Many standards systems refer to the ILO agreements and conventions on forced labour, and have incorporated them into their standards. However, they’ve traditionally relied almost exclusively on social audits to monitor compliance, resulting in low identification rates even in high-risk areas. For standards to become more effective as tools to eradicate forced labour in certified supply chains, there’s a need to identify new risk-based tools to assess farms where there's a high chance it could be taking place. Owing to the often hidden, entrenched and culture- and region-specific nature of forced labour, new approaches to monitoring compliance need to be rooted in workers’ experiences, drawing on data from professionals at field level who can support the verification process.
Improving detection and remediation of forced labour in agriculture
Introduction
Project details
Project title: Integrating new data to improve risk assessments and detection of forced labour in agricultural supply chains
Lead organisation: Sustainable Agriculture Network (SAN)
Partners: UTZ and Ergon Associates
Duration: November 2017 to December 2018
Lead organisation: Sustainable Agriculture Network (SAN)
Partners: UTZ and Ergon Associates
Duration: November 2017 to December 2018
Forced labour
Risk-based approaches
SAN, Rainforest Alliance and Ergon Associates came together to find new methods of identifying high-risk locations beyond the tools available to auditors.
The organisations wanted to build an evidence base to support standards to better learn about the drivers of forced labour and improve monitoring and remediation practices, with a focus on high-risk farms. First, they developed a methodology to assess vulnerability to forced labour in agricultural supply chains, consisting of a set of indicators, feasible means of measurement, weighting, user information and data sources.
Download project outputs
Report | Forced Labour - India Country Pilot Methodology
A report produced for the ISEAL Alliance Innovations Fund project “Integrating new data to improve risk assessments and detection of forced labour vulnerability in agricultural supply chains”.
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