Heterogeneous Demand for Quality Soybean in Northern Ghana

Agricultural commercialization is considered a critical pathway for economic development in Sub Saharan Africa. However, lack of market information and poor infrastructure may impede this development. We employ discrete choice models to investigate traders preferences for high quality soybean traits and explore heterogeneity across traders using primary data from northern Ghana. Random parameter logit (RPL) and latent class logit (LCL) models were both fit to the data and used to estimate marginal willingness to pay (MWTP) for soybean trade attributes. Results suggest that traders significantly discount the price of soybean attributes such as light brown, small size, varieties other than Jenguma" and soybean mixed with foreign materials like stones, straw and husk. However, traders were willing to pay a higher price for a bag of soybean that meets all the preferred trade attributes. We also find significant heterogeneity among traders explained partly by socioeconomic and trade characteristics of the respondents. Three distinct classes were identified per the LCL results with unique preferences, suggesting both skepticism and high price discounting. To ensure consistent information on quality and price of soybean among supply chain actors, there is the need for establishment of multi-stakeholder platform for consensus building on quality standards. Acknowledgement : This material is based upon work supported by the United States Agency for International Development, as part of the Feed the Future initiative, under the CGIAR Fund, award number BFS?G?11?00002, and the predecessor fund the Food Security and Crisis Mitigation II grant, award number EEM?G?00?04?00013. The data was partly funded by the Office of International Program (OIP), UIUC; Soybean Innovation Lab (SIL) and the Savanna Agricultural Research Institute of the Council for Scientific and Industrial Research (CSIR-SARI).

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JEL Codes:
C93; C92

 Record created 2018-10-02, last modified 2020-10-28

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