Integrating new data to improve risk assessments and detection of forced labour in agricultural supply chains
Guatemala, India and Kenya
November 2017-October 2018
In the short term, the methodology will support standards systems as well as companies and organizations that are working to assess forced labour in their agricultural supply chains.
UTZ, Ergon Associates and SAN Technical Partners IMO India, Africert and FIIT.
ISEAL Innovations Fund
About the project
Abusive working conditions in agriculture, including forced labour, are often hidden. While many standard systems have incorporated ILO agreements and conventions to address labour issues, the scale of the problem does not appear to be matched by the rates of identification of non-compliance with labour standards. SAN and Utz had discussed whether this could indicate that farms applying for certification are some of the best performers, but also likely that detection needs to be improved. Standard systems use, almost exclusively, audits to monitor compliance and there is a need to explore how to improve existing methods and develop new ones to better identify and detect forced labour.
A further challenge exists in knowing what to do when forced labour is discovered. While general guidelines on remediation exist (SEDEX (Operational Guidelines on Forced Labor), the ILO (Recommendation 203, June 2014), and the UN Principles on Business and Human Rights), effective and appropriate remediation strategies are locally specific and need to be addressed from the perspective of the victim.
In this context, the project aims to improve the assessment and detection of forced labour in agricultural supply chains by designing a methodology to better use existing knowledge and information and collect and integrate new data. It is expected to lead to a more nuanced and effective way of targeting local assessments and detection efforts on higher risk locations.
The project also compiles initial information on locally appropriate and victim-centered considerations for remediating forced labour when detected, and aims to support standards systems and organizations in better understanding their role in developing effective remediation strategies.
The longer-term outcome is an improved assessment of vulnerability and detection of forced labour in agricultural supply chains by the adoption and effective implementation of the methodology by standard systems and wider organizations.