2024. A network-driven data collection approach for agri-food value chains. Discussion Paper 2256. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/144207 |
| spellingShingle | data agrifood systems value chains networks arabica coffee soybeans rice potatoes Ambler, Kate Bloem, Jeffrey R. de Brauw, Alan Herskowitz, Sylvan Wagner, Julia A network-driven data collection approach for agri-food value chains |
| title | A network-driven data collection approach for agri-food value chains |
| title_full | A network-driven data collection approach for agri-food value chains |
| title_fullStr | A network-driven data collection approach for agri-food value chains |
| title_full_unstemmed | A network-driven data collection approach for agri-food value chains |
| title_short | A network-driven data collection approach for agri-food value chains |
| title_sort | network driven data collection approach for agri food value chains |
| topic | data agrifood systems value chains networks arabica coffee soybeans rice potatoes |
| url | https://hdl.handle.net/10568/144207 |
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