Periodic Reporting for period 1 - MISSINGMARKETS (It’s (also) what you produce: Experimental Evidence on Creating Markets for Quality in Low-income Countries)
Reporting period: 2023-10-01 to 2026-03-31
More than half of the world’s extreme poor – over 500 million people – live in Sub-Saharan Africa. The vast majority reside in rural areas and depend on agriculture, where productivity remains desperately low. This keeps welfare low today – and constrains it in the future – as agricultural productivity growth is essential both for structural transformation and for mitigating the worst impacts of climate change. This is the motivation for bringing the green revolution – which has so far bypassed the continent – to Sub-Saharan Africa. Despite this compelling logic, a decade of research on technology adoption in African agriculture has produced disappointing results. Interventions aimed at increasing farmers’ ability to invest in inputs to produce more of what they already grow have not proven transformative.
This research project investigates a complementary explanation for why income and productivity in African agriculture remain low: it is what farmers produce – the quality and value added of their output – that keeps them poor. And farmers produce low quality because there is little or no demand for high-quality or higher–value-added outputs from smallholders. As low-quality limits prices and therefore income, missing markets for quality may help explain the persistently low returns to farming.
At the core of this project lies the hypothesis that markets for quality are missing because of a fundamental information problem: quality is difficult to observe before purchase. Working with an interdisciplinary team of researchers in Sweden, Italy, and Uganda – spanning economics, food science, and computer science – the project investigates technological and institutional solutions to overcome these information failures that suppress demand for high-quality products from small-scale producers, both in export and domestic markets, and how such solutions can, in turn, help ignite a green (quality) revolution in Africa.
This research project investigates a complementary explanation for why income and productivity in African agriculture remain low: it is what farmers produce – the quality and value added of their output – that keeps them poor. And farmers produce low quality because there is little or no demand for high-quality or higher–value-added outputs from smallholders. As low-quality limits prices and therefore income, missing markets for quality may help explain the persistently low returns to farming.
At the core of this project lies the hypothesis that markets for quality are missing because of a fundamental information problem: quality is difficult to observe before purchase. Working with an interdisciplinary team of researchers in Sweden, Italy, and Uganda – spanning economics, food science, and computer science – the project investigates technological and institutional solutions to overcome these information failures that suppress demand for high-quality products from small-scale producers, both in export and domestic markets, and how such solutions can, in turn, help ignite a green (quality) revolution in Africa.
The project investigates how missing markets for quality constrain income growth and productivity among smallholder farmers in Africa and develops technological and institutional solutions to address these barriers. Research activities have focused on understanding how farmers can move from producing low-quality, unprocessed commodities to higher-value, processed goods, and how technology can support this transition.
A major scientific and technological achievement is the development of a new quality-verification system that enables real-time assessment of processed agricultural products at the farmgate. Initial work explored the use of near-infrared spectral scanning for detecting coffee quality attributes, but this approach proved too limited in accuracy and portability under field conditions. The research team therefore designed an alternative solution based on image recognition and machine learning. By combining laboratory experiments and field validation, they developed algorithms that classify coffee samples with very high accuracy and integrated them into a smartphone application that provides automatic quality assessment and purchasing recommendations to buyers.
This innovation allows buyers to verify product quality directly at the point of purchase, making high-quality output observable and therefore eligible for price premiums. In doing so, it creates new incentives for smallholders to invest in quality upgrading and supports their participation in higher-value market segments. It also reduces waste and inefficiencies in the supply chain by identifying low-quality products before they are traded.
Field studies using this technology are now under way and will generate rigorous evidence on whether enabling smallholders to process and sell higher-quality products increases their incomes and productivity. The project has also established collaborations with local and international partners, including the United Nations World Food Programme, to explore how these technological and institutional innovations can be embedded in real-world procurement systems and scaled more widely.
A major scientific and technological achievement is the development of a new quality-verification system that enables real-time assessment of processed agricultural products at the farmgate. Initial work explored the use of near-infrared spectral scanning for detecting coffee quality attributes, but this approach proved too limited in accuracy and portability under field conditions. The research team therefore designed an alternative solution based on image recognition and machine learning. By combining laboratory experiments and field validation, they developed algorithms that classify coffee samples with very high accuracy and integrated them into a smartphone application that provides automatic quality assessment and purchasing recommendations to buyers.
This innovation allows buyers to verify product quality directly at the point of purchase, making high-quality output observable and therefore eligible for price premiums. In doing so, it creates new incentives for smallholders to invest in quality upgrading and supports their participation in higher-value market segments. It also reduces waste and inefficiencies in the supply chain by identifying low-quality products before they are traded.
Field studies using this technology are now under way and will generate rigorous evidence on whether enabling smallholders to process and sell higher-quality products increases their incomes and productivity. The project has also established collaborations with local and international partners, including the United Nations World Food Programme, to explore how these technological and institutional innovations can be embedded in real-world procurement systems and scaled more widely.
The project has advanced understanding of how information barriers in agricultural markets limit value creation and income growth among smallholder farmers, and it has demonstrated a new way to overcome these barriers using affordable, scalable technology. By developing a system that makes product quality observable after processing, the research provides the foundation for smallholders to participate directly in higher-value market segments from which they were previously excluded. This represents a significant conceptual and empirical shift in development economics — from focusing solely on productivity and input adoption to addressing how market information shapes incentives for quality upgrading and value creation.
The project also breaks new ground technologically. The integration of image recognition and machine learning into a smartphone-based quality-verification tool creates a new methodological approach for field-level quality assessment under real-world conditions. Making such a tool operational in low-connectivity, rural environments demonstrates that advanced digital technologies can be adapted to smallholder contexts at scale. Practitioners in the coffee industry have also expressed interest in the potential of such tool.
Beyond research, the results open pathways for wider practical application. For example, practitioners in the coffee industry have expressed interest in the potential of the smartphone app we have developed. Moreover, collaboration with local actors and with the United Nations World Food Programme shows strong potential for integrating these innovations into institutional procurement programs, which could generate substantial income gains and efficiency improvements. Further uptake will depend on continued testing across crops and settings, adaptation to different regulatory frameworks, and potential partnerships with private-sector buyers and technology developers to support large-scale deployment.
If successful, these developments could redefine how value chains in low-income countries are organized — enabling inclusive growth by linking farmers more directly to markets that reward quality and innovation.
The project also breaks new ground technologically. The integration of image recognition and machine learning into a smartphone-based quality-verification tool creates a new methodological approach for field-level quality assessment under real-world conditions. Making such a tool operational in low-connectivity, rural environments demonstrates that advanced digital technologies can be adapted to smallholder contexts at scale. Practitioners in the coffee industry have also expressed interest in the potential of such tool.
Beyond research, the results open pathways for wider practical application. For example, practitioners in the coffee industry have expressed interest in the potential of the smartphone app we have developed. Moreover, collaboration with local actors and with the United Nations World Food Programme shows strong potential for integrating these innovations into institutional procurement programs, which could generate substantial income gains and efficiency improvements. Further uptake will depend on continued testing across crops and settings, adaptation to different regulatory frameworks, and potential partnerships with private-sector buyers and technology developers to support large-scale deployment.
If successful, these developments could redefine how value chains in low-income countries are organized — enabling inclusive growth by linking farmers more directly to markets that reward quality and innovation.